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<ASSISTANT_TASK:> Python Code: %matplotlib inline import numpy as np import tensorflow as tf import matplotlib.pyplot as plt from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('MNIST_data', validation_size=0) img = mnist.train.images[2] plt.imshow(img.reshape((28, 28)), cmap='Greys_r') # mnist.train.images[0] # Size of the encoding layer (the hidden layer) encoding_dim = 32 # feel free to change this value image_shape = mnist.train.images.shape[1] inputs_ = tf.placeholder(tf.float32,shape=(None,image_shape),name='inputs') targets_ = tf.placeholder(tf.float32,shape=(None,image_shape),name='targets') # Output of hidden layer encoded = tf.layers.dense(inputs_,encoding_dim,activation=tf.nn.relu) # Output layer logits logits = tf.layers.dense(encoded,image_shape) # linear activation # Sigmoid output from logits decoded = tf.sigmoid(logits) # Sigmoid cross-entropy loss loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits,labels=targets_) # Mean of the loss cost = tf.reduce_mean(loss) # Adam optimizer opt = tf.train.AdamOptimizer(0.001).minimize(cost) # Create the session sess = tf.Session() epochs = 20 batch_size = 200 sess.run(tf.global_variables_initializer()) for e in range(epochs): for ii in range(mnist.train.num_examples//batch_size): batch = mnist.train.next_batch(batch_size) feed = {inputs_: batch[0], targets_: batch[0]} batch_cost, _ = sess.run([cost, opt], feed_dict=feed) print("Epoch: {}/{}...".format(e+1, epochs), "Training loss: {:.4f}".format(batch_cost)) fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4)) in_imgs = mnist.test.images[:10] reconstructed, compressed = sess.run([decoded, encoded], feed_dict={inputs_: in_imgs}) for images, row in zip([in_imgs, reconstructed], axes): for img, ax in zip(images, row): ax.imshow(img.reshape((28, 28)), cmap='Greys_r') ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) fig.tight_layout(pad=0.1) sess.close() <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Below I'm plotting an example image from the MNIST dataset. These are 28x28 grayscale images of handwritten digits. Step2: We'll train an autoencoder with these images by flattening them into 784 length vectors. The images from this dataset are already normalized such that the values are between 0 and 1. Let's start by building basically the simplest autoencoder with a single ReLU hidden layer. This layer will be used as the compressed representation. Then, the encoder is the input layer and the hidden layer. The decoder is the hidden layer and the output layer. Since the images are normalized between 0 and 1, we need to use a sigmoid activation on the output layer to get values matching the input. Step3: Training Step4: Here I'll write a bit of code to train the network. I'm not too interested in validation here, so I'll just monitor the training loss. Step5: Checking out the results

<ASSISTANT_TASK:> Python Code: import pandas as pd from os import path from sklearn.ensemble import RandomForestClassifier import numpy as np from sklearn.ensemble import ExtraTreesClassifier import sklearn # Edit path if need be (shouldn't need to b/c we all have the same folder structure) CSV_PATH_1 = '../Videos/all_data' CSV_PATH_2 = '../Videos2/all_data2' FILE_EXTENSION = '_all.csv' GENRES = ['country', 'edm', 'pop', 'rap', 'rock'] # Containers for the data frames genre_dfs = {} all_genres = None # Read in the 5 genre's of CV's for genre in GENRES: genre_csv_path_1 = path.join(CSV_PATH_1, genre) + FILE_EXTENSION genre_csv_path_2 = path.join(CSV_PATH_2, genre) + FILE_EXTENSION df_1 = pd.read_csv(genre_csv_path_1) df_2 = pd.read_csv(genre_csv_path_2) df_1 = df_1.drop('Unnamed: 0',1) df_2 = df_2.drop('Unnamed: 0',1) df_combined = pd.concat([df_1,df_2],ignore_index=True) genre_dfs[genre] = df_combined all_genres = pd.concat(genre_dfs.values()) all_genres.head() # genre_dfs is now a dictionary that contains the 5 different data frames # all_genres is a dataframe that contains all of the data def genre_to_ordinal(genre_in): if(genre_in == "country"): return 0 elif(genre_in == "pop"): return 1 elif(genre_in == "rock"): return 2 elif(genre_in == "edm"): return 3 elif(genre_in == "rap"): return 4 else: return genre_in all_genres['genre_ordinal'] = all_genres.genre.apply(genre_to_ordinal) # Adding is_country flag def is_country(genre_in): if(genre_in == "country"): return 1 else: return 0 all_genres['is_country'] = all_genres.genre.apply(is_country) # Adding is_country flag def is_rock(genre_in): if(genre_in == "rock"): return 1 else: return 0 all_genres['is_rock'] = all_genres.genre.apply(is_rock) # Adding is_edm flag def is_edm(genre_in): if(genre_in == "edm"): return 1 else: return 0 all_genres['is_edm'] = all_genres.genre.apply(is_edm) # Adding is_rap flag def is_rap(genre_in): if(genre_in == "rap"): return 1 else: return 0 all_genres['is_rap'] = all_genres.genre.apply(is_rap) # Adding is_country flag def is_pop(genre_in): if(genre_in == "pop"): return 1 else: return 0 all_genres['is_pop'] = all_genres.genre.apply(is_pop) # Subset all_genres to group by individual genres country_records = all_genres[all_genres["genre"] == "country"] rock_records = all_genres[all_genres["genre"] == "rock"] pop_records = all_genres[all_genres["genre"] == "pop"] edm_records = all_genres[all_genres["genre"] == "edm"] rap_records = all_genres[all_genres["genre"] == "rap"] # From the subsets above, create train and test sets from each country_train = country_records.head(len(country_records) / 2) country_test = country_records.tail(len(country_records) / 2) rock_train = rock_records.head(len(rock_records) / 2) rock_test = rock_records.tail(len(rock_records) / 2) pop_train = pop_records.head(len(pop_records) / 2) pop_test = pop_records.tail(len(pop_records) / 2) edm_train = edm_records.head(len(edm_records) / 2) edm_test = edm_records.tail(len(edm_records) / 2) rap_train = rap_records.head(len(rap_records) / 2) rap_test = rap_records.tail(len(rap_records) / 2) # Create big training and big test set for analysis training_set = pd.concat([country_train,rock_train,pop_train,edm_train,rap_train]) test_set = pd.concat([country_test,rock_test,pop_test,edm_test,rap_test]) training_set = training_set.fillna(0) test_set = test_set.fillna(0) print "Training Records:\t" , len(training_set) print "Test Records:\t\t" , len(test_set) # training_set.head() # Predicting based solely on non-color features, using RF clf = RandomForestClassifier(n_estimators=11) meta_data_features = ['rating', 'likes','dislikes','length','viewcount'] y, _ = pd.factorize(training_set['genre_ordinal']) clf = clf.fit(training_set[meta_data_features], y) z, _ = pd.factorize(test_set['genre_ordinal']) print clf.score(test_set[meta_data_features],z) pd.crosstab(test_set.genre_ordinal, clf.predict(test_set[meta_data_features]),rownames=["Actual"], colnames=["Predicted"]) def gen_new_headers(old_headers): headers = ['colors_' + str(x+1) + '_' for x in range(10)] h = [] for x in headers: h.append(x + 'red') h.append(x + 'blue') h.append(x + 'green') return old_headers + h + ['genre'] clf = RandomForestClassifier(n_estimators=11) color_features = gen_new_headers([])[:-1] # Predicting based solely on colors y, _ = pd.factorize(training_set['genre_ordinal']) clf = clf.fit(training_set[color_features], y) z, _ = pd.factorize(test_set['genre_ordinal']) print clf.score(test_set[color_features],z) pd.crosstab(test_set.genre_ordinal, clf.predict(test_set[color_features]),rownames=["Actual"], colnames=["Predicted"]) clf = RandomForestClassifier(n_estimators=11) all_features = meta_data_features + color_features # Predicting based on colors and non-color features y, _ = pd.factorize(training_set['genre_ordinal']) clf = clf.fit(training_set[all_features], y) z, _ = pd.factorize(test_set['genre_ordinal']) print clf.score(test_set[all_features],z) pd.crosstab(test_set.genre_ordinal, clf.predict(test_set[all_features]),rownames=["Actual"], colnames=["Predicted"]) clf = RandomForestClassifier(n_estimators=11) all_features = meta_data_features + color_features print all_features # Predicting based on colors and non-color features y, _ = pd.factorize(training_set['is_pop']) clf = clf.fit(training_set[all_features], y) z, _ = pd.factorize(test_set['is_pop']) print clf.score(test_set[all_features],z) pd.crosstab(test_set.is_pop, clf.predict(test_set[all_features]),rownames=["Actual"], colnames=["Predicted"]) clf = RandomForestClassifier(n_estimators=11) all_features = meta_data_features + color_features # Predicting based on colors and non-color features y, _ = pd.factorize(training_set['is_rap']) clf = clf.fit(training_set[all_features], y) z, _ = pd.factorize(test_set['is_rap']) print clf.score(test_set[all_features],z) pd.crosstab(test_set.is_rap, clf.predict(test_set[all_features]),rownames=["Actual"], colnames=["Predicted"]) def multi_RF_averages(is_genre,num_iterations): clf = RandomForestClassifier(n_estimators=11) loop_indices = range(0,num_iterations) cumsum = 0 for i in loop_indices: y, _ = pd.factorize(training_set[is_genre]) clf = clf.fit(training_set[all_features], y) z, _ = pd.factorize(test_set[is_genre]) cumsum = cumsum + clf.score(test_set[all_features],z) print "Average Score for",len(loop_indices),is_genre,"iterations:", cumsum/len(loop_indices) return clf pop_class = multi_RF_averages("is_pop",50) rap_class = multi_RF_averages("is_rap",50) rock_class = multi_RF_averages("is_rock",50) edm_class = multi_RF_averages("is_edm",50) country_class = multi_RF_averages("is_country",50) from sklearn.externals import joblib # only use these to generate pickle files for website # joblib.dump(pop_class, 'classifiers/pop_class.pkl') # joblib.dump(rap_class, 'classifiers/rap_class.pkl') # joblib.dump(rock_class, 'classifiers/rock_class.pkl') # joblib.dump(edm_class, 'classifiers/edm_class.pkl') # joblib.dump(country_class, 'classifiers/country_class.pkl') # Removing EDM for better analysis - makes is_pop and is_rap much more accurate training_set = pd.concat([country_train,rock_train,pop_train,rap_train]) test_set = pd.concat([country_test,rock_test,pop_test,rap_test]) multi_RF_averages("is_pop",50) multi_RF_averages("is_rap",50) multi_RF_averages("is_rock",50) multi_RF_averages("is_edm",50) multi_RF_averages("is_country",50) training_set = pd.concat([country_train,rock_train,edm_train,rap_train,pop_train]) test_set = pd.concat([rock_test]) multi_RF_averages("is_rock",50) test_set = pd.concat([rap_test]) multi_RF_averages("is_rap",50) test_set = pd.concat([country_test]) multi_RF_averages("is_country",50) test_set = pd.concat([pop_test]) multi_RF_averages("is_pop",50) test_set = pd.concat([edm_test]) multi_RF_averages("is_edm",50) test_set = pd.concat([edm_test,rock_test]) multi_RF_averages("is_edm",50) multi_RF_averages("is_rock",50) model = ExtraTreesClassifier() training_set = pd.concat([country_train,pop_train,rap_train,rock_train,edm_train]) y, _ = pd.factorize(training_set['is_rock']) model.fit(training_set[all_features], y) # display the relative importance of each attribute print model.feature_importances_ df = pd.DataFrame() df['index'] = all_features y, _ = pd.factorize(training_set['is_rap']) model.fit(training_set[all_features], y) df['rap'] = model.feature_importances_ y, _ = pd.factorize(training_set['is_rock']) model.fit(training_set[all_features], y) df['rock'] = model.feature_importances_ y, _ = pd.factorize(training_set['is_country']) model.fit(training_set[all_features], y) df['country'] = model.feature_importances_ y, _ = pd.factorize(training_set['is_edm']) model.fit(training_set[all_features], y) df['edm'] = model.feature_importances_ y, _ = pd.factorize(training_set['is_pop']) model.fit(training_set[all_features], y) df['pop'] = model.feature_importances_ df = df.set_index('index') df = df.transpose() df.head() lol = lol = df.values.tolist() cols = [] for x in df.columns: cols.append(x) import plotly.offline as py # a little wordplay import plotly.graph_objs as go offline.init_notebook_mode() title = 'Feature Importance By Genre' labels = [ ] mode_size = [8, 8, 12, 8] line_size = [2, 2, 4, 2] x_data = cols y_data = df.values.tolist() traces = [] for i in range(0, 4): traces.append(go.Scatter( x=x_data, y=y_data[i], mode='lines', connectgaps=True, )) layout = go.Layout( yaxis=dict( showgrid=False, zeroline=False, showline=False, showticklabels=False, ), autosize=False, margin=dict( autoexpand=True, l=100, r=20, t=110, ), showlegend=False, ) annotations = [] # Adding labels for y_trace, label in zip(y_data, labels): # labeling the left_side of the plot annotations.append(dict(xref='paper', x=0.05, y=y_trace[0], xanchor='right', yanchor='middle', text=label + ' {}%'.format(y_trace[0]), font=dict(family='Arial', size=16, ), showarrow=False)) # labeling the right_side of the plot annotations.append(dict(xref='paper', x=0.95, y=y_trace[11], xanchor='left', yanchor='middle', text='{}%'.format(y_trace[11]), font=dict(family='Arial', size=16, ), showarrow=False)) # Title annotations.append(dict(xref='paper', yref='paper', x=0.0, y=1.05, xanchor='left', yanchor='bottom', text='Feature Importance By Genre', font=dict(family='Arial', size=30, ), showarrow=False)) # Source # annotations.append(dict(xref='paper', yref='paper', x=0.5, y=-0.1, # xanchor='center', yanchor='top', # text='Source: PewResearch Center & ' + # 'Storytelling with data', # font=dict(family='Arial', # size=12, # ), # showarrow=False)) layout['annotations'] = annotations fig = go.Figure(data=traces, layout=layout) py.iplot(fig, filename='news-source') import seaborn as sns sns.set_style("whitegrid") ax = sns.pointplot(x="likes", y="rating",data=df) sns.plt.show() import seaborn as sns sns.set_style("whitegrid") tips = sns.load_dataset("tips") print tips ax = sns.pointplot(x="time", y="total_bill", data=tips) sns.plt.show() <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Ordinal Genres Step2: We add in some boolean genre classifiers to make our analysis more fine-grained. Rather than saying "we predict this video is country with 50% confidence", we could say "we predict this video is not edm with 90% confidence" and so on. Step3: Test and Train Sets Step4: Generating Random Forest - Viewer Statistics Step5: As shown above, this method yields relatively poor results. This is because there's no distinct clusters being created by our random forest, and simple viewer statistics tell us nothing about what kind of video we're watching. However, we see that country, rap and pop are initially somewhat distinct (diagonal is the highest value), and rock and edm are getting mistaken for one another. Let's see if we can't make something of this. Step6: This actually yields worse results than just the viewer statistics, because the color of a video by itself does not determine the genre. If rappers only had red in their videos and rockers only had black this might be somewhat accurate, but that's just not the case. But, what if we pair these findings with our initial viewer statistics? Step7: Singling Out Pop and Rap Step8: What we're seeing above is a confusion matrix that, based on our training data, predicts whether or not a video in the test set is a pop video or not. In the "predicted" row, 0 means it predicts it's not a pop video, and that the 1 is. Likewise with the actual, 0 shows that the video actually wasn't a pop video, and the 1 shows that it was. Step9: The following creates several files that describe our classifiers. Our website will later Step10: We ran the above test with all genres, and as shown in above analysis, our country and edm typically have very low accuracy. We've seen above that edm and rock videos are getting mixed up with one another, so we assume that something is characteristic of these 2 genres that's not of everything else. We take out the edm values from our training and test datasets, hoping to improve accuracy. Step11: So, what does this tell us? Based on our training data, we have the best chance of accurately classifying something as pop or not pop (under these conditions). Step12: Rock and EDM have suprisingly distinct classifiers. We should dive into the videos and see what this means. Step13: Selecting Most Valuable Features per Genre - Rock

<ASSISTANT_TASK:> Python Code: features.applymap(np.isreal).apply(pd.value_counts) features.apply(lambda x: stats.shapiro(x)) numeric_feats = features.dtypes[features.dtypes != "object"].index skewness = features[numeric_feats].apply(lambda x: skew(x.dropna())) #compute skewness print skewness def show_qqplot(x, data): fig = plt.figure() ax = fig.add_subplot(111) stats.probplot(data[x], dist="norm", plot=pylab) ax.set_title(x) pylab.show() for name in features.columns: show_qqplot(name, features) skewed_feats = skewness[skewness < -0.75] skewed_feats = skewed_feats.index features[skewed_feats] = np.exp2(features[skewed_feats]) features.head() numeric_feats = features.dtypes[features.dtypes != "object"].index print features[numeric_feats].apply(lambda x: skew(x.dropna())) show_qqplot("lp2", features) y = features.cra X_train = features.drop(["cra"], axis=1) from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, Lasso, LassoCV, LassoLarsCV from sklearn.model_selection import cross_val_score def vector_norm(w): return np.sqrt(np.sum(w**2)) def rmse_cv(model): rmse= np.sqrt(-cross_val_score(model, X_train, y, scoring="neg_mean_squared_error", cv = 10)) return(rmse) def coefficients_graphic(model, title): coef = pd.Series(model.coef_, index = X_train.columns) matplotlib.rcParams['figure.figsize'] = (8.0, 10.0) coef.plot(kind = "barh") plt.title(title) def residuals_graph(model): matplotlib.rcParams['figure.figsize'] = (6.0, 6.0) preds = pd.DataFrame({"preds":model.predict(X_train), "true":y}) preds["residuals"] = preds["true"] - preds["preds"] preds.plot(x = "preds", y = "residuals",kind = "scatter") def cv_rmse_graph(cv_rmse, alpha_levels): cv_rmse = pd.Series(cv_rmse, index = alpha_levels) cv_rmse.plot(title = "Validation - Just Do It") plt.xlabel("alpha") plt.ylabel("rmse") clf = Ridge(alpha=0) clf.fit(X_train, y) vector_norm(clf.coef_) coefficients_graphic(clf, "Coefficients in the Ridge Model Not Regularized") residuals_graph(clf) alphas = [0, 0.05, 0.1, 0.3, 1, 3, 5, 10, 15, 30, 40, 50, 75] cv_rmse = [rmse_cv(Ridge(alpha = level)).mean() for level in alphas] cv_rmse_graph(cv_rmse, alphas) clf = Ridge(alpha=40) clf.fit(X_train, y) vector_norm(clf.coef_) coefficients_graphic(clf, "Coefficients in Regularized Ridge Model") residuals_graph(clf) model_lasso = Lasso(alpha = [0]).fit(X_train, y) coef_lasso = pd.Series(model_lasso.coef_, index = X_train.columns) print("Lasso picked " + str(sum(coef_lasso != 0)) + " variables and eliminated the other " + str(sum(coef_lasso == 0)) + " variables") coefficients_graphic(model_lasso, "Coefficients in the Lasso Model Not Regularized") residuals_graph(model_lasso) alphas = [0, 0.001, 0.01, 0.02, 0.03, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1] cv_rmse = [rmse_cv(Lasso(alpha = level)).mean() for level in alphas] cv_rmse_graph(cv_rmse, alphas) model_lasso = Lasso(alpha = 0.06).fit(X_train, y) coef_lasso = pd.Series(model_lasso.coef_, index = X_train.columns) print("Lasso picked " + str(sum(coef_lasso != 0)) + " variables and eliminated the other " + str(sum(coef_lasso == 0)) + " variables") print coef_lasso coefficients_graphic(model_lasso, "Coefficients in Regularized Lasso Model") residuals_graph(model_lasso) from sklearn.neighbors import KNeighborsRegressor max_n_neighbors = int(y.shape[0] - 0.1*y.shape[0]) neighbors = range(1,max_n_neighbors) cv_rmse = [rmse_cv(KNeighborsRegressor(n_neighbors = level)).mean() for level in neighbors] cv_rmse_graph(cv_rmse, neighbors) best_knn_fit = KNeighborsRegressor(n_neighbors = 22) residuals_graph(model_lasso) teste = pd.read_csv("graduados_teste.csv") <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Checking for skew in data Step2: Skewness Step3: Q-QPlot Step4: Through our analysis, we conclude that the only feature in the necessity of being transformed is LP2. We conclude it by Step5: 2. Ridge and LASSO Regression Step6: Ridge Regression Step7: Using Regularization Step8: LASSO Regression Step9: Using regularization Step10: 3. KNN Step11: Analysis

<ASSISTANT_TASK:> Python Code: # import python packages here... import mpmath mpmath.plot([mpmath.cos, mpmath.sin], [-4, 4]) mpmath.plot(lambda x: mpmath.exp(x) * mpmath.li(x), [1, 4]) mpmath.cplot(lambda z: z, [-10, 10], [-10, 10]) mpmath.cplot(lambda z: z, [-10, 10], [-10, 10], points=100000) mpmath.cplot(mpmath.gamma, [-10, 10], [-10, 10], points=100000) f = lambda z: (z * 2 - 1)*(z + 2 - mpmath.j)**2 / (z * 2 + 2 - 2 * mpmath.j) mpmath.cplot(f, [-5, 5], [-3, 3], points=100000) f = lambda x, y: mpmath.sin(x+y) * mpmath.cos(y) mpmath.splot(f) #, [-mpmath.pi, mpmath.pi], [-mpmath.pi, mpmath.pi]) r, R = 1, 2.5 f = lambda u, v: [r*mpmath.cos(u), (R+r*mpmath.sin(u))*mpmath.cos(v), (R+r*mpmath.sin(u))*mpmath.sin(v)] mpmath.splot(f) #, [0, 2*mpmath.pi], [0, 2*mpmath.pi]) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Arbitrary-precision floating-point arithmetic Step2: Complex function plots Step3: Use the points argument to increase the resolution. Step4: 3D surface plots

<ASSISTANT_TASK:> Python Code: # Set up paths/ os import os import sys this_path=os.getcwd() os.chdir("../data") sys.path.insert(0, this_path) # Load datasets import pandas as pd df = pd.read_csv("MedHelp-posts.csv",index_col=0) df.head(2) df_users = pd.read_csv("MedHelp-users.csv",index_col=0) df_users.head(2) # 1 classify users as professionals and general public: df_users['is expert']=0 for user_id in df_users.index: user_description=df_users.loc[user_id,['user description']].values if ( "," in user_description[0]): print(user_description[0]) df_users.loc[user_id,['is expert']]=1 # Save database: df_users.to_csv("MedHelp-users-class.csv") is_expert=df_users['is expert'] == 1 is_expert.value_counts() # Select user_id from DB where is_professional = 1 experts_ids = df_users[df_users['is expert'] == 1 ].index.values experts_ids non_experts_ids = df_users[df_users['is expert'] == 0 ].index.values # Select * where user_id in experts_ids #df_users.loc[df_users.index.isin(experts_ids)] df_experts=df.loc[df['user id'].isin(experts_ids)] print('Total of posts from expert users {}'.format(len(df_experts))) print('Total of posts {}'.format(len(df))) print('Ratio {}'.format(len(df_experts)/len(df))) del df_experts # Tokenize data import nltk tokenizer = nltk.RegexpTokenizer(r'\w+') # Get the length of tokens into a columns df_text = df['text'].str.lower() df_token = df_text.apply(tokenizer.tokenize) df['token length'] = df_token.apply(len) # Get list of tokens from text in first article: #for text in df_text.values: # ttext = tokenizer.tokenize(text.lower()) # lenght_text=len(ttext) # break import matplotlib.pyplot as plt import seaborn as sns import matplotlib.mlab as mlab from matplotlib import gridspec from scipy.stats import norm import numpy as np from scipy.optimize import curve_fit from lognormal import lognormal, lognormal_stats,truncated_normal from scipy.stats import truncnorm plt.rcParams['text.usetex'] = True plt.rcParams['text.latex.unicode'] = True plt.rcParams.update({'font.size': 24}) nbins=100 fig = plt.figure() #fig=plt.figure(figsize=(2,1)) #fig.set_size_inches(6.6,3.3) gs = gridspec.GridSpec(2, 1) #plt.subplots_adjust(left=0.1,right=1.0,bottom=0.17,top=0.9) #plt.suptitle('Text length (words count)') fig.text(0.04,0.5,'Distribution',va='center',rotation='vertical') #X ticks xmax=1000 x=np.arange(0,xmax,100) #xtics xx=np.arange(1,xmax,1) # Panel 1 ax1=plt.subplot(gs[0]) ax1.set_xlim([0, xmax]) ax1.set_xticks(x) ax1.tick_params(labelbottom='off') #plt.ylabel('') #Class 0 X=df.loc[df['user id'].isin(non_experts_ids)]['token length'].values n,bins,patches=plt.hist(X,nbins,normed=1,facecolor='cyan',align='mid') popt,pcov = curve_fit(truncated_normal,bins[:nbins],n) c0,=plt.plot(xx,truncated_normal(xx,*popt),color='blue',label='non expert') plt.legend(handles=[c0],bbox_to_anchor=(0.45, 0.95), loc=2, borderaxespad=0.) print(popt) mu=X.mean() var=X.var() print("Class 0: Mean,variance: ({},{})".format(mu,var)) # Panel 2 ax2=plt.subplot(gs[1]) ax2.set_xlim([0, xmax]) ax2.set_xticks(x) #ax2.set_yticks(np.arange(0,8,2)) #plt.ylabel('Normal distribution') #Class 1 X=df.loc[df['user id'].isin(experts_ids)]['token length'].values #(mu,sigma) = norm.fit(X) n,bins,patches=plt.hist(X,nbins,normed=1,facecolor='orange',align='mid') popt,pcov = curve_fit(lognormal,bins[:nbins],n) #c1,=plt.plot(xx,mlab.normpdf(xx, mu, sigma),color='darkorange',label='layered') c1,=plt.plot(xx,lognormal(xx,*popt),color='red',label='expert') plt.legend(handles=[c1],bbox_to_anchor=(0.45, 0.95), loc=2, borderaxespad=0.) print("Class 1: Mean,variance:",lognormal_stats(*popt)) #plt.xlabel('Volume ratio (theor./expt.)') plt.show() <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Only 10 out of 505 users are experts! Step2: Length ot text

<ASSISTANT_TASK:> Python Code: import numpy as np file = "tsp.txt" # file = "test2.txt" data = open(file, 'r').readlines() n = int(data[0]) graph = {} for i,v in enumerate(data[1:]): graph[i] = tuple(map(float, v.strip().split(" "))) dist_val = np.zeros([n,n]) for i in range(n): for k in range(n): dist_val[i,k] = dist_val[k,i] = np.sqrt((graph[k][0]-graph[i][0])**2 + (graph[k][1]-graph[i][1])**2) print (graph) %matplotlib inline import matplotlib.pyplot as plt values = list(graph.values()) y = [values[i][0] for i in range(len(values))] x = [values[i][1] for i in range(len(values))] plt.scatter(y,x) plt.show() import collections def to_key(a): my_str = "" for i in a: my_str += str(int(i)) return my_str def to_subset(v, n): a = np.zeros(n) a[v] = 1 return a def create_all_subset(n): A = collections.defaultdict(dict) for m in range(1,n): for a in (itertools.combinations(range(n), m)): key = a + tuple([0 for i in range(n-m)]) print (a, tuple([0 for i in range(n-m)]), key, m, n) for j in range(n): A[to_key(key)][j] = np.inf A[to_key(to_subset(0,n))][0] = 0 return A # res= to_subset([2,3],5) # print (res) # print (to_key(res)) # A = create_all_subset(3) # print (A) # print (index_to_set(10,'25')) # print(set_to_index([1,3])) import itertools def powerset(iterable): "powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)" s = list(iterable) return itertools.chain.from_iterable(itertools.combinations(s, r) for r in range(1,len(s)+1)) def index_to_set(index, n='8'): fmt = '{0:0'+n+'b}' res = fmt.format(index) mylist = list(res) mylist.reverse() print (res) mylist = np.asarray(mylist, dtype=int) ret = np.where(mylist==1) # ret = [] # for i, j in enumerate(mylist): # if j=="1": # ret.append(i) return list(ret[0]) def set_to_index(my_set): # i = [1, 5, 7] ret = 0 for i in my_set: ret += 2**i return ret print ("~~ Test") # print (set_to_index([1])) # print (index_to_set(set_to_index([1]))) ex_all_sets = powerset(range(5)) for s in ex_all_sets: print ("~~ Original set:", s) print ("index:", set_to_index(s)) print ("recovered set:", index_to_set(set_to_index(s),'5')) A = np.full([2**n, n], np.inf) A[set_to_index([0]),0]=0 for i in range(0, n): A[set_to_index([i]),i] = dist_val[i,0] print (set_to_index([i]), dist_val[i,0]) from tqdm import tqdm def _dist(k, j): return np.sqrt((graph[k][0]-graph[j][0])**2 + (graph[k][1]-graph[j][1])**2) FULL = range(n) for m in range(1,n): # all_sets = powerset(range(1,m)) all_sets = itertools.combinations(FULL, m+1) print ("Subset Size:",m) for _set in all_sets: if not _set: continue _set = list(_set) # print ("Len Set", len(_set)) set2_idx = set_to_index(_set) for j in _set: _set2 = _set.copy() _set2.remove(j) if j==0 or not _set2: continue # print ("_set2", _set2) _set2_idx = set_to_index(_set2) # print ("handle Set", _set2, "idx",_set2_idx, "j:", j) minval = np.inf for k in _set2: # print ("idxSet:", _set2_idx, "k:", k, "dist", A[_set2_idx,k]) val = A[_set2_idx,k] + dist_val[k,j] if val < minval: minval = val # print ("minval",minval) A[set2_idx,j] = minval # print (A) my_set = [i for i in range(n)] print ("Full Set", my_set, set_to_index(my_set)) minval = np.inf for j in range(1,n): val = A[set_to_index(my_set),j] + dist_val[j,0] if val < minval: minval = val print ("minval", minval) # print (A[set_to_index(my_set),:]) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Draw points Step2: Initialize the 2-D Array Step3: Run the Dynamic Programming algorithm

<ASSISTANT_TASK:> Python Code: import pandas as pd import numpy as np from IPython.display import Image import matplotlib.pyplot as plt # Import the random forest package from sklearn.ensemble import RandomForestClassifier from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score filename ="CrowdstormingDataJuly1st.csv" Data = pd.read_csv(filename) Data.ix[:10,:13] Data.ix[:10,13:28] # Remove the players without rater 1 / 2 (ie: without photo) because we won't be # able to train or test the values (this can be done as bonus later) Data_hasImage = Data[pd.notnull(Data['photoID'])] # Group by player and do the sum of every column, except for mean_rater (skin color) that we need to move away during the calculation (we don't want to sum skin color values !) Data_aggregated = Data_hasImage.drop(['refNum', 'refCountry'], 1) Data_aggregated = Data_aggregated.groupby(['playerShort', 'position'])['games','yellowCards', 'yellowReds', 'redCards'].sum() Data_aggregated = Data_aggregated.reset_index() # Take information of skin color for each player Data_nbGames_skinColor = Data_hasImage Data_nbGames_skinColor.drop_duplicates('playerShort') Data_nbGames_skinColor['skinColor']=(Data_nbGames_skinColor['rater1']+Data_hasImage['rater2'])/2 Data_nbGames_skinColor = pd.DataFrame(Data_nbGames_skinColor[['playerShort','skinColor']]) Data_aggregated = pd.merge(left=Data_aggregated,right=Data_nbGames_skinColor, how='left', left_on='playerShort', right_on='playerShort') Data_aggregated = Data_aggregated.drop_duplicates('playerShort') Data_aggregated = Data_aggregated.reset_index(drop=True) Data_aggregated # Input x = Data_aggregated x = x.drop(['playerShort'], 1) # We have to convert every columns to floats, to be able to train our model mapping = {'Center Back': 1, 'Attacking Midfielder': 2, 'Right Midfielder': 3, 'Center Midfielder': 4, 'Defensive Midfielder': 5, 'Goalkeeper':6, 'Left Fullback':7, 'Left Midfielder':8, 'Right Fullback':9, 'Center Forward':10, 'Left Winger':11, 'Right Winger':12} x = x.replace({'position': mapping}) x # Output with the same length as the input, that will contains the associated cluster y = pd.DataFrame(index=x.index, columns=['targetCluster']) y.head() # K Means Cluster model = KMeans(n_clusters=2) model = model.fit(x) model # We got a model with two clusters model.labels_ # View the results # Set the size of the plot plt.figure(figsize=(14,7)) # Create a colormap for the two clusters colormap = np.array(['blue', 'lime']) # Plot the Model Classification PARTIALLY plt.scatter((0.5*x.yellowCards + x.yellowReds + x.redCards)/x.games, x.skinColor, c=colormap[model.labels_], s=40) plt.xlabel('Red cards per game (yellow = half a red card)') plt.ylabel('Skin color') plt.title('K Mean Classification') plt.show() cluster = pd.DataFrame(pd.Series(model.labels_, name='cluster')) Data_Clustered = Data_aggregated Data_Clustered['cluster'] = cluster Data_Clustered score = silhouette_score(x, model.labels_) score x_noSkinColor = x.drop(['skinColor'], 1) model = KMeans(n_clusters=2) model = model.fit(x_noSkinColor) score_noSkinColor = silhouette_score(x_noSkinColor, model.labels_) score_noSkinColor score_noSkinColor / score x_noPosition = x.drop(['position'], 1) model = KMeans(n_clusters=2) model = model.fit(x_noPosition) score_noPosition= silhouette_score(x_noPosition, model.labels_) score_noPosition score_noPosition / score x_noGameNumber = x.drop(['games'], 1) model = KMeans(n_clusters=2) model = model.fit(x_noGameNumber) score_noGameNumber = silhouette_score(x_noGameNumber, model.labels_) score_noGameNumber score_noGameNumber / score <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: 1) Peeking into the Data Step2: II. Preparing data Step3: 2) Getting rif of referees and grouping data by soccer player Step4: III. Unsupervized machine learning Step5: (We show only skin color and number of "red cards" because it's a 2D plot, but we actually used 5 parameters Step6: So, do we have any new information ? What can we conclude of this ? Step7: We got a silhouette score of 58%, which is honestly not enough to predict precisely the skin color of new players. A value closer to +1 would have indicated with higher confidence a difference between the clusters. 60% is enough to distinguish the two clusters but, still, we cannot rely on this model. Step8: Seems like removing skin color from the input didn't change anything for the clustering performance ! Step9: Player position doesn't have much impact either. We can try to remove the number of games, but it won't make sense

<ASSISTANT_TASK:> Python Code: # Import directives #%pylab notebook %pylab inline pylab.rcParams['figure.figsize'] = (6, 6) #import warnings #warnings.filterwarnings('ignore') import math import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import axes3d from ipywidgets import interact def plot2d(x, fmt="ok"): plt.axis('equal') plt.axis([-5, 5, -5, 5]) plt.xticks(np.arange(-5, 5, 1)) plt.yticks(np.arange(-5, 5, 1)) plt.axhline(y=0, color='k') plt.axvline(x=0, color='k') plt.plot(x[:,0], x[:,1], fmt) plt.grid() # Define initial points A = np.array([[0., 0.], [1., 0.], [1., 1.], [0., 0.]]) # Define the rotation angle theta = np.radians(30) # Define the rotation matrix R = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) # Rotate points Aprime = np.dot(R, A.T).T # Print and plot print(A) print(Aprime) plot2d(A, fmt="-ok") plot2d(Aprime, fmt="-or") def plot3d(x, axis=None, fmt="ok"): if axis is None: fig = plt.figure() axis = axes3d.Axes3D(fig) axis.scatter(x[:,0], x[:,1], x[:,2], fmt) axis.plot(x[:,0], x[:,1], x[:,2], fmt) # Define initial points A = np.array([[0., 0., 0.], [1., 0., 0.], [0., 0., 0.], [0., 1., 0.], [0., 0., 0.], [0., 0., 1.]]) # Define the rotation angle theta = np.radians(90) # Define the rotation matrices Rx = np.array([[1., 0., 0.], [0., np.cos(theta), -np.sin(theta)], [0., np.sin(theta), np.cos(theta)]]) Ry = np.array([[np.cos(theta), 0., np.sin(theta)], [0., 1., 0. ], [-np.sin(theta), 0., np.cos(theta)]]) Rz = np.array([[np.cos(theta), -np.sin(theta), 0.], [np.sin(theta), np.cos(theta), 0.], [0., 0., 1.]]) # Rotate points Ax = np.dot(Rx, A.T).T Ay = np.dot(Ry, A.T).T Az = np.dot(Rz, A.T).T # Plot fig = plt.figure() ax = axes3d.Axes3D(fig) plot3d(A, axis=ax, fmt="-ok") plot3d(Ax, axis=ax, fmt=":or") plot3d(Ay, axis=ax, fmt=":og") plot3d(Az, axis=ax, fmt=":ob") ax.text(1, 0, 0, "x", color="r") ax.text(0, 1, 0, "y", color="g") ax.text(0, 0, 1, "z", color="b") # Define initial points A = np.array([[0., 0., 0.], [1., 0., 0.], [0., 0., 0.], [0., 1., 0.], [0., 0., 0.], [0., 0., 1.]]) # Define the rotation angle theta = np.radians(10) u = np.array([1., 1., 0.]) ux, uy, uz = u[0], u[1], u[2] c = np.cos(theta) s = np.sin(theta) # Define the rotation matrices R = np.array([[ux**2 * (1-c) + c, ux*uy * (1-c) - uz*s, ux*uz * (1-c) + uy*s], [ux*uy * (1-c) + uz*s, ux**2 * (1-c) + c, uy*uz * (1-c) - ux*s], [ux*uz * (1-c) - uy*s, uy*uz * (1-c) + ux*s, uz**2 * (1-c) + c]]) # Rotate points Ar = np.dot(R, A.T).T # Plot fig = plt.figure() ax = axes3d.Axes3D(fig) plot3d(A, axis=ax, fmt="-ok") plot3d(np.array([np.zeros(3), u]), axis=ax, fmt="--ok") plot3d(Ar, axis=ax, fmt=":or") ax.text(1, 0, 0, "x", color="k") ax.text(0, 1, 0, "y", color="k") ax.text(0, 0, 1, "z", color="k") @interact(a=(-5., 5., 0.1), b=(-5., 5., 0.1), c=(-5., 5., 0.1)) def plot(a, b, c): plt.axis('equal') plt.axis([-5, 5, -5, 5]) plt.xticks(np.arange(-5,5,1)) plt.yticks(np.arange(-5,5,1)) plt.axhline(y=0, color='k') plt.axvline(x=0, color='k') x = np.array([-10., 10.]) f = lambda x: a/(-b) * x + c/(-b) try: plt.plot(x, f(x)) except ZeroDivisionError: print("b should not be equal to 0") plt.grid() # Setup the plot def plot(a, b, c, p, p2): plt.axis('equal') plt.axis([-5, 5, -5, 5]) plt.xticks(np.arange(-5,5,1)) plt.yticks(np.arange(-5,5,1)) plt.axhline(y=0, color='k') plt.axvline(x=0, color='k') x = np.array([-10., 10.]) f = lambda x: a/(-b) * x + c/(-b) plt.plot(x, f(x)) plt.scatter(*p) # Plot the projection point plt.scatter(*p2) plt.plot((p2[0], p[0]), (p2[1], p[1])) #plt.arrow(*p2, *p) # TODO: doesn't work... plt.grid() # Define the distance and projection functions def distance(a, b, c, p): d1 = (a*p[0] + b*p[1] + c) d2 = math.sqrt(math.pow(a, 2.) + math.pow(b, 2.)) d = abs(d1)/d2 return d def projection(a, b, c, p): p2 = ((b*(b*p[0] - a*p[1]) - a*c)/(math.pow(a,2.)+math.pow(b,2.)), (a*(-b*p[0] + a*p[1]) - b*c)/(math.pow(a,2.)+math.pow(b,2.))) return p2 # Define the line and the point a = 2. b = 1. c = -2. p = (-4., 2.) # Compute the distance and the projection point on the line d = distance(a, b, c, p) p2 = projection(a, b, c, p) print("Distance:", d) print("Projection point:", p2) # Plot the line and the point plot(a, b, c, p, p2) # TODO... def angle_point_to_equation(angle_degree, p): angle_radian = math.radians(angle_degree) a = math.tan(angle_radian) b = -1 c = -math.tan(angle_radian) * p[0] + p[1] return a, b, c angle_degree = 30 p0 = (3, 2) a, b, c = angle_point_to_equation(angle_degree, p0) p = (-4., 2.) # Compute the distance and the projection point on the line d = distance(a, b, c, p) p2 = projection(a, b, c, p) print("Distance:", d) print("Projection point:", p2) # Plot the line and the point plot(a, b, c, p, p2) plt.scatter(*p0) # Define initial points to project a = np.array([0., 1., 2.]) # Define camera's position c = np.array([0., 0., 0.]) # Define viewer's position e = np.array([0., 0., -1.]) # Define the orientation of the camera theta = np.array([np.radians(0), np.radians(0), np.radians(0)]) theta_x, theta_y, theta_z = theta[0], theta[1], theta[2] # Define the rotation matrices Rx = np.array([[1., 0., 0.], [0., np.cos(theta_x), np.sin(theta_x)], [0., -np.sin(theta_x), np.cos(theta_x)]]) Ry = np.array([[np.cos(theta_y), 0., -np.sin(theta_y)], [0., 1., 0. ], [np.sin(theta_y), 0., np.cos(theta_y)]]) Rz = np.array([[np.cos(theta_z), np.sin(theta_z), 0.], [-np.sin(theta_z), np.cos(theta_z), 0.], [0., 0., 1.]]) d = np.dot(Rx, Ry) d = np.dot(d, Rz) d = np.dot(d, a-c) ## TODO: which version is correct ? The one above or the one below ? #d = a - c #d = np.dot(Rz, d) #d = np.dot(Ry, d) #d = np.dot(Rx, d) print("d:", d) b = np.array([e[2]/d[2] * d[0] - e[0], e[2]/d[2] * d[1] - e[1]]) print("b:", b) # Alternative to compute b Rf = np.array([[1., 0., -e[0]/e[2], 0.], [0., 1., -e[1]/e[2], 0.], [0., 0., 1., 0.], [0., 0., 1./e[2], 0.]]) f = np.dot(Rf, np.concatenate([d, np.ones(1)])) b = np.array([f[0]/f[3], f[1]/f[3]]) print("b:", b) plot2d(np.array([b, b]), "ok") @interact(theta_x=(-90., 90., 1.), theta_y=(-90., 90., 1.), theta_z=(-90., 90., 1.)) def projection(theta_x, theta_y, theta_z): # Define initial points to project A = np.array([[-1., 0., 1.], [ 1., 0., 1.], [-1., 0., 2.], [ 1., 0., 2.], [-1., 0., 5.], [ 1., 0., 5.], [-1., 0., 15.], [ 1., 0., 15.]]) # Define camera's position c = np.array([0., -2., 0.]) C = np.tile(c, (A.shape[0], 1)) # Define viewer's position e = np.array([0., 0., -1.]) # Define the orientation of the camera theta = np.radians(np.array([theta_x, theta_y, theta_z])) theta_x, theta_y, theta_z = theta[0], theta[1], theta[2] # Define the rotation matrices Rx = np.array([[1., 0., 0.], [0., np.cos(theta_x), np.sin(theta_x)], [0., -np.sin(theta_x), np.cos(theta_x)]]) Ry = np.array([[np.cos(theta_y), 0., -np.sin(theta_y)], [0., 1., 0. ], [np.sin(theta_y), 0., np.cos(theta_y)]]) Rz = np.array([[np.cos(theta_z), np.sin(theta_z), 0.], [-np.sin(theta_z), np.cos(theta_z), 0.], [0., 0., 1.]]) d = np.dot(Rx, Ry) d = np.dot(d, Rz) d = np.dot(d, (A-C).T) ## TODO: which version is correct ? The one above or the one below ? #d = a - c #d = np.dot(Rz, d) #d = np.dot(Ry, d) #d = np.dot(Rx, d) print("d:", d) b = np.array([e[2]/d[2] * d[0] - e[0], e[2]/d[2] * d[1] - e[1]]) print("b:", b) # Alternative to compute b Rf = np.array([[1., 0., -e[0]/e[2], 0.], [0., 1., -e[1]/e[2], 0.], [0., 0., 1., 0.], [0., 0., 1./e[2], 0.]]) # Add a line of ones d = np.vstack([d, np.ones(d.shape[1])]) f = np.dot(Rf, d) b = np.array([f[0]/f[3], f[1]/f[3]]) print("b:", b) plot2d(b.T, "ok") plot2d(b.T, "-k") <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: 2D transformations Step2: Rotation around the origin Step3: 3D transformations Step4: Rotation around the x axis Step5: Rotation around a given axis Step6: Projections Step7: Distance from a point to a line Step8: Line defined by two points Step9: Line defined by a point and an angle Step10: Project 3D points on a plane without perspective Step11: Multiple points version

<ASSISTANT_TASK:> Python Code: %matplotlib inline import bigbang.mailman as mailman import bigbang.graph as graph import bigbang.process as process from bigbang.parse import get_date reload(process) import pandas as pd import datetime import matplotlib.pyplot as plt import numpy as np import math import pytz import pickle import os pd.options.display.mpl_style = 'default' # pandas has a set of preferred graph formatting options urls = ["http://www.ietf.org/mail-archive/text/ietf-privacy/", "http://lists.w3.org/Archives/Public/public-privacy/"] mlists = [mailman.open_list_archives(url,"../archives") for url in urls] activities = [process.activity(ml) for ml in mlists] a = activities[1] # for the first mailing list ta = a.sum(0) # sum along the first axis ta.sort() ta[-10:].plot(kind='barh', width=1) levdf = process.sorted_lev(a) # creates a slightly more nuanced edit distance matrix # and sorts by rows/columns that have the best candidates levdf_corner = levdf.iloc[:25,:25] # just take the top 25 fig = plt.figure(figsize=(15, 12)) plt.pcolor(levdf_corner) plt.yticks(np.arange(0.5, len(levdf_corner.index), 1), levdf_corner.index) plt.xticks(np.arange(0.5, len(levdf_corner.columns), 1), levdf_corner.columns, rotation='vertical') plt.colorbar() plt.show() consolidates = [] # gather pairs of names which have a distance of less than 10 for col in levdf.columns: for index, value in levdf.loc[levdf[col] < 10, col].iteritems(): if index != col: # the name shouldn't be a pair for itself consolidates.append((col, index)) print str(len(consolidates)) + ' candidates for consolidation.' c = process.consolidate_senders_activity(a, consolidates) print 'We removed: ' + str(len(a.columns) - len(c.columns)) + ' columns.' lev_c = process.sorted_lev(c) levc_corner = lev_c.iloc[:25,:25] fig = plt.figure(figsize=(15, 12)) plt.pcolor(levc_corner) plt.yticks(np.arange(0.5, len(levc_corner.index), 1), levc_corner.index) plt.xticks(np.arange(0.5, len(levc_corner.columns), 1), levc_corner.columns, rotation='vertical') plt.colorbar() plt.show() fig, axes = plt.subplots(nrows=2, figsize=(15, 12)) ta = a.sum(0) # sum along the first axis ta.sort() ta[-20:].plot(kind='barh',ax=axes[0], width=1, title='Before consolidation') tc = c.sum(0) tc.sort() tc[-20:].plot(kind='barh',ax=axes[1], width=1, title='After consolidation') plt.show() reload(process) grouped = tc.groupby(process.domain_name_from_email) domain_groups = grouped.size() domain_groups.sort(ascending=True) domain_groups[-20:].plot(kind='barh', width=1, title="Number of participants at domain") domain_messages_sum = grouped.sum() domain_messages_sum.sort(ascending=True) domain_messages_sum[-20:].plot(kind='barh', width=1, title="Number of messages from domain") <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Import the BigBang modules as needed. These should be in your Python environment if you've installed BigBang correctly. Step2: Also, let's import a number of other dependencies we'll use later. Step3: Now let's load the data for analysis. Step4: This variable is for the range of days used in computing rolling averages. Step5: This might be useful for seeing the distribution (does the top message sender dominate?) or for identifying key participants to talk to. Step6: For this still naive measure (edit distance on a normalized string), it appears that there are many duplicates in the &lt;10 range, but that above that the edit distance of short email addresses at common domain names can take over. Step7: We can create the same color plot with the consolidated dataframe to see how the distribution has changed. Step8: Of course, there are still some duplicates, mostly people who are using the same name, but with a different email address at an unrelated domain name. Step9: Okay, not dramatically different, but the consolidation makes the head heavier. There are more people close to that high end, a stronger core group and less a power distribution smoothly from one or two people. Step10: Pandas lets us group by the results of a keying function, which we can use to group participants sending from email addresses with the same domain. Step11: We can also aggregate the number of messages that come from addresses at each domain.

<ASSISTANT_TASK:> Python Code: from pprint import pprint %%HTML <p style="color:red;font-size: 150%;">Classes are more than that in Python. Classes are objects too.</p> %%HTML <p style="color:red;font-size: 150%;">Yes, objects.</p> %%HTML <p style="color:red;font-size: 150%;">As soon as you use the keyword class, Python executes it and creates an OBJECT. The instruction</p> class ObjectCreator(object): pass %%HTML <p style="color:red;font-size: 150%;">This object (the class) is itself capable of creating objects (the instances), and this is why it's a class.</p> object_creator_class = ObjectCreator print(object_creator_class) from copy import copy ObjectCreatorCopy = copy(ObjectCreator) print(ObjectCreatorCopy) print("copy ObjectCreatorCopy is not ObjectCreator: ", ObjectCreatorCopy is not ObjectCreator) print("variable object_creator_class is ObjectCreator: ", object_creator_class is ObjectCreator) print("ObjectCreator has an attribute 'new_attribute': ", hasattr(ObjectCreator, 'new_attribute')) ObjectCreator.new_attribute = 'foo' # you can add attributes to a class print("ObjectCreator has an attribute 'new_attribute': ", hasattr(ObjectCreator, 'new_attribute')) print("attribute 'new_attribute': ", ObjectCreator.new_attribute) def echo(o): print(o) # you can pass a class as a parameter print("return value of passing Object Creator to {}: ".format(echo), echo(ObjectCreator)) %%HTML <p style="color:red;font-size: 150%;">Since classes are objects, you can create them on the fly, like any object.</p> def get_class_by(name): class Foo: pass class Bar: pass classes = { 'foo': Foo, 'bar': Bar } return classes.get(name, None) for class_ in (get_class_by(name) for name in ('foo', 'bar', )): pprint(class_) print(type(1)) print(type("1")) print(type(int)) print(type(ObjectCreator)) print(type(type)) classes = Foo, Bar = [type(name, (), {}) for name in ('Foo', 'Bar')] for class_ in classes: pprint(class_) classes_with_attributes = Foo, Bar = [type(name, (), namespace) for name, namespace in zip( ('Foo', 'Bar'), ( {'assigned_attr': 'foo_attr'}, {'assigned_attr': 'bar_attr'} ) ) ] for class_ in classes_with_attributes: pprint([item for item in vars(class_).items()]) def an_added_function(self): return "I am an added function." Foo.added = an_added_function foo = Foo() print(foo.added()) %%HTML <p style="color:red;font-size: 150%;">[Creating a class on the fly, dynamically] is what Python does when you use the keyword class, and it does so by using a metaclass.</p> %%HTML <p style="color:red;font-size: 150%;">Metaclasses are the 'stuff' that creates classes.</p> %%HTML <p style="color:red;font-size: 150%;">Well, metaclasses are what create these objects. They are the classes' classes.</p> %%HTML <p style="color:red;font-size: 150%;">Everything, and I mean everything, is an object in Python. That includes ints, strings, functions and classes. All of them are objects. And all of them have been created from a class (which is also an object).</p> class MyType(type): pass class MySpecialClass(metaclass=MyType): pass msp = MySpecialClass() type(msp) type(MySpecialClass) type(MyType) %%HTML <p style="color:red;font-size: 150%;">"Build a class"? This is a task for metaclasses. The following implementation comes from Python 3 Patterns, Recipes and Idioms.</p> class Singleton(type): instance = None def __call__(cls, *args, **kwargs): if not cls.instance: cls.instance = super(Singleton, cls).__call__(*args, **kwargs) return cls.instance class ASingleton(metaclass=Singleton): pass a = ASingleton() b = ASingleton() print(a is b) print(hex(id(a))) print(hex(id(b))) %%HTML <p style="color:red;font-size: 150%;">The tasks of the two methods are very clear and distinct: __new__() shall perform actions needed when creating a new instance while __init__ deals with object initialization.</p> class MyClass: def __new__(cls, *args, **kwargs): obj = super().__new__(cls, *args, **kwargs) # do something here obj.one = 1 return obj # instance of the container class, so __init__ is called %%HTML <p style="color:red;font-size: 150%;"> Anyway, __init__() will be called only if you return an instance of the container class. </p> my_class = MyClass() my_class.one class MyInt: def __new__(cls, *args, **kwargs): obj = super().__new__(cls, *args, **kwargs) obj.join = ':'.join return obj mi = MyInt() print(mi.join(str(n) for n in range(10))) class MyBool(int): def __repr__(self): return 'MyBool.' + ['False', 'True'][self] t = MyBool(1) t bool(2) == 1 MyBool(2) == 1 %%HTML <p style="color:red;font-size: 150%;">In many classes we use __init__ to mutate the newly constructed object, typically by storing or otherwise using the arguments to __init__. But we can’t do this with a subclass of int (or any other immuatable) because they are immutable.</p> bool.__doc__ class NewBool(int): def __new__(cls, value): # bool return int.__new__(cls, bool(value)) y = NewBool(56) y == 1 <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: creates in memory an object with the name "ObjectCreator". Step2: But still, it's an object, and therefore Step3: you can copy it Step4: you can add attributes to it Step5: you can pass it as a function parameter Step6: But it's not so dynamic, since you still have to write the whole class yourself. Step7: Well, type has a completely different ability, it can also create classes on the fly. type can take the description of a class as parameters, and return a class. Step8: type accepts a dictionary to define the attributes of the class. So Step9: Eventually you'll want to add methods to your class. Just define a function with the proper signature and assign it as an attribute. Step10: You see where we are going Step11: You define classes in order to create objects, right? Step12: Changing to blog post entitled Python 3 OOP Part 5—Metaclasses Step13: Metaclasses are a very advanced topic in Python, but they have many practical uses. For example, by means of a custom metaclass you may log any time a class is instanced, which can be important for applications that shall keep a low memory usage or have to monitor it. Step14: The constructor mechanism in Python is on the contrary very important, and it is implemented by two methods, instead of just one Step15: Subclassing int Step16: The solution to the problem is to use new. Here we will show that it works, and later we will explain elsewhere exactly what happens.

<ASSISTANT_TASK:> Python Code: # DO NOT EDIT ! from pyesdoc.ipython.model_topic import NotebookOutput # DO NOT EDIT ! DOC = NotebookOutput('cmip6', 'nasa-giss', 'sandbox-2', 'atmos') # Set as follows: DOC.set_author("name", "email") # TODO - please enter value(s) # Set as follows: DOC.set_contributor("name", "email") # TODO - please enter value(s) # Set publication status: # 0=do not publish, 1=publish. DOC.set_publication_status(0) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.overview.model_overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.overview.model_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.overview.model_family') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "AGCM" # "ARCM" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.overview.basic_approximations') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "primitive equations" # "non-hydrostatic" # "anelastic" # "Boussinesq" # "hydrostatic" # "quasi-hydrostatic" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.resolution.horizontal_resolution_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.resolution.canonical_horizontal_resolution') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.resolution.range_horizontal_resolution') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.resolution.number_of_vertical_levels') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.resolution.high_top') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_dynamics') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_shortwave_radiative_transfer') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_longwave_radiative_transfer') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.orography.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "present day" # "modified" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.orography.changes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "related to ice sheets" # "related to tectonics" # "modified mean" # "modified variance if taken into account in model (cf gravity waves)" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "spectral" # "fixed grid" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "finite elements" # "finite volumes" # "finite difference" # "centered finite difference" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_order') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "second" # "third" # "fourth" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.horizontal_pole') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "filter" # "pole rotation" # "artificial island" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.grid_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Gaussian" # "Latitude-Longitude" # "Cubed-Sphere" # "Icosahedral" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.vertical.coordinate_type') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "isobaric" # "sigma" # "hybrid sigma-pressure" # "hybrid pressure" # "vertically lagrangian" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.timestepping_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Adams-Bashforth" # "explicit" # "implicit" # "semi-implicit" # "leap frog" # "multi-step" # "Runge Kutta fifth order" # "Runge Kutta second order" # "Runge Kutta third order" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.prognostic_variables') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "surface pressure" # "wind components" # "divergence/curl" # "temperature" # "potential temperature" # "total water" # "water vapour" # "water liquid" # "water ice" # "total water moments" # "clouds" # "radiation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_boundary_condition') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "sponge layer" # "radiation boundary condition" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_heat') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_wind') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.lateral_boundary.condition') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "sponge layer" # "radiation boundary condition" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "iterated Laplacian" # "bi-harmonic" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Heun" # "Roe and VanLeer" # "Roe and Superbee" # "Prather" # "UTOPIA" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_characteristics') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Eulerian" # "modified Euler" # "Lagrangian" # "semi-Lagrangian" # "cubic semi-Lagrangian" # "quintic semi-Lagrangian" # "mass-conserving" # "finite volume" # "flux-corrected" # "linear" # "quadratic" # "quartic" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conserved_quantities') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "dry mass" # "tracer mass" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conservation_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "conservation fixer" # "Priestley algorithm" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "VanLeer" # "Janjic" # "SUPG (Streamline Upwind Petrov-Galerkin)" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_characteristics') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "2nd order" # "4th order" # "cell-centred" # "staggered grid" # "semi-staggered grid" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_staggering_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Arakawa B-grid" # "Arakawa C-grid" # "Arakawa D-grid" # "Arakawa E-grid" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conserved_quantities') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Angular momentum" # "Horizontal momentum" # "Enstrophy" # "Mass" # "Total energy" # "Vorticity" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conservation_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "conservation fixer" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.aerosols') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "sulphate" # "nitrate" # "sea salt" # "dust" # "ice" # "organic" # "BC (black carbon / soot)" # "SOA (secondary organic aerosols)" # "POM (particulate organic matter)" # "polar stratospheric ice" # "NAT (nitric acid trihydrate)" # "NAD (nitric acid dihydrate)" # "STS (supercooled ternary solution aerosol particle)" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_integration') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "wide-band model" # "correlated-k" # "exponential sum fitting" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.transport_calculation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "two-stream" # "layer interaction" # "bulk" # "adaptive" # "multi-stream" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_intervals') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.greenhouse_gas_complexity') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "CO2" # "CH4" # "N2O" # "CFC-11 eq" # "CFC-12 eq" # "HFC-134a eq" # "Explicit ODSs" # "Explicit other fluorinated gases" # "O3" # "H2O" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.ODS') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "CFC-12" # "CFC-11" # "CFC-113" # "CFC-114" # "CFC-115" # "HCFC-22" # "HCFC-141b" # "HCFC-142b" # "Halon-1211" # "Halon-1301" # "Halon-2402" # "methyl chloroform" # "carbon tetrachloride" # "methyl chloride" # "methylene chloride" # "chloroform" # "methyl bromide" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.other_flourinated_gases') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "HFC-134a" # "HFC-23" # "HFC-32" # "HFC-125" # "HFC-143a" # "HFC-152a" # "HFC-227ea" # "HFC-236fa" # "HFC-245fa" # "HFC-365mfc" # "HFC-43-10mee" # "CF4" # "C2F6" # "C3F8" # "C4F10" # "C5F12" # "C6F14" # "C7F16" # "C8F18" # "c-C4F8" # "NF3" # "SF6" # "SO2F2" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.physical_representation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "bi-modal size distribution" # "ensemble of ice crystals" # "mean projected area" # "ice water path" # "crystal asymmetry" # "crystal aspect ratio" # "effective crystal radius" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.optical_methods') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "T-matrix" # "geometric optics" # "finite difference time domain (FDTD)" # "Mie theory" # "anomalous diffraction approximation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.physical_representation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "cloud droplet number concentration" # "effective cloud droplet radii" # "droplet size distribution" # "liquid water path" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.optical_methods') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "geometric optics" # "Mie theory" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_inhomogeneity.cloud_inhomogeneity') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Monte Carlo Independent Column Approximation" # "Triplecloud" # "analytic" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.physical_representation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "number concentration" # "effective radii" # "size distribution" # "asymmetry" # "aspect ratio" # "mixing state" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.optical_methods') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "T-matrix" # "geometric optics" # "finite difference time domain (FDTD)" # "Mie theory" # "anomalous diffraction approximation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_gases.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_radiation.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_radiation.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_integration') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "wide-band model" # "correlated-k" # "exponential sum fitting" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_radiation.transport_calculation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "two-stream" # "layer interaction" # "bulk" # "adaptive" # "multi-stream" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_intervals') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_GHG.greenhouse_gas_complexity') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "CO2" # "CH4" # "N2O" # "CFC-11 eq" # "CFC-12 eq" # "HFC-134a eq" # "Explicit ODSs" # "Explicit other fluorinated gases" # "O3" # "H2O" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_GHG.ODS') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "CFC-12" # "CFC-11" # "CFC-113" # "CFC-114" # "CFC-115" # "HCFC-22" # "HCFC-141b" # "HCFC-142b" # "Halon-1211" # "Halon-1301" # "Halon-2402" # "methyl chloroform" # "carbon tetrachloride" # "methyl chloride" # "methylene chloride" # "chloroform" # "methyl bromide" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_GHG.other_flourinated_gases') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "HFC-134a" # "HFC-23" # "HFC-32" # "HFC-125" # "HFC-143a" # "HFC-152a" # "HFC-227ea" # "HFC-236fa" # "HFC-245fa" # "HFC-365mfc" # "HFC-43-10mee" # "CF4" # "C2F6" # "C3F8" # "C4F10" # "C5F12" # "C6F14" # "C7F16" # "C8F18" # "c-C4F8" # "NF3" # "SF6" # "SO2F2" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.physical_reprenstation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "bi-modal size distribution" # "ensemble of ice crystals" # "mean projected area" # "ice water path" # "crystal asymmetry" # "crystal aspect ratio" # "effective crystal radius" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.optical_methods') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "T-matrix" # "geometric optics" # "finite difference time domain (FDTD)" # "Mie theory" # "anomalous diffraction approximation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.physical_representation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "cloud droplet number concentration" # "effective cloud droplet radii" # "droplet size distribution" # "liquid water path" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.optical_methods') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "geometric optics" # "Mie theory" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_inhomogeneity.cloud_inhomogeneity') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Monte Carlo Independent Column Approximation" # "Triplecloud" # "analytic" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.physical_representation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "number concentration" # "effective radii" # "size distribution" # "asymmetry" # "aspect ratio" # "mixing state" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.optical_methods') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "T-matrix" # "geometric optics" # "finite difference time domain (FDTD)" # "Mie theory" # "anomalous diffraction approximation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_gases.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Mellor-Yamada" # "Holtslag-Boville" # "EDMF" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.scheme_type') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "TKE prognostic" # "TKE diagnostic" # "TKE coupled with water" # "vertical profile of Kz" # "non-local diffusion" # "Monin-Obukhov similarity" # "Coastal Buddy Scheme" # "Coupled with convection" # "Coupled with gravity waves" # "Depth capped at cloud base" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.closure_order') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.counter_gradient') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_type') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "mass-flux" # "adjustment" # "plume ensemble" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_method') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "CAPE" # "bulk" # "ensemble" # "CAPE/WFN based" # "TKE/CIN based" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.processes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "vertical momentum transport" # "convective momentum transport" # "entrainment" # "detrainment" # "penetrative convection" # "updrafts" # "downdrafts" # "radiative effect of anvils" # "re-evaporation of convective precipitation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.microphysics') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "tuning parameter based" # "single moment" # "two moment" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_type') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "mass-flux" # "cumulus-capped boundary layer" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "same as deep (unified)" # "included in boundary layer turbulence" # "separate diagnosis" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.processes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "convective momentum transport" # "entrainment" # "detrainment" # "penetrative convection" # "re-evaporation of convective precipitation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.microphysics') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "tuning parameter based" # "single moment" # "two moment" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.microphysics_precipitation.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_precipitation.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_precipitation.hydrometeors') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "liquid rain" # "snow" # "hail" # "graupel" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_cloud_microphysics.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_cloud_microphysics.processes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "mixed phase" # "cloud droplets" # "cloud ice" # "ice nucleation" # "water vapour deposition" # "effect of raindrops" # "effect of snow" # "effect of graupel" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.atmos_coupling') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "atmosphere_radiation" # "atmosphere_microphysics_precipitation" # "atmosphere_turbulence_convection" # "atmosphere_gravity_waves" # "atmosphere_solar" # "atmosphere_volcano" # "atmosphere_cloud_simulator" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.uses_separate_treatment') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.processes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "entrainment" # "detrainment" # "bulk cloud" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.prognostic_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.diagnostic_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.prognostic_variables') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "cloud amount" # "liquid" # "ice" # "rain" # "snow" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.optical_cloud_properties.cloud_overlap_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "random" # "maximum" # "maximum-random" # "exponential" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.optical_cloud_properties.cloud_inhomogeneity') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "prognostic" # "diagnostic" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.function_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.function_order') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.convection_coupling') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "coupled with deep" # "coupled with shallow" # "not coupled with convection" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "prognostic" # "diagnostic" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.function_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.function_order') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.convection_coupling') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "coupled with deep" # "coupled with shallow" # "not coupled with convection" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.isscp_attributes.top_height_estimation_method') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "no adjustment" # "IR brightness" # "visible optical depth" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.isscp_attributes.top_height_direction') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "lowest altitude level" # "highest altitude level" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.run_configuration') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Inline" # "Offline" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_grid_points') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_sub_columns') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_levels') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.frequency') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "surface" # "space borne" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.gas_absorption') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.effective_radius') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.lidar_inputs.ice_types') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "ice spheres" # "ice non-spherical" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.lidar_inputs.overlap') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "max" # "random" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.sponge_layer') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Rayleigh friction" # "Diffusive sponge layer" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.background') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "continuous spectrum" # "discrete spectrum" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.subgrid_scale_orography') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "effect on drag" # "effect on lifting" # "enhanced topography" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.source_mechanisms') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "linear mountain waves" # "hydraulic jump" # "envelope orography" # "low level flow blocking" # "statistical sub-grid scale variance" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.calculation_method') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "non-linear calculation" # "more than two cardinal directions" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.propagation_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "linear theory" # "non-linear theory" # "includes boundary layer ducting" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.dissipation_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "total wave" # "single wave" # "spectral" # "linear" # "wave saturation vs Richardson number" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.source_mechanisms') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "convection" # "precipitation" # "background spectrum" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.calculation_method') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "spatially dependent" # "temporally dependent" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.propagation_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "linear theory" # "non-linear theory" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.dissipation_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "total wave" # "single wave" # "spectral" # "linear" # "wave saturation vs Richardson number" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.solar_pathways.pathways') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "SW radiation" # "precipitating energetic particles" # "cosmic rays" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.solar_constant.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "fixed" # "transient" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.solar_constant.fixed_value') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.solar_constant.transient_characteristics') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.orbital_parameters.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "fixed" # "transient" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.orbital_parameters.fixed_reference_date') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.orbital_parameters.transient_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.orbital_parameters.computation_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Berger 1978" # "Laskar 2004" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.insolation_ozone.solar_ozone_impact') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.volcanos.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.volcanos.volcanoes_treatment.volcanoes_implementation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "high frequency solar constant anomaly" # "stratospheric aerosols optical thickness" # "Other: [Please specify]" # TODO - please enter value(s) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Document Authors Step2: Document Contributors Step3: Document Publication Step4: Document Table of Contents Step5: 1.2. Model Name Step6: 1.3. Model Family Step7: 1.4. Basic Approximations Step8: 2. Key Properties --&gt; Resolution Step9: 2.2. Canonical Horizontal Resolution Step10: 2.3. Range Horizontal Resolution Step11: 2.4. Number Of Vertical Levels Step12: 2.5. High Top Step13: 3. Key Properties --&gt; Timestepping Step14: 3.2. Timestep Shortwave Radiative Transfer Step15: 3.3. Timestep Longwave Radiative Transfer Step16: 4. Key Properties --&gt; Orography Step17: 4.2. Changes Step18: 5. Grid --&gt; Discretisation Step19: 6. Grid --&gt; Discretisation --&gt; Horizontal Step20: 6.2. Scheme Method Step21: 6.3. Scheme Order Step22: 6.4. Horizontal Pole Step23: 6.5. Grid Type Step24: 7. Grid --&gt; Discretisation --&gt; Vertical Step25: 8. Dynamical Core Step26: 8.2. Name Step27: 8.3. Timestepping Type Step28: 8.4. Prognostic Variables Step29: 9. Dynamical Core --&gt; Top Boundary Step30: 9.2. Top Heat Step31: 9.3. Top Wind Step32: 10. Dynamical Core --&gt; Lateral Boundary Step33: 11. Dynamical Core --&gt; Diffusion Horizontal Step34: 11.2. Scheme Method Step35: 12. Dynamical Core --&gt; Advection Tracers Step36: 12.2. Scheme Characteristics Step37: 12.3. Conserved Quantities Step38: 12.4. Conservation Method Step39: 13. Dynamical Core --&gt; Advection Momentum Step40: 13.2. Scheme Characteristics Step41: 13.3. Scheme Staggering Type Step42: 13.4. Conserved Quantities Step43: 13.5. Conservation Method Step44: 14. Radiation Step45: 15. Radiation --&gt; Shortwave Radiation Step46: 15.2. Name Step47: 15.3. Spectral Integration Step48: 15.4. Transport Calculation Step49: 15.5. Spectral Intervals Step50: 16. Radiation --&gt; Shortwave GHG Step51: 16.2. ODS Step52: 16.3. Other Flourinated Gases Step53: 17. Radiation --&gt; Shortwave Cloud Ice Step54: 17.2. Physical Representation Step55: 17.3. Optical Methods Step56: 18. Radiation --&gt; Shortwave Cloud Liquid Step57: 18.2. Physical Representation Step58: 18.3. Optical Methods Step59: 19. Radiation --&gt; Shortwave Cloud Inhomogeneity Step60: 20. Radiation --&gt; Shortwave Aerosols Step61: 20.2. Physical Representation Step62: 20.3. Optical Methods Step63: 21. Radiation --&gt; Shortwave Gases Step64: 22. Radiation --&gt; Longwave Radiation Step65: 22.2. Name Step66: 22.3. Spectral Integration Step67: 22.4. Transport Calculation Step68: 22.5. Spectral Intervals Step69: 23. Radiation --&gt; Longwave GHG Step70: 23.2. ODS Step71: 23.3. Other Flourinated Gases Step72: 24. Radiation --&gt; Longwave Cloud Ice Step73: 24.2. Physical Reprenstation Step74: 24.3. Optical Methods Step75: 25. Radiation --&gt; Longwave Cloud Liquid Step76: 25.2. Physical Representation Step77: 25.3. Optical Methods Step78: 26. Radiation --&gt; Longwave Cloud Inhomogeneity Step79: 27. Radiation --&gt; Longwave Aerosols Step80: 27.2. Physical Representation Step81: 27.3. Optical Methods Step82: 28. Radiation --&gt; Longwave Gases Step83: 29. Turbulence Convection Step84: 30. Turbulence Convection --&gt; Boundary Layer Turbulence Step85: 30.2. Scheme Type Step86: 30.3. Closure Order Step87: 30.4. Counter Gradient Step88: 31. Turbulence Convection --&gt; Deep Convection Step89: 31.2. Scheme Type Step90: 31.3. Scheme Method Step91: 31.4. Processes Step92: 31.5. Microphysics Step93: 32. Turbulence Convection --&gt; Shallow Convection Step94: 32.2. Scheme Type Step95: 32.3. Scheme Method Step96: 32.4. Processes Step97: 32.5. Microphysics Step98: 33. Microphysics Precipitation Step99: 34. Microphysics Precipitation --&gt; Large Scale Precipitation Step100: 34.2. Hydrometeors Step101: 35. Microphysics Precipitation --&gt; Large Scale Cloud Microphysics Step102: 35.2. Processes Step103: 36. Cloud Scheme Step104: 36.2. Name Step105: 36.3. Atmos Coupling Step106: 36.4. Uses Separate Treatment Step107: 36.5. Processes Step108: 36.6. Prognostic Scheme Step109: 36.7. Diagnostic Scheme Step110: 36.8. Prognostic Variables Step111: 37. Cloud Scheme --&gt; Optical Cloud Properties Step112: 37.2. Cloud Inhomogeneity Step113: 38. Cloud Scheme --&gt; Sub Grid Scale Water Distribution Step114: 38.2. Function Name Step115: 38.3. Function Order Step116: 38.4. Convection Coupling Step117: 39. Cloud Scheme --&gt; Sub Grid Scale Ice Distribution Step118: 39.2. Function Name Step119: 39.3. Function Order Step120: 39.4. Convection Coupling Step121: 40. Observation Simulation Step122: 41. Observation Simulation --&gt; Isscp Attributes Step123: 41.2. Top Height Direction Step124: 42. Observation Simulation --&gt; Cosp Attributes Step125: 42.2. Number Of Grid Points Step126: 42.3. Number Of Sub Columns Step127: 42.4. Number Of Levels Step128: 43. Observation Simulation --&gt; Radar Inputs Step129: 43.2. Type Step130: 43.3. Gas Absorption Step131: 43.4. Effective Radius Step132: 44. Observation Simulation --&gt; Lidar Inputs Step133: 44.2. Overlap Step134: 45. Gravity Waves Step135: 45.2. Sponge Layer Step136: 45.3. Background Step137: 45.4. Subgrid Scale Orography Step138: 46. Gravity Waves --&gt; Orographic Gravity Waves Step139: 46.2. Source Mechanisms Step140: 46.3. Calculation Method Step141: 46.4. Propagation Scheme Step142: 46.5. Dissipation Scheme Step143: 47. Gravity Waves --&gt; Non Orographic Gravity Waves Step144: 47.2. Source Mechanisms Step145: 47.3. Calculation Method Step146: 47.4. Propagation Scheme Step147: 47.5. Dissipation Scheme Step148: 48. Solar Step149: 49. Solar --&gt; Solar Pathways Step150: 50. Solar --&gt; Solar Constant Step151: 50.2. Fixed Value Step152: 50.3. Transient Characteristics Step153: 51. Solar --&gt; Orbital Parameters Step154: 51.2. Fixed Reference Date Step155: 51.3. Transient Method Step156: 51.4. Computation Method Step157: 52. Solar --&gt; Insolation Ozone Step158: 53. Volcanos Step159: 54. Volcanos --&gt; Volcanoes Treatment

<ASSISTANT_TASK:> Python Code: import sys # system module import pandas as pd # data package import matplotlib.pyplot as plt # graphics module import datetime as dt # date and time module import numpy as np # foundation for pandas import requests from bs4 import BeautifulSoup %matplotlib inline # check versions (overkill, but why not?) print('Python version: ', sys.version) print('Pandas version: ', pd.__version__) print('Today: ', dt.date.today()) plt.style.use('ggplot') url = 'table1_2015.xls' headers = ['Incidents','Offenses','Victims1','Known offenders2'] data_2015 = pd.read_excel(url, skiprows=3, skipfooter=3, parse_cols="A,B,C,D,E", headers = None, names = ["Motivation","Incidents","Offenses","Victims","Known Offenders"]) religion_2015 = data_2015[12:27] #columns = ['Incidents','Offenses', 'Victims','Known Offenders'] #original_2015 = religion_2015.copy(deep=True) #for col in columns: # new_val = religion_2015.iloc[5][col] + religion_2015[col][7:14].sum() #print(new_val) #religion_2015 = religion_2015.set_value('Anti-Other Religion',col,new_val) #religion_2015.ix['Anti-Other Religion',col] = new_val religion_2015 url = 'table1_2014.xls' headers = ['Incidents','Offenses','Victims1','Known offenders2'] data_2014 = pd.read_excel(url, skiprows=3, skipfooter=3, parse_cols="A,B,C,D,E",headers = None, names = ["Motivation","Incidents","Offenses","Victims","Known Offenders"]) religion_2014 = data_2014[9:17] url = 'table1_2013.xls' headers = ['Incidents','Offenses','Victims1','Known offenders2'] data_2013 = pd.read_excel(url, skiprows=3, skipfooter=3, parse_cols="A,B,C,D,E",headers = None, names = ["Motivation","Incidents","Offenses","Victims","Known Offenders"]) religion_2013 = data_2013[9:17] url = 'table1_2012.xls' headers = ['Incidents','Offenses','Victims1','Known offenders2'] data_2012 = pd.read_excel(url, skiprows=3, skipfooter=3, parse_cols="A,B,C,D,E",headers = None, names = ["Motivation","Incidents","Offenses","Victims","Known Offenders"]) religion_2012 = data_2012[8:16] url = 'table1_2011.xls' headers = ['Incidents','Offenses','Victims1','Known offenders2'] data_2011 = pd.read_excel(url, skiprows=3, skipfooter=3, parse_cols="A,B,C,D,E",headers = None, names = ["Motivation","Incidents","Offenses","Victims","Known Offenders"]) religion_2011 = data_2011[8:16] url = 'table1_2010.xls' headers = ['Incidents','Offenses','Victims1','Known offenders2'] data_2010 = pd.read_excel(url, skiprows=3, skipfooter=3, parse_cols="A,B,C,D,E",headers = None, names = ["Motivation","Incidents","Offenses","Victims","Known Offenders"]) religion_2010 = data_2010[7:15] url = 'table1_2008.xls' headers = ['Incidents','Offenses','Victims1','Known offenders2'] data_2008 = pd.read_excel(url, skiprows=3, skipfooter=3, parse_cols="A,B,C,D,E",headers = None, names = ["Motivation","Incidents","Offenses","Victims","Known Offenders"]) religion_2008 = data_2008[7:15] url = 'table1_2007.xls' headers = ['Incidents','Offenses','Victims1','Known offenders2'] data_2007 = pd.read_excel(url, skiprows=3, skipfooter=3, parse_cols="A,B,C,D,E",headers = None, names = ["Motivation","Incidents","Offenses","Victims","Known Offenders"]) religion_2007 = data_2007[7:15] url = 'table1_2006.xls' headers = ['Incidents','Offenses','Victims1','Known offenders2'] data_2006 = pd.read_excel(url, skiprows=3, skipfooter=3, parse_cols="A,B,C,D,E",headers = None, names = ["Motivation","Incidents","Offenses","Victims","Known Offenders"]) religion_2006 = data_2006[7:15] url = 'table1_2005.xls' headers = ['Incidents','Offenses','Victims1','Known offenders2'] data_2005 = pd.read_excel(url, skiprows=3, skipfooter=3, parse_cols="A,B,C,D,E",headers = None, names = ["Motivation","Incidents","Offenses","Victims","Known Offenders"]) religion_2005 = data_2005[7:15] target = 'source_2004.txt' target = open(target, "w") url = "https://www2.fbi.gov/ucr/hc2004/hctable1.htm" data_2004 = requests.get(url) data_2004_soup = BeautifulSoup(data_2004.content, 'html.parser') data_2004_soup religion_part = data_2004_soup.find_all('tr') for row_number in range(9,17): row = religion_part[row_number] tmp_string = '' table_header = row.find('th') table_values = row.find_all('td') tmp_string += table_header.text + ' ' for tb in table_values: tmp_string += tb.text + ' ' tmp_string = tmp_string[:-1].replace('\n','') + '\n' target.write(tmp_string) target.close() # Global Variables all_years = [] # list of all the DataFrames. sourcenames = ["source_"+str(year)+".txt" for year in range(1996,2005)] # list of source files names for 1996-2003, to be converted to .csv targetnames = ["table1_"+str(year)+".csv" for year in range(1996,2005)] # List of name of all .csv files, to be imported in DataFrames datanames = ["religion_"+str(year) for year in range(1996,2005)] # List of name of all dataframes, to be created e.g religion_1998,religion1999 ''' Steps for cleaing and converting the files to .csv format, and loading them in pandas DataFrames, using year 2003 as example: ''' # Loop through the years 1996 to 2003 and repeat the same steps. for i in range(9): source = sourcenames[i] target = targetnames[i] try: #Open the source file e.g source_2003 source = open(source,"r",) except: print("Could not open the source file") else: # Open the target file e.g table1_2003.csv target = open(target, "w") lines = source.readlines(); rows = len(lines) cols = 5 # Loop through each line in the source file: for line in lines: # Remove the endline character i.e '\n' line = line.replace('\n','') # Remove all the commas ',' from the line. line = line.replace(",","") # Split the line into an array, using empty space as split character line_elements= line.split(' ') # Check if the number of array elements are greater than. If so, array[:-4] are part of the index in the table: join these elements into one element. if len(line_elements) > 5: # join the resulting elemets into a string using ',' as join character, and ending the string with newline character '\n'. new_line = " ".join(line_elements[:-4]) + ',' + ','.join(line_elements[-4:]) + '\n' else: # join the resulting elemets into a string using ',' as join character, and ending the string with newline character '\n'. new_line = ','.join(line_elements) + '\n' # write the resutling string to target file. target.write(new_line) # Close the target and source files. source.close() target.close() url = targetnames[i] # Use pandas_readcsv(filename) method to read the .csv file into DataFrames. Set DataFrame headers to ["Motivation","Incidents","Offenses","Victims","Known Offenders"]. Name the returned DataFrame as religion_2003. exec('%s = pd.read_csv(url, engine = "python", names = ["Motivation","Incidents","Offenses","Victims","Known Offenders"])' % (datanames[i])) # Save religion_2003 to all_years array of DataFrames. exec('all_years.append(%s)' % (datanames[i])) # adding DataFrames for years 2005-2015 excluding 2009 into the all_years list all_years.extend([religion_2005,religion_2006,religion_2007,religion_2008,religion_2010,religion_2011,religion_2012,religion_2013,religion_2014]) print('Variable dtypes:\n', religion_2000.dtypes, sep='') religion_1996 rel = religion_1996['Motivation'] rel religion_2003 #Variables and Description # List of Indices (Motivation) in a DataFrame for a particular yaer header_rows = ['All Religion','Anti-Jewish','Anti-Catholic','Anti-Protestants','Anti-Islamic','Anti-Other Religion','Anti-Multiple Religion,Group','Anti-Atheism/Agnosticism/etc.'] # List of headers in a DataFrame for particular yaer columns = ['Incidents','Offenses', 'Victims','Known Offenders'] # List of headers for the new DataFrame all_years_headers = [] #List of list of all values in the DataFrames for all years all_years_list=[] # List of the new indices, representing all reported years, for the new DataFrams. all_years_keys = [] ''' Folloing Steps Are taken for Combining the Data: ''' ''' Combine 8 Motivations with the different data values' headers: * Use the 8 motivations : ['All Religion','Anti-Jewish','Anti-Catholic','Anti-Protestants','Anti-Islamic',' Anti-Other Religion','Anti-Multiple Religion,Group','Anti-Atheism/Agnosticism/etc.'] * Use the 4 Data Values headers = ['Incidents','Offenses', 'Victims','Known Offenders'] * Create 32 headers such that for each motivation, there are 4 different headers for the different data values. * E.g for 'Anti-Jewish' motivation, the resulting headers will be Anti-Jewish: Incidents,Anti-Jewish: Offenses, Anti-Jewish: Victims', and Anti-Jewish: Known Offenders. * all_years_headers is the list of all the generated headers. ''' for row in header_rows: for col in columns: header_val = row + ': ' + str(col) all_years_headers.append(header_val) ''' Generate a list called all_years_keys, which will correspond to the indices of the new DataFrame. ''' for i in list(range(1996,2009)) + list(range(2010, 2015)): all_years_keys.append(str(i)) count = 0 ''' Create the combined DataFrame: ''' # Loop through all_year - the list of the DataFrames representing each year * for single_year in all_years: tmp_list =[] # Within each DataFrameLoop through all rows : for row in range(8): current_row = single_year.iloc[row] # Within each row, loop through all column values for col in columns: # add the column values into a temporary list tmp_list.append(current_row[col]) # Add the temporary list cosisting of all the data values of the data frame into all_years_list. all_years_list.append(tmp_list) count+=1 ''' Create the DataFrame using all_years_list as data, all_years_keys as indices, all_years_headers as headers. Name this DataFrame hc, representing hate crimes ''' hc = pd.DataFrame(all_years_list, columns= all_years_headers, index = all_years_keys) hc anti_islam = hc['Anti-Islamic: Incidents'] anti_islam.plot(kind='line', grid = True, title = 'Anti-Islam Hate Crimes', sharey = True, sharex = True, use_index = True, legend = True, fontsize = 10 ) print(anti_islam) anti_islam_2011 = anti_islam[5] anti_islam_2010 = anti_islam[4] anti_islam_2012 = anti_islam[6] percentage_change_2011 = (((anti_islam_2011 - anti_islam_2010)/anti_islam_2010)*100) percentage_change_2012 = (((anti_islam_2012 - anti_islam_2011)/anti_islam_2011)*100) print("Hate Crimes against Muslims growth in 2011 from 2010: ", percentage_change_2011, '%') print("Hate Crimes against Muslims growth in 2010 from 2011: ", percentage_change_2012, '%') anti_islam_before_2011 = anti_islam[:5].mean() anti_islam_after_2011 = anti_islam[6:].mean() print('Average hate crimes against Muslims before 2011: ', anti_islam_before_2011) print('Average hate crimes against Muslims before 2011: ', anti_islam_after_2011) avg = (((anti_islam_after_2011 - anti_islam_before_2011)/anti_islam_before_2011)*100) print('Percentage increased in the average number of hate crimes against Muslims after 2011: ', avg) anti_religion = hc['All Religion: Incidents'] anti_religion.plot(kind='line', title = 'Hate Crimes Against All Religion', sharey = True, sharex = True, use_index = True, legend = True) anti_religion_2011 = anti_religion[5] anti_religion_2010 = anti_religion[4] anti_religion_2012 = anti_religion[6] avg_before_2011 = anti_religion[:5].mean() avg_after_2011 = anti_religion[6:].mean() avg_after_2008 = anti_religion[13:].mean() print('Average Number of Crimes before 2011 : ', avg_before_2011) print('Avearage Number of Crimes after 2011 : ', avg_after_2011) print('Avearage Number of Crimes after 2008 : ', avg_after_2008) print('Hate Crimes in 2011 : ', anti_religion_2011) anti_muslim_percentage= (hc['Anti-Islamic: Incidents']/hc['All Religion: Incidents'])*100 anti_muslim_percentage.plot(kind = 'line', title = 'Percentage of Hate Crimes Against Muslims Among All Religion', sharey = True, sharex = True, use_index = True) avg_before_2011 = something[:5].mean() #not including 2011 in either average before or after 2011 avg_after_2011 = something[6:].mean() perc_increase = (((avg_after_2011 - avg_before_2011)/avg_before_2011)*100) print(avg_before_2011, avg_after_2011, perc_increase) growth_list = [] <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Data Import (2005 - 2015) Step2: 2014 Data Step3: 2013 Data Step4: 2012 Data Step5: 2011 Data Step6: 2010 Data Step7: 2009 Data Step8: 2007 Data Step9: 2006 Data Step10: 2005 Data Step11: Data Web Scraping Step12: Data Import 1996 - 2004 Steps for Data Collection using example of 2003 Step13: DataFrame Description for a particular year Step14: Combining DataFrames for all years into one DataFrame Step15: Q Step16: Answer Step17: Q Step18: Answer Step19: Answer Step20: Answer

<ASSISTANT_TASK:> Python Code: # DO NOT EDIT ! from pyesdoc.ipython.model_topic import NotebookOutput # DO NOT EDIT ! DOC = NotebookOutput('cmip6', 'messy-consortium', 'sandbox-1', 'atmos') # Set as follows: DOC.set_author("name", "email") # TODO - please enter value(s) # Set as follows: DOC.set_contributor("name", "email") # TODO - please enter value(s) # Set publication status: # 0=do not publish, 1=publish. DOC.set_publication_status(0) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.overview.model_overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.overview.model_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.overview.model_family') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "AGCM" # "ARCM" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.overview.basic_approximations') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "primitive equations" # "non-hydrostatic" # "anelastic" # "Boussinesq" # "hydrostatic" # "quasi-hydrostatic" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.resolution.horizontal_resolution_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.resolution.canonical_horizontal_resolution') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.resolution.range_horizontal_resolution') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.resolution.number_of_vertical_levels') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.resolution.high_top') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_dynamics') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_shortwave_radiative_transfer') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_longwave_radiative_transfer') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.orography.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "present day" # "modified" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.key_properties.orography.changes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "related to ice sheets" # "related to tectonics" # "modified mean" # "modified variance if taken into account in model (cf gravity waves)" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "spectral" # "fixed grid" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "finite elements" # "finite volumes" # "finite difference" # "centered finite difference" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_order') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "second" # "third" # "fourth" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.horizontal_pole') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "filter" # "pole rotation" # "artificial island" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.grid_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Gaussian" # "Latitude-Longitude" # "Cubed-Sphere" # "Icosahedral" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.grid.discretisation.vertical.coordinate_type') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "isobaric" # "sigma" # "hybrid sigma-pressure" # "hybrid pressure" # "vertically lagrangian" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.timestepping_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Adams-Bashforth" # "explicit" # "implicit" # "semi-implicit" # "leap frog" # "multi-step" # "Runge Kutta fifth order" # "Runge Kutta second order" # "Runge Kutta third order" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.prognostic_variables') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "surface pressure" # "wind components" # "divergence/curl" # "temperature" # "potential temperature" # "total water" # "water vapour" # "water liquid" # "water ice" # "total water moments" # "clouds" # "radiation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_boundary_condition') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "sponge layer" # "radiation boundary condition" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_heat') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_wind') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.lateral_boundary.condition') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "sponge layer" # "radiation boundary condition" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "iterated Laplacian" # "bi-harmonic" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Heun" # "Roe and VanLeer" # "Roe and Superbee" # "Prather" # "UTOPIA" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_characteristics') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Eulerian" # "modified Euler" # "Lagrangian" # "semi-Lagrangian" # "cubic semi-Lagrangian" # "quintic semi-Lagrangian" # "mass-conserving" # "finite volume" # "flux-corrected" # "linear" # "quadratic" # "quartic" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conserved_quantities') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "dry mass" # "tracer mass" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conservation_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "conservation fixer" # "Priestley algorithm" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "VanLeer" # "Janjic" # "SUPG (Streamline Upwind Petrov-Galerkin)" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_characteristics') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "2nd order" # "4th order" # "cell-centred" # "staggered grid" # "semi-staggered grid" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_staggering_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Arakawa B-grid" # "Arakawa C-grid" # "Arakawa D-grid" # "Arakawa E-grid" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conserved_quantities') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Angular momentum" # "Horizontal momentum" # "Enstrophy" # "Mass" # "Total energy" # "Vorticity" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conservation_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "conservation fixer" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.aerosols') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "sulphate" # "nitrate" # "sea salt" # "dust" # "ice" # "organic" # "BC (black carbon / soot)" # "SOA (secondary organic aerosols)" # "POM (particulate organic matter)" # "polar stratospheric ice" # "NAT (nitric acid trihydrate)" # "NAD (nitric acid dihydrate)" # "STS (supercooled ternary solution aerosol particle)" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_integration') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "wide-band model" # "correlated-k" # "exponential sum fitting" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.transport_calculation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "two-stream" # "layer interaction" # "bulk" # "adaptive" # "multi-stream" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_intervals') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.greenhouse_gas_complexity') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "CO2" # "CH4" # "N2O" # "CFC-11 eq" # "CFC-12 eq" # "HFC-134a eq" # "Explicit ODSs" # "Explicit other fluorinated gases" # "O3" # "H2O" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.ODS') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "CFC-12" # "CFC-11" # "CFC-113" # "CFC-114" # "CFC-115" # "HCFC-22" # "HCFC-141b" # "HCFC-142b" # "Halon-1211" # "Halon-1301" # "Halon-2402" # "methyl chloroform" # "carbon tetrachloride" # "methyl chloride" # "methylene chloride" # "chloroform" # "methyl bromide" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.other_flourinated_gases') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "HFC-134a" # "HFC-23" # "HFC-32" # "HFC-125" # "HFC-143a" # "HFC-152a" # "HFC-227ea" # "HFC-236fa" # "HFC-245fa" # "HFC-365mfc" # "HFC-43-10mee" # "CF4" # "C2F6" # "C3F8" # "C4F10" # "C5F12" # "C6F14" # "C7F16" # "C8F18" # "c-C4F8" # "NF3" # "SF6" # "SO2F2" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.physical_representation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "bi-modal size distribution" # "ensemble of ice crystals" # "mean projected area" # "ice water path" # "crystal asymmetry" # "crystal aspect ratio" # "effective crystal radius" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.optical_methods') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "T-matrix" # "geometric optics" # "finite difference time domain (FDTD)" # "Mie theory" # "anomalous diffraction approximation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.physical_representation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "cloud droplet number concentration" # "effective cloud droplet radii" # "droplet size distribution" # "liquid water path" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.optical_methods') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "geometric optics" # "Mie theory" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_inhomogeneity.cloud_inhomogeneity') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Monte Carlo Independent Column Approximation" # "Triplecloud" # "analytic" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.physical_representation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "number concentration" # "effective radii" # "size distribution" # "asymmetry" # "aspect ratio" # "mixing state" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.optical_methods') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "T-matrix" # "geometric optics" # "finite difference time domain (FDTD)" # "Mie theory" # "anomalous diffraction approximation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.shortwave_gases.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_radiation.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_radiation.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_integration') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "wide-band model" # "correlated-k" # "exponential sum fitting" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_radiation.transport_calculation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "two-stream" # "layer interaction" # "bulk" # "adaptive" # "multi-stream" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_intervals') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_GHG.greenhouse_gas_complexity') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "CO2" # "CH4" # "N2O" # "CFC-11 eq" # "CFC-12 eq" # "HFC-134a eq" # "Explicit ODSs" # "Explicit other fluorinated gases" # "O3" # "H2O" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_GHG.ODS') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "CFC-12" # "CFC-11" # "CFC-113" # "CFC-114" # "CFC-115" # "HCFC-22" # "HCFC-141b" # "HCFC-142b" # "Halon-1211" # "Halon-1301" # "Halon-2402" # "methyl chloroform" # "carbon tetrachloride" # "methyl chloride" # "methylene chloride" # "chloroform" # "methyl bromide" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_GHG.other_flourinated_gases') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "HFC-134a" # "HFC-23" # "HFC-32" # "HFC-125" # "HFC-143a" # "HFC-152a" # "HFC-227ea" # "HFC-236fa" # "HFC-245fa" # "HFC-365mfc" # "HFC-43-10mee" # "CF4" # "C2F6" # "C3F8" # "C4F10" # "C5F12" # "C6F14" # "C7F16" # "C8F18" # "c-C4F8" # "NF3" # "SF6" # "SO2F2" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.physical_reprenstation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "bi-modal size distribution" # "ensemble of ice crystals" # "mean projected area" # "ice water path" # "crystal asymmetry" # "crystal aspect ratio" # "effective crystal radius" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.optical_methods') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "T-matrix" # "geometric optics" # "finite difference time domain (FDTD)" # "Mie theory" # "anomalous diffraction approximation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.physical_representation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "cloud droplet number concentration" # "effective cloud droplet radii" # "droplet size distribution" # "liquid water path" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.optical_methods') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "geometric optics" # "Mie theory" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_cloud_inhomogeneity.cloud_inhomogeneity') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Monte Carlo Independent Column Approximation" # "Triplecloud" # "analytic" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.physical_representation') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "number concentration" # "effective radii" # "size distribution" # "asymmetry" # "aspect ratio" # "mixing state" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.optical_methods') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "T-matrix" # "geometric optics" # "finite difference time domain (FDTD)" # "Mie theory" # "anomalous diffraction approximation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.radiation.longwave_gases.general_interactions') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "scattering" # "emission/absorption" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Mellor-Yamada" # "Holtslag-Boville" # "EDMF" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.scheme_type') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "TKE prognostic" # "TKE diagnostic" # "TKE coupled with water" # "vertical profile of Kz" # "non-local diffusion" # "Monin-Obukhov similarity" # "Coastal Buddy Scheme" # "Coupled with convection" # "Coupled with gravity waves" # "Depth capped at cloud base" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.closure_order') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.counter_gradient') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_type') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "mass-flux" # "adjustment" # "plume ensemble" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_method') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "CAPE" # "bulk" # "ensemble" # "CAPE/WFN based" # "TKE/CIN based" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.processes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "vertical momentum transport" # "convective momentum transport" # "entrainment" # "detrainment" # "penetrative convection" # "updrafts" # "downdrafts" # "radiative effect of anvils" # "re-evaporation of convective precipitation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.microphysics') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "tuning parameter based" # "single moment" # "two moment" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_type') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "mass-flux" # "cumulus-capped boundary layer" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "same as deep (unified)" # "included in boundary layer turbulence" # "separate diagnosis" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.processes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "convective momentum transport" # "entrainment" # "detrainment" # "penetrative convection" # "re-evaporation of convective precipitation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.microphysics') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "tuning parameter based" # "single moment" # "two moment" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.microphysics_precipitation.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_precipitation.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_precipitation.hydrometeors') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "liquid rain" # "snow" # "hail" # "graupel" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_cloud_microphysics.scheme_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_cloud_microphysics.processes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "mixed phase" # "cloud droplets" # "cloud ice" # "ice nucleation" # "water vapour deposition" # "effect of raindrops" # "effect of snow" # "effect of graupel" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.atmos_coupling') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "atmosphere_radiation" # "atmosphere_microphysics_precipitation" # "atmosphere_turbulence_convection" # "atmosphere_gravity_waves" # "atmosphere_solar" # "atmosphere_volcano" # "atmosphere_cloud_simulator" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.uses_separate_treatment') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.processes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "entrainment" # "detrainment" # "bulk cloud" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.prognostic_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.diagnostic_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.prognostic_variables') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "cloud amount" # "liquid" # "ice" # "rain" # "snow" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.optical_cloud_properties.cloud_overlap_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "random" # "maximum" # "maximum-random" # "exponential" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.optical_cloud_properties.cloud_inhomogeneity') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "prognostic" # "diagnostic" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.function_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.function_order') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.convection_coupling') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "coupled with deep" # "coupled with shallow" # "not coupled with convection" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "prognostic" # "diagnostic" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.function_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.function_order') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.convection_coupling') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "coupled with deep" # "coupled with shallow" # "not coupled with convection" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.isscp_attributes.top_height_estimation_method') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "no adjustment" # "IR brightness" # "visible optical depth" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.isscp_attributes.top_height_direction') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "lowest altitude level" # "highest altitude level" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.run_configuration') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Inline" # "Offline" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_grid_points') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_sub_columns') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_levels') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.frequency') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "surface" # "space borne" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.gas_absorption') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.effective_radius') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.lidar_inputs.ice_types') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "ice spheres" # "ice non-spherical" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.observation_simulation.lidar_inputs.overlap') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "max" # "random" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.sponge_layer') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Rayleigh friction" # "Diffusive sponge layer" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.background') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "continuous spectrum" # "discrete spectrum" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.subgrid_scale_orography') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "effect on drag" # "effect on lifting" # "enhanced topography" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.source_mechanisms') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "linear mountain waves" # "hydraulic jump" # "envelope orography" # "low level flow blocking" # "statistical sub-grid scale variance" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.calculation_method') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "non-linear calculation" # "more than two cardinal directions" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.propagation_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "linear theory" # "non-linear theory" # "includes boundary layer ducting" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.dissipation_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "total wave" # "single wave" # "spectral" # "linear" # "wave saturation vs Richardson number" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.source_mechanisms') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "convection" # "precipitation" # "background spectrum" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.calculation_method') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "spatially dependent" # "temporally dependent" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.propagation_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "linear theory" # "non-linear theory" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.dissipation_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "total wave" # "single wave" # "spectral" # "linear" # "wave saturation vs Richardson number" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.solar_pathways.pathways') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "SW radiation" # "precipitating energetic particles" # "cosmic rays" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.solar_constant.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "fixed" # "transient" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.solar_constant.fixed_value') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.solar_constant.transient_characteristics') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.orbital_parameters.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "fixed" # "transient" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.orbital_parameters.fixed_reference_date') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.orbital_parameters.transient_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.orbital_parameters.computation_method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Berger 1978" # "Laskar 2004" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.solar.insolation_ozone.solar_ozone_impact') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.volcanos.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.atmos.volcanos.volcanoes_treatment.volcanoes_implementation') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "high frequency solar constant anomaly" # "stratospheric aerosols optical thickness" # "Other: [Please specify]" # TODO - please enter value(s) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Document Authors Step2: Document Contributors Step3: Document Publication Step4: Document Table of Contents Step5: 1.2. Model Name Step6: 1.3. Model Family Step7: 1.4. Basic Approximations Step8: 2. Key Properties --&gt; Resolution Step9: 2.2. Canonical Horizontal Resolution Step10: 2.3. Range Horizontal Resolution Step11: 2.4. Number Of Vertical Levels Step12: 2.5. High Top Step13: 3. Key Properties --&gt; Timestepping Step14: 3.2. Timestep Shortwave Radiative Transfer Step15: 3.3. Timestep Longwave Radiative Transfer Step16: 4. Key Properties --&gt; Orography Step17: 4.2. Changes Step18: 5. Grid --&gt; Discretisation Step19: 6. Grid --&gt; Discretisation --&gt; Horizontal Step20: 6.2. Scheme Method Step21: 6.3. Scheme Order Step22: 6.4. Horizontal Pole Step23: 6.5. Grid Type Step24: 7. Grid --&gt; Discretisation --&gt; Vertical Step25: 8. Dynamical Core Step26: 8.2. Name Step27: 8.3. Timestepping Type Step28: 8.4. Prognostic Variables Step29: 9. Dynamical Core --&gt; Top Boundary Step30: 9.2. Top Heat Step31: 9.3. Top Wind Step32: 10. Dynamical Core --&gt; Lateral Boundary Step33: 11. Dynamical Core --&gt; Diffusion Horizontal Step34: 11.2. Scheme Method Step35: 12. Dynamical Core --&gt; Advection Tracers Step36: 12.2. Scheme Characteristics Step37: 12.3. Conserved Quantities Step38: 12.4. Conservation Method Step39: 13. Dynamical Core --&gt; Advection Momentum Step40: 13.2. Scheme Characteristics Step41: 13.3. Scheme Staggering Type Step42: 13.4. Conserved Quantities Step43: 13.5. Conservation Method Step44: 14. Radiation Step45: 15. Radiation --&gt; Shortwave Radiation Step46: 15.2. Name Step47: 15.3. Spectral Integration Step48: 15.4. Transport Calculation Step49: 15.5. Spectral Intervals Step50: 16. Radiation --&gt; Shortwave GHG Step51: 16.2. ODS Step52: 16.3. Other Flourinated Gases Step53: 17. Radiation --&gt; Shortwave Cloud Ice Step54: 17.2. Physical Representation Step55: 17.3. Optical Methods Step56: 18. Radiation --&gt; Shortwave Cloud Liquid Step57: 18.2. Physical Representation Step58: 18.3. Optical Methods Step59: 19. Radiation --&gt; Shortwave Cloud Inhomogeneity Step60: 20. Radiation --&gt; Shortwave Aerosols Step61: 20.2. Physical Representation Step62: 20.3. Optical Methods Step63: 21. Radiation --&gt; Shortwave Gases Step64: 22. Radiation --&gt; Longwave Radiation Step65: 22.2. Name Step66: 22.3. Spectral Integration Step67: 22.4. Transport Calculation Step68: 22.5. Spectral Intervals Step69: 23. Radiation --&gt; Longwave GHG Step70: 23.2. ODS Step71: 23.3. Other Flourinated Gases Step72: 24. Radiation --&gt; Longwave Cloud Ice Step73: 24.2. Physical Reprenstation Step74: 24.3. Optical Methods Step75: 25. Radiation --&gt; Longwave Cloud Liquid Step76: 25.2. Physical Representation Step77: 25.3. Optical Methods Step78: 26. Radiation --&gt; Longwave Cloud Inhomogeneity Step79: 27. Radiation --&gt; Longwave Aerosols Step80: 27.2. Physical Representation Step81: 27.3. Optical Methods Step82: 28. Radiation --&gt; Longwave Gases Step83: 29. Turbulence Convection Step84: 30. Turbulence Convection --&gt; Boundary Layer Turbulence Step85: 30.2. Scheme Type Step86: 30.3. Closure Order Step87: 30.4. Counter Gradient Step88: 31. Turbulence Convection --&gt; Deep Convection Step89: 31.2. Scheme Type Step90: 31.3. Scheme Method Step91: 31.4. Processes Step92: 31.5. Microphysics Step93: 32. Turbulence Convection --&gt; Shallow Convection Step94: 32.2. Scheme Type Step95: 32.3. Scheme Method Step96: 32.4. Processes Step97: 32.5. Microphysics Step98: 33. Microphysics Precipitation Step99: 34. Microphysics Precipitation --&gt; Large Scale Precipitation Step100: 34.2. Hydrometeors Step101: 35. Microphysics Precipitation --&gt; Large Scale Cloud Microphysics Step102: 35.2. Processes Step103: 36. Cloud Scheme Step104: 36.2. Name Step105: 36.3. Atmos Coupling Step106: 36.4. Uses Separate Treatment Step107: 36.5. Processes Step108: 36.6. Prognostic Scheme Step109: 36.7. Diagnostic Scheme Step110: 36.8. Prognostic Variables Step111: 37. Cloud Scheme --&gt; Optical Cloud Properties Step112: 37.2. Cloud Inhomogeneity Step113: 38. Cloud Scheme --&gt; Sub Grid Scale Water Distribution Step114: 38.2. Function Name Step115: 38.3. Function Order Step116: 38.4. Convection Coupling Step117: 39. Cloud Scheme --&gt; Sub Grid Scale Ice Distribution Step118: 39.2. Function Name Step119: 39.3. Function Order Step120: 39.4. Convection Coupling Step121: 40. Observation Simulation Step122: 41. Observation Simulation --&gt; Isscp Attributes Step123: 41.2. Top Height Direction Step124: 42. Observation Simulation --&gt; Cosp Attributes Step125: 42.2. Number Of Grid Points Step126: 42.3. Number Of Sub Columns Step127: 42.4. Number Of Levels Step128: 43. Observation Simulation --&gt; Radar Inputs Step129: 43.2. Type Step130: 43.3. Gas Absorption Step131: 43.4. Effective Radius Step132: 44. Observation Simulation --&gt; Lidar Inputs Step133: 44.2. Overlap Step134: 45. Gravity Waves Step135: 45.2. Sponge Layer Step136: 45.3. Background Step137: 45.4. Subgrid Scale Orography Step138: 46. Gravity Waves --&gt; Orographic Gravity Waves Step139: 46.2. Source Mechanisms Step140: 46.3. Calculation Method Step141: 46.4. Propagation Scheme Step142: 46.5. Dissipation Scheme Step143: 47. Gravity Waves --&gt; Non Orographic Gravity Waves Step144: 47.2. Source Mechanisms Step145: 47.3. Calculation Method Step146: 47.4. Propagation Scheme Step147: 47.5. Dissipation Scheme Step148: 48. Solar Step149: 49. Solar --&gt; Solar Pathways Step150: 50. Solar --&gt; Solar Constant Step151: 50.2. Fixed Value Step152: 50.3. Transient Characteristics Step153: 51. Solar --&gt; Orbital Parameters Step154: 51.2. Fixed Reference Date Step155: 51.3. Transient Method Step156: 51.4. Computation Method Step157: 52. Solar --&gt; Insolation Ozone Step158: 53. Volcanos Step159: 54. Volcanos --&gt; Volcanoes Treatment

<ASSISTANT_TASK:> Python Code: def hi(): print('Hello world!') hi() def cobbDouglas(A,alpha,k): ''' Computes output per worker y given A, alpha, and a value of capital per worker k Args: A (float): TFP alpha (float): Cobb-Douglas parameter k (float or numpy array): capital per worker Returns float or numpy array''' return A*k**alpha # Use cobbDouglas to plot the production function for a bunch of values of alpha between 0 and 1. def solow_example(A,alpha,delta,s,n,K0,L0,T): '''Returns DataFrame with simulated values for a Solow model with labor growth and constant TFP Args: A (float): TFP alpha (float): Cobb-Douglas production function parameter delta (float): capital deprection rate s (float): saving rate n (float): labor force growth rate K0 (float): initial capital stock L0 (float): initial labor force T (int): number of periods to simulate Returns: pandas DataFrame with columns: 'capital', 'labor', 'output', 'consumption', 'investment', 'capital_pw','output_pw', 'consumption_pw', 'investment_pw' ''' # Initialize a variable called capital as a (T+1)x1 array of zeros and set first value to K0 capital = np.zeros(T+1) capital[0] = K0 # Initialize a variable called labor as a (T+1)x1 array of zeros and set first value to L0 labor = np.zeros(T+1) labor[0] = L0 # Compute all capital and labor values by iterating over t from 0 through T for t in np.arange(T): labor[t+1] = (1+n)*labor[t] capital[t+1] = s*A*capital[t]**alpha*labor[t]**(1-alpha) + (1-delta)*capital[t] # Store the simulated capital df in a pandas DataFrame called data df = pd.DataFrame({'capital':capital,'labor':labor}) # Create columns in the DataFrame to store computed values of the other endogenous variables df['output'] = df['capital']**alpha*df['labor']**(1-alpha) df['consumption'] = (1-s)*df['output'] df['investment'] = df['output'] - df['consumption'] # Create columns in the DataFrame to store capital per worker, output per worker, consumption per worker, and investment per worker df['capital_pw'] = df['capital']/df['labor'] df['output_pw'] = df['output']/df['labor'] df['consumption_pw'] = df['consumption']/df['labor'] df['investment_pw'] = df['investment']/df['labor'] return df # Create the DataFrame with simulated values df = solow_example(A=10,alpha=0.35,delta=0.1,s=0.15,n=0.01,K0=20,L0=1,T=100) # Create a 2x2 grid of plots of the capital per worker, output per worker, consumption per worker, and investment per worker fig = plt.figure(figsize=(12,8)) ax = fig.add_subplot(2,2,1) ax.plot(df['capital_pw'],lw=3) ax.grid() ax.set_title('Capital per worker') ax = fig.add_subplot(2,2,2) ax.plot(df['output_pw'],lw=3) ax.grid() ax.set_title('Output per worker') ax = fig.add_subplot(2,2,3) ax.plot(df['consumption_pw'],lw=3) ax.grid() ax.set_title('Consumption per worker') ax = fig.add_subplot(2,2,4) ax.plot(df['investment_pw'],lw=3) ax.grid() ax.set_title('Investment per worker') df1 = solow_example(A=10,alpha=0.35,delta=0.1,s=0.15,n=0.01,K0=20,L0=1,T=100) df2 = solow_example(A=10,alpha=0.35,delta=0.1,s=0.15,n=0.01,K0=10,L0=1,T=100) # Create a 2x2 grid of plots of the capital per worker, output per worker, consumption per worker, and investment per worker fig = plt.figure(figsize=(12,8)) ax = fig.add_subplot(2,2,1) ax.plot(df1['capital_pw'],lw=3) ax.plot(df2['capital_pw'],lw=3) ax.grid() ax.set_title('Capital per worker') ax = fig.add_subplot(2,2,2) ax.plot(df1['output_pw'],lw=3) ax.plot(df2['output_pw'],lw=3) ax.grid() ax.set_title('Output per worker') ax = fig.add_subplot(2,2,3) ax.plot(df1['consumption_pw'],lw=3) ax.plot(df2['consumption_pw'],lw=3) ax.grid() ax.set_title('Consumption per worker') ax = fig.add_subplot(2,2,4) ax.plot(df1['investment_pw'],lw=3,label='$k_0=20$') ax.plot(df2['investment_pw'],lw=3,label='$k_0=10$') ax.grid() ax.set_title('Investment per worker') ax.legend(loc='lower right') df1 = solow_example(A=5,alpha=0.35,delta=0.1,s=0.15,n=0.01,K0=10,L0=1,T=100) df2 = solow_example(A=10,alpha=0.35,delta=0.1,s=0.15,n=0.01,K0=10,L0=1,T=100) df3 = solow_example(A=15,alpha=0.35,delta=0.1,s=0.15,n=0.01,K0=10,L0=1,T=100) # Create a 2x2 grid of plots of the capital per worker, output per worker, consumption per worker, and investment per worker fig = plt.figure(figsize=(12,8)) ax = fig.add_subplot(2,2,1) ax.plot(df1['capital_pw'],lw=3) ax.plot(df2['capital_pw'],lw=3) ax.plot(df3['capital_pw'],lw=3) ax.grid() ax.set_title('Capital per worker') ax = fig.add_subplot(2,2,2) ax.plot(df1['output_pw'],lw=3) ax.plot(df2['output_pw'],lw=3) ax.plot(df3['output_pw'],lw=3) ax.grid() ax.set_title('Output per worker') ax = fig.add_subplot(2,2,3) ax.plot(df1['consumption_pw'],lw=3) ax.plot(df2['consumption_pw'],lw=3) ax.plot(df3['consumption_pw'],lw=3) ax.grid() ax.set_title('Consumption per worker') ax = fig.add_subplot(2,2,4) ax.plot(df1['investment_pw'],lw=3,label='$A=20$') ax.plot(df2['investment_pw'],lw=3,label='$A=10$') ax.plot(df3['investment_pw'],lw=3,label='$A=10$') ax.grid() ax.set_title('Investment per worker') ax.legend(loc='lower right',ncol=3) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Notice that in the previous example, the function takes no arguments and returns nothing. It just does the task that it's supposed to. Step2: Note the cobbDouglas() has a docstring. The docstring is optional, but it tells users about the function. The contents of the docstring can be accessed with the help() function. It's good practice to make use of doc strings. Step3: The Solow model with exogenous population growth Step4: Example Step5: Example Step6: Example

<ASSISTANT_TASK:> Python Code: from search import * from notebook import psource, heatmap, gaussian_kernel, show_map, final_path_colors, display_visual, plot_NQueens # Needed to hide warnings in the matplotlib sections import warnings warnings.filterwarnings("ignore") %matplotlib inline import networkx as nx import matplotlib.pyplot as plt from matplotlib import lines from ipywidgets import interact import ipywidgets as widgets from IPython.display import display import time psource(Problem) psource(Node) psource(GraphProblem) romania_map = UndirectedGraph(dict( Arad=dict(Zerind=75, Sibiu=140, Timisoara=118), Bucharest=dict(Urziceni=85, Pitesti=101, Giurgiu=90, Fagaras=211), Craiova=dict(Drobeta=120, Rimnicu=146, Pitesti=138), Drobeta=dict(Mehadia=75), Eforie=dict(Hirsova=86), Fagaras=dict(Sibiu=99), Hirsova=dict(Urziceni=98), Iasi=dict(Vaslui=92, Neamt=87), Lugoj=dict(Timisoara=111, Mehadia=70), Oradea=dict(Zerind=71, Sibiu=151), Pitesti=dict(Rimnicu=97), Rimnicu=dict(Sibiu=80), Urziceni=dict(Vaslui=142))) romania_map.locations = dict( Arad=(91, 492), Bucharest=(400, 327), Craiova=(253, 288), Drobeta=(165, 299), Eforie=(562, 293), Fagaras=(305, 449), Giurgiu=(375, 270), Hirsova=(534, 350), Iasi=(473, 506), Lugoj=(165, 379), Mehadia=(168, 339), Neamt=(406, 537), Oradea=(131, 571), Pitesti=(320, 368), Rimnicu=(233, 410), Sibiu=(207, 457), Timisoara=(94, 410), Urziceni=(456, 350), Vaslui=(509, 444), Zerind=(108, 531)) romania_problem = GraphProblem('Arad', 'Bucharest', romania_map) romania_locations = romania_map.locations print(romania_locations) # node colors, node positions and node label positions node_colors = {node: 'white' for node in romania_map.locations.keys()} node_positions = romania_map.locations node_label_pos = { k:[v[0],v[1]-10] for k,v in romania_map.locations.items() } edge_weights = {(k, k2) : v2 for k, v in romania_map.graph_dict.items() for k2, v2 in v.items()} romania_graph_data = { 'graph_dict' : romania_map.graph_dict, 'node_colors': node_colors, 'node_positions': node_positions, 'node_label_positions': node_label_pos, 'edge_weights': edge_weights } show_map(romania_graph_data) psource(SimpleProblemSolvingAgentProgram) class vacuumAgent(SimpleProblemSolvingAgentProgram): def update_state(self, state, percept): return percept def formulate_goal(self, state): goal = [state7, state8] return goal def formulate_problem(self, state, goal): problem = state return problem def search(self, problem): if problem == state1: seq = ["Suck", "Right", "Suck"] elif problem == state2: seq = ["Suck", "Left", "Suck"] elif problem == state3: seq = ["Right", "Suck"] elif problem == state4: seq = ["Suck"] elif problem == state5: seq = ["Suck"] elif problem == state6: seq = ["Left", "Suck"] return seq state1 = [(0, 0), [(0, 0), "Dirty"], [(1, 0), ["Dirty"]]] state2 = [(1, 0), [(0, 0), "Dirty"], [(1, 0), ["Dirty"]]] state3 = [(0, 0), [(0, 0), "Clean"], [(1, 0), ["Dirty"]]] state4 = [(1, 0), [(0, 0), "Clean"], [(1, 0), ["Dirty"]]] state5 = [(0, 0), [(0, 0), "Dirty"], [(1, 0), ["Clean"]]] state6 = [(1, 0), [(0, 0), "Dirty"], [(1, 0), ["Clean"]]] state7 = [(0, 0), [(0, 0), "Clean"], [(1, 0), ["Clean"]]] state8 = [(1, 0), [(0, 0), "Clean"], [(1, 0), ["Clean"]]] a = vacuumAgent(state1) print(a(state6)) print(a(state1)) print(a(state3)) def tree_breadth_search_for_vis(problem): Search through the successors of a problem to find a goal. The argument frontier should be an empty queue. Don't worry about repeated paths to a state. [Figure 3.7] # we use these two variables at the time of visualisations iterations = 0 all_node_colors = [] node_colors = {k : 'white' for k in problem.graph.nodes()} #Adding first node to the queue frontier = deque([Node(problem.initial)]) node_colors[Node(problem.initial).state] = "orange" iterations += 1 all_node_colors.append(dict(node_colors)) while frontier: #Popping first node of queue node = frontier.popleft() # modify the currently searching node to red node_colors[node.state] = "red" iterations += 1 all_node_colors.append(dict(node_colors)) if problem.goal_test(node.state): # modify goal node to green after reaching the goal node_colors[node.state] = "green" iterations += 1 all_node_colors.append(dict(node_colors)) return(iterations, all_node_colors, node) frontier.extend(node.expand(problem)) for n in node.expand(problem): node_colors[n.state] = "orange" iterations += 1 all_node_colors.append(dict(node_colors)) # modify the color of explored nodes to gray node_colors[node.state] = "gray" iterations += 1 all_node_colors.append(dict(node_colors)) return None def breadth_first_tree_search(problem): "Search the shallowest nodes in the search tree first." iterations, all_node_colors, node = tree_breadth_search_for_vis(problem) return(iterations, all_node_colors, node) all_node_colors = [] romania_problem = GraphProblem('Arad', 'Bucharest', romania_map) a, b, c = breadth_first_tree_search(romania_problem) display_visual(romania_graph_data, user_input=False, algorithm=breadth_first_tree_search, problem=romania_problem) def tree_depth_search_for_vis(problem): Search through the successors of a problem to find a goal. The argument frontier should be an empty queue. Don't worry about repeated paths to a state. [Figure 3.7] # we use these two variables at the time of visualisations iterations = 0 all_node_colors = [] node_colors = {k : 'white' for k in problem.graph.nodes()} #Adding first node to the stack frontier = [Node(problem.initial)] node_colors[Node(problem.initial).state] = "orange" iterations += 1 all_node_colors.append(dict(node_colors)) while frontier: #Popping first node of stack node = frontier.pop() # modify the currently searching node to red node_colors[node.state] = "red" iterations += 1 all_node_colors.append(dict(node_colors)) if problem.goal_test(node.state): # modify goal node to green after reaching the goal node_colors[node.state] = "green" iterations += 1 all_node_colors.append(dict(node_colors)) return(iterations, all_node_colors, node) frontier.extend(node.expand(problem)) for n in node.expand(problem): node_colors[n.state] = "orange" iterations += 1 all_node_colors.append(dict(node_colors)) # modify the color of explored nodes to gray node_colors[node.state] = "gray" iterations += 1 all_node_colors.append(dict(node_colors)) return None def depth_first_tree_search(problem): "Search the deepest nodes in the search tree first." iterations, all_node_colors, node = tree_depth_search_for_vis(problem) return(iterations, all_node_colors, node) all_node_colors = [] romania_problem = GraphProblem('Arad', 'Bucharest', romania_map) display_visual(romania_graph_data, user_input=False, algorithm=depth_first_tree_search, problem=romania_problem) def breadth_first_search_graph(problem): "[Figure 3.11]" # we use these two variables at the time of visualisations iterations = 0 all_node_colors = [] node_colors = {k : 'white' for k in problem.graph.nodes()} node = Node(problem.initial) node_colors[node.state] = "red" iterations += 1 all_node_colors.append(dict(node_colors)) if problem.goal_test(node.state): node_colors[node.state] = "green" iterations += 1 all_node_colors.append(dict(node_colors)) return(iterations, all_node_colors, node) frontier = deque([node]) # modify the color of frontier nodes to blue node_colors[node.state] = "orange" iterations += 1 all_node_colors.append(dict(node_colors)) explored = set() while frontier: node = frontier.popleft() node_colors[node.state] = "red" iterations += 1 all_node_colors.append(dict(node_colors)) explored.add(node.state) for child in node.expand(problem): if child.state not in explored and child not in frontier: if problem.goal_test(child.state): node_colors[child.state] = "green" iterations += 1 all_node_colors.append(dict(node_colors)) return(iterations, all_node_colors, child) frontier.append(child) node_colors[child.state] = "orange" iterations += 1 all_node_colors.append(dict(node_colors)) node_colors[node.state] = "gray" iterations += 1 all_node_colors.append(dict(node_colors)) return None all_node_colors = [] romania_problem = GraphProblem('Arad', 'Bucharest', romania_map) display_visual(romania_graph_data, user_input=False, algorithm=breadth_first_search_graph, problem=romania_problem) def graph_search_for_vis(problem): Search through the successors of a problem to find a goal. The argument frontier should be an empty queue. If two paths reach a state, only use the first one. [Figure 3.7] # we use these two variables at the time of visualisations iterations = 0 all_node_colors = [] node_colors = {k : 'white' for k in problem.graph.nodes()} frontier = [(Node(problem.initial))] explored = set() # modify the color of frontier nodes to orange node_colors[Node(problem.initial).state] = "orange" iterations += 1 all_node_colors.append(dict(node_colors)) while frontier: # Popping first node of stack node = frontier.pop() # modify the currently searching node to red node_colors[node.state] = "red" iterations += 1 all_node_colors.append(dict(node_colors)) if problem.goal_test(node.state): # modify goal node to green after reaching the goal node_colors[node.state] = "green" iterations += 1 all_node_colors.append(dict(node_colors)) return(iterations, all_node_colors, node) explored.add(node.state) frontier.extend(child for child in node.expand(problem) if child.state not in explored and child not in frontier) for n in frontier: # modify the color of frontier nodes to orange node_colors[n.state] = "orange" iterations += 1 all_node_colors.append(dict(node_colors)) # modify the color of explored nodes to gray node_colors[node.state] = "gray" iterations += 1 all_node_colors.append(dict(node_colors)) return None def depth_first_graph_search(problem): Search the deepest nodes in the search tree first. iterations, all_node_colors, node = graph_search_for_vis(problem) return(iterations, all_node_colors, node) all_node_colors = [] romania_problem = GraphProblem('Arad', 'Bucharest', romania_map) display_visual(romania_graph_data, user_input=False, algorithm=depth_first_graph_search, problem=romania_problem) def best_first_graph_search_for_vis(problem, f): Search the nodes with the lowest f scores first. You specify the function f(node) that you want to minimize; for example, if f is a heuristic estimate to the goal, then we have greedy best first search; if f is node.depth then we have breadth-first search. There is a subtlety: the line "f = memoize(f, 'f')" means that the f values will be cached on the nodes as they are computed. So after doing a best first search you can examine the f values of the path returned. # we use these two variables at the time of visualisations iterations = 0 all_node_colors = [] node_colors = {k : 'white' for k in problem.graph.nodes()} f = memoize(f, 'f') node = Node(problem.initial) node_colors[node.state] = "red" iterations += 1 all_node_colors.append(dict(node_colors)) if problem.goal_test(node.state): node_colors[node.state] = "green" iterations += 1 all_node_colors.append(dict(node_colors)) return(iterations, all_node_colors, node) frontier = PriorityQueue('min', f) frontier.append(node) node_colors[node.state] = "orange" iterations += 1 all_node_colors.append(dict(node_colors)) explored = set() while frontier: node = frontier.pop() node_colors[node.state] = "red" iterations += 1 all_node_colors.append(dict(node_colors)) if problem.goal_test(node.state): node_colors[node.state] = "green" iterations += 1 all_node_colors.append(dict(node_colors)) return(iterations, all_node_colors, node) explored.add(node.state) for child in node.expand(problem): if child.state not in explored and child not in frontier: frontier.append(child) node_colors[child.state] = "orange" iterations += 1 all_node_colors.append(dict(node_colors)) elif child in frontier: incumbent = frontier[child] if f(child) < incumbent: del frontier[child] frontier.append(child) node_colors[child.state] = "orange" iterations += 1 all_node_colors.append(dict(node_colors)) node_colors[node.state] = "gray" iterations += 1 all_node_colors.append(dict(node_colors)) return None def uniform_cost_search_graph(problem): "[Figure 3.14]" #Uniform Cost Search uses Best First Search algorithm with f(n) = g(n) iterations, all_node_colors, node = best_first_graph_search_for_vis(problem, lambda node: node.path_cost) return(iterations, all_node_colors, node) all_node_colors = [] romania_problem = GraphProblem('Arad', 'Bucharest', romania_map) display_visual(romania_graph_data, user_input=False, algorithm=uniform_cost_search_graph, problem=romania_problem) def depth_limited_search_graph(problem, limit = -1): ''' Perform depth first search of graph g. if limit >= 0, that is the maximum depth of the search. ''' # we use these two variables at the time of visualisations iterations = 0 all_node_colors = [] node_colors = {k : 'white' for k in problem.graph.nodes()} frontier = [Node(problem.initial)] explored = set() cutoff_occurred = False node_colors[Node(problem.initial).state] = "orange" iterations += 1 all_node_colors.append(dict(node_colors)) while frontier: # Popping first node of queue node = frontier.pop() # modify the currently searching node to red node_colors[node.state] = "red" iterations += 1 all_node_colors.append(dict(node_colors)) if problem.goal_test(node.state): # modify goal node to green after reaching the goal node_colors[node.state] = "green" iterations += 1 all_node_colors.append(dict(node_colors)) return(iterations, all_node_colors, node) elif limit >= 0: cutoff_occurred = True limit += 1 all_node_colors.pop() iterations -= 1 node_colors[node.state] = "gray" explored.add(node.state) frontier.extend(child for child in node.expand(problem) if child.state not in explored and child not in frontier) for n in frontier: limit -= 1 # modify the color of frontier nodes to orange node_colors[n.state] = "orange" iterations += 1 all_node_colors.append(dict(node_colors)) # modify the color of explored nodes to gray node_colors[node.state] = "gray" iterations += 1 all_node_colors.append(dict(node_colors)) return 'cutoff' if cutoff_occurred else None def depth_limited_search_for_vis(problem): Search the deepest nodes in the search tree first. iterations, all_node_colors, node = depth_limited_search_graph(problem) return(iterations, all_node_colors, node) all_node_colors = [] romania_problem = GraphProblem('Arad', 'Bucharest', romania_map) display_visual(romania_graph_data, user_input=False, algorithm=depth_limited_search_for_vis, problem=romania_problem) def iterative_deepening_search_for_vis(problem): for depth in range(sys.maxsize): iterations, all_node_colors, node=depth_limited_search_for_vis(problem) if iterations: return (iterations, all_node_colors, node) all_node_colors = [] romania_problem = GraphProblem('Arad', 'Bucharest', romania_map) display_visual(romania_graph_data, user_input=False, algorithm=iterative_deepening_search_for_vis, problem=romania_problem) def greedy_best_first_search(problem, h=None): Greedy Best-first graph search is an informative searching algorithm with f(n) = h(n). You need to specify the h function when you call best_first_search, or else in your Problem subclass. h = memoize(h or problem.h, 'h') iterations, all_node_colors, node = best_first_graph_search_for_vis(problem, lambda n: h(n)) return(iterations, all_node_colors, node) all_node_colors = [] romania_problem = GraphProblem('Arad', 'Bucharest', romania_map) display_visual(romania_graph_data, user_input=False, algorithm=greedy_best_first_search, problem=romania_problem) def astar_search_graph(problem, h=None): A* search is best-first graph search with f(n) = g(n)+h(n). You need to specify the h function when you call astar_search, or else in your Problem subclass. h = memoize(h or problem.h, 'h') iterations, all_node_colors, node = best_first_graph_search_for_vis(problem, lambda n: n.path_cost + h(n)) return(iterations, all_node_colors, node) all_node_colors = [] romania_problem = GraphProblem('Arad', 'Bucharest', romania_map) display_visual(romania_graph_data, user_input=False, algorithm=astar_search_graph, problem=romania_problem) def recursive_best_first_search_for_vis(problem, h=None): [Figure 3.26] Recursive best-first search # we use these two variables at the time of visualizations iterations = 0 all_node_colors = [] node_colors = {k : 'white' for k in problem.graph.nodes()} h = memoize(h or problem.h, 'h') def RBFS(problem, node, flimit): nonlocal iterations def color_city_and_update_map(node, color): node_colors[node.state] = color nonlocal iterations iterations += 1 all_node_colors.append(dict(node_colors)) if problem.goal_test(node.state): color_city_and_update_map(node, 'green') return (iterations, all_node_colors, node), 0 # the second value is immaterial successors = node.expand(problem) if len(successors) == 0: color_city_and_update_map(node, 'gray') return (iterations, all_node_colors, None), infinity for s in successors: color_city_and_update_map(s, 'orange') s.f = max(s.path_cost + h(s), node.f) while True: # Order by lowest f value successors.sort(key=lambda x: x.f) best = successors[0] if best.f > flimit: color_city_and_update_map(node, 'gray') return (iterations, all_node_colors, None), best.f if len(successors) > 1: alternative = successors[1].f else: alternative = infinity node_colors[node.state] = 'gray' node_colors[best.state] = 'red' iterations += 1 all_node_colors.append(dict(node_colors)) result, best.f = RBFS(problem, best, min(flimit, alternative)) if result[2] is not None: color_city_and_update_map(node, 'green') return result, best.f else: color_city_and_update_map(node, 'red') node = Node(problem.initial) node.f = h(node) node_colors[node.state] = 'red' iterations += 1 all_node_colors.append(dict(node_colors)) result, bestf = RBFS(problem, node, infinity) return result all_node_colors = [] romania_problem = GraphProblem('Arad', 'Bucharest', romania_map) display_visual(romania_graph_data, user_input=False, algorithm=recursive_best_first_search_for_vis, problem=romania_problem) all_node_colors = [] # display_visual(romania_graph_data, user_input=True, algorithm=breadth_first_tree_search) algorithms = { "Breadth First Tree Search": tree_breadth_search_for_vis, "Depth First Tree Search": tree_depth_search_for_vis, "Breadth First Search": breadth_first_search_graph, "Depth First Graph Search": graph_search_for_vis, "Best First Graph Search": best_first_graph_search_for_vis, "Uniform Cost Search": uniform_cost_search_graph, "Depth Limited Search": depth_limited_search_for_vis, "Iterative Deepening Search": iterative_deepening_search_for_vis, "Greedy Best First Search": greedy_best_first_search, "A-star Search": astar_search_graph, "Recursive Best First Search": recursive_best_first_search_for_vis} display_visual(romania_graph_data, algorithm=algorithms, user_input=True) psource(recursive_best_first_search) recursive_best_first_search(romania_problem).solution() puzzle = EightPuzzle((2, 4, 3, 1, 5, 6, 7, 8, 0)) assert puzzle.check_solvability((2, 4, 3, 1, 5, 6, 7, 8, 0)) recursive_best_first_search(puzzle).solution() goal = [1, 2, 3, 4, 5, 6, 7, 8, 0] # Heuristics for 8 Puzzle Problem import math def linear(node): return sum([1 if node.state[i] != goal[i] else 0 for i in range(8)]) def manhattan(node): state = node.state index_goal = {0:[2,2], 1:[0,0], 2:[0,1], 3:[0,2], 4:[1,0], 5:[1,1], 6:[1,2], 7:[2,0], 8:[2,1]} index_state = {} index = [[0,0], [0,1], [0,2], [1,0], [1,1], [1,2], [2,0], [2,1], [2,2]] x, y = 0, 0 for i in range(len(state)): index_state[state[i]] = index[i] mhd = 0 for i in range(8): for j in range(2): mhd = abs(index_goal[i][j] - index_state[i][j]) + mhd return mhd def sqrt_manhattan(node): state = node.state index_goal = {0:[2,2], 1:[0,0], 2:[0,1], 3:[0,2], 4:[1,0], 5:[1,1], 6:[1,2], 7:[2,0], 8:[2,1]} index_state = {} index = [[0,0], [0,1], [0,2], [1,0], [1,1], [1,2], [2,0], [2,1], [2,2]] x, y = 0, 0 for i in range(len(state)): index_state[state[i]] = index[i] mhd = 0 for i in range(8): for j in range(2): mhd = (index_goal[i][j] - index_state[i][j])**2 + mhd return math.sqrt(mhd) def max_heuristic(node): score1 = manhattan(node) score2 = linear(node) return max(score1, score2) # Solving the puzzle puzzle = EightPuzzle((2, 4, 3, 1, 5, 6, 7, 8, 0)) puzzle.check_solvability((2, 4, 3, 1, 5, 6, 7, 8, 0)) # checks whether the initialized configuration is solvable or not astar_search(puzzle).solution() astar_search(puzzle, linear).solution() astar_search(puzzle, manhattan).solution() astar_search(puzzle, sqrt_manhattan).solution() astar_search(puzzle, max_heuristic).solution() recursive_best_first_search(puzzle, manhattan).solution() puzzle_1 = EightPuzzle((2, 4, 3, 1, 5, 6, 7, 8, 0)) puzzle_2 = EightPuzzle((1, 2, 3, 4, 5, 6, 0, 7, 8)) puzzle_3 = EightPuzzle((1, 2, 3, 4, 5, 7, 8, 6, 0)) %%timeit astar_search(puzzle_1) astar_search(puzzle_2) astar_search(puzzle_3) %%timeit astar_search(puzzle_1, linear) astar_search(puzzle_2, linear) astar_search(puzzle_3, linear) %%timeit astar_search(puzzle_1, manhattan) astar_search(puzzle_2, manhattan) astar_search(puzzle_3, manhattan) %%timeit astar_search(puzzle_1, sqrt_manhattan) astar_search(puzzle_2, sqrt_manhattan) astar_search(puzzle_3, sqrt_manhattan) %%timeit astar_search(puzzle_1, max_heuristic) astar_search(puzzle_2, max_heuristic) astar_search(puzzle_3, max_heuristic) %%timeit recursive_best_first_search(puzzle_1, linear) recursive_best_first_search(puzzle_2, linear) recursive_best_first_search(puzzle_3, linear) psource(hill_climbing) class TSP_problem(Problem): subclass of Problem to define various functions def two_opt(self, state): Neighbour generating function for Traveling Salesman Problem neighbour_state = state[:] left = random.randint(0, len(neighbour_state) - 1) right = random.randint(0, len(neighbour_state) - 1) if left > right: left, right = right, left neighbour_state[left: right + 1] = reversed(neighbour_state[left: right + 1]) return neighbour_state def actions(self, state): action that can be excuted in given state return [self.two_opt] def result(self, state, action): result after applying the given action on the given state return action(state) def path_cost(self, c, state1, action, state2): total distance for the Traveling Salesman to be covered if in state2 cost = 0 for i in range(len(state2) - 1): cost += distances[state2[i]][state2[i + 1]] cost += distances[state2[0]][state2[-1]] return cost def value(self, state): value of path cost given negative for the given state return -1 * self.path_cost(None, None, None, state) distances = {} all_cities = [] for city in romania_map.locations.keys(): distances[city] = {} all_cities.append(city) all_cities.sort() print(all_cities) import numpy as np for name_1, coordinates_1 in romania_map.locations.items(): for name_2, coordinates_2 in romania_map.locations.items(): distances[name_1][name_2] = np.linalg.norm( [coordinates_1[0] - coordinates_2[0], coordinates_1[1] - coordinates_2[1]]) distances[name_2][name_1] = np.linalg.norm( [coordinates_1[0] - coordinates_2[0], coordinates_1[1] - coordinates_2[1]]) def hill_climbing(problem): From the initial node, keep choosing the neighbor with highest value, stopping when no neighbor is better. [Figure 4.2] def find_neighbors(state, number_of_neighbors=100): finds neighbors using two_opt method neighbors = [] for i in range(number_of_neighbors): new_state = problem.two_opt(state) neighbors.append(Node(new_state)) state = new_state return neighbors # as this is a stochastic algorithm, we will set a cap on the number of iterations iterations = 10000 current = Node(problem.initial) while iterations: neighbors = find_neighbors(current.state) if not neighbors: break neighbor = argmax_random_tie(neighbors, key=lambda node: problem.value(node.state)) if problem.value(neighbor.state) <= problem.value(current.state): Note that it is based on negative path cost method current.state = neighbor.state iterations -= 1 return current.state tsp = TSP_problem(all_cities) hill_climbing(tsp) psource(simulated_annealing) psource(exp_schedule) initial = (0, 0) grid = [[3, 7, 2, 8], [5, 2, 9, 1], [5, 3, 3, 1]] directions4 problem = PeakFindingProblem(initial, grid, directions4) solutions = {problem.value(simulated_annealing(problem)) for i in range(100)} max(solutions) grid = gaussian_kernel() heatmap(grid, cmap='jet', interpolation='spline16') directions8 problem = PeakFindingProblem(initial, grid, directions8) %%timeit solutions = {problem.value(simulated_annealing(problem)) for i in range(100)} max(solutions) %%timeit solution = problem.value(hill_climbing(problem)) solution = problem.value(hill_climbing(problem)) solution grid = [[0, 0, 0, 1, 4], [0, 0, 2, 8, 10], [0, 0, 2, 4, 12], [0, 2, 4, 8, 16], [1, 4, 8, 16, 32]] heatmap(grid, cmap='jet', interpolation='spline16') problem = PeakFindingProblem(initial, grid, directions8) solution = problem.value(hill_climbing(problem)) solution solutions = {problem.value(simulated_annealing(problem)) for i in range(100)} max(solutions) psource(genetic_algorithm) psource(recombine) psource(mutate) psource(init_population) target = 'Genetic Algorithm' # The ASCII values of uppercase characters ranges from 65 to 91 u_case = [chr(x) for x in range(65, 91)] # The ASCII values of lowercase characters ranges from 97 to 123 l_case = [chr(x) for x in range(97, 123)] gene_pool = [] gene_pool.extend(u_case) # adds the uppercase list to the gene pool gene_pool.extend(l_case) # adds the lowercase list to the gene pool gene_pool.append(' ') # adds the space character to the gene pool max_population = 100 mutation_rate = 0.07 # 7% def fitness_fn(sample): # initialize fitness to 0 fitness = 0 for i in range(len(sample)): # increment fitness by 1 for every matching character if sample[i] == target[i]: fitness += 1 return fitness population = init_population(max_population, gene_pool, len(target)) parents = select(2, population, fitness_fn) # The recombine function takes two parents as arguments, so we need to unpack the previous variable child = recombine(*parents) child = mutate(child, gene_pool, mutation_rate) population = [mutate(recombine(*select(2, population, fitness_fn)), gene_pool, mutation_rate) for i in range(len(population))] current_best = max(population, key=fitness_fn) print(current_best) current_best_string = ''.join(current_best) print(current_best_string) ngen = 1200 # maximum number of generations # we set the threshold fitness equal to the length of the target phrase # i.e the algorithm only terminates whne it has got all the characters correct # or it has completed 'ngen' number of generations f_thres = len(target) def genetic_algorithm_stepwise(population, fitness_fn, gene_pool=[0, 1], f_thres=None, ngen=1200, pmut=0.1): for generation in range(ngen): population = [mutate(recombine(*select(2, population, fitness_fn)), gene_pool, pmut) for i in range(len(population))] # stores the individual genome with the highest fitness in the current population current_best = ''.join(max(population, key=fitness_fn)) print(f'Current best: {current_best}\t\tGeneration: {str(generation)}\t\tFitness: {fitness_fn(current_best)}\r', end='') # compare the fitness of the current best individual to f_thres fittest_individual = fitness_threshold(fitness_fn, f_thres, population) # if fitness is greater than or equal to f_thres, we terminate the algorithm if fittest_individual: return fittest_individual, generation return max(population, key=fitness_fn) , generation psource(genetic_algorithm) population = init_population(max_population, gene_pool, len(target)) solution, generations = genetic_algorithm_stepwise(population, fitness_fn, gene_pool, f_thres, ngen, mutation_rate) edges = { 'A': [0, 1], 'B': [0, 3], 'C': [1, 2], 'D': [2, 3] } population = init_population(8, ['R', 'G'], 4) print(population) def fitness(c): return sum(c[n1] != c[n2] for (n1, n2) in edges.values()) solution = genetic_algorithm(population, fitness, gene_pool=['R', 'G']) print(solution) print(fitness(solution)) population = init_population(100, range(8), 8) print(population[:5]) def fitness(q): non_attacking = 0 for row1 in range(len(q)): for row2 in range(row1+1, len(q)): col1 = int(q[row1]) col2 = int(q[row2]) row_diff = row1 - row2 col_diff = col1 - col2 if col1 != col2 and row_diff != col_diff and row_diff != -col_diff: non_attacking += 1 return non_attacking solution = genetic_algorithm(population, fitness, f_thres=25, gene_pool=range(8)) print(solution) print(fitness(solution)) psource(NQueensProblem) nqp = NQueensProblem(8) %%timeit depth_first_tree_search(nqp) dfts = depth_first_tree_search(nqp).solution() plot_NQueens(dfts) %%timeit breadth_first_tree_search(nqp) bfts = breadth_first_tree_search(nqp).solution() plot_NQueens(bfts) %%timeit uniform_cost_search(nqp) ucs = uniform_cost_search(nqp).solution() plot_NQueens(ucs) psource(NQueensProblem.h) %%timeit astar_search(nqp) astar = astar_search(nqp).solution() plot_NQueens(astar) psource(and_or_graph_search) vacuum_world = GraphProblemStochastic('State_1', ['State_7', 'State_8'], vacuum_world) plan = and_or_graph_search(vacuum_world) plan def run_plan(state, problem, plan): if problem.goal_test(state): return True if len(plan) is not 2: return False predicate = lambda x: run_plan(x, problem, plan[1][x]) return all(predicate(r) for r in problem.result(state, plan[0])) run_plan('State_1', vacuum_world, plan) psource(OnlineDFSAgent) psource(LRTAStarAgent) one_dim_state_space LRTA_problem = OnlineSearchProblem('State_3', 'State_5', one_dim_state_space) lrta_agent = LRTAStarAgent(LRTA_problem) lrta_agent('State_3') lrta_agent('State_4') lrta_agent('State_3') lrta_agent('State_4') lrta_agent('State_5') <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: CONTENTS Step2: PROBLEM Step3: The Problem class has six methods. Step4: The Node class has nine methods. The first is the __init__ method. Step5: Have a look at our romania_map, which is an Undirected Graph containing a dict of nodes as keys and neighbours as values. Step6: It is pretty straightforward to understand this romania_map. The first node Arad has three neighbours named Zerind, Sibiu, Timisoara. Each of these nodes are 75, 140, 118 units apart from Arad respectively. And the same goes with other nodes. Step7: Romania Map Visualisation Step8: Let's get started by initializing an empty graph. We will add nodes, place the nodes in their location as shown in the book, add edges to the graph. Step9: We have completed building our graph based on romania_map and its locations. It's time to display it here in the notebook. This function show_map(node_colors) helps us do that. We will be calling this function later on to display the map at each and every interval step while searching, using variety of algorithms from the book. Step10: Voila! You see, the romania map as shown in the Figure[3.2] in the book. Now, see how different searching algorithms perform with our problem statements. Step11: The SimpleProblemSolvingAgentProgram class has six methods Step12: Now, we will define all the 8 states and create an object of the above class. Then, we will pass it different states and check the output Step14: SEARCHING ALGORITHMS VISUALIZATION Step15: Now, we use ipywidgets to display a slider, a button and our romania map. By sliding the slider we can have a look at all the intermediate steps of a particular search algorithm. By pressing the button Visualize, you can see all the steps without interacting with the slider. These two helper functions are the callback functions which are called when we interact with the slider and the button. Step17: 2. DEPTH-FIRST TREE SEARCH Step18: 3. BREADTH-FIRST GRAPH SEARCH Step21: 4. DEPTH-FIRST GRAPH SEARCH Step23: 5. BEST FIRST SEARCH Step24: 6. UNIFORM COST SEARCH Step26: 7. DEPTH LIMITED SEARCH Step27: 8. ITERATIVE DEEPENING SEARCH Step29: 9. GREEDY BEST FIRST SEARCH Step31: 10. A* SEARCH Step33: 11. RECURSIVE BEST FIRST SEARCH Step34: RECURSIVE BEST-FIRST SEARCH Step35: This is how recursive_best_first_search can solve the romania_problem Step36: recursive_best_first_search can be used to solve the 8 puzzle problem too, as discussed later. Step37: A* HEURISTICS Step38: Heuristics Step39: We can solve the puzzle using the astar_search method. Step40: This case is solvable, let's proceed. Step41: In the following cells, we use different heuristic functions. Step42: And here's how recursive_best_first_search can be used to solve this problem too. Step43: Even though all the heuristic functions give the same solution, the difference lies in the computation time. Step44: The default heuristic function is the same as the linear heuristic function, but we'll still check both. Step45: We can infer that the manhattan heuristic function works the fastest. Step46: It is quite a lot slower than astar_search as we can see. Step53: We will find an approximate solution to the traveling salespersons problem using this algorithm. Step54: We will use cities from the Romania map as our cities for this problem. Step55: Next, we need to populate the individual lists inside the dictionary with the manhattan distance between the cities. Step59: The way neighbours are chosen currently isn't suitable for the travelling salespersons problem. Step60: An instance of the TSP_problem class will be created. Step61: We can now generate an approximate solution to the problem by calling hill_climbing. Step62: The solution looks like this. Step63: The temperature is gradually decreased over the course of the iteration. Step64: Next, we'll define a peak-finding problem and try to solve it using Simulated Annealing. Step65: We want to allow only four directions, namely N, S, E and W. Step66: Define a problem with these parameters. Step67: We'll run simulated_annealing a few times and store the solutions in a set. Step68: Hence, the maximum value is 9. Step69: Let's use the heatmap function from notebook.py to plot this. Step70: Let's define the problem. Step71: We'll solve the problem just like we did last time. Step72: The peak is at 1.0 which is how gaussian distributions are defined. Step73: As you can see, Hill-Climbing is about 24 times faster than Simulated Annealing. Step74: The peak value is 32 at the lower right corner. Step75: Solution by Hill Climbing Step76: Solution by Simulated Annealing Step77: Notice that even though both algorithms started at the same initial state, Step78: The algorithm takes the following input Step79: The method picks at random a point and merges the parents (x and y) around it. Step80: We pick a gene in x to mutate and a gene from the gene pool to replace it with. Step81: The function takes as input the number of individuals in the population, the gene pool and the length of each individual/state. It creates individuals with random genes and returns the population when done. Step82: We then need to define our gene pool, i.e the elements which an individual from the population might comprise of. Here, the gene pool contains all uppercase and lowercase letters of the English alphabet and the space character. Step83: We now need to define the maximum size of each population. Larger populations have more variation but are computationally more expensive to run algorithms on. Step84: As our population is not very large, we can afford to keep a relatively large mutation rate. Step85: Great! Now, we need to define the most important metric for the genetic algorithm, i.e the fitness function. This will simply return the number of matching characters between the generated sample and the target phrase. Step86: Before we run our genetic algorithm, we need to initialize a random population. We will use the init_population function to do this. We need to pass in the maximum population size, the gene pool and the length of each individual, which in this case will be the same as the length of the target phrase. Step87: We will now define how the individuals in the population should change as the number of generations increases. First, the select function will be run on the population to select two individuals with high fitness values. These will be the parents which will then be recombined using the recombine function to generate the child. Step88: Next, we need to apply a mutation according to the mutation rate. We call the mutate function on the child with the gene pool and mutation rate as the additional arguments. Step89: The above lines can be condensed into Step90: The individual with the highest fitness can then be found using the max function. Step91: Let's print this out Step92: We see that this is a list of characters. This can be converted to a string using the join function Step93: We now need to define the conditions to terminate the algorithm. This can happen in two ways Step94: To generate ngen number of generations, we run a for loop ngen number of times. After each generation, we calculate the fitness of the best individual of the generation and compare it to the value of f_thres using the fitness_threshold function. After every generation, we print out the best individual of the generation and the corresponding fitness value. Lets now write a function to do this. Step95: The function defined above is essentially the same as the one defined in search.py with the added functionality of printing out the data of each generation. Step96: We have defined all the required functions and variables. Let's now create a new population and test the function we wrote above. Step97: The genetic algorithm was able to converge! Step98: Edge 'A' connects nodes 0 and 1, edge 'B' connects nodes 0 and 3 etc. Step99: We created and printed the population. You can see that the genes in the individuals are random and there are 8 individuals each with 4 genes. Step100: Great! Now we will run the genetic algorithm and see what solution it gives. Step101: The algorithm converged to a solution. Let's check its score Step102: The solution has a score of 4. Which means it is optimal, since we have exactly 4 edges in our graph, meaning all are valid! Step103: We have a population of 100 and each individual has 8 genes. The gene pool is the integers from 0 to 7, in string form. Above you can see the first five individuals. Step104: Note that the best score achievable is 28. That is because for each queen we only check for the queens after her. For the first queen we check 7 other queens, for the second queen 6 others and so on. In short, the number of checks we make is the sum 7+6+5+...+1. Which is equal to 7*(7+1)/2 = 28. Step105: Above you can see the solution and its fitness score, which should be no less than 25. Step106: In csp.ipynb we have seen that the N-Queens problem can be formulated as a CSP and can be solved by Step107: Let's use depth_first_tree_search first. Step108: breadth_first_tree_search Step109: uniform_cost_search Step110: depth_first_tree_search is almost 20 times faster than breadth_first_tree_search and more than 200 times faster than uniform_cost_search. Step111: astar_search is faster than both uniform_cost_search and breadth_first_tree_search. Step112: AND-OR GRAPH SEARCH Step113: The search is carried out by two functions and_search and or_search that recursively call each other, traversing nodes sequentially. Step114: ONLINE DFS AGENT Step115: It maintains two dictionaries untried and unbacktracked. Step116: H stores the heuristic cost of the paths the agent may travel to. Step117: Let's define an instance of OnlineSearchProblem. Step118: Now we initialize a LRTAStarAgent object for the problem we just defined. Step119: We'll pass the percepts [State_3, State_4, State_3, State_4, State_5] one-by-one to our agent to see what action it comes up with at each timestep. Step120: If you manually try to see what the optimal action should be at each step, the outputs of the lrta_agent will start to make sense if it doesn't already.

<ASSISTANT_TASK:> Python Code: d = triand['dh'].data d_cut = (d > 15) & (d < 21) triand_dist = triand[d_cut] c_triand = _c_triand[d_cut] print(len(triand_dist)) plt.hist(triand_dist['<Vmag>'].data) ptf_triand = ascii.read("/Users/adrian/projects/streams/data/observing/triand.txt") ptf_c = coord.SkyCoord(ra=ptf_triand['ra']*u.deg, dec=ptf_triand['dec']*u.deg) print ptf_triand.colnames, len(ptf_triand) obs_dist = distance(ptf_triand['Vmag'].data) ((obs_dist > 12*u.kpc) & (obs_dist < 25*u.kpc)).sum() ptf_triand[0] rrlyr_d = np.genfromtxt("/Users/adrian/projects/triand-rrlyrae/data/RRL_ALL.txt", skiprows=2, dtype=None, names=['l','b','vhel','vgsr','src','ra','dec','name','dist']) obs_rrlyr = rrlyr_d[rrlyr_d['src'] == 'PTF'] fig,ax = plt.subplots(1,1,figsize=(10,8)) # ax.plot(c.galactic.l.degree, c.galactic.b.degree, linestyle='none', # marker='o', markersize=4, alpha=0.75) # ALL RR LYRAE ax.plot(c_triand.galactic.l.degree, c_triand.galactic.b.degree, linestyle='none', marker='o', markersize=5, alpha=0.75) ax.plot(ptf_c.galactic.l.degree, ptf_c.galactic.b.degree, linestyle='none', marker='o', markerfacecolor='none', markeredgewidth=2, markersize=12, alpha=0.75) ax.plot(obs_rrlyr['l'], obs_rrlyr['b'], linestyle='none', mec='r', marker='o', markerfacecolor='none', markeredgewidth=2, markersize=12, alpha=0.75) # x = np.linspace(-10,40,100) # x[x < 0] += 360. # y = np.linspace(30,45,100) # x,y = map(np.ravel, np.meshgrid(x,y)) # ccc = coord.SkyCoord(ra=x*u.deg,dec=y*u.deg) # ax.plot(ccc.galactic.l.degree, ccc.galactic.b.degree, linestyle='none') ax.set_xlim(97,162) ax.set_ylim(-37,-13) ax.set_xlabel("$l$ [deg]") ax.set_ylabel("$b$ [deg]") fig,ax = plt.subplots(1,1,figsize=(10,8)) ax.plot(c_triand.galactic.l.degree, c_triand.galactic.b.degree, linestyle='none', marker='o', markersize=4, alpha=0.75) ax.plot(ptf_c.galactic.l.degree, ptf_c.galactic.b.degree, linestyle='none', marker='o', markerfacecolor='none', markeredgewidth=2, markersize=8, alpha=0.75) ax.plot(obs_rrlyr['l'], obs_rrlyr['b'], linestyle='none', mec='r', marker='o', markerfacecolor='none', markeredgewidth=2, markersize=8, alpha=0.75) ax.plot(c_triand.galactic.l.degree[10], c_triand.galactic.b.degree[10], linestyle='none', marker='o', markersize=25, alpha=0.75) ax.set_xlim(97,162) ax.set_ylim(-37,-13) c_triand.icrs[10] brani = ascii.read("/Users/adrian/projects/triand-rrlyrae/brani_sample/TriAnd.dat") blaschko = brani[(brani['objectID'] == "13322281016459551106") | (brani['objectID'] == "13879390364114107826")] for b in blaschko: row = ptf_triand[np.argmin(np.sqrt((ptf_triand['ra'] - b['ra'])**2 + (ptf_triand['dec'] - b['dec'])**2))] print(row['name']) print(coord.SkyCoord(ra=row['ra']*u.deg, dec=row['dec']*u.deg).galactic) zip(obs_rrlyr['l'], obs_rrlyr['b']) d = V_to_dist(triand['<Vmag>'].data).to(u.kpc).value bins = np.arange(1., 60+5, 3) plt.figure(figsize=(10,8)) n,bins,patches = plt.hist(triand['dh'].data, bins=bins, alpha=0.5, label='Catalina') for pa in patches: if pa.xy[0] < 15. or pa.xy[0] > 40.: pa.set_alpha(0.2) # other_bins = np.arange(0, 15+2., 2.) # plt.hist(V_to_dist(triand['<Vmag>'].data), bins=other_bins, alpha=0.2, color='k') # other_bins = np.arange(40, 60., 2.) # plt.hist(V_to_dist(triand['<Vmag>'].data), bins=other_bins, alpha=0.2, color='k') plt.hist(V_to_dist(ptf_triand['Vmag'].data), bins=bins, alpha=0.5, label='PTF/MDM') plt.xlabel("Distance [kpc]") plt.ylabel("Number") # plt.ylim(0,35) plt.legend(fontsize=20) plt.axvline(18.) plt.axvline(28.) import emcee import triangle from scipy.misc import logsumexp ((distance(triand['<Vmag>'].data) > (15.*u.kpc)) & (distance(triand['<Vmag>'].data) < (40.*u.kpc))).sum() !head -n3 /Users/adrian/projects/triand-rrlyrae/data/triand_giants.txt d = np.loadtxt("/Users/adrian/projects/triand-rrlyrae/data/triand_giants.txt", skiprows=1) d2 = np.genfromtxt("/Users/adrian/projects/triand-rrlyrae/data/TriAnd_Mgiant.txt", skiprows=2) plt.plot(d[:,0], d[:,2], linestyle='none') plt.plot(d2[:,0], d2[:,3], linestyle='none') ix = (d[:,2] < 100) & (d[:,2] > -50) ix = np.ones_like(ix).astype(bool) plt.plot(d[ix,0], d[ix,2], linestyle='none') plt.plot(d[ix,0], -1*d[ix,0] + 170, marker=None) plt.xlabel('l [deg]') plt.ylabel('v_r [km/s]') plt.figure() plt.plot(d[ix,0], d[ix,1], linestyle='none') plt.xlabel('l [deg]') plt.ylabel('b [deg]') def ln_normal(x, mu, sigma): return -0.5*np.log(2*np.pi) - np.log(sigma) - 0.5*((x-mu)/sigma)**2 # def ln_prior(p): # m,b,V = p # if m > 0. or m < -50: # return -np.inf # if b < 0 or b > 500: # return -np.inf # if V <= 0.: # return -np.inf # return -np.log(V) # def ln_likelihood(p, l, vr, sigma_vr): # m,b,V = p # sigma = np.sqrt(sigma_vr**2 + V**2) # return ln_normal(vr, m*l + b, sigma) # mixture model - f_ol is outlier fraction def ln_prior(p): m,b,V,f_ol = p if m > 0. or m < -50: return -np.inf if b < 0 or b > 500: return -np.inf if V <= 0.: return -np.inf if f_ol > 1. or f_ol < 0.: return -np.inf return -np.log(V) def likelihood(p, l, vr, sigma_vr): m,b,V,f_ol = p sigma = np.sqrt(sigma_vr**2 + V**2) term1 = ln_normal(vr, m*l + b, sigma) term2 = ln_normal(vr, 0., 120.) return np.array([term1, term2]) def ln_likelihood(p, *args): m,b,V,f_ol = p x = likelihood(p, *args) # coefficients b = np.zeros_like(x) b[0] = 1-f_ol b[1] = f_ol return logsumexp(x,b=b, axis=0) def ln_posterior(p, *args): lnp = ln_prior(p) if np.isinf(lnp): return -np.inf return lnp + ln_likelihood(p, *args).sum() def outlier_prob(p, *args): m,b,V,f_ol = p p1,p2 = likelihood(p, *args) return f_ol*np.exp(p2) / ((1-f_ol)*np.exp(p1) + f_ol*np.exp(p2)) vr_err = 2 # km/s nwalkers = 32 sampler = emcee.EnsembleSampler(nwalkers=nwalkers, dim=4, lnpostfn=ln_posterior, args=(d[ix,0],d[ix,2],vr_err)) p0 = np.zeros((nwalkers,sampler.dim)) p0[:,0] = np.random.normal(-1, 0.1, size=nwalkers) p0[:,1] = np.random.normal(150, 0.1, size=nwalkers) p0[:,2] = np.random.normal(25, 0.5, size=nwalkers) p0[:,3] = np.random.normal(0.1, 0.01, size=nwalkers) for pp in p0: lnp = ln_posterior(pp, *sampler.args) if not np.isfinite(lnp): print("you suck") pos,prob,state = sampler.run_mcmc(p0, N=100) sampler.reset() pos,prob,state = sampler.run_mcmc(pos, N=1000) fig = triangle.corner(sampler.flatchain, labels=[r'$\mathrm{d}v/\mathrm{d}l$', r'$v_0$', r'$\sigma_v$', r'$f_{\rm halo}$']) figsize = (12,8) MAP = sampler.flatchain[sampler.flatlnprobability.argmax()] pout = outlier_prob(MAP, d[ix,0], d[ix,2], vr_err) plt.figure(figsize=figsize) cl = plt.scatter(d[ix,0], d[ix,2], c=(1-pout), s=30, cmap='RdYlGn', vmin=0, vmax=1) cbar = plt.colorbar(cl) cbar.set_clim(0,1) # plt.plot(d[ix,0], d[ix,2], linestyle='none', marker='o', ms=4) plt.xlabel(r'$l\,[{\rm deg}]$') plt.ylabel(r'$v_r\,[{\rm km\,s}^{-1}]$') ls = np.linspace(d[ix,0].min(), d[ix,0].max(), 100) for i in np.random.randint(len(sampler.flatchain), size=100): m,b,V,f_ol = sampler.flatchain[i] plt.plot(ls, m*ls+b, color='#555555', alpha=0.1, marker=None) best_m,best_b,best_V,best_f_ol = MAP plt.plot(ls, best_m*ls + best_b, color='k', alpha=1, marker=None) plt.plot(ls, best_m*ls + best_b + best_V, color='k', alpha=1, marker=None, linestyle='--') plt.plot(ls, best_m*ls + best_b - best_V, color='k', alpha=1, marker=None, linestyle='--') plt.xlim(ls.max()+2, ls.min()-2) plt.title("{:.1f}% halo stars".format(best_f_ol*100.)) print(((1-pout) > 0.75).tolist()) print best_m, best_b, best_V print "MAP velocity dispersion: {:.2f} km/s".format(best_V) high_p = (1-pout) > 0.8 plt.figure(figsize=figsize) cl = plt.scatter(d[high_p,0], d[high_p,1], c=d[high_p,2]-d[high_p,2].mean(), s=30, cmap='coolwarm', vmin=-40, vmax=40) cbar = plt.colorbar(cl) ax = plt.gca() ax.set_axis_bgcolor('#555555') plt.xlim(ls.max()+2,ls.min()-2) plt.ylim(-50,-10) plt.xlabel(r'$l\,[{\rm deg}]$') plt.ylabel(r'$b\,[{\rm deg}]$') plt.title(r'$P_{\rm TriAnd} > 0.8$', y=1.02) rrlyr_d = np.genfromtxt("/Users/adrian/projects/triand-rrlyrae/data/RRL_ALL.txt", skiprows=2, dtype=None) !cat "/Users/adrian/projects/triand-rrlyrae/data/RRL_ALL.txt" rrlyr_d = np.genfromtxt("/Users/adrian/projects/triand-rrlyrae/data/RRL_ALL.txt", skiprows=2) rrlyr_vr_err = 10. MAP = sampler.flatchain[sampler.flatlnprobability.argmax()] pout = outlier_prob(MAP, rrlyr_d[:,0], rrlyr_d[:,3], rrlyr_vr_err) plt.figure(figsize=figsize) cl = plt.scatter(rrlyr_d[:,0], rrlyr_d[:,1], c=(1-pout), s=30, cmap='RdYlGn', vmin=0, vmax=1) cbar = plt.colorbar(cl) cbar.set_clim(0,1) # plt.plot(d[ix,0], d[ix,2], linestyle='none', marker='o', ms=4) plt.xlabel(r'$l\,[{\rm deg}]$') plt.ylabel(r'$b\,[{\rm deg}]$') plt.xlim(ls.max()+2,ls.min()-2) plt.ylim(-50,-10) plt.title("RR Lyrae") MAP = sampler.flatchain[sampler.flatlnprobability.argmax()] pout = outlier_prob(MAP, rrlyr_d[:,0], rrlyr_d[:,3], rrlyr_vr_err) plt.figure(figsize=figsize) cl = plt.scatter(rrlyr_d[:,0], rrlyr_d[:,3], c=(1-pout), s=30, cmap='RdYlGn', vmin=0, vmax=1) cbar = plt.colorbar(cl) cbar.set_clim(0,1) # plt.plot(d[ix,0], d[ix,2], linestyle='none', marker='o', ms=4) plt.xlabel(r'$l\,[{\rm deg}]$') plt.ylabel(r'$v_r\,[{\rm km\,s}^{-1}]$') ls = np.linspace(d[ix,0].min(), d[ix,0].max(), 100) best_m,best_b,best_V,best_f_ol = MAP plt.plot(ls, best_m*ls + best_b, color='k', alpha=1, marker=None) plt.plot(ls, best_m*ls + best_b + best_V, color='k', alpha=1, marker=None, linestyle='--') plt.plot(ls, best_m*ls + best_b - best_V, color='k', alpha=1, marker=None, linestyle='--') plt.xlim(ls.max()+2, ls.min()-2) plt.title("RR Lyrae") <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Stars I actually observed Step2: Data for the observed stars Step3: Comparison of stars observed with Catalina Step4: Issues Step5: Possible Blaschko stars Step6: For Kathryn's proposal Step7: Now read in RR Lyrae data, compute prob for each star

<ASSISTANT_TASK:> Python Code: # Perform standard imports import spacy nlp = spacy.load('en_core_web_sm') # From Spacy Basics: doc = nlp(u'This is the first sentence. This is another sentence. This is the last sentence.') for sent in doc.sents: print(sent) print(doc[1]) print(doc.sents[1]) doc_sents = [sent for sent in doc.sents] doc_sents # Now you can access individual sentences: print(doc_sents[1]) type(doc_sents[1]) print(doc_sents[1].start, doc_sents[1].end) # Parsing the segmentation start tokens happens during the nlp pipeline doc2 = nlp(u'This is a sentence. This is a sentence. This is a sentence.') for token in doc2: print(token.is_sent_start, ' '+token.text) # SPACY'S DEFAULT BEHAVIOR doc3 = nlp(u'"Management is doing things right; leadership is doing the right things." -Peter Drucker') for sent in doc3.sents: print(sent) # ADD A NEW RULE TO THE PIPELINE def set_custom_boundaries(doc): for token in doc[:-1]: if token.text == ';': doc[token.i+1].is_sent_start = True return doc nlp.add_pipe(set_custom_boundaries, before='parser') nlp.pipe_names # Re-run the Doc object creation: doc4 = nlp(u'"Management is doing things right; leadership is doing the right things." -Peter Drucker') for sent in doc4.sents: print(sent) # And yet the new rule doesn't apply to the older Doc object: for sent in doc3.sents: print(sent) # Find the token we want to change: doc3[7] # Try to change the .is_sent_start attribute: doc3[7].is_sent_start = True nlp = spacy.load('en_core_web_sm') # reset to the original mystring = u"This is a sentence. This is another.\n\nThis is a \nthird sentence." # SPACY DEFAULT BEHAVIOR: doc = nlp(mystring) for sent in doc.sents: print([token.text for token in sent]) # CHANGING THE RULES from spacy.pipeline import SentenceSegmenter def split_on_newlines(doc): start = 0 seen_newline = False for word in doc: if seen_newline: yield doc[start:word.i] start = word.i seen_newline = False elif word.text.startswith('\n'): # handles multiple occurrences seen_newline = True yield doc[start:] # handles the last group of tokens sbd = SentenceSegmenter(nlp.vocab, strategy=split_on_newlines) nlp.add_pipe(sbd) doc = nlp(mystring) for sent in doc.sents: print([token.text for token in sent]) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Doc.sents is a generator Step2: However, you can build a sentence collection by running doc.sents and saving the result to a list Step3: <font color=green>NOTE Step4: sents are Spans Step5: Adding Rules Step6: <font color=green>Notice we haven't run doc2.sents, and yet token.is_sent_start was set to True on two tokens in the Doc.</font> Step7: <font color=green>The new rule has to run before the document is parsed. Here we can either pass the argument before='parser' or first=True. Step8: Why not change the token directly? Step9: <font color=green>spaCy refuses to change the tag after the document is parsed to prevent inconsistencies in the data.</font> Step10: <font color=green>While the function split_on_newlines can be named anything we want, it's important to use the name sbd for the SentenceSegmenter.</font>

<ASSISTANT_TASK:> Python Code: from __future__ import print_function %matplotlib inline import openpathsampling as paths import numpy as np import matplotlib.pyplot as plt import os import openpathsampling.visualize as ops_vis from IPython.display import SVG # note that this log will overwrite the log from the previous notebook #import logging.config #logging.config.fileConfig("logging.conf", disable_existing_loggers=False) %%time flexible = paths.AnalysisStorage("ad_tps.nc") # opening as AnalysisStorage is a little slower, but speeds up the move_summary engine = flexible.engines[0] flex_scheme = flexible.schemes[0] print("File size: {0} for {1} steps, {2} snapshots".format( flexible.file_size_str, len(flexible.steps), len(flexible.snapshots) )) flex_scheme.move_summary(flexible.steps) replica_history = ops_vis.ReplicaEvolution(replica=0) tree = ops_vis.PathTree( flexible.steps[0:25], replica_history ) tree.options.css['scale_x'] = 3 SVG(tree.svg()) # can write to svg file and open with programs that can read SVG with open("flex_tps_tree.svg", 'w') as f: f.write(tree.svg()) print("Decorrelated trajectories:", len(tree.generator.decorrelated_trajectories)) %%time full_history = ops_vis.PathTree( flexible.steps, ops_vis.ReplicaEvolution( replica=0 ) ) n_decorrelated = len(full_history.generator.decorrelated_trajectories) print("All decorrelated trajectories:", n_decorrelated) path_lengths = [len(step.active[0].trajectory) for step in flexible.steps] plt.hist(path_lengths, bins=40, alpha=0.5); print("Maximum:", max(path_lengths), "("+(max(path_lengths)*engine.snapshot_timestep).format("%.3f")+")") print ("Average:", "{0:.2f}".format(np.mean(path_lengths)), "("+(np.mean(path_lengths)*engine.snapshot_timestep).format("%.3f")+")") from openpathsampling.numerics import HistogramPlotter2D psi = flexible.cvs['psi'] phi = flexible.cvs['phi'] deg = 180.0 / np.pi path_density = paths.PathDensityHistogram(cvs=[phi, psi], left_bin_edges=(-180/deg,-180/deg), bin_widths=(2.0/deg,2.0/deg)) path_dens_counter = path_density.histogram([s.active[0].trajectory for s in flexible.steps]) tick_labels = np.arange(-np.pi, np.pi+0.01, np.pi/4) plotter = HistogramPlotter2D(path_density, xticklabels=tick_labels, yticklabels=tick_labels, label_format="{:4.2f}") ax = plotter.plot(cmap="Blues") ops_traj = flexible.steps[1000].active[0].trajectory traj = ops_traj.to_mdtraj() traj # Here's how you would then use NGLView: #import nglview as nv #view = nv.show_mdtraj(traj) #view flexible.close() <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Load the file, and from the file pull our the engine (which tells us what the timestep was) and the move scheme (which gives us a starting point for much of the analysis). Step2: That tell us a little about the file we're dealing with. Now we'll start analyzing the contents of that file. We used a very simple move scheme (only shooting), so the main information that the move_summary gives us is the acceptance of the only kind of move in that scheme. See the MSTIS examples for more complicated move schemes, where you want to make sure that frequency at which the move runs is close to what was expected. Step3: Replica history tree and decorrelated trajectories Step4: Path length distribution Step5: Path density histogram Step6: Now we've built the path density histogram, and we want to visualize it. We have a convenient plot_2d_histogram function that works in this case, and takes the histogram, desired plot tick labels and limits, and additional matplotlib named arguments to plt.pcolormesh. Step7: Convert to MDTraj for analysis by external tools

<ASSISTANT_TASK:> Python Code: import matplotlib.pyplot as plt import numpy as np import pandas as pd %matplotlib inline import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model, decomposition, datasets from sklearn.metrics import accuracy_score digits = datasets.load_digits() X_digits = digits.data y_digits = digits.target i = y_digits.shape[0] split = np.random.random(i) train = split < 0.7 test = split >= 0.7 X_digits_train = X_digits[train] X_digits_test = X_digits[test] y_digits_train = y_digits[train] y_digits_test = y_digits[test] clf = linear_model.LogisticRegression() clf.fit(X_digits_train, y_digits_train) predictions = clf.predict(X_digits_test) print(accuracy_score(y_digits_test, predictions)) pca = decomposition.PCA() pca.fit(X_digits_train) #pca.transform(X_digits_train)[:,:2].shape z = 2 clf = linear_model.LogisticRegression() clf.fit(pca.transform(X_digits_train)[:,:z], y_digits_train) predictions = clf.predict(pca.transform(X_digits_test)[:,:z]) print(accuracy_score(y_digits_test, predictions)) # http://scikit-learn.org/stable/auto_examples/plot_digits_pipe.html#example-plot-digits-pipe-py import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model, decomposition, datasets from sklearn.pipeline import Pipeline from sklearn.model_selection import GridSearchCV logistic = linear_model.LogisticRegression() pca = decomposition.PCA() pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)]) digits = datasets.load_digits() X_digits = digits.data y_digits = digits.target ############################################################################### # Plot the PCA spectrum pca.fit(X_digits) fig, ax = plt.subplots(1,1) ax.plot(pca.explained_variance_, linewidth=2) ax.set_xlabel('n_components') ax.set_ylabel('explained_variance_') ############################################################################### # Prediction n_components = [20, 40, 64] Cs = np.logspace(-4, 4, 10) Cs #Parameters of pipelines can be set using ‘__’ separated parameter names: estimator = GridSearchCV(pipe, dict(pca__n_components=n_components, logistic__C=Cs)) estimator.fit(X_digits, y_digits) print('# components:', estimator.best_estimator_.named_steps['pca'].n_components) print('C:', estimator.best_estimator_.named_steps['logistic'].C) print(estimator) # http://scikit-learn.org/stable/auto_examples/feature_stacker.html#example-feature-stacker-py # Author: Andreas Mueller <amueller@ais.uni-bonn.de> # # License: BSD 3 clause from sklearn.pipeline import Pipeline, FeatureUnion from sklearn.model_selection import GridSearchCV from sklearn.svm import SVC from sklearn.datasets import load_iris from sklearn.decomposition import PCA from sklearn.feature_selection import SelectKBest iris = load_iris() X, y = iris.data, iris.target # This dataset is way to high-dimensional. Better do PCA: pca = PCA(n_components=2) # Maybe some original features where good, too? selection = SelectKBest(k=1) # Build estimator from PCA and Univariate selection: combined_features = FeatureUnion([("pca", pca), ("univ_select", selection)]) # Use combined features to transform dataset: X_features = combined_features.fit(X, y).transform(X) svm = SVC(kernel="linear") # Do grid search over k, n_components and C: pipeline = Pipeline([("features", combined_features), ("svm", svm)]) # numpy.arange, numpy.linspace, numpy.logspace, and range are all useful in creating options to evaluate param_grid = dict(features__pca__n_components=[1,2,3], features__univ_select__k=[1,2,3], svm__C=[0.1, 1, 10]) grid_search = GridSearchCV(pipeline, param_grid=param_grid) grid_search.fit(X, y) print('PCA components:', grid_search.best_estimator_.named_steps['features'].get_params()['pca'].n_components) print('Original features used:', grid_search.best_estimator_.named_steps['features'].get_params()['univ_select'].k) print('C:', grid_search.best_estimator_.named_steps['svm'].C) print(grid_search.best_estimator_) from sklearn.datasets import fetch_20newsgroups twenty_train = fetch_20newsgroups(subset='train', categories=['comp.graphics', 'sci.med'], shuffle=True, random_state=0) print(twenty_train.target_names) # Looking at an example print(twenty_train.data[0]) # The first step is converting the text into numerical data we can work with # We will use a bag-of-words approach - counting the occurance of words from sklearn.feature_extraction.text import (CountVectorizer, TfidfTransformer) # Using a pipeline pipe = Pipeline([('counts', CountVectorizer()), ('tfidf', TfidfTransformer())]) output = pipe.fit(twenty_train.data).transform(twenty_train.data) output # Adding a classifier pipe = Pipeline([('counts', CountVectorizer()), ('tfidf', TfidfTransformer()), ('classifier', linear_model.LogisticRegression())]) # We can compare different parameters at each stage param_grid = dict(counts__ngram_range=[(1,1), (1,2)], counts__stop_words=[None, 'english'], classifier__C=[0.1, 1, 10, 30, 100]) X = twenty_train.data[:] y = twenty_train.target[:] grid_search = GridSearchCV(pipe, param_grid=param_grid) grid_search.fit(X, y) # Getting the best parameters print('# of words:', grid_search.best_estimator_.named_steps['counts'].ngram_range) print('Stop words:', grid_search.best_estimator_.named_steps['counts'].stop_words) print('C:', grid_search.best_estimator_.named_steps['classifier'].C) import numpy as np from sklearn.base import TransformerMixin class ModelTransformer(TransformerMixin): Wrap a classifier model so that it can be used in a pipeline def __init__(self, model): self.model = model def fit(self, *args, **kwargs): self.model.fit(*args, **kwargs) return self def transform(self, X, **transform_params): return self.model.predict_proba(X) def predict_proba(self, X, **transform_params): return self.transform(X, **transform_params) class VarTransformer(TransformerMixin): Compute the variance def transform(self, X, **transform_params): var = X.var(axis=1) return var.reshape((var.shape[0],1)) def fit(self, X, y=None, **fit_params): return self class MedianTransformer(TransformerMixin): Compute the median def transform(self, X, **transform_params): median = np.median(X, axis=1) return median.reshape((median.shape[0],1)) def fit(self, X, y=None, **fit_params): return self class ChannelExtractor(TransformerMixin): Extract a single channel for downstream processing def __init__(self, channel): self.channel = channel def transform(self, X, **transformer_params): return X[:,:,self.channel] def fit(self, X, y=None, **fit_params): return self class FFTTransformer(TransformerMixin): Convert to the frequency domain and then sum over bins def transform(self, X, **transformer_params): fft = np.fft.rfft(X, axis=1) fft = np.abs(fft) fft = np.cumsum(fft, axis=1) bin_size = 10 max_freq = 60 return np.column_stack([fft[:,i] - fft[:,i-bin_size] for i in range(bin_size, max_freq, bin_size)]) def fit(self, X, y=None, **fit_params): return self This cell is not expected to run correctly. We don't have all the packages needed. If you want to run this example download the repository and the source data. import numpy as np import os import pickle from sklearn.cross_validation import cross_val_score, StratifiedShuffleSplit from sklearn.pipeline import Pipeline, FeatureUnion from sklearn.ensemble import RandomForestClassifier import get_traces import transformers as trans def build_pipeline(X): Helper function to build the pipeline of feature transformations. We do the same thing to each channel so rather than manually copying changes for all channels this is automatically generated channels = X.shape[2] pipeline = Pipeline([ ('features', FeatureUnion([ ('select_%d_pipeline' % i, Pipeline([('select_%d' % i, trans.ChannelExtractor(i)), ('channel features', FeatureUnion([ ('var', trans.VarTransformer()), ('median', trans.MedianTransformer()), ('fft', trans.FFTTransformer()), ])), ]) ) for i in range(channels)])), ('classifier', trans.ModelTransformer(RandomForestClassifier( n_estimators=500, max_depth=None, min_samples_split=1, random_state=0))), ]) return pipeline def get_transformed_data(patient, func=get_traces.get_training_traces): Load in all the data X = [] channels = get_traces.get_num_traces(patient) # Reading in 43 Gb of data . . . for i in range(channels): x, y = func(patient, i) X.append(x) return (np.dstack(X), y) all_labels = [] all_predictions = np.array([]) folders = [i for i in os.listdir(get_traces.directory) if i[0] != '.'] folders.sort() for folder in folders: print('Starting %s' % folder) print('getting data') X, y = get_transformed_data(folder) print(X.shape) print('stratifiedshufflesplit') cv = StratifiedShuffleSplit(y, n_iter=5, test_size=0.2, random_state=0,) print('cross_val_score') pipeline = build_pipeline(X) # Putting this in a list is unnecessary for just one pipeline - use to compare multiple pipelines scores = [ cross_val_score(pipeline, X, y, cv=cv, scoring='roc_auc') ] print('displaying results') for score, label in zip(scores, ['pipeline',]): print("AUC: {:.2%} (+/- {:.2%}), {:}".format(score.mean(), score.std(), label)) clf = pipeline print('Fitting full model') clf.fit(X, y) print('Getting test data') testing_data, files = get_transformed_data(folder, get_traces.get_testing_traces) print('Generating predictions') predictions = clf.predict_proba(testing_data) print(predictions.shape, len(files)) with open('%s_randomforest_predictions.pkl' % folder, 'wb') as f: pickle.dump((files, predictions[:,1]), f) from sklearn import datasets diabetes = datasets.load_diabetes() # Description at http://www4.stat.ncsu.edu/~boos/var.select/diabetes.html # Ten baseline variables, age, sex, body mass index, average blood pressure, and six blood serum measurements # were obtained for each of n = 442 diabetes patients, # as well as the response of interest, a quantitative measure of disease progression one year after baseline. X = diabetes.data # independent variables y = diabetes.target # dependent val print(X.shape) print(y.shape) import pandas as pd data = pd.DataFrame(X, columns=['age', 'sex', 'bmi', 'map', 'tc', 'ldl', 'hdl', 'tch', 'ltg', 'glu']) data.info() from sklearn import linear_model bmi = X[:, 2].reshape(-1, 1) outcome = y reg = linear_model.LinearRegression() reg.fit(bmi, outcome) predicted_outcome = reg.predict(bmi) plt.plot(predicted_outcome, outcome, 'k.') plt.xlabel("Predicted outcome") plt.ylabel("Clinical outcome") print('Directly trained model predictions:', predicted_outcome[:10]) from sklearn.externals import joblib joblib.dump(reg, 'diabetes_prediction_model.pkl') reg2 = joblib.load('diabetes_prediction_model.pkl') predicted_outcome2 = reg2.predict(bmi) print('Saved model predictions:', predicted_outcome[:10]) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: In previous weeks we have covered preprocessing our data, dimensionality reduction, clustering, regression and classification. This week we will be pulling these processes together into a complete project. Step2: Scikit learn includes functionality for structuring our code and easily exploring the impact of different parameters not only in the machine learning algorithm we choose but at every stage of our solution. Step3: FeatureUnion Step4: Text classification Step13: Advanced Pipeline Step14: Saving a model

<ASSISTANT_TASK:> Python Code: # Declaring both Boolean values a = True b = False # Capturing True from an expression x = 2 < 3 # Capturing False from an expression y = 5 > 9 # Example of assigning None, and changing it. some_obj = None if 2 < 3: some_obj = True <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: To stay practical, it is important to understand that you won't be assigning True and False values to variables as much as you will be receiving them. We talked in the "Comparison Operators" series about said operators, and what they return. How do we capture True and False from an expression? Step2: The general format for if-else control flow in Python is the following

<ASSISTANT_TASK:> Python Code: rst(O.publish) def emit(obs): log('.........EMITTING........') sleep(0.1) obs.on_next(rand()) obs.on_completed() rst(title='Reminder: 2 subscribers on a cold stream:') s = O.create(emit) d = subs(s), subs(s.delay(100)) rst(title='Now 2 subscribers on a PUBLISHED (hot) stream', sleep=0.4) sp = s.publish() subs(sp, name='subs1') subs(sp.delay(100), name='subs2') log('now connect') # this creates a 'single, intermediate subscription between stream and subs' d = sp.connect() # will only see the finish, since subscribed too late d = subs(sp, name='subs3') rst(O.publish_value) def sideeffect(*x): log('sideffect', x) print('Everybody gets the initial value and the events, sideeffect only once per ev') src = O.interval(500).take(20).do_action(sideeffect) published = src.publish_value(42) subs(published), subs(published.delay(100)) d = published.connect() sleep(1.3) log('disposing now') d.dispose() # not yet in RXPy rst(O.multicast) # show actions on intermediate subject: show = False def emit(obs): 'instead of range we allow some logging:' for i in (1, 2): v = rand() log('emitting', v) obs.on_next(v) log('complete') obs.on_completed() class MySubject: def __init__(self): self.rx_subj = Subject() if show: log('New Subject %s created' % self) def __str__(self): return str(hash(self))[-4:] def __getattr__(self, a): 'called at any attr. access, logging it' if not a.startswith('__') and show: log('RX called', a, 'on MySub\n') return getattr(self.rx_subj, a) subject1 = MySubject() subject2 = MySubject() source = O.create(emit).multicast(subject2) # a "subscription" *is* a disposable # (the normal d we return all the time): d, observer = subs(source, return_subscriber=True) ds1 = subject1.subscribe(observer) ds2 = subject2.subscribe(observer) print ('we have now 3 subscriptions, only two will see values.') print('start multicast stream (calling connect):') connected = source.connect() d.dispose() rst(O.let) # show actions on intermediate subject: show = True def emit(obs): 'instead of range we allow some logging:' v = rand() log('emitting', v) obs.on_next(v) log('complete') obs.on_completed() source = O.create(emit) # following the RXJS example: header("without let") d = subs(source.concat(source)) d = subs(source.concat(source)) header("now with let") d = subs(source.let(lambda o: o.concat(o))) d = subs(source.let(lambda o: o.concat(o))) # TODO: Not understood: # "This operator allows for a fluent style of writing queries that use the same sequence multiple times." # ... I can't verify this, the source sequence is not duplicated but called every time like a cold obs. rst(O.replay) def emit(obs): 'continuous emission' for i in range(0, 5): v = 'nr %s, value %s' % (i, rand()) log('emitting', v, '\n') obs.on_next(v) sleep(0.2) def sideeffect(*v): log("sync sideeffect (0.2s)", v, '\n') sleep(0.2) log("end sideeffect", v, '\n') def modified_stream(o): log('modified_stream (take 2)') return o.map(lambda x: 'MODIFIED FOR REPLAY: %s' % x).take(2) header("playing and replaying...") subject = Subject() cold = O.create(emit).take(3).do_action(sideeffect) assert not getattr(cold, 'connect', None) hot = cold.multicast(subject) connect = hot.connect # present now. #d, observer = subs(hot, return_subscriber=True, name='normal subscriber\n') #d1 = subject.subscribe(observer) published = hot.replay(modified_stream, 1000, 50000) d2 = subs(published, name='Replay Subs 1\n') #header("replaying again") #d = subs(published, name='Replay Subs 2\n') log('calling connect now...') d3 = hot.connect() def mark(x): return 'marked %x' % x def side_effect(x): log('sideeffect %s\n' % x) for i in 1, 2: s = O.interval(100).take(3).do_action(side_effect) if i == 2: sleep(1) header("now with publish - no more sideeffects in the replays") s = s.publish() reset_start_time() published = s.replay(lambda o: o.map(mark).take(3).repeat(2), 3) d = subs(s, name='Normal\n') d = subs(published, name='Replayer A\n') d = subs(published, name='Replayer B\n') if i == 2: d = s.connect() rst(O.interval(1).publish) publ = O.interval(1000).take(2).publish().ref_count() # be aware about potential race conditions here subs(publ) subs(publ) rst(O.interval(1).share) def sideffect(v): log('sideeffect %s\n' % v) publ = O.interval(200).take(2).do_action(sideeffect).share() ''' When the number of observers subscribed to published observable goes from 0 to 1, we connect to the underlying observable sequence. published.subscribe(createObserver('SourceA')); When the second subscriber is added, no additional subscriptions are added to the underlying observable sequence. As a result the operations that result in side effects are not repeated per subscriber. ''' subs(publ, name='SourceA') subs(publ, name='SourceB') rst(O.interval(1).publish().connect) published = O.create(emit).publish() def emit(obs): for i in range(0, 10): log('emitting', i, obs.__class__.__name__, hash(obs)) # going nowhere obs.on_next(i) sleep(0.1) import thread thread.start_new_thread(published.connect, ()) sleep(0.5) d = subs(published, scheduler=new_thread_scheduler) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: ... and then only emits the last item in its sequence publish_last Step2: ... via multicast Step3: ... and then emits the complete sequence, even to those who subscribe after the sequence has begun replay Step4: If you apply the Replay operator to an Observable Step5: ... but I want it to go away once all of its subscribers unsubscribe ref_count, share Step6: ... and then I want to ask it to start connect

<ASSISTANT_TASK:> Python Code: from __future__ import print_function import pydot # Create a graph and set defaults dot = pydot.Dot() dot.set('rankdir', 'TB') dot.set('concentrate', True) dot.set_node_defaults(shape='record') # Add nodes and edges node = pydot.Node(1, label="FROM") dot.add_node(node) node = pydot.Node(2, label="TO") dot.add_node(node) dot.add_edge( pydot.Edge(1,2)) from IPython.core.display import SVG img = dot.create_svg() SVG( data=img ) for n in dot.get_nodes(): n.set('style', 'filled') n.set('fillcolor', 'aliceblue') n.set('fontsize', '10') n.set('fontname', 'Trebuchet MS, Tahoma, Verdana, Arial, Helvetica, sans-serif') SVG( data=dot.create_svg() ) from IPython.core.display import Image Image( data=dot.create_png() ) import graphviz as gv g1 = gv.Graph(format='svg') g1.node('A', 'Node A', tooltip='tooltip for node A') g1.node('B') g1.edge('A', 'B') # Render into "example.svg" file g1.render( filename="example") # Create a graph dot = gv.Digraph(comment='The Round Table', engine='dot') dot.node('A', 'King Arthur', color="blue", fillcolor="lightgray", style="filled", fontcolor="red", fontname="Verdana") dot.node('B', 'Sir Bedevere the Wise') dot.node('L', 'Sir Lancelot the Brave', shape="rectangle") dot.edges(['AB', 'AL']) dot.edge('B', 'L', constraint='false', color="blue") # Render in notebook by just outputting the graph as the result of a cell dot src = gv.Source('digraph "countdown" { rankdir=LR; 3 -> 2 -> 1 -> "Go!" }') # Again, the result can be rendered directly in the Notebook src <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: There are many available Python packages providing APIs for Graphviz. In no particular order Step2: Render Step3: We can also set properties on the graph nodes Step4: It is also possible to render as PNG and display as image (though the quality, in general, will be lower) Step5: Graphviz Step6: Render Step7: Furthermode, the package provides a useful facility for notebooks Step8: It is also possible to directly provide a buffer containing a graph written in dot language, by using the Source class

<ASSISTANT_TASK:> Python Code: import os import numpy as np import pandas from scipy.optimize import curve_fit import scipy.linalg import scipy.stats from scipy.interpolate import interp1d,splev,splrep from scipy.ndimage import map_coordinates,gaussian_filter import matplotlib.pyplot as plt import matplotlib.colors from matplotlib.ticker import LogFormatter import seaborn as sns import astropy.units as u import astropy.constants as const import hissw from sunpy.map import Map,GenericMap import h5py from ChiantiPy.tools import filters as ch_filters import synthesizAR from synthesizAR.instruments import InstrumentHinodeEIS from synthesizAR.util import EISCube,EMCube from synthesizAR.atomic import EmissionModel %matplotlib inline eis = InstrumentHinodeEIS([7.5e3,1.25e4]*u.s) frequencies = [250,750,'750-ion',2500,5000] temperature_bin_edges = 10.**(np.arange(5.6, 7.0, 0.05))*u.K emission_model = EmissionModel.restore('/data/datadrive1/ar_forward_modeling/systematic_ar_study/emission_model1109_full/') resolved_wavelengths = np.sort(u.Quantity([rw for ion in emission_model.ions for rw in ion.resolved_wavelengths])) pressure_const = 1e15*u.K*u.cm**(-3) class FakeLoop(object): electron_temperature = np.logspace(5.5,7.5,100)*u.K density = pressure_const/electron_temperature fake_loop = FakeLoop() i_temperature,i_density = emission_model.interpolate_to_mesh_indices(fake_loop) contribution_functions = {} line_names = {} for ion in emission_model.ions: for rw in ion.resolved_wavelengths: i_rw = np.where(ion.wavelength==rw)[0][0] emiss = map_coordinates(ion.emissivity[:,:,i_rw].value, np.vstack([i_temperature,i_density]),order=3)*ion.emissivity.unit ioneq = splev(fake_loop.electron_temperature.value, splrep(emission_model.temperature_mesh[:,0].value, ion.fractional_ionization[:,0].value,k=1),ext=1) line_names[rw] = '{} {}'.format(ion.chianti_ion.meta['name'],rw.value) contribution_functions[rw] = (1./(np.pi*4.*u.steradian)*0.83 *ioneq*ion.chianti_ion.abundance*emiss/fake_loop.density *(const.h.cgs*const.c.cgs)/rw.to(u.cm)/u.photon) line_intensities = {'{}'.format(freq):{} for freq in frequencies} for freq in frequencies: for channel in eis.channels: tmp = EISCube('../data/eis_intensity_{}_tn{}_t7500-12500.h5'.format(channel['name'],freq)) if type(freq) == int: tmp.data = (gaussian_filter(tmp.data.value,(channel['gaussian_width']['y'].value, channel['gaussian_width']['x'].value,0.)))*tmp.data.unit for rw in resolved_wavelengths: i_center = np.where(np.isclose(tmp.wavelength.value,rw.value,atol=1.1e-2,rtol=0.))[0] if len(i_center) == 0: continue line_intensities['{}'.format(freq)][rw] = tmp[i_center-5:i_center+5].integrated_intensity fig = plt.figure(figsize=(17,15)) plt.subplots_adjust(right=0.85) cax = fig.add_axes([0.88, 0.12, 0.025, 0.75]) for i,rw in enumerate(resolved_wavelengths): tmp = (line_intensities['750'][rw] .submap(u.Quantity((270,450),u.arcsec),u.Quantity((90,360),u.arcsec)) ) ax = fig.add_subplot(5,5,i+1,projection=tmp) im = tmp.plot(axes=ax,annotate=False,title=False, norm=matplotlib.colors.SymLogNorm(1,vmin=1e2,vmax=1e5) ) ax.set_title(r'{:.3f} {}'.format(rw.value,rw.unit.to_string(format='latex'))) cbar = fig.colorbar(im,cax=cax) k_matrix = [] intensity_matrix = [] line_names = [] for rw in resolved_wavelengths: line_name = '{}_{}'.format(rw.value,rw.unit) line_names.append(line_name) k_matrix.append(contribution_functions[rw].value.tolist()) for freq in frequencies: line_intensities['{}'.format(freq)][rw].save('../data/eis_integrated_intensity_{}_{}.fits'.format(freq,line_name)) demreg_runner = hissw.ScriptMaker(extra_paths=['/home/wtb2/Documents/codes/demreg/idl/'], ssw_path_list=['vobs','ontology']) static_input_vars = { 'log_temperature':np.log10(fake_loop.electron_temperature.value).tolist(), 'temperature_bins':temperature_bin_edges.value.tolist(), 'k_matrix':k_matrix, 'names':line_names, 'error_ratio':0.25, 'gloci':1,'reg_tweak':1,'timed':1 } save_vars = ['dem','edem','elogt','chisq','dn_reg'] demreg_script = ; load intensity from each channel/line names = {{ names }} eis_file_list = find_file('{{ fits_file_glob }}') read_sdo,eis_file_list,ind,intensity ; load the contribution functions or response functions (called K in Hannah and Kontar 2012) k_matrix = {{ k_matrix }} ; load temperature array over which K is computed log_temperature = {{ log_temperature }} ; temperature bins temperature_bins = {{ temperature_bins }} ; crude estimate of intensity errors intensity_errors = intensity*{{ error_ratio }} ; inversion method parameters reg_tweak={{ reg_tweak }} timed={{ timed }} gloci={{ gloci }} ; run the inversion method dn2dem_pos_nb,intensity,intensity_errors,$ k_matrix,log_temperature,temperature_bins,$ dem,edem,elogt,chisq,dn_reg,$ timed=timed,gloci=gloci,reg_tweak=reg_tweak for freq in frequencies: input_vars = static_input_vars.copy() input_vars['fits_file_glob'] = '/home/wtb2/Documents/projects/loops-workshop-2017-talk/data/eis_integrated_intensity_{}_*.fits'.format(freq) tmp = demreg_runner.run([(demreg_script,input_vars)],save_vars=save_vars,cleanup=True,verbose=True) tmp_cube = EMCube(np.swapaxes(tmp['dem'].T,0,1)*np.diff(temperature_bin_edges.value)*(u.cm**(-5)), (line_intensities['{}'.format(freq)][resolved_wavelengths[0]].meta),temperature_bin_edges) tmp_cube.save('../data/em_cubes_demreg_tn{}_t7500-12500.h5'.format(freq)) foo = EMCube.restore('../data/em_cubes_demreg_tn750_t7500-12500.h5') fig = plt.figure(figsize=(20,15)) plt.subplots_adjust(right=0.87) cax = fig.add_axes([0.88, 0.12, 0.025, 0.75]) plt.subplots_adjust(hspace=0.1) for i in range(foo.temperature_bin_edges.shape[0]-1): # apply a filter to the tmp = foo[i].submap(u.Quantity([250,500],u.arcsec),u.Quantity([150,400],u.arcsec)) #tmp.data = gaussian_filter(tmp.data, # eis.channels[0]['gaussian_width']['x'].value # ) # set up axes properly and add plot ax = fig.add_subplot(6,5,i+1,projection=tmp) im = tmp.plot(axes=ax, annotate=False, cmap=matplotlib.cm.get_cmap('magma'), norm=matplotlib.colors.SymLogNorm(1, vmin=1e25, vmax=1e29) ) # set title and labels ax.set_title(r'${t0:.2f}-{t1:.2f}$ {uni}'.format(t0=np.log10(tmp.meta['temp_a']), t1=np.log10(tmp.meta['temp_b']),uni='K')) if i<25: ax.coords[0].set_ticklabel_visible(False) else: ax.set_xlabel(r'$x$ ({})'.format(u.Unit(tmp.meta['cunit1']))) if i%5==0: ax.set_ylabel(r'$y$ ({})'.format(u.Unit(tmp.meta['cunit2']))) else: ax.coords[1].set_ticklabel_visible(False) cbar = fig.colorbar(im,cax=cax) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: First, load the emission model. Step2: List the resolved wavelengths for convenience. Step3: and calculate the contribution functions. Step4: Now, load the time-average intensities and slice in wavelength and integrate at the desired indicies corresponding to the resolved wavelength. This gives us a map of integrated intensity for each of the lines that we are interested in. Step5: Peek at the integrated intensities. Step6: Reshape the data so that it can be passed to the demreg script. Step8: Now run the inversion code.

<ASSISTANT_TASK:> Python Code: %matplotlib inline import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import numpy as np from sklearn.preprocessing import scale houses = pd.read_csv('house_prices.csv') plt.figure(1) plt.subplot(211) plt.xlabel('sq. feet') plt.ylabel('price (\'000)') plt.scatter(houses['sqft'], houses['price']) plt.subplot(212) plt.xlabel('no. of rooms') plt.ylabel('price (\'000)') plt.scatter(houses['rooms'], houses['price']) plt.tight_layout() X = houses[['sqft', 'rooms']].as_matrix() X = np.column_stack([np.ones([X.shape[0]]), X]) y = houses[['price']].as_matrix().ravel() # Hypothesis function def h(theta, X): return np.matmul(X, theta) # Cost function def J(theta, X, y): d = h(theta, X) - y return 0.5 * np.dot(d, d.T) # One step of gradient descent def descend(theta, X, y, alpha=0.01): error = h(theta, X) - y t = theta - alpha * np.matmul(X.T, error) return t, np.dot(error, error.T) theta = np.zeros([X.shape[1]]) for i in range(50): theta, cost = descend(theta, X, y) if i % 10 == 0: print("epoch: {0}, cost: {1}".format(i, cost)) print("epoch: {0}, cost: {1}".format(i, cost)) print("theta: {0}".format(theta)) X_scaled = scale(X) y_scaled = scale(y) plt.figure(1) plt.subplot(211) plt.xlabel('sq. feet') plt.ylabel('price') plt.scatter(X_scaled[:, 1], y_scaled) plt.subplot(212) plt.xlabel('no. of rooms') plt.ylabel('price') plt.scatter(X_scaled[:, 2], y_scaled) plt.tight_layout() def fit(X, y): theta = np.zeros([X.shape[1]]) theta, cost = descend(theta, X, y) for i in range(10000): cost_ = cost theta, cost = descend(theta, X, y) if cost_ - cost < 1e-7: break if i % 10 == 0: print("epoch: {0}, cost: {1}".format(i, cost)) print("epoch: {0}, cost: {1}".format(i, cost)) print("theta: {0}".format(theta)) fit(X_scaled, y_scaled) from sklearn.linear_model import LinearRegression l = LinearRegression(fit_intercept=False) l.fit(X_scaled, y_scaled) l.coef_ # One step of gradient descent def stochastic_descend(theta, X, y, alpha=0.01): X_sample = np.random.choice(X.shape[0], 47) error = h(theta, X[X_sample]) - y[X_sample] t = theta - alpha * np.matmul(X[X_sample].T, error) return t, np.dot(error, error.T) X[4] def stochastic_fit(X, y): theta = np.zeros([X.shape[1]]) theta, cost = descend(theta, X, y) for i in range(10000): cost_ = cost theta, cost = descend(theta, X, y) if cost_ - cost < 1e-7: break if i % 10 == 0: print("epoch: {0}, cost: {1}".format(i, cost)) print("epoch: {0}, cost: {1}".format(i, cost)) print("theta: {0}".format(theta)) stochastic_fit(X_scaled, y_scaled) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: A little searching leads us to the Portland housing prices dataset that's used as an example in the lecture. We load the dataset from the CSV file. Step2: Let's plot the output variable (the price of a house) against each of the input variables (area in sq. feet, number of bedrooms) to get a little intuition about the data. Step3: Let's transform our data into the right matrix format. Note that we add a column of one's to the $X$ matrix, to be multiplied with $\theta_0$. Step4: Next we implement the hypothesis and cost functions and the parameter update using gradient descent. Step5: We are now ready to fit the model using gradient descent. Let's initialize our parameters to 0 and run 50 iterations of gradient descent to see how it behaves. Step6: That doesn't look good. We expected the cost to steadily decrease as gradient descent progressed. Instead, the cost function diverged so much it exceeded our ability to represent it as a floating-point number. What happened? Step7: We can plot the data again to visualize the effect of the scaling operation. Step8: Let us write a function to fit the model such that it automatically stops once the improvement in the value of the cost function is below a certain threshold. Step9: Let's try to fit the model again with our scaled input and output matrices. Step10: Success! Step11: The parameters are close enough to consider our solution correct.

<ASSISTANT_TASK:> Python Code: import h2o h2o.init() import os.path PATH = os.path.expanduser("~/h2o-3/") test_df = h2o.import_file(PATH + "bigdata/laptop/mnist/test.csv.gz") train_df = h2o.import_file(PATH + "/bigdata/laptop/mnist/train.csv.gz") y = "C785" x = train_df.names[0:784] train_df[y] = train_df[y].asfactor() test_df[y] = test_df[y].asfactor() def lenet(num_classes): import mxnet as mx data = mx.symbol.Variable('data') # first conv conv1 = mx.symbol.Convolution(data=data, kernel=(5,5), num_filter=20) tanh1 = mx.symbol.Activation(data=conv1, act_type="tanh") pool1 = mx.symbol.Pooling(data=tanh1, pool_type="max", kernel=(2,2), stride=(2,2)) # second conv conv2 = mx.symbol.Convolution(data=pool1, kernel=(5,5), num_filter=50) tanh2 = mx.symbol.Activation(data=conv2, act_type="tanh") pool2 = mx.symbol.Pooling(data=tanh2, pool_type="max", kernel=(2,2), stride=(2,2)) # first fullc flatten = mx.symbol.Flatten(data=pool2) fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=500) tanh3 = mx.symbol.Activation(data=fc1, act_type="tanh") # second fullc fc2 = mx.symbol.FullyConnected(data=tanh3, num_hidden=num_classes) # loss lenet = mx.symbol.SoftmaxOutput(data=fc2, name='softmax') return lenet nclasses = 10 mxnet_model = lenet(nclasses) model_filename="/tmp/symbol_lenet-py.json" mxnet_model.save(model_filename) # pip install graphviz # sudo apt-get install graphviz import mxnet as mx import graphviz mx.viz.plot_network(mxnet_model, shape={"data":(1, 1, 28, 28)}, node_attrs={"shape":'rect',"fixedsize":'false'}) !head -n 20 $model_filename from h2o.estimators.deepwater import H2ODeepWaterEstimator lenet_model = H2ODeepWaterEstimator( epochs=10, learning_rate=1e-3, mini_batch_size=64, network_definition_file=model_filename, # network='lenet', ## equivalent pre-configured model image_shape=[28,28], problem_type='dataset', ## Not 'image' since we're not passing paths to image files, but raw numbers ignore_const_cols=False, ## We need to keep all 28x28=784 pixel values, even if some are always 0 channels=1 ) lenet_model.train(x=train_df.names, y=y, training_frame=train_df, validation_frame=test_df) error = lenet_model.model_performance(valid=True).mean_per_class_error() print "model error:", error def cnn(num_classes): import mxnet as mx data = mx.symbol.Variable('data') inputdropout = mx.symbol.Dropout(data=data, p=0.1) # first convolution conv1 = mx.symbol.Convolution(data=data, kernel=(5,5), num_filter=50) tanh1 = mx.symbol.Activation(data=conv1, act_type="relu") pool1 = mx.symbol.Pooling(data=tanh1, pool_type="max", pad=(1,1), kernel=(3,3), stride=(2,2)) # second convolution conv2 = mx.symbol.Convolution(data=pool1, kernel=(5,5), num_filter=100) tanh2 = mx.symbol.Activation(data=conv2, act_type="relu") pool2 = mx.symbol.Pooling(data=tanh2, pool_type="max", pad=(1,1), kernel=(3,3), stride=(2,2)) # first fully connected layer flatten = mx.symbol.Flatten(data=pool2) fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=1024) relu3 = mx.symbol.Activation(data=fc1, act_type="relu") inputdropout = mx.symbol.Dropout(data=fc1, p=0.5) # second fully connected layer flatten = mx.symbol.Flatten(data=relu3) fc2 = mx.symbol.FullyConnected(data=flatten, num_hidden=1024) relu4 = mx.symbol.Activation(data=fc2, act_type="relu") inputdropout = mx.symbol.Dropout(data=fc2, p=0.5) # third fully connected layer fc3 = mx.symbol.FullyConnected(data=relu4, num_hidden=num_classes) # loss cnn = mx.symbol.SoftmaxOutput(data=fc3, name='softmax') return cnn nclasses = 10 mxnet_model = cnn(nclasses) model_filename="/tmp/symbol_cnn-py.json" mxnet_model.save(model_filename) from h2o.estimators.deepwater import H2ODeepWaterEstimator print("Importing the lenet model architecture for training in H2O") model = H2ODeepWaterEstimator( epochs=20, learning_rate=1e-3, mini_batch_size=64, network_definition_file=model_filename, image_shape=[28,28], channels=1, ignore_const_cols=False ## We need to keep all 28x28=784 pixel values, even if some are always 0 ) model.train(x=train_df.names, y=y, training_frame=train_df, validation_frame=test_df) error = model.model_performance(valid=True).mean_per_class_error() print "model error:", error %matplotlib inline import matplotlib import numpy as np import scipy.io import matplotlib.pyplot as plt from IPython.display import Image, display import warnings warnings.filterwarnings("ignore") df = test_df.as_data_frame() import numpy as np image = df.T[int(np.random.random()*784)] image.shape plt.imshow(image[:-1].reshape(28, 28), plt.cm.gray); print image[-1] image_hf = h2o.H2OFrame.from_python(image.to_dict()) prediction = model.predict(image_hf) prediction['predict'] <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Specify the response and predictor columns Step2: Convert the number to a class Step3: Train Deep Learning model and validate on test set Step4: Here we instantiate our lenet model using 10 classes Step5: To import the model inside the DeepWater training engine we need to save the model to a file Step6: The model is just the structure of the network expressed as a json dict Step7: Importing the LeNET model architecture for training in H2O Step8: A More powerful Architecture Step9: Visualizing the results

<ASSISTANT_TASK:> Python Code: %matplotlib inline import numpy as np from scipy import stats import pandas as pd import matplotlib.pyplot as plt import statsmodels.api as sm from statsmodels.graphics.api import qqplot print(sm.datasets.sunspots.NOTE) dta = sm.datasets.sunspots.load_pandas().data dta.index = pd.Index(pd.date_range("1700", end="2009", freq="A-DEC")) del dta["YEAR"] dta.plot(figsize=(12,4)); fig = plt.figure(figsize=(12,8)) ax1 = fig.add_subplot(211) fig = sm.graphics.tsa.plot_acf(dta.values.squeeze(), lags=40, ax=ax1) ax2 = fig.add_subplot(212) fig = sm.graphics.tsa.plot_pacf(dta, lags=40, ax=ax2) arma_mod20 = sm.tsa.statespace.SARIMAX(dta, order=(2,0,0), trend='c').fit(disp=False) print(arma_mod20.params) arma_mod30 = sm.tsa.statespace.SARIMAX(dta, order=(3,0,0), trend='c').fit(disp=False) print(arma_mod20.aic, arma_mod20.bic, arma_mod20.hqic) print(arma_mod30.params) print(arma_mod30.aic, arma_mod30.bic, arma_mod30.hqic) sm.stats.durbin_watson(arma_mod30.resid) fig = plt.figure(figsize=(12,4)) ax = fig.add_subplot(111) ax = plt.plot(arma_mod30.resid) resid = arma_mod30.resid stats.normaltest(resid) fig = plt.figure(figsize=(12,4)) ax = fig.add_subplot(111) fig = qqplot(resid, line='q', ax=ax, fit=True) fig = plt.figure(figsize=(12,8)) ax1 = fig.add_subplot(211) fig = sm.graphics.tsa.plot_acf(resid, lags=40, ax=ax1) ax2 = fig.add_subplot(212) fig = sm.graphics.tsa.plot_pacf(resid, lags=40, ax=ax2) r,q,p = sm.tsa.acf(resid, fft=True, qstat=True) data = np.c_[range(1,41), r[1:], q, p] table = pd.DataFrame(data, columns=['lag', "AC", "Q", "Prob(>Q)"]) print(table.set_index('lag')) predict_sunspots = arma_mod30.predict(start='1990', end='2012', dynamic=True) fig, ax = plt.subplots(figsize=(12, 8)) dta.loc['1950':].plot(ax=ax) predict_sunspots.plot(ax=ax, style='r'); def mean_forecast_err(y, yhat): return y.sub(yhat).mean() mean_forecast_err(dta.SUNACTIVITY, predict_sunspots) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Sunspots Data Step2: Does our model obey the theory? Step3: This indicates a lack of fit.

<ASSISTANT_TASK:> Python Code: !curl -Lo conda_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py import conda_installer conda_installer.install() !/root/miniconda/bin/conda info -e !pip install --pre deepchem import deepchem deepchem.__version__ !pip install biopython import Bio Bio.__version__ from Bio.Seq import Seq my_seq = Seq("AGTACACATTG") my_seq my_seq.complement() my_seq.reverse_complement() !wget https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta from Bio import SeqIO for seq_record in SeqIO.parse('ls_orchid.fasta', 'fasta'): print(seq_record.id) print(repr(seq_record.seq)) print(len(seq_record)) from Bio.Seq import Seq from Bio.Alphabet import IUPAC my_seq = Seq("ACAGTAGAC", IUPAC.unambiguous_dna) my_seq my_seq.alphabet my_prot = Seq("AAAAA", IUPAC.protein) # Alanine pentapeptide my_prot my_prot.alphabet print(len(my_prot)) my_prot[0] my_prot[0:3] my_prot + my_prot my_prot + my_seq from Bio.Seq import Seq from Bio.Alphabet import IUPAC coding_dna = Seq("ATGATCTCGTAA", IUPAC.unambiguous_dna) coding_dna template_dna = coding_dna.reverse_complement() template_dna messenger_rna = coding_dna.transcribe() messenger_rna messenger_rna.back_transcribe() coding_dna.translate() coding_dna = Seq("ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG", IUPAC.unambiguous_dna) coding_dna.translate() coding_dna.translate(to_stop=True) from Bio.Alphabet import generic_dna gene = Seq("GTGAAAAAGATGCAATCTATCGTACTCGCACTTTCCCTGGTTCTGGTCGCTCCCATGGCA" + \ "GCACAGGCTGCGGAAATTACGTTAGTCCCGTCAGTAAAATTACAGATAGGCGATCGTGAT" + \ "AATCGTGGCTATTACTGGGATGGAGGTCACTGGCGCGACCACGGCTGGTGGAAACAACAT" + \ "TATGAATGGCGAGGCAATCGCTGGCACCTACACGGACCGCCGCCACCGCCGCGCCACCAT" + \ "AAGAAAGCTCCTCATGATCATCACGGCGGTCATGGTCCAGGCAAACATCACCGCTAA", generic_dna) # We specify a "table" to use a different translation table for bacterial proteins gene.translate(table="Bacterial") gene.translate(table="Bacterial", to_stop=True) from Bio.SeqRecord import SeqRecord help(SeqRecord) from Bio.SeqRecord import SeqRecord simple_seq = Seq("GATC") simple_seq_r = SeqRecord(simple_seq) simple_seq_r.id = "AC12345" simple_seq_r.description = "Made up sequence" print(simple_seq_r.id) print(simple_seq_r.description) !wget https://raw.githubusercontent.com/biopython/biopython/master/Tests/GenBank/NC_005816.fna from Bio import SeqIO record = SeqIO.read("NC_005816.fna", "fasta") record record.id record.name record.description !wget https://raw.githubusercontent.com/biopython/biopython/master/Tests/GenBank/NC_005816.gb from Bio import SeqIO record = SeqIO.read("NC_005816.gb", "genbank") record <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: We'll use pip to install biopython Step2: Parsing Sequence Records Step3: Let's take a look at what the contents of this file look like Step4: Sequence Objects Step5: If we want to code a protein sequence, we can do that just as easily. Step6: We can take the length of sequences and index into them like strings. Step7: You can also use slice notation on sequences to get subsequences. Step8: You can concatenate sequences if they have the same type so this works. Step9: But this fails Step10: Transcription Step11: Note that these sequences match those in the image below. You might be confused about why the template_dna sequence is shown reversed. The reason is that by convention, the template strand is read in the reverse direction. Step12: We can also perform a "back-transcription" to recover the original coding strand from the messenger RNA. Step13: Translation Step14: Let's now consider a longer genetic sequence that has some more interesting structure for us to look at. Step15: In both of the sequences above, '*' represents the stop codon. A stop codon is a sequence of 3 nucleotides that turns off the protein machinery. In DNA, the stop codons are 'TGA', 'TAA', 'TAG'. Note that this latest sequence has multiple stop codons. It's possible to run the machinery up to the first stop codon and pause too. Step16: We're going to introduce a bit of terminology here. A complete coding sequence CDS is a nucleotide sequence of messenger RNA which is made of a whole number of codons (that is, the length of the sequence is a multiple of 3), starts with a "start codon" and ends with a "stop codon". A start codon is basically the opposite of a stop codon and is mostly commonly the sequence "AUG", but can be different (especially if you're dealing with something like bacterial DNA). Step17: Handling Annotated Sequences Step18: Let's write a bit of code involving SeqRecord and see how it comes out looking. Step19: Let's now see how we can use SeqRecord to parse a large fasta file. We'll pull down a file hosted on the biopython site. Step20: Note how there's a number of annotations attached to the SeqRecord object! Step21: Let's now look at the same sequence, but downloaded from GenBank. We'll download the hosted file from the biopython tutorial website as before.

<ASSISTANT_TASK:> Python Code: # DO NOT EDIT ! from pyesdoc.ipython.model_topic import NotebookOutput # DO NOT EDIT ! DOC = NotebookOutput('cmip6', 'cmcc', 'sandbox-3', 'ocnbgchem') # Set as follows: DOC.set_author("name", "email") # TODO - please enter value(s) # Set as follows: DOC.set_contributor("name", "email") # TODO - please enter value(s) # Set publication status: # 0=do not publish, 1=publish. DOC.set_publication_status(0) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.model_overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.model_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.model_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Geochemical" # "NPZD" # "PFT" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.elemental_stoichiometry') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Fixed" # "Variable" # "Mix of both" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.elemental_stoichiometry_details') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.prognostic_variables') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.diagnostic_variables') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.damping') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.passive_tracers_transport.method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "use ocean model transport time step" # "use specific time step" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.passive_tracers_transport.timestep_if_not_from_ocean') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.biology_sources_sinks.method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "use ocean model transport time step" # "use specific time step" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.biology_sources_sinks.timestep_if_not_from_ocean') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Offline" # "Online" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Use that of ocean model" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.use_different_scheme') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.atmospheric_deposition') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "from file (climatology)" # "from file (interannual variations)" # "from Atmospheric Chemistry model" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.river_input') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "from file (climatology)" # "from file (interannual variations)" # "from Land Surface model" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.sediments_from_boundary_conditions') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.sediments_from_explicit_model') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CO2_exchange_present') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CO2_exchange_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "OMIP protocol" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.O2_exchange_present') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.O2_exchange_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "OMIP protocol" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.DMS_exchange_present') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.DMS_exchange_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2_exchange_present') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2_exchange_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2O_exchange_present') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2O_exchange_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC11_exchange_present') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC11_exchange_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC12_exchange_present') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC12_exchange_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.SF6_exchange_present') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.SF6_exchange_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.13CO2_exchange_present') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.13CO2_exchange_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.14CO2_exchange_present') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.14CO2_exchange_type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.other_gases') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "OMIP protocol" # "Other protocol" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.pH_scale') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Sea water" # "Free" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.constants_if_not_OMIP') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.sulfur_cycle_present') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.nutrients_present') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Nitrogen (N)" # "Phosphorous (P)" # "Silicium (S)" # "Iron (Fe)" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.nitrous_species_if_N') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Nitrates (NO3)" # "Amonium (NH4)" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.nitrous_processes_if_N') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Dentrification" # "N fixation" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.upper_trophic_levels_definition') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.upper_trophic_levels_treatment') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "None" # "Generic" # "PFT including size based (specify both below)" # "Size based only (specify below)" # "PFT only (specify below)" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.pft') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Diatoms" # "Nfixers" # "Calcifiers" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.size_classes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Microphytoplankton" # "Nanophytoplankton" # "Picophytoplankton" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.zooplankton.type') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "None" # "Generic" # "Size based (specify below)" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.zooplankton.size_classes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "Microzooplankton" # "Mesozooplankton" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.disolved_organic_matter.bacteria_present') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.disolved_organic_matter.lability') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "None" # "Labile" # "Semi-labile" # "Refractory" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.particules.method') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Diagnostic" # "Diagnostic (Martin profile)" # "Diagnostic (Balast)" # "Prognostic" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.particules.types_if_prognostic') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "POC" # "PIC (calcite)" # "PIC (aragonite" # "BSi" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.particules.size_if_prognostic') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "No size spectrum used" # "Full size spectrum" # "Discrete size classes (specify which below)" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.particules.size_if_discrete') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.particules.sinking_speed_if_prognostic') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Constant" # "Function of particule size" # "Function of particule type (balast)" # "Other: [Please specify]" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.carbon_isotopes') # PROPERTY VALUE(S): # Set as follows: DOC.set_value("value") # Valid Choices: # "C13" # "C14)" # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.abiotic_carbon') # PROPERTY VALUE: # Set as follows: DOC.set_value(value) # Valid Choices: # True # False # TODO - please enter value(s) # PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.alkalinity') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # Valid Choices: # "Prognostic" # "Diagnostic)" # TODO - please enter value(s) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Document Authors Step2: Document Contributors Step3: Document Publication Step4: Document Table of Contents Step5: 1.2. Model Name Step6: 1.3. Model Type Step7: 1.4. Elemental Stoichiometry Step8: 1.5. Elemental Stoichiometry Details Step9: 1.6. Prognostic Variables Step10: 1.7. Diagnostic Variables Step11: 1.8. Damping Step12: 2. Key Properties --&gt; Time Stepping Framework --&gt; Passive Tracers Transport Step13: 2.2. Timestep If Not From Ocean Step14: 3. Key Properties --&gt; Time Stepping Framework --&gt; Biology Sources Sinks Step15: 3.2. Timestep If Not From Ocean Step16: 4. Key Properties --&gt; Transport Scheme Step17: 4.2. Scheme Step18: 4.3. Use Different Scheme Step19: 5. Key Properties --&gt; Boundary Forcing Step20: 5.2. River Input Step21: 5.3. Sediments From Boundary Conditions Step22: 5.4. Sediments From Explicit Model Step23: 6. Key Properties --&gt; Gas Exchange Step24: 6.2. CO2 Exchange Type Step25: 6.3. O2 Exchange Present Step26: 6.4. O2 Exchange Type Step27: 6.5. DMS Exchange Present Step28: 6.6. DMS Exchange Type Step29: 6.7. N2 Exchange Present Step30: 6.8. N2 Exchange Type Step31: 6.9. N2O Exchange Present Step32: 6.10. N2O Exchange Type Step33: 6.11. CFC11 Exchange Present Step34: 6.12. CFC11 Exchange Type Step35: 6.13. CFC12 Exchange Present Step36: 6.14. CFC12 Exchange Type Step37: 6.15. SF6 Exchange Present Step38: 6.16. SF6 Exchange Type Step39: 6.17. 13CO2 Exchange Present Step40: 6.18. 13CO2 Exchange Type Step41: 6.19. 14CO2 Exchange Present Step42: 6.20. 14CO2 Exchange Type Step43: 6.21. Other Gases Step44: 7. Key Properties --&gt; Carbon Chemistry Step45: 7.2. PH Scale Step46: 7.3. Constants If Not OMIP Step47: 8. Tracers Step48: 8.2. Sulfur Cycle Present Step49: 8.3. Nutrients Present Step50: 8.4. Nitrous Species If N Step51: 8.5. Nitrous Processes If N Step52: 9. Tracers --&gt; Ecosystem Step53: 9.2. Upper Trophic Levels Treatment Step54: 10. Tracers --&gt; Ecosystem --&gt; Phytoplankton Step55: 10.2. Pft Step56: 10.3. Size Classes Step57: 11. Tracers --&gt; Ecosystem --&gt; Zooplankton Step58: 11.2. Size Classes Step59: 12. Tracers --&gt; Disolved Organic Matter Step60: 12.2. Lability Step61: 13. Tracers --&gt; Particules Step62: 13.2. Types If Prognostic Step63: 13.3. Size If Prognostic Step64: 13.4. Size If Discrete Step65: 13.5. Sinking Speed If Prognostic Step66: 14. Tracers --&gt; Dic Alkalinity Step67: 14.2. Abiotic Carbon Step68: 14.3. Alkalinity

<ASSISTANT_TASK:> Python Code: from IPython.display import Image # Add your filename and uncomment the following line: Image(filename='bad graph.jpg') <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Violations of graphical excellence and integrity

<ASSISTANT_TASK:> Python Code: #@title Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import tensorflow as tf import pandas as pd CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species'] SPECIES = ['Setosa', 'Versicolor', 'Virginica'] train_path = tf.keras.utils.get_file( "iris_training.csv", "https://storage.googleapis.com/download.tensorflow.org/data/iris_training.csv") test_path = tf.keras.utils.get_file( "iris_test.csv", "https://storage.googleapis.com/download.tensorflow.org/data/iris_test.csv") train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0) test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0) train.head() train_y = train.pop('Species') test_y = test.pop('Species') # The label column has now been removed from the features. train.head() def input_evaluation_set(): features = {'SepalLength': np.array([6.4, 5.0]), 'SepalWidth': np.array([2.8, 2.3]), 'PetalLength': np.array([5.6, 3.3]), 'PetalWidth': np.array([2.2, 1.0])} labels = np.array([2, 1]) return features, labels def input_fn(features, labels, training=True, batch_size=256): An input function for training or evaluating # Convert the inputs to a Dataset. dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels)) # Shuffle and repeat if you are in training mode. if training: dataset = dataset.shuffle(1000).repeat() return dataset.batch(batch_size) # Feature columns describe how to use the input. my_feature_columns = [] for key in train.keys(): my_feature_columns.append(tf.feature_column.numeric_column(key=key)) # Build a DNN with 2 hidden layers with 30 and 10 hidden nodes each. classifier = tf.estimator.DNNClassifier( feature_columns=my_feature_columns, # Two hidden layers of 30 and 10 nodes respectively. hidden_units=[30, 10], # The model must choose between 3 classes. n_classes=3) # Train the Model. classifier.train( input_fn=lambda: input_fn(train, train_y, training=True), steps=5000) eval_result = classifier.evaluate( input_fn=lambda: input_fn(test, test_y, training=False)) print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result)) # Generate predictions from the model expected = ['Setosa', 'Versicolor', 'Virginica'] predict_x = { 'SepalLength': [5.1, 5.9, 6.9], 'SepalWidth': [3.3, 3.0, 3.1], 'PetalLength': [1.7, 4.2, 5.4], 'PetalWidth': [0.5, 1.5, 2.1], } def input_fn(features, batch_size=256): An input function for prediction. # Convert the inputs to a Dataset without labels. return tf.data.Dataset.from_tensor_slices(dict(features)).batch(batch_size) predictions = classifier.predict( input_fn=lambda: input_fn(predict_x)) for pred_dict, expec in zip(predictions, expected): class_id = pred_dict['class_ids'][0] probability = pred_dict['probabilities'][class_id] print('Prediction is "{}" ({:.1f}%), expected "{}"'.format( SPECIES[class_id], 100 * probability, expec)) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: 事前作成された Estimator Step2: データセット Step3: 次に、Keras と Pandas を使用して、Iris データセットをダウンロードして解析します。トレーニング用とテスト用に別々のデータセットを維持することに注意してください。 Step4: データを検査し、4 つの浮動小数型の特徴量カラムと 1 つの int32 ラベルがあることを確認します。 Step5: 各データセットに対し、モデルが予測するようにトレーニングされるラベルを分割します。 Step6: Estimator を使ったプログラミングの概要 Step8: 入力関数を自分で作成すれば、features ディクショナリと label リストを好みに合わせて生成できるようにすることができますが、あらゆる種類のデータを解析できる TensorFlow の Dataset API を使用することをお勧めします。 Step9: 特徴量カラムを定義する Step10: 特徴量カラムは、ここに示すものよりもはるかに高度なものに構築することができます。特徴量カラムの詳細については、こちらのガイドをご覧ください。 Step11: トレーニングして評価して予測する Step12: Estimator が期待するとおり、引数を取らない入力関数を指定しながら、input_fn 呼び出しを lambda にラッピングして引数をキャプチャするところに注意してください。steps 引数はメソッドに対して、あるトレーニングステップ数を完了した後にトレーニングを停止するように指定しています。 Step14: train メソッドへの呼び出しとは異なり、評価するsteps 引数を渡していません。eval の input_fn データの単一のエポックのみを返します。 Step15: predict メソッドは Python イテラブルを返し、各サンプルの予測結果のディクショナリを生成します。次のコードを使って、予測とその確率を出力します。

<ASSISTANT_TASK:> Python Code: from sympy import * init_session() def const_model(E, nu, const="plane_stress"): if const == "plane_stress": fac = E/(1 - nu**2) C = fac*Matrix([ [1, nu, 0], [nu, 1, 0], [0, 0, (1 - nu)/2]]) elif const == "plane_strain": fac = E*(1 - nu)/((1 - 2*nu)*(1 + nu)) C = fac*Matrix([ [1, nu/(1 - nu), 0], [nu/(1 - nu), 1, 0], [0, 0, (1 - 2*nu)/(2*(1 - nu))]]) return C r, s = symbols("r s") N = S(1)/4 *Matrix([ [(1 - r)*(1 - s)], [(1 + r)*(1 - s)], [(1 + r)*(1 + s)], [(1 - r)*(1 + s)]]) display(N) H = zeros(2, 8) for cont in range(4): H[0, 2*cont] = N[cont] H[1, 2*cont + 1] = N[cont] display(H.T) dHdr = zeros(2, 4) for cont in range(4): dHdr[0, cont] = diff(N[cont], r) dHdr[1, cont] = diff(N[cont], s) display(dHdr) def gauss_int2d(f, x, y): acu = 0 pts = [-1/sqrt(3), 1/sqrt(3)] w = [1, 1] for i in range(2): for j in range(2): acu += f.subs({x: pts[i], y: pts[j]})*w[i]*w[j] return acu def jaco(dHdr, coord_el): return simplify(dHdr * coord_el) def jaco_inv(dHdr, coord_el): jac = jaco(dHdr, coord_el) return Matrix([[jac[1, 1], -jac[0, 1]], [-jac[1, 0], jac[0, 0]]])/jac.det() def B_mat(dHdr, coord_el): dHdx = jaco_inv(dHdr, coord_el) * dHdr B = zeros(3, 8) for cont in range(4): B[0, 2*cont] = dHdx[0, cont] B[1, 2*cont + 1] = dHdx[1, cont] B[2, 2*cont] = dHdx[1, cont] B[2, 2*cont + 1] = dHdx[0, cont] return simplify(B) def local_mass(H, coord_el, rho): det = jaco(dHdr, coord_el).det() integrand = rho * det * expand(H.T * H) mass_mat = zeros(8, 8) for row in range(8): for col in range(row, 8): mass_mat[row, col] = gauss_int2d(integrand[row, col], r, s) mass_mat[col, row] = mass_mat[row, col] return mass_mat def local_stiff(dHdr, coord_el, C): det = jaco(dHdr, coord_el).det() B = B_mat(dHdr, coord_el) integrand = det * expand(B.T * C * B) stiff_mat = zeros(8, 8) for row in range(8): for col in range(row, 8): stiff_mat[row, col] = gauss_int2d(integrand[row, col], r, s) stiff_mat[col, row] = stiff_mat[row, col] return stiff_mat def assembler(coords, elems, mat_props, const="plane_stress"): ncoords = coords.shape[0] stiff_glob = zeros(2*ncoords, 2*ncoords) mass_glob = zeros(2*ncoords, 2*ncoords) for el_cont, elem in enumerate(elems): E, nu, rho = mat_props[el_cont] C = const_model(E, nu, const=const) coord_el = coords[elem, :] stiff_loc = local_stiff(dHdr, coord_el, C) mass_loc = local_mass(H, coord_el, rho) for row in range(4): for col in range(4): row_glob = elem[row] col_glob = elem[col] # Stiffness matrix stiff_glob[2*row_glob, 2*col_glob] += stiff_loc[2*row, 2*col] stiff_glob[2*row_glob, 2*col_glob + 1] += stiff_loc[2*row, 2*col + 1] stiff_glob[2*row_glob + 1, 2*col_glob] += stiff_loc[2*row + 1, 2*col] stiff_glob[2*row_glob + 1, 2*col_glob + 1] += stiff_loc[2*row + 1, 2*col + 1] # Mass matrix mass_glob[2*row_glob, 2*col_glob] += mass_loc[2*row, 2*col] mass_glob[2*row_glob, 2*col_glob + 1] += mass_loc[2*row, 2*col + 1] mass_glob[2*row_glob + 1, 2*col_glob] += mass_loc[2*row + 1, 2*col] mass_glob[2*row_glob + 1, 2*col_glob + 1] += mass_loc[2*row + 1, 2*col + 1] return stiff_glob, mass_glob coords = Matrix([ [-1, -1], [1, -1], [1, 1], [-1, 1]]) elems = [[0, 1, 2, 3]] mat_props = [[S(8)/3, S(1)/3, 1]] stiff, mass = assembler(coords, elems, mat_props, const="plane_strain") stiff mass coords = Matrix([ [-1, -1], [0, -1], [1, -1], [-1, 0], [0, 0], [1, 0], [-1, 1], [0, 1], [1, 1]]) elems = [[0, 1, 4, 3], [1, 2, 5, 4], [3, 4, 7, 6], [4, 5, 8, 7]] mat_props = [[16, S(1)/3, 1]]*4 stiff, _ = assembler(coords, elems, mat_props) stiff_exact = Matrix([ [8, 3, -5, 0, 0, 0, 1, 0, -4, -3, 0, 0, 0, 0, 0, 0, 0, 0], [3, 8, 0, 1, 0, 0, 0, -5, -3, -4, 0, 0, 0, 0, 0, 0, 0, 0], [-5, 0, 16, 0, -5, 0, -4, 3, 2, 0, -4, -3, 0, 0, 0, 0, 0, 0], [0, 1, 0, 16, 0, 1, 3, -4, 0, -10, -3, -4, 0, 0, 0, 0, 0, 0], [0, 0, -5, 0, 8, -3, 0, 0, -4, 3, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, -3, 8, 0, 0, 3, -4, 0, -5, 0, 0, 0, 0, 0, 0], [1, 0, -4, 3, 0, 0, 16, 0, -10, 0, 0, 0, 1, 0, -4, -3, 0, 0], [0, -5, 3, -4, 0, 0, 0, 16, 0, 2, 0, 0, 0, -5, -3, -4, 0, 0], [-4, -3, 2, 0, -4, 3, -10, 0, 32, 0, -10, 0, -4, 3, 2, 0, -4, -3], [-3, -4, 0, -10, 3, -4, 0, 2, 0, 32, 0, 2, 3, -4, 0, -10, -3, -4], [0, 0, -4, -3, 1, 0, 0, 0, -10, 0, 16, 0, 0, 0, -4, 3, 1, 0], [0, 0, -3, -4, 0, -5, 0, 0, 0, 2, 0, 16, 0, 0, 3, -4, 0, -5], [0, 0, 0, 0, 0, 0, 1, 0, -4, 3, 0, 0, 8, -3, -5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, -5, 3, -4, 0, 0, -3, 8, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, -4, -3, 2, 0, -4, 3, -5, 0, 16, 0, -5, 0], [0, 0, 0, 0, 0, 0, -3, -4, 0, -10, 3, -4, 0, 1, 0, 16, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, -4, -3, 1, 0, 0, 0, -5, 0, 8, 3], [0, 0, 0, 0, 0, 0, 0, 0, -3, -4, 0, -5, 0, 0, 0, 1, 3, 8]]) stiff_exact - stiff coords = Matrix([ [0, 0], [1, 0], [2, 0], [0, 1], [1, 1], [2, 1]]) elems = [[0, 1, 4, 3], [1, 2, 5, 4]] mat_props = [[1, S(3)/10, 1]]*4 stiff, mass = assembler(coords, elems, mat_props, const="plane_stress") load = zeros(12, 1) load[5] = -S(1)/2 load[11] = -S(1)/2 load stiff2 = stiff.copy() stiff2[0, :] = eye(12)[0, :] stiff2[:, 0] = eye(12)[:, 0] stiff2[1, :] = eye(12)[1, :] stiff2[:, 1] = eye(12)[:, 1] stiff2[6, :] = eye(12)[6, :] stiff2[:, 6] = eye(12)[:, 6] stiff2[7, :] = eye(12)[7, :] stiff2[:, 7] = eye(12)[:, 7] sol = linsolve((stiff2, load)) sol from IPython.core.display import HTML def css_styling(): styles = open('./styles/custom_barba.css', 'r').read() return HTML(styles) css_styling() <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Interpolation functions and matrices Step2: The interpolation matrix is a matrix with the interpolation Step3: The local derivatives matrix is formed with the derivatives of the interpolation functions Step4: Gauss integration Step5: Local matrices generation Step6: We can re-arrange the derivatives of the interpolation function as a matrix that Step7: With these elements we can form the local stiffness and mass matrices. Step8: Assembly process Step9: Example Step10: Example Step11: Example

<ASSISTANT_TASK:> Python Code: import numpy as np from astropy.table import Table, join from astropy import units as u from astropy.coordinates import SkyCoord, search_around_sky from IPython.display import clear_output import pickle import os import sys sys.path.append("..") from mltier1 import (get_center, Field, MultiMLEstimator, MultiMLEstimatorOld, parallel_process, get_sigma_all, get_sigma_all_old, describe) %load_ext autoreload %autoreload from IPython.display import clear_output %pylab inline save_intermediate = True plot_intermediate = True idp = "../idata/final_pdf_v0.9" if not os.path.isdir(idp): os.makedirs(idp) # Busy week Edinburgh 2017 ra_down = 172.09 ra_up = 187.5833 dec_down = 46.106 dec_up = 56.1611 # Busy week Hatfield 2017 ra_down = 170. ra_up = 190. dec_down = 46.8 dec_up = 55.9 # Full field July 2017 ra_down = 160. ra_up = 232. dec_down = 42. dec_up = 62. field = Field(170.0, 190.0, 46.8, 55.9) field_full = Field(160.0, 232.0, 42.0, 62.0) combined_all = Table.read("../pw.fits") lofar_all = Table.read("../data/LOFAR_HBA_T1_DR1_catalog_v0.9.srl.fixed.fits") #lofar_all = Table.read("data/LOFAR_HBA_T1_DR1_merge_ID_optical_v0.8.fits") np.array(combined_all.colnames) np.array(lofar_all.colnames) lofar = field_full.filter_catalogue(lofar_all, colnames=("RA", "DEC")) combined = field_full.filter_catalogue(combined_all, colnames=("ra", "dec")) combined["colour"] = combined["i"] - combined["W1mag"] combined_aux_index = np.arange(len(combined)) coords_combined = SkyCoord(combined['ra'], combined['dec'], unit=(u.deg, u.deg), frame='icrs') coords_lofar = SkyCoord(lofar['RA'], lofar['DEC'], unit=(u.deg, u.deg), frame='icrs') combined_matched = (~np.isnan(combined["i"]) & ~np.isnan(combined["W1mag"])) # Matched i-W1 sources combined_panstarrs = (~np.isnan(combined["i"]) & np.isnan(combined["W1mag"])) # Sources with only i-band combined_wise =(np.isnan(combined["i"]) & ~np.isnan(combined["W1mag"])) # Sources with only W1-band combined_i = combined_matched | combined_panstarrs combined_w1 = combined_matched | combined_wise #combined_only_i = combined_panstarrs & ~combined_matched #combined_only_w1 = combined_wise & ~combined_matched print("Total - ", len(combined)) print("i and W1 - ", np.sum(combined_matched)) print("Only i - ", np.sum(combined_panstarrs)) print("With i - ", np.sum(combined_i)) print("Only W1 - ", np.sum(combined_wise)) print("With W1 - ", np.sum(combined_w1)) colour_limits = [0.0, 0.5, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 3.5, 4.0] # Start with the W1-only, i-only and "less than lower colour" bins colour_bin_def = [{"name":"only W1", "condition": combined_wise}, {"name":"only i", "condition": combined_panstarrs}, {"name":"-inf to {}".format(colour_limits[0]), "condition": (combined["colour"] < colour_limits[0])}] # Get the colour bins for i in range(len(colour_limits)-1): name = "{} to {}".format(colour_limits[i], colour_limits[i+1]) condition = ((combined["colour"] >= colour_limits[i]) & (combined["colour"] < colour_limits[i+1])) colour_bin_def.append({"name":name, "condition":condition}) # Add the "more than higher colour" bin colour_bin_def.append({"name":"{} to inf".format(colour_limits[-1]), "condition": (combined["colour"] >= colour_limits[-1])}) combined["category"] = np.nan for i in range(len(colour_bin_def)): combined["category"][colour_bin_def[i]["condition"]] = i np.sum(np.isnan(combined["category"])) numbers_combined_bins = np.array([np.sum(a["condition"]) for a in colour_bin_def]) numbers_combined_bins bin_list, centers, Q_0_colour, n_m, q_m = pickle.load(open("../lofar_params.pckl", "rb")) likelihood_ratio_function = MultiMLEstimator(Q_0_colour, n_m, q_m, centers) likelihood_ratio_function_old = MultiMLEstimatorOld(Q_0_colour, n_m, q_m, centers) radius = 15 selection = ~np.isnan(combined["category"]) # Avoid the dreaded sources with no actual data catalogue = combined[selection] def apply_ml(i, likelihood_ratio_function): idx_0 = idx_i[idx_lofar == i] d2d_0 = d2d[idx_lofar == i] category = catalogue["category"][idx_0].astype(int) mag = catalogue["i"][idx_0] mag[category == 0] = catalogue["W1mag"][idx_0][category == 0] lofar_ra = lofar[i]["RA"] lofar_dec = lofar[i]["DEC"] lofar_pa = lofar[i]["PA"] lofar_maj_err = lofar[i]["E_Maj"] lofar_min_err = lofar[i]["E_Min"] c_ra = catalogue["ra"][idx_0] c_dec = catalogue["dec"][idx_0] c_ra_err = catalogue["raErr"][idx_0] c_dec_err = catalogue["decErr"][idx_0] sigma, sigma_maj, sigma_min = get_sigma_all(lofar_maj_err, lofar_min_err, lofar_pa, lofar_ra, lofar_dec, c_ra, c_dec, c_ra_err, c_dec_err) lr_0 = likelihood_ratio_function(mag, d2d_0.arcsec, sigma, sigma_maj, sigma_min, category) chosen_index = np.argmax(lr_0) result = [combined_aux_index[selection][idx_0[chosen_index]], # Index (d2d_0.arcsec)[chosen_index], # distance lr_0[chosen_index]] # LR return result from mltier1 import fr_u, fr_u_old def check_ml(i, likelihood_ratio_function, likelihood_ratio_function_old, verbose=True): idx_0 = idx_i[idx_lofar == i] d2d_0 = d2d[idx_lofar == i] category = catalogue["category"][idx_0].astype(int) mag = catalogue["i"][idx_0] mag[category == 0] = catalogue["W1mag"][idx_0][category == 0] lofar_ra = lofar[i]["RA"] lofar_dec = lofar[i]["DEC"] lofar_pa = lofar[i]["PA"] lofar_maj_err = lofar[i]["E_Maj"] lofar_min_err = lofar[i]["E_Min"] c_ra = catalogue["ra"][idx_0] c_dec = catalogue["dec"][idx_0] c_ra_err = catalogue["raErr"][idx_0] c_dec_err = catalogue["decErr"][idx_0] sigma, sigma_maj, sigma_min = get_sigma_all_old(lofar_maj_err, lofar_min_err, lofar_pa, lofar_ra, lofar_dec, c_ra, c_dec, c_ra_err, c_dec_err) sigma_0_0, det_sigma = get_sigma_all(lofar_maj_err, lofar_min_err, lofar_pa, lofar_ra, lofar_dec, c_ra, c_dec, c_ra_err, c_dec_err) fr = fr_u(d2d_0.arcsec, sigma_0_0, det_sigma) fr_old = np.array(fr_u_old(d2d_0.arcsec, sigma, sigma_maj, sigma_min)) if verbose: print("NEW - s00: {}; sdet: {}; fr: {}".format(sigma_0_0, det_sigma, fr)) print("OLD - s: {}; smin: {}; smaj: {}; fr: {}".format( np.array(sigma), np.array(sigma_maj), np.array(sigma_min), fr_old)) lr_0 = likelihood_ratio_function(mag, d2d_0.arcsec, sigma_0_0, det_sigma, category) lr_0_old = likelihood_ratio_function_old(mag, d2d_0.arcsec, sigma, sigma_maj, sigma_min, category) chosen_index = np.argmax(lr_0) chosen_index_old = np.argmax(lr_0_old) ix, dist, lr = (combined_aux_index[selection][idx_0[chosen_index]], # Index (d2d_0.arcsec)[chosen_index], # distance lr_0[chosen_index]) ix_old, dist_old, lr_old = (combined_aux_index[selection][idx_0[chosen_index_old]], # Index (d2d_0.arcsec)[chosen_index_old], # distance lr_0[chosen_index_old] ) if verbose: print("NEW res - Ix: {}; dist: {}; LR: {}".format(ix, dist, lr)) # LR print("OLD res - Ix: {}; dist: {}; LR: {}".format(ix_old, dist_old, lr_old)) return (sigma_0_0, det_sigma, fr, np.array(sigma), np.array(sigma_maj), np.array(sigma_min), fr_old, ix, dist, lr, ix_old, dist_old, lr_old, (lofar_maj_err, lofar_min_err, lofar_pa, lofar_ra, lofar_dec, c_ra, c_dec, c_ra_err, c_dec_err)) idx_lofar, idx_i, d2d, d3d = search_around_sky( coords_lofar, coords_combined[selection], radius*u.arcsec) idx_lofar_unique = np.unique(idx_lofar) list_i = [141, 235, 396, 412, 418, 711, 858, 887, 932, 965, 1039, 1389, 1680, 1699, 1787, 1927, 2168, 2267, 2339, 2410, 2548, 2838, 2969, 3136, 3163, 3265, 3348, 3353, 3401] for i in range(100000): s00, det_s, fr, s, s_maj, s_min, fr_o, ix, dist, lr, ix_o, dist_o, lr_o, p = check_ml(idx_lofar_unique[i], likelihood_ratio_function, likelihood_ratio_function_old, verbose=False) if (ix != ix_o) and ((lr > 6) or (lr_o > 6)): print(i) #print(ix, dist, lr) #print(ix_o, dist_o, lr_o) #print(s00, det_s, fr, s, s_maj, s_min, fr_o) #print(p) list_i = [141, 235, 396, 412, 418, 711, 858, 887, 932, 965, 1039, 1389, 1680, 1699, 1787, 1927, 2168, 2267, 2339, 2410, 2548, 2838, 2969, 3136, 3163, 3265, 3348, 3353, 3401, 3654, 3687, 4022, 4074, 4083, 4164, 4263] for i in list_i: s00, det_s, fr, s, s_maj, s_min, fr_o, ix, dist, lr, ix_o, dist_o, lr_o, p = check_ml(idx_lofar_unique[i], likelihood_ratio_function, likelihood_ratio_function_old, verbose=False) if ix != ix_o: print(i) print(ix, dist, lr) print(ix_o, dist_o, lr_o) print(s00, det_s, fr, s, s_maj, s_min, fr_o) print(p) import multiprocessing n_cpus_total = multiprocessing.cpu_count() n_cpus = max(1, n_cpus_total-1) def ml(i): return apply_ml(i, likelihood_ratio_function) res = parallel_process(idx_lofar_unique, ml, n_jobs=n_cpus) lofar["lr"] = np.nan # Likelihood ratio lofar["lr_dist"] = np.nan # Distance to the selected source lofar["lr_index"] = np.nan # Index of the PanSTARRS source in combined (lofar["lr_index"][idx_lofar_unique], lofar["lr_dist"][idx_lofar_unique], lofar["lr"][idx_lofar_unique]) = list(map(list, zip(*res))) total_sources = len(idx_lofar_unique) combined_aux_index = np.arange(len(combined)) lofar["lrt"] = lofar["lr"] lofar["lrt"][np.isnan(lofar["lr"])] = 0 q0 = np.sum(Q_0_colour) def completeness(lr, threshold, q0): n = len(lr) lrt = lr[lr < threshold] return 1. - np.sum((q0 * lrt)/(q0 * lrt + (1 - q0)))/float(n)/q0 def reliability(lr, threshold, q0): n = len(lr) lrt = lr[lr > threshold] return 1. - np.sum((1. - q0)/(q0 * lrt + (1 - q0)))/float(n)/q0 completeness_v = np.vectorize(completeness, excluded=[0]) reliability_v = np.vectorize(reliability, excluded=[0]) n_test = 100 threshold_mean = np.percentile(lofar["lrt"], 100*(1 - q0)) thresholds = np.arange(0., 10., 0.01) thresholds_fine = np.arange(0.1, 1., 0.001) completeness_t = completeness_v(lofar["lrt"], thresholds, q0) reliability_t = reliability_v(lofar["lrt"], thresholds, q0) average_t = (completeness_t + reliability_t)/2 completeness_t_fine = completeness_v(lofar["lrt"], thresholds_fine, q0) reliability_t_fine = reliability_v(lofar["lrt"], thresholds_fine, q0) average_t_fine = (completeness_t_fine + reliability_t_fine)/2 threshold_sel = thresholds_fine[np.argmax(average_t_fine)] plt.rcParams["figure.figsize"] = (15,6) subplot(1,2,1) plot(thresholds, completeness_t, "r-") plot(thresholds, reliability_t, "g-") plot(thresholds, average_t, "k-") vlines(threshold_sel, 0.9, 1., "k", linestyles="dashed") vlines(threshold_mean, 0.9, 1., "y", linestyles="dashed") ylim([0.9, 1.]) xlabel("Threshold") ylabel("Completeness/Reliability") subplot(1,2,2) plot(thresholds_fine, completeness_t_fine, "r-") plot(thresholds_fine, reliability_t_fine, "g-") plot(thresholds_fine, average_t_fine, "k-") vlines(threshold_sel, 0.9, 1., "k", linestyles="dashed") #vlines(threshold_mean, 0.9, 1., "y", linestyles="dashed") ylim([0.97, 1.]) xlabel("Threshold") ylabel("Completeness/Reliability") print(threshold_sel) plt.rcParams["figure.figsize"] = (15,6) subplot(1,2,1) hist(lofar[lofar["lrt"] != 0]["lrt"], bins=200) vlines([threshold_sel], 0, 5000) ylim([0,5000]) subplot(1,2,2) hist(np.log10(lofar[lofar["lrt"] != 0]["lrt"]+1), bins=200) vlines(np.log10(threshold_sel+1), 0, 5000) ticks, _ = xticks() xticks(ticks, ["{:.1f}".format(10**t-1) for t in ticks]) ylim([0,5000]); lofar["lr_index_sel"] = lofar["lr_index"] lofar["lr_index_sel"][lofar["lrt"] < threshold_sel] = np.nan combined["lr_index_sel"] = combined_aux_index.astype(float) pwl = join(lofar, combined, join_type='left', keys='lr_index_sel', uniq_col_name='{col_name}{table_name}', table_names=['_input', '']) pwl_columns = pwl.colnames for col in pwl_columns: fv = pwl[col].fill_value if (isinstance(fv, np.float64) and (fv != 1e+20)): print(col, fv) pwl[col].fill_value = 1e+20 columns_save = ['Source_Name', 'RA', 'E_RA', 'DEC', 'E_DEC', 'Peak_flux', 'E_Peak_flux', 'Total_flux', 'E_Total_flux', 'Maj', 'E_Maj', 'Min', 'E_Min', 'PA', 'E_PA', 'Isl_rms', 'S_Code', 'Mosaic_ID', 'AllWISE', 'objID', 'ra', 'dec', 'raErr', 'decErr', 'W1mag', 'W1magErr', 'i', 'iErr', 'colour', 'category', 'lr', 'lr_dist'] pwl[columns_save].filled().write('lofar_pw_pdf.fits', format="fits") <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: General configuration Step2: Area limits Step3: Load data Step4: Filter catalogues Step5: Additional data Step6: Sky coordinates Step7: Class of sources in the combined catalogue Step8: Colour categories Step9: We get the number of sources of the combined catalogue in each colour category. It will be used at a later stage to compute the $Q_0$ values Step10: Maximum Likelihood Step11: ML match Step12: Run the cross-match Step13: Run the ML matching Step14: Threshold and selection Step15: Save combined catalogue

<ASSISTANT_TASK:> Python Code: %matplotlib inline import numpy as np import matplotlib.pyplot as plt import netCDF4 as nc import climlab ncep_filename = 'air.mon.1981-2010.ltm.nc' # This will try to read the data over the internet. #ncep_url = "http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.derived/" #ncep_air = nc.Dataset( ncep_url + 'pressure/' + ncep_filename ) # Or to read from local disk ncep_air = nc.Dataset( ncep_filename ) level = ncep_air.variables['level'][:] lat = ncep_air.variables['lat'][:] # A log-pressure height coordinate zstar = -np.log(level/1000) # Take averages of the temperature data Tzon = np.mean(ncep_air.variables['air'][:],axis=(0,3)) Tglobal = np.average( Tzon , weights=np.cos(np.deg2rad(lat)), axis=1) + climlab.constants.tempCtoK # Note the useful conversion factor. climlab.constants has lots of commonly used constant pre-defined # Here we are plotting with respect to log(pressure) but labeling the axis in pressure units fig = plt.figure( figsize=(8,6) ) ax = fig.add_subplot(111) ax.plot( Tglobal , zstar ) yticks = np.array([1000., 750., 500., 250., 100., 50., 20., 10.]) ax.set_yticks(-np.log(yticks/1000.)) ax.set_yticklabels(yticks) ax.set_xlabel('Temperature (K)', fontsize=16) ax.set_ylabel('Pressure (hPa)', fontsize=16 ) ax.set_title('Global, annual mean sounding from NCEP Reanalysis', fontsize = 24) ax.grid() # Repeating code from previous notebook ... set up a column model with observed temperatures. # initialize a grey radiation model with 30 levels col = climlab.GreyRadiationModel() # interpolate to 30 evenly spaced pressure levels lev = col.lev Tinterp = np.interp(lev, np.flipud(level), np.flipud(Tglobal)) # Initialize model with observed temperatures col.Ts[:] = Tglobal[0] col.Tatm[:] = Tinterp # The tuned value of absorptivity eps = 0.0534 # set it col.subprocess.LW.absorptivity = eps # Pure radiative equilibrium # Make a clone of our first model re = climlab.process_like(col) # Run out to equilibrium re.integrate_years(2.) # Check for energy balance re.ASR - re.OLR # And set up a RadiativeConvective model, rce = climlab.RadiativeConvectiveModel(adj_lapse_rate=6.) # Set our tuned absorptivity value rce.subprocess.LW.absorptivity = eps # Run out to equilibrium rce.integrate_years(2.) # Check for energy balance rce.ASR - rce.OLR # A handy re-usable routine for making a plot of the temperature profiles # We will plot temperatures with respect to log(pressure) to get a height-like coordinate def plot_sounding(collist): color_cycle=['r', 'g', 'b', 'y', 'm'] # col is either a column model object or a list of column model objects if isinstance(collist, climlab.Process): # make a list with a single item collist = [collist] fig = plt.figure() ax = fig.add_subplot(111) for i, col in enumerate(collist): zstar = -np.log(col.lev/climlab.constants.ps) ax.plot(col.Tatm, zstar, color=color_cycle[i]) ax.plot(col.Ts, 0, 'o', markersize=12, color=color_cycle[i]) #ax.invert_yaxis() yticks = np.array([1000., 750., 500., 250., 100., 50., 20., 10.]) ax.set_yticks(-np.log(yticks/1000.)) ax.set_yticklabels(yticks) ax.set_xlabel('Temperature (K)') ax.set_ylabel('Pressure (hPa)') ax.grid() return ax # Make a plot to compare observations and Radiative-Convective Equilibrium plot_sounding([col, re, rce]) # To read from internet #datapath = "http://ramadda.atmos.albany.edu:8080/repository/opendap/latest/Top/Users/Brian+Rose/CESM+runs/som_input/" #endstr = "/entry.das" ozone_filename = 'ozone_1.9x2.5_L26_2000clim_c091112.nc' datapath = '' endstr = '' ozone = nc.Dataset( datapath + ozone_filename + endstr ) print ozone.variables['O3'] lat = ozone.variables['lat'][:] lon = ozone.variables['lon'][:] lev = ozone.variables['lev'][:] print lev O3_zon = np.mean( ozone.variables['O3'],axis=(0,3) ) O3_global = np.sum( O3_zon * np.cos(np.deg2rad(lat)), axis=1 ) / np.sum( np.cos(np.deg2rad(lat) ) ) O3_global.shape fig = plt.figure(figsize=(15,5)) ax1 = fig.add_subplot(1,2,1) cax = ax1.contourf(lat, np.log(lev/climlab.constants.ps), O3_zon * 1.E6) ax1.invert_yaxis() ax1.set_xlabel('Latitude', fontsize=16) ax1.set_ylabel('Pressure (hPa)', fontsize=16 ) yticks = np.array([1000., 500., 250., 100., 50., 20., 10., 5.]) ax1.set_yticks( np.log(yticks/1000.) ) ax1.set_yticklabels( yticks ) ax1.set_title('Ozone concentration (annual mean)', fontsize = 16); plt.colorbar(cax) ax2 = fig.add_subplot(1,2,2) ax2.plot( O3_global * 1.E6, np.log(lev/climlab.constants.ps) ) ax2.invert_yaxis() ax2.set_xlabel('Ozone (ppm)', fontsize=16) ax2.set_ylabel('Pressure (hPa)', fontsize=16 ) yticks = np.array([1000., 500., 250., 100., 50., 20., 10., 5.]) ax2.set_yticks( np.log(yticks/1000.) ) ax2.set_yticklabels( yticks ) ax2.set_title('Global mean ozone concentration', fontsize = 16); # Create the column with appropriate vertical coordinate, surface albedo and convective adjustment band1 = climlab.BandRCModel(lev=lev, adj_lapse_rate=6) print band1 band1.absorber_vmr band1.state band1.integrate_years(2) # Check for energy balance band1.ASR - band1.OLR # Add another line to our graph! plot_sounding([col, re, rce, band1]) band2 = climlab.process_like(band1) print band2 band2.absorber_vmr['O3'] = O3_global band2.absorber_vmr # Run the model out to equilibrium! band2.integrate_years(2.) # Add another line to our graph! plot_sounding([col, re, rce, band1, band2]) band2.absorber_vmr['CO2'] # Let's double CO2 and calculate radiative forcing band3 = climlab.process_like(band2) band3.absorber_vmr['CO2'] *= 2. band3.absorber_vmr['CO2'] band3.compute_diagnostics() print 'The radiative forcing for doubling CO2 is %f W/m2.' % (band2.OLR - band3.OLR) # and make another copy, which we will integrate out to equilibrium band4 = climlab.process_like(band3) band4.integrate_years(3) band4.ASR - band4.OLR DeltaT = band4.Ts - band2.Ts print 'The Equilibrium Climate Sensitivity is %f K.' % DeltaT # We multiply the H2O mixing ratio by 1000 to get units of g / kg # (amount of water vapor per mass of air) plt.plot( band2.absorber_vmr['H2O'] *1000, lev, label='before 2xCO2') plt.plot( band4.absorber_vmr['H2O'] * 1000., lev, label='after 2xCO2') plt.xlabel('g/kg H2O') plt.ylabel('pressure (hPa)') plt.legend(loc='upper right') plt.grid() # This reverses the axis so pressure decreases upward plt.gca().invert_yaxis() # First, make a new clone noh2o = climlab.process_like(band2) # See what absorbing gases are currently present noh2o.absorber_vmr # double the CO2 ! noh2o.absorber_vmr['CO2'] *= 2 # Check out the list of subprocesses print noh2o # Remove the process that changes the H2O noh2o.remove_subprocess('H2O') print noh2o noh2o.absorber_vmr noh2o.integrate_years(3) noh2o.ASR - noh2o.OLR # Repeat the same plot of water vapor profiles, but add a third curve # We'll plot the new model as a dashed line to make it easier to see. plt.plot( band2.absorber_vmr['H2O'] *1000, lev, label='before 2xCO2') plt.plot( band4.absorber_vmr['H2O'] * 1000., lev, label='after 2xCO2') plt.plot( noh2o.absorber_vmr['H2O'] * 1000., lev, linestyle='--', label='No H2O feedback') plt.xlabel('g/kg H2O') plt.ylabel('pressure (hPa)') plt.legend(loc='upper right') plt.grid() # This reverses the axis so pressure decreases upward plt.gca().invert_yaxis() DeltaT_noh2o = noh2o.Ts - band2.Ts print 'The Equilibrium Climate Sensitivity with water vapor feedback is %f K.' % DeltaT_noh2o DeltaT - DeltaT_noh2o # Create the column with appropriate vertical coordinate, surface albedo and convective adjustment tuneband = climlab.BandRCModel(lev=lev, adj_lapse_rate=6, albedo_sfc=0.1) print tuneband tuneband.absorber_vmr['O3'] = O3_global tuneband.absorber_vmr tuneband.compute_diagnostics() tuneband.absorber_vmr tuneband.subprocess.LW.absorption_cross_section # This set of parameters gives correct Ts and ASR # But seems weird... the radiative forcing for doubling CO2 is tiny #tuneband.subprocess.SW.reflectivity[15] = 0.255 #tuneband.subprocess.LW.absorption_cross_section['CO2'][1] = 2. #tuneband.subprocess.LW.absorption_cross_section['H2O'][2] = 0.45 # Just tune the surface albedo, won't get correct ASR but get correct Ts tuneband = climlab.BandRCModel(lev=lev, adj_lapse_rate=6, albedo_sfc=0.22) tuneband.integrate_converge() tuneband.Ts tuneband.ASR tuneband.OLR tuneband.param tband2 = climlab.process_like(tuneband) tband2.subprocess['LW'].absorber_vmr['CO2'] *= 2 tband2.compute_diagnostics() tband2.OLR tband2.step_forward() tband2.OLR tband2.integrate_converge() tband2.Ts ncep_air ncep_air.variables.keys() latmodel = climlab.BandRCModel(lat=lat, lev=lev, adj_lapse_rate=6, albedo_sfc=0.22) print latmodel latmodel.Tatm.shape ncep_air.variables['air'].shape tuneband.Ts tuneband.subprocess.SW.albedo_sfc = 0.4 tuneband.integrate_converge() tuneband.Ts tuneband. # make a model on the same grid as the ozone model = climlab.BandRCModel(lev=lev, lat=lat, albedo_sfc=0.22) insolation = climlab.radiation.insolation.AnnualMeanInsolation(domains=model.Ts.domain) model.add_subprocess('insolation', insolation) model.subprocess.SW.flux_from_space = model.subprocess.insolation.insolation print model model.subprocess.insolation.insolation <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: First, let's just repeat the calculations we did in the previous notebook RadiativeConvectiveEquilibrium.ipynb Step2: Stratospheric ozone Step3: The pressure levels in this dataset are Step4: Take the global average of the ozone climatology, and plot it as a function of pressure (or height) Step5: This shows that most of the ozone is indeed in the stratosphere, and peaks near the top of the stratosphere. Step6: Check out the list of subprocesses. Step7: Ozone and CO2 are both specified in the model. The default, as you see above, is zero ozone, and constant (well-mixed) CO2 at a volume mixing ratio of 3.8E-4 or 380 ppm. Step8: Now put in the ozone Step9: Once we include ozone we get a well-defined stratosphere. Step10: We've just increased CO2 from 380 ppm to 760 ppm. Step11: Investigating the role of water vapor feedback in climate sensitivity Step12: Water vapor decreases from the surface upward mostly because the temperature decreases! Step13: Notice that the subprocess labelled 'H2O' is gone from the list. Step14: But this will be held fixed now as the climate changes in noh2o. Step15: Indeed, the water vapor is identical in the new equilibrium climate to the old pre-CO2-increase model. Step16: So the effect of the water vapor feedback on the climate sensitivity to doubled CO2 is Step17: We get about an additional degree of warming from the water vapor increase.

<ASSISTANT_TASK:> Python Code: import sisl import numpy as np import matplotlib.pyplot as plt %matplotlib inline graphene = sisl.geom.graphene() print(graphene) H = sisl.Hamiltonian(graphene) print(H) H[0, 0] = 0.0 H[1, 1] = 0.0 H[0, 1] = -2.7 H[1, 0] = -2.7 H[0, 1, (-1, 0)] = -2.7 H[0, 1, (0, -1)] = -2.7 H[1, 0, (1, 0)] = -2.7 H[1, 0, (0, 1)] = -2.7 print(H) print("Gamma:", H.eigh()) print("K:", H.eigh(k=[2./3,1./3,0])) for ia, io in H.geometry.iter_orbitals(local=False): # This loops over all atoms and the orbitals # corresponding to the atom. # In this case the geometry has one orbital per atom, hence # ia == io # in all cases. # In order to figure out which atoms atom `ia` is connected # to, we must find those atoms. # To do this we access the geometry attached to the # Hamiltonian (H.geom) # and use a function called `close` which returns ALL # atomic indices within certain ranges of a given point or atom idx = H.geometry.close(ia, R = [0.1, 1.43]) # the argument R has two entries: # 0.1 and 1.43 # Each value represents a radii of a sphere. # The `close` function will then return # a list of equal length of the R argument (i.e. a list with # two values). # idx[0] is the first element and is also a list # of all atoms within a sphere of 0.1 AA of atom `ia`. # This should obviously only contain the atom it-self. # The second element, idx[1], contains all atoms within a sphere # with radius of 1.43 AA, but not including those within 0.1 AA. # In this case this is then all atoms that are the nearest neighbour # atoms. # Now we know the on-site atoms (idx[0]) and the nearest neighbour # atoms (idx[1]), all we need to do is set the Hamiltonian # elements: # on-site (0. eV) H[io, idx[0]] = 0. # nearest-neighbour (-2.7 eV) H[io, idx[1]] = -2.7 print(H) print("Gamma:", H.eigh()) print("K:", H.eigh(k=[2./3,1./3,0])) band = sisl.BandStructure(H, [[0., 0.], [2./3, 1./3], [1./2, 1./2], [1., 1.]], 301, [r'$\Gamma$', 'K', 'M', r'$\Gamma$']) eigs = band.apply.array.eigh() # Retrieve the tick-marks and the linear k points xtick, xtick_label = band.lineartick() lk = band.lineark() plt.plot(lk, eigs) plt.ylabel('Eigenspectrum [eV]') plt.gca().xaxis.set_ticks(xtick) plt.gca().set_xticklabels(xtick_label) # Also plot x-major lines at the ticks ymin, ymax = plt.gca().get_ylim() for tick in xtick: plt.plot([tick,tick], [ymin,ymax], 'k') <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Instead of manually defining the graphene system with associated atomic coordinates and lattice vectors we use the build-in sisl capability of defining the graphene structure with a default atomic distance of $d = 1.42\,A$. Step2: Some basic information of the geometry is shown above Step3: Now H is a Hamiltonian object. It works equivalently to a matrix and one may assign elements and extract elements as though it were a matrix, we will return to the intricate details of the Hamiltonian object later. Step4: Now we need to set the coupling elements Step5: This will only couple the first and second atom in the primary unit-cell. But we also require couplings from the primary unit-cell to the neighbouring supercells. Remember that nsc = [3, 3, 1]. Step6: Now all matrix elements are set, i.e. 2 on-site and 6 nearest neighbour couplings, lets assert this Step7: We find 8 non-zero elements, as there should be. Remark, even though we set the on-site terms to $0$, they are interpreted as non-zero elements due to explicitly setting them. Step8: Looping the atoms and orbitals in the Hamiltonian Step9: The above loop is equivalent to the previously explicitly set values, so printing the structure will yield the same information, we have just specified all values again. Step10: After having setup the Hamilton, we may easily calculate the eigenvalues at any $\mathbf k$ (in reduced coordinates $\mathbf k\in]-0.5 Step11: We may also create a bandstructure of the Hamiltonian. Step12: Now eigs contains all the eigenvalues of the Hamiltonian object for all the $k$-points.

<ASSISTANT_TASK:> Python Code: %matplotlib inline # %load depthprobe_ex.py import numpy as np import bornagain as ba from bornagain import deg, angstrom, nm # layer thicknesses in angstroms t_Ti = 130.0 * angstrom t_Pt = 320.0 * angstrom t_Ti_top = 100.0 * angstrom t_TiO2 = 30.0 * angstrom # beam data ai_min = 0.0 * deg # minimum incident angle ai_max = 1.0 * deg # maximum incident angle n_ai_bins = 500 # number of bins in incident angle axis beam_sample_ratio = 0.01 # beam-to-sample size ratio wl = 10 * angstrom # wavelength in angstroms # angular beam divergence from https://mlz-garching.de/maria d_ang = np.degrees(3.0e-03)*deg # spread width for incident angle n_points = 50 # number of points to convolve over n_sig = 3 # number of sigmas to convolve over # wavelength divergence from https://mlz-garching.de/maria d_wl = 0.1*wl # spread width for the wavelength n_points_wl = 50 n_sig_wl = 2 # depth position span z_min = -100 * nm # 300 nm to the sample and substrate z_max = 100 * nm # 100 nm to the ambient layer n_z_bins = 500 def get_sample(): Constructs a sample with one resonating Ti/Pt layer # define materials m_Si = ba.MaterialBySLD("Si", 2.07e-06, 2.38e-11) m_Ti = ba.MaterialBySLD("Ti", 2.8e-06, 5.75e-10) m_Pt = ba.MaterialBySLD("Pt", 6.36e-06, 1.9e-09) m_TiO2 = ba.MaterialBySLD("TiO2", 2.63e-06, 5.4e-10) m_D2O = ba.MaterialBySLD("D2O", 6.34e-06, 1.13e-13) # create layers l_Si = ba.Layer(m_Si) l_Ti = ba.Layer(m_Ti, 130.0 * angstrom) l_Pt = ba.Layer(m_Pt, 320.0 * angstrom) l_Ti_top = ba.Layer(m_Ti, 100.0 * angstrom) l_TiO2 = ba.Layer(m_TiO2, 30.0 * angstrom) l_D2O = ba.Layer(m_D2O) # construct sample sample = ba.MultiLayer() sample.addLayer(l_Si) # put your code here (1 line), take care of correct indents sample.addLayer(l_Ti) sample.addLayer(l_Pt) sample.addLayer(l_Ti_top) sample.addLayer(l_TiO2) sample.addLayer(l_D2O) return sample def get_simulation(): Returns a depth-probe simulation. footprint = ba.FootprintFactorSquare(beam_sample_ratio) simulation = ba.DepthProbeSimulation() simulation.setBeamParameters(wl, n_ai_bins, ai_min, ai_max, footprint) simulation.setZSpan(n_z_bins, z_min, z_max) fwhm2sigma = 2*np.sqrt(2*np.log(2)) # add angular beam divergence # put your code here (2 lines) # add wavelength divergence # put your code here (2 lines) return simulation def run_simulation(): Runs simulation and returns its result. sample = get_sample() simulation = get_simulation() simulation.setSample(sample) simulation.runSimulation() return simulation.result() if __name__ == '__main__': result = run_simulation() ba.plot_simulation_result(result) %load depthprobe.py <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step3: Evanescent wave intensity Step4: Solution

<ASSISTANT_TASK:> Python Code: DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE from urllib.request import urlretrieve from os.path import isfile, isdir from tqdm import tqdm import problem_unittests as tests import tarfile cifar10_dataset_folder_path = 'cifar-10-batches-py' tar_gz_path = 'cifar-10-python.tar.gz' class DLProgress(tqdm): last_block = 0 def hook(self, block_num=1, block_size=1, total_size=None): self.total = total_size self.update((block_num - self.last_block) * block_size) self.last_block = block_num if not isfile(tar_gz_path): with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar: urlretrieve( 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz', tar_gz_path, pbar.hook) if not isdir(cifar10_dataset_folder_path): with tarfile.open(tar_gz_path) as tar: tar.extractall() tar.close() tests.test_folder_path(cifar10_dataset_folder_path) %matplotlib inline %config InlineBackend.figure_format = 'retina' import helper import numpy as np # Explore the dataset batch_id = 1 sample_id = 5 helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id) def normalize(x): Normalize a list of sample image data in the range of 0 to 1 : x: List of image data. The image shape is (32, 32, 3) : return: Numpy array of normalize data return (x - x.min())/(x.max() - x.min()) tests.test_normalize(normalize) import sklearn.preprocessing label_binarizer = sklearn.preprocessing.LabelBinarizer() label_binarizer.fit(range(10)) def one_hot_encode(x): One hot encode a list of sample labels. Return a one-hot encoded vector for each label. : x: List of sample Labels : return: Numpy array of one-hot encoded labels return label_binarizer.transform(x) tests.test_one_hot_encode(one_hot_encode) # Preprocess Training, Validation, and Testing Data helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode) import pickle import problem_unittests as tests import helper # Load the Preprocessed Validation data valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb')) import tensorflow as tf def neural_net_image_input(image_shape): Return a Tensor for a batch of image input : image_shape: Shape of the images : return: Tensor for image input. return tf.placeholder(tf.float32, [None, image_shape[0], image_shape[1], image_shape[2]], 'x') def neural_net_label_input(n_classes): Return a Tensor for a batch of label input : n_classes: Number of classes : return: Tensor for label input. return tf.placeholder(tf.float32, [None, n_classes], 'y') def neural_net_keep_prob_input(): Return a Tensor for keep probability : return: Tensor for keep probability. return tf.placeholder(tf.float32, name='keep_prob') tf.reset_default_graph() tests.test_nn_image_inputs(neural_net_image_input) tests.test_nn_label_inputs(neural_net_label_input) tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input) def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides, ): Apply convolution then max pooling to x_tensor :param x_tensor: TensorFlow Tensor :param conv_num_outputs: Number of outputs for the convolutional layer :param conv_ksize: kernal size 2-D Tuple for the convolutional layer :param conv_strides: Stride 2-D Tuple for convolution :param pool_ksize: kernal size 2-D Tuple for pool :param pool_strides: Stride 2-D Tuple for pool : return: A tensor that represents convolution and max pooling of x_tensor weights = tf.Variable(tf.truncated_normal([conv_ksize[0], conv_ksize[1], x_tensor.get_shape().as_list()[-1], conv_num_outputs], stddev=0.1)) bias = tf.Variable(tf.zeros([conv_num_outputs])) conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x_tensor, weights, [1, conv_strides[0], conv_strides[1], 1], 'SAME'), bias)) return tf.nn.max_pool(conv1, [1, pool_ksize[0], pool_ksize[1], 1], [1, pool_strides[0], pool_strides[1], 1], 'SAME') DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE tests.test_con_pool(conv2d_maxpool) def flatten(x_tensor): Flatten x_tensor to (Batch Size, Flattened Image Size) : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions. : return: A tensor of size (Batch Size, Flattened Image Size). size = x_tensor.get_shape().as_list() return tf.reshape(x_tensor, shape=[-1, size[1] * size[2] * size[3]]) tests.test_flatten(flatten) def fully_conn(x_tensor, num_outputs): Apply a fully connected layer to x_tensor using weight and bias : x_tensor: A 2-D tensor where the first dimension is batch size. : num_outputs: The number of output that the new tensor should be. : return: A 2-D tensor where the second dimension is num_outputs. weights = tf.Variable(tf.truncated_normal([x_tensor.get_shape().as_list()[1], num_outputs], stddev=0.1)) bias = tf.Variable(tf.zeros([num_outputs])) return tf.nn.relu(tf.nn.bias_add(tf.matmul(x_tensor, weights), bias)) tests.test_fully_conn(fully_conn) def output(x_tensor, num_outputs): Apply a output layer to x_tensor using weight and bias : x_tensor: A 2-D tensor where the first dimension is batch size. : num_outputs: The number of output that the new tensor should be. : return: A 2-D tensor where the second dimension is num_outputs. weights = tf.Variable(tf.truncated_normal([x_tensor.get_shape().as_list()[1], num_outputs], stddev=0.1)) bias = tf.Variable(tf.zeros([num_outputs])) return tf.nn.bias_add(tf.matmul(x_tensor, weights), bias) tests.test_output(output) def conv_net(x, keep_prob): Create a convolutional neural network model : x: Placeholder tensor that holds image data. : keep_prob: Placeholder tensor that hold dropout keep probability. : return: Tensor that represents logits conv1 = conv2d_maxpool(x, 64, [2, 2], [1, 1], [2, 2], [2, 2]) conv1 = conv2d_maxpool(conv1, 128, [3, 3], [1, 1], [2, 2], [2, 2]) conv1 = conv2d_maxpool(conv1, 256, [3, 3], [1, 1], [2, 2], [2, 2]) conv1 = flatten(conv1) conv1 = fully_conn(conv1, 2500) conv1 = tf.nn.dropout(conv1, keep_prob) return output(conv1, 10) ############################## ## Build the Neural Network ## ############################## # Remove previous weights, bias, inputs, etc.. tf.reset_default_graph() # Inputs x = neural_net_image_input((32, 32, 3)) y = neural_net_label_input(10) keep_prob = neural_net_keep_prob_input() # Model logits = conv_net(x, keep_prob) # Name logits Tensor, so that is can be loaded from disk after training logits = tf.identity(logits, name='logits') # Loss and Optimizer cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y)) optimizer = tf.train.AdamOptimizer().minimize(cost) # Accuracy correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy') tests.test_conv_net(conv_net) def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch): Optimize the session on a batch of images and labels : session: Current TensorFlow session : optimizer: TensorFlow optimizer function : keep_probability: keep probability : feature_batch: Batch of Numpy image data : label_batch: Batch of Numpy label data session.run(optimizer, feed_dict={x: feature_batch, y: label_batch , keep_prob: keep_probability}) tests.test_train_nn(train_neural_network) def print_stats(session, feature_batch, label_batch, cost, accuracy): Print information about loss and validation accuracy : session: Current TensorFlow session : feature_batch: Batch of Numpy image data : label_batch: Batch of Numpy label data : cost: TensorFlow cost function : accuracy: TensorFlow accuracy function cost_val = session.run(cost, feed_dict={x: feature_batch, y: label_batch , keep_prob: 1}) accuracy_val = session.run(accuracy, feed_dict={x: valid_features, y: valid_labels , keep_prob: 1}) print("Cost: {} Accuracy: {}".format(cost_val, accuracy_val)) # TODO: Tune Parameters epochs = 50 batch_size = 256 keep_probability = 0.75 print('Checking the Training on a Single Batch...') with tf.Session() as sess: # Initializing the variables sess.run(tf.global_variables_initializer()) # Training cycle for epoch in range(epochs): batch_i = 1 for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size): train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels) print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='') print_stats(sess, batch_features, batch_labels, cost, accuracy) save_model_path = './image_classification' print('Training...') with tf.Session() as sess: # Initializing the variables sess.run(tf.global_variables_initializer()) # Training cycle for epoch in range(epochs): # Loop over all batches n_batches = 5 for batch_i in range(1, n_batches + 1): for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size): train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels) print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='') print_stats(sess, batch_features, batch_labels, cost, accuracy) # Save Model saver = tf.train.Saver() save_path = saver.save(sess, save_model_path) %matplotlib inline %config InlineBackend.figure_format = 'retina' import tensorflow as tf import pickle import helper import random # Set batch size if not already set try: if batch_size: pass except NameError: batch_size = 64 save_model_path = './image_classification' n_samples = 4 top_n_predictions = 3 def test_model(): Test the saved model against the test dataset test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb')) loaded_graph = tf.Graph() with tf.Session(graph=loaded_graph) as sess: # Load model loader = tf.train.import_meta_graph(save_model_path + '.meta') loader.restore(sess, save_model_path) # Get Tensors from loaded model loaded_x = loaded_graph.get_tensor_by_name('x:0') loaded_y = loaded_graph.get_tensor_by_name('y:0') loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0') loaded_logits = loaded_graph.get_tensor_by_name('logits:0') loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0') # Get accuracy in batches for memory limitations test_batch_acc_total = 0 test_batch_count = 0 for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size): test_batch_acc_total += sess.run( loaded_acc, feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0}) test_batch_count += 1 print('Testing Accuracy: {}\n'.format(test_batch_acc_total/test_batch_count)) # Print Random Samples random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples))) random_test_predictions = sess.run( tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions), feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0}) helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions) test_model() <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Image Classification Step2: Explore the Data Step4: Implement Preprocess Functions Step6: One-hot encode Step7: Randomize Data Step8: Check Point Step12: Build the network Step15: Convolution and Max Pooling Layer Step17: Flatten Layer Step19: Fully-Connected Layer Step22: Output Layer Step24: Create Convolutional Model Step26: Show Stats Step27: Hyperparameters Step28: Train on a Single CIFAR-10 Batch Step29: Fully Train the Model Step31: Checkpoint

<ASSISTANT_TASK:> Python Code: import re import time import pprint from qumulo.rest_client import RestClient rc = RestClient("qumulo.test", 8000) rc.login("admin", "*********"); def create_policy_for_diff(rc, policy_name, path='/', minutes=10): try: dets = rc.fs.get_file_attr(path=path) except: print("!!! Unable to find directory: %s" % path) return policy = rc.snapshot.create_policy( name = policy_name, directory_id = dets['id'], schedule_info = {"creation_schedule":{ "frequency":"SCHEDULE_HOURLY_OR_LESS", "fire_every":1, "fire_every_interval":"FIRE_IN_MINUTES", "window_start_hour":0, "window_start_minute":0, "window_end_hour":23, "window_end_minute":59, "on_days":["MON","TUE","WED","THU","FRI","SAT","SUN"], "timezone":"America/Los_Angeles" }, "expiration_time_to_live":"%sminutes" % minutes } ) print("Created policy on directory '%s': %s expires after %s" % ( path, policy['name'], policy['schedules'][0]['expiration_time_to_live'])) def diff_snaps(rc, policy_name): snap_count = 2 # set up for the 1st loop paths = [] while snap_count >= 2: all_snaps = rc.snapshot.list_snapshot_statuses()['entries'] short_list = filter(lambda s: s['name'] == policy_name, all_snaps) snaps = sorted(short_list, key=lambda s: s['id']) if len(snaps) < 2: break print("Diff times: %s -> %s" % (snaps[0]['timestamp'][0:19], snaps[1]['timestamp'][0:19])) diff = rc.snapshot.get_all_snapshot_tree_diff(snaps[1]['id'], snaps[0]['id']) for d in diff: for e in d['entries']: if e['path'][-1] == "/": continue # it's a directory sz = None owner = None try: dets = rc.fs.get_file_attr(e['path']) sz = dets['size'] owner = dets['owner_details']['id_value'] except: pass if e['op'] == 'DELETE' and sz is not None: continue # don't add deletes for existing files paths.append({'op': e['op'], 'path': e['path'], 'size': sz, 'owner': owner, 'snapshot_id': snaps[1]['id']}) # delete the oldest snapshot rc.snapshot.delete_snapshot(snaps[0]['id']) snap_count = len(snaps) - 1 return paths create_policy_for_diff(rc, 'EveryMinuteForDiffs') diff_list = diff_snaps(rc, 'EveryMinuteForDiffs') print("Found %s file changes." % len(diff_list)) owners = {} ops = {} diffs = {} for d in diff_list: owners[d['owner']] = 1 if d['op'] not in ops: ops[d['op']] = 1 ops[d['op']] += 1 diffs[d['snapshot_id']] = 1 print("Ops: %s" % ' | '.join(["%s:%s" % (k,v) for k, v in ops.items()])) print("Diff count: %s" % len(diffs)) print("Owner count: %s" % len(owners)) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Create Snapshot Policy Step2: Diff all snapshots in a policy

<ASSISTANT_TASK:> Python Code: from keras.datasets import imdb idx = imdb.get_word_index() idx_arr = sorted(idx, key=idx.get) idx_arr[:10] idx2word = {v: k for k, v in idx.iteritems()} path = get_file('imdb_full.pkl', origin='https://s3.amazonaws.com/text-datasets/imdb_full.pkl', md5_hash='d091312047c43cf9e4e38fef92437263') f = open(path, 'rb') (x_train, labels_train), (x_test, labels_test) = pickle.load(f) len(x_train) ', '.join(map(str, x_train[0])) idx2word[23022] ' '.join([idx2word[o] for o in x_train[0]]) labels_train[:10] vocab_size = 5000 trn = [np.array([i if i<vocab_size-1 else vocab_size-1 for i in s]) for s in x_train] test = [np.array([i if i<vocab_size-1 else vocab_size-1 for i in s]) for s in x_test] lens = np.array(map(len, trn)) (lens.max(), lens.min(), lens.mean()) seq_len = 500 trn = sequence.pad_sequences(trn, maxlen=seq_len, value=0) test = sequence.pad_sequences(test, maxlen=seq_len, value=0) trn.shape model = Sequential([ Embedding(vocab_size, 32, input_length=seq_len), Flatten(), Dense(100, activation='relu'), Dropout(0.7), Dense(1, activation='sigmoid')]) model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy']) model.summary() model.fit(trn, labels_train, validation_data=(test, labels_test), nb_epoch=2, batch_size=64, verbose=2) conv1 = Sequential([ Embedding(vocab_size, 32, input_length=seq_len, dropout=0.2), Dropout(0.2), Convolution1D(64, 5, border_mode='same', activation='relu'), Dropout(0.2), MaxPooling1D(), Flatten(), Dense(100, activation='relu'), Dropout(0.7), Dense(1, activation='sigmoid')]) conv1.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy']) conv1.fit(trn, labels_train, validation_data=(test, labels_test), nb_epoch=4, batch_size=64, verbose=2) conv1.save_weights(model_path + 'conv1.h5') conv1.load_weights(model_path + 'conv1.h5') def get_glove_dataset(dataset): Download the requested glove dataset from files.fast.ai and return a location that can be passed to load_vectors. # see wordvectors.ipynb for info on how these files were # generated from the original glove data. md5sums = {'6B.50d': '8e1557d1228decbda7db6dfd81cd9909', '6B.100d': 'c92dbbeacde2b0384a43014885a60b2c', '6B.200d': 'af271b46c04b0b2e41a84d8cd806178d', '6B.300d': '30290210376887dcc6d0a5a6374d8255'} glove_path = os.path.abspath('data/glove/results') %mkdir -p $glove_path return get_file(dataset, 'http://files.fast.ai/models/glove/' + dataset + '.tgz', cache_subdir=glove_path, md5_hash=md5sums.get(dataset, None), untar=True) def load_vectors(loc): return (load_array(loc+'.dat'), pickle.load(open(loc+'_words.pkl','rb')), pickle.load(open(loc+'_idx.pkl','rb'))) vecs, words, wordidx = load_vectors(get_glove_dataset('6B.50d')) def create_emb(): n_fact = vecs.shape[1] emb = np.zeros((vocab_size, n_fact)) for i in range(1,len(emb)): word = idx2word[i] if word and re.match(r"^[a-zA-Z0-9\-]*$", word): src_idx = wordidx[word] emb[i] = vecs[src_idx] else: # If we can't find the word in glove, randomly initialize emb[i] = normal(scale=0.6, size=(n_fact,)) # This is our "rare word" id - we want to randomly initialize emb[-1] = normal(scale=0.6, size=(n_fact,)) emb/=3 return emb emb = create_emb() model = Sequential([ Embedding(vocab_size, 50, input_length=seq_len, dropout=0.2, weights=[emb], trainable=False), Dropout(0.25), Convolution1D(64, 5, border_mode='same', activation='relu'), Dropout(0.25), MaxPooling1D(), Flatten(), Dense(100, activation='relu'), Dropout(0.7), Dense(1, activation='sigmoid')]) model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy']) model.fit(trn, labels_train, validation_data=(test, labels_test), nb_epoch=2, batch_size=64, verbose=2) model.layers[0].trainable=True model.optimizer.lr=1e-4 model.fit(trn, labels_train, validation_data=(test, labels_test), nb_epoch=1, batch_size=64, verbose = 2) model.save_weights(model_path+'glove50.h5') from keras.layers import Merge graph_in = Input ((vocab_size, 50)) convs = [ ] for fsz in range (3, 6): x = Convolution1D(64, fsz, border_mode='same', activation="relu")(graph_in) x = MaxPooling1D()(x) x = Flatten()(x) convs.append(x) out = Merge(mode="concat")(convs) graph = Model(graph_in, out) emb = create_emb() model = Sequential ([ Embedding(vocab_size, 50, input_length=seq_len, dropout=0.2, weights=[emb]), Dropout (0.2), graph, Dropout (0.5), Dense (100, activation="relu"), Dropout (0.7), Dense (1, activation='sigmoid') ]) model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy']) model.fit(trn, labels_train, validation_data=(test, labels_test), nb_epoch=2, batch_size=64, verbose=2) model.layers[0].trainable=False model.optimizer.lr=1e-5 model.fit(trn, labels_train, validation_data=(test, labels_test), nb_epoch=2, batch_size=64, verbose=2) model = Sequential([ Embedding(vocab_size, 32, input_length=seq_len, mask_zero=True, W_regularizer=l2(1e-6), dropout=0.2), LSTM(100, consume_less='gpu'), Dense(1, activation='sigmoid')]) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.summary() model.fit(trn, labels_train, validation_data=(test, labels_test), nb_epoch=5, batch_size=64, verbose=2) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: This is the word list Step2: ...and this is the mapping from id to word Step3: We download the reviews using code copied from keras.datasets Step4: Here's the 1st review. As you see, the words have been replaced by ids. The ids can be looked up in idx2word. Step5: The first word of the first review is 23022. Let's see what that is. Step6: Here's the whole review, mapped from ids to words. Step7: The labels are 1 for positive, 0 for negative. Step8: Reduce vocab size by setting rare words to max index. Step9: Look at distribution of lengths of sentences. Step10: Pad (with zero) or truncate each sentence to make consistent length. Step11: This results in nice rectangular matrices that can be passed to ML algorithms. Reviews shorter than 500 words are pre-padded with zeros, those greater are truncated. Step12: Create simple models Step13: The stanford paper that this dataset is from cites a state of the art accuracy (without unlabelled data) of 0.883. So we're short of that, but on the right track. Step14: That's well past the Stanford paper's accuracy - another win for CNNs! Step16: Pre-trained vectors Step17: The glove word ids and imdb word ids use different indexes. So we create a simple function that creates an embedding matrix using the indexes from imdb, and the embeddings from glove (where they exist). Step18: We pass our embedding matrix to the Embedding constructor, and set it to non-trainable. Step19: We already have beaten our previous model! But let's fine-tune the embedding weights - especially since the words we couldn't find in glove just have random embeddings. Step20: As expected, that's given us a nice little boost. Step21: Multi-size CNN Step22: We use the functional API to create multiple conv layers of different sizes, and then concatenate them. Step23: We then replace the conv/max-pool layer in our original CNN with the concatenated conv layers. Step24: Interestingly, I found that in this case I got best results when I started the embedding layer as being trainable, and then set it to non-trainable after a couple of epochs. I have no idea why! Step25: This more complex architecture has given us another boost in accuracy.

<ASSISTANT_TASK:> Python Code: import datetime import matplotlib.pyplot as plt import pandas as pd import pinkfish as pf import strategy # Format price data. pd.options.display.float_format = '{:0.2f}'.format pd.set_option('display.max_rows', None) %matplotlib inline # Set size of inline plots '''note: rcParams can't be in same cell as import matplotlib or %matplotlib inline %matplotlib notebook: will lead to interactive plots embedded within the notebook, you can zoom and resize the figure %matplotlib inline: only draw static images in the notebook ''' plt.rcParams["figure.figsize"] = (10, 7) # symbol: (description, multiplier) equities = { 'ES=F': ('E-Mini S&P 500 Futures', 50), 'YM=F': ('Mini Dow Jones Futures', 5), 'NQ=F': ('Nasdaq 100 Futures', 20), } interest_rates = { 'ZB=F': ('U.S. Treasury Bond Futures', 1000), 'ZN=F': ('10-Year T-Note Futures', 1000), 'ZF=F': ('Five-Year US Treasury Note Futures', 1000), 'ZT=F': ('2-Year T-Note Futures', 2000) } # https://www.cmegroup.com/markets/agriculture.html#products agricultural_commodities = { # Grains 'ZC=F': ('Corn Futures', 5000), 'KE=F': ('KC HRW Wheat Futures', 5000), 'ZS=F': ('Soybean Futures', 50), 'KE=F': ('KC HRW Wheat Futures', 50), 'ZR=F': ('Rough Rice Futures', 2000), 'ZM=F': ('Soybean Meal Futures', 100), 'ZL=F': ('Soybean Oil Futures', 600), 'GF=F': ('Feeder Cattle Futures', 500), 'HE=F': ('Lean Hogs Futures', 400), 'CC=F': ('Cocoa Futures', 10), 'KC=F': ('Coffee Futures', 375), 'CT=F': ('Cotton Futures', 50000), 'LBS=F': ('Lumber Futures', 110), 'SB=F': ('Sugar #11 Futures', 1120) } non_agricultural_commodities = { 'GC=F': ('Gold Futures', 100), 'SI=F': ('Silver Futures', 5000), 'PL=F': ('Platinum Futures', 50), 'HG=F': ('Copper Futures', 25000), 'PA=F': ('Palladium Futures', 100), 'CL=F': ('Crude Oil Futures', 1000), 'HO=F': ('Heating Oil Futures', 42000), 'NG=F': ('Natural Gas Futures', 10000), 'RB=F': ('RBOB Gasoline Futures', 42000) } # https://www.cmegroup.com/markets/fx.html#products currency_multiplier = 100 currencies = { # G10 'DX=F': ('U.S. Dollar Index', currency_multiplier), '6E=F': ('Euro FX Futures', currency_multiplier), '6J=F': ('Japanese Yen Futures', currency_multiplier), '6A=F': ('Australian Dollar Futures', currency_multiplier), '6B=F': ('British Pound Futures', currency_multiplier), '6C=F': ('Canadian Dollar Futures', currency_multiplier), '6S=F': ('Swiss Franc Futures', currency_multiplier), '6N=F': ('New Zealand Dollar Futures', currency_multiplier), } futures = {**equities, **interest_rates, **agricultural_commodities, **non_agricultural_commodities, **currencies} ten_largest = ['ZN=F', 'ES=F', 'CL=F', 'GC=F', 'ZC=F', 'KC=F', 'CT=F', 'DX=F'] symbols = list(ten_largest) #symbols = ['ES=F', 'GC=F', 'CL=F'] capital = 100_000 start = datetime.datetime(1900, 1, 1) end = datetime.datetime.now() options = { 'use_adj' : False, 'use_cache' : True, 'sell_short' : False, 'force_stock_market_calendar' : True, 'margin' : 2, 'sma_timeperiod_slow': 100, 'sma_timeperiod_fast': 50, 'use_vola_weight' : True } s = strategy.Strategy(symbols, capital, start, end, options=options) s.run() s.rlog.head() s.tlog.head() s.dbal.tail() pf.print_full(s.stats) weights = {symbol: 1 / len(symbols) for symbol in symbols} totals = s.portfolio.performance_per_symbol(weights=weights) totals corr_df = s.portfolio.correlation_map(s.ts) corr_df benchmark = pf.Benchmark('SPY', s.capital, s.start, s.end, use_adj=True) benchmark.run() pf.plot_equity_curve(s.dbal, benchmark=benchmark.dbal) df = pf.plot_bar_graph(s.stats, benchmark.stats) df kelly = pf.kelly_criterion(s.stats, benchmark.stats) kelly <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Investment Universe Step2: Run Strategy Step3: View log DataFrames Step4: Generate strategy stats - display all available stats Step5: View Performance by Symbol Step6: Run Benchmark, Retrieve benchmark logs, and Generate benchmark stats Step7: Plot Equity Curves Step8: Bar Graph Step9: Analysis

<ASSISTANT_TASK:> Python Code: %%bash source activate root # you need to change here to your env name pip install kaggle-cli %%bash source activate root # you need to change here to your env name rm -rf data mkdir -p data pushd data kg download unzip -q train.zip unzip -q test.zip popd from glob import glob import numpy as np from shutil import move, copyfile %mkdir -p data/train %mkdir -p data/valid %mkdir -p data/sample/train %mkdir -p data/sample/valid %pushd data/train g = glob('*.jpg') shuf = np.random.permutation(g) for i in range(200): copyfile(shuf[i], '../sample/train/' + shuf[i]) shuf = np.random.permutation(g) for i in range(200): copyfile(shuf[i], '../sample/valid/' + shuf[i]) # validation files are moved shuf = np.random.permutation(g) for i in range(1000): move(shuf[i], '../valid/' + shuf[i]) %popd %pushd data/train % mkdir cat dog % mv cat*.jpg cat % mv dog*.jpg dog %popd %pushd data/valid % mkdir cat dog % mv cat*.jpg cat % mv dog*.jpg dog %popd %pushd data/sample/train % mkdir cat dog % mv cat*.jpg cat % mv dog*.jpg dog %popd %pushd data/sample/valid % mkdir cat dog % mv cat*.jpg cat % mv dog*.jpg dog %popd %pushd data/test % mkdir unknown % mv *.jpg unknown %popd <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: download dataset Step2: Copy files to valid and sample Step3: Arrangement files

<ASSISTANT_TASK:> Python Code: def get_val(line): Get the value after the key for a RIS formatted line >>> get_val('AU - Garcia-Pino, Abel') 'Garcia-Pino, Abel' >>> get_val('AU - Uversky, Vladimir N.') 'Uversky, Vladimir N.' >>> get_val('SP - 6933') '6933' >>> get_val('EP - 6947') '6947' #Finish... import doctest doctest.testmod() filein = open('data/achs_chreay114_6557.ris', 'r') articles = [] for line in filein: if line.strip()=='': articles.append(dict()) if line.startswith('AU -'): articles[-1].setdefault('authors', []).append(get_val(line).strip()) if line.startswith('SP -'): #Finish... if line.startswith('EP -'): #finish... filein.close() articles = #Finish... import numpy as np page_lengths = [*FINISH* for d in articles] page_lengths "The average number of pages is {:.1f} and its standard deviation {:.1f}".format(*FINISH*) author_dict = {} for d in articles: for author in d['authors']: author_dict[author] = author_dict.setdefault(author, 0) + 1 author_dict.values() papers_authored = set([p for p in author_dict.values() if p>1]) # Remove repeated elements papers_authored = # We need to convert to a list to sort #Sort the list... # Print the results def get_pages(name, articles): Get the total number of pages for a given author. Return 0 if the author is not in the article authors. pages = 0 for art in articles: if name in art['authors']: pages += art['ep']-art['sp']+1 return pages #Finish... from collections import Counter author_count = Counter() for d in articles: author_count.update(d['authors']) for author, val in author_count.most_common(): if val > 1: pages = get_pages(author, articles) print("{} published {} papers adding up to {} pages".format(author, val, pages)) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Text Parsing. Counting authors in a journal issue Step2: Now we scan all the text file. For each empty line we create a new entry (we'll correct for the blank lines at the beggining of the file later). Step3: Because there were some blank lines at the begining of the file, that created some empty dictionaries that we can now remove (using the fact that empty object evaluate as False) Step4: Analysing the data Step5: Now let's count how many papers in this issue has contributed each author. We'll create a dictionary of authors keys and number of papers as values. Step6: So one author published 7 papers in that issue, another one 3 papers and several have authored 2 papers! Let's see who authored more than one paper. We will print the results sorted by the number of papers authored. We want to get a list such as Step8: Now let's see how many pages they have (presumably) written or supervised... Get something like Step9: As usual, some documentation search would have shown us a module that could have eased the coding. The collections module has a Counter type that is useful for counting things. When fed with a list, Counter counts its elements and stores something similar to a dictionary. Step10: author_dict and author_count are similar objects. But Counters has some useful counting methods, so that we do not need the papers_authored list. Using the Counter object the code is simpler. This prints our final list in a single loop

<ASSISTANT_TASK:> Python Code: import random as rnd class Supplier(): def __init__(self): self.wta = [] # the supplier has n quantities that they can sell # they may be willing to sell this quantity anywhere from a lower price of l # to a higher price of u def set_quantity(self,n,l,u): for i in range(n): p = rnd.uniform(l,u) self.wta.append(p) # return the dictionary of willingness to ask def get_ask(self): return self.wta class Buyer(): def __init__(self): self.wtp = [] # the supplier has n quantities that they can buy # they may be willing to sell this quantity anywhere from a lower price of l # to a higher price of u def set_quantity(self,n,l,u): for i in range(n): p = rnd.uniform(l,u) self.wtp.append(p) # return list of willingness to pay def get_bid(self): return self.wtp class Market(): count = 0 last_price = '' b = [] s = [] def __init__(self,b,s): # buyer list sorted in descending order self.b = sorted(b, reverse=True) # seller list sorted in ascending order self.s = sorted(s, reverse=False) # return the price at which the market clears # assume equal numbers of sincere buyers and sellers def get_clearing_price(self): # buyer makes a bid, starting with the buyer which wants it most for i in range(len(self.b)): if (self.b[i] > self.s[i]): self.count +=1 self.last_price = self.b[i] return self.last_price def get_units_cleared(self): return self.count # make a supplier and get the asks supplier = Supplier() supplier.set_quantity(60,10,30) ask = supplier.get_ask() # make a buyer and get the bids (n,l,u) buyer = Buyer() buyer.set_quantity(60,10,30) bid = buyer.get_bid() # make a market where the buyers and suppliers can meet # the bids and asks are a list of prices market = Market(bid,ask) price = market.get_clearing_price() quantity = market.get_units_cleared() # output the results of the market print("Goods cleared for a price of ",price) print("Units sold are ", quantity) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Example Market

<ASSISTANT_TASK:> Python Code: %matplotlib inline import pandas as pd import numpy as np import pymc3 as pm import matplotlib.pyplot as plt import seaborn import warnings warnings.filterwarnings('ignore') data = pd.read_csv("data.csv", header=None, skiprows=1, names=['age', 'workclass', 'fnlwgt', 'education-categorical', 'educ', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'captial-gain', 'capital-loss', 'hours', 'native-country', 'income']) data data = data[~pd.isnull(data['income'])] data[data['native-country']==" United-States"] income = 1 * (data['income'] == " >50K") age2 = np.square(data['age']) data = data[['age', 'educ', 'hours']] data['age2'] = age2 data['income'] = income income.value_counts() with pm.Model() as model: pm.glm.glm('income ~ age + age2 + educ + hours', data) trace = pm.sample(2000, pm.NUTS(), progressbar=True) trace = trace[1000:] plt.figure(figsize=(9,7)) plt.hist2d(trace['age'], trace['educ'], alpha=.5, cmap="Reds", bins=25) plt.colorbar() seaborn.kdeplot(trace['age'], trace['educ']) plt.xlabel("beta_age") plt.ylabel("beta_educ") plt.show() # Linear model with hours == 50 and educ == 12 lm = lambda x, samples: 1 / (1 + np.exp(-(samples['Intercept'] + samples['age']*x + samples['age2']*np.square(x) + samples['educ']*12 + samples['hours']*50))) # Linear model with hours == 50 and educ == 16 lm2 = lambda x, samples: 1 / (1 + np.exp(-(samples['Intercept'] + samples['age']*x + samples['age2']*np.square(x) + samples['educ']*16 + samples['hours']*50))) # Linear model with hours == 50 and educ == 19 lm3 = lambda x, samples: 1 / (1 + np.exp(-(samples['Intercept'] + samples['age']*x + samples['age2']*np.square(x) + samples['educ']*19 + samples['hours']*50))) # Plot the posterior predictive distributions of P(income > $50K) vs. age pm.glm.plot_posterior_predictive(trace, eval=np.linspace(25, 75, 1000), lm=lm, samples=100, color="blue", alpha=.15) pm.glm.plot_posterior_predictive(trace, eval=np.linspace(25, 75, 1000), lm=lm2, samples=100, color="green", alpha=.15) pm.glm.plot_posterior_predictive(trace, eval=np.linspace(25, 75, 1000), lm=lm3, samples=100, color="red", alpha=.15) plt.ylim([.4,.8]) plt.ylabel("P(Income > $50K)") plt.xlabel("Age") plt.show() b = trace['educ'] plt.hist(np.exp(b), bins=25, normed=True) plt.xlabel("Odds Ratio") plt.show() lb, ub = np.percentile(b, 2.5), np.percentile(b, 97.5) print("P(%.3f < O.R. < %.3f) = 0.95"%(np.exp(3*lb),np.exp(3*ub))) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: The Adult Data Set is commonly used to benchmark machine learning algorithms. The goal is to use demographic features, or variables, to predict whether an individual makes more than \$50,000 per year. The data set is almost 20 years old, and therefore, not perfect for determining the probability that I will make more than \$50K, but it is a nice, simple dataset that can be used to showcase a few benefits of using Bayesian logistic regression over its frequentist counterpart. Step2: The model Step3: Some results Step4: So how do age and education affect the probability of making more than $$50K?$ To answer this question, we can show how the probability of making more than $50K changes with age for a few different education levels. Here, we assume that the number of hours worked per week is fixed at 50. PyMC3 gives us a convenient way to plot the posterior predictive distribution. We need to give the function a linear model and a set of points to evaluate. We will pass in three different linear models Step5: Each curve shows how the probability of earning more than $ 50K$ changes with age. The red curve represents 19 years of education, the green curve represents 16 years of education and the blue curve represents 12 years of education. For all three education levels, the probability of making more than $50K increases with age until approximately age 60, when the probability begins to drop off. Notice that each curve is a little blurry. This is because we are actually plotting 100 different curves for each level of education. Each curve is a draw from our posterior distribution. Because the curves are somewhat translucent, we can interpret dark, narrow portions of a curve as places where we have low uncertainty and light, spread out portions of the curve as places where we have somewhat higher uncertainty about our coefficient values. Step6: Finally, we can find a confidence interval for this quantity. This may be the best part about Bayesian statistics

<ASSISTANT_TASK:> Python Code: %load exercises/3.1-colors.py t = np.arange(0.0, 5.0, 0.2) plt.plot(t, t, , t, t**2, , t, t**3, ) plt.show() t = np.arange(0.0, 5.0, 0.2) plt.plot(t, t, , t, t**2, , t, t**3, ) plt.show() xs, ys = np.mgrid[:4, 9:0:-1] markers = [".", "+", ",", "x", "o", "D", "d", "", "8", "s", "p", "*", "|", "_", "h", "H", 0, 4, "<", "3", 1, 5, ">", "4", 2, 6, "^", "2", 3, 7, "v", "1", "None", None, " ", ""] descripts = ["point", "plus", "pixel", "cross", "circle", "diamond", "thin diamond", "", "octagon", "square", "pentagon", "star", "vertical bar", "horizontal bar", "hexagon 1", "hexagon 2", "tick left", "caret left", "triangle left", "tri left", "tick right", "caret right", "triangle right", "tri right", "tick up", "caret up", "triangle up", "tri up", "tick down", "caret down", "triangle down", "tri down", "Nothing", "Nothing", "Nothing", "Nothing"] fig, ax = plt.subplots(1, 1, figsize=(14, 4)) for x, y, m, d in zip(xs.T.flat, ys.T.flat, markers, descripts): ax.scatter(x, y, marker=m, s=100) ax.text(x + 0.1, y - 0.1, d, size=14) ax.set_axis_off() plt.show() %load exercises/3.2-markers.py t = np.arange(0.0, 5.0, 0.2) plt.plot(t, t, , t, t**2, , t, t**3, ) plt.show() t = np.arange(0.0, 5.0, 0.2) plt.plot(t, t, '-', t, t**2, '--', t, t**3, '-.', t, -t, ':') plt.show() fig, ax = plt.subplots(1, 1) ax.bar([1, 2, 3, 4], [10, 20, 15, 13], ls='dashed', ec='r', lw=5) plt.show() t = np.arange(0., 5., 0.2) # red dashes, blue squares and green triangles plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^') plt.show() %load exercises/3.3-properties.py t = np.arange(0.0, 5.0, 0.1) a = np.exp(-t) * np.cos(2*np.pi*t) plt.plot(t, a, ) plt.show() %load http://matplotlib.org/mpl_examples/color/colormaps_reference.py # For those with v1.2 or higher Reference for colormaps included with Matplotlib. This reference example shows all colormaps included with Matplotlib. Note that any colormap listed here can be reversed by appending "_r" (e.g., "pink_r"). These colormaps are divided into the following categories: Sequential: These colormaps are approximately monochromatic colormaps varying smoothly between two color tones---usually from low saturation (e.g. white) to high saturation (e.g. a bright blue). Sequential colormaps are ideal for representing most scientific data since they show a clear progression from low-to-high values. Diverging: These colormaps have a median value (usually light in color) and vary smoothly to two different color tones at high and low values. Diverging colormaps are ideal when your data has a median value that is significant (e.g. 0, such that positive and negative values are represented by different colors of the colormap). Qualitative: These colormaps vary rapidly in color. Qualitative colormaps are useful for choosing a set of discrete colors. For example:: color_list = plt.cm.Set3(np.linspace(0, 1, 12)) gives a list of RGB colors that are good for plotting a series of lines on a dark background. Miscellaneous: Colormaps that don't fit into the categories above. import numpy as np import matplotlib.pyplot as plt cmaps = [('Sequential', ['binary', 'Blues', 'BuGn', 'BuPu', 'gist_yarg', 'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu', 'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']), ('Sequential (2)', ['afmhot', 'autumn', 'bone', 'cool', 'copper', 'gist_gray', 'gist_heat', 'gray', 'hot', 'pink', 'spring', 'summer', 'winter']), ('Diverging', ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr', 'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'seismic']), ('Qualitative', ['Accent', 'Dark2', 'hsv', 'Paired', 'Pastel1', 'Pastel2', 'Set1', 'Set2', 'Set3', 'spectral']), ('Miscellaneous', ['gist_earth', 'gist_ncar', 'gist_rainbow', 'gist_stern', 'jet', 'brg', 'CMRmap', 'cubehelix', 'gnuplot', 'gnuplot2', 'ocean', 'rainbow', 'terrain', 'flag', 'prism'])] nrows = max(len(cmap_list) for cmap_category, cmap_list in cmaps) gradient = np.linspace(0, 1, 256) gradient = np.vstack((gradient, gradient)) def plot_color_gradients(cmap_category, cmap_list): fig, axes = plt.subplots(nrows=nrows) fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99) axes[0].set_title(cmap_category + ' colormaps', fontsize=14) for ax, name in zip(axes, cmap_list): ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name)) pos = list(ax.get_position().bounds) x_text = pos[0] - 0.01 y_text = pos[1] + pos[3]/2. fig.text(x_text, y_text, name, va='center', ha='right', fontsize=10) # Turn off *all* ticks & spines, not just the ones with colormaps. for ax in axes: ax.set_axis_off() for cmap_category, cmap_list in cmaps: plot_color_gradients(cmap_category, cmap_list) plt.show() fig, (ax1, ax2) = plt.subplots(1, 2) z = np.random.random((10, 10)) ax1.imshow(z, interpolation='none', cmap='gray') ax2.imshow(z, interpolation='none', cmap='coolwarm') plt.show() plt.scatter([1, 2, 3, 4], [4, 3, 2, 1]) plt.title(r'$\sigma_i=15$', fontsize=20) plt.show() t = np.arange(0.0, 5.0, 0.01) s = np.cos(2*np.pi*t) plt.plot(t, s, lw=2) plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5), arrowprops=dict(facecolor='black', shrink=0.05)) plt.ylim(-2, 2) plt.show() import matplotlib.patches as mpatches styles = mpatches.ArrowStyle.get_styles() ncol = 2 nrow = (len(styles)+1) // ncol figheight = (nrow+0.5) fig = plt.figure(figsize=(4.0*ncol/0.85, figheight/0.85)) fontsize = 0.4 * 70 ax = fig.add_axes([0, 0, 1, 1]) ax.set_xlim(0, 4*ncol) ax.set_ylim(0, figheight) def to_texstring(s): s = s.replace("<", r"$<$") s = s.replace(">", r"$>$") s = s.replace("|", r"$|$") return s for i, (stylename, styleclass) in enumerate(sorted(styles.items())): x = 3.2 + (i//nrow)*4 y = (figheight - 0.7 - i%nrow) p = mpatches.Circle((x, y), 0.2, fc="w") ax.add_patch(p) ax.annotate(to_texstring(stylename), (x, y), (x-1.2, y), ha="right", va="center", size=fontsize, arrowprops=dict(arrowstyle=stylename, patchB=p, shrinkA=50, shrinkB=5, fc="w", ec="k", connectionstyle="arc3,rad=-0.25", ), bbox=dict(boxstyle="square", fc="w")) ax.set_axis_off() plt.show() %load exercises/3.4-arrows.py t = np.arange(0.0, 5.0, 0.01) s = np.cos(2*np.pi*t) plt.plot(t, s, lw=2) plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5), arrowprops=dict()) plt.ylim(-2, 2) plt.show() bars = plt.bar([1, 2, 3, 4], [10, 12, 15, 17]) plt.setp(bars[0], hatch='x', facecolor='w') plt.setp(bars[1], hatch='xx-', facecolor='orange') plt.setp(bars[2], hatch='+O.', facecolor='c') plt.setp(bars[3], hatch='*', facecolor='y') plt.show() import matplotlib print(matplotlib.matplotlib_fname()) import matplotlib as mpl import matplotlib.pyplot as plt mpl.rcdefaults() # for when re-running this cell fig, (ax1, ax2) = plt.subplots(1, 2) ax1.plot([1, 2, 3, 4]) mpl.rc('lines', linewidth=2, linestyle='-.') # Equivalent older, but still valid syntax #mpl.rcParams['lines.linewidth'] = 2 #mpl.rcParams['lines.linestyle'] = '-.' ax2.plot([1, 2, 3, 4]) plt.show() <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Markers Step2: Exercise 3.2 Step3: Linestyles Step4: It is a bit confusing, but the line styles mentioned above are only valid for lines. Whenever you are dealing with the linestyles of the edges of "Patch" objects, you will need to use words instead of the symbols. So "solid" instead of "-", and "dashdot" instead of "-.". This issue will be fixed for the v2.1 release and allow these specifications to be used interchangably. Step5: Plot attributes Step6: | Property | Value Type Step8: Colormaps Step9: When colormaps are created in mpl, they get "registered" with a name. This allows one to specify a colormap to use by name. Step10: Mathtext Step11: Annotations and Arrows Step12: There are all sorts of boxes for your text, and arrows you can use, and there are many different ways to connect the text to the point that you want to annotate. For a complete tutorial on this topic, go to the Annotation Guide. In the meantime, here is a table of the kinds of arrows that can be drawn Step13: Exercise 3.4 Step14: Hatches Step15: Transforms Step16: You can also change the rc settings during runtime within a python script or interactively from the python shell. All of the rc settings are stored in a dictionary-like variable called matplotlib.rcParams, which is global to the matplotlib package. rcParams can be modified directly. Newer versions of matplotlib can use rc(), for example

<ASSISTANT_TASK:> Python Code: !pip install ott-jax !pip install POT # import JAX and OTT import jax import jax.numpy as jnp import ott from ott.geometry import pointcloud from ott.core import sinkhorn # import OT, from POT import numpy as np import ot # misc import matplotlib.pyplot as plt plt.rc('font', size = 20) import mpl_toolkits.axes_grid1 import timeit def solve_ot(a, b, x, y, 𝜀, threshold): _, log = ot.sinkhorn(a, b, ot.dist(x,y), 𝜀, stopThr=threshold, method='sinkhorn_stabilized', log=True, numItermax=1000) f, g = 𝜀 * log['logu'], 𝜀 * log['logv'] f, g = f - np.mean(f), g + np.mean(f) # center variables, useful if one wants to compare them reg_ot = np.sum(f * a) + np.sum(g * b) if log['err'][-1] < threshold else np.nan return f, g, reg_ot @jax.jit def solve_ott(a, b, x, y, 𝜀, threshold): out = sinkhorn.sinkhorn(pointcloud.PointCloud(x, y, epsilon=𝜀), a, b, threshold=threshold, lse_mode=True, jit=False, max_iterations=1000) f, g = out.f, out.g f, g = f - np.mean(f), g + np.mean(f) # center variables, useful if one wants to compare them reg_ot = jnp.where(out.converged, jnp.sum(f * a) + jnp.sum(g * b), jnp.nan) return f, g, reg_ot dim = 3 def run_simulation(rng, n, 𝜀, threshold, solver_spec): # setting global variables helps avoir a timeit bug. global solver_ global a, b, x , y # extract specificities of solver. solver_, env, name = solver_spec # draw data at random using JAX rng, *rngs = jax.random.split(rng, 5) x = jax.random.uniform(rngs[0], (n, dim)) y = jax.random.uniform(rngs[1], (n, dim)) + 0.1 a = jax.random.uniform(rngs[2], (n,)) b = jax.random.uniform(rngs[3], (n,)) a = a / jnp.sum(a) b = b / jnp.sum(b) # map to numpy if needed if env == 'np': a, b, x, y = map(np.array,(a, b, x, y)) timeit_res = %timeit -o solver_(a, b, x, y, 𝜀, threshold) out = solver_(a, b, x, y, 𝜀, threshold) exec_time = np.nan if np.isnan(out[-1]) else timeit_res.best return exec_time, out POT = (solve_ot, 'np', 'POT') OTT = (solve_ott, 'jax', 'OTT') rng = jax.random.PRNGKey(0) solvers = (POT, OTT) n_range = 2 ** np.arange(8, 13) 𝜀_range = 10 ** np.arange(-2.0, 0.0) threshold = 1e-2 exec_time = {} reg_ot = {} for solver_spec in solvers: solver, env, name = solver_spec print('----- ', name) exec_time[name] = np.ones((len(n_range), len(𝜀_range))) * np.nan reg_ot[name] = np.ones((len(n_range), len(𝜀_range))) * np.nan for i, n in enumerate(n_range): for j, 𝜀 in enumerate(𝜀_range): exec, out = run_simulation(rng, n, 𝜀, threshold, solver_spec) exec_time[name][i, j] = exec reg_ot[name][i, j] = out[-1] list_legend = [] fig = plt.figure(figsize=(14,8)) for solver_spec, marker, col in zip(solvers,('p','o'), ('blue','red')): solver, env, name = solver_spec p = plt.plot(exec_time[name], marker=marker, color=col, markersize=16, markeredgecolor='k', lw=3) p[0].set_linestyle('dotted') p[1].set_linestyle('solid') list_legend += [name + r' $\varepsilon $=' + "{:.2g}".format(𝜀) for 𝜀 in 𝜀_range] plt.xticks(ticks=np.arange(len(n_range)), labels=n_range) plt.legend(list_legend) plt.yscale('log') plt.xlabel('dimension $n$') plt.ylabel('time (s)') plt.title(r'Execution Time vs Dimension for OTT and POT for two $\varepsilon$ values') plt.show() fig = plt.figure(figsize=(12,8)) ax = plt.gca() im = ax.imshow(reg_ot['OTT'].T - reg_ot['POT'].T) plt.xticks(ticks=np.arange(len(n_range)), labels=n_range) plt.yticks(ticks=np.arange(len(𝜀_range)), labels=𝜀_range) plt.xlabel('dimension $n$') plt.ylabel(r'regularization $\varepsilon$') plt.title('Gap in objective, >0 when OTT is better') divider = mpl_toolkits.axes_grid1.make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.1) plt.colorbar(im, cax=cax) plt.show() for name in ('POT','OTT'): print('----', name) print('Objective') print(reg_ot[name]) print('Execution Time') print(exec_time[name]) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: ... and import them, along with their numerical environments, jax and numpy. Step2: Regularized OT in a nutshell Step3: To test both solvers, we run simulations using a random seed to generate random point clouds of size $n$. Random generation is carried out using jax.random, to ensure reproducibility. A solver provides three pieces of info Step4: Defines the two solvers used in this experiment Step5: Runs simulations with varying $n$ and $\varepsilon$ Step6: Plots results in terms of time and difference in objective. Step7: For good measure, we also show the differences in objectives between the two solvers. We substract the objective returned by POT to that returned by OTT.

<ASSISTANT_TASK:> Python Code: #@title Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #@test {"skip": true} !pip install tensorflow-lattice pydot import tensorflow as tf import logging import numpy as np import pandas as pd import sys import tensorflow_lattice as tfl from tensorflow import feature_column as fc logging.disable(sys.maxsize) # UCI Statlog (Heart) dataset. csv_file = tf.keras.utils.get_file( 'heart.csv', 'http://storage.googleapis.com/download.tensorflow.org/data/heart.csv') training_data_df = pd.read_csv(csv_file).sample( frac=1.0, random_state=41).reset_index(drop=True) training_data_df.head() LEARNING_RATE = 0.1 BATCH_SIZE = 128 NUM_EPOCHS = 100 # Lattice layer expects input[i] to be within [0, lattice_sizes[i] - 1.0], so lattice_sizes = [3, 2, 2, 2, 2, 2, 2] combined_calibrators = tfl.layers.ParallelCombination() # ############### age ############### calibrator = tfl.layers.PWLCalibration( # Every PWLCalibration layer must have keypoints of piecewise linear # function specified. Easiest way to specify them is to uniformly cover # entire input range by using numpy.linspace(). input_keypoints=np.linspace( training_data_df['age'].min(), training_data_df['age'].max(), num=5), # You need to ensure that input keypoints have same dtype as layer input. # You can do it by setting dtype here or by providing keypoints in such # format which will be converted to desired tf.dtype by default. dtype=tf.float32, # Output range must correspond to expected lattice input range. output_min=0.0, output_max=lattice_sizes[0] - 1.0, ) combined_calibrators.append(calibrator) # ############### sex ############### # For boolean features simply specify CategoricalCalibration layer with 2 # buckets. calibrator = tfl.layers.CategoricalCalibration( num_buckets=2, output_min=0.0, output_max=lattice_sizes[1] - 1.0, # Initializes all outputs to (output_min + output_max) / 2.0. kernel_initializer='constant') combined_calibrators.append(calibrator) # ############### cp ############### calibrator = tfl.layers.PWLCalibration( # Here instead of specifying dtype of layer we convert keypoints into # np.float32. input_keypoints=np.linspace(1, 4, num=4, dtype=np.float32), output_min=0.0, output_max=lattice_sizes[2] - 1.0, monotonicity='increasing', # You can specify TFL regularizers as a tuple ('regularizer name', l1, l2). kernel_regularizer=('hessian', 0.0, 1e-4)) combined_calibrators.append(calibrator) # ############### trestbps ############### calibrator = tfl.layers.PWLCalibration( # Alternatively, you might want to use quantiles as keypoints instead of # uniform keypoints input_keypoints=np.quantile(training_data_df['trestbps'], np.linspace(0.0, 1.0, num=5)), dtype=tf.float32, # Together with quantile keypoints you might want to initialize piecewise # linear function to have 'equal_slopes' in order for output of layer # after initialization to preserve original distribution. kernel_initializer='equal_slopes', output_min=0.0, output_max=lattice_sizes[3] - 1.0, # You might consider clamping extreme inputs of the calibrator to output # bounds. clamp_min=True, clamp_max=True, monotonicity='increasing') combined_calibrators.append(calibrator) # ############### chol ############### calibrator = tfl.layers.PWLCalibration( # Explicit input keypoint initialization. input_keypoints=[126.0, 210.0, 247.0, 286.0, 564.0], dtype=tf.float32, output_min=0.0, output_max=lattice_sizes[4] - 1.0, # Monotonicity of calibrator can be decreasing. Note that corresponding # lattice dimension must have INCREASING monotonicity regardless of # monotonicity direction of calibrator. monotonicity='decreasing', # Convexity together with decreasing monotonicity result in diminishing # return constraint. convexity='convex', # You can specify list of regularizers. You are not limited to TFL # regularizrs. Feel free to use any :) kernel_regularizer=[('laplacian', 0.0, 1e-4), tf.keras.regularizers.l1_l2(l1=0.001)]) combined_calibrators.append(calibrator) # ############### fbs ############### calibrator = tfl.layers.CategoricalCalibration( num_buckets=2, output_min=0.0, output_max=lattice_sizes[5] - 1.0, # For categorical calibration layer monotonicity is specified for pairs # of indices of categories. Output for first category in pair will be # smaller than output for second category. # # Don't forget to set monotonicity of corresponding dimension of Lattice # layer to '1'. monotonicities=[(0, 1)], # This initializer is identical to default one('uniform'), but has fixed # seed in order to simplify experimentation. kernel_initializer=tf.keras.initializers.RandomUniform( minval=0.0, maxval=lattice_sizes[5] - 1.0, seed=1)) combined_calibrators.append(calibrator) # ############### restecg ############### calibrator = tfl.layers.CategoricalCalibration( num_buckets=3, output_min=0.0, output_max=lattice_sizes[6] - 1.0, # Categorical monotonicity can be partial order. monotonicities=[(0, 1), (0, 2)], # Categorical calibration layer supports standard Keras regularizers. kernel_regularizer=tf.keras.regularizers.l1_l2(l1=0.001), kernel_initializer='constant') combined_calibrators.append(calibrator) lattice = tfl.layers.Lattice( lattice_sizes=lattice_sizes, monotonicities=[ 'increasing', 'none', 'increasing', 'increasing', 'increasing', 'increasing', 'increasing' ], output_min=0.0, output_max=1.0) model = tf.keras.models.Sequential() model.add(combined_calibrators) model.add(lattice) features = training_data_df[[ 'age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg' ]].values.astype(np.float32) target = training_data_df[['target']].values.astype(np.float32) model.compile( loss=tf.keras.losses.mean_squared_error, optimizer=tf.keras.optimizers.Adagrad(learning_rate=LEARNING_RATE)) model.fit( features, target, batch_size=BATCH_SIZE, epochs=NUM_EPOCHS, validation_split=0.2, shuffle=False, verbose=0) model.evaluate(features, target) # We are going to have 2-d embedding as one of lattice inputs. lattice_sizes = [3, 2, 2, 3, 3, 2, 2] model_inputs = [] lattice_inputs = [] # ############### age ############### age_input = tf.keras.layers.Input(shape=[1], name='age') model_inputs.append(age_input) age_calibrator = tfl.layers.PWLCalibration( # Every PWLCalibration layer must have keypoints of piecewise linear # function specified. Easiest way to specify them is to uniformly cover # entire input range by using numpy.linspace(). input_keypoints=np.linspace( training_data_df['age'].min(), training_data_df['age'].max(), num=5), # You need to ensure that input keypoints have same dtype as layer input. # You can do it by setting dtype here or by providing keypoints in such # format which will be converted to desired tf.dtype by default. dtype=tf.float32, # Output range must correspond to expected lattice input range. output_min=0.0, output_max=lattice_sizes[0] - 1.0, monotonicity='increasing', name='age_calib', )( age_input) lattice_inputs.append(age_calibrator) # ############### sex ############### # For boolean features simply specify CategoricalCalibration layer with 2 # buckets. sex_input = tf.keras.layers.Input(shape=[1], name='sex') model_inputs.append(sex_input) sex_calibrator = tfl.layers.CategoricalCalibration( num_buckets=2, output_min=0.0, output_max=lattice_sizes[1] - 1.0, # Initializes all outputs to (output_min + output_max) / 2.0. kernel_initializer='constant', name='sex_calib', )( sex_input) lattice_inputs.append(sex_calibrator) # ############### cp ############### cp_input = tf.keras.layers.Input(shape=[1], name='cp') model_inputs.append(cp_input) cp_calibrator = tfl.layers.PWLCalibration( # Here instead of specifying dtype of layer we convert keypoints into # np.float32. input_keypoints=np.linspace(1, 4, num=4, dtype=np.float32), output_min=0.0, output_max=lattice_sizes[2] - 1.0, monotonicity='increasing', # You can specify TFL regularizers as tuple ('regularizer name', l1, l2). kernel_regularizer=('hessian', 0.0, 1e-4), name='cp_calib', )( cp_input) lattice_inputs.append(cp_calibrator) # ############### trestbps ############### trestbps_input = tf.keras.layers.Input(shape=[1], name='trestbps') model_inputs.append(trestbps_input) trestbps_calibrator = tfl.layers.PWLCalibration( # Alternatively, you might want to use quantiles as keypoints instead of # uniform keypoints input_keypoints=np.quantile(training_data_df['trestbps'], np.linspace(0.0, 1.0, num=5)), dtype=tf.float32, # Together with quantile keypoints you might want to initialize piecewise # linear function to have 'equal_slopes' in order for output of layer # after initialization to preserve original distribution. kernel_initializer='equal_slopes', output_min=0.0, output_max=lattice_sizes[3] - 1.0, # You might consider clamping extreme inputs of the calibrator to output # bounds. clamp_min=True, clamp_max=True, monotonicity='increasing', name='trestbps_calib', )( trestbps_input) lattice_inputs.append(trestbps_calibrator) # ############### chol ############### chol_input = tf.keras.layers.Input(shape=[1], name='chol') model_inputs.append(chol_input) chol_calibrator = tfl.layers.PWLCalibration( # Explicit input keypoint initialization. input_keypoints=[126.0, 210.0, 247.0, 286.0, 564.0], output_min=0.0, output_max=lattice_sizes[4] - 1.0, # Monotonicity of calibrator can be decreasing. Note that corresponding # lattice dimension must have INCREASING monotonicity regardless of # monotonicity direction of calibrator. monotonicity='decreasing', # Convexity together with decreasing monotonicity result in diminishing # return constraint. convexity='convex', # You can specify list of regularizers. You are not limited to TFL # regularizrs. Feel free to use any :) kernel_regularizer=[('laplacian', 0.0, 1e-4), tf.keras.regularizers.l1_l2(l1=0.001)], name='chol_calib', )( chol_input) lattice_inputs.append(chol_calibrator) # ############### fbs ############### fbs_input = tf.keras.layers.Input(shape=[1], name='fbs') model_inputs.append(fbs_input) fbs_calibrator = tfl.layers.CategoricalCalibration( num_buckets=2, output_min=0.0, output_max=lattice_sizes[5] - 1.0, # For categorical calibration layer monotonicity is specified for pairs # of indices of categories. Output for first category in pair will be # smaller than output for second category. # # Don't forget to set monotonicity of corresponding dimension of Lattice # layer to '1'. monotonicities=[(0, 1)], # This initializer is identical to default one ('uniform'), but has fixed # seed in order to simplify experimentation. kernel_initializer=tf.keras.initializers.RandomUniform( minval=0.0, maxval=lattice_sizes[5] - 1.0, seed=1), name='fbs_calib', )( fbs_input) lattice_inputs.append(fbs_calibrator) # ############### restecg ############### restecg_input = tf.keras.layers.Input(shape=[1], name='restecg') model_inputs.append(restecg_input) restecg_calibrator = tfl.layers.CategoricalCalibration( num_buckets=3, output_min=0.0, output_max=lattice_sizes[6] - 1.0, # Categorical monotonicity can be partial order. monotonicities=[(0, 1), (0, 2)], # Categorical calibration layer supports standard Keras regularizers. kernel_regularizer=tf.keras.regularizers.l1_l2(l1=0.001), kernel_initializer='constant', name='restecg_calib', )( restecg_input) lattice_inputs.append(restecg_calibrator) lattice = tfl.layers.Lattice( lattice_sizes=lattice_sizes, monotonicities=[ 'increasing', 'none', 'increasing', 'increasing', 'increasing', 'increasing', 'increasing' ], output_min=0.0, output_max=1.0, name='lattice', )( lattice_inputs) model_output = tfl.layers.PWLCalibration( input_keypoints=np.linspace(0.0, 1.0, 5), name='output_calib', )( lattice) model = tf.keras.models.Model( inputs=model_inputs, outputs=model_output) tf.keras.utils.plot_model(model, rankdir='LR') feature_names = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg'] features = np.split( training_data_df[feature_names].values.astype(np.float32), indices_or_sections=len(feature_names), axis=1) target = training_data_df[['target']].values.astype(np.float32) model.compile( loss=tf.keras.losses.mean_squared_error, optimizer=tf.keras.optimizers.Adagrad(LEARNING_RATE)) model.fit( features, target, batch_size=BATCH_SIZE, epochs=NUM_EPOCHS, validation_split=0.2, shuffle=False, verbose=0) model.evaluate(features, target) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: TFL レイヤーを使用した Keras モデルの作成 Step2: 必要なパッケージをインポートします。 Step3: UCI Statlog (心臓) データセットをダウンロードします。 Step4: このガイドのトレーニングに使用するデフォルト値を設定します。 Step5: Sequential Keras モデル Step6: <code>tfl.layers.ParallelCombination</code>レイヤーを使用して、Sequential モデルを作成できるようにするために並行して実行する必要があるキャリブレーションレイヤーをグループ化します。 Step7: 各特徴のキャリブレーションレイヤーを作成し、それを並行コンビネーションレイヤーに追加します。数値特徴の場合はtfl.layers.PWLCalibration、カテゴリカル特徴の場合はtfl.layers.CategoricalCalibrationを使用します。 Step8: 次に、キャリブレータの出力を非線形に融合するラティスレイヤーを作成します。 Step9: 次に、キャリブレータとラティスレイヤーを組み合わせて Sequential モデルを作成します。 Step10: トレーニングは、他の Keras モデルと同じように機能します。 Step11: Functional Keras モデル Step12: 各特徴ごとに入力レイヤーを作成してからキャリブレーションレイヤーを作成する必要があります。数値特徴の場合はtfl.layers.PWLCalibration、カテゴリカル特徴の場合はtfl.layers.CategoricalCalibrationを使用します。 Step13: 次に、キャリブレータの出力を非線形に融合するラティスレイヤーを作成します。 Step14: モデルに柔軟性を追加するために、出力キャリブレーションレイヤーを追加します。 Step15: 入力と出力を使用してモデルを作成できるようになりました。 Step16: トレーニングは、他の Keras モデルと同じように機能します。このセットアップでは、入力された特徴が個別のテンソルとして渡されることに注意してください。

<ASSISTANT_TASK:> Python Code: # These are all the modules we'll be using later. Make sure you can import them # before proceeding further. %matplotlib inline from __future__ import print_function import collections import math import numpy as np import os import random import tensorflow as tf import zipfile from matplotlib import pylab from six.moves import range from six.moves.urllib.request import urlretrieve from sklearn.manifold import TSNE url = 'http://mattmahoney.net/dc/' def maybe_download(filename, expected_bytes): Download a file if not present, and make sure it's the right size. if not os.path.exists(filename): filename, _ = urlretrieve(url + filename, filename) statinfo = os.stat(filename) if statinfo.st_size == expected_bytes: print('Found and verified %s' % filename) else: print(statinfo.st_size) raise Exception( 'Failed to verify ' + filename + '. Can you get to it with a browser?') return filename filename = maybe_download('text8.zip', 31344016) def read_data(filename): Extract the first file enclosed in a zip file as a list of words with zipfile.ZipFile(filename) as f: data = tf.compat.as_str(f.read(f.namelist()[0])).split() return data words = read_data(filename) print('Data size %d' % len(words)) vocabulary_size = 50000 def build_dataset(words): count = [['UNK', -1]] count.extend(collections.Counter(words).most_common(vocabulary_size - 1)) dictionary = dict() for word, _ in count: dictionary[word] = len(dictionary) data = list() unk_count = 0 for word in words: if word in dictionary: index_into_count = dictionary[word] else: index_into_count = 0 # dictionary['UNK'] unk_count = unk_count + 1 data.append(index_into_count) count[0][1] = unk_count reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys())) return data, count, dictionary, reverse_dictionary data, count, dictionary, reverse_dictionary = build_dataset(words) print('Most common words (+UNK)', count[:5]) print('Sample data', data[:10]) del words # Hint to reduce memory. data_index = 0 def generate_batch(batch_size, num_skips, skip_window): global data_index assert batch_size % num_skips == 0 assert num_skips <= 2 * skip_window batch = np.ndarray(shape=(batch_size), dtype=np.int32) labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32) span = 2 * skip_window + 1 # [ skip_window target skip_window ] buffer = collections.deque(maxlen=span) for _ in range(span): buffer.append(data[data_index]) data_index = (data_index + 1) % len(data) for i in range(batch_size // num_skips): target = skip_window # target label at the center of the buffer targets_to_avoid = [ skip_window ] for j in range(num_skips): while target in targets_to_avoid: target = random.randint(0, span - 1) targets_to_avoid.append(target) batch[i * num_skips + j] = buffer[skip_window] labels[i * num_skips + j, 0] = buffer[target] buffer.append(data[data_index]) data_index = (data_index + 1) % len(data) return batch, labels print('data:', [reverse_dictionary[di] for di in data[:8]]) for num_skips, skip_window in [(2, 1), (4, 2)]: data_index = 0 batch, labels = generate_batch(batch_size=8, num_skips=num_skips, skip_window=skip_window) print('\nwith num_skips = %d and skip_window = %d:' % (num_skips, skip_window)) print(' batch:', [reverse_dictionary[bi] for bi in batch]) print(' labels:', [reverse_dictionary[li] for li in labels.reshape(8)]) batch_size = 128 embedding_size = 128 # Dimension of the embedding vector. skip_window = 1 # How many words to consider left and right. num_skips = 2 # How many times to reuse an input to generate a label. # We pick a random validation set to sample nearest neighbors. here we limit the # validation samples to the words that have a low numeric ID, which by # construction are also the most frequent. valid_size = 16 # Random set of words to evaluate similarity on. valid_window = 100 # Only pick dev samples in the head of the distribution. valid_examples = np.array(random.sample(range(valid_window), valid_size)) num_sampled = 64 # Number of negative examples to sample. graph = tf.Graph() with graph.as_default(), tf.device('/cpu:0'): # Input data. train_dataset = tf.placeholder(tf.int32, shape=[batch_size]) train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1]) valid_dataset = tf.constant(valid_examples, dtype=tf.int32) # Variables. embeddings = tf.Variable( tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)) softmax_weights = tf.Variable( tf.truncated_normal([vocabulary_size, embedding_size], stddev=1.0 / math.sqrt(embedding_size))) softmax_biases = tf.Variable(tf.zeros([vocabulary_size])) # Model. # Look up embeddings for inputs. embed = tf.nn.embedding_lookup(embeddings, train_dataset) # Compute the softmax loss, using a sample of the negative labels each time. loss = tf.reduce_mean( tf.nn.sampled_softmax_loss(softmax_weights, softmax_biases, embed, train_labels, num_sampled, vocabulary_size)) # Optimizer. # Note: The optimizer will optimize the softmax_weights AND the embeddings. # This is because the embeddings are defined as a variable quantity and the # optimizer's `minimize` method will by default modify all variable quantities # that contribute to the tensor it is passed. # See docs on `tf.train.Optimizer.minimize()` for more details. optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss) # Compute the similarity between minibatch examples and all embeddings. # We use the cosine distance: norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True)) normalized_embeddings = embeddings / norm valid_embeddings = tf.nn.embedding_lookup( normalized_embeddings, valid_dataset) similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings)) num_steps = 100001 with tf.Session(graph=graph) as session: tf.initialize_all_variables().run() print('Initialized') average_loss = 0 for step in range(num_steps): batch_data, batch_labels = generate_batch( batch_size, num_skips, skip_window) feed_dict = {train_dataset : batch_data, train_labels : batch_labels} _, l = session.run([optimizer, loss], feed_dict=feed_dict) average_loss += l if step % 2000 == 0: if step > 0: average_loss = average_loss / 2000 # The average loss is an estimate of the loss over the last 2000 batches. print('Average loss at step %d: %f' % (step, average_loss)) average_loss = 0 # note that this is expensive (~20% slowdown if computed every 500 steps) if step % 10000 == 0: sim = similarity.eval() for i in range(valid_size): valid_word = reverse_dictionary[valid_examples[i]] top_k = 8 # number of nearest neighbors nearest = (-sim[i, :]).argsort()[1:top_k+1] log = 'Nearest to %s:' % valid_word for k in range(top_k): close_word = reverse_dictionary[nearest[k]] log = '%s %s,' % (log, close_word) print(log) final_embeddings = normalized_embeddings.eval() num_points = 400 tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000) two_d_embeddings = tsne.fit_transform(final_embeddings[1:num_points+1, :]) # why ruclidean distance here, and not cosine? def plot(embeddings, labels): assert embeddings.shape[0] >= len(labels), 'More labels than embeddings' pylab.figure(figsize=(15,15)) # in inches for i, label in enumerate(labels): x, y = embeddings[i,:] pylab.scatter(x, y) pylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points', ha='right', va='bottom') pylab.show() words = [reverse_dictionary[i] for i in range(1, num_points+1)] plot(two_d_embeddings, words) data_index = 0 def generate_batch(batch_size, skip_window): assert skip_window == 1 # Handling of this value is hard-coded here. global data_index batch = np.ndarray(shape=(batch_size, 2), dtype=np.int32) labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32) span = 2*skip_window + 1 # [ skip_window target skip_window ] buffer = collections.deque(maxlen=span) for _ in range(span): buffer.append(data[data_index]) data_index = (data_index + 1) % len(data) for i in range(batch_size): target = skip_window # target label at the center of the buffer batch[i, 0] = buffer[skip_window-1] batch[i, 1] = buffer[skip_window+1] labels[i, 0] = buffer[target] buffer.append(data[data_index]) data_index = (data_index + 1) % len(data) return batch, labels print('data:', [reverse_dictionary[di] for di in data[:8]]) for skip_window in [1]: data_index = 0 batch, labels = generate_batch(batch_size=8, skip_window=skip_window) print('\nwith skip_window = %d:' % skip_window) print(' batch:', [[reverse_dictionary[m] for m in bi] for bi in batch]) print(' labels:', [reverse_dictionary[li] for li in labels.reshape(8)]) batch_size = 128 embedding_size = 128 # Dimension of the embedding vector. skip_window = 1 # How many words to consider left and right. num_skips = 2 # How many times to reuse an input to generate a label. # We pick a random validation set to sample nearest neighbors. here we limit the # validation samples to the words that have a low numeric ID, which by # construction are also the most frequent. valid_size = 16 # Random set of words to evaluate similarity on. valid_window = 100 # Only pick dev samples in the head of the distribution. valid_examples = np.array(random.sample(range(valid_window), valid_size)) num_sampled = 64 # Number of negative examples to sample. graph = tf.Graph() with graph.as_default(), tf.device('/cpu:0'): # Input data. span = 2*skip_window + 1 # [ skip_window target skip_window ] train_dataset = tf.placeholder(tf.int32, shape=[batch_size, (span-1)]) train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1]) valid_dataset = tf.constant(valid_examples, dtype=tf.int32) # Variables. embeddings = tf.Variable( tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)) softmax_weights = tf.Variable( tf.truncated_normal([vocabulary_size, embedding_size], stddev=1.0 / math.sqrt(embedding_size))) softmax_biases = tf.Variable(tf.zeros([vocabulary_size])) # Model. # Look up embeddings for inputs. assert skip_window == 1 # Handling of this value is hard-coded here. embed0 = tf.nn.embedding_lookup(embeddings, train_dataset[:,0]) embed1 = tf.nn.embedding_lookup(embeddings, train_dataset[:,1]) embed = (embed0 + embed1)/(span-1) # Compute the softmax loss, using a sample of the negative labels each time. loss = tf.reduce_mean( tf.nn.sampled_softmax_loss(softmax_weights, softmax_biases, embed, train_labels, num_sampled, vocabulary_size)) # Optimizer. optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss) # Compute the similarity between minibatch examples and all embeddings. # We use the cosine distance: norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True)) normalized_embeddings = embeddings / norm valid_embeddings = tf.nn.embedding_lookup( normalized_embeddings, valid_dataset) similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings)) num_steps = 100001 with tf.Session(graph=graph) as session: tf.initialize_all_variables().run() print('Initialized') average_loss = 0 for step in range(num_steps): batch_data, batch_labels = generate_batch( batch_size, skip_window) feed_dict = {train_dataset : batch_data, train_labels : batch_labels} _, l = session.run([optimizer, loss], feed_dict=feed_dict) average_loss += l if step % 2000 == 0: if step > 0: average_loss = average_loss / 2000 # The average loss is an estimate of the loss over the last 2000 batches. print('Average loss at step %d: %f' % (step, average_loss)) average_loss = 0 # note that this is expensive (~20% slowdown if computed every 500 steps) if step % 10000 == 0: sim = similarity.eval() for i in xrange(valid_size): valid_word = reverse_dictionary[valid_examples[i]] top_k = 8 # number of nearest neighbors nearest = (-sim[i, :]).argsort()[1:top_k+1] log = 'Nearest to %s:' % valid_word for k in xrange(top_k): close_word = reverse_dictionary[nearest[k]] log = '%s %s,' % (log, close_word) print(log) final_embeddings = normalized_embeddings.eval() <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step2: Download the data from the source website if necessary. Step4: Read the data into a string. Step5: Build the dictionary and replace rare words with UNK token. Step6: Function to generate a training batch for the skip-gram model. Step7: Train a skip-gram model. Step8: Problem

<ASSISTANT_TASK:> Python Code: import numpy as np import pandas as pd csvframe=pd.read_csv('myCSV_01.csv') csvframe # 也可以通过read_table来读写数据 pd.read_table('myCSV_01.csv',sep=',') pd.read_csv('myCSV_02.csv',header=None) pd.read_csv('myCSV_02.csv',names=['white','red','blue','green','animal']) pd.read_csv('myCSV_03.csv',index_col=['colors','status']) pd.read_csv('myCSV_04.csv',sep='\s+') pd.read_csv('myCSV_05.csv',sep='\D*',header=None,engine='python') pd.read_table('myCSV_06.csv',sep=',',skiprows=[0,1,3,6]) pd.read_csv('myCSV_02.csv',skiprows=[2],nrows=3,header=None) out = pd.Series() i=0 pieces = pd.read_csv('myCSV_01.csv',chunksize=3) for piece in pieces: print piece out.set_value(i,piece['white'].sum()) i += 1 out frame = pd.DataFrame(np.arange(4).reshape((2,2))) print frame.to_html() frame = pd.DataFrame(np.random.random((4,4)), index=['white','black','red','blue'], columns=['up','down','left','right']) frame s = ['<HTML>'] s.append('<HEAD><TITLE>MY DATAFRAME</TITLE></HEAD>') s.append('<BODY>') s.append(frame.to_html()) s.append('</BODY></HTML>') html=''.join(s) with open('myFrame.html','w') as html_file: html_file.write(html) web_frames = pd.read_html('myFrame.html') web_frames[0] # 以网址作为参数 ranking = pd.read_html('http://www.meccanismocomplesso.org/en/meccanismo-complesso-sito-2/classifica-punteggio/') ranking[0] from lxml import objectify xml = objectify.parse('books.xml') xml root =xml.getroot() root.Book.Author root.Book.PublishDate root.getchildren() [child.tag for child in root.Book.getchildren()] [child.text for child in root.Book.getchildren()] def etree2df(root): column_names=[] for i in range(0,len(root.getchildren()[0].getchildren())): column_names.append(root.getchildren()[0].getchildren()[i].tag) xml_frame = pd.DataFrame(columns=column_names) for j in range(0,len(root.getchildren())): obj = root.getchildren()[j].getchildren() texts = [] for k in range(0,len(column_names)): texts.append(obj[k].text) row = dict(zip(column_names,texts)) row_s=pd.Series(row) row_s.name=j xml_frame = xml_frame.append(row_s) return xml_frame etree2df(root) pd.read_excel('data.xlsx') pd.read_excel('data.xlsx','Sheet2') frame = pd.DataFrame(np.random.random((4,4)), index=['exp1','exp2','exp3','exp4'], columns=['Jan2015','Feb2015','Mar2015','Apr2015']) frame frame.to_excel('data2.xlsx') frame = pd.DataFrame(np.arange(16).reshape((4,4)), index=['white','black','red','blue'], columns=['up','down','right','left']) frame.to_json('frame.json') # 读取json pd.read_json('frame.json') from pandas.io.pytables import HDFStore store = HDFStore('mydata.h5') store['obj1']=frame store['obj1'] frame.to_pickle('frame.pkl') pd.read_pickle('frame.pkl') frame=pd.DataFrame(np.arange(20).reshape((4,5)), columns=['white','red','blue','black','green']) frame from sqlalchemy import create_engine enegine=create_engine('sqlite:///foo.db') frame.to_sql('colors',enegine) pd.read_sql('colors',enegine) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: 读取没有head的数据 Step2: 可以指定header Step3: 创建一个具有等级结构的DataFrame对象，可以添加index_col选项,数据文件格式 Step4: Regexp 解析TXT文件 Step5: 读取有字母分隔的数据 Step6: 读取文本文件跳过一些不必要的行 Step7: 从TXT文件中读取部分数据 Step8: 实例 ： Step9: 写入文件 Step10: 创建复杂的DataFrame Step11: HTML读表格 Step12: 读写xml文件 Step13: 读写Excel文件 Step14: JSON数据 Step15: HDF5数据 Step16: pickle数据 Step17: 数据库连接

<ASSISTANT_TASK:> Python Code: # Load pickled data import pickle # TODO: Fill this in based on where you saved the training and testing data data_dir = "data/" training_file = data_dir + "train.p" validation_file = data_dir + "valid.p" testing_file = data_dir + "test.p" with open(training_file, mode='rb') as f: train = pickle.load(f) with open(validation_file, mode='rb') as f: valid = pickle.load(f) with open(testing_file, mode='rb') as f: test = pickle.load(f) X_train, y_train = train['features'], train['labels'] X_valid, y_valid = valid['features'], valid['labels'] X_test, y_test = test['features'], test['labels'] ### Replace each question mark with the appropriate value. ### Use python, pandas or numpy methods rather than hard coding the results import numpy as np # TODO: Number of training examples n_train = len(X_train) # TODO: Number of validation examples n_validation = len(X_valid) # TODO: Number of testing examples. n_test = len(X_test) # TODO: What's the shape of an traffic sign image? image_shape = X_train[0].shape # TODO: How many unique classes/labels there are in the dataset. n_classes = np.unique(y_train).size print("Number of training examples =", n_train) print("Number of testing examples =", n_test) print("Image data shape =", image_shape) print("Number of classes =", n_classes) ### Data exploration visualization code goes here. ### Feel free to use as many code cells as needed. import matplotlib.pyplot as plt import mpl_toolkits.axes_grid1.inset_locator as insetLoc from mpl_toolkits.axes_grid1 import ImageGrid import scipy.stats as stats import pandas as pd import csv # Visualizations will be shown in the notebook. %matplotlib inline y_all = np.concatenate([y_train, y_valid, y_test]) # Prepare a figure to display data summary fig, axes = plt.subplots(1, 5, figsize=(15, 20), sharey = 'all') plot_data = [X_test, y_all, y_train, y_valid, y_test] titles = ['Examples', 'All', 'Training', 'Validation', 'Test'] classes, class_indices = np.unique(y_train, return_inverse = True) # Prepare grid for display of class examples in first subplot class_labels = []; with open('signnames.csv', newline='\n') as csvfile: nameReader = csv.reader(csvfile, delimiter=',') for row in nameReader: class_labels.append(row[1]) class_labels = class_labels[1:] axes[0].set_xticks([]) axes[0].set_title(titles[0]) n_examples = 5 grid = ImageGrid(fig, 151, nrows_ncols=(n_classes, n_examples), axes_pad=0.025) # Get indices of class examples class_examples = [] for i in range(n_classes): example_indices = np.where(y_test == classes[i]) class_examples.extend(example_indices[0][0:n_examples]) class_examples.reverse() # Display class examples for i in range(n_classes*n_examples): grid[i].imshow(plot_data[0][class_examples[i]]) grid[i].axis('off') grid[i].set_xticks([]) grid[i].set_yticks([]) # Display histogram for each data set and compare distributions for i in range(1, len(axes)): arr = axes[i].hist(plot_data[i], bins = range(n_classes+1), normed = 1, orientation = 'horizontal', rwidth = 0.95, ) axes[i].set_title(titles[i]) for j in range(len(arr[0])): axes[i].text(arr[0][j],arr[1][j]+0.5,"{:.0f}".format(arr[0][j]*len(plot_data[i]))) if i > 1: observed = np.bincount(plot_data[i])/len(plot_data[i]) expected = np.bincount(y_all)/len(y_all) chisq, p = stats.chisquare(f_obs= observed, f_exp= expected) axes[i].set_xlabel('Proportion of Set\n(chisq = ' + str(chisq) + ',\np = ' + str(p) + ')') axes[i].set_ylim((0, 42)) axes[i].set_yticks([]) axes[1].set_xlabel('Proportion of Samples') axes[0].set_ylim(0, 43) axes[0].set_yticks(np.arange(0, n_classes)+0.5) axes[0].set_yticklabels(class_labels) print(' ') # for some reason this supresses output from somewhere else ### Preprocess the data here. It is required to normalize the data. Other preprocessing steps could include ### converting to grayscale, etc. ### Feel free to use as many code cells as needed. import cv2 def preprocess_images(images): # Convert image color to Lab images = [cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) for image in images] # Image correction clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8)) #for i in range(len(images)): # l, a, b = cv2.split(images[i]) # l = clahe.apply(l) # images[i] = cv2.merge((l, a, b)) images = [clahe.apply(image) for image in images] # Convert image color back to RGB #images = [cv2.cvtColor(image, cv2.COLOR_LAB2RGB) for image in images] # Add third (singleton) dimension images = [np.expand_dims(image, axis = 2) for image in images] # Normalize values for input to range [-1, 1] images = np.divide(np.subtract(np.array(images, dtype = 'float'), 128), 128) return images def show_examples(images, labels, n_examples): # Select n_examples examples from each class classes = np.unique(labels) class_examples_indices = [] for i in range(len(classes)): example_indices = np.where(labels == classes[i]) random_select = np.random.randint(0, len(example_indices[0])) class_examples_indices.append(example_indices[0][random_select]) class_examples = [images[index] for index in class_examples_indices] # Display class examples fig, axes = plt.subplots(1, n_examples, figsize=(4, 4*n_examples)) for i in range(0, n_examples): axes[i].imshow(class_examples[i]) axes[i].axis('off') axes[i].set_xticks([]) axes[i].set_yticks([]) # Apply image preprocessing but reverse final normalization for viewing images class_examples = preprocess_images(class_examples) class_examples = np.multiply(np.add(np.array(class_examples, dtype = 'float'), 128), 128) # Display preprocessed class examples fig, axes = plt.subplots(1, n_examples, figsize=(4, 4*n_examples)) for i in range(0, n_examples): axes[i].imshow(np.squeeze(class_examples[i], axis = 2), cmap = "gray") axes[i].axis('off') axes[i].set_xticks([]) axes[i].set_yticks([]) return # Preprocess data X_train_preprocessed = preprocess_images(X_train) X_valid_preprocessed = preprocess_images(X_valid) X_test_preprocessed = preprocess_images(X_test) # Display some examples show_examples(X_test, y_test, 5) ### Define your architecture here. ### Feel free to use as many code cells as needed. from tensorflow.contrib.layers import flatten def CNN(x, keep_prob): # Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer mu = 0 sigma = 0.1 # Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x10. conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 10), mean = mu, stddev = sigma)) conv1_b = tf.Variable(tf.zeros(10)) conv1 = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b # Activation. conv1 = tf.nn.relu(conv1) # Dropout conv1 = tf.nn.dropout(conv1, keep_prob) # Pooling. Input = 28x28x12. Output = 14x14x12. conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') # Layer 2: Convolutional. Output = 10x10x16. conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 10, 16), mean = mu, stddev = sigma)) conv2_b = tf.Variable(tf.zeros(16)) conv2 = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b # Activation. conv2 = tf.nn.relu(conv2) # Dropout conv2 = tf.nn.dropout(conv2, keep_prob) # Pooling. Input = 10x10x16. Output = 5x5x16. conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') # Flatten. Input = 5x5x16. Output = 400. fc0 = flatten(conv2) # Layer 3: Fully Connected. Input = 400. Output = 120. fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma)) fc1_b = tf.Variable(tf.zeros(120)) fc1 = tf.matmul(fc0, fc1_W) + fc1_b # Activation. fc1 = tf.nn.relu(fc1) # Layer 4: Fully Connected. Input = 120. Output = 84. fc2_W = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma)) fc2_b = tf.Variable(tf.zeros(84)) fc2 = tf.matmul(fc1, fc2_W) + fc2_b # Activation. fc2 = tf.nn.relu(fc2) # Layer 5: Fully Connected. Input = 84. Output = n_classes. fc3_W = tf.Variable(tf.truncated_normal(shape=(84, n_classes), mean = mu, stddev = sigma)) fc3_b = tf.Variable(tf.zeros(n_classes)) logits = tf.matmul(fc2, fc3_W) + fc3_b return logits ### Train your model here. ### Calculate and report the accuracy on the training and validation set. ### Once a final model architecture is selected, ### the accuracy on the test set should be calculated and reported as well. ### Feel free to use as many code cells as needed. import tensorflow as tf from sklearn.utils import shuffle EPOCHS = 10 BATCH_SIZE = 128 x = tf.placeholder(tf.float32, (None, 32, 32, 1)) y = tf.placeholder(tf.int32, (None)) keep_prob = tf.placeholder(tf.float32, (None)) one_hot_y = tf.one_hot(y, n_classes) rate = 0.001 logits = CNN(x, keep_prob) cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y, logits=logits) loss_operation = tf.reduce_mean(cross_entropy) optimizer = tf.train.AdamOptimizer(learning_rate = rate) training_operation = optimizer.minimize(loss_operation) correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1)) accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) saver = tf.train.Saver() def evaluate(X_data, y_data): num_examples = len(X_data) total_accuracy = 0 sess = tf.get_default_session() for offset in range(0, num_examples, BATCH_SIZE): batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE] accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 1.0}) total_accuracy += (accuracy * len(batch_x)) return total_accuracy / num_examples with tf.Session() as sess: sess.run(tf.global_variables_initializer()) num_examples = len(X_train_preprocessed) print("Training...") print() for i in range(EPOCHS): print("EPOCH {} ...".format(i+1)) X_train_preprocessed, y_train = shuffle(X_train_preprocessed, y_train) for offset in range(0, num_examples, BATCH_SIZE): end = offset + BATCH_SIZE batch_x, batch_y = X_train_preprocessed[offset:end], y_train[offset:end] sess.run(training_operation, feed_dict={x: batch_x, y: batch_y, keep_prob: 0.6}) validation_accuracy = evaluate(X_valid_preprocessed, y_valid) print("Validation Accuracy = {:.3f}".format(validation_accuracy)) print() saver.save(sess, './cnn') print("Model saved") with tf.Session() as sess: saver.restore(sess, tf.train.latest_checkpoint('.')) test_accuracy = evaluate(X_test_preprocessed, y_test) print("Test Accuracy = {:.3f}".format(test_accuracy)) ### Load the images and plot them here. ### Feel free to use as many code cells as needed. import glob import math # Read images web_images = [] for file in glob.glob("web_images\*.jpg"): image = cv2.cvtColor(cv2.imread(file), cv2.COLOR_BGR2RGB) web_images.append(np.array(image)) # Show raw images fig, axes = plt.subplots(1, len(web_images), figsize=(8, 48), dpi = 80) for i in range(len(web_images)): axes[i].imshow(web_images[i]) axes[i].set_xlabel(str(web_images[i].shape)) axes[i].set_xticks([]) axes[i].set_yticks([]) # Resize/pad images for input desired_size = (32, 32) for i in range(len(web_images)): image = web_images[i] scale = min(desired_size[0]/image.shape[0], desired_size[1]/image.shape[1]) image = cv2.resize(image, None, image, scale, scale, interpolation = cv2.INTER_AREA) im_shape = image.shape x_pad = (desired_size[0] - im_shape[0])/2 y_pad = (desired_size[1] - im_shape[1])/2 left_pad = math.floor(x_pad) right_pad = math.ceil(x_pad) top_pad = math.floor(y_pad) bottom_pad = math.ceil(y_pad) image = image.transpose(2, 0, 1) new_image = np.zeros((3,)+desired_size, dtype = "uint8") for j in range(3): new_image[j] = np.pad(image[j], ((left_pad,right_pad),(top_pad,bottom_pad)), mode = "edge") web_images[i] = new_image.transpose(1, 2, 0) # Show images after resizing/padding fig, axes = plt.subplots(1, len(web_images), figsize=(8, 48), dpi = 80) for i in range(len(web_images)): axes[i].imshow(web_images[i]) axes[i].set_xlabel(str(web_images[i].shape)) axes[i].set_xticks([]) axes[i].set_yticks([]) ### Run the predictions here and use the model to output the prediction for each image. ### Make sure to pre-process the images with the same pre-processing pipeline used earlier. ### Feel free to use as many code cells as needed. # Create input images (preprocessed) and expected labels X_web = preprocess_images(web_images) labels_web = ["General caution", "No entry", "Road work", "Stop", "Yield"] y_web = [] for i in range(len(labels_web)): y_web.append(class_labels.index(labels_web[i])) expected = [class_labels[index] for index in y_web] print("Input classes: ", y_web) print("Expected labels: ", expected) # Predict image labels with tf.Session() as sess: saver.restore(sess, tf.train.latest_checkpoint('.')) output = sess.run(tf.argmax(logits, 1), feed_dict={x: X_web, y: y_web, keep_prob: 1.0}) predictions = [class_labels[index] for index in output] print("Output classes: ", output) print("Predicted labels: ", predictions) sess.close() ### Calculate the accuracy for these 5 new images. ### For example, if the model predicted 1 out of 5 signs correctly, it's 20% accurate on these new images. with tf.Session() as sess: saver.restore(sess, tf.train.latest_checkpoint('.')) accuracy = sess.run(accuracy_operation, feed_dict={x: X_web, y: y_web, keep_prob: 1.0}) print("Web test accuracy = {:.3f}".format(accuracy)) sess.close() ### Print out the top five softmax probabilities for the predictions on the German traffic sign images found on the web. ### Feel free to use as many code cells as needed. with tf.Session() as sess: saver.restore(sess, tf.train.latest_checkpoint('.')) top_probs = sess.run(tf.nn.top_k(tf.nn.softmax(logits), k=5), feed_dict={x: X_web, y: y_web, keep_prob: 1.0}) print("Top softmax probabilities:\n", top_probs.values) print("Top classes: ", top_probs.indices) sess.close() ### Visualize your network's feature maps here. ### Feel free to use as many code cells as needed. # image_input: the test image being fed into the network to produce the feature maps # tf_activation: should be a tf variable name used during your training procedure that represents the calculated state of a specific weight layer # activation_min/max: can be used to view the activation contrast in more detail, by default matplot sets min and max to the actual min and max values of the output # plt_num: used to plot out multiple different weight feature map sets on the same block, just extend the plt number for each new feature map entry def outputFeatureMap(image_input, tf_activation, activation_min=-1, activation_max=-1 ,plt_num=1): # Here make sure to preprocess your image_input in a way your network expects # with size, normalization, ect if needed # image_input = # Note: x should be the same name as your network's tensorflow data placeholder variable # If you get an error tf_activation is not defined it may be having trouble accessing the variable from inside a function activation = tf_activation.eval(session=sess,feed_dict={x : image_input}) print(activation.shape) featuremaps = activation.shape[3] plt.figure(plt_num, figsize=(15,15)) for featuremap in range(featuremaps): plt.subplot(6,8, featuremap+1) # sets the number of feature maps to show on each row and column plt.title('FeatureMap ' + str(featuremap)) # displays the feature map number if activation_min != -1 & activation_max != -1: plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", vmin =activation_min, vmax=activation_max, cmap="gray") elif activation_max != -1: plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", vmax=activation_max, cmap="gray") elif activation_min !=-1: plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", vmin=activation_min, cmap="gray") else: plt.imshow(activation[0,:,:, featuremap], interpolation="nearest", cmap="gray") with tf.Session() as sess: saver.restore(sess, tf.train.latest_checkpoint('.')) outputFeatureMap(X_web, conv2) sess.close() <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Step 1 Step2: Include an exploratory visualization of the dataset Step3: Step 2 Step4: Model Architecture Step5: Train, Validate and Test the Model Step6: Step 3 Step7: Predict the Sign Type for Each Image Step8: Analyze Performance Step9: Output Top 5 Softmax Probabilities For Each Image Found on the Web Step10: Project Writeup

<ASSISTANT_TASK:> Python Code: import pandas as pd import numpy as np #importar los modulos pandas y numpy con los alias convencionales from pandas import Series, DataFrame #Crear una serie desde un ndarray s = pd.Series(np.arange(0,5), index=['a', 'b', 'c', 'd', 'e']) s s.index type(s) s0 = Series(np.random.random(10)) #crea una serie con 10 valores aleatorios s0 #Crear serie desde un dictionario d = {'a':100,'b':124,'c':700,'d':430,'e':400} s1 = pd.Series(d) s1 #Crear serie desde un valor escalar s2 = pd.Series(23., s1.index) s2 # Acceder a los valores de la serie print(s2.index) print(s2.values) lpaises=['Venezuela','República Dominicana','India','Guatemala','Alemania','Arabia Saudita','Argentina','Francia','Filipinas','Indonesia','Haití','México','Nicaragua'] lpoblacion=[31108.08,10528.39,1311050.53,16342.90,81413.15,31540.37,43416.75,66808.38,100699.40,257563.82,10711.07,127017.22,6082.03] seriepp=Series(lpoblacion,lpaises) seriepp['India'] #seleccionar el indice India #Ver cuales de los paises seleccionado superan los 50 millones de personas seriepp[seriepp>50000] seried = seriepp.to_dict() # convertir en un diccionario seried seriepp.name = 'Población algunos paises' seriepp['India'] seriepp[['India','Aruba','Venezuela']] #Seleccionar desde una lista, en caso de no estar el elemento NaN 'México' in seriepp #Verificar si México esta en la serie 'Estados Unidos' in seriepp #Verificar si Estados Unidos esta en la serie seriepp.append(Series({'Ginea':12608.59})) seriepp=seriepp.append(Series({'Ginea':12608.59})) #Agregar un elemento a una serie seriepp seriepp=seriepp.append(Series({'Otro Nación':np.NAN})) seriepp seriepp.notnull() # retorna una serie boleana donde identifca los valores no son nulos #Crear un DataFrame desde un dictionario AntillaMayores = {'ciudad':['Santo Domingo','La Habana','San Juan','Kington','Puerto Principe'], 'pais':['República Dominicana','Cuba','Puerto Rico','Jaimaica','Haití'], 'poblacion':[10528.39,11389.56,3474.18,2725.94,10711.07]} dfAntillasMay = DataFrame(AntillaMayores) dfAntillasMay #Crear un DataFram desde multiples listas #Creando varias listas con datos de paises de antillas menores territorios independient #fuente de datos: https://es.wikipedia.org/wiki/Antillas_Menores pais = ['Antigua y Barbuda', 'Barbados', 'Dominica', 'Granada', 'San Cristóbal y Nieve', 'San Vicente y las Granadinas', 'Santa Lucía', 'Trinidad y Tobago'] idiomaOficial = ['Inglés','Inglés','Inglés','Inglés','Inglés','Inglés','Inglés','Inglés'] superficieTerritorial = [443,431,754, 344, 261,389, 616, 5128 ] poblacion = [68722 , 279912 , 69278 , 89502 , 38958, 117534, 160145, 1075066] etiquetas = ['pais','idioma','superficie','población'] listacolumnas = [pais,idiomaOficial,superficieTerritorial,poblacion] datazipped = list(zip(etiquetas,listacolumnas)) dataantillas = dict(datazipped) dfantillasmenores = DataFrame(dataantillas) dfantillasmenores # Cambiando los nombres de las columnas # complicarla un poco dfantillasmenores.rename(columns={'idioma': 'Idioma Oficial','pais':'País','población':'Población','superficie':'Superficie Territorial'},inplace=True) dfantillasmenores #Broadcasting o cambio en memoria, permite agregar nuevos datos o agregar columnas #según se necesite, ejemplo una nueva colunma para asignar la visitas de turistas en el 2017 dfantillasmenores['Turismo 2017(MM)']=1 #De forma automática asignamos 1 dfantillasmenores dfantillasmenores['Turismo 2017(MM)']=dfantillasmenores['Población']/dfantillasmenores['Superficie Territorial']**3 dfantillasmenores <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Series es similar a un arreglo de numpy(ndarray) con la excepción de que estos poseen un axis labels. Step2: Ejercicio 1 Step3: Data Frames se define como arreglo bidimensional etiquetadas, cada columna es una Serie.

<ASSISTANT_TASK:> Python Code: # Load Biospytial modules and etc. %matplotlib inline import sys sys.path.append('/apps') sys.path.append('..') #sys.path.append('../../spystats') import django django.setup() import pandas as pd import matplotlib.pyplot as plt import numpy as np ## Use the ggplot style plt.style.use('ggplot') from external_plugins.spystats.spystats import tools as sptools import scipy vm = sptools.ExponentialVariogram(sill=0.3,range_a=0.4) %time xx,yy,z = sptools.simulatedGaussianFieldAsPcolorMesh(vm) plt.imshow(z) x = xx[1,:] y = yy[:,1] import scipy.fftpack as fft c_delta = lambda d : np.hstack(((2 + d),-1,np.zeros(128 - 3),-1)) c_base = c_delta(0.1) c_base C = scipy.linalg.circulant(c_base) n = C.shape[1] C_inv = np.linalg.inv(C) plt.imshow(C_inv, interpolation = 'None') C_inv.max() plop = fft.ifft(fft.fft(c_base) ** -1) #plop = fft.rifft(fft.rfft(c_base) ** -1) / n plop.real.max() C_inv2 = scipy.linalg.circulant(plop.real) plt.imshow(C_inv2, interpolation = 'None') C_chol = np.linalg.cholesky(C_inv) z = np.random.normal(0, 1, [n]) mmm = np.matmul(C_chol, z) plt.plot(mmm); lambda_vec = fft.fft(c_base) Lambda_aux = np.power(lambda_vec, -0.5) z_re = np.random.normal(0, 1, [n]) z_im = np.random.normal(0, 1, [n]) z = z_re + 1j * z_im # z = np.random.normal(0, 1, [n]) x = fft.fft(Lambda_aux.real * z).real / np.sqrt(n) plt.plot(x); #c_delta = lambda d : np.hstack(((4 + d),-1,np.zeros(128 - 3),-1)) #c_delta = lambda d : np.hstack(((0),-1,np.zeros(128 - 3),-1)) n = 64 N = 64 delta = 0.1 c_base = np.zeros([n, N]) c_base[0, 0] = 4 + delta c_base[0, 1] = -1 c_base[0, 2] = -1 c_base[1, 0] = -1 c_base[2, 0] = -1 c_base[0, N-1] = -1 c_base[0, N-2] = -1 c_base[N-1, 0] = -1 c_base[N-2, 0] = -1 c_base %%time lambda_mat = fft.fft2(c_base) z_re = np.random.normal(0, 1, [n, N]) z_im = np.random.normal(0, 1, [n, N]) z = z_re + 1j * z_im x = fft.fft2((lambda_mat ** -0.5) * z).real / np.sqrt(n *N) plt.imshow(x, interpolation = 'None') %%time n = 64 N = 64 delta = 0.0001 c_base = np.zeros([n, N]) c_base[0, 0] = 4 + delta c_base[0, 1] = -1 #c_base[0, 2] = -1 c_base[1, 0] = -1 #c_base[2, 0] = -1 c_base[0, N-1] = -1 #c_base[0, N-2] = -1 c_base[N-1, 0] = -1 #c_base[N-2, 0] = -1 lambda_mat = fft.fft2(c_base) z_re = np.random.normal(0, 1, [n, N]) z_im = np.random.normal(0, 1, [n, N]) z = z_re + 1j * z_im x = fft.fft2((lambda_mat ** -0.5) * z).real / np.sqrt(n *N) plt.imshow(x, interpolation = 'none') ## Simulate random noise (Normal distributed) zr = scipy.stats.norm.rvs(size=(C.size,2),loc=0,scale=1) zr.dtype=np.complex_ #plt.hist(zr.real) from scipy.fftpack import ifft2, fft2 Lm = scipy.sqrt(C.shape[0]*C.shape[0]) * fft2(C) Lm.shape zr.shape Lm.size %time v = fft2(scipy.sqrt(Lm) * zr.reshape(Lm.shape)) x = v.real x.shape plt.imshow(x,interpolation='None') cc = scipy.linalg.inv(C) plt.plot(cc[:,0]) n = x.shape[0] mm = scipy.stats.multivariate_normal(np.zeros(n),cc) mmm = mm.rvs() plt.imshow(mmm.reshape(100,100)) scipy.stats.multivariate_normal? nn = mm.rvs() from scipy.fftpack import ifftn import matplotlib.pyplot as plt import matplotlib.cm as cm N = 30 f, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(2, 3, sharex='col', sharey='row') xf = np.zeros((N,N)) xf[0, 5] = 1 xf[0, N-5] = 1 Z = ifftn(xf) ax1.imshow(xf, cmap=cm.Reds) ax4.imshow(np.real(Z), cmap=cm.gray) xf = np.zeros((N, N)) xf[5, 0] = 1 xf[N-5, 0] = 1 Z = ifftn(xf) ax2.imshow(xf, cmap=cm.Reds) ax5.imshow(np.real(Z), cmap=cm.gray) xf = np.zeros((N, N)) xf[5, 10] = 1 xf[N-5, N-10] = 1 Z = ifftn(xf) ax3.imshow(xf, cmap=cm.Reds) ax6.imshow(np.real(Z), cmap=cm.gray) plt.show() <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: For benchmarking we will perfom a GF simulation. Step2: Simulation of a temporal GMRF with DFT Step3: Now let's build the circulant matrix for the tourus Step4: Algorithm to simulate GMRF with block-circulant Step5: Example to perform a FFT in two dimensions

<ASSISTANT_TASK:> Python Code: !ls -ltr /data spark df = spark.read.format("csv").option("header","true")\ .option("inferSchema","true").load("/data/Combined_Cycle_Power_Plant.csv") df.show() df.cache() df.limit(10).toPandas().head() from pyspark.ml.feature import * vectorizer = VectorAssembler() vectorizer.setInputCols(["AT", "V", "AP", "RH"]) vectorizer.setOutputCol("features") df_vect = vectorizer.transform(df) df_vect.show(10, False) print(vectorizer.explainParams()) from pyspark.ml.regression import LinearRegression lr = LinearRegression() print(lr.explainParams()) lr.setLabelCol("EP") lr.setFeaturesCol("features") model = lr.fit(df_vect) type(model) print("R2:", model.summary.r2) print("Intercept: ", model.intercept, "Coefficients", model.coefficients) df_pred = model.transform(df_vect) df_pred.show() from pyspark.ml.evaluation import RegressionEvaluator evaluator = RegressionEvaluator() print(evaluator.explainParams()) evaluator = RegressionEvaluator(labelCol = "EP", predictionCol = "prediction", metricName = "rmse") evaluator.evaluate(df_pred) from pyspark.ml.pipeline import Pipeline, PipelineModel pipeline = Pipeline() print(pipeline.explainParams()) pipeline.setStages([vectorizer, lr]) pipelineModel = pipeline.fit(df) pipeline.getStages() lr_model = pipelineModel.stages[1] lr_model .coefficients pipelineModel.transform(df).show() evaluator.evaluate(pipelineModel.transform(df)) pipelineModel.save("/tmp/lr-pipeline") !tree /tmp/lr-pipeline saved_model = PipelineModel.load("/tmp/lr-pipeline") saved_model.stages[1].coefficients saved_model.transform(df).show() df_train, df_test = df.randomSplit(weights=[0.7, 0.3], seed = 200) pipelineModel = pipeline.fit(df_train) evaluator.evaluate(pipelineModel.transform(df_test)) from pyspark.ml.tuning import ParamGridBuilder, TrainValidationSplit paramGrid = ParamGridBuilder()\ .addGrid(lr.regParam, [0.1, 0.01]) \ .addGrid(lr.fitIntercept, [False, True])\ .addGrid(lr.elasticNetParam, [0.0, 0.5, 1.0])\ .build() # In this case the estimator is simply the linear regression. # A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator. tvs = TrainValidationSplit(estimator=lr, estimatorParamMaps=paramGrid, evaluator=evaluator, trainRatio=0.8) tuned_model = tvs.fit(vectorizer.transform(df_train)) tuned_model.bestModel, tuned_model.validationMetrics df_test_pred = tuned_model.transform(vectorizer.transform(df_test)) df_test_pred.show() evaluator.evaluate(df_test_pred) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Load Data Step2: Convert Spark Dataframe to Pandas Dataframe Step3: Verctorize the features Step4: Fit Linear Regression Model Step5: View model summary Step6: Predict Step7: Evaluate Step8: Build a pipeline Step9: Save the pipeline to disk to persist the model Step10: Load the persisted model from the disk Step11: Tune the model

<ASSISTANT_TASK:> Python Code: from taxii2client import Collection from stix2 import CompositeDataSource, FileSystemSource, TAXIICollectionSource # create FileSystemStore fs = FileSystemSource("/tmp/stix2_source") # create TAXIICollectionSource colxn = Collection('http://127.0.0.1:5000/trustgroup1/collections/91a7b528-80eb-42ed-a74d-c6fbd5a26116/', user="user1", password="Password1") ts = TAXIICollectionSource(colxn) # add them both to the CompositeDataSource cs = CompositeDataSource() cs.add_data_sources([fs,ts]) # get an object that is only in the filesystem intrusion_set = cs.get('intrusion-set--f3bdec95-3d62-42d9-a840-29630f6cdc1a') print(intrusion_set.serialize(pretty=True)) # get an object that is only in the TAXII collection ind = cs.get('indicator--a740531e-63ff-4e49-a9e1-a0a3eed0e3e7') print(ind.serialize(pretty=True)) import sys from stix2 import Filter # create filter for STIX objects that have external references to MITRE ATT&CK framework f = Filter("external_references.source_name", "=", "mitre-attack") # create filter for STIX objects that are not of SDO type Attack-Pattnern f1 = Filter("type", "!=", "attack-pattern") # create filter for STIX objects that have the "threat-report" label f2 = Filter("labels", "in", "threat-report") # create filter for STIX objects that have been modified past the timestamp f3 = Filter("modified", ">=", "2017-01-28T21:33:10.772474Z") # create filter for STIX objects that have been revoked f4 = Filter("revoked", "=", True) from stix2 import MemoryStore, FileSystemStore, FileSystemSource fs = FileSystemStore("/tmp/stix2_store") fs_source = FileSystemSource("/tmp/stix2_source") # attach filter to FileSystemStore fs.source.filters.add(f) # attach multiple filters to FileSystemStore fs.source.filters.add([f1,f2]) # can also attach filters to a Source # attach multiple filters to FileSystemSource fs_source.filters.add([f3, f4]) mem = MemoryStore() # As it is impractical to only use MemorySink or MemorySource, # attach a filter to a MemoryStore mem.source.filters.add(f) # attach multiple filters to a MemoryStore mem.source.filters.add([f1,f2]) from stix2 import Campaign, Identity, Indicator, Malware, Relationship mem = MemoryStore() cam = Campaign(name='Charge', description='Attack!') idy = Identity(name='John Doe', identity_class="individual") ind = Indicator(pattern_type='stix', pattern="[file:hashes.MD5 = 'd41d8cd98f00b204e9800998ecf8427e']") mal = Malware(name="Cryptolocker", is_family=False, created_by_ref=idy) rel1 = Relationship(ind, 'indicates', mal,) rel2 = Relationship(mal, 'targets', idy) rel3 = Relationship(cam, 'uses', mal) mem.add([cam, idy, ind, mal, rel1, rel2, rel3]) print(mem.creator_of(mal).serialize(pretty=True)) rels = mem.relationships(mal) len(rels) mem.relationships(mal, relationship_type='indicates') mem.relationships(mal, source_only=True) mem.relationships(mal, target_only=True) mem.related_to(mal, target_only=True, relationship_type='uses') <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Filters Step2: For Filters to be applied to a query, they must be either supplied with the query call or attached to a DataStore, more specifically to a DataSource whether that DataSource is a part of a DataStore or stands by itself. Step3: Note Step4: If a STIX object has a created_by_ref property, you can use the creator_of() method to retrieve the Identity object that created it. Step5: Use the relationships() method to retrieve all the relationship objects that reference a STIX object. Step6: You can limit it to only specific relationship types Step7: You can limit it to only relationships where the given object is the source Step8: And you can limit it to only relationships where the given object is the target Step9: Finally, you can retrieve all STIX objects related to a given STIX object using related_to(). This calls relationships() but then performs the extra step of getting the objects that these Relationships point to. related_to() takes all the same arguments that relationships() does.

<ASSISTANT_TASK:> Python Code: # Run this cell, but please don't change it. # These lines import the Numpy and Datascience modules. import numpy as np from datascience import * # These lines do some fancy plotting magic. import matplotlib %matplotlib inline import matplotlib.pyplot as plt plt.style.use('fivethirtyeight') import warnings warnings.simplefilter('ignore', FutureWarning) # These lines load the tests. from client.api.assignment import load_assignment tests = load_assignment('lab07.ok') # For the curious: this is how to display a YouTube video in a # Jupyter notebook. The argument to YouTubeVideo is the part # of the URL (called a "query parameter") that identifies the # video. For example, the full URL for this video is: # https://www.youtube.com/watch?v=wE8NDuzt8eg from IPython.display import YouTubeVideo YouTubeVideo("wE8NDuzt8eg") faithful = Table.read_table("faithful.csv") faithful ... duration_mean = ... duration_std = ... wait_mean = ... wait_std = ... faithful_standard = Table().with_columns( "duration (standard units)", ..., "wait (standard units)", ...) faithful_standard _ = tests.grade('q1_3') ... r = ... r _ = tests.grade('q1_6') def plot_data_and_line(dataset, x, y, point_0, point_1): Makes a scatter plot of the dataset, along with a line passing through two points. dataset.scatter(x, y, label="data") plt.plot(make_array(point_0.item(0), point_1.item(0)), make_array(point_0.item(1), point_1.item(1)), label="regression line") plt.legend(bbox_to_anchor=(1.5,.8)) plot_data_and_line(faithful_standard, "duration (standard units)", "wait (standard units)", make_array(-2, -2*r), make_array(2, 2*r)) slope = ... slope intercept = slope*(-duration_mean) + wait_mean intercept _ = tests.grade('q2_1') two_minute_predicted_waiting_time = ... five_minute_predicted_waiting_time = ... # Here is a helper function to print out your predictions # (you don't need to modify it): def print_prediction(duration, predicted_waiting_time): print("After an eruption lasting", duration, "minutes, we predict you'll wait", predicted_waiting_time, "minutes until the next eruption.") print_prediction(2, two_minute_predicted_waiting_time) print_prediction(5, five_minute_predicted_waiting_time) plot_data_and_line(faithful, "duration", "wait", make_array(2, two_minute_predicted_waiting_time), make_array(5, five_minute_predicted_waiting_time)) faithful_predictions = ... faithful_predictions _ = tests.grade("q3_2") faithful_residuals = ... faithful_residuals _ = tests.grade("q3_3") faithful_residuals.scatter("duration", "residual", color="r") faithful_residuals.scatter("duration", "wait", label="actual waiting time", color="blue") plt.scatter(faithful_residuals.column("duration"), faithful_residuals.column("residual"), label="residual", color="r") plt.plot(make_array(2, 5), make_array(two_minute_predicted_waiting_time, five_minute_predicted_waiting_time), label="regression line") plt.legend(bbox_to_anchor=(1.7,.8)); zero_minute_predicted_waiting_time = ... two_point_five_minute_predicted_waiting_time = ... hour_predicted_waiting_time = ... print_prediction(0, zero_minute_predicted_waiting_time) print_prediction(2.5, two_point_five_minute_predicted_waiting_time) print_prediction(60, hour_predicted_waiting_time) _ = tests.grade('q4_1') # For your convenience, you can run this cell to run all the tests at once! import os print("Running all tests...") _ = [tests.grade(q[:-3]) for q in os.listdir("tests") if q.startswith('q')] print("Finished running all tests.") # Run this cell to submit your work *after* you have passed all of the test cells. # It's ok to run this cell multiple times. Only your final submission will be scored. !TZ=America/Los_Angeles jupyter nbconvert --output=".lab07_$(date +%m%d_%H%M)_submission.html" lab07.ipynb && echo "Submitted successfully." <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: 1. How Faithful is Old Faithful? Step2: Some of Old Faithful's eruptions last longer than others. When it has a long eruption, there's generally a longer wait until the next eruption. Step3: We would like to use linear regression to make predictions, but that won't work well if the data aren't roughly linearly related. To check that, we should look at the data. Step4: Question 2 Step5: Question 4 Step6: You'll notice that this plot looks exactly the same as the last one! The data really are different, but the axes are scaled differently. (The method scatter scales the axes so the data fill up the available space.) So it's important to read the ticks on the axes. Step8: 2. The regression line Step9: How would you take a point in standard units and convert it back to original units? We'd have to "stretch" its horizontal position by duration_std and its vertical position by wait_std. Step10: We know that the regression line passes through the point (duration_mean, wait_mean). You might recall from high-school algebra that the equation for the line is therefore Step11: 3. Investigating the regression line Step12: The next cell plots the line that goes between those two points, which is (a segment of) the regression line. Step13: Question 2 Step14: Question 3 Step15: Here is a plot of the residuals you computed. Each point corresponds to one eruption. It shows how much our prediction over- or under-estimated the waiting time. Step16: There isn't really a pattern in the residuals, which confirms that it was reasonable to try linear regression. It's true that there are two separate clouds; the eruption durations seemed to fall into two distinct clusters. But that's just a pattern in the eruption durations, not a pattern in the relationship between eruption durations and waiting times. Step17: However, unless you have a strong reason to believe that the linear regression model is true, you should be wary of applying your prediction model to data that are very different from the training data. Step18: Question 2. Do you believe any of these values are reliable predictions? If you don't believe some of them, say why.

<ASSISTANT_TASK:> Python Code: # Load libraries # Math import numpy as np # Visualization %matplotlib notebook import matplotlib.pyplot as plt plt.rcParams.update({'figure.max_open_warning': 0}) from mpl_toolkits.axes_grid1 import make_axes_locatable from scipy import ndimage # Print output of LFR code import subprocess # Sparse matrix import scipy.sparse import scipy.sparse.linalg # 3D visualization import pylab from mpl_toolkits.mplot3d import Axes3D from matplotlib import pyplot # Import data import scipy.io # Import functions in lib folder import sys sys.path.insert(1, 'lib') # Import helper functions %load_ext autoreload %autoreload 2 from lib.utils import construct_kernel from lib.utils import compute_kernel_kmeans_EM from lib.utils import compute_purity # Import distance function import sklearn.metrics.pairwise # Remove warnings import warnings warnings.filterwarnings("ignore") # Load dataset: W is the Adjacency Matrix and Cgt is the ground truth clusters mat = scipy.io.loadmat('datasets/mnist_2000_graph.mat') W = mat['W'] n = W.shape[0] Cgt = mat['Cgt'] - 1; Cgt = Cgt.squeeze() nc = len(np.unique(Cgt)) print('Number of nodes =',n) print('Number of classes =',nc); # Degree Matrix d = scipy.sparse.csr_matrix.sum(W,axis=-1) # Compute D^(-0.5) d_sqrt = np.sqrt(d) d_sqrt_inv = 1./d_sqrt D_sqrt_inv = scipy.sparse.diags(d_sqrt_inv.A.squeeze(), 0) # Create Identity matrix I = scipy.sparse.identity(d.size, dtype=W.dtype) # Construct A A = I - D_sqrt_inv*W*D_sqrt_inv # Perform EVD on A U = scipy.sparse.linalg.eigsh(A, k=4, which='SM') fig = plt.figure(1) ax = fig.gca(projection='3d') ax.scatter(U[1][:,1], U[1][:,2], U[1][:,3], c=Cgt) plt.title('$Y^*$') # Your code here #lamb, Y_star = scipy.sparse.linalg.eigsh(A, k=4, which='SM') # Normalize the rows of Y* with the L2 norm, i.e. ||y_i||_2 = 1 #Y_star = Y_star/np.sqrt(np.sum((Y_star)**2)) Y_star = U[1] Y_star = ( Y_star.T / np.sqrt(np.sum(Y_star**2,axis=1)+1e-10) ).T # Your code here # Run standard K-Means Ker=construct_kernel(Y_star,'linear') n = Y_star.shape[0] Theta= np.ones(n) [C_kmeans, En_kmeans]=compute_kernel_kmeans_EM(nc,Ker,Theta,10) accuracy = compute_purity(C_kmeans,Cgt,nc) print('accuracy = ',accuracy,'%') fig = plt.figure(2) ax = fig.gca(projection='3d') plt.scatter(Y_star[:,1], U[1][:,2], U[1][:,3], c=Cgt) plt.title('$Y^*$') <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Question 1 Step2: Question 6

<ASSISTANT_TASK:> Python Code: %pylab inline from Geant4 import * from IPython.display import Image class MyDetectorConstruction(G4VUserDetectorConstruction): "My Detector Construction" def __init__(self): G4VUserDetectorConstruction.__init__(self) self.solid = {} self.logical = {} self.physical = {} self.create_world(side = 4000, material = "G4_AIR") self.create_cylinder(name = "vacuum", radius = 200, length = 320, translation = [0,0,900], material = "G4_Galactic", colour = [1.,1.,1.,0.1], mother = 'world') self.create_cylinder(name = "upper_scatter", radius = 10, length = 0.01, translation = [0,0,60], material = "G4_Ta", colour = [1.,1.,1.,0.7], mother = 'vacuum') self.create_cylinder(name = "lower_scatter", radius = 30, length = 0.01, translation = [0,0,20], material = "G4_Al", colour = [1.,1.,1.,0.7], mother = 'vacuum') self.create_applicator_aperture(name = "apature_1", inner_side = 142, outer_side = 182, thickness = 6, translation = [0,0,449], material = "G4_Fe", colour = [1,1,1,0.7], mother = 'world') self.create_applicator_aperture(name = "apature_2", inner_side = 130, outer_side = 220, thickness = 12, translation = [0,0,269], material = "G4_Fe", colour = [1,1,1,0.7], mother = 'world') self.create_applicator_aperture(name = "apature_3", inner_side = 110, outer_side = 180, thickness = 12, translation = [0,0,140], material = "G4_Fe", colour = [1,1,1,0.7], mother = 'world') self.create_applicator_aperture(name = "apature_4", inner_side = 100, outer_side = 140, thickness = 12, translation = [0,0,59], material = "G4_Fe", colour = [1,1,1,0.7], mother = 'world') self.create_applicator_aperture(name = "cutout", inner_side = 100, outer_side = 120, thickness = 6, translation = [0,0,50], material = "G4_Fe", colour = [1,1,1,0.7], mother = 'world') self.create_cube(name = "phantom", side = 500, translation = [0,0,-250], material = "G4_WATER", colour = [0,0,1,0.4], mother = 'world') def create_world(self, **kwargs): material = gNistManager.FindOrBuildMaterial(kwargs['material']) side = kwargs['side'] self.solid['world'] = G4Box("world", side/2., side/2., side/2.) self.logical['world'] = G4LogicalVolume(self.solid['world'], material, "world") self.physical['world'] = G4PVPlacement(G4Transform3D(), self.logical['world'], "world", None, False, 0) visual = G4VisAttributes() visual.SetVisibility(False) self.logical['world'].SetVisAttributes(visual) def create_cylinder(self, **kwargs): name = kwargs['name'] radius = kwargs['radius'] length = kwargs['length'] translation = G4ThreeVector(*kwargs['translation']) material = gNistManager.FindOrBuildMaterial(kwargs['material']) visual = G4VisAttributes(G4Color(*kwargs['colour'])) mother = self.physical[kwargs['mother']] self.solid[name] = G4Tubs(name, 0., radius, length/2., 0., 2*pi) self.logical[name] = G4LogicalVolume(self.solid[name], material, name) self.physical[name] = G4PVPlacement(None, translation, name, self.logical[name], mother, False, 0) self.logical[name].SetVisAttributes(visual) def create_cube(self, **kwargs): name = kwargs['name'] side = kwargs['side'] translation = G4ThreeVector(*kwargs['translation']) material = gNistManager.FindOrBuildMaterial(kwargs['material']) visual = G4VisAttributes(G4Color(*kwargs['colour'])) mother = self.physical[kwargs['mother']] self.solid[name] = G4Box(name, side/2., side/2., side/2.) self.logical[name] = G4LogicalVolume(self.solid[name], material, name) self.physical[name] = G4PVPlacement(None, translation, name, self.logical[name], mother, False, 0) self.logical[name].SetVisAttributes(visual) def create_applicator_aperture(self, **kwargs): name = kwargs['name'] inner_side = kwargs['inner_side'] outer_side = kwargs['outer_side'] thickness = kwargs['thickness'] translation = G4ThreeVector(*kwargs['translation']) material = gNistManager.FindOrBuildMaterial(kwargs['material']) visual = G4VisAttributes(G4Color(*kwargs['colour'])) mother = self.physical[kwargs['mother']] inner_box = G4Box("inner", inner_side/2., inner_side/2., thickness/2. + 1) outer_box = G4Box("outer", outer_side/2., outer_side/2., thickness/2.) self.solid[name] = G4SubtractionSolid(name, outer_box, inner_box) self.logical[name] = G4LogicalVolume(self.solid[name], material, name) self.physical[name] = G4PVPlacement(None, translation, name, self.logical[name], mother, False, 0) self.logical[name].SetVisAttributes(visual) # ----------------------------------------------------------------- def Construct(self): # return the world volume return self.physical['world'] # set geometry detector = MyDetectorConstruction() gRunManager.SetUserInitialization(detector) # set physics list physics_list = FTFP_BERT() gRunManager.SetUserInitialization(physics_list) class MyPrimaryGeneratorAction(G4VUserPrimaryGeneratorAction): "My Primary Generator Action" def __init__(self): G4VUserPrimaryGeneratorAction.__init__(self) particle_table = G4ParticleTable.GetParticleTable() electron = particle_table.FindParticle(G4String("e-")) positron = particle_table.FindParticle(G4String("e+")) gamma = particle_table.FindParticle(G4String("gamma")) beam = G4ParticleGun() beam.SetParticleEnergy(6*MeV) beam.SetParticleMomentumDirection(G4ThreeVector(0,0,-1)) beam.SetParticleDefinition(electron) beam.SetParticlePosition(G4ThreeVector(0,0,1005)) self.particleGun = beam def GeneratePrimaries(self, event): self.particleGun.GeneratePrimaryVertex(event) primary_generator_action = MyPrimaryGeneratorAction() gRunManager.SetUserAction(primary_generator_action) # Initialise gRunManager.Initialize() %%file macros/raytrace.mac /vis/open RayTracer /vis/rayTracer/headAngle 340. /vis/rayTracer/eyePosition 200 200 250 cm /vis/rayTracer/trace images/world.jpg gUImanager.ExecuteMacroFile('macros/raytrace.mac') # Show image Image(filename="images/world.jpg") %%file macros/dawn.mac /vis/open DAWNFILE /vis/scene/create /vis/scene/add/volume /vis/scene/add/trajectories smooth /vis/modeling/trajectories/create/drawByCharge /vis/modeling/trajectories/drawByCharge-0/default/setDrawStepPts true /vis/modeling/trajectories/drawByCharge-0/default/setStepPtsSize 2 /vis/scene/endOfEventAction accumulate 1000 /vis/scene/add/hits /vis/sceneHandler/attach #/vis/scene/add/axes 0. 0. 0. 10. cm /vis/viewer/set/targetPoint 0.0 0.0 300.0 mm /vis/viewer/set/viewpointThetaPhi 90 0 /vis/viewer/zoom 1 gUImanager.ExecuteMacroFile('macros/dawn.mac') gRunManager.BeamOn(50) !mv g4_00.prim images/world.prim !dawn -d images/world.prim !convert images/world.eps images/world.png Image("images/world.png") !G4VRML_DEST_DIR=. !G4VRMLFILE_MAX_FILE_NUM=1 !G4VRMLFILE_VIEWER=echo gApplyUICommand("/vis/open VRML2FILE") gRunManager.BeamOn(1) !mv g4_00.wrl images/world.wrl %load_ext version_information %version_information matplotlib, numpy <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Setting the requirements for a simulation Step2: Now that you have made your geometry class, time to load it up Step3: The physics list Step4: Generating the beam Step5: And this is now loading up the generator we have just made Step6: Initialise the simulation Step7: Seeing the geometry Step8: Seeing the particle tracks Step9: Once we have defined how we want to see the tracks we can beam on our pretend linac with 50 electrons. Step10: The beam on created a prim file which needs to be converted to png for viewing. Step11: And here is our wonderful simulation Step12: Versions

<ASSISTANT_TASK:> Python Code: import matplotlib as mpl mpl.use('TkAgg') import matplotlib.pyplot as plt %matplotlib inline import bacteriopop_utils import feature_selection_utils import load_data loaded_data = data = load_data.load_data() loaded_data.shape loaded_data[loaded_data['phylum'].isnull()].head(3) loaded_data.head() bacteriopop_utils.filter_by_abundance(dataframe=loaded_data, low= 0.6).head() bacteriopop_utils.reduce_data(dataframe=loaded_data, min_abundance= 0.6, phylo_column='genus', oxygen='high').head() raw_dmd_data = bacteriopop_utils.reduce_data( dataframe=loaded_data, min_abundance= 0.01, phylo_column='genus', oxygen='Low') data_dict = bacteriopop_utils.break_apart_experiments(raw_dmd_data) data_dict.keys() # Can't view generators very easily!!! data_dict.itervalues() # But we can make a list from them and grab the 0th item first_df = list(data_dict.itervalues())[0] first_df.head(3) first_df[first_df['genus'] == 'other'].head() first_df[first_df['genus'] != ''].pivot(index='genus', columns='week', values='abundance') raw_dmd_data.columns DMD_input_dict = \ bacteriopop_utils.prepare_DMD_matrices(raw_dmd_data, groupby_level = "genus") type(DMD_input_dict) DMD_input_dict[('Low', 1)] DMD_input_dict[('Low', 1)].shape DMD_input_dict[('Low', 1)].groupby('week')['abundance'].sum() DMD_test_matrix = DMD_input_dict[('Low', 1)] # Who is in there? DMD_test_matrix.reset_index()['genus'].unique() # following example 1: https://pythonhosted.org/modred/tutorial_modaldecomp.html import modred as MR num_modes = 1 modes, eig_vals = MR.compute_POD_matrices_snaps_method(DMD_test_matrix, range(num_modes)) modes eig_vals extracted_features = bacteriopop_utils.extract_features( dataframe = loaded_data, column_list = ['kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'oxygen', 'abundance'] # default list was: ['kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'length', 'abundance', 'project'] ) extracted_features.head() extracted_features.shape pca_results = feature_selection_utils.pca_bacteria( data = extracted_features.head(100), n_components = 10) pca_results.components_ feature_selection_utils.calculate_features_target_correlation( data = extracted_features.head(100), features = extracted_features.columns.tolist(), target='abundance', method="Pearson") <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: make sure none of the phyla are NA (checking 160304 update to load_data.py Step2: Test filter and reduce functions using a high threshold, which selects for genus==Methylobacter Step3: Demo of DMD data prep Step4: Errors are thrown by functions below if you drop min_abunance below. I think it is hanging up on multiple "other" rows. Step5: We can get each dataframe out like this Step6: DMD Step7: I'm stuck at the installation of modred Step8: Feature extraction and PCA Step9: Just do PCA on a tiny bit of the data as a demo Step10: Do correlations for a tiny subset of the data.

<ASSISTANT_TASK:> Python Code: import inflect # for string manipulation import numpy as np import pandas as pd import scipy as sp import scipy.stats as st import matplotlib.pyplot as plt %matplotlib inline filename = '/Users/excalibur/py/nanodegree/intro_ds/final_project/improved-dataset/turnstile_weather_v2.csv' # import data data = pd.read_csv(filename) entries_hourly_by_row = data['ENTRIESn_hourly'].values def map_column_to_entries_hourly(column): instances = column.values # e.g., longitude_instances = data['longitude'].values # reduce entries_hourly = {} # e.g., longitude_entries_hourly = {} for i in np.arange(len(instances)): if instances[i] in entries_hourly: entries_hourly[instances[i]] += float(entries_hourly_by_row[i]) else: entries_hourly[instances[i]] = float(entries_hourly_by_row[i]) return entries_hourly # e.g., longitudes, entries def create_df(entries_hourly_dict, column1name): # e.g, longitude_df = pd.DataFrame(data=longitude_entries_hourly.items(), columns=['longitude','entries']) df = pd.DataFrame(data=entries_hourly_dict.items(), columns=[column1name,'entries']) return df # e.g, longitude_df rain_entries_hourly = map_column_to_entries_hourly(data['rain']) rain_df = create_df(rain_entries_hourly, 'rain') rain_days = data[data['rain'] == 1] no_rain_days = data[data['rain'] == 0] def plot_box(sample1, sample2): plt.boxplot([sample2, sample1], vert=False) plt.title('NUMBER OF ENTRIES PER SAMPLE') plt.xlabel('ENTRIESn_hourly') plt.yticks([1, 2], ['Sample 2', 'Sample 1']) plt.show() def describe_samples(sample1, sample2): size1, min_max1, mean1, var1, skew1, kurt1 = st.describe(sample1) size2, min_max2, mean2, var2, skew2, kurt2 = st.describe(sample2) med1 = np.median(sample1) med2 = np.median(sample2) std1 = np.std(sample1) std2 = np.std(sample2) print "Sample 1 (rainy days):\n min = {0}, max = {1},\n mean = {2:.2f}, median = {3}, var = {4:.2f}, std = {5:.2f}".format(min_max1[0], min_max1[1], mean1, med1, var1, std1) print "Sample 2 (non-rainy days):\n min = {0}, max = {1},\n mean = {2:.2f}, median = {3}, var = {4:.2f}, std = {5:.2f}".format(min_max2[0], min_max2[1], mean2, med2, var2, std2) class MannWhitneyU: def __init__(self,n): self.n = n self.num_of_tests = 1000 self.sample1 = 0 self.sample2 = 0 def sample_and_test(self, plot, describe): self.sample1 = np.random.choice(rain_days['ENTRIESn_hourly'], size=self.n, replace=False) self.sample2 = np.random.choice(no_rain_days['ENTRIESn_hourly'], size=self.n, replace=False) ### the following two self.sample2 assignments are for testing purposes ### #self.sample2 = self.sample1 # test when samples are same #self.sample2 = np.random.choice(np.random.randn(self.n),self.n) # test for when samples are very different if plot == True: plot_box(self.sample1,self.sample2) if describe == True: describe_samples(self.sample1,self.sample2) return st.mannwhitneyu(self.sample1, self.sample2) def effect_sizes(self, U): # Wendt's rank-biserial correlation r = (1 - np.true_divide((2*U),(self.n*self.n))) # Cohen's d s = np.sqrt(np.true_divide((((self.n-1)*np.std(self.sample1)**2) + ((self.n-1)*np.std(self.sample2)**2)), (self.n+self.n-2))) d = np.true_divide((np.mean(self.sample1) - np.mean(self.sample2)), s) return r,d def trial_series(self): success = 0 U_values = [] p_values = [] d_values = [] r_values = [] for i in np.arange(self.num_of_tests): U, p = self.sample_and_test(False, False) r, d = self.effect_sizes(U) U_values.append(U) # scipy.stats.mannwhitneyu returns p for a one-sided hypothesis, # so multiply by 2 for two-sided p_values.append(p*2) d_values.append(d) r_values.append(r) if p <= 0.05: success += 1 print "n = {0}".format(self.n) print "average U value: {0:.2f}".format(np.mean(U_values)) print "number of times p <= 0.05: {0}/{1} ({2}%)".format(success, self.num_of_tests, (np.true_divide(success,self.num_of_tests)*100)) print "average p value: {0:.2f}".format(np.mean(p_values)) print "average rank-biserial r value: {0:.2f}".format(np.mean(r_values)) print "average Cohen's d value: {0:.2f}".format(np.mean(d_values)) plt.hist(p_values, color='green', alpha=0.3) plt.show() sample_sizes = [30, 100, 500, 1500, 3000, 5000, 9585] for n in sample_sizes: MannWhitneyU(n).trial_series() print "Shape of rainy-days data:" +str(rain_days.shape) N = rain_days.shape[0] print "N = " + str(N) print "0.05 * N = " + str(0.05 * N) n = 450 mwu = MannWhitneyU(n) U, p = mwu.sample_and_test(True,True) r, d = mwu.effect_sizes(U) print "\nMann-Whitney U test results:" print "n = {0}".format(n) print "U = {0}".format(U) print "p = {0:.2f}".format(np.mean(p)) print "rank-biserial r value: {0:.2f}".format(np.mean(r)) print "Cohen's d value: {0:.2f}".format(np.mean(d)) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Functions for Getting, Mapping, and Plotting Data Step2: Function for Basic Statistics Step3: Formulas Implemented Step4: Section 1. Statistical Test Step5: As witnessed above, when rainy and non-rainy days from the data set are considered populations (as opposed to samples themselves), it takes significantly large sample sizes from each population (e.g., $n = 3000$, which is more than $30\%$ of the total number of rainy days in the data set) to attain low $p$-values¹ frequently enough to reject the null hypothesis of the Mann-Whitney $U$ test² with the critical values proposed below. Step6: The Mann-Whitney $U$ test is a nonparametric test of the null hypothesis that the distributions of two populations are the same.

<ASSISTANT_TASK:> Python Code: %matplotlib inline import matplotlib.pyplot as plt import scipy.optimize plt.plot([1, 2, 3], [10, 30, 20], "o-") plt.xlabel("Unit of time (t)") plt.ylabel("Price of one unit of energy (c)") plt.title("Cost of energy on the market") plt.show(); # Price of energy on the market price = [10, 30, 20] plt.plot(price); stock_max = 100 # battery capacity # Coefficients of the linear objective function to be minimized p = -np.array(price) # 2-D array which, when matrix-multiplied by x, gives the values of the upper-bound inequality constraints at x. A = [[-1, 0, 0], [ 1, 0, 0], [-1, -1, 0], [ 1, 1, 0], [-1, -1, -1], [ 1, 1, 1]] # 1-D array of values representing the upper-bound of each inequality constraint (row) in A. b = [stock_max, 0, stock_max, 0, stock_max, 0] # Sequence of (min, max) pairs for each element in x, defining the bounds on that parameter. # Use None for one of min or max when there is no bound in that direction. # By default bounds are (0, None) (non-negative). # If a sequence containing a single tuple is provided, then min and max will be applied to all variables in the problem. x0_bounds = (None, None) x1_bounds = (None, None) x2_bounds = (None, None) bounds = (x0_bounds, x1_bounds, x2_bounds) scipy.optimize.linprog(p, A_ub=A, b_ub=b, bounds=bounds) # Cost of energy on the market #price = [10, 30, 20] # -> -100, 100, 0 #price = [10, 30, 10, 30] # -> [-100., 100., -100., 100.] #price = [10, 30, 10, 30, 30] # -> [-100., 100., -100., 100., 0.] #price = [10, 20, 30, 40] # -> [-100., 0., 0., 100.] price = [10, 30, 20, 50] price = [10, 30, 20, 50] plt.plot(price); p = -np.array(price) A = np.repeat(np.tril(np.ones(len(price))), 2, axis=0) A[::2, :] *= -1 A b = np.zeros(A.shape[0]) b[::2] = stock_max b bounds = tuple((None, None) for _p in price) bounds %%time res = scipy.optimize.linprog(p, A_ub=A, b_ub=b, bounds=bounds) # , method='revised simplex' res res.x.round(decimals=2) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Scipy's syntax Step2: TODO Step3: Hand write the model Step4: Automatically make the model

<ASSISTANT_TASK:> Python Code: import numpy as np # crear un arreglo arr = np.arange(0,11) # desplegar el arreglo arr #obtener el valor del indice 8 arr[8] #obtener los valores de un rango arr[1:5] #obtener los valores de otro rango arr[0:5] # reemplazar valores en un rango determinado arr[0:5]=100 # desplegar el arreglo arr # Generar nuevamente el arreglo arr = np.arange(0,11) # desplegar arr # corte de un arreglo slice_of_arr = arr[0:6] # desplegar el corte slice_of_arr # cambiar valores del corte slice_of_arr[:]=99 # desplegar los valores del corte slice_of_arr # desplegar arreglo arr # para obtener una copia se debe hacer explicitamente arr_copy = arr.copy() # desplegar el arreglo copia arr_copy # generar un arreglo 2D arr_2d = np.array(([5,10,15],[20,25,30],[35,40,45])) #Show arr_2d # indices de filas arr_2d[1] # Formato es arr_2d[row][col] o arr_2d[row,col] # Seleccionar un solo elemento arr_2d[1][0] # Seleccionar un solo elemento arr_2d[1,0] # Cortes en 2D # forma (2,2) desde la esquina superior derecha arr_2d[:2,1:] #forma desde la ultima fila arr_2d[2] # forma desde la ultima fila arr_2d[2,:] # longitud de un arreglo arr_length = arr2d.shape[1] arr = np.arange(1,11) arr arr > 4 bool_arr = arr>4 bool_arr arr[bool_arr] arr[arr>2] x = 2 arr[arr>x] <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: seleccion utilizando los corchetes Step2: Reemplazar valores Step3: Tomar en cuenta que los cambios tambien se realizaron al arreglo original Step4: La información no es copiada para evitar problemas de memoria Step5: Indices en un arreglo 2D (matrices) Step6: Seleccion

<ASSISTANT_TASK:> Python Code: import os import os.path as op # Step 2 itasser_download_link = 'my_download_link' # Step 3 itasser_version_number = '5.1' # Step 4 itasser_archive = itasser_download_link.split('/')[-1] os.mkdir(op.expanduser('~/software/itasser/')) os.chdir(op.expanduser('~/software/itasser/')) !wget $itasser_download_link !tar -jxf $itasser_archive # Step 5 os.mkdir(op.expanduser('~/software/itasser/ITLIB')) !./I-TASSER5.1/download_lib.pl -libdir ITLIB # Step 2 tmhmm_download_link = 'my_download_link' # Step 3 os.mkdir(op.expanduser('~/software/tmhmm/')) os.chdir(op.expanduser('~/software/tmhmm/')) !wget $tmhmm_download_link !tar -zxf tmhmm-2.0c.Linux.tar.gz # Replace perl path os.chdir(op.expanduser('~/software/tmhmm/tmhmm-2.0c/bin')) !perl -i -pe 's{^#!/usr/local/bin/perl}{#!/usr/bin/perl}' tmhmm !perl -i -pe 's{^#!/usr/local/bin/perl -w}{#!/usr/bin/perl -w}' tmhmmformat.pl # Create symbolic links !ln -s $HOME/software/tmhmm/tmhmm-2.0c/bin/* /srv/venv/bin/ <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: I-TASSER Step2: TMHMM

<ASSISTANT_TASK:> Python Code: import numpy as np import matplotlib import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D %matplotlib inline NN = 6 NF = 5 MM = 5 C = np.zeros((MM, NN)) C[0, 0] = 1; C[0, 1] = -1 # element 1 C[1, 0] = 1; C[1, 2] = -1 # element 2 C[2, 0] = 1; C[2, 3] = -1 # element 3 C[3, 0] = 1; C[3, 4] = -1 # element 4 C[4, 0] = 1; C[4, 5] = -1 # element 4 CL = C[:, 0:(NN - NF)] CF = C[:, (NN - NF):] print(C) q = [5., -1.5, 5., -7.5, 2.5] # force densities in kN/m corresponding to every element qQ = np.diagflat(q) gammaF = np.array([-5. + -5.*1j, 3. + -5.*1j, 5. + 3.*1j, -1. + 6.*1j, -5. + 5.*1j ]) fL = np.array([0. + (-5.)*1j]) gammaL = np.zeros(NN - NF) DL = np.zeros((NN - NF, NN - NF)) DF = np.zeros((NN - NF, NF)) DL = np.dot(np.transpose(CL), np.dot(qQ, CL)) DF = np.dot(np.transpose(CL), np.dot(qQ, CF)) gammaL = np.linalg.solve(DL, fL - np.dot(DF, gammaF)) gammaL fig = plt.figure(figsize=(9,9)) ax = fig.gca(aspect='equal') ax.scatter(gammaF.real, gammaF.imag, color='b') ax.scatter(gammaL.real, gammaL.imag, color='r') for i in range(MM): if q[i] > 0.: col = 'b' else: col = 'r' ax.plot([gammaL.real[0], gammaF.real[i]], [gammaL.imag[0], gammaF.imag[i]], color = col) ax.set_xlabel('$x$') ax.set_ylabel('$y$') NN = 6 NF = 4 MM = 5 C = np.zeros((MM, NN)) C[0, 0] = 1; C[0, 2] = -1 # element 1 C[1, 0] = 1; C[1, 1] = -1 # element 2 C[2, 1] = 1; C[2, 5] = -1 # element 3 C[3, 0] = 1; C[3, 3] = -1 # element 4 C[4, 1] = 1; C[4, 4] = -1 # element 5 CL = C[:, 0:(NN - NF)] CF = C[:, (NN - NF):] print(C) gammaF = np.array([-5. + 0.*1j, 0. + 2.5*1j, 0 - 2.5*1j, 5. + 0.*1j]) q = [-5. + 1.35*1j, -5. + -5.*1j, -5. + 1.35*1j, 0., 0.] # force densities in kN/m corresponding to every element qQ = np.diagflat(q) print(qQ) fL = np.array([0. + (0.)*1j]) gammaL = np.zeros(NN - NF) DL = np.zeros((NN - NF, NN - NF)) DF = np.zeros((NN - NF, NF)) DL = np.dot(np.transpose(CL), np.dot(qQ, CL)) DF = np.dot(np.transpose(CL), np.dot(qQ, CF)) gammaL = np.linalg.solve(DL, fL - np.dot(DF, gammaF)) print(gammaL) fig = plt.figure(figsize=(9,9)) ax = fig.gca(aspect='equal') ax.scatter(gammaF.real, gammaF.imag, color='b') ax.scatter(gammaL.real, gammaL.imag, color='r') ax.set_xlabel('$x$') ax.set_ylabel('$y$') gamma = np.concatenate((gammaL, gammaF), axis=0) d = np.absolute((np.dot(C, gamma))) f = q*d V = np.imag(f) DM = -V*d print(DM) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: 1 Classical 2D Force density equations Step2: We consider now the equilibrium of the node $i$ joining nodes $j, k, l$ through members $m, n, r$, respectively Step3: 1.3 Restrained nodes Step4: 1.4 External forces Step5: 1.5 Solving for the free nodes Step6: 2 Force density for active bending members

<ASSISTANT_TASK:> Python Code: !pip install -q scann import tensorflow as tf import numpy as np from datetime import datetime PROJECT_ID = 'yourProject' # Change to your project. BUCKET = 'yourBucketName' # Change to the bucket you created. REGION = 'yourTrainingRegion' # Change to your AI Platform Training region. EMBEDDING_FILES_PREFIX = f'gs://{BUCKET}/bqml/item_embeddings/embeddings-*' OUTPUT_INDEX_DIR = f'gs://{BUCKET}/bqml/scann_index' try: from google.colab import auth auth.authenticate_user() print("Colab user is authenticated.") except: pass from index_builder.builder import indexer indexer.build(EMBEDDING_FILES_PREFIX, OUTPUT_INDEX_DIR) if tf.io.gfile.exists(OUTPUT_INDEX_DIR): print("Removing {} contents...".format(OUTPUT_INDEX_DIR)) tf.io.gfile.rmtree(OUTPUT_INDEX_DIR) print("Creating output: {}".format(OUTPUT_INDEX_DIR)) tf.io.gfile.makedirs(OUTPUT_INDEX_DIR) timestamp = datetime.utcnow().strftime('%y%m%d%H%M%S') job_name = f'ks_bqml_build_scann_index_{timestamp}' !gcloud ai-platform jobs submit training {job_name} \ --project={PROJECT_ID} \ --region={REGION} \ --job-dir={OUTPUT_INDEX_DIR}/jobs/ \ --package-path=index_builder/builder \ --module-name=builder.task \ --config='index_builder/config.yaml' \ --runtime-version=2.2 \ --python-version=3.7 \ --\ --embedding-files-path={EMBEDDING_FILES_PREFIX} \ --output-dir={OUTPUT_INDEX_DIR} \ --num-leaves=500 !gsutil ls {OUTPUT_INDEX_DIR} from index_server.matching import ScaNNMatcher scann_matcher = ScaNNMatcher(OUTPUT_INDEX_DIR) vector = np.random.rand(50) scann_matcher.match(vector, 5) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Import libraries Step2: Configure GCP environment settings Step3: Authenticate your GCP account Step4: Build the ANN index Step5: Build the index using AI Platform Training Step6: After the AI Platform Training job finishes, check that the scann_index folder has been created in your Cloud Storage bucket Step7: Test the ANN index

<ASSISTANT_TASK:> Python Code: from pymongo import MongoClient import pandas as pd from datetime import datetime client = MongoClient() client = MongoClient('localhost', 27017) db = client.airbnb cursor = db.Rawdata.find() data = pd.DataFrame(list(cursor)) data.head(1) data.columns data = data.drop("listing_url",axis=1) data = data.drop("scrape_id",axis=1) data = data.drop("name",axis=1) data = data.drop("notes",axis=1) data = data.drop("access",axis=1) data = data.drop("thumbnail_url",axis=1) data = data.drop("medium_url",axis=1) data = data.drop("picture_url",axis=1) data = data.drop("xl_picture_url",axis=1) data = data.drop("host_url",axis=1) data = data.drop("host_thumbnail_url",axis=1) data = data.drop("host_picture_url",axis=1) data = data.drop("street",axis=1) data = data.drop("neighbourhood",axis=1) data = data.drop("neighbourhood_cleansed",axis=1) data = data.drop("city",axis=1) data = data.drop("state",axis=1) data = data.drop("zipcode",axis=1) data = data.drop("market",axis=1) data = data.drop("smart_location",axis=1) data = data.drop("country_code",axis=1) data = data.drop("country",axis=1) data = data.drop("is_location_exact",axis=1) data = data.drop("property_type",axis=1) data = data.drop("bed_type",axis=1) data = data.drop("amenities",axis=1) data = data.drop("square_feet",axis=1) data = data.drop("weekly_price",axis=1) data = data.drop("monthly_price",axis=1) data = data.drop("availability_30",axis=1) data = data.drop("availability_60",axis=1) data = data.drop("availability_90",axis=1) data = data.drop("calendar_last_scraped",axis=1) data = data.drop("license",axis=1) data = data.drop("jurisdiction_names",axis=1) data = data.drop("first_review",axis=1) data = data.drop("last_review",axis=1) data = data.drop("Shampoo",axis=1) data = data.drop("nearest_attr_lat",axis=1) data = data.drop("nearest_attr_long",axis=1) data = data.drop("Dryer",axis=1) data = data.drop("Doorman",axis=1) data = data.drop("Essentials",axis=1) #data = data.drop("translation missing: en.hosting_amenity_50",axis=1) data = data.drop("Washer",axis=1) data = data.drop("Washer / Dryer",axis=1) data = data.drop("First aid kit",axis=1) data = data.drop("Smoke detector",axis=1) #data = data.drop("translation missing: en.hosting_amenity_49",axis=1) data = data.drop("Hangers",axis=1) data = data.drop("Fire extinguisher",axis=1) data = data.drop("Iron",axis=1) data = data.drop("Carbon monoxide detector",axis=1) data = data.drop("Wireless Internet",axis=1) data = data.drop("Laptop friendly workspace",axis=1) data = data.drop("Hot tub",axis=1) data = data.drop("Dog(s)",axis=1) data = data.drop("Cat(s)",axis=1) data = data.drop("Buzzer/wireless intercom",axis=1) data = data.drop("Hair dryer",axis=1) data = data.drop("Safety card",axis=1) data = data.drop("last_scraped",axis=1) data = data.drop("house_rules",axis=1) data = data.drop("interaction",axis=1) data = data.drop("transit",axis=1) data = data.drop("neighborhood_overview",axis=1) data = data.drop("experiences_offered",axis=1) data = data.drop("id",axis=1) data = data.drop("summary",axis=1) data = data.drop("space",axis=1) data = data.drop("description",axis=1) data = data.drop("host_id",axis=1) data = data.drop("host_name",axis=1) data = data.drop("host_about",axis=1) data = data.drop("latitude",axis=1) data = data.drop("longitude",axis=1) data = data.drop("host_neighbourhood",axis=1) data = data.drop("host_location",axis=1) data = data.drop("calendar_updated",axis=1) data = data.drop("host_listings_count",axis=1) #data = data.drop("Unnamed: 0",axis=1) data = data.drop("calculated_host_listings_count",axis=1) data = data.drop("host_acceptance_rate",axis=1) data.head(1) data = data.drop("",axis=1) data.dtypes data.host_response_rate.tail() x = pd.to_datetime(data.host_since) x.head() today = '2016-04-01' y = datetime.strptime('2017-04-01', '%Y-%m-%d') x[16] y-x data["host_since_days"] = y-x data = data.drop("host_since",axis=1) data = data.drop("host_response_time",axis=1) data.head(1) test_host_response = data.host_response_rate a = test_host_response.map(lambda x: str(x)[:-1]) for i in range(0,len(a)): print(i) if a[i] == "na": continue if a[i] == "": continue else: a[i] = int(a[i]) a[i] = a[i]/100 data["host_response_rate"] = a test_superhost = data['host_is_superhost'] a = test_superhost.str.replace('t', '1') a = a.str.replace('f', '0') test_superhost.isnull().sum() data['host_is_superhost'] = a data = data.drop("host_verifications",axis=1) data.head() df_dummies1= pd.get_dummies(data, prefix='neighbourhood', columns=['neighbourhood_group_cleansed']) df_dummies2= pd.get_dummies(df_dummies1, prefix='roomtype', columns=['room_type']) test_profilepic = df_dummies2['host_has_profile_pic'] a = test_profilepic.str.replace('t', '1') a = a.str.replace('f', '0') df_dummies2["host_has_profile_pic"] = a test_host_identity_verified = df_dummies2['host_identity_verified'] a = test_host_identity_verified.str.replace('t', '1') a = a.str.replace('f', '0') df_dummies2["host_identity_verified"] = a df_dummies2 = df_dummies2.drop("has_availability",axis=1) df_dummies2 = df_dummies2.drop("requires_license",axis=1) test_instant_bookable = df_dummies2['instant_bookable'] a = test_instant_bookable.str.replace('t', '1') a = a.str.replace('f', '0') df_dummies2['instant_bookable'] = a df_dummies2.head() df_dummies3= pd.get_dummies(df_dummies2, prefix='cancellation_policy', columns=['cancellation_policy']) test_instant_bookable = df_dummies3['require_guest_profile_picture'] a = test_instant_bookable.str.replace('t', '1') a = a.str.replace('f', '0') df_dummies3['require_guest_profile_picture'] = a df_dummies3["require_guest_phone_verification"].head() df_dummies3["require_guest_phone_verification"].isnull().sum() test_phone = df_dummies3['require_guest_phone_verification'] a = test_phone.str.replace('t', '1') a = a.str.replace('f', '0') df_dummies3['require_guest_phone_verification'] = a df_dummies3.head() df_dummies3.nearest_attr_rating.head() df_dummies3.nearest_attr_rating.isnull().sum() len(df_dummies3) data.host_since_days[0] from datetime import timedelta data.host_since_days[0].days data.host_since_days.head() data.host_since_days[1] data['host_since_days'] = data['host_since_days'].apply(lambda x: x.days if pd.isnull(x) == False else 0) #pd.DataFrame.to_csv(df_dummies3, "preprocessed_data.csv") <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Drop columns that are not important Step2: Convert string date to type date, then convert it to number of days since user became a host till date of analysis (4th april 2017) Step3: convert host_response_rate from string to integer. if "na", convert to not a number (NaN) Step4: for i in range(0,len(a)) Step5: a = test_superhost.astype("str") Step6: data['host_is_superhost'] = data['host_is_superhost'].astype("str") Step7: convert columns with string names to dummy variables

<ASSISTANT_TASK:> Python Code: from __future__ import print_function from numpy import random from keras.datasets import mnist # helps in loading the MNIST dataset from keras.models import Sequential from keras.layers import Input, Dense, Dropout, Activation, Flatten from keras.layers import Convolution2D, MaxPooling2D from keras.utils import np_utils from keras import backend as K from keras.models import load_model from keras.utils.np_utils import probas_to_classes import matplotlib.pyplot as plt import numpy as np import matplotlib.pyplot as plt import matplotlib.cm as cm import time import cv2 #to plot inside the notebook itself %matplotlib inline # to be able to reproduce the same randomness random.seed(42) # No of rows and columns in the image img_rows = 28 img_cols = 28 #No of output classes (0-9) nb_classes = 10 (X_train, y_train), (X_test, y_test) = mnist.load_data() # Show the results of the split print("Training set has {} samples.".format(X_train.shape[0])) print("Testing set has {} samples.".format(X_test.shape[0])) print("\n") # Show the number of rows and columns print("Row pixels in each image : {}.".format(X_train.shape[1])) print("Column pixels in each image : {}.".format(X_train.shape[2])) print("\n") print("Successfully Downloaded and Loaded the dataset") # Show the handwritten image plt.imshow(X_train[0], cmap=cm.binary) if K.image_dim_ordering() == 'th': X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols) X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols) input_shape = (1, img_rows, img_cols) else: X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1) X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1) input_shape = (img_rows, img_cols, 1) print("The data is reshaped to the respective format!" , input_shape) X_train = X_train.astype('float32') #converted to float so that it can hold floating values between 0-1 X_test = X_test.astype('float32') #converted to float so that it can hold floating values between 0-1 X_train /= 255 X_test /= 255 print("In Integer form : ", y_train,y_test) Y_train = np_utils.to_categorical(y_train, nb_classes) #converted to their binary forms Y_test = np_utils.to_categorical(y_test, nb_classes) #converted to their binary forms print("In Binary form : ", Y_train,Y_test) print("Preprocessing of Data is Done Successfully...") pool_size = (2, 2) kernel_size = (3, 3) model = Sequential() model.add(Convolution2D(32, kernel_size[0], kernel_size[1], border_mode='valid', input_shape=input_shape)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=pool_size)) model.add(Flatten()) model.add(Dense(128)) model.add(Activation('relu')) model.add(Dense(nb_classes)) model.add(Activation('softmax')) print("Successfully built the DNN Model!") model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'] ) print("Model Compilation completed!") from IPython.display import SVG from keras.utils.visualize_util import model_to_dot SVG(model_to_dot(model).create(prog='dot', format='svg')) batch_size = 128 nb_epoch=10 start = time.time() model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1,validation_data=(X_test, Y_test)) done = time.time() elapsed = (done - start)/60 print("Model trained Successfully : Took - {} mins!".format(elapsed)) score = model.evaluate(X_test, Y_test, verbose=0) print('Test Loss Value:', score[0]) print('Test Accuracy Value:', score[1]) pool_size = (2, 2) kernel_size = (3, 3) rmodel = Sequential() rmodel.add(Convolution2D(32, kernel_size[0], kernel_size[1], border_mode='valid', input_shape=input_shape)) rmodel.add(Activation('relu')) rmodel.add(Convolution2D(64, kernel_size[0], kernel_size[1])) rmodel.add(Activation('relu')) rmodel.add(MaxPooling2D(pool_size=pool_size)) rmodel.add(Dropout(0.25)) rmodel.add(Flatten()) rmodel.add(Dense(128)) rmodel.add(Activation('relu')) rmodel.add(Dropout(0.5)) rmodel.add(Dense(nb_classes)) rmodel.add(Activation('softmax')) print("Successfully built the Refined DNN Model!") rmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) print("Refined Model Compilation completed!") from IPython.display import SVG from keras.utils.visualize_util import model_to_dot SVG(model_to_dot(rmodel).create(prog='dot', format='svg')) batch_size = 128 nb_epoch=10 start = time.time() rmodel.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, validation_data=(X_test, Y_test)) done = time.time() elapsed = (done - start)/60 print("Refined Model trained Successfully : Took - {} mins!".format(elapsed)) score = rmodel.evaluate(X_test, Y_test, verbose=0) print('Test Loss Value:', score[0]) print('Test Accuracy Value:', score[1]) import os, os.path imgs = [] path = "/home/joel/PROJECTS/Udacity-MLND/Udacity-MLND-Capstone-Handwritting-Digit-Recognition/images" count=0 for f in os.listdir(path): imgs.append(cv2.imread(os.path.join(path,f))) count+=1 print("Successfully loaded {} images".format(count)) X_pred = [] for img in imgs: # Convert the color image to rgb gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) # Invert black and white color(since in opencv white is considered 255 and black 0 but we need vice versa in order to match with the dataset) invert_gray = (255-gray) # Resize the image to 28,28 pixels as per the mnist dataset format resized = cv2.resize(invert_gray, (28, 28)) # Convert the image format from (28,28) to (28,28,1) in order for the model to recognize resized = np.asarray(resized) resized.shape+=1, #scale the color channel from 0-255 to 0-1 resized/=255 X_pred.append(resized) X_pred = np.asarray(X_pred) print(X_pred.shape) # Predict the output proba = rmodel.predict(X_pred) # Convert the predicted output to respective integer number answers = probas_to_classes(proba) #plot the image and the predicted number i=0 for img in imgs: plt.figure() plt.imshow(img, cmap=cm.binary) plt.suptitle("The predicted digit is : " + str(answers[i])) i+=1 <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Load the Dataset Step2: Visualize an Image sample Step3: Preprocess the Data Step4: The MNIST dataset contains grayscale images where the color channel value varies from 0 to 255. In order to reduce the computational load and training difficulty, we will map the values from 0 - 255 to 0 - 1 by dividing each pixel values by 255. Run the below code to do this. Step5: The target labels y_train,y_test are in the form of numerical integers(0-9), we need to convert them to binary form in order for the neural network to perform mapping from input to output correctly and efficiently. Run the below code to do this. Step6: Implementing the Deep Neural Network (DNN) Step7: Compile the Model Step8: Visualize the Model Step9: Train the DNN Model Step10: In the neural network terminology Step11: Refinement of the Deep Neural Network(DNN) Step12: Compile the Refined Model Step13: Visualize the Refined Model Step14: Train the Refined DNN Model Step15: Evaluate the Refined DNN Model Step16: Test the Refined Model with Real Data Step17: Preprocess the Loaded Image Step18: Predict the digit in the Images

<ASSISTANT_TASK:> Python Code: import spvcm.api as spvcm #package API spvcm.both.Generic # abstract customizable class, ignores rho/lambda, equivalent to MVCM spvcm.both.MVCM # no spatial effect spvcm.both.SESE # both spatial error (SE) spvcm.both.SESMA # response-level SE, region-level spatial moving average spvcm.both.SMASE # response-level SMA, region-level SE spvcm.both.SMASMA # both levels SMA spvcm.upper.SE # response-level uncorrelated, region-level SE spvcm.upper.SMA # response-level uncorrelated, region-level SMA spvcm.lower.SE # response-level SE, region-level uncorrelated spvcm.lower.SMA # response-level SMA, region-level uncorrelated #seaborn is required for the traceplots import pysal as ps import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import geopandas as gpd %matplotlib inline data = ps.pdio.read_files(ps.examples.get_path('south.shp')) gdf = gpd.read_file(ps.examples.get_path('south.shp')) data = data[data.STATE_NAME != 'District of Columbia'] X = data[['UE90', 'PS90', 'RD90']].values N = X.shape[0] Z = data.groupby('STATE_NAME')[['FP89', 'GI89']].mean().values J = Z.shape[0] Y = data.HR90.values.reshape(-1,1) W2 = ps.queen_from_shapefile(ps.examples.get_path('us48.shp'), idVariable='STATE_NAME') W2 = ps.w_subset(W2, ids=data.STATE_NAME.unique().tolist()) #only keep what's in the data W1 = ps.queen_from_shapefile(ps.examples.get_path('south.shp'), idVariable='FIPS') W1 = ps.w_subset(W1, ids=data.FIPS.tolist()) #again, only keep what's in the data W1.transform = 'r' W2.transform = 'r' membership = data.STATE_NAME.apply(lambda x: W2.id_order.index(x)).values Delta_frame = pd.get_dummies(data.STATE_NAME) Delta = Delta_frame.values vcsma = spvcm.upper.SMA(Y, X, M=W2, Z=Z, membership=membership, n_samples=5000, configs=dict(tuning=1000, adapt_step=1.01)) vcsma.trace.varnames vcsma.trace.varnames trace_dataframe = vcsma.trace.to_df() trace_dataframe.head() trace_dataframe.mean() fig, ax = vcsma.trace.plot() plt.show() vcsma.trace['Lambda',-4:] #last 4 draws of lambda vcsma.trace[['Tau2', 'Sigma2'], 0:2] #the first 2 variance parameters vcsma_p = spvcm.upper.SMA(Y, X, M=W2, Z=Z, membership=membership, n_samples=5000, n_jobs=3, #run 3 chains configs=dict(tuning=500, adapt_step=1.01)) vcsma_p.trace[0, 'Betas', -1] #the last draw of Beta on the first chain. vcsma_p.trace[1, 'Betas', -1] #the last draw of Beta on the second chain vcsma_p.trace.plot(burn=1000, thin=10) plt.suptitle('SMA of Homicide Rate in Southern US Counties', y=0, fontsize=20) #plt.savefig('trace.png') #saves to a file called "trace.png" plt.show() vcsma_p.trace.plot(burn=-100, varnames='Lambda') #A negative burn-in works like negative indexing in Python & R plt.suptitle('First 100 iterations of $\lambda$', fontsize=20, y=.02) plt.show() #so this plots Lambda in the first 100 iterations. df = vcsma.trace.to_df() df.describe() vcsma.trace.summarize() from statsmodels.api import tsa #if you don't have it, try removing the comment and: #! pip install statsmodels plt.plot(tsa.pacf(vcsma.trace['Lambda', -2500:])) tsa.pacf(df.Lambda)[0:3] betas = [c for c in df.columns if c.startswith('Beta')] f,ax = plt.subplots(len(betas), 2, figsize=(10,8)) for i, col in enumerate(betas): ax[i,0].plot(tsa.acf(df[col].values)) ax[i,1].plot(tsa.pacf(df[col].values)) #the pacf plots take a while ax[i,0].set_title(col +' (ACF)') ax[i,1].set_title('(PACF)') f.tight_layout() plt.show() gstats = spvcm.diagnostics.geweke(vcsma, varnames='Tau2') #takes a while print(gstats) plt.plot(gstats[0]['Tau2'][:-1]) spvcm.diagnostics.mcse(vcsma, varnames=['Tau2', 'Sigma2']) spvcm.diagnostics.psrf(vcsma_p, varnames=['Tau2', 'Sigma2']) spvcm.diagnostics.hpd_interval(vcsma, varnames=['Betas', 'Lambda', 'Sigma2']) vcsma.trace.map(np.percentile, varnames=['Lambda', 'Tau2', 'Sigma2'], #arguments to pass to the function go last q=[25, 50, 75]) vcsma.trace.to_csv('./model_run.csv') tr = spvcm.Trace.from_csv('./model_run.csv') print(tr.varnames) tr.plot(varnames=['Tau2']) vcsma.draw() vcsma.sample(10) vcsma.cycles vcsma_p.sample(10) vcsma_p.cycles print(vcsma.state.keys()) example = spvcm.upper.SMA(Y, X, M=W2, Z=Z, membership=membership, n_samples=250, extra_traced_params = ['DeltaAlphas'], configs=dict(tuning=500, adapt_step=1.01)) example.trace.varnames vcsma.configs vcsma.configs.Lambda.accepted vcsma.configs.Lambda.accepted / float(vcsma.cycles) example = spvcm.upper.SMA(Y, X, M=W2, Z=Z, membership=membership, n_samples=500, configs=dict(tuning=250, adapt_step=1.01, debug=True)) example.configs.Lambda._cache[-1] #let's only look at the last one from spvcm.steps import Metropolis, Slice example = spvcm.upper.SMA(Y, X, M=W2, Z=Z, membership=membership, n_samples=500, configs=dict(tuning=250, adapt_step=1.01, debug=True, ar_low=.1, ar_hi=.4)) example.configs.Lambda.ar_hi, example.configs.Lambda.ar_low example_slicer = spvcm.upper.SMA(Y, X, M=W2, Z=Z, membership=membership, n_samples=500, configs=dict(Lambda_method='slice')) example_slicer.trace.plot(varnames='Lambda') plt.show() example_slicer.configs.Lambda.adapt, example_slicer.configs.Lambda.width vcsese = spvcm.both.SESE(Y, X, W=W1, M=W2, Z=Z, membership=membership, n_samples=0) vcsese.configs vcsese.configs.Lambda.max_tuning = 0 vcsese.configs.Lambda.jump = .25 Delta = vcsese.state.Delta DeltaZ = Delta.dot(Z) vcsese.state.Betas_mean0 = ps.spreg.OLS(Y, np.hstack((X, DeltaZ))).betas vcsese.state.Lambda = -.25 vcsese.state.Betas += np.random.uniform(-10, 10, size=(vcsese.state.p,1)) from scipy import stats def Lambda_prior(val): if (val < 0) or (val > 1): return -np.inf return np.log(stats.beta.pdf(val, 2,1)) def Rho_prior(val): if (val > .5) or (val < -.5): return -np.inf return np.log(stats.truncnorm.pdf(val, -.5, .5, loc=0, scale=.5)) vcsese.state.LogLambda0 = Lambda_prior vcsese.state.LogRho0 = Rho_prior %timeit vcsese.draw() %time vcsese.sample(100) vcsese.sample(10) vcsese.state.Psi_1 #lower-level covariance vcsese.state.Psi_2 #upper-level covariance vcsma.state.Psi_2 #upper-level covariance vcsma.state.Psi_2i vcsma.state.Psi_1 <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Depending on the structure of the model, you need at least Step2: Reading in the data, we'll extract these values we need from the dataframe. Step3: Then, we'll construct some queen contiguity weights from the files to show how to run a model. Step4: With the data, upper-level weights, and lower-level weights, we can construct a membership vector or a dummy data matrix. For now, I'll create the membership vector. Step5: But, we could also build the dummy variable matrix using pandas, if we have a suitable categorical variable Step6: Every call to the sampler is of the following form Step7: This models, spvcm.upper.SMA, is a variance components/varying intercept model with a state-level SMA-correlated error. Step8: The results and state of the sampler are stored within the vcsma object. I'll step through the most important parts of this object. Step9: In this case, Lambda is the upper-level moving average parameter, Alphas is the vector of correlated group-level random effects, Tau2 is the upper-level variance, Betas are the marginal effects, and Sigma2 is the lower-level error variance. Step10: the dataframe will have columns containing the elements of the parameters and each row is a single iteration of the sampler Step11: You can write this out to a csv or analyze it in memory like a typical pandas dataframes Step12: The second is a method to plot the traces Step13: The trace object can be sliced by (chain, parameter, index) tuples, or any subset thereof. Step14: We only ran a single chain, so the first index is assumed to be zero. You can run more than one chain in parallel, using the builtin python multiprocessing library Step15: and the chain plotting works also for the multi-chain traces. In addition, there are quite a few traceplot options, and all the plots are returned by the methods as matplotlib objects, so they can also be saved using plt.savefig(). Step16: To get stuff like posterior quantiles, you can use the attendant pandas dataframe functionality, like describe. Step17: There is also a trace.summarize function that will compute various things contained in spvcm.diagnostics on the chain. It takes a while for large chains, because the statsmodels.tsa.AR estimator is much slower than the ar estimator in R. If you have rpy2 installed and CODA installed in your R environment, I attempt to use R directly. Step18: So, 5000 iterations, but many parameters have an effective sample size that's much less than this. There's debate about whether it's necesasry to thin these samples in accordance with the effective size, and I think you should thin your sample to the effective size and see if it affects your HPD/Standard Errorrs. Step19: For example, a plot of the partial autocorrelation in $\lambda$, the upper-level spatial moving average parameter, over the last half of the chain is Step20: So, the chain is close-to-first order Step21: We could do this for many parameters, too. An Autocorrelation/Partial Autocorrelation plot can be made of the marginal effects by Step22: As far as the builtin diagnostics for convergence and simulation quality, the diagnostics module exposes a few things Step23: Typically, this means the chain is converged at the given "bin" count if the line stays within $\pm2$. The geweke statistic is a test of differences in means between the given chunk of the chain and the remaining chain. If it's outside of +/- 2 in the early part of the chain, you should discard observations early in the chain. If you get extreme values of these statistics throughout, you need to keep running the chain. Step24: We can also compute Monte Carlo Standard Errors like in the mcse R package, which represent the intrinsic error contained in the estimate Step25: Another handy statistic is the Partial Scale Reduction factor, which measures of how likely a set of chains run in parallel have converged to the same stationary distribution. It provides the difference in variance between between chains vs. within chains. Step26: Highest posterior density intervals provide a kind of interval estimate for parameters in Bayesian models Step27: Sometimes, you want to apply arbitrary functions to each parameter trace. To do this, I've written a map function that works like the python builtin map. For example, if you wanted to get arbitrary percentiles from the chain Step28: In addition, you can pop the trace results pretty simply to a .csv file and analyze it elsewhere, like if you want to use use the coda Bayesian Diagnostics package in R. Step29: And, you can even load traces from csvs Step30: Working with models Step31: And sample steps forward an arbitrary number of times Step32: At this point, we did 5000 initial samples and 11 extra samples. Thus Step33: Parallel models can suspend/resume sampling too Step34: Under the hood, it's the draw method that actually ends up calling one run of model._iteration, which is where the actual statistical code lives. Then, it updates all model.traced_params by adding their current value in model.state to model.trace. In addition, model._finalize is called the first time sampling is run, which computes some of the constants & derived quantities that save computing time. Step35: If you want to track how something (maybe a hyperparameter) changes over sampling, you can pass extra_traced_params to the model declaration Step36: configs Step37: Since vcsma is an upper-level-only model, the Rho config is skipped. But, we can look at the Lambda config. The number of accepted lambda draws is contained in Step38: so, the acceptance rate is Step39: Also, if you want to get verbose output from the metropolis sampler, there is a "debug" flag Step40: Which stores the information about each iteration in a list, accessible from model.configs.&lt;parameter&gt;._cache Step41: Configuration of the MCMC steps is done using the config options dictionary, like done in spBayes in R. The actual configuration classes exist in spvcm.steps Step42: Most of the common options are Step43: Working with models Step44: This sets up a two-level spatial error model with the default uninformative configuration. This means the prior precisions are all I * .001*, prior means are all 0, spatial parameters are set to -1/(n-1), and prior scale factors are set arbitrarily. Step45: So, for example, if we wanted to turn off adaptation in the upper-level parameter, and fix the Metrpolis jump variance to .25 Step46: Priors Step47: Starting Values Step48: Sometimes, it's suggested that you start the beta vector randomly, rather than at zero. For the parallel sampling, the model starting values are adjusted to induce overdispersion in the start values. Step49: Spatial Priors Step50: And then assigning to their symbols, LogLambda0 and LogRho0 in the state Step51: Performance Step52: To make it easy to work with the model, you can interrupt and resume sampling using keyboard interrupts (ctrl-c or the stop button in the notebook). Step53: Under the Hood

<ASSISTANT_TASK:> Python Code: import pandas as pd from pandas import Series, DataFrame series_1 = Series([-2, -1, 0, 1, 2, 3, 4, 5]) series_1 series_1.values series_1.index series_2 = Series([1, 2, 3], index=['a', 'b', 'c']) series_2 series_2.index series_2['a'] series_2[['a', 'b']] series_2[series_2 > 1] series_2 * 2 numbers_1 = {'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5} series_3 = Series(numbers_1) series_3 numbers_2_index = ['a', 'b', 'c', 'd'] numbers_2 = {'a': 1, 'b': 2, 'c': 3} series_4 = Series(numbers_2, index=numbers_2_index) series_4 pd.isnull(series_4) # same as series_4.isnull() pd.notnull(series_4) series_3 + series_4 series_4.name = 'numbers' series_4.index.name = 'letter' series_4 series_4.index = ['1', '2', '3', 'x'] # update index series_4 data_1 = { 'pet': ['Toffee', 'Candy', 'Cake', 'Sussy'], 'age': [3, 1, 2, 4], } frame_1 = DataFrame(data_1) frame_1 frame_1['age'] # dict-like notation frame_1.pet # attribute frame_2 = DataFrame(data_1, columns=['pet', 'age', 'toy'], index=['one', 'two', 'three', 'four']) frame_2 frame_2.ix['four'] # row frame_2.toy = 'bone' frame_2 frame_2.toy = Series(['bone', None, 'bone', 'bone'], index=['one', 'two', 'three', 'four']) frame_2 frame_2['likes_bone_toy'] = frame_2.toy == 'bone' frame_2 frame_2.T # transpose frame_2.columns.name = 'Number' frame_2.index.name = 'Pet' frame_2 frame_2.values frame_2.index 'one' in frame_2.index frame_2.reindex(['four', 'three', 'two', 'one'], fill_value=0) fill_numbers = Series(['blue', 'yellow', 'green'], index=[0, 4, 8]) fill_numbers.reindex(range(12), method='ffill') <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Using a dict for Series Step2: DataFrame Step3: Interpolation / filling of values when reindex (Series)

<ASSISTANT_TASK:> Python Code: # HTTP Client for Python import requests # Cytoscape port number PORT_NUMBER = 1234 BASE_URL = "https://raw.githubusercontent.com/ls-cwi/eXamine/master/data/" # The Base path for the CyRest API BASE = 'http://localhost:' + str(PORT_NUMBER) + '/v1/' #Helper command to call a command via HTTP POST def executeRestCommand(namespace="", command="", args={}): postString = BASE + "commands/" + namespace + "/" + command res = requests.post(postString,json=args) return res # First we import our demo network executeRestCommand("network", "import url", {"indexColumnSourceInteraction":"1", "indexColumnTargetInteraction":"2", "url": BASE_URL + "edges_karate.gml"}) # Next we import node annotations executeRestCommand("table", "import url", {"firstRowAsColumnNames":"true", "keyColumnIndex" : "1", "startLoadRow" : "1", "dataTypeList":"s,sl", "url": BASE_URL + "nodes_karate.txt"}) executeRestCommand("network", "select", {"nodeList" : "all"}) executeRestCommand("examine", "generate groups", {"selectedGroupColumns" : "Community"}) # Adjust the visualization settings executeRestCommand("examine", "update settings", {"labelColumn" : "label", "URL" : "label", "showScore" : "false", "selectedGroupColumns" : "Community"}) # Select groups for demarcation in the visualization executeRestCommand("examine", "select groups", {"selectedGroups":"A,B,C,D,E,F"}) # Launch the interactive eXamine visualization executeRestCommand("examine", "interact", {}) # Export a graphic instead of interacting with it # use absolute path; writes in Cytoscape directory if not changed executeRestCommand("examine", "export", {"path": "test.svg"}) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Importing network and node-specific annotation Step2: We then import node-specific annotation directly from the eXamine repository on github. The imported file contains set membership information for each node. Note that it is important to ensure that set-membership information is imported as List of String, as indicated by sl. Additionaly, note that the default list separator is a pipe character. Step3: Set-based visualization using eXamine Step4: We then select six groups. Step5: There are two options Step6: The command below launches the eXamine window. If this window is blank, simply resize the window to force a redraw of the scene.

<ASSISTANT_TASK:> Python Code: %load_ext autoreload %autoreload 2 import sys import warnings warnings.filterwarnings("ignore") import torch import numpy as np from tqdm import tqdm_notebook, tqdm sys.path.append('../..') from batchflow import Notifier, Pipeline, Dataset, I, W, V, L, B from batchflow.monitor import * # Set GPU %env CUDA_VISIBLE_DEVICES=0 DEVICE = torch.device('cuda:0') torch.ones((1, 1, 1), device=DEVICE) BAR = 't' # can be changed to 'n' to use Jupyter Notebook progress bar for item in Notifier(BAR)(range(5)): print(item) %time for item in Notifier('t')(range(100000)): pass %time for item in tqdm(range(100000)): pass %time for item in Notifier('n')(range(100000)): pass %time for item in tqdm_notebook(range(100000)): pass with monitor_cpu(frequency=0.1) as cpu_monitor: for _ in Notifier(BAR)(range(10)): _ = np.random.random((1000, 10000)) cpu_monitor.visualize() with monitor_resource(['uss', 'gpu', 'gpu_memory'], frequency=0.1) as (uss_monitor, gpu_monitor, gpum_monitor): for _ in Notifier(BAR)(range(42)): cpu_data = np.random.random((1000, 10000)) gpu_data = torch.ones((256, 512, 2096), device=DEVICE) gpu_op = torch.mvlgamma(torch.erfinv(gpu_data), 1) # intense operation torch.cuda.empty_cache() uss_monitor.visualize() gpu_monitor.visualize() gpum_monitor.visualize() notifier = Notifier(BAR, monitors=['memory', 'cpu']) for _ in notifier(range(100)): _ = np.random.random((1000, 100)) notifier.visualize() pipeline = ( Pipeline() .init_variable('loss_history', []) .init_variable('image') .update(V('loss_history', mode='a'), 100 * 2 ** (-I())) .update(V('image'), L(np.random.random)((30, 30))) ) << Dataset(10) pipeline.reset('all') _ = pipeline.run(1, n_iters=10, notifier=BAR) pipeline.reset('all') _ = pipeline.run(1, n_iters=10, notifier=Notifier(BAR, monitors='loss_history')) pipeline.notifier.visualize() pipeline.reset('all') _ = pipeline.run(1, n_iters=50, notifier=Notifier(BAR, monitors=['cpu', 'loss_history'], file='notifications.txt')) pipeline.notifier.visualize() !head notifications.txt -n 13 pipeline.reset('all') _ = pipeline.run(1, n_iters=10, notifier=Notifier('n', graphs=['memory', 'loss_history'])) pipeline.reset('all') _ = pipeline.run(1, n_iters=100, notifier=Notifier('n', graphs=['memory', 'loss_history', 'image'], frequency=10)) def custom_plotter(ax=None, container=None, **kwargs): Zero-out center area of the image, change plot parameters. container['data'][10:20, 10:20] = 0 ax.imshow(container['data']) ax.set_title(container['name'], fontsize=18) ax.set_xlabel('axis one', fontsize=18) ax.set_ylabel('axis two', fontsize=18) pipeline.reset('all') _ = pipeline.run(1, n_iters=100, notifier=Notifier('n', graphs=[{'source': 'memory', 'name': 'my custom monitor'}, {'source': 'image', 'name': 'amazing plot', 'plot_function': custom_plotter}], frequency=10) ) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Notifier Step2: As some of the loops are running hundreds of iterations per second, we should take special care of speed of updating Step3: Monitors Step4: Under the hood Monitor creates a separate process, that checks the state of a resource every frequency seconds and can fetch collected data on demand. Step5: This feature is immensely helpful during both research and deploy stages, so we included it in the Notifier itself Step6: Pipeline Step7: Vanilla pipeline Step8: Track pipeline variables Step9: Obviously, we can use the same resource monitors, as before, by passing additional items to monitors. There is also file argument, that allows us to log the progress to an external storage Step10: Live plots Step11: It can work with images also. As the rendering of plots might take some time, we want to do so once every 10 iterations and achieve so by using frequency parameter Step13: Advanced usage of Notifier

<ASSISTANT_TASK:> Python Code: import pandas as pd r_cols = ['user_id', 'movie_id', 'rating'] ratings = pd.read_csv('e:/sundog-consult/udemy/datascience/ml-100k/u.data', sep='\t', names=r_cols, usecols=range(3)) ratings.head() import numpy as np movieProperties = ratings.groupby('movie_id').agg({'rating': [np.size, np.mean]}) movieProperties.head() movieNumRatings = pd.DataFrame(movieProperties['rating']['size']) movieNormalizedNumRatings = movieNumRatings.apply(lambda x: (x - np.min(x)) / (np.max(x) - np.min(x))) movieNormalizedNumRatings.head() movieDict = {} with open(r'e:/sundog-consult/udemy/datascience/ml-100k/u.item') as f: temp = '' for line in f: fields = line.rstrip('\n').split('|') movieID = int(fields[0]) name = fields[1] genres = fields[5:25] genres = map(int, genres) movieDict[movieID] = (name, genres, movieNormalizedNumRatings.loc[movieID].get('size'), movieProperties.loc[movieID].rating.get('mean')) movieDict[1] from scipy import spatial def ComputeDistance(a, b): genresA = a[1] genresB = b[1] genreDistance = spatial.distance.cosine(genresA, genresB) popularityA = a[2] popularityB = b[2] popularityDistance = abs(popularityA - popularityB) return genreDistance + popularityDistance ComputeDistance(movieDict[2], movieDict[4]) print movieDict[2] print movieDict[4] import operator def getNeighbors(movieID, K): distances = [] for movie in movieDict: if (movie != movieID): dist = ComputeDistance(movieDict[movieID], movieDict[movie]) distances.append((movie, dist)) distances.sort(key=operator.itemgetter(1)) neighbors = [] for x in range(K): neighbors.append(distances[x][0]) return neighbors K = 10 avgRating = 0 neighbors = getNeighbors(1, K) for neighbor in neighbors: avgRating += movieDict[neighbor][3] print movieDict[neighbor][0] + " " + str(movieDict[neighbor][3]) avgRating /= float(K) avgRating movieDict[1] <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Now, we'll group everything by movie ID, and compute the total number of ratings (each movie's popularity) and the average rating for every movie Step2: The raw number of ratings isn't very useful for computing distances between movies, so we'll create a new DataFrame that contains the normalized number of ratings. So, a value of 0 means nobody rated it, and a value of 1 will mean it's the most popular movie there is. Step3: Now, let's get the genre information from the u.item file. The way this works is there are 19 fields, each corresponding to a specific genre - a value of '0' means it is not in that genre, and '1' means it is in that genre. A movie may have more than one genre associated with it. Step4: For example, here's the record we end up with for movie ID 1, "Toy Story" Step5: Now let's define a function that computes the "distance" between two movies based on how similar their genres are, and how similar their popularity is. Just to make sure it works, we'll compute the distance between movie ID's 2 and 4 Step6: Remember the higher the distance, the less similar the movies are. Let's check what movies 2 and 4 actually are - and confirm they're not really all that similar Step7: Now, we just need a little code to compute the distance between some given test movie (Toy Story, in this example) and all of the movies in our data set. When the sort those by distance, and print out the K nearest neighbors Step8: While we were at it, we computed the average rating of the 10 nearest neighbors to Toy Story Step9: How does this compare to Toy Story's actual average rating?

<ASSISTANT_TASK:> Python Code: import pandas as pd import numpy as np import tensorflow as tf import tflearn from tflearn.data_utils import to_categorical reviews = pd.read_csv('reviews.txt', header=None) labels = pd.read_csv('labels.txt', header=None) from collections import Counter total_counts = Counter() for index,review in reviews.iterrows(): for word in review[0].split(' '): total_counts[word] += 1 print("Total words in data set: ", len(total_counts)) vocab = sorted(total_counts, key=total_counts.get, reverse=True)[:10000] print(vocab[:60]) print(vocab[-1], ': ', total_counts[vocab[-1]]) word2idx = {key:idx for (idx,key) in enumerate(vocab)} ## create the word-to-index dictionary def text_to_vector(text): word_vector = np.zeros(len(word2idx),dtype=int) for word in text.split(' '): if word2idx.get(word,None) != None: word_vector[word2idx[word]] += 1 return word_vector text_to_vector('The tea is for a party to celebrate ' 'the movie so she has no time for a cake')[:65] word_vectors = np.zeros((len(reviews), len(vocab)), dtype=np.int_) for ii, (_, text) in enumerate(reviews.iterrows()): word_vectors[ii] = text_to_vector(text[0]) # Printing out the first 5 word vectors word_vectors[:5, :23] Y = (labels=='positive').astype(np.int_) records = len(labels) shuffle = np.arange(records) np.random.shuffle(shuffle) test_fraction = 0.9 train_split, test_split = shuffle[:int(records*test_fraction)], shuffle[int(records*test_fraction):] trainX, trainY = word_vectors[train_split,:], to_categorical(Y.values[train_split], 2) testX, testY = word_vectors[test_split,:], to_categorical(Y.values[test_split], 2) trainY # Network building def build_model(): # This resets all parameters and variables, leave this here tf.reset_default_graph() # Start the network graph #Input net = tflearn.input_data([None, 10000]) #Hidden layers 250, 10 net = tflearn.fully_connected(net, 400, activation='ReLU') net = tflearn.fully_connected(net, 10, activation='ReLU') #output net = tflearn.fully_connected(net, 2, activation='softmax') #Training specifications net = tflearn.regression(net, optimizer='sgd', learning_rate=0.1, loss='categorical_crossentropy') # End of network graph model = tflearn.DNN(net) return model model = build_model() # Training model.fit(trainX, trainY, validation_set=0.1, show_metric=True, batch_size=128, n_epoch=50) predictions = (np.array(model.predict(testX))[:,0] >= 0.5).astype(np.int_) test_accuracy = np.mean(predictions == testY[:,0], axis=0) print("Test accuracy: ", test_accuracy) # Helper function that uses your model to predict sentiment def test_sentence(sentence): positive_prob = model.predict([text_to_vector(sentence.lower())])[0][1] print('Sentence: {}'.format(sentence)) print('P(positive) = {:.3f} :'.format(positive_prob), 'Positive' if positive_prob > 0.5 else 'Negative') sentence = "Moonlight is by far the best movie of 2016." test_sentence(sentence) sentence = "It's amazing anyone could be talented enough to make something this spectacularly awful" test_sentence(sentence) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Preparing the data Step2: Counting word frequency Step3: Let's keep the first 10000 most frequent words. As Andrew noted, most of the words in the vocabulary are rarely used so they will have little effect on our predictions. Below, we'll sort vocab by the count value and keep the 10000 most frequent words. Step4: What's the last word in our vocabulary? We can use this to judge if 10000 is too few. If the last word is pretty common, we probably need to keep more words. Step5: The last word in our vocabulary shows up in 30 reviews out of 25000. I think it's fair to say this is a tiny proportion of reviews. We are probably fine with this number of words. Step6: Text to vector function Step7: If you do this right, the following code should return Step8: Now, run through our entire review data set and convert each review to a word vector. Step9: Train, Validation, Test sets Step10: Building the network Step11: Intializing the model Step12: Training the network Step13: Testing Step14: Try out your own text!

<ASSISTANT_TASK:> Python Code: from oemof.solph import EnergySystem import pandas as pd # initialize energy system energysystem = EnergySystem(timeindex=pd.date_range('1/1/2016', periods=168, freq='H')) # import example data with scaled demands and feedin timeseries of renewables # as dataframe data = pd.read_csv("data/example_data.csv", sep=",") #print(data.demand_el[0:10]) #print(data.keys()) from oemof.solph import Bus, Flow, Sink, Source, LinearTransformer ### BUS # create electricity bus b_el = Bus(label="b_el") # add excess sink to help avoid infeasible problems Sink(label="excess_el", inputs={b_el: Flow()}) Source(label="shortage_el", outputs={b_el: Flow(variable_costs=1000)}) ### DEMAND # add electricity demand Sink(label="demand_el", inputs={b_el: Flow(nominal_value=85, actual_value=data['demand_el'], fixed=True)}) ### SUPPLY # add wind and pv feedin Source(label="wind", outputs={b_el: Flow(actual_value=data['wind'], nominal_value=60, fixed=True)}); Source(label="pv", outputs={b_el: Flow(actual_value=data['pv'], nominal_value=200, fixed=True)}); from oemof.solph import OperationalModel import oemof.outputlib import matplotlib.pyplot as plt def optimize(energysystem): ### optimize # create operational model om = OperationalModel(es=energysystem) # solve using the cbc solver om.solve(solver='cbc', solve_kwargs={'tee': False}) # save LP-file om.write('sector_coupling.lp', io_options={'symbolic_solver_labels': True}) # write back results from optimization object to energysystem om.results(); def plot(energysystem, bus_label, bus_type): # define colors cdict = {'wind': '#00bfff', 'pv': '#ffd700', 'pp_gas': '#8b1a1a', 'pp_chp_extraction': '#838b8b', 'excess_el': '#8b7355', 'shortage_el': '#000000', 'heater_rod': 'darkblue', 'pp_chp': 'green', 'demand_el': 'lightgreen', 'demand_th': '#ce4aff', 'heat_pump': 'red', 'leaving_bev': 'darkred', 'bev_storage': 'orange'} # create multiindex dataframe with result values esplot = oemof.outputlib.DataFramePlot(energy_system=energysystem) # select input results of electrical bus (i.e. power delivered by plants) esplot.slice_unstacked(bus_label=bus_label, type=bus_type, date_from='2016-01-03 00:00:00', date_to='2016-01-06 00:00:00') # set colorlist for esplot colorlist = esplot.color_from_dict(cdict) # set plot attributes esplot.plot(color=colorlist, title="January 2016", stacked=True, width=1, kind='bar') esplot.ax.set_ylabel('Power') esplot.ax.set_xlabel('Date') esplot.set_datetime_ticks(tick_distance=24, date_format='%d-%m') esplot.outside_legend(reverse=True) plt.show() optimize(energysystem) plot(energysystem, "b_el", "to_bus") # add gas bus b_gas = Bus(label="b_gas", balanced=False) # add gas power plant LinearTransformer(label="pp_gas", inputs={b_gas: Flow(summed_max_flow=200)}, outputs={b_el: Flow(nominal_value=40, variable_costs=40)}, conversion_factors={b_el: 0.50}); optimize(energysystem) plot(energysystem, "b_el", "to_bus") # add heat bus b_heat = Bus(label="b_heat", balanced=True) # add heat demand Sink(label="demand_th", inputs={b_heat: Flow(nominal_value=60, actual_value=data['demand_th'], fixed=True)}) # add heater rod LinearTransformer(label="heater_rod", inputs={b_el: Flow()}, outputs={b_heat: Flow(variable_costs=10)}, conversion_factors={b_heat: 0.98}); optimize(energysystem) plot(energysystem, "b_heat", "to_bus") # COP can be calculated beforehand, assuming the heat reservoir temperature # is infinite random timeseries for COP import numpy as np COP = np.random.uniform(low=3.0, high=5.0, size=(168,)) # add heater rod #LinearTransformer(label="heater_rod", # inputs={b_el: Flow()}, # outputs={b_heat: Flow(variable_costs=10)}, # conversion_factors={b_heat: 0.98}); # add heat pump LinearTransformer(label="heat_pump", inputs={b_el: Flow()}, outputs={b_heat: Flow(nominal_value=20, variable_costs=10)}, conversion_factors={b_heat: COP}); optimize(energysystem) plot(energysystem, "b_heat", "to_bus") # add CHP with fixed ratio of heat and power (back-pressure turbine) LinearTransformer(label='pp_chp', inputs={b_gas: Flow()}, outputs={b_el: Flow(nominal_value=30, variable_costs=42), b_heat: Flow(nominal_value=40)}, conversion_factors={b_el: 0.3, b_heat: 0.4}); from oemof.solph import VariableFractionTransformer # add CHP with variable ratio of heat and power (extraction turbine) VariableFractionTransformer(label='pp_chp_extraction', inputs={b_gas: Flow()}, outputs={b_el: Flow(nominal_value=30, variable_costs=42), b_heat: Flow(nominal_value=40)}, conversion_factors={b_el: 0.3, b_heat: 0.4}, conversion_factor_single_flow={b_el: 0.5}); optimize(energysystem) plot(energysystem, "b_el", "to_bus") from oemof.solph import Storage charging_power = 20 bev_battery_cap = 50 # add mobility bus b_bev = Bus(label="b_bev", balanced=True) # add transformer to transport electricity from grid to mobility sector LinearTransformer(label="transport_el_bev", inputs={b_el: Flow()}, outputs={b_bev: Flow(variable_costs=10, nominal_value=charging_power, max=data['bev_charging_power'])}, conversion_factors={b_bev: 1.0}) # add BEV storage Storage(label='bev_storage', inputs={b_bev: Flow()}, outputs={b_bev: Flow()}, nominal_capacity=bev_battery_cap, capacity_min=data['bev_cap_min'], capacity_max=data['bev_cap_max'], capacity_loss=0.00, initial_capacity=None, inflow_conversion_factor=1.0, outflow_conversion_factor=1.0, nominal_input_capacity_ratio=1.0, nominal_output_capacity_ratio=1.0, fixed_costs=35) # add sink for leaving vehicles Sink(label="leaving_bev", inputs={b_bev: Flow(nominal_value=bev_battery_cap, actual_value=data['bev_sink'], fixed=True)}) # add source for returning vehicles Source(label="returning_bev", outputs={b_bev: Flow(nominal_value=bev_battery_cap, actual_value=data['bev_source'], fixed=True)}); optimize(energysystem) plot(energysystem, "b_bev", "from_bus") plot(energysystem, "b_el", "to_bus") plot(energysystem, "b_el", "from_bus") <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Import input data Step2: Add entities to energy system Step3: Optimize energy system and plot results Step4: Adding the gas sector Step5: Adding the heat sector Step6: Adding a heat pump Step7: Adding a combined heat and power plant Step8: Adding the mobility sector

<ASSISTANT_TASK:> Python Code: from sklearn.datasets import load_linnerud linnerud = load_linnerud() chinups = linnerud.data[:,0] fig, ax = plt.subplots() ax.hist( # complete ax.set_xlabel('chinups', fontsize=14) ax.set_ylabel('N', fontsize=14) fig.tight_layout() fig, ax = plt.subplots() ax.hist(# complete ax.hist(# complete ax.set_xlabel('chinups', fontsize=14) ax.set_ylabel('N', fontsize=14) fig.tight_layout() bins = np.append(# complete fig, ax = plt.subplots() ax.hist( # complete ax.set_xlabel('chinups', fontsize=14) ax.set_ylabel('N', fontsize=14) fig.tight_layout() fig, ax = plt.subplots() ax.hist(chinups, histtype = 'step') # this is the code for the rug plot ax.plot(chinups, np.zeros_like(chinups), '|', color='k', ms = 25, mew = 4) ax.set_xlabel('chinups', fontsize=14) ax.set_ylabel('N', fontsize=14) fig.tight_layout() # execute this cell from sklearn.neighbors import KernelDensity def kde_sklearn(data, grid, bandwidth = 1.0, **kwargs): kde_skl = KernelDensity(bandwidth = bandwidth, **kwargs) kde_skl.fit(data[:, np.newaxis]) log_pdf = kde_skl.score_samples(grid[:, np.newaxis]) # sklearn returns log(density) return np.exp(log_pdf) grid = # complete PDFtophat = kde_sklearn( # complete fig, ax = plt.subplots() ax.plot( # complete ax.set_xlabel('chinups', fontsize=14) ax.set_ylabel('PDF', fontsize=14) fig.tight_layout() PDFtophat1 = # complete PDFtophat5 = # complete fig, ax = plt.subplots() ax.plot(# complete ax.plot(# complete ax.set_xlabel('chinups', fontsize=14) ax.set_ylabel('PDF', fontsize=14) fig.tight_layout() ax.legend() PDFgaussian = # complete PDFepanechnikov = # complete fig, ax = plt.subplots() ax.plot(# complete ax.plot(# complete ax.legend(loc = 2) ax.set_xlabel('chinups', fontsize=14) ax.set_ylabel('PDF', fontsize=14) fig.tight_layout() x = np.arange(0, 6*np.pi, 0.1) y = np.cos(x) fig, ax=plt.subplots() ax.plot(x,y, lw = 2) ax.set_xlabel('X') ax.set_ylabel('Y') ax.set_xlim(0, 6*np.pi) fig.tight_layout() import seaborn as sns fig, ax = plt.subplots() ax.plot(x,y, lw = 2) ax.set_xlabel('X') ax.set_ylabel('Y') ax.set_xlim(0, 6*np.pi) fig.tight_layout() sns.set_style(# complete # complete # complete # complete # default color palette current_palette = sns.color_palette() sns.palplot(current_palette) # set palette to colorblind sns.set_palette("colorblind") current_palette = sns.color_palette() sns.palplot(current_palette) iris = sns.load_dataset("iris") iris # note - kde, and rug all set to True, set to False to turn them off with sns.axes_style("dark"): sns.displot(iris['petal_length'], bins=20, kde=True, rug=True) plt.tight_layout() fig, ax = plt.subplots() ax.scatter( # complete ax.set_xlabel("petal length (cm)") ax.set_ylabel("petal width (cm)") fig.tight_layout() np.random.seed(2016) xexample = np.random.normal(loc = 0.2, scale = 1.1, size = 10000) yexample = np.random.normal(loc = -0.2, scale = 0.9, size = 10000) fig, ax = plt.subplots() ax.scatter(xexample, yexample) ax.set_xlabel('X', fontsize=14) ax.set_ylabel('Y', fontsize=14) fig.tight_layout() # hexbin w/ bins = "log" returns the log of counts/bin # mincnt = 1 displays only hexpix with at least 1 source present fig, ax = plt.subplots() cax = ax.hexbin(xexample, yexample, bins = "log", cmap = "viridis", mincnt = 1) ax.set_xlabel('X', fontsize=14) ax.set_ylabel('Y', fontsize=14) fig.tight_layout() plt.colorbar(cax) fig, ax = plt.subplots() sns.kdeplot(x=xexample, y=yexample, shade=False) ax.set_xlabel('X', fontsize=14) ax.set_ylabel('Y', fontsize=14) fig.tight_layout() sns.jointplot(x=iris['petal_length'], y=iris['petal_width']) plt.tight_layout() sns.jointplot(# complete plt.tight_layout() sns.pairplot(iris[["sepal_length", "sepal_width", "petal_length", "petal_width"]]) plt.tight_layout() sns.pairplot(iris, vars = ["sepal_length", "sepal_width", "petal_length", "petal_width"], hue = "species", diag_kind = 'kde') g = sns.PairGrid(iris, vars = ["sepal_length", "sepal_width", "petal_length", "petal_width"], hue = "species", diag_sharey=False) g.map_lower(sns.kdeplot) g.map_upper(plt.scatter, edgecolor='white') g.map_diag(sns.kdeplot, lw=3) g.add_legend() <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Problem 1a Step2: Something is wrong here - the choice of bin centers and number of bins suggest that there is a 0% probability that middle aged men can do 10 chinups. This is intuitively incorrect; we will now adjust the bins in the histogram. Step3: These small changes significantly change the estimator for the PDF. With fewer bins we get something closer to a continuous distribution, while shifting the bin centers reduces the probability to zero at 9 chinups. Step4: Ending the lie Step5: Of course, even rug plots are not a perfect solution. Many of the chinup measurements are repeated, and those instances cannot be easily isolated above. One (slightly) better solution is to vary the transparency of the rug "whiskers" using alpha = 0.3 in the whiskers plot call. But this too is far from perfect. Step6: Problem 1e Step7: In this representation, each "block" has a height of 0.25. The bandwidth is too narrow to provide any overlap between the blocks. This choice of kernel and bandwidth produces an estimate that is essentially a histogram with a large number of bins. It gives no sense of continuity for the distribution. Now, we examine the difference (relative to histograms) upon changing the the width (i.e. kernel) of the blocks. Step8: It turns out blocks are not an ideal representation for continuous data (see discussion on histograms above). Now we will explore the resulting PDF from other kernels. Step9: So, what is the optimal choice of bandwidth and kernel? Unfortunately, there is no hard and fast rule, as every problem will likely have a different optimization. Typically, the choice of bandwidth is far more important than the choice of kernel. In the case where the PDF is likely to be gaussian (or close to gaussian), then Silverman's rule of thumb can be used Step10: Seaborn Step11: These plots look identical, but it is possible to change the style with seaborn. Step12: The folks behind seaborn have thought a lot about color palettes, which is a good thing. Remember - the choice of color for plots is one of the most essential aspects of visualization. A poor choice of colors can easily mask interesting patterns or suggest structure that is not real. To learn more about what is available, see the seaborn color tutorial. Step13: which we will now change to colorblind, which is clearer to those that are colorblind. Step14: Now that we have covered the basics of seaborn (and the above examples truly only scratch the surface of what is possible), we will explore the power of seaborn for higher dimension data sets. We will load the famous Iris data set, which measures 4 different features of 3 different types of Iris flowers. There are 150 different flowers in the data set. Step15: Now that we have a sense of the data structure, it is useful to examine the distribution of features. Above, we went to great pains to produce histograms, KDEs, and rug plots. seaborn handles all of that effortlessly with the displot function. Step16: Of course, this data set lives in a 4D space, so plotting more than univariate distributions is important. Fortunately, seaborn makes it very easy to produce handy summary plots. Step17: Of course, when there are many many data points, scatter plots become difficult to interpret. As in the example below Step18: Here, we see that there are many points, clustered about the origin, but we have no sense of the underlying density of the distribution. 2D histograms, such as plt.hist2d(), can alleviate this problem. I prefer to use plt.hexbin() which is a little easier on the eyes (though note - these histograms are just as subject to the same issues discussed above). Step19: While the above plot provides a significant improvement over the scatter plot by providing a better sense of the density near the center of the distribution, the binedge effects are clearly present. An even better solution, like before, is a density estimate, which is easily built into seaborn via the kdeplot function. Step20: This plot is much more appealing (and informative) than the previous two. For the first time we can clearly see that the distribution is not actually centered on the origin. Now we will move back to the Iris data set. Step21: But! Histograms and scatter plots can be problematic as we have discussed many times before. Step22: That is much nicer than what was presented above. However - we still have a problem in that our data live in 4D, but we are (mostly) limited to 2D projections of that data. One way around this is via the seaborn version of a pairplot, which plots the distribution of every variable in the data set against each other. (Here is where the integration with pandas DataFrames becomes so powerful.) Step23: For data sets where we have classification labels, we can even color the various points using the hue option, and produce KDEs along the diagonal with diag_type = 'kde'. Step24: Even better - there is an option to create a PairGrid which allows fine tuned control of the data as displayed above, below, and along the diagonal. In this way it becomes possible to avoid having symmetric redundancy, which is not all that informative. In the example below, we will show scatter plots and contour plots simultaneously.

<ASSISTANT_TASK:> Python Code: from osgeo import gdal import numpy as np betasso_dem_name = '/Users/gtucker/Dev/dem_analysis_with_gdal/czo_1m_bt1.img' geo = gdal.Open(betasso_dem_name) zb = geo.ReadAsArray() zb[np.where(zb<0.0)[0],np.where(zb<0.0)[1]] = 0.0 import matplotlib.pyplot as plt %matplotlib inline plt.imshow(zb, vmin=1600.0, vmax=2350.0) np.amax(zb) def slope_gradient(z): Calculate absolute slope gradient elevation array. x, y = np.gradient(z) #slope = (np.pi/2. - np.arctan(np.sqrt(x*x + y*y))) slope = np.sqrt(x*x + y*y) return slope sb = slope_gradient(zb) plt.imshow(sb, vmin=0.0, vmax=1.0, cmap='pink') def aspect(z): Calculate aspect from DEM. x, y = np.gradient(z) return np.arctan2(-x, y) ab = aspect(zb) plt.imshow(ab) abdeg = (180./np.pi)*ab # convert to degrees n, bins, patches = plt.hist(abdeg.flatten(), 50, normed=1, facecolor='green', alpha=0.75) def hillshade(z, azimuth=315.0, angle_altitude=45.0): Generate a hillshade image from DEM. Notes: adapted from example on GeoExamples blog, published March 24, 2014, by Roger Veciana i Rovira. x, y = np.gradient(z) slope = np.pi/2. - np.arctan(np.sqrt(x*x + y*y)) aspect = np.arctan2(-x, y) azimuthrad = azimuth*np.pi / 180. altituderad = angle_altitude*np.pi / 180. shaded = np.sin(altituderad) * np.sin(slope)\ + np.cos(altituderad) * np.cos(slope)\ * np.cos(azimuthrad - aspect) return 255*(shaded + 1)/2 hb = hillshade(zb) plt.imshow(hb, cmap='gray') <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Open and read data from the DEM Step2: If the previous two lines worked, zb should be a 2D numpy array that contains the DEM elevations. There are some cells along the edge of the grid with invalid data. Let's set their elevations to zero, using the numpy where function Step3: Now let's make a color image of the data. To do this, we'll need Pylab and a little "magic". Step4: Questions Step6: Make a slope map Step7: Let's see what it looks like Step9: Questions Step10: We can make a histogram (frequency diagram) of aspect. Here 0 degrees is east-facing, 90 is north-facing, 180 is west-facing, and -90 is south-facing. Step12: Questions

<ASSISTANT_TASK:> Python Code: DON'T MODIFY ANYTHING IN THIS CELL import helper import problem_unittests as tests source_path = 'data/small_vocab_en' target_path = 'data/small_vocab_fr' source_text = helper.load_data(source_path) target_text = helper.load_data(target_path) view_sentence_range = (0, 10) DON'T MODIFY ANYTHING IN THIS CELL import numpy as np print('Dataset Stats') print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()}))) sentences = source_text.split('\n') word_counts = [len(sentence.split()) for sentence in sentences] print('Number of sentences: {}'.format(len(sentences))) print('Average number of words in a sentence: {}'.format(np.average(word_counts))) print() print('English sentences {} to {}:'.format(*view_sentence_range)) print('\n'.join(source_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]])) print() print('French sentences {} to {}:'.format(*view_sentence_range)) print('\n'.join(target_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]])) def get_ids(input_text, vocab_to_int): Returns a list of word IDs for each word in the input :param input_text: Input string :param vocab_to_int: A mapping of word to wordID. :return: A list of [IDs] for each sentence in the input. return [[vocab_to_int[word] for word in sentence.split()] for sentence in input_text] def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int): Convert source and target text to proper word ids :param source_text: String that contains all the source text. :param target_text: String that contains all the target text. :param source_vocab_to_int: Dictionary to go from the source words to an id :param target_vocab_to_int: Dictionary to go from the target words to an id :return: A tuple of lists (source_id_text, target_id_text) # TODO: Implement Function source_sentences = [sentence for sentence in source_text.split('\n')] target_sentences = [sentence + ' <EOS>' for sentence in target_text.split('\n')] source_id_text = get_ids(source_sentences, source_vocab_to_int) target_id_text = get_ids(target_sentences, target_vocab_to_int) return source_id_text, target_id_text DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE tests.test_text_to_ids(text_to_ids) DON'T MODIFY ANYTHING IN THIS CELL helper.preprocess_and_save_data(source_path, target_path, text_to_ids) DON'T MODIFY ANYTHING IN THIS CELL import numpy as np import helper import problem_unittests as tests (source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess() DON'T MODIFY ANYTHING IN THIS CELL from distutils.version import LooseVersion import warnings import tensorflow as tf from tensorflow.python.layers.core import Dense # Check TensorFlow Version assert LooseVersion(tf.__version__) >= LooseVersion('1.1'), 'Please use TensorFlow version 1.1 or newer' print('TensorFlow Version: {}'.format(tf.__version__)) # Check for a GPU if not tf.test.gpu_device_name(): warnings.warn('No GPU found. Please use a GPU to train your neural network.') else: print('Default GPU Device: {}'.format(tf.test.gpu_device_name())) def model_inputs(): Create TF Placeholders for input, targets, learning rate, and lengths of source and target sequences. :return: Tuple (input, targets, learning rate, keep probability, target sequence length, max target sequence length, source sequence length) # TODO: Implement Function inputs = tf.placeholder(tf.int32, [None, None], name='input') targets = tf.placeholder(tf.int32, [None, None], name='target') learning_rate = tf.placeholder(tf.float32, name='learning_rate') keep_prob = tf.placeholder(tf.float32, name='keep_prob') target_seq_length = tf.placeholder(tf.int32, [None], name='target_sequence_length') max_target_seq_length = tf.reduce_max(target_seq_length) source_seq_length = tf.placeholder(tf.int32, [None], name='source_sequence_length') return inputs, targets, learning_rate, keep_prob, target_seq_length, max_target_seq_length, source_seq_length DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE tests.test_model_inputs(model_inputs) def process_decoder_input(target_data, target_vocab_to_int, batch_size): Preprocess target data for encoding :param target_data: Target Placehoder :param target_vocab_to_int: Dictionary to go from the target words to an id :param batch_size: Batch Size :return: Preprocessed target data # TODO: Implement Function end = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1]) return tf.concat([tf.fill([batch_size, 1], target_vocab_to_int['<GO>']), end], 1) DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE tests.test_process_encoding_input(process_decoder_input) from imp import reload reload(tests) def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, source_sequence_length, source_vocab_size, encoding_embedding_size): Create encoding layer :param rnn_inputs: Inputs for the RNN :param rnn_size: RNN Size :param num_layers: Number of layers :param keep_prob: Dropout keep probability :param source_sequence_length: a list of the lengths of each sequence in the batch :param source_vocab_size: vocabulary size of source data :param encoding_embedding_size: embedding size of source data :return: tuple (RNN output, RNN state) # TODO: Implement Function embed_encoder_input = tf.contrib.layers.embed_sequence(rnn_inputs, source_vocab_size, encoding_embedding_size) # Create an LSTM cell wrapped in a DropOutWrapper def create_lstm_cell(rnn_size): encoder_cell = tf.contrib.rnn.LSTMCell(rnn_size, initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=42)) return tf.contrib.rnn.DropoutWrapper(encoder_cell, output_keep_prob=keep_prob) encoder_cell = tf.contrib.rnn.MultiRNNCell([create_lstm_cell(rnn_size) for _ in range(num_layers)]) return tf.nn.dynamic_rnn(encoder_cell, embed_encoder_input, sequence_length=source_sequence_length, dtype=tf.float32) DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE tests.test_encoding_layer(encoding_layer) def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, target_sequence_length, max_summary_length, output_layer, keep_prob): Create a decoding layer for training :param encoder_state: Encoder State :param dec_cell: Decoder RNN Cell :param dec_embed_input: Decoder embedded input :param target_sequence_length: The lengths of each sequence in the target batch :param max_summary_length: The length of the longest sequence in the batch :param output_layer: Function to apply the output layer :param keep_prob: Dropout keep probability :return: BasicDecoderOutput containing training logits and sample_id # TODO: Implement Function # Try if a dropout has to be added here for the Decoder RNN cell. helper = tf.contrib.seq2seq.TrainingHelper(inputs=dec_embed_input, sequence_length=target_sequence_length, time_major=False) decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, helper, encoder_state, output_layer) output = tf.contrib.seq2seq.dynamic_decode(decoder, impute_finished=True, maximum_iterations=max_summary_length)[0] return output DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE tests.test_decoding_layer_train(decoding_layer_train) def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, max_target_sequence_length, vocab_size, output_layer, batch_size, keep_prob): Create a decoding layer for inference :param encoder_state: Encoder state :param dec_cell: Decoder RNN Cell :param dec_embeddings: Decoder embeddings :param start_of_sequence_id: GO ID :param end_of_sequence_id: EOS Id :param max_target_sequence_length: Maximum length of target sequences :param vocab_size: Size of decoder/target vocabulary :param decoding_scope: TenorFlow Variable Scope for decoding :param output_layer: Function to apply the output layer :param batch_size: Batch size :param keep_prob: Dropout keep probability :return: BasicDecoderOutput containing inference logits and sample_id # TODO: Implement Function start_token = tf.tile(tf.constant([start_of_sequence_id], dtype=tf.int32), [batch_size], name='start_token') helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(dec_embeddings, start_token, end_of_sequence_id) decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, helper, encoder_state, output_layer) decoder_output = tf.contrib.seq2seq.dynamic_decode(decoder, impute_finished=True, maximum_iterations=max_target_sequence_length)[0] return decoder_output DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE tests.test_decoding_layer_infer(decoding_layer_infer) def decoding_layer(dec_input, encoder_state, target_sequence_length, max_target_sequence_length, rnn_size, num_layers, target_vocab_to_int, target_vocab_size, batch_size, keep_prob, decoding_embedding_size): Create decoding layer :param dec_input: Decoder input :param encoder_state: Encoder state :param target_sequence_length: The lengths of each sequence in the target batch :param max_target_sequence_length: Maximum length of target sequences :param rnn_size: RNN Size :param num_layers: Number of layers :param target_vocab_to_int: Dictionary to go from the target words to an id :param target_vocab_size: Size of target vocabulary :param batch_size: The size of the batch :param keep_prob: Dropout keep probability :param decoding_embedding_size: Decoding embedding size :return: Tuple of (Training BasicDecoderOutput, Inference BasicDecoderOutput) # TODO: Implement Function decoded_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, decoding_embedding_size])) decoded_embed_input = tf.nn.embedding_lookup(decoded_embeddings, dec_input) # Create an LSTM cell wrapped in a DropOutWrapper def create_lstm_cell(rnn_size): encoder_cell = tf.contrib.rnn.LSTMCell(rnn_size, initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=42)) return encoder_cell decoded_cell = tf.contrib.rnn.MultiRNNCell([create_lstm_cell(rnn_size) for _ in range(num_layers)]) output_layer = Dense(target_vocab_size, kernel_initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1)) with tf.variable_scope("decode"): training_logits = decoding_layer_train(encoder_state, decoded_cell, decoded_embed_input, target_sequence_length, max_target_sequence_length, output_layer, keep_prob) with tf.variable_scope("decode", reuse=True): inference_logits = decoding_layer_infer(encoder_state, decoded_cell, decoded_embeddings, target_vocab_to_int['<GO>'], target_vocab_to_int['<EOS>'], max_target_sequence_length, len(target_vocab_to_int), output_layer, batch_size, keep_prob) return training_logits, inference_logits DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE tests.test_decoding_layer(decoding_layer) def seq2seq_model(input_data, target_data, keep_prob, batch_size, source_sequence_length, target_sequence_length, max_target_sentence_length, source_vocab_size, target_vocab_size, enc_embedding_size, dec_embedding_size, rnn_size, num_layers, target_vocab_to_int): Build the Sequence-to-Sequence part of the neural network :param input_data: Input placeholder :param target_data: Target placeholder :param keep_prob: Dropout keep probability placeholder :param batch_size: Batch Size :param source_sequence_length: Sequence Lengths of source sequences in the batch :param target_sequence_length: Sequence Lengths of target sequences in the batch :param source_vocab_size: Source vocabulary size :param target_vocab_size: Target vocabulary size :param enc_embedding_size: Decoder embedding size :param dec_embedding_size: Encoder embedding size :param rnn_size: RNN Size :param num_layers: Number of layers :param target_vocab_to_int: Dictionary to go from the target words to an id :return: Tuple of (Training BasicDecoderOutput, Inference BasicDecoderOutput) # TODO: Implement Function _, encoding_state = encoding_layer(input_data, rnn_size, num_layers, keep_prob, source_sequence_length, source_vocab_size, enc_embedding_size) decoding_input = process_decoder_input(target_data, target_vocab_to_int, batch_size) training_dec_output, inference_dec_output = decoding_layer(decoding_input, encoding_state, target_sequence_length, max_target_sentence_length, rnn_size, num_layers, target_vocab_to_int, target_vocab_size, batch_size, keep_prob, dec_embedding_size) return training_dec_output, inference_dec_output DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE tests.test_seq2seq_model(seq2seq_model) # Number of Epochs epochs = 8 # Batch Size batch_size = 128 # RNN Size rnn_size = 256 # Number of Layers num_layers = 3 # Embedding Size encoding_embedding_size = 128 decoding_embedding_size = 128 # Learning Rate learning_rate = 0.001 # Dropout Keep Probability keep_probability = 0.6 display_step = 100 DON'T MODIFY ANYTHING IN THIS CELL save_path = 'checkpoints/dev' (source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess() max_target_sentence_length = max([len(sentence) for sentence in source_int_text]) train_graph = tf.Graph() with train_graph.as_default(): input_data, targets, lr, keep_prob, target_sequence_length, max_target_sequence_length, source_sequence_length = model_inputs() #sequence_length = tf.placeholder_with_default(max_target_sentence_length, None, name='sequence_length') input_shape = tf.shape(input_data) train_logits, inference_logits = seq2seq_model(tf.reverse(input_data, [-1]), targets, keep_prob, batch_size, source_sequence_length, target_sequence_length, max_target_sequence_length, len(source_vocab_to_int), len(target_vocab_to_int), encoding_embedding_size, decoding_embedding_size, rnn_size, num_layers, target_vocab_to_int) training_logits = tf.identity(train_logits.rnn_output, name='logits') inference_logits = tf.identity(inference_logits.sample_id, name='predictions') masks = tf.sequence_mask(target_sequence_length, max_target_sequence_length, dtype=tf.float32, name='masks') with tf.name_scope("optimization"): # Loss function cost = tf.contrib.seq2seq.sequence_loss( training_logits, targets, masks) # Optimizer optimizer = tf.train.AdamOptimizer(lr) # Gradient Clipping gradients = optimizer.compute_gradients(cost) capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None] train_op = optimizer.apply_gradients(capped_gradients) DON'T MODIFY ANYTHING IN THIS CELL def pad_sentence_batch(sentence_batch, pad_int): Pad sentences with <PAD> so that each sentence of a batch has the same length max_sentence = max([len(sentence) for sentence in sentence_batch]) return [sentence + [pad_int] * (max_sentence - len(sentence)) for sentence in sentence_batch] def get_batches(sources, targets, batch_size, source_pad_int, target_pad_int): Batch targets, sources, and the lengths of their sentences together for batch_i in range(0, len(sources)//batch_size): start_i = batch_i * batch_size # Slice the right amount for the batch sources_batch = sources[start_i:start_i + batch_size] targets_batch = targets[start_i:start_i + batch_size] # Pad pad_sources_batch = np.array(pad_sentence_batch(sources_batch, source_pad_int)) pad_targets_batch = np.array(pad_sentence_batch(targets_batch, target_pad_int)) # Need the lengths for the _lengths parameters pad_targets_lengths = [] for target in pad_targets_batch: pad_targets_lengths.append(len(target)) pad_source_lengths = [] for source in pad_sources_batch: pad_source_lengths.append(len(source)) yield pad_sources_batch, pad_targets_batch, pad_source_lengths, pad_targets_lengths DON'T MODIFY ANYTHING IN THIS CELL def get_accuracy(target, logits): Calculate accuracy max_seq = max(target.shape[1], logits.shape[1]) if max_seq - target.shape[1]: target = np.pad( target, [(0,0),(0,max_seq - target.shape[1])], 'constant') if max_seq - logits.shape[1]: logits = np.pad( logits, [(0,0),(0,max_seq - logits.shape[1])], 'constant') return np.mean(np.equal(target, logits)) # Split data to training and validation sets train_source = source_int_text[batch_size:] train_target = target_int_text[batch_size:] valid_source = source_int_text[:batch_size] valid_target = target_int_text[:batch_size] (valid_sources_batch, valid_targets_batch, valid_sources_lengths, valid_targets_lengths ) = next(get_batches(valid_source, valid_target, batch_size, source_vocab_to_int['<PAD>'], target_vocab_to_int['<PAD>'])) with tf.Session(graph=train_graph) as sess: sess.run(tf.global_variables_initializer()) for epoch_i in range(epochs): for batch_i, (source_batch, target_batch, sources_lengths, targets_lengths) in enumerate( get_batches(train_source, train_target, batch_size, source_vocab_to_int['<PAD>'], target_vocab_to_int['<PAD>'])): _, loss = sess.run( [train_op, cost], {input_data: source_batch, targets: target_batch, lr: learning_rate, target_sequence_length: targets_lengths, source_sequence_length: sources_lengths, keep_prob: keep_probability}) if batch_i % display_step == 0 and batch_i > 0: batch_train_logits = sess.run( inference_logits, {input_data: source_batch, source_sequence_length: sources_lengths, target_sequence_length: targets_lengths, keep_prob: 1.0}) batch_valid_logits = sess.run( inference_logits, {input_data: valid_sources_batch, source_sequence_length: valid_sources_lengths, target_sequence_length: valid_targets_lengths, keep_prob: 1.0}) train_acc = get_accuracy(target_batch, batch_train_logits) valid_acc = get_accuracy(valid_targets_batch, batch_valid_logits) print('Epoch {:>3} Batch {:>4}/{} - Train Accuracy: {:>6.4f}, Validation Accuracy: {:>6.4f}, Loss: {:>6.4f}' .format(epoch_i, batch_i, len(source_int_text) // batch_size, train_acc, valid_acc, loss)) # Save Model saver = tf.train.Saver() saver.save(sess, save_path) print('Model Trained and Saved') DON'T MODIFY ANYTHING IN THIS CELL # Save parameters for checkpoint helper.save_params(save_path) DON'T MODIFY ANYTHING IN THIS CELL import tensorflow as tf import numpy as np import helper import problem_unittests as tests _, (source_vocab_to_int, target_vocab_to_int), (source_int_to_vocab, target_int_to_vocab) = helper.load_preprocess() load_path = helper.load_params() def sentence_to_seq(sentence, vocab_to_int): Convert a sentence to a sequence of ids :param sentence: String :param vocab_to_int: Dictionary to go from the words to an id :return: List of word ids # TODO: Implement Function return [vocab_to_int.get(word, vocab_to_int['<UNK>']) for word in sentence.lower().split()] DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE tests.test_sentence_to_seq(sentence_to_seq) translate_sentence = 'he saw a old yellow truck .' DON'T MODIFY ANYTHING IN THIS CELL translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int) loaded_graph = tf.Graph() with tf.Session(graph=loaded_graph) as sess: # Load saved model loader = tf.train.import_meta_graph(load_path + '.meta') loader.restore(sess, load_path) input_data = loaded_graph.get_tensor_by_name('input:0') logits = loaded_graph.get_tensor_by_name('predictions:0') target_sequence_length = loaded_graph.get_tensor_by_name('target_sequence_length:0') source_sequence_length = loaded_graph.get_tensor_by_name('source_sequence_length:0') keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0') translate_logits = sess.run(logits, {input_data: [translate_sentence]*batch_size, target_sequence_length: [len(translate_sentence)*2]*batch_size, source_sequence_length: [len(translate_sentence)]*batch_size, keep_prob: 1.0})[0] print('Input') print(' Word Ids: {}'.format([i for i in translate_sentence])) print(' English Words: {}'.format([source_int_to_vocab[i] for i in translate_sentence])) print('\nPrediction') print(' Word Ids: {}'.format([i for i in translate_logits])) print(' French Words: {}'.format(" ".join([target_int_to_vocab[i] for i in translate_logits]))) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Language Translation Step3: Explore the Data Step7: Implement Preprocessing Function Step9: Preprocess all the data and save it Step11: Check Point Step13: Check the Version of TensorFlow and Access to GPU Step16: Build the Neural Network Step19: Process Decoder Input Step22: Encoding Step25: Decoding - Training Step28: Decoding - Inference Step31: Build the Decoding Layer Step34: Build the Neural Network Step35: Neural Network Training Step37: Build the Graph Step41: Batch and pad the source and target sequences Step44: Train Step46: Save Parameters Step48: Checkpoint Step51: Sentence to Sequence Step53: Translate

<ASSISTANT_TASK:> Python Code: %matplotlib inline import sklearn import scipy.stats as stats import scipy.optimize import matplotlib.pyplot as plt import seaborn as sns import time import numpy as np import os import pandas as pd !pip install -U pymc3>=3.8 import pymc3 as pm print(pm.__version__) import theano.tensor as tt import theano #!pip install arviz import arviz as az !mkdir ../figures # https://github.com/probml/pyprobml/blob/master/scripts/schools8_pymc3.py # Data of the Eight Schools Model J = 8 y = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]) sigma = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]) print(np.mean(y)) print(np.median(y)) names = [] for t in range(8): names.append("{}".format(t)) # Plot raw data fig, ax = plt.subplots() y_pos = np.arange(8) ax.errorbar(y, y_pos, xerr=sigma, fmt="o") ax.set_yticks(y_pos) ax.set_yticklabels(names) ax.invert_yaxis() # labels read top-to-bottom plt.title("8 schools") plt.savefig("../figures/schools8_data.png") plt.show() # Centered model with pm.Model() as Centered_eight: mu_alpha = pm.Normal("mu_alpha", mu=0, sigma=5) sigma_alpha = pm.HalfCauchy("sigma_alpha", beta=5) alpha = pm.Normal("alpha", mu=mu_alpha, sigma=sigma_alpha, shape=J) obs = pm.Normal("obs", mu=alpha, sigma=sigma, observed=y) log_sigma_alpha = pm.Deterministic("log_sigma_alpha", tt.log(sigma_alpha)) np.random.seed(0) with Centered_eight: trace_centered = pm.sample(1000, chains=4, return_inferencedata=False) pm.summary(trace_centered).round(2) # PyMC3 gives multiple warnings about divergences # Also, see r_hat ~ 1.01, ESS << nchains*1000, especially for sigma_alpha # We can solve these problems below by using a non-centered parameterization. # In practice, for this model, the results are very similar. # Display the total number and percentage of divergent chains diverging = trace_centered["diverging"] print("Number of Divergent Chains: {}".format(diverging.nonzero()[0].size)) diverging_pct = diverging.nonzero()[0].size / len(trace_centered) * 100 print("Percentage of Divergent Chains: {:.1f}".format(diverging_pct)) dir(trace_centered) trace_centered.varnames with Centered_eight: # fig, ax = plt.subplots() az.plot_autocorr(trace_centered, var_names=["mu_alpha", "sigma_alpha"], combined=True) plt.savefig("schools8_centered_acf_combined.png", dpi=300) with Centered_eight: # fig, ax = plt.subplots() az.plot_autocorr(trace_centered, var_names=["mu_alpha", "sigma_alpha"]) plt.savefig("schools8_centered_acf.png", dpi=300) with Centered_eight: az.plot_forest(trace_centered, var_names="alpha", hdi_prob=0.95, combined=True) plt.savefig("schools8_centered_forest_combined.png", dpi=300) with Centered_eight: az.plot_forest(trace_centered, var_names="alpha", hdi_prob=0.95, combined=False) plt.savefig("schools8_centered_forest.png", dpi=300) # Non-centered parameterization with pm.Model() as NonCentered_eight: mu_alpha = pm.Normal("mu_alpha", mu=0, sigma=5) sigma_alpha = pm.HalfCauchy("sigma_alpha", beta=5) alpha_offset = pm.Normal("alpha_offset", mu=0, sigma=1, shape=J) alpha = pm.Deterministic("alpha", mu_alpha + sigma_alpha * alpha_offset) # alpha = pm.Normal('alpha', mu=mu_alpha, sigma=sigma_alpha, shape=J) obs = pm.Normal("obs", mu=alpha, sigma=sigma, observed=y) log_sigma_alpha = pm.Deterministic("log_sigma_alpha", tt.log(sigma_alpha)) np.random.seed(0) with NonCentered_eight: trace_noncentered = pm.sample(1000, chains=4) pm.summary(trace_noncentered).round(2) # Samples look good: r_hat = 1, ESS ~= nchains*1000 with NonCentered_eight: az.plot_autocorr(trace_noncentered, var_names=["mu_alpha", "sigma_alpha"], combined=True) plt.savefig("schools8_noncentered_acf_combined.png", dpi=300) with NonCentered_eight: az.plot_forest(trace_noncentered, var_names="alpha", combined=True, hdi_prob=0.95) plt.savefig("schools8_noncentered_forest_combined.png", dpi=300) az.plot_forest( [trace_centered, trace_noncentered], model_names=["centered", "noncentered"], var_names="alpha", combined=True, hdi_prob=0.95, ) plt.axvline(np.mean(y), color="k", linestyle="--") az.plot_forest( [trace_centered, trace_noncentered], model_names=["centered", "noncentered"], var_names="alpha", kind="ridgeplot", combined=True, hdi_prob=0.95, ); # Plot the "funnel of hell" # Based on # https://github.com/twiecki/WhileMyMCMCGentlySamples/blob/master/content/downloads/notebooks/GLM_hierarchical_non_centered.ipynb fig, axs = plt.subplots(ncols=2, sharex=True, sharey=True) x = pd.Series(trace_centered["mu_alpha"], name="mu_alpha") y = pd.Series(trace_centered["log_sigma_alpha"], name="log_sigma_alpha") axs[0].plot(x, y, ".") axs[0].set(title="Centered", xlabel="µ", ylabel="log(sigma)") # axs[0].axhline(0.01) x = pd.Series(trace_noncentered["mu_alpha"], name="mu") y = pd.Series(trace_noncentered["log_sigma_alpha"], name="log_sigma_alpha") axs[1].plot(x, y, ".") axs[1].set(title="NonCentered", xlabel="µ", ylabel="log(sigma)") # axs[1].axhline(0.01) plt.savefig("schools8_funnel.png", dpi=300) xlim = axs[0].get_xlim() ylim = axs[0].get_ylim() x = pd.Series(trace_centered["mu_alpha"], name="mu") y = pd.Series(trace_centered["log_sigma_alpha"], name="log sigma_alpha") sns.jointplot(x, y, xlim=xlim, ylim=ylim) plt.suptitle("centered") plt.savefig("schools8_centered_joint.png", dpi=300) x = pd.Series(trace_noncentered["mu_alpha"], name="mu") y = pd.Series(trace_noncentered["log_sigma_alpha"], name="log sigma_alpha") sns.jointplot(x, y, xlim=xlim, ylim=ylim) plt.suptitle("noncentered") plt.savefig("schools8_noncentered_joint.png", dpi=300) group = 0 fig, axs = plt.subplots(ncols=2, sharex=True, sharey=True, figsize=(10, 5)) x = pd.Series(trace_centered["alpha"][:, group], name=f"alpha {group}") y = pd.Series(trace_centered["log_sigma_alpha"], name="log_sigma_alpha") axs[0].plot(x, y, ".") axs[0].set(title="Centered", xlabel=r"$\alpha_0$", ylabel=r"$\log(\sigma_\alpha)$") x = pd.Series(trace_noncentered["alpha"][:, group], name=f"alpha {group}") y = pd.Series(trace_noncentered["log_sigma_alpha"], name="log_sigma_alpha") axs[1].plot(x, y, ".") axs[1].set(title="NonCentered", xlabel=r"$\alpha_0$", ylabel=r"$\log(\sigma_\alpha)$") xlim = axs[0].get_xlim() ylim = axs[0].get_ylim() plt.savefig("schools8_funnel_group0.png", dpi=300) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Data Step2: Centered model Step3: Non-centered Step4: Funnel of hell

<ASSISTANT_TASK:> Python Code: # If you want the figures to appear in the notebook, # and you want to interact with them, use # %matplotlib notebook # If you want the figures to appear in the notebook, # and you don't want to interact with them, use # %matplotlib inline # If you want the figures to appear in separate windows, use # %matplotlib qt5 # tempo switch from one to another, you have to select Kernel->Restart %matplotlib notebook from modsim import * condition = Condition(g = 9.8, m = 75, area = 1, rho = 1.2, v_term = 60, duration = 30, length0 = 100, angle = (270 - 45), k = 20) def make_system(condition): Makes a System object for the given conditions. condition: Condition with height, g, m, diameter, rho, v_term, and duration returns: System with init, g, m, rho, C_d, area, and ts unpack(condition) theta = np.deg2rad(angle) x, y = pol2cart(theta, length0) P = Vector(x, y) V = Vector(0, 0) init = State(x=P.x, y=P.y, vx=V.x, vy=V.y) C_d = 2 * m * g / (rho * area * v_term**2) ts = linspace(0, duration, 501) return System(init=init, g=g, m=m, rho=rho, C_d=C_d, area=area, length0=length0, k=k, ts=ts) system = make_system(condition) system system.init def slope_func(state, t, system): Computes derivatives of the state variables. state: State (x, y, x velocity, y velocity) t: time system: System object with length0, m, k returns: sequence (vx, vy, ax, ay) x, y, vx, vy = state unpack(system) ax = x*(g*y - vx**2 - vy**2)/(x**2 + y**2) ay = -(g*x**2 + y*(vx**2 + vy**2))/(x**2 + y**2) return vx, vy, ax, ay slope_func(system.init, 0, system) %time run_odeint(system, slope_func) xs = system.results.x ys = system.results.y newfig() plot(xs, label='x') plot(ys, label='y') decorate(xlabel='Time (s)', ylabel='Position (m)') vxs = system.results.vx vys = system.results.vy newfig() plot(vxs, label='vx') plot(vys, label='vy') decorate(xlabel='Time (s)', ylabel='Velocity (m/s)') newfig() plot(xs, ys, label='trajectory') decorate(xlabel='x position (m)', ylabel='y position (m)') newfig() decorate(xlabel='x position (m)', ylabel='y position (m)', xlim=[-100, 100], ylim=[-200, -50], legend=False) for x, y in zip(xs, ys): plot(x, y, 'bo', update=True) sleep(0.01) def animate2d(xs, ys, speedup=1): Animate the results of a projectile simulation. xs: x position as a function of time ys: y position as a function of time speedup: how much to divide `dt` by # get the time intervals between elements ts = xs.index dts = np.diff(ts) dts = np.append(dts, 0) # decorate the plot newfig() decorate(xlabel='x position (m)', ylabel='y position (m)', xlim=[xs.min(), xs.max()], ylim=[ys.min(), ys.max()], legend=False) # loop through the values for x, y, dt in zip(xs, ys, dts): plot(x, y, 'bo', update=True) sleep(dt / speedup) animate2d(system.results.x, system.results.y) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Pendulum Step3: Now here's a version of make_system that takes a Condition object as a parameter. Step4: Let's make a System Step6: To write the slope function, we can get the expressions for ax and ay directly from SymPy and plug them in. Step7: As always, let's test the slope function with the initial conditions. Step8: And then run the simulation. Step9: Visualizing the results Step10: The simplest way to visualize the results is to plot x and y as functions of time. Step11: We can plot the velocities the same way. Step12: Another way to visualize the results is to plot y versus x. The result is the trajectory through the plane of motion. Step13: We can also animate the trajectory. If there's an error in the simulation, we can sometimes spot it by looking at animations. Step15: Here's a function that encapsulates that code and runs the animation in (approximately) real time.

<ASSISTANT_TASK:> Python Code: from pyCHX.chx_packages import * %matplotlib notebook plt.rcParams.update({'figure.max_open_warning': 0}) plt.rcParams.update({ 'image.origin': 'lower' }) plt.rcParams.update({ 'image.interpolation': 'none' }) import pickle as cpk from pyCHX.chx_xpcs_xsvs_jupyter_V1 import * import itertools #from pyCHX.XPCS_SAXS import get_QrQw_From_RoiMask %run /home/yuzhang/pyCHX_link/pyCHX/chx_generic_functions.py #%matplotlib notebook %matplotlib inline scat_geometry = 'saxs' #suport 'saxs', 'gi_saxs', 'ang_saxs' (for anisotropics saxs or flow-xpcs) #scat_geometry = 'ang_saxs' #scat_geometry = 'gi_waxs' #scat_geometry = 'gi_saxs' analysis_type_auto = True #if True, will take "analysis type" option from data acquisition func series qphi_analysis = False #if True, will do q-phi (anisotropic analysis for transmission saxs) isotropic_Q_mask = 'normal' #'wide' # 'normal' # 'wide' ## select wich Q-mask to use for rings: 'normal' or 'wide' phi_Q_mask = 'phi_4x_20deg' ## select wich Q-mask to use for phi analysis q_mask_name = '' force_compress = False #True #force to compress data bin_frame = False #generally make bin_frame as False para_compress = True #parallel compress run_fit_form = False #run fit form factor run_waterfall = False #True #run waterfall analysis run_profile_plot = False #run prolfile plot for gi-saxs run_t_ROI_Inten = True #run ROI intensity as a function of time run_get_mass_center = False # Analysis for mass center of reflective beam center run_invariant_analysis = False run_one_time = True #run one-time cal_g2_error = False #True #calculate g2 signal to noise #run_fit_g2 = True #run fit one-time, the default function is "stretched exponential" fit_g2_func = 'stretched' run_two_time = True #run two-time run_four_time = False #True #True #False #run four-time run_xsvs= False #False #run visibility analysis att_pdf_report = True #attach the pdf report to CHX olog qth_interest = 1 #the intested single qth use_sqnorm = True #if True, use sq to normalize intensity use_SG = True # False #if True, use the Sawitzky-Golay filter for <I(pix)> use_imgsum_norm= True #if True use imgsum to normalize intensity for one-time calculatoin pdf_version='_%s'%get_today_date() #for pdf report name run_dose = True #True # True #False #run dose_depend analysis if scat_geometry == 'gi_saxs':run_xsvs= False;use_sqnorm=False if scat_geometry == 'gi_waxs':use_sqnorm = False if scat_geometry != 'saxs':qphi_analysis = False;scat_geometry_ = scat_geometry else:scat_geometry_ = ['','ang_'][qphi_analysis]+ scat_geometry if scat_geometry != 'gi_saxs':run_profile_plot = False scat_geometry taus=None;g2=None;tausb=None;g2b=None;g12b=None;taus4=None;g4=None;times_xsv=None;contrast_factorL=None; lag_steps = None CYCLE= '2019_1' #change clycle here path = '/XF11ID/analysis/%s/masks/'%CYCLE username = getpass.getuser() username = 'commisionning' username = 'petrash' data_dir0 = create_user_folder(CYCLE, username) print( data_dir0 ) uid = 'd099ce48' #(scan num: 3567 (Measurement: 500k, 9kHz 5k CoralPor uid = '0587b05b' #(scan num: 3570 (Measurement: 4M, 100Hz, 200 testing data processing CoralPor uid = 'ad658cdf' #(scan num: 3571 (Measurement: 4M, 100Hz, 200 testing data processing CoralPor uid = '9f849990' #(scan num: 3573 (Measurement: 500k, 9 kHz, 2000 testing data processing CoralPor uid = '25171c35-ce50-450b-85a0-ba9e116651e3' uid = uid[:8] print('The current uid for analysis is: %s...'%uid) #get_last_uids( -1) sud = get_sid_filenames(db[uid]) for pa in sud[2]: if 'master.h5' in pa: data_fullpath = pa print ('scan_id, full-uid, data path are: %s--%s--%s'%(sud[0], sud[1], data_fullpath )) #start_time, stop_time = '2017-2-24 12:23:00', '2017-2-24 13:42:00' #sids, uids, fuids = find_uids(start_time, stop_time) data_dir = os.path.join(data_dir0, '%s/'%(sud[1])) os.makedirs(data_dir, exist_ok=True) print('Results from this analysis will be stashed in the directory %s' % data_dir) uidstr = 'uid=%s'%uid md = get_meta_data( uid ) md_blue = md.copy() #md_blue #md_blue['detectors'][0] #if md_blue['OAV_mode'] != 'none': # cx , cy = md_blue[md_blue['detectors'][0]+'_beam_center_x'], md_blue[md_blue['detectors'][0]+'_beam_center_x'] #else: # cx , cy = md_blue['beam_center_x'], md_blue['beam_center_y'] #print(cx,cy) detectors = sorted(get_detectors(db[uid])) print('The detectors are:%s'%detectors) if len(detectors) >1: md['detector'] = detectors[1] print( md['detector']) if md['detector'] =='eiger4m_single_image' or md['detector'] == 'image': reverse= True rot90= False elif md['detector'] =='eiger500K_single_image': reverse= True rot90=True elif md['detector'] =='eiger1m_single_image': reverse= True rot90=False print('Image reverse: %s\nImage rotate 90: %s'%(reverse, rot90)) try: cx , cy = md_blue['beam_center_x'], md_blue['beam_center_y'] print(cx,cy) except: print('Will find cx,cy later.') if analysis_type_auto:#if True, will take "analysis type" option from data acquisition func series try: qphi_analysis_ = md['analysis'] #if True, will do q-phi (anisotropic analysis for transmission saxs) print(md['analysis']) if qphi_analysis_ == 'iso': qphi_analysis = False elif qphi_analysis_ == '': qphi_analysis = False else: qphi_analysis = True except: print('There is no analysis in metadata.') print('Will %s qphis analysis.'%['NOT DO','DO'][qphi_analysis]) if scat_geometry != 'saxs':qphi_analysis = False;scat_geometry_ = scat_geometry else:scat_geometry_ = ['','ang_'][qphi_analysis]+ scat_geometry if scat_geometry != 'gi_saxs':run_profile_plot = False print(scat_geometry_) #isotropic_Q_mask scat_geometry ##For SAXS roi_path = '/XF11ID/analysis/2019_1/masks/' roi_date = 'Feb6' if scat_geometry =='saxs': if qphi_analysis == False: if isotropic_Q_mask == 'normal': #print('Here') q_mask_name='rings' if md['detector'] =='eiger4m_single_image' or md['detector'] == 'image': #for 4M fp = roi_path + 'roi_mask_%s_4M_norm.pkl'%roi_date elif md['detector'] =='eiger500K_single_image': #for 500K fp = roi_path + 'roi_mask_%s_500K_norm.pkl'%roi_date elif isotropic_Q_mask == 'wide': q_mask_name='wide_rings' if md['detector'] =='eiger4m_single_image' or md['detector'] == 'image': #for 4M fp = roi_path + 'roi_mask_%s_4M_wide.pkl'%roi_date elif md['detector'] =='eiger500K_single_image': #for 500K fp = roi_path + 'roi_mask_%s_500K_wide.pkl'%roi_date elif qphi_analysis: if phi_Q_mask =='phi_4x_20deg': q_mask_name='phi_4x_20deg' if md['detector'] =='eiger4m_single_image' or md['detector'] == 'image': #for 4M fp = roi_path + 'roi_mask_%s_4M_phi_4x_20deg.pkl'%roi_date elif md['detector'] =='eiger500K_single_image': #for 500K fp = roi_path + 'roi_mask_%s_500K_phi_4x_20deg.pkl'%roi_date #fp = 'XXXXXXX.pkl' roi_mask,qval_dict = cpk.load( open(fp, 'rb' ) ) #for load the saved roi data #print(fp) ## Gi_SAXS elif scat_geometry =='gi_saxs': # dynamics mask fp = '/XF11ID/analysis/2018_2/masks/uid=460a2a3a_roi_mask.pkl' roi_mask,qval_dict = cpk.load( open(fp, 'rb' ) ) #for load the saved roi data print('The dynamic mask is: %s.'%fp) # static mask fp = '/XF11ID/analysis/2018_2/masks/uid=460a2a3a_roi_masks.pkl' roi_masks,qval_dicts = cpk.load( open(fp, 'rb' ) ) #for load the saved roi data print('The static mask is: %s.'%fp) # q-map fp = '/XF11ID/analysis/2018_2/masks/uid=460a2a3a_qmap.pkl' #print(fp) qr_map, qz_map, ticks, Qrs, Qzs, Qr, Qz, inc_x0,refl_x0, refl_y0 = cpk.load( open(fp, 'rb' ) ) print('The qmap is: %s.'%fp) ## WAXS elif scat_geometry =='gi_waxs': fp = '/XF11ID/analysis/2018_2/masks/uid=db5149a1_roi_mask.pkl' roi_mask,qval_dict = cpk.load( open(fp, 'rb' ) ) #for load the saved roi data print(roi_mask.shape) #qval_dict #roi_mask = shift_mask(roi_mask, 10,30) #if shift mask to get new mask show_img(roi_mask, aspect=1.0, image_name = fp)#, center=center[::-1]) #%run /home/yuzhang/pyCHX_link/pyCHX/chx_generic_functions.py imgs = load_data( uid, md['detector'], reverse= reverse, rot90=rot90 ) md.update( imgs.md );Nimg = len(imgs); #md['beam_center_x'], md['beam_center_y'] = cx, cy #if 'number of images' not in list(md.keys()): md['number of images'] = Nimg pixel_mask = 1- np.int_( np.array( imgs.md['pixel_mask'], dtype= bool) ) print( 'The data are: %s' %imgs ) #md['acquire period' ] = md['cam_acquire_period'] #md['exposure time'] = md['cam_acquire_time'] mdn = md.copy() if md['detector'] =='eiger1m_single_image': Chip_Mask=np.load( '/XF11ID/analysis/2017_1/masks/Eiger1M_Chip_Mask.npy') elif md['detector'] =='eiger4m_single_image' or md['detector'] == 'image': Chip_Mask= np.array(np.load( '/XF11ID/analysis/2017_1/masks/Eiger4M_chip_mask.npy'), dtype=bool) BadPix = np.load('/XF11ID/analysis/2018_1/BadPix_4M.npy' ) Chip_Mask.ravel()[BadPix] = 0 elif md['detector'] =='eiger500K_single_image': #print('here') Chip_Mask= np.load( '/XF11ID/analysis/2017_1/masks/Eiger500K_Chip_Mask.npy') #to be defined the chip mask Chip_Mask = np.rot90(Chip_Mask) pixel_mask = np.rot90( 1- np.int_( np.array( imgs.md['pixel_mask'], dtype= bool)) ) else: Chip_Mask = 1 #show_img(Chip_Mask) print(Chip_Mask.shape, pixel_mask.shape) use_local_disk = True import shutil,glob save_oavs = False if len(detectors)==2: if '_image' in md['detector']: pref = md['detector'][:-5] else: pref=md['detector'] for k in [ 'beam_center_x', 'beam_center_y','cam_acquire_time','cam_acquire_period','cam_num_images', 'wavelength', 'det_distance', 'photon_energy']: md[k] = md[ pref + '%s'%k] if 'OAV_image' in detectors: try: #tifs = list( db[uid].data( 'OAV_image') )[0] #print(len(tifs)) save_oavs_tifs( uid, data_dir ) save_oavs = True ## show all images #fig, ax = show_tif_series( tifs, Nx = None, vmin=1.0, vmax=20, logs=False, # cmap= cm.gray, figsize=[4,6] ) ##show one image #show_img(tifs[0],cmap= cm.gray,) except: pass print_dict( md, ['suid', 'number of images', 'uid', 'scan_id', 'start_time', 'stop_time', 'sample', 'Measurement', 'acquire period', 'exposure time', 'det_distance', 'beam_center_x', 'beam_center_y', ] ) if scat_geometry =='gi_saxs': inc_x0 = md['beam_center_x'] inc_y0 = imgs[0].shape[0] - md['beam_center_y'] refl_x0 = md['beam_center_x'] refl_y0 = 1000 #imgs[0].shape[0] - 1758 print( "inc_x0, inc_y0, ref_x0,ref_y0 are: %s %s %s %s."%(inc_x0, inc_y0, refl_x0, refl_y0) ) else: if md['detector'] =='eiger4m_single_image' or md['detector'] == 'image' or md['detector']=='eiger1m_single_image': inc_x0 = imgs[0].shape[0] - md['beam_center_y'] inc_y0= md['beam_center_x'] elif md['detector'] =='eiger500K_single_image': inc_y0 = imgs[0].shape[1] - md['beam_center_y'] inc_x0 = imgs[0].shape[0] - md['beam_center_x'] print(inc_x0, inc_y0) ###for this particular uid, manually give x0/y0 #inc_x0 = 1041 #inc_y0 = 1085 dpix, lambda_, Ldet, exposuretime, timeperframe, center = check_lost_metadata( md, Nimg, inc_x0 = inc_x0, inc_y0= inc_y0, pixelsize = 7.5*10*(-5) ) if scat_geometry =='gi_saxs':center=center[::-1] setup_pargs=dict(uid=uidstr, dpix= dpix, Ldet=Ldet, lambda_= lambda_, exposuretime=exposuretime, timeperframe=timeperframe, center=center, path= data_dir) print_dict( setup_pargs ) setup_pargs if scat_geometry == 'gi_saxs': mask_path = '/XF11ID/analysis/2018_2/masks/' mask_name = 'July13_2018_4M.npy' elif scat_geometry == 'saxs': mask_path = '/XF11ID/analysis/2019_1/masks/' if md['detector'] =='eiger4m_single_image' or md['detector'] == 'image': mask_name = 'Feb6_2019_4M_SAXS.npy' elif md['detector'] =='eiger500K_single_image': mask_name = 'Feb6_2019_500K_SAXS.npy' elif scat_geometry == 'gi_waxs': mask_path = '/XF11ID/analysis/2018_2/masks/' mask_name = 'July20_2018_1M_WAXS.npy' mask = load_mask(mask_path, mask_name, plot_ = False, image_name = uidstr + '_mask', reverse= reverse, rot90=rot90 ) mask = mask * pixel_mask * Chip_Mask show_img(mask,image_name = uidstr + '_mask', save=True, path=data_dir, aspect=1, center=center[::-1]) mask_load=mask.copy() imgsa = apply_mask( imgs, mask ) img_choice_N = 3 img_samp_index = random.sample( range(len(imgs)), img_choice_N) avg_img = get_avg_img( imgsa, img_samp_index, plot_ = False, uid =uidstr) if avg_img.max() == 0: print('There are no photons recorded for this uid: %s'%uid) print('The data analysis should be terminated! Please try another uid.') #show_img( imgsa[1000], vmin=.1, vmax= 1e1, logs=True, aspect=1, # image_name= uidstr + '_img_avg', save=True, path=data_dir, cmap = cmap_albula ) print(center[::-1]) show_img( imgsa[ 5], vmin = -1, vmax = 20, logs=False, aspect=1, #save_format='tif', image_name= uidstr + '_img_avg', save=True, path=data_dir, cmap=cmap_albula,center=center[::-1]) # select subregion, hard coded center beam location #show_img( imgsa[180+40*3/0.05][110:110+840*2, 370:370+840*2], vmin = 0.01, vmax = 20, logs=False, aspect=1, #save_format='tif', # image_name= uidstr + '_img_avg', save=True, path=data_dir, cmap=cmap_albula,center=[845,839]) compress=True photon_occ = len( np.where(avg_img)[0] ) / ( imgsa[0].size) #compress = photon_occ < .4 #if the photon ocupation < 0.5, do compress print ("The non-zeros photon occupation is %s."%( photon_occ)) print("Will " + 'Always ' + ['NOT', 'DO'][compress] + " apply compress process.") if md['detector'] =='eiger4m_single_image' or md['detector'] == 'image': good_start = 5 #make the good_start at least 0 elif md['detector'] =='eiger500K_single_image': good_start = 100 #5 #make the good_start at least 0 elif md['detector'] =='eiger1m_single_image' or md['detector'] == 'image': good_start = 5 bin_frame = False # True #generally make bin_frame as False if bin_frame: bin_frame_number=4 acquisition_period = md['acquire period'] timeperframe = acquisition_period * bin_frame_number else: bin_frame_number =1 force_compress = False #force_compress = True import time t0= time.time() if not use_local_disk: cmp_path = '/nsls2/xf11id1/analysis/Compressed_Data' else: cmp_path = '/tmp_data/compressed' cmp_path = '/nsls2/xf11id1/analysis/Compressed_Data' if bin_frame_number==1: cmp_file = '/uid_%s.cmp'%md['uid'] else: cmp_file = '/uid_%s_bined--%s.cmp'%(md['uid'],bin_frame_number) filename = cmp_path + cmp_file mask2, avg_img, imgsum, bad_frame_list = compress_eigerdata(imgs, mask, md, filename, force_compress= force_compress, para_compress= para_compress, bad_pixel_threshold = 1e14, reverse=reverse, rot90=rot90, bins=bin_frame_number, num_sub= 100, num_max_para_process= 500, with_pickle=True, direct_load_data =use_local_disk, data_path = data_fullpath, ) min_inten = 10 good_start = max(good_start, np.where( np.array(imgsum) > min_inten )[0][0] ) print ('The good_start frame number is: %s '%good_start) FD = Multifile(filename, good_start, len(imgs)//bin_frame_number ) #FD = Multifile(filename, good_start, 100) uid_ = uidstr + '_fra_%s_%s'%(FD.beg, FD.end) print( uid_ ) plot1D( y = imgsum[ np.array( [i for i in np.arange(good_start, len(imgsum)) if i not in bad_frame_list])], title =uidstr + '_imgsum', xlabel='Frame', ylabel='Total_Intensity', legend='imgsum' ) Nimg = Nimg/bin_frame_number run_time(t0) mask = mask * pixel_mask * Chip_Mask mask_copy = mask.copy() mask_copy2 = mask.copy() #%run ~/pyCHX_link/pyCHX/chx_generic_functions.py try: if md['experiment']=='printing': #p = md['printing'] #if have this printing key, will do error function fitting to find t_print0 find_tp0 = True t_print0 = ps( y = imgsum[:400] ) * timeperframe print( 'The start time of print: %s.' %(t_print0 ) ) else: find_tp0 = False print('md[experiment] is not "printing" -> not going to look for t_0') t_print0 = None except: find_tp0 = False print('md[experiment] is not "printing" -> not going to look for t_0') t_print0 = None show_img( avg_img, vmin=1e-3, vmax= 1e1, logs=True, aspect=1, #save_format='tif', image_name= uidstr + '_img_avg', save=True, path=data_dir, center=center[::-1], cmap = cmap_albula ) good_end= None # 2000 if good_end is not None: FD = Multifile(filename, good_start, min( len(imgs)//bin_frame_number, good_end) ) uid_ = uidstr + '_fra_%s_%s'%(FD.beg, FD.end) print( uid_ ) re_define_good_start =False if re_define_good_start: good_start = 180 #good_end = 19700 good_end = len(imgs) FD = Multifile(filename, good_start, good_end) uid_ = uidstr + '_fra_%s_%s'%(FD.beg, FD.end) print( FD.beg, FD.end) bad_frame_list = get_bad_frame_list( imgsum, fit='both', plot=True,polyfit_order = 30, scale= 3.5, good_start = good_start, good_end=good_end, uid= uidstr, path=data_dir) print( 'The bad frame list length is: %s'%len(bad_frame_list) ) imgsum_y = imgsum[ np.array( [i for i in np.arange( len(imgsum)) if i not in bad_frame_list])] imgsum_x = np.arange( len( imgsum_y)) save_lists( [imgsum_x, imgsum_y], label=['Frame', 'Total_Intensity'], filename=uidstr + '_img_sum_t', path= data_dir ) plot1D( y = imgsum_y, title = uidstr + '_img_sum_t', xlabel='Frame', c='b', ylabel='Total_Intensity', legend='imgsum', save=True, path=data_dir) #%run /home/yuzhang/pyCHX_link/pyCHX/chx_packages.py if md['detector'] =='eiger4m_single_image' or md['detector'] == 'image': pass elif md['detector'] =='eiger500K_single_image': #if md['cam_acquire_period'] <= 0.00015: #will check this logic if imgs[0].dtype == 'uint16': print('Create dynamic mask for 500K due to 9K data acquistion!!!') bdp = find_bad_pixels_FD( bad_frame_list, FD, img_shape = avg_img.shape, threshold=20 ) mask = mask_copy2.copy() mask *=bdp mask_copy = mask.copy() show_img( mask, image_name='New Mask_uid=%s'%uid ) setup_pargs #%run ~/pyCHX_link/pyCHX/chx_generic_functions.py %run ~/pyCHX_link/pyCHX/XPCS_SAXS.py if scat_geometry =='saxs': ## Get circular average| * Do plot and save q~iq mask = mask_copy.copy() hmask = create_hot_pixel_mask( avg_img, threshold = 1e8, center=center, center_radius= 10) qp_saxs, iq_saxs, q_saxs = get_circular_average( avg_img * Chip_Mask , mask * hmask, pargs=setup_pargs ) plot_circular_average( qp_saxs, iq_saxs, q_saxs, pargs=setup_pargs, show_pixel=True, xlim=[qp_saxs.min(), qp_saxs.max()*1.0], ylim = [iq_saxs.min(), iq_saxs.max()*2] ) mask =np.array( mask * hmask, dtype=bool) if scat_geometry =='saxs': if run_fit_form: form_res = fit_form_factor( q_saxs,iq_saxs, guess_values={'radius': 2500, 'sigma':0.05, 'delta_rho':1E-10 }, fit_range=[0.0001, 0.015], fit_variables={'radius': T, 'sigma':T, 'delta_rho':T}, res_pargs=setup_pargs, xlim=[0.0001, 0.015]) qr = np.array( [qval_dict[k][0] for k in sorted( qval_dict.keys())] ) if qphi_analysis == False: try: qr_cal, qr_wid = get_QrQw_From_RoiMask( roi_mask, setup_pargs ) print(len(qr)) if (qr_cal - qr).sum() >=1e-3: print( 'The loaded ROI mask might not be applicable to this UID: %s.'%uid) print('Please check the loaded roi mask file.') except: print('Something is wrong with the roi-mask. Please check the loaded roi mask file.') show_ROI_on_image( avg_img*roi_mask, roi_mask, center, label_on = False, rwidth = 840, alpha=.9, save=True, path=data_dir, uid=uidstr, vmin= 1e-3, vmax= 1e-1, #np.max(avg_img), aspect=1, show_roi_edge=True, show_ang_cor = True) plot_qIq_with_ROI( q_saxs, iq_saxs, np.unique(qr), logs=True, uid=uidstr, xlim=[q_saxs.min(), q_saxs.max()*1.02],#[0.0001,0.08], ylim = [iq_saxs.min(), iq_saxs.max()*1.02], save=True, path=data_dir) roi_mask = roi_mask * mask if scat_geometry =='saxs': Nimg = FD.end - FD.beg time_edge = create_time_slice( Nimg, slice_num= 10, slice_width= 1, edges = None ) time_edge = np.array( time_edge ) + good_start #print( time_edge ) qpt, iqst, qt = get_t_iqc( FD, time_edge, mask*Chip_Mask, pargs=setup_pargs, nx=1500, show_progress= False ) plot_t_iqc( qt, iqst, time_edge, pargs=setup_pargs, xlim=[qt.min(), qt.max()], ylim = [iqst.min(), iqst.max()], save=True ) if run_invariant_analysis: if scat_geometry =='saxs': invariant = get_iq_invariant( qt, iqst ) time_stamp = time_edge[:,0] * timeperframe if scat_geometry =='saxs': plot_q2_iq( qt, iqst, time_stamp,pargs=setup_pargs,ylim=[ -0.001, 0.01] , xlim=[0.007,0.2],legend_size= 6 ) if scat_geometry =='saxs': plot_time_iq_invariant( time_stamp, invariant, pargs=setup_pargs, ) if False: iq_int = np.zeros( len(iqst) ) fig, ax = plt.subplots() q = qt for i in range(iqst.shape[0]): yi = iqst[i] * q**2 iq_int[i] = yi.sum() time_labeli = 'time_%s s'%( round( time_edge[i][0] * timeperframe, 3) ) plot1D( x = q, y = yi, legend= time_labeli, xlabel='Q (A-1)', ylabel='I(q)*Q^2', title='I(q)*Q^2 ~ time', m=markers[i], c = colors[i], ax=ax, ylim=[ -0.001, 0.01] , xlim=[0.007,0.2], legend_size=4) #print( iq_int ) if scat_geometry =='gi_saxs': plot_qzr_map( qr_map, qz_map, inc_x0, ticks = ticks, data= avg_img, uid= uidstr, path = data_dir ) if scat_geometry =='gi_saxs': #roi_masks, qval_dicts = get_gisaxs_roi( Qrs, Qzs, qr_map, qz_map, mask= mask ) show_qzr_roi( avg_img, roi_masks, inc_x0, ticks[:4], alpha=0.5, save=True, path=data_dir, uid=uidstr ) if scat_geometry =='gi_saxs': Nimg = FD.end - FD.beg time_edge = create_time_slice( N= Nimg, slice_num= 3, slice_width= 2, edges = None ) time_edge = np.array( time_edge ) + good_start print( time_edge ) qrt_pds = get_t_qrc( FD, time_edge, Qrs, Qzs, qr_map, qz_map, mask=mask, path=data_dir, uid = uidstr ) plot_qrt_pds( qrt_pds, time_edge, qz_index = 0, uid = uidstr, path = data_dir ) if scat_geometry =='gi_saxs': if run_profile_plot: xcorners= [ 1100, 1250, 1250, 1100 ] ycorners= [ 850, 850, 950, 950 ] waterfall_roi_size = [ xcorners[1] - xcorners[0], ycorners[2] - ycorners[1] ] waterfall_roi = create_rectangle_mask( avg_img, xcorners, ycorners ) #show_img( waterfall_roi * avg_img, aspect=1,vmin=.001, vmax=1, logs=True, ) wat = cal_waterfallc( FD, waterfall_roi, qindex= 1, bin_waterfall=True, waterfall_roi_size = waterfall_roi_size,save =True, path=data_dir, uid=uidstr) if scat_geometry =='gi_saxs': if run_profile_plot: plot_waterfallc( wat, qindex=1, aspect=None, vmin=1, vmax= np.max( wat), uid=uidstr, save =True, path=data_dir, beg= FD.beg) if scat_geometry =='gi_saxs': show_qzr_roi( avg_img, roi_mask, inc_x0, ticks[:4], alpha=0.5, save=True, path=data_dir, uid=uidstr ) ## Get 1D Curve (Q||-intensity¶) qr_1d_pds = cal_1d_qr( avg_img, Qr, Qz, qr_map, qz_map, inc_x0= None, mask=mask, setup_pargs=setup_pargs ) plot_qr_1d_with_ROI( qr_1d_pds, qr_center=np.unique( np.array(list( qval_dict.values() ) )[:,0] ), loglog=True, save=True, uid=uidstr, path = data_dir) if scat_geometry =='gi_waxs': #badpixel = np.where( avg_img[:600,:] >=300 ) #roi_mask[badpixel] = 0 show_ROI_on_image( avg_img, roi_mask, label_on = True, alpha=.5, save=True, path=data_dir, uid=uidstr, vmin=0.1, vmax=5) qind, pixelist = roi.extract_label_indices(roi_mask) noqs = len(np.unique(qind)) print(noqs) nopr = np.bincount(qind, minlength=(noqs+1))[1:] nopr roi_inten = check_ROI_intensity( avg_img, roi_mask, ring_number= 2, uid =uidstr ) #roi starting from 1 qth_interest = 2 #the second ring. #qth_interest starting from 1 if scat_geometry =='saxs' or scat_geometry =='gi_waxs': if run_waterfall: wat = cal_waterfallc( FD, roi_mask, qindex= qth_interest, save =True, path=data_dir, uid=uidstr) plot_waterfallc( wat, qth_interest, aspect= None, vmin=1e-1, vmax= wat.max(), uid=uidstr, save =True, path=data_dir, beg= FD.beg, cmap = cmap_vge ) q_mask_name ring_avg = None if run_t_ROI_Inten: times_roi, mean_int_sets = cal_each_ring_mean_intensityc(FD, roi_mask, timeperframe = None, multi_cor=True ) plot_each_ring_mean_intensityc( times_roi, mean_int_sets, uid = uidstr, save=True, path=data_dir ) roi_avg = np.average( mean_int_sets, axis=0) if run_get_mass_center: cx, cy = get_mass_center_one_roi(FD, roi_mask, roi_ind=25) if run_get_mass_center: fig,ax=plt.subplots(2) plot1D( cx, m='o', c='b',ax=ax[0], legend='mass center-refl_X', ylim=[940, 960], ylabel='posX (pixel)') plot1D( cy, m='s', c='r',ax=ax[1], legend='mass center-refl_Y', ylim=[1540, 1544], xlabel='frames',ylabel='posY (pixel)') define_good_series = False #define_good_series = True if define_good_series: good_start = 200 FD = Multifile(filename, beg = good_start, end = 600) #end=1000) uid_ = uidstr + '_fra_%s_%s'%(FD.beg, FD.end) print( uid_ ) if use_sqnorm:#for transmision SAXS norm = get_pixelist_interp_iq( qp_saxs, iq_saxs, roi_mask, center) print('Using circular average in the normalization of G2 for SAXS scattering.') elif use_SG:#for Gi-SAXS or WAXS avg_imgf = sgolay2d( avg_img, window_size= 11, order= 5) * mask norm=np.ravel(avg_imgf)[pixelist] print('Using smoothed image by SavitzkyGolay filter in the normalization of G2.') else: norm= None print('Using simple (average) normalization of G2.') if use_imgsum_norm: imgsum_ = imgsum print('Using frame total intensity for intensity normalization in g2 calculation.') else: imgsum_ = None import time if run_one_time: t0 = time.time() if cal_g2_error: g2,lag_steps,g2_err = cal_g2p(FD,roi_mask,bad_frame_list,good_start, num_buf = 8, num_lev= None,imgsum= imgsum_, norm=norm, cal_error= True ) else: g2,lag_steps = cal_g2p(FD,roi_mask,bad_frame_list,good_start, num_buf = 8, num_lev= None,imgsum= imgsum_, norm=norm, cal_error= False ) run_time(t0) #g2_err.shape, g2.shape lag_steps = lag_steps[:g2.shape[0]] g2.shape[1] if run_one_time: taus = lag_steps * timeperframe try: g2_pds = save_g2_general( g2, taus=taus,qr= np.array( list( qval_dict.values() ) )[:g2.shape[1],0], qz = np.array( list( qval_dict.values() ) )[:g2.shape[1],1], uid=uid_+'_g2.csv', path= data_dir, return_res=True ) except: g2_pds = save_g2_general( g2, taus=taus,qr= np.array( list( qval_dict.values() ) )[:g2.shape[1],0], uid=uid_+'_'+q_mask_name+'_g2.csv', path= data_dir, return_res=True ) if cal_g2_error: try: g2_err_pds = save_g2_general( g2_err, taus=taus,qr= np.array( list( qval_dict.values() ) )[:g2.shape[1],0], qz = np.array( list( qval_dict.values() ) )[:g2.shape[1],1], uid=uid_+'_g2_err.csv', path= data_dir, return_res=True ) except: g2_err_pds = save_g2_general( g2_err, taus=taus,qr= np.array( list( qval_dict.values() ) )[:g2.shape[1],0], uid=uid_+'_'+q_mask_name+'_g2_err.csv', path= data_dir, return_res=True ) #g2.shape if run_one_time: g2_fit_result, taus_fit, g2_fit = get_g2_fit_general( g2, taus, function = fit_g2_func, vlim=[0.95, 1.05], fit_range= None, fit_variables={'baseline':False, 'beta': True, 'alpha':True,'relaxation_rate':True,}, guess_values={'baseline':1.0,'beta': 0.03,'alpha':1.0,'relaxation_rate':0.0005}, guess_limits = dict( baseline =[.9, 1.3], alpha=[0, 2], beta = [0, 1], relaxation_rate= [1e-7, 1000]) ,) g2_fit_paras = save_g2_fit_para_tocsv(g2_fit_result, filename= uid_ +'_'+q_mask_name +'_g2_fit_paras.csv', path=data_dir ) scat_geometry_ if run_one_time: if cal_g2_error: g2_fit_err = np.zeros_like(g2_fit) plot_g2_general( g2_dict={1:g2, 2:g2_fit}, taus_dict={1:taus, 2:taus_fit}, vlim=[0.95, 1.05], g2_err_dict= {1:g2_err, 2: g2_fit_err}, qval_dict = dict(itertools.islice(qval_dict.items(),g2.shape[1])), fit_res= g2_fit_result, geometry= scat_geometry_,filename= uid_+'_g2', path= data_dir, function= fit_g2_func, ylabel='g2', append_name= '_fit') else: plot_g2_general( g2_dict={1:g2, 2:g2_fit}, taus_dict={1:taus, 2:taus_fit}, vlim=[0.95, 1.05], qval_dict = dict(itertools.islice(qval_dict.items(),g2.shape[1])), fit_res= g2_fit_result, geometry= scat_geometry_,filename= uid_+'_g2', path= data_dir, function= fit_g2_func, ylabel='g2', append_name= '_fit') if run_one_time: if True: fs, fe = 0, 8 #fs,fe=0, 6 qval_dict_ = {k:qval_dict[k] for k in list(qval_dict.keys())[fs:fe] } D0, qrate_fit_res = get_q_rate_fit_general( qval_dict_, g2_fit_paras['relaxation_rate'][fs:fe], geometry= scat_geometry_ ) plot_q_rate_fit_general( qval_dict_, g2_fit_paras['relaxation_rate'][fs:fe], qrate_fit_res, geometry= scat_geometry_,uid=uid_ , path= data_dir ) else: D0, qrate_fit_res = get_q_rate_fit_general( qval_dict, g2_fit_paras['relaxation_rate'], fit_range=[0, 26], geometry= scat_geometry_ ) plot_q_rate_fit_general( qval_dict, g2_fit_paras['relaxation_rate'], qrate_fit_res, geometry= scat_geometry_,uid=uid_ , show_fit=False, path= data_dir, plot_all_range=False) #plot1D( x= qr, y=g2_fit_paras['beta'], ls='-', m = 'o', c='b', ylabel=r'$\beta$', xlabel=r'$Q( \AA^{-1} ) $' ) define_good_series = False #define_good_series = True if define_good_series: good_start = 5 FD = Multifile(filename, beg = good_start, end = 1000) uid_ = uidstr + '_fra_%s_%s'%(FD.beg, FD.end) print( uid_ ) data_pixel = None if run_two_time: data_pixel = Get_Pixel_Arrayc( FD, pixelist, norm= norm ).get_data() import time t0=time.time() g12b=None if run_two_time: g12b = auto_two_Arrayc( data_pixel, roi_mask, index = None ) if run_dose: np.save( data_dir + 'uid=%s_g12b'%uid, g12b) run_time( t0 ) if run_two_time: show_C12(g12b, q_ind= 2, qlabel=dict(itertools.islice(qval_dict.items(),g2.shape[1])),N1= FD.beg,logs=False, N2=min( FD.end,10000), vmin= 1.0, vmax=1.18,timeperframe=timeperframe,save=True, path= data_dir, uid = uid_ ,cmap=plt.cm.jet)#cmap=cmap_albula) multi_tau_steps = True if run_two_time: if lag_steps is None: num_bufs=8 noframes = FD.end - FD.beg num_levels = int(np.log( noframes/(num_bufs-1))/np.log(2) +1) +1 tot_channels, lag_steps, dict_lag = multi_tau_lags(num_levels, num_bufs) max_taus= lag_steps.max() #max_taus= lag_steps.max() max_taus = Nimg t0=time.time() #tausb = np.arange( g2b.shape[0])[:max_taus] *timeperframe if multi_tau_steps: lag_steps_ = lag_steps[ lag_steps <= g12b.shape[0] ] g2b = get_one_time_from_two_time(g12b)[lag_steps_] tausb = lag_steps_ *timeperframe else: tausb = (np.arange( g12b.shape[0]) *timeperframe)[:-200] g2b = (get_one_time_from_two_time(g12b))[:-200] run_time(t0) g2b_pds = save_g2_general( g2b, taus=tausb, qr= np.array( list( qval_dict.values() ) )[:g2.shape[1],0], qz=None, uid=uid_+'_'+q_mask_name+'_g2b.csv', path= data_dir, return_res=True ) if run_two_time: g2b_fit_result, tausb_fit, g2b_fit = get_g2_fit_general( g2b, tausb, function = fit_g2_func, vlim=[0.95, 1.05], fit_range= None, fit_variables={'baseline':False, 'beta': True, 'alpha':True,'relaxation_rate':True}, guess_values={'baseline':1.0,'beta': 0.15,'alpha':1.0,'relaxation_rate':1e-3,}, guess_limits = dict( baseline =[1, 1.8], alpha=[0, 2], beta = [0, 1], relaxation_rate= [1e-8, 5000]) ) g2b_fit_paras = save_g2_fit_para_tocsv(g2b_fit_result, filename= uid_ +'_'+q_mask_name+'_g2b_fit_paras.csv', path=data_dir ) #plot1D( x = tausb[1:], y =g2b[1:,0], ylim=[0.95, 1.46], xlim = [0.0001, 10], m='', c='r', ls = '-', # logx=True, title='one_time_corelation', xlabel = r"$\tau $ $(s)$", ) if run_two_time: plot_g2_general( g2_dict={1:g2b, 2:g2b_fit}, taus_dict={1:tausb, 2:tausb_fit}, vlim=[0.95, 1.05], qval_dict=dict(itertools.islice(qval_dict.items(),g2.shape[1])), fit_res= g2b_fit_result, geometry=scat_geometry_,filename=uid_+'_g2', path= data_dir, function= fit_g2_func, ylabel='g2', append_name= '_b_fit') if run_two_time: D0b, qrate_fit_resb = get_q_rate_fit_general( dict(itertools.islice(qval_dict.items(),g2.shape[1])), g2b_fit_paras['relaxation_rate'], fit_range=[0, 10], geometry= scat_geometry_ ) #qval_dict, g2b_fit_paras['relaxation_rate'] if run_two_time: if True: fs, fe = 0,8 #fs, fe = 0,12 qval_dict_ = {k:qval_dict[k] for k in list(qval_dict.keys())[fs:fe] } D0b, qrate_fit_resb = get_q_rate_fit_general( qval_dict_, g2b_fit_paras['relaxation_rate'][fs:fe], geometry= scat_geometry_ ) plot_q_rate_fit_general( qval_dict_, g2b_fit_paras['relaxation_rate'][fs:fe], qrate_fit_resb, geometry= scat_geometry_,uid=uid_ +'_two_time' , path= data_dir ) else: D0b, qrate_fit_resb = get_q_rate_fit_general( qval_dict, g2b_fit_paras['relaxation_rate'], fit_range=[0, 10], geometry= scat_geometry_ ) plot_q_rate_fit_general( qval_dict, g2b_fit_paras['relaxation_rate'], qrate_fit_resb, geometry= scat_geometry_,uid=uid_ +'_two_time', show_fit=False,path= data_dir, plot_all_range= True ) if run_two_time and run_one_time: plot_g2_general( g2_dict={1:g2, 2:g2b}, taus_dict={1:taus, 2:tausb},vlim=[0.99, 1.007], qval_dict=dict(itertools.islice(qval_dict.items(),g2.shape[1])), g2_labels=['from_one_time', 'from_two_time'], geometry=scat_geometry_,filename=uid_+'_g2_two_g2', path= data_dir, ylabel='g2', ) #run_dose = True if run_dose: get_two_time_mulit_uids( [uid], roi_mask, norm= norm, bin_frame_number=1, path= data_dir0, force_generate=False, compress_path = cmp_path + '/' ) try: print( md['transmission'] ) except: md['transmission'] =1 exposuretime if run_dose: N = len(imgs) print(N) #exposure_dose = md['transmission'] * exposuretime* np.int_([ N/16, N/8, N/4 ,N/2, 3*N/4, N*0.99 ]) exposure_dose = md['transmission'] * exposuretime* np.int_([ N/8, N/4 ,N/2, 3*N/4, N*0.99 ]) print( exposure_dose ) if run_dose: taus_uids, g2_uids = get_series_one_time_mulit_uids( [ uid ], qval_dict, good_start=good_start, path= data_dir0, exposure_dose = exposure_dose, num_bufs =8, save_g2= False, dead_time = 0, trans = [ md['transmission'] ] ) if run_dose: plot_dose_g2( taus_uids, g2_uids, ylim=[1.0, 1.2], vshift= 0.00, qval_dict = qval_dict, fit_res= None, geometry= scat_geometry_, filename= '%s_dose_analysis'%uid_, path= data_dir, function= None, ylabel='g2_Dose', g2_labels= None, append_name= '' ) if run_dose: qth_interest = 1 plot_dose_g2( taus_uids, g2_uids, qth_interest= qth_interest, ylim=[0.98, 1.2], vshift= 0.00, qval_dict = qval_dict, fit_res= None, geometry= scat_geometry_, filename= '%s_dose_analysis'%uidstr, path= data_dir, function= None, ylabel='g2_Dose', g2_labels= None, append_name= '' ) 0.33/0.00134 if run_four_time: t0=time.time() g4 = get_four_time_from_two_time(g12b, g2=g2b)[:int(max_taus)] run_time(t0) if run_four_time: taus4 = np.arange( g4.shape[0])*timeperframe g4_pds = save_g2_general( g4, taus=taus4, qr=np.array( list( qval_dict.values() ) )[:,0], qz=None, uid=uid_ +'_g4.csv', path= data_dir, return_res=True ) if run_four_time: plot_g2_general( g2_dict={1:g4}, taus_dict={1:taus4},vlim=[0.95, 1.05], qval_dict=qval_dict, fit_res= None, geometry=scat_geometry_,filename=uid_+'_g4',path= data_dir, ylabel='g4') #run_xsvs =True if run_xsvs: max_cts = get_max_countc(FD, roi_mask ) #max_cts = 15 #for eiger 500 K qind, pixelist = roi.extract_label_indices( roi_mask ) noqs = len( np.unique(qind) ) nopr = np.bincount(qind, minlength=(noqs+1))[1:] #time_steps = np.array( utils.geometric_series(2, len(imgs) ) ) time_steps = [0,1] #only run the first two levels num_times = len(time_steps) times_xsvs = exposuretime + (2**( np.arange( len(time_steps) ) ) -1 ) * timeperframe print( 'The max counts are: %s'%max_cts ) if run_xsvs: if roi_avg is None: times_roi, mean_int_sets = cal_each_ring_mean_intensityc(FD, roi_mask, timeperframe = None, ) roi_avg = np.average( mean_int_sets, axis=0) t0=time.time() spec_bins, spec_his, spec_std, spec_sum = xsvsp( FD, np.int_(roi_mask), norm=None, max_cts=int(max_cts+2), bad_images=bad_frame_list, only_two_levels=True ) spec_kmean = np.array( [roi_avg * 2**j for j in range( spec_his.shape[0] )] ) run_time(t0) spec_pds = save_bin_his_std( spec_bins, spec_his, spec_std, filename=uid_+'_spec_res.csv', path=data_dir ) if run_xsvs: ML_val, KL_val,K_ = get_xsvs_fit( spec_his, spec_sum, spec_kmean, spec_std, max_bins=2, fit_range=[1,60], varyK= False ) #print( 'The observed average photon counts are: %s'%np.round(K_mean,4)) #print( 'The fitted average photon counts are: %s'%np.round(K_,4)) print( 'The difference sum of average photon counts between fit and data are: %s'%np.round( abs(np.sum( spec_kmean[0,:] - K_ )),4)) print( '#'*30) qth= 0 print( 'The fitted M for Qth= %s are: %s'%(qth, ML_val[qth]) ) print( K_[qth]) print( '#'*30) if run_xsvs: qr = [qval_dict[k][0] for k in list(qval_dict.keys()) ] plot_xsvs_fit( spec_his, ML_val, KL_val, K_mean = spec_kmean, spec_std=spec_std, xlim = [0,10], vlim =[.9, 1.1], uid=uid_, qth= qth_interest, logy= True, times= times_xsvs, q_ring_center=qr, path=data_dir) plot_xsvs_fit( spec_his, ML_val, KL_val, K_mean = spec_kmean, spec_std = spec_std, xlim = [0,15], vlim =[.9, 1.1], uid=uid_, qth= None, logy= True, times= times_xsvs, q_ring_center=qr, path=data_dir ) if run_xsvs: contrast_factorL = get_contrast( ML_val) spec_km_pds = save_KM( spec_kmean, KL_val, ML_val, qs=qr, level_time=times_xsvs, uid=uid_, path = data_dir ) #spec_km_pds if run_xsvs: plot_g2_contrast( contrast_factorL, g2b, times_xsvs, tausb, qr, vlim=[0.8,1.2], qth = qth_interest, uid=uid_,path = data_dir, legend_size=14) plot_g2_contrast( contrast_factorL, g2b, times_xsvs, tausb, qr, vlim=[0.8,1.2], qth = None, uid=uid_,path = data_dir, legend_size=4) #from chxanalys.chx_libs import cmap_vge, cmap_albula, Javascript md['mask_file']= mask_path + mask_name md['roi_mask_file']= fp md['mask'] = mask #md['NOTEBOOK_FULL_PATH'] = data_dir + get_current_pipeline_fullpath(NFP).split('/')[-1] md['good_start'] = good_start md['bad_frame_list'] = bad_frame_list md['avg_img'] = avg_img md['roi_mask'] = roi_mask md['setup_pargs'] = setup_pargs if scat_geometry == 'gi_saxs': md['Qr'] = Qr md['Qz'] = Qz md['qval_dict'] = qval_dict md['beam_center_x'] = inc_x0 md['beam_center_y']= inc_y0 md['beam_refl_center_x'] = refl_x0 md['beam_refl_center_y'] = refl_y0 elif scat_geometry == 'gi_waxs': md['beam_center_x'] = center[1] md['beam_center_y']= center[0] else: md['qr']= qr #md['qr_edge'] = qr_edge md['qval_dict'] = qval_dict md['beam_center_x'] = center[1] md['beam_center_y']= center[0] md['beg'] = FD.beg md['end'] = FD.end md['t_print0'] = t_print0 md['qth_interest'] = qth_interest md['metadata_file'] = data_dir + 'uid=%s_md.pkl'%uid psave_obj( md, data_dir + 'uid=%s_md.pkl'%uid ) #save the setup parameters save_dict_csv( md, data_dir + 'uid=%s_md.csv'%uid, 'w') Exdt = {} if scat_geometry == 'gi_saxs': for k,v in zip( ['md', 'roi_mask','qval_dict','avg_img','mask','pixel_mask', 'imgsum', 'bad_frame_list', 'qr_1d_pds'], [md, roi_mask, qval_dict, avg_img,mask,pixel_mask, imgsum, bad_frame_list, qr_1d_pds] ): Exdt[ k ] = v elif scat_geometry == 'saxs': for k,v in zip( ['md', 'q_saxs', 'iq_saxs','iqst','qt','roi_mask','qval_dict','avg_img','mask','pixel_mask', 'imgsum', 'bad_frame_list'], [md, q_saxs, iq_saxs, iqst, qt,roi_mask, qval_dict, avg_img,mask,pixel_mask, imgsum, bad_frame_list] ): Exdt[ k ] = v elif scat_geometry == 'gi_waxs': for k,v in zip( ['md', 'roi_mask','qval_dict','avg_img','mask','pixel_mask', 'imgsum', 'bad_frame_list'], [md, roi_mask, qval_dict, avg_img,mask,pixel_mask, imgsum, bad_frame_list] ): Exdt[ k ] = v if run_waterfall:Exdt['wat'] = wat if run_t_ROI_Inten:Exdt['times_roi'] = times_roi;Exdt['mean_int_sets']=mean_int_sets if run_one_time: if run_invariant_analysis: for k,v in zip( ['taus','g2','g2_fit_paras', 'time_stamp','invariant'], [taus,g2,g2_fit_paras,time_stamp,invariant] ):Exdt[ k ] = v else: for k,v in zip( ['taus','g2','g2_fit_paras' ], [taus,g2,g2_fit_paras ] ):Exdt[ k ] = v if run_two_time: for k,v in zip( ['tausb','g2b','g2b_fit_paras', 'g12b'], [tausb,g2b,g2b_fit_paras,g12b] ):Exdt[ k ] = v #for k,v in zip( ['tausb','g2b','g2b_fit_paras', ], [tausb,g2b,g2b_fit_paras] ):Exdt[ k ] = v if run_dose: for k,v in zip( [ 'taus_uids', 'g2_uids' ], [taus_uids, g2_uids] ):Exdt[ k ] = v if run_four_time: for k,v in zip( ['taus4','g4'], [taus4,g4] ):Exdt[ k ] = v if run_xsvs: for k,v in zip( ['spec_kmean','spec_pds','times_xsvs','spec_km_pds','contrast_factorL'], [ spec_kmean,spec_pds,times_xsvs,spec_km_pds,contrast_factorL] ):Exdt[ k ] = v #%run chxanalys_link/chxanalys/Create_Report.py export_xpcs_results_to_h5( 'uid=%s_%s_Res.h5'%(md['uid'],q_mask_name), data_dir, export_dict = Exdt ) #extract_dict = extract_xpcs_results_from_h5( filename = 'uid=%s_Res.h5'%md['uid'], import_dir = data_dir ) #g2npy_filename = data_dir + '/' + 'uid=%s_g12b.npy'%uid #print(g2npy_filename) #if os.path.exists( g2npy_filename): # print('Will delete this file=%s.'%g2npy_filename) # os.remove( g2npy_filename ) #extract_dict = extract_xpcs_results_from_h5( filename = 'uid=%s_Res.h5'%md['uid'], import_dir = data_dir ) #extract_dict = extract_xpcs_results_from_h5( filename = 'uid=%s_Res.h5'%md['uid'], import_dir = data_dir ) pdf_out_dir = os.path.join('/XF11ID/analysis/', CYCLE, username, 'Results/') pdf_filename = "XPCS_Analysis_Report2_for_uid=%s%s%s.pdf"%(uid,pdf_version,q_mask_name) if run_xsvs: pdf_filename = "XPCS_XSVS_Analysis_Report_for_uid=%s%s%s.pdf"%(uid,pdf_version,q_mask_name) #%run /home/yuzhang/chxanalys_link/chxanalys/Create_Report.py data_dir make_pdf_report( data_dir, uid, pdf_out_dir, pdf_filename, username, run_fit_form,run_one_time, run_two_time, run_four_time, run_xsvs, run_dose, report_type= scat_geometry, report_invariant= run_invariant_analysis, md = md ) #%run /home/yuzhang/chxanalys_link/chxanalys/chx_olog.py if att_pdf_report: os.environ['HTTPS_PROXY'] = 'https://proxy:8888' os.environ['no_proxy'] = 'cs.nsls2.local,localhost,127.0.0.1' update_olog_uid_with_file( uid[:6], text='Add XPCS Analysis PDF Report', filename=pdf_out_dir + pdf_filename, append_name='_R1' ) if save_oavs: os.environ['HTTPS_PROXY'] = 'https://proxy:8888' os.environ['no_proxy'] = 'cs.nsls2.local,localhost,127.0.0.1' update_olog_uid_with_file( uid[:6], text='Add OVA images', filename= data_dir + 'uid=%s_OVA_images.png'%uid, append_name='_img' ) # except: uid #save_current_pipeline( NFP, data_dir) #get_current_pipeline_fullpath(NFP) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Control Runs Here Step2: Make a directory for saving results Step3: Load Metadata & Image Data Step4: Don't Change the lines below here Step5: Load ROI defined by "XPCS_Setup" Pipeline Step6: Load ROI mask depending on data analysis type Step7: get data Step8: Load Chip mask depeding on detector Step9: Overwrite Some Metadata if Wrong Input Step10: Apply Mask Step11: Check several frames average intensity Step12: Compress Data Step13: Get bad frame list by a polynominal fit Step14: Creat new mask by masking the bad pixels and get new avg_img Step15: Plot time~ total intensity of each frame Step16: Get Dynamic Mask (currently designed for 500K) Step17: Static Analysis Step18: Time Depedent I(q) Analysis Step19: GiSAXS Scattering Geometry Step20: Static Analysis for gisaxs Step21: Make a Profile Plot Step22: Dynamic Analysis for gi_saxs Step23: GiWAXS Scattering Geometry Step24: Extract the labeled array Step25: Number of pixels in each q box Step26: Check one ROI intensity Step27: Do a waterfall analysis Step28: Analysis for mass center of reflective beam center Step29: One time Correlation Step30: Fit g2 Step31: For two-time Step32: Run Dose dependent analysis Step33: Four Time Correlation Step34: Speckle Visiblity Step35: Do historam Step36: Do historam fit by negtive binominal function with maximum likehood method Step37: Plot fit results Step38: Get contrast Step39: Plot contrast with g2 results Step40: Export Results to a HDF5 File Step41: Creat PDF Report Step42: Attach the PDF report to Olog Step43: Save the OVA image Step44: The End! Step45: Save the current pipeline in Results folder

<ASSISTANT_TASK:> Python Code: import pypsa import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns sns.set(rc={"figure.figsize": (9, 5)}) network = pypsa.Network() for i in range(3): network.add("Bus", "electric bus {}".format(i), v_nom=20.0) network.add("Bus", "heat bus {}".format(i), carrier="heat") network.buses network.buses["carrier"].value_counts() for i in range(3): network.add( "Line", "line {}".format(i), bus0="electric bus {}".format(i), bus1="electric bus {}".format((i + 1) % 3), x=0.1, s_nom=1000, ) network.lines for i in range(3): network.add( "Link", "heat pump {}".format(i), bus0="electric bus {}".format(i), bus1="heat bus {}".format(i), p_nom=100, efficiency=3.0, ) network.links network.add("Carrier", "gas", co2_emissions=0.27) network.add("Carrier", "biomass", co2_emissions=0.0) network.carriers network.add( "Generator", "gas generator", bus="electric bus 0", p_nom=100, marginal_cost=50, carrier="gas", efficiency=0.3, ) network.add( "Generator", "biomass generator", bus="electric bus 1", p_nom=100, marginal_cost=100, efficiency=0.3, carrier="biomass", ) for i in range(3): network.add( "Generator", "boiler {}".format(i), bus="heat bus {}".format(i), p_nom=1000, efficiency=0.9, marginal_cost=20.0, carrier="gas", ) network.generators for i in range(3): network.add( "Load", "electric load {}".format(i), bus="electric bus {}".format(i), p_set=i * 10, ) for i in range(3): network.add( "Load", "heat load {}".format(i), bus="heat bus {}".format(i), p_set=(3 - i) * 10, ) network.loads def run_lopf(): network.lopf() df = pd.concat( [ network.generators_t.p.loc["now"], network.links_t.p0.loc["now"], network.loads_t.p.loc["now"], ], keys=["Generators", "Links", "Line"], names=["Component", "index"], ).reset_index(name="Production") sns.barplot(data=df, x="index", y="Production", hue="Component") plt.title(f"Objective: {network.objective}") plt.xticks(rotation=90) plt.tight_layout() run_lopf() network.links.marginal_cost = 10 run_lopf() network.add("GlobalConstraint", "co2_limit", sense="<=", constant=0.0) run_lopf() <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Add three buses of AC and heat carrier each Step2: Add three lines in a ring Step3: Connect the electric to the heat buses with heat pumps with COP 3 Step4: Add carriers Step5: Add a gas generator at bus 0, a biomass generator at bus 1 and a boiler at all heat buses Step6: Add electric loads and heat loads. Step7: We define a function for the LOPF Step8: Now, rerun with marginal costs for the heat pump operation. Step9: Finally, rerun with no CO2 emissions.

<ASSISTANT_TASK:> Python Code: import numpy as np import pandas as pd import matplotlib.pyplot as plt %matplotlib inline X_train = np.linspace(0, 1, 100) X_test = np.linspace(0, 1, 1000) @np.vectorize def target(x): return x > 0.5 Y_train = target(X_train) + np.random.randn(*X_train.shape) * 0.1 Y_test = target(X_test) + np.random.randn(*X_test.shape) * 0.1 plt.figure(figsize = (16, 9)); plt.scatter(X_train, Y_train, s=50); plt.title('Train dataset'); plt.xlabel('X'); plt.ylabel('Y'); def loss_mse(predict, true): return np.mean((predict - true) ** 2) def stamp_fit(x, y): root_prediction = np.mean(y) root_loss = loss_mse(root_prediction, y) gain = [] _, thresholds = np.histogram(x) thresholds = thresholds[1:-1] for i in thresholds: left_predict = np.mean(y[x < i]) left_weight = np.sum(x < i) / x.shape[0] right_predict = np.mean(y[x >= i]) right_weight = np.sum(x >= i) / x.shape[0] loss = left_weight * loss_mse(left_predict, y[x < i]) + right_weight * loss_mse(right_predict, y[x >= i]) gain.append(root_loss - loss) threshold = thresholds[np.argmax(gain)] left_predict = np.mean(y[x < threshold]) right_predict = np.mean(y[x >= threshold]) return threshold, left_predict, right_predict @np.vectorize def stamp_predict(x, threshold, predict_l, predict_r): prediction = predict_l if x < threshold else predict_r return prediction predict_params = stamp_fit(X_train, Y_train) prediction = stamp_predict(X_test, *predict_params) loss_mse(prediction, Y_test) plt.figure(figsize = (16, 9)); plt.scatter(X_test, Y_test, s=50); plt.plot(X_test, prediction, 'r'); plt.title('Test dataset'); plt.xlabel('X'); plt.ylabel('Y'); from sklearn.tree import DecisionTreeRegressor def get_grid(data): x_min, x_max = data[:, 0].min() - 1, data[:, 0].max() + 1 y_min, y_max = data[:, 1].min() - 1, data[:, 1].max() + 1 return np.meshgrid(np.arange(x_min, x_max, 0.01), np.arange(y_min, y_max, 0.01)) data_x = np.random.normal(size=(100, 2)) data_y = (data_x[:, 0] ** 2 + data_x[:, 1] ** 2) ** 0.5 plt.figure(figsize=(8, 8)); plt.scatter(data_x[:, 0], data_x[:, 1], c=data_y, s=100, cmap='spring'); plt.figure(figsize=(20, 6)) for i in range(3): clf = DecisionTreeRegressor(random_state=42) indecies = np.random.randint(data_x.shape[0], size=int(data_x.shape[0] * 0.9)) clf.fit(data_x[indecies], data_y[indecies]) xx, yy = get_grid(data_x) predicted = clf.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape) plt.subplot2grid((1, 3), (0, i)) plt.pcolormesh(xx, yy, predicted, cmap='winter') plt.scatter(data_x[:, 0], data_x[:, 1], c=data_y, s=30, cmap='winter', edgecolor='k') plt.figure(figsize=(14, 14)) for i, max_depth in enumerate([2, 4, None]): for j, min_samples_leaf in enumerate([15, 5, 1]): clf = DecisionTreeRegressor(max_depth=max_depth, min_samples_leaf=min_samples_leaf) clf.fit(data_x, data_y) xx, yy = get_grid(data_x) predicted = clf.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape) plt.subplot2grid((3, 3), (i, j)) plt.pcolormesh(xx, yy, predicted, cmap='spring') plt.scatter(data_x[:, 0], data_x[:, 1], c=data_y, s=30, cmap='spring', edgecolor='k') plt.title('max_depth=' + str(max_depth) + ', min_samples_leaf: ' + str(min_samples_leaf)) def median(X): return np.median(X) def make_sample_cauchy(n_samples): sample = np.random.standard_cauchy(size=n_samples) return sample X = make_sample_cauchy(int(1e2)) plt.hist(X, bins=int(1e1)); med = median(X) med def make_sample_bootstrap(X): size = X.shape[0] idx_range = range(size) new_idx = np.random.choice(idx_range, size, replace=True) return X[new_idx] K = 500 median_boot_samples = [] for i in range(K): boot_sample = make_sample_bootstrap(X) meadian_boot_sample = median(boot_sample) median_boot_samples.append(meadian_boot_sample) median_boot_samples = np.array(median_boot_samples) mean = np.mean(median_boot_samples) std = np.std(median_boot_samples) print(mean, std) plt.hist(median_boot_samples, bins=int(50)); from sklearn.ensemble import RandomForestRegressor clf = RandomForestRegressor(n_estimators=100) clf.fit(data_x, data_y) xx, yy = get_grid(data_x) predicted = clf.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape) plt.figure(figsize=(8, 8)); plt.pcolormesh(xx, yy, predicted, cmap='spring'); plt.scatter(data_x[:, 0], data_x[:, 1], c=data_y, s=100, cmap='spring', edgecolor='k'); from sklearn.datasets import load_boston data = load_boston() X = data.data y = data.target from sklearn.model_selection import KFold, cross_val_score cv = KFold(shuffle=True, random_state=1011) regr = DecisionTreeRegressor() print(cross_val_score(regr, X, y, cv=cv, scoring='r2').mean()) from sklearn.ensemble import BaggingRegressor from sklearn.ensemble import RandomForestRegressor # usuall cv code <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: <a id='tree'></a> Step2: <a id='stamp'></a> Step3: <a id='lim'></a> Step4: Sensitivity with respect to the subsample Step5: Sensitivity with respect to the hyper parameters Step6: To overcome this disadvantages, we will consider bagging or bootstrap aggregation Step7: So, our model median will be Step8: Exact variance formula for sample cauchy median is following Step9: Second, for $K$ bootstrap samples your shoud estimate its median. Step10: Now we can obtain mean and variance from median_boot_samples as we are usually done it in statistics Step11: Please, put your estimation of std rounded to the 3 decimals at the form Step12: <a id='rf'></a> Step13: You can note, that all boundaries become much more smoother. Now we will compare methods on the Boston Dataset Step14: Task 1 Step15: Find best parameter with CV. Please put score at the https

<ASSISTANT_TASK:> Python Code: import numpy as np from scipy import spatial def sim1(n): v1 = np.random.randint(0, 100, n) v2 = np.random.randint(0, 100, n) return 1 - spatial.distance.cosine(v1, v2) def sim2(n): v1 = np.random.randint(0, 100, n) v2 = np.random.randint(0, 100, n) return np.dot(v1, v2) / np.linalg.norm(v1) / np.linalg.norm(v2) import math def sim3(n): v1 = np.random.randint(0, 100, n) v2 = np.random.randint(0, 100, n) return sum(v1 * v2) / math.sqrt(sum(v1 ** 2)) / math.sqrt(sum(v2 ** 2)) from itertools import izip def dot_product(v1, v2): return sum(map(lambda x: x[0] * x[1], izip(v1, v2))) def sim4(n): v1 = np.random.randint(0, 100, n) v2 = np.random.randint(0, 100, n) prod = dot_product(v1, v2) len1 = math.sqrt(dot_product(v1, v1)) len2 = math.sqrt(dot_product(v2, v2)) return prod / (len1 * len2) %timeit sim1(400) %timeit sim2(400) %timeit sim3(400) %timeit sim4(400) from datetime import datetime as dt start = dt.now() start.date(), start.time(), start dt.now() - start import logging fmtstr = '%(asctime)s [%(levelname)s][%(name)s] %(message)s' datefmtstr = '%Y/%m/%d %H:%M:%S' if len(logging.getLogger().handlers) >= 1: logging.getLogger().handlers[0].setFormatter(logging.Formatter(fmtstr, datefmtstr)) else: logging.basicConfig(format=fmtstr, datefmt=datefmtstr) # 如果直接呼叫 logging.warning，就是使用root logger logging.warning("please set %d in %s", 100, "length") # 在root logger下面增加child logger aaa_logger = logging.getLogger('aaa') bbb_logger = aaa_logger.getChild('bbb') ccc_logger = bbb_logger.getChild('ccc') aaa_logger.warn("hello") bbb_logger.warn("hello") # 當logger是樹狀結構時，logger的名稱會變成 aaa.bbb.ccc ccc_logger.warn("hello") <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: 在python中計算cosine similarity最快的方法是什麼? Step2: 結論 Step3: logging Step4: 如果從某個module呼叫時，就用

<ASSISTANT_TASK:> Python Code: # Run some setup code for this notebook. import random import numpy as np from cs231n.data_utils import load_CIFAR10 import matplotlib.pyplot as plt # This is a bit of magic to make matplotlib figures appear inline in the notebook # rather than in a new window. %matplotlib inline plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' # Some more magic so that the notebook will reload external python modules; # see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython %load_ext autoreload %autoreload 2 # Load the raw CIFAR-10 data. cifar10_dir = 'cs231n/datasets/cifar-10-batches-py' X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir) # As a sanity check, we print out the size of the training and test data. print 'Training data shape: ', X_train.shape print 'Training labels shape: ', y_train.shape print 'Test data shape: ', X_test.shape print 'Test labels shape: ', y_test.shape # Visualize some examples from the dataset. # We show a few examples of training images from each class. classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] num_classes = len(classes) samples_per_class = 7 for y, cls in enumerate(classes): idxs = np.flatnonzero(y_train == y) idxs = np.random.choice(idxs, samples_per_class, replace=False) for i, idx in enumerate(idxs): plt_idx = i * num_classes + y + 1 plt.subplot(samples_per_class, num_classes, plt_idx) plt.imshow(X_train[idx].astype('uint8')) plt.axis('off') if i == 0: plt.title(cls) plt.show() # Subsample the data for more efficient code execution in this exercise num_training = 5000 mask = range(num_training) X_train = X_train[mask] y_train = y_train[mask] num_test = 500 mask = range(num_test) X_test = X_test[mask] y_test = y_test[mask] # Reshape the image data into rows X_train = np.reshape(X_train, (X_train.shape[0], -1)) X_test = np.reshape(X_test, (X_test.shape[0], -1)) print X_train.shape, X_test.shape from cs231n.classifiers import KNearestNeighbor # Create a kNN classifier instance. # Remember that training a kNN classifier is a noop: # the Classifier simply remembers the data and does no further processing classifier = KNearestNeighbor() classifier.train(X_train, y_train) # Open cs231n/classifiers/k_nearest_neighbor.py and implement # compute_distances_two_loops. # Test your implementation: dists = classifier.compute_distances_two_loops(X_test) print dists.shape # We can visualize the distance matrix: each row is a single test example and # its distances to training examples plt.imshow(dists, interpolation='none') plt.show() # Now implement the function predict_labels and run the code below: # We use k = 1 (which is Nearest Neighbor). y_test_pred = classifier.predict_labels(dists, k=1) # Compute and print the fraction of correctly predicted examples num_correct = np.sum(y_test_pred == y_test) accuracy = float(num_correct) / num_test print 'Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy) y_test_pred = classifier.predict_labels(dists, k=5) num_correct = np.sum(y_test_pred == y_test) accuracy = float(num_correct) / num_test print 'Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy) # Now lets speed up distance matrix computation by using partial vectorization # with one loop. Implement the function compute_distances_one_loop and run the # code below: dists_one = classifier.compute_distances_one_loop(X_test) # To ensure that our vectorized implementation is correct, we make sure that it # agrees with the naive implementation. There are many ways to decide whether # two matrices are similar; one of the simplest is the Frobenius norm. In case # you haven't seen it before, the Frobenius norm of two matrices is the square # root of the squared sum of differences of all elements; in other words, reshape # the matrices into vectors and compute the Euclidean distance between them. difference = np.linalg.norm(dists - dists_one, ord='fro') print 'Difference was: %f' % (difference, ) if difference < 0.001: print 'Good! The distance matrices are the same' else: print 'Uh-oh! The distance matrices are different' # Now implement the fully vectorized version inside compute_distances_no_loops # and run the code dists_two = classifier.compute_distances_no_loops(X_test) # check that the distance matrix agrees with the one we computed before: difference = np.linalg.norm(dists - dists_two, ord='fro') print 'Difference was: %f' % (difference, ) if difference < 0.001: print 'Good! The distance matrices are the same' else: print 'Uh-oh! The distance matrices are different' # Let's compare how fast the implementations are def time_function(f, *args): Call a function f with args and return the time (in seconds) that it took to execute. import time tic = time.time() f(*args) toc = time.time() return toc - tic two_loop_time = time_function(classifier.compute_distances_two_loops, X_test) print 'Two loop version took %f seconds' % two_loop_time one_loop_time = time_function(classifier.compute_distances_one_loop, X_test) print 'One loop version took %f seconds' % one_loop_time no_loop_time = time_function(classifier.compute_distances_no_loops, X_test) print 'No loop version took %f seconds' % no_loop_time # you should see significantly faster performance with the fully vectorized implementation num_folds = 5 k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100] X_train_folds = [] y_train_folds = [] ################################################################################ # TODO: # # Split up the training data into folds. After splitting, X_train_folds and # # y_train_folds should each be lists of length num_folds, where # # y_train_folds[i] is the label vector for the points in X_train_folds[i]. # # Hint: Look up the numpy array_split function. # ################################################################################ X_train_folds = np.array_split(X_train, num_folds, axis = 0) Y_train_folds = np.array_split(y_train, num_folds) ################################################################################ # END OF YOUR CODE # ################################################################################ # A dictionary holding the accuracies for different values of k that we find # when running cross-validation. After running cross-validation, # k_to_accuracies[k] should be a list of length num_folds giving the different # accuracy values that we found when using that value of k. k_to_accuracies = {} ################################################################################ # TODO: # # Perform k-fold cross validation to find the best value of k. For each # # possible value of k, run the k-nearest-neighbor algorithm num_folds times, # # where in each case you use all but one of the folds as training data and the # # last fold as a validation set. Store the accuracies for all fold and all # # values of k in the k_to_accuracies dictionary. # ################################################################################ for k in k_choices: k_to_accuracies[k] = [] for i in xrange(num_folds): train_index = range(num_folds) del train_index[i] X_temp = np.zeros((0, X_train.shape[1])) Y_temp = [] for j in train_index: X_temp = np.append(X_temp, X_train_folds[j], axis = 0) Y_temp = np.append(Y_temp, Y_train_folds[j]) classifier = KNearestNeighbor() classifier.train(X_temp, Y_temp) dists = classifier.compute_distances_no_loops(X_train_folds[i]) y_test_pred = classifier.predict_labels(dists, k=k) # Compute and print the fraction of correctly predicted examples num_correct = np.sum(y_test_pred == Y_train_folds[i]) accuracy = float(num_correct) / Y_train_folds[i].shape[0] k_to_accuracies[k].append(accuracy) ################################################################################ # END OF YOUR CODE # ################################################################################ # Print out the computed accuracies for k in sorted(k_to_accuracies): for accuracy in k_to_accuracies[k]: print 'k = %d, accuracy = %f' % (k, accuracy) # plot the raw observations for k in k_choices: accuracies = k_to_accuracies[k] plt.scatter([k] * len(accuracies), accuracies) # plot the trend line with error bars that correspond to standard deviation accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())]) accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())]) plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std) plt.title('Cross-validation on k') plt.xlabel('k') plt.ylabel('Cross-validation accuracy') plt.show() # Based on the cross-validation results above, choose the best value for k, # retrain the classifier using all the training data, and test it on the test # data. You should be able to get above 28% accuracy on the test data. best_k = 10 classifier = KNearestNeighbor() classifier.train(X_train, y_train) y_test_pred = classifier.predict(X_test, k=best_k) # Compute and display the accuracy num_correct = np.sum(y_test_pred == y_test) accuracy = float(num_correct) / num_test print 'Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps Step2: Inline Question #1 Step3: You should expect to see approximately 27% accuracy. Now lets try out a larger k, say k = 5 Step5: You should expect to see a slightly better performance than with k = 1. Step6: Cross-validation

<ASSISTANT_TASK:> Python Code: %matplotlib inline %config InlineBackend.figure_format = 'retina' import numpy as np import pandas as pd import matplotlib.pyplot as plt data_path = 'Bike-Sharing-Dataset/hour.csv' rides = pd.read_csv(data_path) rides.head() rides[:24*30].plot(x='dteday', y='cnt') dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday'] for each in dummy_fields: dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False) rides = pd.concat([rides, dummies], axis=1) fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', 'weekday', 'atemp', 'mnth', 'workingday', 'hr'] data = rides.drop(fields_to_drop, axis=1) data.head() quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed'] # Store scalings in a dictionary so we can convert back later scaled_features = {} for each in quant_features: mean, std = data[each].mean(), data[each].std() scaled_features[each] = [mean, std] data.loc[:, each] = (data[each] - mean)/std # Save the last 21 days test_data = data[-21*24:] data = data[:-21*24] # Separate the data into features and targets target_fields = ['cnt', 'casual', 'registered'] features, targets = data.drop(target_fields, axis=1), data[target_fields] test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields] # Hold out the last 60 days of the remaining data as a validation set train_features, train_targets = features[:-60*24], targets[:-60*24] val_features, val_targets = features[-60*24:], targets[-60*24:] # check data shape print("input samples:", train_features.shape[0]) print("input features:", train_features.shape[1]) batch = np.random.choice(train_features.index, size=128) count = 0 for record, target in zip(train_features.ix[batch].values, train_targets.ix[batch]['cnt']): if count == 0: print(record.shape) print(target.shape) inputs = np.array(record, ndmin=2).T targets = np.array(target, ndmin=2).T print(inputs.shape) print(targets.shape) count += 1 print("count:", count) class NeuralNetwork(object): def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate): # Set number of nodes in input, hidden and output layers. self.input_nodes = input_nodes self.hidden_nodes = hidden_nodes self.output_nodes = output_nodes # Initialize weights self.weights_input_to_hidden = np.random.normal(0.0, self.hidden_nodes**-0.5, (self.hidden_nodes, self.input_nodes)) self.weights_hidden_to_output = np.random.normal(0.0, self.output_nodes**-0.5, (self.output_nodes, self.hidden_nodes)) self.lr = learning_rate #### Set this to your implemented sigmoid function #### # Activation function is the sigmoid function self.activation_function = (lambda x: 1 / (1 + np.exp(-x))) def train(self, inputs_list, targets_list): # Convert inputs list to 2d array inputs = np.array(inputs_list, ndmin=2).T targets = np.array(targets_list, ndmin=2).T # shape symbol: i - numInputs (56), h - numHidden, o - numOutputs # inputs(i, 1) , not batched # targets(1, 1) #### Implement the forward pass here #### ### Forward pass ### # TODO: Hidden layer # shape: inputs(i, 1).T dot weights(h, i).T => (1, h) hidden_inputs = np.dot(inputs.T, self.weights_input_to_hidden.T) # signals into hidden layer hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer # TODO: Output layer # shape: inputs(1, h) dot weights(o, h).T => (1, o) final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output.T) # signals into final output layer final_outputs = final_inputs # signals from final output layer #### Implement the backward pass here #### ### Backward pass ### # TODO: Output error # shape(1, o) output_errors = targets - final_outputs # Output layer error is the difference between desired target and actual output. # TODO: Backpropagated error # shape: inputs(1, o) dot weights(o, h) => (1, h) hidden_errors = np.dot(output_errors, self.weights_hidden_to_output) # errors propagated to the hidden layer hidden_grad = hidden_errors * hidden_outputs * (1 - hidden_outputs) # hidden layer gradients # TODO: Update the weights # shape: (1, o).T dot (1, h) => (o, h) self.weights_hidden_to_output += self.lr * np.dot(output_errors.T, hidden_outputs) # update hidden-to-output weights with gradient descent step # shape: (1, h).T dot (1, i) => (h, i) #self.weights_input_to_hidden += self.lr * np.dot(hidden_grad.T, inputs.T) # update input-to-hidden weights with gradient descent step self.weights_input_to_hidden += self.lr * hidden_grad.T * inputs.T # update input-to-hidden weights with gradient descent step def run(self, inputs_list): # Run a forward pass through the network inputs = np.array(inputs_list, ndmin=2).T #### Implement the forward pass here #### # TODO: Hidden layer #hidden_inputs = # signals into hidden layer #hidden_outputs = # signals from hidden layer hidden_inputs = np.dot(inputs.T, self.weights_input_to_hidden.T) # signals into hidden layer hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer # TODO: Output layer #final_inputs = # signals into final output layer #final_outputs = # signals from final output layer final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output.T) # signals into final output layer final_outputs = final_inputs # signals from final output layer return final_outputs.T def MSE(y, Y): return np.mean((y-Y)**2) import sys ### Set the hyperparameters here ### epochs = 2000 # 100 learning_rate = 0.0699 # 0.1 hidden_nodes = 28 # input feature has 56, half is 28 # 2 output_nodes = 1 N_i = train_features.shape[1] network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate) losses = {'train':[], 'validation':[]} for e in range(epochs): #if False: # Go through a random batch of 128 records from the training data set batch = np.random.choice(train_features.index, size=128) for record, target in zip(train_features.ix[batch].values, train_targets.ix[batch]['cnt']): network.train(record, target) # Printing out the training progress train_loss = MSE(network.run(train_features), train_targets['cnt'].values) val_loss = MSE(network.run(val_features), val_targets['cnt'].values) sys.stdout.write("\rProgress: " + str(100 * e/float(epochs))[:4] \ + "% ... Training loss: " + str(train_loss)[:5] \ + " ... Validation loss: " + str(val_loss)[:5]) losses['train'].append(train_loss) losses['validation'].append(val_loss) plt.plot(losses['train'], label='Training loss') plt.plot(losses['validation'], label='Validation loss') plt.legend() plt.ylim(ymax=0.5) fig, ax = plt.subplots(figsize=(8,4)) mean, std = scaled_features['cnt'] predictions = network.run(test_features)*std + mean ax.plot(predictions[0], label='Prediction') ax.plot((test_targets['cnt']*std + mean).values, label='Data') ax.set_xlim(right=len(predictions)) ax.legend() dates = pd.to_datetime(rides.ix[test_data.index]['dteday']) dates = dates.apply(lambda d: d.strftime('%b %d')) ax.set_xticks(np.arange(len(dates))[12::24]) _ = ax.set_xticklabels(dates[12::24], rotation=45) import unittest inputs = [0.5, -0.2, 0.1] targets = [0.4] test_w_i_h = np.array([[0.1, 0.4, -0.3], [-0.2, 0.5, 0.2]]) test_w_h_o = np.array([[0.3, -0.1]]) class TestMethods(unittest.TestCase): ########## # Unit tests for data loading ########## def test_data_path(self): # Test that file path to dataset has been unaltered self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv') def test_data_loaded(self): # Test that data frame loaded self.assertTrue(isinstance(rides, pd.DataFrame)) ########## # Unit tests for network functionality ########## def test_activation(self): network = NeuralNetwork(3, 2, 1, 0.5) # Test that the activation function is a sigmoid self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5)))) def test_train(self): # Test that weights are updated correctly on training network = NeuralNetwork(3, 2, 1, 0.5) network.weights_input_to_hidden = test_w_i_h.copy() network.weights_hidden_to_output = test_w_h_o.copy() print(network.weights_input_to_hidden) network.train(inputs, targets) print(network.weights_input_to_hidden) self.assertTrue(np.allclose(network.weights_hidden_to_output, np.array([[ 0.37275328, -0.03172939]]))) self.assertTrue(np.allclose(network.weights_input_to_hidden, np.array([[ 0.10562014, 0.39775194, -0.29887597], [-0.20185996, 0.50074398, 0.19962801]]))) def test_run(self): # Test correctness of run method network = NeuralNetwork(3, 2, 1, 0.5) network.weights_input_to_hidden = test_w_i_h.copy() network.weights_hidden_to_output = test_w_h_o.copy() self.assertTrue(np.allclose(network.run(inputs), 0.09998924)) suite = unittest.TestLoader().loadTestsFromModule(TestMethods()) unittest.TextTestRunner().run(suite) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Load and prepare the data Step2: Checking out the data Step3: Dummy variables Step4: Scaling target variables Step5: Splitting the data into training, testing, and validation sets Step6: We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set). Step7: Time to build the network Step8: Training the network Step9: Check out your predictions Step10: Thinking about your results

<ASSISTANT_TASK:> Python Code: import IPython print("pyspark version:" + str(sc.version)) print("Ipython version:" + str(IPython.__version__)) # map x = sc.parallelize([1,2,3]) # sc = spark context, parallelize creates an RDD from the passed object y = x.map(lambda x: (x,x**2)) print(x.collect()) # collect copies RDD elements to a list on the driver print(y.collect()) # flatMap x = sc.parallelize([1,2,3]) y = x.flatMap(lambda x: (x, 100*x, x**2)) print(x.collect()) print(y.collect()) # mapPartitions x = sc.parallelize([1,2,3], 2) def f(iterator): yield sum(iterator) y = x.mapPartitions(f) print(x.glom().collect()) # glom() flattens elements on the same partition print(y.glom().collect()) # mapPartitionsWithIndex x = sc.parallelize([1,2,3], 2) def f(partitionIndex, iterator): yield (partitionIndex,sum(iterator)) y = x.mapPartitionsWithIndex(f) print(x.glom().collect()) # glom() flattens elements on the same partition print(y.glom().collect()) # getNumPartitions x = sc.parallelize([1,2,3], 2) y = x.getNumPartitions() print(x.glom().collect()) print(y) # filter x = sc.parallelize([1,2,3]) y = x.filter(lambda x: x%2 == 1) # filters out even elements print(x.collect()) print(y.collect()) # distinct x = sc.parallelize(['A','A','B']) y = x.distinct() print(x.collect()) print(y.collect()) # sample x = sc.parallelize(range(7)) ylist = [x.sample(withReplacement=False, fraction=0.5) for i in range(5)] # call 'sample' 5 times print('x = ' + str(x.collect())) for cnt,y in zip(range(len(ylist)), ylist): print('sample:' + str(cnt) + ' y = ' + str(y.collect())) # takeSample x = sc.parallelize(range(7)) ylist = [x.takeSample(withReplacement=False, num=3) for i in range(5)] # call 'sample' 5 times print('x = ' + str(x.collect())) for cnt,y in zip(range(len(ylist)), ylist): print('sample:' + str(cnt) + ' y = ' + str(y)) # no collect on y # union x = sc.parallelize(['A','A','B']) y = sc.parallelize(['D','C','A']) z = x.union(y) print(x.collect()) print(y.collect()) print(z.collect()) # intersection x = sc.parallelize(['A','A','B']) y = sc.parallelize(['A','C','D']) z = x.intersection(y) print(x.collect()) print(y.collect()) print(z.collect()) # sortByKey x = sc.parallelize([('B',1),('A',2),('C',3)]) y = x.sortByKey() print(x.collect()) print(y.collect()) # sortBy x = sc.parallelize(['Cat','Apple','Bat']) def keyGen(val): return val[0] y = x.sortBy(keyGen) print(y.collect()) # glom x = sc.parallelize(['C','B','A'], 2) y = x.glom() print(x.collect()) print(y.collect()) # cartesian x = sc.parallelize(['A','B']) y = sc.parallelize(['C','D']) z = x.cartesian(y) print(x.collect()) print(y.collect()) print(z.collect()) # groupBy x = sc.parallelize([1,2,3]) y = x.groupBy(lambda x: 'A' if (x%2 == 1) else 'B' ) print(x.collect()) print([(j[0],[i for i in j[1]]) for j in y.collect()]) # y is nested, this iterates through it # pipe x = sc.parallelize(['A', 'Ba', 'C', 'AD']) y = x.pipe('grep -i "A"') # calls out to grep, may fail under Windows print(x.collect()) print(y.collect()) # foreach from __future__ import print_function x = sc.parallelize([1,2,3]) def f(el): '''side effect: append the current RDD elements to a file''' f1=open("./foreachExample.txt", 'a+') print(el,file=f1) open('./foreachExample.txt', 'w').close() # first clear the file contents y = x.foreach(f) # writes into foreachExample.txt print(x.collect()) print(y) # foreach returns 'None' # print the contents of foreachExample.txt with open("./foreachExample.txt", "r") as foreachExample: print (foreachExample.read()) # foreachPartition from __future__ import print_function x = sc.parallelize([1,2,3],5) def f(parition): '''side effect: append the current RDD partition contents to a file''' f1=open("./foreachPartitionExample.txt", 'a+') print([el for el in parition],file=f1) open('./foreachPartitionExample.txt', 'w').close() # first clear the file contents y = x.foreachPartition(f) # writes into foreachExample.txt print(x.glom().collect()) print(y) # foreach returns 'None' # print the contents of foreachExample.txt with open("./foreachPartitionExample.txt", "r") as foreachExample: print (foreachExample.read()) # collect x = sc.parallelize([1,2,3]) y = x.collect() print(x) # distributed print(y) # not distributed # reduce x = sc.parallelize([1,2,3]) y = x.reduce(lambda obj, accumulated: obj + accumulated) # computes a cumulative sum print(x.collect()) print(y) # fold x = sc.parallelize([1,2,3]) neutral_zero_value = 0 # 0 for sum, 1 for multiplication y = x.fold(neutral_zero_value,lambda obj, accumulated: accumulated + obj) # computes cumulative sum print(x.collect()) print(y) # aggregate x = sc.parallelize([2,3,4]) neutral_zero_value = (0,1) # sum: x+0 = x, product: 1*x = x seqOp = (lambda aggregated, el: (aggregated[0] + el, aggregated[1] * el)) combOp = (lambda aggregated, el: (aggregated[0] + el[0], aggregated[1] * el[1])) y = x.aggregate(neutral_zero_value,seqOp,combOp) # computes (cumulative sum, cumulative product) print(x.collect()) print(y) # max x = sc.parallelize([1,3,2]) y = x.max() print(x.collect()) print(y) # min x = sc.parallelize([1,3,2]) y = x.min() print(x.collect()) print(y) # sum x = sc.parallelize([1,3,2]) y = x.sum() print(x.collect()) print(y) # count x = sc.parallelize([1,3,2]) y = x.count() print(x.collect()) print(y) # histogram (example #1) x = sc.parallelize([1,3,1,2,3]) y = x.histogram(buckets = 2) print(x.collect()) print(y) # histogram (example #2) x = sc.parallelize([1,3,1,2,3]) y = x.histogram([0,0.5,1,1.5,2,2.5,3,3.5]) print(x.collect()) print(y) # mean x = sc.parallelize([1,3,2]) y = x.mean() print(x.collect()) print(y) # variance x = sc.parallelize([1,3,2]) y = x.variance() # divides by N print(x.collect()) print(y) # stdev x = sc.parallelize([1,3,2]) y = x.stdev() # divides by N print(x.collect()) print(y) # sampleStdev x = sc.parallelize([1,3,2]) y = x.sampleStdev() # divides by N-1 print(x.collect()) print(y) # sampleVariance x = sc.parallelize([1,3,2]) y = x.sampleVariance() # divides by N-1 print(x.collect()) print(y) # countByValue x = sc.parallelize([1,3,1,2,3]) y = x.countByValue() print(x.collect()) print(y) # top x = sc.parallelize([1,3,1,2,3]) y = x.top(num = 3) print(x.collect()) print(y) # takeOrdered x = sc.parallelize([1,3,1,2,3]) y = x.takeOrdered(num = 3) print(x.collect()) print(y) # take x = sc.parallelize([1,3,1,2,3]) y = x.take(num = 3) print(x.collect()) print(y) # first x = sc.parallelize([1,3,1,2,3]) y = x.first() print(x.collect()) print(y) # collectAsMap x = sc.parallelize([('C',3),('A',1),('B',2)]) y = x.collectAsMap() print(x.collect()) print(y) # keys x = sc.parallelize([('C',3),('A',1),('B',2)]) y = x.keys() print(x.collect()) print(y.collect()) # values x = sc.parallelize([('C',3),('A',1),('B',2)]) y = x.values() print(x.collect()) print(y.collect()) # reduceByKey x = sc.parallelize([('B',1),('B',2),('A',3),('A',4),('A',5)]) y = x.reduceByKey(lambda agg, obj: agg + obj) print(x.collect()) print(y.collect()) # reduceByKeyLocally x = sc.parallelize([('B',1),('B',2),('A',3),('A',4),('A',5)]) y = x.reduceByKeyLocally(lambda agg, obj: agg + obj) print(x.collect()) print(y) # countByKey x = sc.parallelize([('B',1),('B',2),('A',3),('A',4),('A',5)]) y = x.countByKey() print(x.collect()) print(y) # join x = sc.parallelize([('C',4),('B',3),('A',2),('A',1)]) y = sc.parallelize([('A',8),('B',7),('A',6),('D',5)]) z = x.join(y) print(x.collect()) print(y.collect()) print(z.collect()) # leftOuterJoin x = sc.parallelize([('C',4),('B',3),('A',2),('A',1)]) y = sc.parallelize([('A',8),('B',7),('A',6),('D',5)]) z = x.leftOuterJoin(y) print(x.collect()) print(y.collect()) print(z.collect()) # rightOuterJoin x = sc.parallelize([('C',4),('B',3),('A',2),('A',1)]) y = sc.parallelize([('A',8),('B',7),('A',6),('D',5)]) z = x.rightOuterJoin(y) print(x.collect()) print(y.collect()) print(z.collect()) # partitionBy x = sc.parallelize([(0,1),(1,2),(2,3)],2) y = x.partitionBy(numPartitions = 3, partitionFunc = lambda x: x) # only key is passed to paritionFunc print(x.glom().collect()) print(y.glom().collect()) # combineByKey x = sc.parallelize([('B',1),('B',2),('A',3),('A',4),('A',5)]) createCombiner = (lambda el: [(el,el**2)]) mergeVal = (lambda aggregated, el: aggregated + [(el,el**2)]) # append to aggregated mergeComb = (lambda agg1,agg2: agg1 + agg2 ) # append agg1 with agg2 y = x.combineByKey(createCombiner,mergeVal,mergeComb) print(x.collect()) print(y.collect()) # aggregateByKey x = sc.parallelize([('B',1),('B',2),('A',3),('A',4),('A',5)]) zeroValue = [] # empty list is 'zero value' for append operation mergeVal = (lambda aggregated, el: aggregated + [(el,el**2)]) mergeComb = (lambda agg1,agg2: agg1 + agg2 ) y = x.aggregateByKey(zeroValue,mergeVal,mergeComb) print(x.collect()) print(y.collect()) # foldByKey x = sc.parallelize([('B',1),('B',2),('A',3),('A',4),('A',5)]) zeroValue = 1 # one is 'zero value' for multiplication y = x.foldByKey(zeroValue,lambda agg,x: agg*x ) # computes cumulative product within each key print(x.collect()) print(y.collect()) # groupByKey x = sc.parallelize([('B',5),('B',4),('A',3),('A',2),('A',1)]) y = x.groupByKey() print(x.collect()) print([(j[0],[i for i in j[1]]) for j in y.collect()]) # flatMapValues x = sc.parallelize([('A',(1,2,3)),('B',(4,5))]) y = x.flatMapValues(lambda x: [i**2 for i in x]) # function is applied to entire value, then result is flattened print(x.collect()) print(y.collect()) # mapValues x = sc.parallelize([('A',(1,2,3)),('B',(4,5))]) y = x.mapValues(lambda x: [i**2 for i in x]) # function is applied to entire value print(x.collect()) print(y.collect()) # groupWith x = sc.parallelize([('C',4),('B',(3,3)),('A',2),('A',(1,1))]) y = sc.parallelize([('B',(7,7)),('A',6),('D',(5,5))]) z = sc.parallelize([('D',9),('B',(8,8))]) a = x.groupWith(y,z) print(x.collect()) print(y.collect()) print(z.collect()) print("Result:") for key,val in list(a.collect()): print(key, [list(i) for i in val]) # cogroup x = sc.parallelize([('C',4),('B',(3,3)),('A',2),('A',(1,1))]) y = sc.parallelize([('A',8),('B',7),('A',6),('D',(5,5))]) z = x.cogroup(y) print(x.collect()) print(y.collect()) for key,val in list(z.collect()): print(key, [list(i) for i in val]) # sampleByKey x = sc.parallelize([('A',1),('B',2),('C',3),('B',4),('A',5)]) y = x.sampleByKey(withReplacement=False, fractions={'A':0.5, 'B':1, 'C':0.2}) print(x.collect()) print(y.collect()) # subtractByKey x = sc.parallelize([('C',1),('B',2),('A',3),('A',4)]) y = sc.parallelize([('A',5),('D',6),('A',7),('D',8)]) z = x.subtractByKey(y) print(x.collect()) print(y.collect()) print(z.collect()) # subtract x = sc.parallelize([('C',4),('B',3),('A',2),('A',1)]) y = sc.parallelize([('C',8),('A',2),('D',1)]) z = x.subtract(y) print(x.collect()) print(y.collect()) print(z.collect()) # keyBy x = sc.parallelize([1,2,3]) y = x.keyBy(lambda x: x**2) print(x.collect()) print(y.collect()) # repartition x = sc.parallelize([1,2,3,4,5],2) y = x.repartition(numPartitions=3) print(x.glom().collect()) print(y.glom().collect()) # coalesce x = sc.parallelize([1,2,3,4,5],2) y = x.coalesce(numPartitions=1) print(x.glom().collect()) print(y.glom().collect()) # zip x = sc.parallelize(['B','A','A']) y = x.map(lambda x: ord(x)) # zip expects x and y to have same #partitions and #elements/partition z = x.zip(y) print(x.collect()) print(y.collect()) print(z.collect()) # zipWithIndex x = sc.parallelize(['B','A','A'],2) y = x.zipWithIndex() print(x.glom().collect()) print(y.collect()) # zipWithUniqueId x = sc.parallelize(['B','A','A'],2) y = x.zipWithUniqueId() print(x.glom().collect()) print(y.collect()) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: <a href="http Step2: <a href="http Step3: <a href="http Step4: <a href="http Step5: <a href="http Step6: <a href="http Step7: <a href="http Step8: <a href="http Step9: <a href="http Step10: <a href="http Step11: <a href="http Step12: <a href="http Step13: <a href="http Step14: <a href="http Step15: <a href="http Step16: <a href="http Step17: <a href="http Step18: <a href="http Step19: <a href="http Step20: <a href="http Step21: <a href="http Step22: <a href="http Step23: <a href="http Step24: <a href="http Step25: <a href="http Step26: <a href="http Step27: <a href="http Step28: <a href="http Step29: <a href="http Step30: <a href="http Step31: <a href="http Step32: <a href="http Step33: <a href="http Step34: <a href="http Step35: <a href="http Step36: <a href="http Step37: <a href="http Step38: <a href="http Step39: <a href="http Step40: <a href="http Step41: <a href="http Step42: <a href="http Step43: <a href="http Step44: <a href="http Step45: <a href="http Step46: <a href="http Step47: <a href="http Step48: <a href="http Step49: <a href="http Step50: <a href="http Step51: <a href="http Step52: <a href="http Step53: <a href="http Step54: <a href="http Step55: <a href="http Step56: <a href="http Step57: <a href="http Step58: <a href="http Step59: <a href="http Step60: <a href="http Step61: <a href="http Step62: <a href="http Step63: <a href="http Step64: <a href="http Step65: <a href="http

<ASSISTANT_TASK:> Python Code: import numpy #assuming the data file is in the data/ folder numpy.loadtxt(fname='data/inflammation-01.csv', delimiter=',') data = numpy.loadtxt(fname='data/inflammation-01.csv', delimiter=',') print(data) weight_kg = 55 #assigns value 55 to weight_kg print(weight_kg) #we can print to the screen print("weight in kg", weight_kg) weight_kg = 70 print("weight in kg", weight_kg) weight_kg * 2 weight_lb = weight_kg * 2.2 print('weigh in lb:', weight_lb) print("weight in lb:", weight_kg*2.2) print(data) whos print(data) print(type(data)) #we can get type of object print(data.shape) print('first value in data', data[0,0]) #use index in square brackets print('4th value in data', data[0,3]) #use index in square brackets print('first value in 3rd row data', data[3,0]) #use index in square brackets !head -3 data/inflammation-01.csv print('middle value in data', data[30,20]) # get the middle value - notice here i didn't use print data[0:4, 0:10] #select whole sections of matrix, 1st 10 days & 4 patients data[5:10,0:10] data[:3, 36:] element = 'oxygen' print('first three characters:', element[0:3]) print('last three characters:', element[3:6]) print(element[:4]) print(element[4:]) print(:) #oxygen print(element[-1]) print(element[-2]) print(element[2:-1]) doubledata = data * 2.0 #we can perform math on array doubledata data[:3, 36:] doubledata[:3, 36:] tripledata = doubledata + data print('tripledata:') print(tripledata[:3, 36:]) print(data.mean()) print('maximum inflammation: ', data.max()) print('minimum inflammation: ', data.min()) print('standard deviation:', data.std()) %matplotlib inline import matplotlib.pyplot as plt data plt.imshow(data) image = plt.imshow(data) plt.savefig('timsheatmap.png') avg_inflam = data.mean(axis=0) #asix zero is by each day print(data.mean(axis=0)) print(data.mean(axis=0).shape) #Nx1 vector of averages print(data.mean(axis=1)) #avg inflam per patient across all days print(data.mean(axis=1).shape) print(avg_inflam) day_avg_plot = plt.plot(avg_inflam) data.mean(axis=0).shape data.shape data.mean(axis=1).shape max_plot = plt.plot(data.max(axis=0)) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Importing a library akin to getting lab equipment out of a locker and setting up on bench Step2: numpy.loadtex() is a function call, runs loadtxt in numpy Step3: print above shows several things at once by separating with commas Step4: whos #ipython command to see what variables & mods you have Step5: What does the following program print out? Step6: data refers to N-dimensional array Step7: data has 60 rows and 40 columns Step8: programming languages like MATLAB and R start counting at 1 Step9: slice 0 Step10: dont' have to include uper and lower bound Step11: A section of an array is called a slice. We can take slices of character strings as well Step12: operation on arrays is done on each individual element of the array Step13: we can also do arithmetic operation with another array of same shape (same dims) Step14: we can do more than simple arithmetic Step15: mean is a method of the array (function) Step16: however, we are usually more interested in partial stats, e.g. max value per patient or the avg value per day Step17: let's visualize this data with matplotlib library Step18: nice, but ipython/jupyter proved us with 'magic' functions and one lets us display our plot inline Step19: now let's look at avg inflammation over days (columns) Step20: avg per day across all patients in the var day_avg_plot

<ASSISTANT_TASK:> Python Code: import setup_mysql_database import numpy as np # numerical libraries import scipy as sp import pandas as pd # for data analysis import pandas.io.sql as sql # for interfacing with MySQL database from scipy import linalg # linear algebra libraries from scipy import optimize from __future__ import division, print_function # good defensive measure import matplotlib as mpl # a big library with plotting functionality import matplotlib.pyplot as plt # a subset of matplotlib with most of the useful tools %matplotlib inline # extract from MySQL database info on rank points and height for both winner and loser, store in dataframe with engine.begin() as connection: rawdata = pd.read_sql_query(SELECT winner_rank_points, loser_rank_points, winner_ht, loser_ht FROM matches \ WHERE tourney_date > '20160101' \ AND winner_rank_points IS NOT NULL \ AND loser_rank_points IS NOT NULL \ AND winner_ht IS NOT NULL \ AND loser_ht IS NOT NULL, connection) # this nx2 array contains the differences in rankings points and the differences in height X = pd.concat([rawdata.iloc[:,1]-rawdata.iloc[:,0],rawdata.iloc[:,3]-rawdata.iloc[:,2]],axis=1).values # this nx1 binary array indicates whether the match was a "success" or a "failure", as predicted by ranking differences y = (X[:,0] > 0) # for numerical well-behavedness, we need to scale and center the data X=(X-np.mean(X,axis=0))/np.std(X,axis=0) # plot the normalized data fig, ax = plt.subplots(1,1) ax.plot(X[y,0],X[y,1],"ro") ax.plot(X[~y,0],X[~y,1],"bo") ax.set_xlabel('Rank difference') ax.set_ylabel('Height') ax.set_title('Higher-rank-wins as a function of rank difference and height') # change y from True/False binary into 1/0 binary yv=y*1 # prepend column of 1s to X Xv=np.insert(X,0,1,axis=1) def sigmoid(z): ''' Usage: sigmoid(z) Description: Computes value of sigmoid function for scalar. For vector or matrix, computes values of sigmoid function for each entry. ''' return 1/(1+np.exp(-z)); # define a cost function def costFunction(theta,X,y,lam): ''' Computes the cost and gradient for logistic regression. Input: theta (3x1 vector of parameters) X (nx3 matrix of feature values, first column all 1s) y (nx1 binary vector of outcomes, 1=higher ranked player won, 0 otherwise) lam (scalar: regularization paramter) Output: cost (scalar value of cost) ''' # number of data points m = len(y) # make sure vectors are column vectors theta = theta.reshape(-1,1) y = y.reshape(-1,1) # input to sigmoid function will be a column vector z = np.dot(X,theta) # cost function J = (1/m)*np.sum(np.dot(-y.transpose(),np.log(sigmoid(z))) - \ np.dot((1-y.transpose()),np.log(1-sigmoid(z)))) + \ (lam/(2*m))*np.sum(theta[1:len(theta)+1]**2); # gradient regterm = np.insert(theta[1:len(theta)+1],0,0) grad = (1/m)*np.sum((sigmoid(z) - y)*X,0) + (lam/m)*regterm return J, grad # check that cost function works theta = np.array([1,2,3]) lam = 10 cost, grad = costFunction(theta, Xv, yv,lam) print("cost:", cost) print("grad:", grad) def callbackF(theta): global NFeval global Xv global yv global lam cost,grad = costFunction(theta,Xv,yv,lam) print("%4d %3.6f %3.6f %3.6f %3.6f %3.6f %3.6f %3.6f" % \ (NFeval, theta[0], theta[1], theta[2], cost, grad[0], grad[1], grad[2])) NFeval+=1 # run optimization NFeval = 1 initial_theta = np.array([0.,0.,0.]) print("iter t1 t2 t3 cost grad1 grad2 grad3") res = sp.optimize.minimize(lambda t: costFunction(t,Xv,yv,lam), initial_theta, method='CG',\ jac=True,options={'maxiter':100,'disp':True}, callback=callbackF) # plot the normalized data with regression line theta = res.x fig, ax = plt.subplots(1,1) ax.plot(X[y,0],X[y,1],"ro") ax.plot(X[~y,0],X[~y,1],"bo") xplot = np.array([-1,1]) yplot = (-1/theta[2])*(theta[1]*xplot+theta[0]) ax.plot(xplot,yplot,'g',linewidth=2) ax.set_xlabel('Rank difference') ax.set_ylabel('Height') ax.set_title('Higher-rank-wins as a function of age and height') ax.set_ylim((-5,5)) # we'll use the SVM package in the scikit library from sklearn import svm # produce a dense grid of points in rectangle around the data def make_meshgrid(x, y, h=.02): Create a mesh of points to plot in Parameters ---------- x: data to base x-axis meshgrid on y: data to base y-axis meshgrid on h: stepsize for meshgrid, optional Returns ------- xx, yy : ndarray x_min, x_max = x.min() - 1, x.max() + 1 y_min, y_max = y.min() - 1, y.max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) return xx, yy # produce a contour plot with predicted outcomes from SVM classifier def plot_contours(ax, clf, xx, yy, **params): Plot the decision boundaries for a classifier. Parameters ---------- ax: matplotlib axes object clf: a classifier xx: meshgrid ndarray yy: meshgrid ndarray params: dictionary of params to pass to contourf, optional Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) out = ax.contourf(xx, yy, Z, **params) return out # extract from MySQL database info on rank points and height for both winner and loser, store in dataframe with engine.begin() as connection: rawdata = pd.read_sql_query(SELECT winner_rank_points, loser_rank_points, winner_age, loser_age, winner_ht, loser_ht \ FROM matches \ WHERE tourney_date > '20170101' \ AND winner_rank_points IS NOT NULL \ AND loser_rank_points IS NOT NULL \ AND winner_age IS NOT NULL \ AND loser_age IS NOT NULL \ AND winner_ht IS NOT NULL \ AND loser_ht IS NOT NULL, connection) # this nx2 array contains the differences in ages and the differences in height X = pd.concat([rawdata.iloc[:,2]-rawdata.iloc[:,3], \ rawdata.iloc[:,4]-rawdata.iloc[:,5]], axis=1).values # this nx1 binary array indicates whether the match was a "success" or a "failure", as predicted by ranking differences y = (rawdata.iloc[:,0]-rawdata.iloc[:,1]).values > 0 # for numerical well-behavedness, we need to scale and center the data X=(X-np.mean(X,axis=0))/np.std(X,axis=0) # plot the normalized data fig, ax = plt.subplots(1,1) ax.plot(X[y,0],X[y,1],"ro") ax.plot(X[~y,0],X[~y,1],"bo") ax.set_xlabel('Age') ax.set_ylabel('Height') ax.set_title('Higher-rank-wins as a function of age and height') # find the SVM classifier clf = svm.SVC() clf.fit(X, y) # generate a dense grid for producing a contour plot X0, X1 = X[:, 0], X[:, 1] xx, yy = make_meshgrid(X0, X1) # feed the grid into the plot_contours routinge fig, ax = plt.subplots(1, 1) plot_contours(ax, clf, xx, yy, cmap=plt.cm.coolwarm, alpha=0.8) ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k') ax.set_xlim(xx.min(), xx.max()) ax.set_ylim(yy.min(), yy.max()) ax.set_xlabel('Rank points') ax.set_ylabel('First serve %') ax.set_xticks(()) ax.set_yticks(()) ax.set_title('SVM classifier for height/age data') # name of database db_name = "tennis" # name of db user username = "testuser" # db password for db user password = "test623" # location of atp data files atpfile_directory = "../data/tennis_atp-master/" # location of odds data files oddsfiles_directory = "../data/odds_data/" #%% # # PACKAGES # import sqlalchemy # pandas-mysql interface library import sqlalchemy.exc # exception handling from sqlalchemy import create_engine # needed to define db interface import glob # for file manipulation import sys # for defining behavior under errors #%% # # This cell tries to connect to the mysql database "db_name" with the login # info supplied above. If it succeeds, it prints out the version number of # mysql, if it fails, it exits gracefully. # # create an engine for interacting with the MySQL database try: eng_str = 'mysql+mysqldb://' + username + ':' + password + '@localhost/' + db_name engine = create_engine(eng_str) connection = engine.connect() version = connection.execute("SELECT VERSION()") print("Database version : ") print(version.fetchone()) # report what went wrong if this fails. except sqlalchemy.exc.DatabaseError as e: reason = e.message print("Error %s:" % (reason)) sys.exit(1) # close the connection finally: if connection: connection.close() else: print("Failed to create connection.") # extract from MySQL database info odds with engine.begin() as connection: rawdata = pd.read_sql_query(SELECT PSW, PSL, WRank, LRank FROM odds \ WHERE PSW IS NOT NULL \ AND PSL IS NOT NULL \ AND WRank IS NOT NULL \ AND LRank IS NOT NULL;, connection) investment = len(rawdata) good_call_idx = (rawdata["LRank"]-rawdata["WRank"]>0) winner_odds = rawdata["PSW"] gain = sum(winner_odds*good_call_idx) + sum(good_call_idx==True) roi = gain - investment print("total invested: ", investment) print("return on investment: ", roi) temp = rawdata.iloc[:,0]*RankIdx.values gain = sum(>0)+ loss = net = gain-loss net investment <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step2: II. <a name="logisticregression"> Logistic regression demo Step3: We'll be using the scipy function optimize.minimize to calculate the classifier. To make this code as easily generalizable as possible, we derive some quantities of interest from the dataframe and store these in a numpy array. The array will be the object that we'll manipulate in most of the following calculations. Step4: In preparation for calculating the regression classifier, we'll prepend a column of 1s to the matrix X and change y into a vector of 1s and 0s (instead of Trues and Falses.) Step5: To perform the regression, we'll need to define the sigmoid function and a cost function. The former can take a scalar, vector, or matrix, and return the elementwise value of Step6: The cost function is designed to take a regularization parameter lambda. For a non-regularized solution, lambda can be set equal to 0. The cost function returns both a cost and the gradient for any given value of parameters $\theta$. Step7: Small test Step8: For diagnostic purposes, we define a callback function that will print information about the state and gradient as the optimization algorithm proceeds. Step9: Finally, we run the optimization. Step10: To see how it did, we replot the data with the logistic classifier superimposed over the top. Step11: Comments Step14: After classifying the SVM classifier, we'll need some helper functions to form contour plots. These helper functions are borrowed from the scikit documentation, http Step16: We'll run our test on a slightly different set of data than last time. Here, we'll still classify matches as 1 if the higher ranked player wins and 0 otherwise, but we'll focus on age and height as our predictive features. Step17: We'll use the scikit svm package to train an SVM classifier on this data. We'll plot the results as a contour graph. Step19: Note that these features produce a fairly mixed set of points, so they are unlikely to be highly predictive. But it is interesting to note the pocket of blue in the lower left corner

<ASSISTANT_TASK:> Python Code: N = 4 def performQueries(l , r , prefix ) : l -= 1 r -= 1 flag = False count = 0 for i in range(26 ) : cnt = prefix[r ][i ] if(l > 0 ) : cnt -= prefix[l - 1 ][i ]  if(cnt % 2 == 1 ) : flag = True count += cnt - 1  else : count += cnt   if(flag ) : count += 1  return count  def preCalculate(s , prefix ) : n = len(s ) for i in range(n ) : prefix[i ][ord(s[i ] ) - ord(' a ' ) ] += 1  for i in range(1 , n ) : for j in range(26 ) : prefix[i ][j ] += prefix[i - 1 ][j ]    s = "amim " prefix =[[ 0 for i in range(26 ) ] for i in range(N ) ] preCalculate(s , prefix ) queries =[[ 1 , 4 ] ,[3 , 4 ] ] q = len(queries ) for i in range(q ) : print(performQueries(queries[i ][0 ] , queries[i ][1 ] , prefix ) )  <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description:

<ASSISTANT_TASK:> Python Code: def fixpoint(S0, f): Result = S0.copy() # don't change S0 while True: NewElements = { x for o in Result for x in f(o) } if NewElements.issubset(Result): return Result Result |= NewElements def fixpoint2(S0, f): Result = S0.copy() # don't change S0 while True: NewElements = f(Result) if NewElements.issubset(Result): return Result Result |= NewElements <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: The function fixpoint2 takes two arguments

<ASSISTANT_TASK:> Python Code: import numpy as np import scipy as ps import pandas as pd import matplotlib.pyplot as plt %matplotlib inline data = pd.read_excel('lab-1-1.xlsx', 'table-1') data.head(len(data)) u = data.values[:, 2] print(u.mean()) print(u.std()) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Положим $L=3.2$ м — длина нити, $g=9.8$ м/c — величина ускорения свободного падения, $M=3$ кг — масса балистического маятника. Step2: Посчитаем средний разброс скоростей.

<ASSISTANT_TASK:> Python Code: import bioframe df = bioframe.read_table( 'https://www.encodeproject.org/files/ENCFF001XKR/@@download/ENCFF001XKR.bed.gz', schema='bed9' ) display(df[0:3]) df = bioframe.read_table( "https://www.encodeproject.org/files/ENCFF401MQL/@@download/ENCFF401MQL.bed.gz", schema='narrowPeak') display(df[0:3]) df = bioframe.read_table( 'https://www.encodeproject.org/files/ENCFF001VRS/@@download/ENCFF001VRS.bed.gz', schema='bed12' ) display(df[0:3]) bioframe.SCHEMAS['bed6'] bw_url = 'http://genome.ucsc.edu/goldenPath/help/examples/bigWigExample.bw' df = bioframe.read_bigwig(bw_url, "chr21", start=10_000_000, end=10_010_000) df.head(5) df['value'] *= 100 df.head(5) chromsizes = bioframe.fetch_chromsizes('hg19') bioframe.to_bigwig(df, chromsizes, 'times100.bw') # note: requires UCSC bedGraphToBigWig binary, which can be installed as # !conda install -y -c bioconda ucsc-bedgraphtobigwig bb_url = 'http://genome.ucsc.edu/goldenPath/help/examples/bigBedExample.bb' bioframe.read_bigbed(bb_url, "chr21", start=48000000).head() bioframe.read_chromsizes( 'https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes' ) bioframe.read_chromsizes('https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes', filter_chroms=False) dm6_url = 'https://hgdownload.soe.ucsc.edu/goldenPath/dm6/database/chromInfo.txt.gz' bioframe.read_chromsizes(dm6_url, filter_chroms=True, chrom_patterns=("^chr2L$", "^chr2R$", "^chr3L$", "^chr3R$", "^chr4$", "^chrX$") ) bioframe.read_chromsizes(dm6_url, chrom_patterns=["^chr\d+L$", "^chr\d+R$", "^chr4$", "^chrX$", "^chrM$"]) chromsizes = bioframe.fetch_chromsizes('hg38') chromsizes[-5:] # # bioframe also has locally stored information for certain assemblies that can be # # read as follows # bioframe.get_seqinfo() # bioframe.get_chromsizes('hg38', unit='primary', type=('chromosome', 'non-nuclear'), ) display( bioframe.fetch_centromeres('hg38')[:3] ) client = bioframe.UCSCClient('hg38') client.fetch_cytoband() <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Bioframe provides multiple methods to convert data stored in common genomic file formats to pandas dataFrames in bioframe.io. Step2: The schema argument looks up file type from a registry of schemas stored in the bioframe.SCHEMAS dictionary Step3: UCSC Big Binary Indexed files (BigWig, BigBed) Step4: Reading genome assembly information Step5: Bioframe provides a convenience function to fetch chromosome sizes from UCSC given an assembly name Step6: Bioframe can also generate a list of centromere positions using information from some UCSC assemblies Step7: These functions are just wrappers for a UCSC client. Users can also use UCSCClient directly

<ASSISTANT_TASK:> Python Code: %load_ext noworkflow %now_set_default graph.height=200 %%now_run -e Tracer def f(x, y=3): "Calculate x!/(x - y)!" return x * f(x - 1, y - 1) if y else 1 a = 10 b = a - 2 c = f(b) print(c) trial = _ trial.dot %%now_prolog {trial.id} var_name({trial.id}, Id, 'b'), slice({trial.id}, Id, Vars) %%now_prolog var_name({trial.id}, 2, Name) %%now_sql select count(id) from function_activation where trial_id={trial.id} trial.duration_text len(list(trial.activations)) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Analysis Step2: Prolog queries Step3: SQL queries Step4: ORM

<ASSISTANT_TASK:> Python Code: from textblob import TextBlob texto = '''In new lawsuits brought against the ride-sharing companies Uber and Lyft, the top prosecutors in Los Angeles and San Francisco counties make an important point about the lightly regulated sharing economy. The consumers who participate deserve a very clear picture of the risks they're taking''' t = TextBlob(texto) print('Tenemos', len(t.sentences), 'oraciones.\n') for sentence in t.sentences: print(sentence) # imprimimos las oraciones for sentence in t.sentences: print(sentence) print("--------------") # y las palabras print(t.words) print(texto.split()) print("el texto de ejemplo contiene", len(t.noun_phrases), "entidades") for element in t.noun_phrases: print("-", element) # jugando con lemas, singulares y plurales for word in t.words: if word.endswith("s"): print(word.lemmatize(), word, word.singularize()) else: print(word.lemmatize(), word, word.pluralize()) # ¿cómo podemos hacer la lematización más inteligente? for element in t.tags: # solo lematizamos sustantivos if element[1] == "NN": print(element[0], element[0].lemmatize(), element[0].pluralize() ) elif element[1] == "NNS": print(element[0], element[0].lemmatize(), element[0].singularize()) # y formas verbales if element[1].startswith("VB"): print(element[0], element[0].lemmatize("v")) # análisis sintáctico print(t.parse()) # de chino a inglés y español oracion_zh = "中国探月工程 亦稱嫦娥工程，是中国启动的第一个探月工程，于2003年3月1日正式启动" t_zh = TextBlob(oracion_zh) print(t_zh.translate(from_lang="zh-CN", to="en")) print(t_zh.translate(from_lang="zh-CN", to="es")) print("--------------") t_es = TextBlob(u"La deuda pública ha marcado nuevos récords en España en el tercer trimestre") print(t_es.translate(to="el")) print(t_es.translate(to="ru")) print(t_es.translate(to="eu")) print(t_es.translate(to="fi")) print(t_es.translate(to="fr")) print(t_es.translate(to="nl")) print(t_es.translate(to="gl")) print(t_es.translate(to="ca")) print(t_es.translate(to="zh")) print(t_es.translate(to="la")) # con el slang no funciona tan bien print("--------------") t_ita = TextBlob("Sono andato a Milano e mi sono divertito un bordello.") print(t_ita.translate(to="en")) print(t_ita.translate(to="es")) # WordNet from textblob import Word from textblob.wordnet import VERB # ¿cuántos synsets tiene "car" word = Word("car") print(word.synsets) # dame los synsets de la palabra "hack" como verbo print(Word("hack").get_synsets(pos=VERB)) # imprime la lista de definiciones de "car" print(Word("car").definitions) # recorre la jerarquía de hiperónimos for s in word.synsets: print(s.hypernym_paths()) # análisis de opinión opinion1 = TextBlob("This new restaurant is great. I had so much fun!! :-P") print(opinion1.sentiment) opinion2 = TextBlob("Google News to close in Spain.") print(opinion2.sentiment) print(opinion1.sentiment.polarity) if opinion1.sentiment.subjectivity > 0.5: print("Hey, esto es una opinion") # corrección ortográfica b1 = TextBlob("I havv goood speling!") print(b1.correct()) b2 = TextBlob("Mi naem iz Jonh!") print(b2.correct()) b3 = TextBlob("Boyz dont cri") print(b3.correct()) b4 = TextBlob("psychological posesion achivemen comitment") print(b4.correct()) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Vamos a crear nuestro primer ejemplo de textblob a través del objeto TextBlob. Piensa en estos textblobs como una especie de cadenas de texto de Python, analaizadas y enriquecidas con algunas características extra. Step2: Procesando oraciones, palabras y entidades Step3: La propiedad .noun_phrases nos permite acceder a la lista de entidades (en realidad, son sintagmas nominales) incluídos en nuestro textblob. Así es como funciona. Step4: Análisis sintático Step5: Traducción automática Step6: WordNet Step7: Análisis de opinion Step8: Otras curiosidades

<ASSISTANT_TASK:> Python Code: import matplotlib.pyplot as plt import numpy as np import torch import torch.nn as nn from torch.nn import Parameter from torch.nn.functional import mse_loss from torch.autograd import Variable from torch.nn.functional import relu def sample_from_ground_truth(n_samples=100, std=0.1): x = torch.FloatTensor(n_samples, 1).uniform_(-1, 1) epsilon = torch.FloatTensor(n_samples, 1).normal_(0, std) y = 2 * x + epsilon return x, y n_samples = 100 std = 3 x, y = sample_from_ground_truth(n_samples=100, std=std) class SimpleMLP(nn.Module): def __init__(self, w=None): super(SimpleMLP, self).__init__() self.w1 = Parameter(torch.FloatTensor((1,))) self.w2 = Parameter(torch.FloatTensor((1,))) if w is None: self.reset_parameters() else: self.set_parameters(w) def reset_parameters(self): self.w1.uniform_(-.1, .1) self.w2.uniform_(-.1, .1) def set_parameters(self, w): with torch.no_grad(): self.w1[0] = w[0] self.w2[0] = w[1] def forward(self, x): return self.w1 * relu(self.w2 * x) from math import fabs def make_grids(x, y, model_constructor, expected_risk_func, grid_size=100): n_samples = len(x) assert len(x) == len(y) # Grid logic x_max, y_max, x_min, y_min = 5, 5, -5, -5 w1 = np.linspace(x_min, x_max, grid_size, dtype=np.float32) w2 = np.linspace(y_min, y_max, grid_size, dtype=np.float32) W1, W2 = np.meshgrid(w1, w2) W = np.concatenate((W1[:, :, None], W2[:, :, None]), axis=2) W = torch.from_numpy(W) # We will store the results in this tensor risks = torch.FloatTensor(n_samples, grid_size, grid_size) expected_risk = torch.FloatTensor(grid_size, grid_size) with torch.no_grad(): for i in range(grid_size): for j in range(grid_size): model = model_constructor(W[i, j]) pred = model(x) loss = mse_loss(pred, y, reduction="none") risks[:, i, j] = loss.view(-1) expected_risk[i, j] = expected_risk_func(W[i, j, 0], W[i, j, 1]) empirical_risk = torch.mean(risks, dim=0) return W1, W2, risks.numpy(), empirical_risk.numpy(), expected_risk.numpy() def expected_risk_simple_mlp(w1, w2): Question: Can you derive this your-self? return .5 * (8 / 3 - (4 / 3) * w1 * w2 + 1 / 3 * w1 ** 2 * w2 ** 2) + std ** 2 W1, W2, risks, empirical_risk, expected_risk = make_grids( x, y, SimpleMLP, expected_risk_func=expected_risk_simple_mlp) from torch.optim import SGD def train(model, x, y, lr=.1, n_epochs=1): optimizer = SGD(model.parameters(), lr=lr) iterate_rec = [] grad_rec = [] for epoch in range(n_epochs): # Iterate over the dataset one sample at a time: # batch_size=1 for this_x, this_y in zip(x, y): this_x = this_x[None, :] this_y = this_y[None, :] optimizer.zero_grad() pred = model(this_x) loss = mse_loss(pred, this_y) loss.backward() with torch.no_grad(): iterate_rec.append( [model.w1.clone()[0], model.w2.clone()[0]] ) grad_rec.append( [model.w1.grad.clone()[0], model.w2.grad.clone()[0]] ) optimizer.step() return np.array(iterate_rec), np.array(grad_rec) init = torch.FloatTensor([3, -4]) model = SimpleMLP(init) iterate_rec, grad_rec = train(model, x, y, lr=.01) print(iterate_rec[-1]) import matplotlib.colors as colors class LevelsNormalize(colors.Normalize): def __init__(self, levels, clip=False): self.levels = levels vmin, vmax = levels[0], levels[-1] colors.Normalize.__init__(self, vmin, vmax, clip) def __call__(self, value, clip=None): quantiles = np.linspace(0, 1, len(self.levels)) return np.ma.masked_array(np.interp(value, self.levels, quantiles)) def plot_map(W1, W2, risks, emp_risk, exp_risk, sample, iter_): all_risks = np.concatenate((emp_risk.ravel(), exp_risk.ravel())) x_center, y_center = emp_risk.shape[0] // 2, emp_risk.shape[1] // 2 risk_at_center = exp_risk[x_center, y_center] low_levels = np.percentile(all_risks[all_risks <= risk_at_center], q=np.linspace(0, 100, 11)) high_levels = np.percentile(all_risks[all_risks > risk_at_center], q=np.linspace(10, 100, 10)) levels = np.concatenate((low_levels, high_levels)) norm = LevelsNormalize(levels=levels) cmap = plt.get_cmap('RdBu_r') fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(12, 4)) risk_levels = levels.copy() risk_levels[0] = min(risks[sample].min(), risk_levels[0]) risk_levels[-1] = max(risks[sample].max(), risk_levels[-1]) ax1.contourf(W1, W2, risks[sample], levels=risk_levels, norm=norm, cmap=cmap) ax1.scatter(iterate_rec[iter_, 0], iterate_rec[iter_, 1], color='orange') if any(grad_rec[iter_] != 0): ax1.arrow(iterate_rec[iter_, 0], iterate_rec[iter_, 1], -0.1 * grad_rec[iter_, 0], -0.1 * grad_rec[iter_, 1], head_width=0.3, head_length=0.5, fc='orange', ec='orange') ax1.set_title('Pointwise risk') ax2.contourf(W1, W2, emp_risk, levels=levels, norm=norm, cmap=cmap) ax2.plot(iterate_rec[:iter_ + 1, 0], iterate_rec[:iter_ + 1, 1], linestyle='-', marker='o', markersize=6, color='orange', linewidth=2, label='SGD trajectory') ax2.legend() ax2.set_title('Empirical risk') cf = ax3.contourf(W1, W2, exp_risk, levels=levels, norm=norm, cmap=cmap) ax3.scatter(iterate_rec[iter_, 0], iterate_rec[iter_, 1], color='orange', label='Current sample') ax3.set_title('Expected risk (ground truth)') plt.colorbar(cf, ax=ax3) ax3.legend() fig.suptitle('Iter %i, sample % i' % (iter_, sample)) plt.show() for sample in range(0, 100, 10): plot_map(W1, W2, risks, empirical_risk, expected_risk, sample, sample) # %load solutions/linear_mlp.py # from matplotlib.animation import FuncAnimation # from IPython.display import HTML # fig, ax = plt.subplots(figsize=(8, 8)) # all_risks = np.concatenate((empirical_risk.ravel(), # expected_risk.ravel())) # x_center, y_center = empirical_risk.shape[0] // 2, empirical_risk.shape[1] // 2 # risk_at_center = expected_risk[x_center, y_center] # low_levels = np.percentile(all_risks[all_risks <= risk_at_center], # q=np.linspace(0, 100, 11)) # high_levels = np.percentile(all_risks[all_risks > risk_at_center], # q=np.linspace(10, 100, 10)) # levels = np.concatenate((low_levels, high_levels)) # norm = LevelsNormalize(levels=levels) # cmap = plt.get_cmap('RdBu_r') # ax.set_title('Pointwise risk') # def animate(i): # for c in ax.collections: # c.remove() # for l in ax.lines: # l.remove() # for p in ax.patches: # p.remove() # risk_levels = levels.copy() # risk_levels[0] = min(risks[i].min(), risk_levels[0]) # risk_levels[-1] = max(risks[i].max(), risk_levels[-1]) # ax.contourf(W1, W2, risks[i], levels=risk_levels, # norm=norm, cmap=cmap) # ax.plot(iterate_rec[:i + 1, 0], iterate_rec[:i + 1, 1], # linestyle='-', marker='o', markersize=6, # color='orange', linewidth=2, label='SGD trajectory') # return [] # anim = FuncAnimation(fig, animate,# init_func=init, # frames=100, interval=300, blit=True) # anim.save("stochastic_landscape_minimal_mlp.mp4") # plt.close(fig) # HTML(anim.to_html5_video()) # fig, ax = plt.subplots(figsize=(8, 7)) # cf = ax.contourf(W1, W2, empirical_risk, levels=levels, norm=norm, cmap=cmap) # ax.plot(iterate_rec[:100 + 1, 0], iterate_rec[:100 + 1, 1], # linestyle='-', marker='o', markersize=6, # color='orange', linewidth=2, label='SGD trajectory') # ax.legend() # plt.colorbar(cf, ax=ax) # ax.set_title('Empirical risk') # fig.savefig('empirical_loss_landscape_minimal_mlp.png') <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Data is generated from a simple model Step2: We propose a minimal single hidden layer perceptron model with a single hidden unit and no bias. The model has two tunable parameters $w_1$, and $w_2$, such that Step4: As in the previous notebook, we define a function to sample from and plot loss landscapes. Step5: risks[k, i, j] holds loss value $\ell(f(w_1^{(i)} , w_2^{(j)}, x_k), y_k)$ for a single data point $(x_k, y_k)$; Step6: Let's define our train loop and train our model Step7: We now plot Step8: Observe and comment. Step9: Utilities to generate the slides figures

<ASSISTANT_TASK:> Python Code: import pandas as pd import numpy as np import tensorflow as tf import tflearn from tflearn.data_utils import to_categorical reviews = pd.read_csv('reviews.txt', header=None) labels = pd.read_csv('labels.txt', header=None) from collections import Counter total_counts = Counter() for _, row in reviews.iterrows(): total_counts.update(row[0].split(' ')) print("Total words in data set: ", len(total_counts)) vocab = sorted(total_counts, key=total_counts.get, reverse=True)[:10000] print(vocab[:60]) print(vocab[-1], ': ', total_counts[vocab[-1]]) word2idx = {word: i for i, word in enumerate(vocab)} def text_to_vector(text): word_vector = np.zeros(len(vocab), dtype=np.int_) for word in text.split(' '): idx = word2idx.get(word, None) if idx is None: continue else: word_vector[idx] += 1 return np.array(word_vector) text_to_vector('The tea is for a party to celebrate ' 'the movie so she has no time for a cake')[:65] word_vectors = np.zeros((len(reviews), len(vocab)), dtype=np.int_) for ii, (_, text) in enumerate(reviews.iterrows()): word_vectors[ii] = text_to_vector(text[0]) # Printing out the first 5 word vectors word_vectors[:5, :23] Y = (labels=='positive').astype(np.int_) records = len(labels) shuffle = np.arange(records) np.random.shuffle(shuffle) test_fraction = 0.9 train_split, test_split = shuffle[:int(records*test_fraction)], shuffle[int(records*test_fraction):] trainX, trainY = word_vectors[train_split,:], to_categorical(Y.values[train_split], 2) testX, testY = word_vectors[test_split,:], to_categorical(Y.values[test_split], 2) trainY # Network building def build_model(): tf.reset_default_graph() # Inputs net = tflearn.input_data([None, 10000]) # Hidden layer(s) net = tflearn.fully_connected(net, 200, activation='ReLU') net = tflearn.fully_connected(net, 25, activation='ReLU') # Output layer net = tflearn.fully_connected(net, 2, activation='softmax') net = tflearn.regression(net, optimizer='sgd', learning_rate=0.1, loss='categorical_crossentropy') model = tflearn.DNN(net) return model model = build_model() # Training model.fit(trainX, trainY, validation_set=0.1, show_metric=True, batch_size=128, n_epoch=50) predictions = (np.array(model.predict(testX))[:,0] >= 0.5).astype(np.int_) test_accuracy = np.mean(predictions == testY[:,0], axis=0) print("Test accuracy: ", test_accuracy) sentence = "Moonlight is by far the best movie of 2016." positive_prob = model.predict([text_to_vector(sentence.lower())])[0][1] print('P(positive) = {:.3f} :'.format(positive_prob), 'Positive' if positive_prob > 0.5 else 'Negative') <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Preparing the data Step2: Counting word frequency Step3: Let's keep the first 10000 most frequent words. As Andrew noted, most of the words in the vocabulary are rarely used so they will have little effect on our predictions. Below, we'll sort vocab by the count value and keep the 10000 most frequent words. Step4: What's the last word in our vocabulary? We can use this to judge if 10000 is too few. If the last word is pretty common, we probably need to keep more words. Step5: The last word in our vocabulary shows up in 30 reviews out of 25000. I think it's fair to say this is a tiny proportion of reviews. We are probably fine with this number of words. Step6: Exercise Step7: If you do this right, the following code should return Step8: Now, run through our entire review data set and convert each review to a word vector. Step9: Train, Validation, Test sets Step10: Building the network Step11: Intializing the model Step12: Training the network Step13: Testing Step14: Try out your own sentence!

<ASSISTANT_TASK:> Python Code: import pg8000 conn = pg8000.connect(database="homework2") conn.rollback() cursor = conn.cursor() statement = "SELECT movie_title FROM uitem WHERE scifi = 1 AND horror = 1 ORDER BY release_date DESC" cursor.execute(statement) for row in cursor: print(row[0]) cursor = conn.cursor() statement = "SELECT COUNT(*) FROM uitem WHERE musical = 1 OR childrens = 1" cursor.execute(statement) for row in cursor: print(row[0]) cursor = conn.cursor() statement = "SELECT DISTINCT(occupation), COUNT(*) FROM uuser GROUP BY occupation HAVING COUNT(*) > 50" cursor.execute(statement) for row in cursor: print(row[0], row[1]) cursor = conn.cursor() statement = "SELECT DISTINCT(movie_title) FROM udata JOIN uitem ON uitem.movie_id = udata.item_id WHERE EXTRACT(YEAR FROM release_date) < 1992 AND rating = 5 GROUP BY movie_title" #TA-STEPHAN: Try using this statement #statement = "SELECT DISTINCT uitem.movie_title, udata.rating FROM uitem JOIN udata ON uitem.movie_id = udata.item_id WHERE documentary = 1 AND udata.rating = 5 AND uitem.release_date < '1992-01-01';" # if "any" has to be taken in the sense of "every": # statement = "SELECT movie_title FROM uitem JOIN udata ON uitem.movie_id = udata.item_id WHERE EXTRACT(YEAR FROM release_date) < 1992 GROUP BY movie_title HAVING MIN(rating) = 5" cursor.execute(statement) for row in cursor: print(row[0]) conn.rollback() cursor = conn.cursor() statement = "SELECT movie_title), AVG(rating) FROM udata JOIN uitem ON uitem.movie_id = udata.item_id WHERE horror = 1 GROUP BY movie_title ORDER BY AVG(rating) LIMIT 10" cursor.execute(statement) for row in cursor: print(row[0], "%0.2f" % row[1]) cursor = conn.cursor() statement = "SELECT movie_title, AVG(rating) FROM udata JOIN uitem ON uitem.movie_id = udata.item_id WHERE horror = 1 GROUP BY movie_title HAVING COUNT(rating) > 10 ORDER BY AVG(rating) LIMIT 10;" cursor.execute(statement) for row in cursor: print(row[0], "%0.2f" % row[1]) <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: If you get an error stating that database "homework2" does not exist, make sure that you followed the instructions above exactly. If necessary, drop the database you created (with, e.g., DROP DATABASE your_database_name) and start again. Step2: Problem set 1 Step3: Problem set 2 Step4: Nicely done. Now, in the cell below, fill in the indicated string with a SQL statement that returns all occupations, along with their count, from the uuser table that have more than fifty users listed for that occupation. (I.e., the occupation librarian is listed for 51 users, so it should be included in these results. There are only 12 lawyers, so lawyer should not be included in the result.) Step5: Problem set 3 Step6: Problem set 4 Step7: BONUS

<ASSISTANT_TASK:> Python Code: import numpy as np import pandas as pd import matplotlib.pyplot as plt import matplotlib matplotlib.style.use('ggplot') # Look Pretty def plotDecisionBoundary(model, X, y): fig = plt.figure() ax = fig.add_subplot(111) padding = 0.6 resolution = 0.0025 colors = ['royalblue','forestgreen','ghostwhite'] # Calculate the boundaris x_min, x_max = X[:, 0].min(), X[:, 0].max() y_min, y_max = X[:, 1].min(), X[:, 1].max() x_range = x_max - x_min y_range = y_max - y_min x_min -= x_range * padding y_min -= y_range * padding x_max += x_range * padding y_max += y_range * padding # Create a 2D Grid Matrix. The values stored in the matrix # are the predictions of the class at at said location xx, yy = np.meshgrid(np.arange(x_min, x_max, resolution), np.arange(y_min, y_max, resolution)) # What class does the classifier say? Z = model.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) # Plot the contour map cs = plt.contourf(xx, yy, Z, cmap=plt.cm.terrain) # Plot the test original points as well... for label in range(len(np.unique(y))): indices = np.where(y == label) plt.scatter(X[indices, 0], X[indices, 1], c=colors[label], label=str(label), alpha=0.8) p = model.get_params() plt.axis('tight') plt.title('K = ' + str(p['n_neighbors'])) # .. your code here .. # .. your code here .. # .. your code here .. # .. your code here .. # .. your code here .. # .. your code here .. # .. your code here .. # .. your code here .. # .. your code here .. # I hope your KNeighbors classifier model from earlier was named 'knn' # If not, adjust the following line: plotDecisionBoundary(knn, X_train, y_train) # .. your code here .. plt.show() <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: A Convenience Function Step2: The Assignment Step3: Copy the wheat_type series slice out of X, and into a series called y. Then drop the original wheat_type column from the X Step4: Do a quick, "ordinal" conversion of y. In actuality our classification isn't ordinal, but just as an experiment... Step5: Do some basic nan munging. Fill each row's nans with the mean of the feature Step6: Split X into training and testing data sets using train_test_split(). Use 0.33 test size, and use random_state=1. This is important so that your answers are verifiable. In the real world, you wouldn't specify a random_state Step7: Create an instance of SKLearn's Normalizer class and then train it using its .fit() method against your training data. The reason you only fit against your training data is because in a real-world situation, you'll only have your training data to train with! In this lab setting, you have both train+test data; but in the wild, you'll only have your training data, and then unlabeled data you want to apply your models to. Step8: With your trained pre-processor, transform both your training AND testing data. Any testing data has to be transformed with your preprocessor that has ben fit against your training data, so that it exist in the same feature-space as the original data used to train your models. Step9: Just like your preprocessing transformation, create a PCA transformation as well. Fit it against your training data, and then project your training and testing features into PCA space using the PCA model's .transform() method. This has to be done because the only way to visualize the decision boundary in 2D would be if your KNN algo ran in 2D as well Step10: Create and train a KNeighborsClassifier. Start with K=9 neighbors. Be sure train your classifier against the pre-processed, PCA- transformed training data above! You do not, of course, need to transform your labels. Step11: Display the accuracy score of your test data/labels, computed by your KNeighbors model. You do NOT have to run .predict before calling .score, since .score will take care of running your predictions for you automatically. Step12: Bonus

<ASSISTANT_TASK:> Python Code: from orangecontrib.associate.fpgrowth import * import pandas as pd from numpy import * questions = correctedScientific.columns correctedScientificText = [[] for _ in range(correctedScientific.shape[0])] for q in questions: for index in range(correctedScientific.shape[0]): r = correctedScientific.index[index] if correctedScientific.loc[r, q]: correctedScientificText[index].append(q) #correctedScientificText len(correctedScientificText) # Get frequent itemsets with support > 25% # run time < 1 min support = 0.20 itemsets = frequent_itemsets(correctedScientificText, math.floor(len(correctedScientificText) * support)) #dict(itemsets) # Generate rules according to confidence, confidence > 85 % # run time < 5 min confidence = 0.80 rules = association_rules(dict(itemsets), confidence) #list(rules) # Transform rules generator into a Dataframe rulesDataframe = pd.DataFrame([(ant, cons, supp, conf) for ant, cons, supp, conf in rules]) rulesDataframe.rename(columns = {0:"antecedants", 1:"consequents", 2:"support", 3:"confidence"}, inplace=True) rulesDataframe.head() # Save the mined rules to file rulesDataframe.to_csv("results/associationRulesMiningSupport"+str(support)+"percentsConfidence"+str(confidence)+"percents.csv") # Sort rules by confidence confidenceSortedRules = rulesDataframe.sort_values(by = ["confidence", "support"], ascending=[False, False]) confidenceSortedRules.head(50) # Sort rules by size of consequent set rulesDataframe["consequentSize"] = rulesDataframe["consequents"].apply(lambda x: len(x)) consequentSortedRules = rulesDataframe.sort_values(by = ["consequentSize", "confidence", "support"], ascending=[False, False, False]) consequentSortedRules.head(50) # Select only pairs (rules with antecedent and consequent of size one) # Sort pairs according to confidence rulesDataframe["fusedRule"] = rulesDataframe[["antecedants", "consequents"]].apply(lambda x: frozenset().union(*x), axis=1) rulesDataframe["ruleSize"] = rulesDataframe["fusedRule"].apply(lambda x: len(x)) pairRules = rulesDataframe.sort_values(by=["ruleSize", "confidence", "support"], ascending=[True, False, False]) pairRules.head(30) correctedScientific.columns # Sort questions by number of apparition in consequents for q in scientificQuestions: rulesDataframe[q+"c"] = rulesDataframe["consequents"].apply(lambda x: 1 if q in x else 0) occurenceInConsequents = rulesDataframe.loc[:,scientificQuestions[0]+"c":scientificQuestions[-1]+"c"].sum(axis=0) occurenceInConsequents.sort_values(inplace=True, ascending=False) occurenceInConsequents # Sort questions by number of apparition in antecedants for q in scientificQuestions: rulesDataframe[q+"a"] = rulesDataframe["antecedants"].apply(lambda x: 1 if q in x else 0) occurenceInAntecedants = rulesDataframe.loc[:,scientificQuestions[0]+"a":scientificQuestions[-1]+"a"].sum(axis=0) occurenceInAntecedants.sort_values(inplace=True, ascending=False) occurenceInAntecedants sortedPrePostProgression = pd.read_csv("../../data/sortedPrePostProgression.csv") sortedPrePostProgression.index = sortedPrePostProgression.iloc[:,0] sortedPrePostProgression = sortedPrePostProgression.drop(sortedPrePostProgression.columns[0], axis = 1) del sortedPrePostProgression.index.name sortedPrePostProgression.loc['occ_ant',:] = 0 sortedPrePostProgression.loc['occ_csq',:] = 0 sortedPrePostProgression for questionA, occsA in enumerate(occurenceInAntecedants): questionVariableName = occurenceInAntecedants.index[questionA][:-1] question = globals()[questionVariableName] questionC = questionVariableName + "c" sortedPrePostProgression.loc['occ_ant',question] = occsA occsC = occurenceInConsequents.loc[questionC] sortedPrePostProgression.loc['occ_csq',question] = occsC #print(questionVariableName+"='"+question+"'") #print("\t"+questionVariableName+"a="+str(occsA)+","+questionC+"="+str(occsC)) #print() sortedPrePostProgression.T <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Search for interesting rules

<ASSISTANT_TASK:> Python Code: # To access the Travel Mode Choice data import statsmodels.datasets # To perform the dataset conversion import pylogit as pl # Access the dataset mode_data = statsmodels.datasets.modechoice.load_pandas() # Get a pandas dataframe of the mode choice data long_df = mode_data["data"] # Look at the dataframe to ensure that it loaded correctly long_df.head() # ind_vars is a list of strings denoting the column # headings of data that varies across choice situations, # but not across alternatives. In our data, this is # the household income and party size. individual_specific_variables = ["hinc", "psize"] # alt_specific_vaars is a list of strings denoting the # column headings of data that vary not only across # choice situations but also across all alternatives. # These are columns such as the "level of service" # variables. alternative_specific_variables = ["invc", "invt", "gc"] # subset_specific_vars is a dictionary. Each key is a # string that denotes a variable that is subset specific. # Each value is a list of alternative ids, over which the # variable actually varies. Note that subset specific # variables vary across choice situations and across some # (but not all) alternatives. This is most common when # using variables that are not meaningfully defined for # all alternatives. An example of this in our dataset is # terminal time ("ttme"). This variable is not meaningfully # defined for the "car" alternative. Therefore, it is always # zero. Note "4" is the id for the "car" alternative subset_specific_variables = {"ttme": [1, 2, 3]} # obs_id_col is the column denoting the id of the choice # situation. If one was using a panel dataset, with multiple # choice situations per unit of observation, the column # denoting the unit of observation would be listed in # ind_vars (i.e. with the individual specific variables) observation_id_column = "individual" # alt_id_col is the column denoting the id of the alternative # corresponding to a given row. alternative_id_column = "mode" # choice_col is the column denoting whether the alternative # on a given row was chosen in the corresponding choice situation choice_column = "choice" # Lastly, alt_name_dict is not necessary. However, it is useful. # It records the names corresponding to each alternative, if there # are any, and allows for the creation of meaningful column names # in the wide-format data (such as when creating the columns # denoting the available alternatives in each choice situation). # The keys of alt_name_dict are the unique alternative ids, and # the values are the names of each alternative. alternative_name_dict = {1: "air", 2: "train", 3: "bus", 4: "car"} # Finally, we can create the wide format dataframe wide_df = pl.convert_long_to_wide(long_df, individual_specific_variables, alternative_specific_variables, subset_specific_variables, observation_id_column, alternative_id_column, choice_column, alternative_name_dict) # Let's look at the created dataframe, transposed for easy viewing wide_df.head().T <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Load the needed dataset Step2: Create the needed variables for the conversion function. Step3: Create the wide-format dataframe

<ASSISTANT_TASK:> Python Code: import math import numpy as np import pandas as pd from pandas import Series, DataFrame # 引入绘图包 import matplotlib.pyplot as plt import seaborn as sns sns.set_style('whitegrid') %matplotlib inline # 投掷硬币10次，正面朝上的次数；重复100次 n, p = 10, .5 np.random.binomial(n, p, 100) sum(np.random.binomial(9, 0.1, 20000) == 0) / 20000 lb = 5 s = np.random.poisson(lb, 10000) count, bins, ignored = plt.hist(s, 14, normed=True) # 取a = -1, b = 0, 样本数10000 a, b = -1, 0 s = np.random.uniform(a, b, 10000) # 所有样本的值均大于a np.all(s >= a) # 所有样本的值均小于b np.all(s < b) # 绘制样本直方图及密度函数 count, bins, ignored = plt.hist(s, 15, normed=True) plt.plot(bins, np.ones_like(bins) / (b - a), linewidth=2, color='r') plt.show() # 取theta = 1，绘制样本直方图及密度函数 theta = 1 f = lambda x: math.e ** (-x / theta) / theta s = np.random.exponential(theta, 10000) count, bins, ignored = plt.hist(s, 100, normed=True) plt.plot(bins, f(bins), linewidth=2, color='r') plt.show() # 取均值0，标准差0.1 mu, sigma = 0, 0.1 s = np.random.normal(mu, sigma, 1000) # 验证均值 abs(mu - np.mean(s)) < 0.01 # 验证标准差 abs(sigma - np.std(s, ddof=1)) < 0.01 # 绘制样本直方图及密度函数 count, bins, ignored = plt.hist(s, 30, normed=True) plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) * np.exp( - (bins - mu)**2 / (2 * sigma**2) ), linewidth=2, color='r') plt.show() <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: 1. 随机变量 Random Variable Step2: 一个现实生活中的例子。一家钻井公司探索九个矿井，预计每个开采成功率为0.1；九个矿井全部开采失败的概率是多少？ Step3: 将试验次数增加，可以模拟出更加逼近准确值的结果。 Step4: 5. 均匀分布 Uniform Distribution Step5: 6. 指数分布 Exponential Distribution Step6: 7. 正态分布 Normal Distribution

<ASSISTANT_TASK:> Python Code: import pandas as pd import matplotlib.pyplot as plt %matplotlib inline import ext_datos as ext import procesar as pro import time_plot as tplt dia1 = ext.extraer_data('dia1') cd .. dia2 = ext.extraer_data('dia2') cd .. dia3 = ext.extraer_data('dia3') cd .. dia4 = ext.extraer_data('dia4') motoresdia1 = pro.procesar(dia1) motoresdia2 = pro.procesar(dia2) motoresdia3 = pro.procesar(dia3) motoresdia4 = pro.procesar(dia4) motoresdia4.motorRpm_m1[motoresdia4.motorRpm_m1>1].plot(kind='hist', bins=50) motoresdia1.motorRpm_m2[motoresdia1.motorRpm_m2>1].plot(kind='hist', bins=50) motoresdia2.motorRpm_m1[motoresdia2.motorRpm_m1>1].plot(kind='hist', bins=50) motoresdia3.motorRpm_m1[motoresdia3.motorRpm_m1>1].plot(kind='hist', bins=50) motoresdia4.motorTemp_m1.plot() motoresdia4[motoresdia4.busCurrent_m1 == 0].busVoltage_m1.plot() motoresdia4[motoresdia4.motorRpm_m1>1].motorRpm_m1.mean() motoresdia3[motoresdia3.motorRpm_m1>1].motorRpm_m1.mean() motoresdia2[motoresdia2.motorRpm_m1>1].motorRpm_m1.mean() motoresdia1[motoresdia1.motorRpm_m2>1].motorRpm_m2.mean() <END_TASK>

<SYSTEM_TASK:> Given the following text description, write Python code to implement the functionality described below step by step <END_TASK> <USER_TASK:> Description: Step1: Importamos las librerías creadas para trabajar Step2: Generamos los datasets de todos los días Step3: Se procesan las listas anteriores, se concatenan por motor según