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standardize datasets (#2)

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- Add Heliconius master file to git LFS tracking (f99d84605b31185762938851a8d5e59b8c02aeb5)
- Update master files (8da36215c45659bb66d7543735de345108e0021b)
- Update license file (16086463ecc976651942b722f16f7a1a6bd203a9)
- Add file containing information about images that did not have aligned taxonomic information (726f8a910349adaac54938b61bc6b391d1ae7041)
- Update description of dataset components (d8d92b6ce4f4a89a9b2d56917dd2b53ecbf3b978)
- Add download and checksum scripts. (338d72156ee93010a76d70fbdd1224d2361b8ca9)


Co-authored-by: Elizabeth Campolongo <egrace479@users.noreply.huggingface.co>

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  Jiggins_Zenodo_Img_Master.csv filter=lfs diff=lfs merge=lfs -text
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+ Jiggins_Heliconius_Master.csv filter=lfs diff=lfs merge=lfs -text
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README.md CHANGED
@@ -11,67 +11,96 @@ tags:
11
  - separated wings
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  - mimicry
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  - CV
 
 
 
 
 
 
14
  pretty_name: Jiggins Heliconius Collection
15
  size_categories:
16
  - 10K<n<100K
17
  language:
18
  - en
 
 
 
 
 
 
 
 
 
 
 
 
 
19
  ---
20
  # Dataset Card for Jiggins Heliconius Collection
21
 
22
  ## Dataset Description
23
 
 
24
  - **Homepage:**
25
  - **Repository:**
26
  - **Paper:**
27
  - **Leaderboard:**
28
  - **Point of Contact:**
 
29
 
30
  ### Dataset Summary
31
 
32
- Subset of the collection records from the research group of Chris Jiggins at the University of Cambridge derived from almost 20 years of field studies.
33
- This subset contains approximately 49,359 RGB images of 12,586 specimens (34,929 images of 9,546 specimens across all Heliconius). Many records include images as well as locality data.
34
- All detached wings were photographed with a DSLR camera with a 100 mm macro-lens in standardized conditions; images and full records with data are stored in the [EarthCape database](https://heliconius.ecdb.io/default.aspx) and on [Zenodo](https://zenodo.org/communities/butterfly?q=&l=list&p=1&s=10&sort=newest) (across 32 records from the Butterfly Genetics Group).
 
 
 
35
 
36
  Both dorsal and ventral images available. Contains both whole butterfly, and 4 wings separate. Large variation in image content.
37
 
38
  <!---
39
  This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
40
  --->
 
41
  ### Supported Tasks and Leaderboards
42
 
43
- [More Information Needed]
44
 
45
  ### Languages
46
 
47
- [More Information Needed]
48
 
49
  ## Dataset Structure
50
 
51
- * **Jiggins_Zenodo_Img_Master.csv:** Information for the approximately 49,0000 unprocessed image files included in the Jiggins Heliconius Collection. Image types are `jpg`, `raw` (.CR2) and `tif`. `genus`, `species`, and `subspecies` are included columns.
52
- * To access the images combine columns `zenodo_link` and `Image_name`:
53
- ```
54
- zenodo_link + '/files/' + Image_name
55
- ```
56
 
57
- * **Jiggins_Zenodo_dorsal_Img_Master.csv:** Subset of 24,318 images from `Jiggins_Zenodo_Img_Master.csv` with a dorsal view of the butterflies (note that some have both dorsal and ventral). This subset includes 12,296 unique specimens. Accessing the images from Zenodo works the same as above, and image types are `jpg`, `raw` (.CR2) and `tif`.
58
 
59
- * **Jiggins_Heliconius_Master.csv:** The 34,929-image subset of all Heliconius images from `Jiggins_Zenodo_Img_Master.csv`. This subset includes 9,546 unique specimens. Accessing the images from Zenodo works the same as above, and image types are `jpg`, `raw` (.CR2) and `tif`. `species` and `subspecies` columns are also included.
60
 
61
 
62
-
63
- * Note: The notebooks that generated these files and stats are included in the `notebooks` folder.
64
-
 
 
 
 
 
 
 
65
 
66
  ### Data Instances
67
 
68
  `Jiggins_Heliconius_Master.csv` contains multiple species of Heliconius (including erato and melpomene), most are labeled down to the subspecies level. The `Jiggins_Zenodo_Img_Master.csv` also contains species from other genera, with just over half labeled to the subspecies level (these are predominantly Heliconius subspecies).
 
69
  Detached wings in four quadrants (generally).
70
  Some subspecies may be photographed differently, needs segmentation preprocessing.
71
 
72
  * **Type:** JPG/jpg/tif(very few)
73
  * **Size (x pixels by y pixels):** Unknown yet
74
- * **Background (color or none):** multiple (needs to be normalized)
75
  * **Fit in frame:**
76
  * **Ruler or Scale:** Some with Ruler
77
  * **Color (ColorChecker, white-balance, None):** None
@@ -90,12 +119,12 @@ CSV Columns are as follows:
90
 
91
  - `CAMID`: Unique identifier for each specimen that was photographed. Each `CAMID` corresponds to multiple images (based on factors such as `View` and `file_type`).
92
  - `X`: Unique identifier for each line in the master CSV.
93
- - `Image_name`: Filename of image.
94
  - `View`: View of the butterfly in the image: `dorsal`, `ventral`, `forewing dorsal`, `hindwing dorsal`, `forewing ventral`, `hindwing ventral`, `dorsal and ventral`.
95
  - `zenodo_name`: Name of the CSV file with metadata used to populate this file from the associated Zenodo record.
96
  - `zenodo_link`: URL for the Zenodo record of the image.
97
  - `Sequence`: Mostly numeric IDs, not unique, please see the associated Zenodo record for more information on the meaning of these designations.
98
- - `Taxonomic_Name`: Indication of the Genus, Species, and possibly, subspecies, of the specimen. For Cross Types, this is just the Genus species pair (all _Heliconius erato_ or _Heliconius melpomene_).
99
  - `Locality`: Likely location of specimen collection, varying levels of specificity. Please see the associated Zenodo record for more information on the meaning of these designations.
100
  - `Sample_accession`: Some type of ID, not unique, please see the associated Zenodo record for more information on the meaning of these designations.
101
  - `Collected_by`: Abbreviations (likely for those collecting the specimen), please see the associated Zenodo record for more information on the meaning of these designations.
@@ -114,44 +143,40 @@ CSV Columns are as follows:
114
  - `species`: Species of the specimen. There are 246 species represented in the full collection, 37 of these are species of Heliconius.
115
  - `subspecies`: Subspecies of the specimen (where available, mostly labeled for Heliconius). There are 155 subspecies represented in the full collection, 110 of which are Heliconius subspecies.
116
  - `genus`: Genus of the specimen. There are 94 unique genera represented in the full collection.
 
 
117
 
118
  **Note:**
119
- - `Jiggins_Heliconius_Master.csv` has all but the `genus` column, since all images are Heliconius.
120
- - `Jiggins_Zenodo_dorsal_Img_Master.csv` does not have the last four columns, but they can be added easily with the appropriate functions in `notebooks/standardize_taxa.ipynb`. This file also has a column `CAM_dupe` indicating whether the `CAMID` has multiple images within this subset.
 
 
121
 
 
122
  ### Data Splits
123
 
124
  [More Information Needed]
 
125
 
126
  ## Dataset Creation
127
 
128
  ### Curation Rationale
129
 
130
- [More Information Needed]
131
-
132
- ### Source Data
133
 
134
- These images are a subset of the [Butterfly Genetics Group's Cambridge butterfly collection](https://zenodo.org/communities/butterfly?q=&f=subject%3ACambridge&l=list&p=1&s=10&sort=newest). This collection of butterfly images comes from the research group of Chris Jiggins at the University of Cambridge derived from almost 20 years of field studies.
135
 
136
- Data is pulled from the Zenodo Records in [`licenses.json`](https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection/blob/main/licenses.json). This file also contains full citation information for all records.
137
-
138
- #### Initial Data Collection and Normalization
139
-
140
- [More Information Needed]
141
-
142
- #### Who are the source language producers?
143
 
144
- [More Information Needed]
145
 
146
- ### Annotations
147
 
148
- #### Annotation process
149
 
150
- [More Information Needed]
151
 
152
- #### Who are the annotators?
153
 
154
- [More Information Needed]
155
 
156
  ### Personal and Sensitive Information
157
 
@@ -159,17 +184,13 @@ None
159
 
160
  ## Considerations for Using the Data
161
 
162
- ### Social Impact of Dataset
163
 
164
- [More Information Needed]
 
 
 
165
 
166
- ### Discussion of Biases
167
-
168
- [More Information Needed]
169
-
170
- ### Other Known Limitations
171
-
172
- [More Information Needed]
173
 
174
  ## Additional Information
175
 
@@ -178,6 +199,7 @@ None
178
  * Christopher Lawrence (Princeton University) - ORCID: 0000-0002-3846-5968
179
  * Chris Jiggins (University of Cambridge) - ORCID: 0000-0002-7809-062X
180
  * Butterfly Genetics Group (University of Cambridge)
 
181
 
182
  ### Licensing Information
183
 
@@ -187,10 +209,10 @@ Images can be matched to their source record through the `zenodo_link` column in
187
 
188
  ### Citation Information
189
 
190
- Christopher Lawrence, Chris Jiggins, Butterfly Genetics Group (University of Cambridge). (2023). Jiggins Heliconius Collection. Hugging Face. https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection.
191
 
192
- If you use this dataset, please cite the original datasets (citations for all 32 Zenodo records are in licenses.json) as well as this curated subset.
193
 
194
  ### Contributions
195
 
196
- The [Imageomics Institute](https://imageomics.org) is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) Institute program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning).
 
11
  - separated wings
12
  - mimicry
13
  - CV
14
+ - erato
15
+ - melpomene
16
+ - hybrids
17
+ - cross types
18
+ - wild
19
+ - lab-bred
20
  pretty_name: Jiggins Heliconius Collection
21
  size_categories:
22
  - 10K<n<100K
23
  language:
24
  - en
25
+ configs:
26
+ - config_name: full_master
27
+ data_files:
28
+ - split: train
29
+ path: "Jiggins_Zenodo_Img_Master.csv"
30
+ - config_name: heliconius
31
+ data_files:
32
+ - split: train
33
+ path: "Jiggins_Heliconius_Master.csv"
34
+ - config_name: dorsal
35
+ data_files:
36
+ - split: train
37
+ path: "Jiggins_Zenodo_dorsal_Img_Master.csv"
38
  ---
39
  # Dataset Card for Jiggins Heliconius Collection
40
 
41
  ## Dataset Description
42
 
43
+ <!--
44
  - **Homepage:**
45
  - **Repository:**
46
  - **Paper:**
47
  - **Leaderboard:**
48
  - **Point of Contact:**
49
+ -->
50
 
51
  ### Dataset Summary
52
 
53
+ Subset of the collection records from Chris Jiggins' research group at the University of Cambridge, collection covers nearly 20 years of field studies.
54
+ This subset contains approximately 44,809 RGB images of 11,991 specimens (34,265 images of 10,109 specimens across all Heliconius). Many records have both images and locality data.
55
+
56
+ Most images were photographed with a DSLR camera with a 100 mm macro-lens in standardized conditions.
57
+ More information can be found at the individual Zenodo record pages.
58
+ Images and full records with data are stored in the [EarthCape database](https://heliconius.ecdb.io/default.aspx) and on [Zenodo](https://zenodo.org/communities/butterfly?q=&l=list&p=1&s=10&sort=newest) (across 31 records from the Butterfly Genetics Group).
59
 
60
  Both dorsal and ventral images available. Contains both whole butterfly, and 4 wings separate. Large variation in image content.
61
 
62
  <!---
63
  This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
64
  --->
65
+ <!--
66
  ### Supported Tasks and Leaderboards
67
 
68
+ [More Information Needed] -->
69
 
70
  ### Languages
71
 
72
+ English, Latin
73
 
74
  ## Dataset Structure
75
 
76
+ * **Jiggins_Zenodo_Img_Master.csv:** Information for the approximately 45,0000 unprocessed image files included in the Jiggins Heliconius Collection. Image types are `jpg`, `raw` (.CR2) and `tif`. `genus`, `species`, and `subspecies` are included columns.
 
 
 
 
77
 
78
+ * **Jiggins_Zenodo_dorsal_Img_Master.csv:** Subset of 22,175 images from `Jiggins_Zenodo_Img_Master.csv` with a dorsal view of the butterflies (note that some have both dorsal and ventral). This subset includes 12,296 unique specimens. Image types and columns are the same as for the Master file.
79
 
80
+ * **Jiggins_Heliconius_Master.csv:** The 34,265-image subset of all Heliconius images from `Jiggins_Zenodo_Img_Master.csv`. This subset includes 10,109 unique specimens. IImage types and columns are the same as for the Master file.
81
 
82
 
83
+ **Notes:**
84
+ - The notebooks that generated these files and stats are included in the `notebooks` folder.
85
+ - The original Jiggins Zenodo Master file was compiled from the CSVs provided with the included Zenodo records from the Butterfly Genetics Group. Christopher Lawrence selected which of these provided columns to include. Further processing and standardization (all documented in the Jupyter Notebooks) was performed by Elizabeth Campolongo.
86
+ - Taxonomic information for records [5526257](https://zenodo.org/record/5526257), [2554218](https://zenodo.org/record/2554218), and [2555086](https://zenodo.org/record/2555086) was recovered from information on their Zenodo pages, as the provided CSVs did not contain that information.
87
+ - Be advised that there may be overlap between images in [record 2548678](https://zenodo.org/records/2548678) and [record 3082688](https://zenodo.org/records/3082688).
88
+ - The `scripts` folder has a download and checksum script.
89
+ - Images are downloaded to the provided images directory with subfolders labeled by the `Taxonomic_Name`, with filenames `<X>_<Image_name>`.
90
+ - The checksum script is called by `download_jiggins_subset.py` to generate an MD5 for all download images and creates a CSV with `filepath`, `filename`, and `md5` columns in the same folder as the source CSV (named `<source CSV>_checksums.csv`). This helps to ensure FAIR and Reproducible results, though this will _**not**_ distinguish between the raw and jpg versions of the same image.
91
+ - A log of the download is also generated in the same folder as the source CSV (named `<source CSV>_log.json`).
92
+ - `metadata/Missing_taxa_Jiggins_Zenodo_Master.csv` contains a record of the images that did not have easily reconcilable taxonomic information (see `notebooks/standardize_datasets.ipynb` for more information on this data). There are 1,630 such images distributed across 18 records.
93
 
94
  ### Data Instances
95
 
96
  `Jiggins_Heliconius_Master.csv` contains multiple species of Heliconius (including erato and melpomene), most are labeled down to the subspecies level. The `Jiggins_Zenodo_Img_Master.csv` also contains species from other genera, with just over half labeled to the subspecies level (these are predominantly Heliconius subspecies).
97
+
98
  Detached wings in four quadrants (generally).
99
  Some subspecies may be photographed differently, needs segmentation preprocessing.
100
 
101
  * **Type:** JPG/jpg/tif(very few)
102
  * **Size (x pixels by y pixels):** Unknown yet
103
+ * **Background (color or none):** multiple (needs to be normalized, often grey or lime green)
104
  * **Fit in frame:**
105
  * **Ruler or Scale:** Some with Ruler
106
  * **Color (ColorChecker, white-balance, None):** None
 
119
 
120
  - `CAMID`: Unique identifier for each specimen that was photographed. Each `CAMID` corresponds to multiple images (based on factors such as `View` and `file_type`).
121
  - `X`: Unique identifier for each line in the master CSV.
122
+ - `Image_name`: Filename of image (not unique, often `CAM<CAMID>_<v or d>`).
123
  - `View`: View of the butterfly in the image: `dorsal`, `ventral`, `forewing dorsal`, `hindwing dorsal`, `forewing ventral`, `hindwing ventral`, `dorsal and ventral`.
124
  - `zenodo_name`: Name of the CSV file with metadata used to populate this file from the associated Zenodo record.
125
  - `zenodo_link`: URL for the Zenodo record of the image.
126
  - `Sequence`: Mostly numeric IDs, not unique, please see the associated Zenodo record for more information on the meaning of these designations.
127
+ - `Taxonomic_Name`: Indication of the Genus, species, and possibly, subspecies, of the specimen. For Cross Types, the hybrid names are reduced to just the two subspecies (from the `Cross_Type` column) and non-specified crosses are labeled `<Genus> <species> cross hybrid`.
128
  - `Locality`: Likely location of specimen collection, varying levels of specificity. Please see the associated Zenodo record for more information on the meaning of these designations.
129
  - `Sample_accession`: Some type of ID, not unique, please see the associated Zenodo record for more information on the meaning of these designations.
130
  - `Collected_by`: Abbreviations (likely for those collecting the specimen), please see the associated Zenodo record for more information on the meaning of these designations.
 
143
  - `species`: Species of the specimen. There are 246 species represented in the full collection, 37 of these are species of Heliconius.
144
  - `subspecies`: Subspecies of the specimen (where available, mostly labeled for Heliconius). There are 155 subspecies represented in the full collection, 110 of which are Heliconius subspecies.
145
  - `genus`: Genus of the specimen. There are 94 unique genera represented in the full collection.
146
+ - `file_url`: URL to download image from Zenodo: `zenodo_link + "/files/" + Image_name`. Allows for sample image display in [data dashboard](https://huggingface.co/spaces/imageomics/dashboard-prototype).
147
+ - `hybrid_stat`: Hybrid status of the sample: `hybrid`, `non-hybrid`, or `None`. Hybrids are determined by an ` x ` or `hybrid` in the `Taxonomic_Name` column, all other images classified to the _subspecies_ level are labeled as `non-hybrid`, and the parent species of the one species-level hybrid is labeled as `non-hybrid` (only one of them is present in the dataset).
148
 
149
  **Note:**
150
+ - `Jiggins_Zenodo_dorsal_Img_Master.csv` also has a column `CAM_dupe` indicating whether the `CAMID` has multiple images within this subset.
151
+ - We do not leave the first instance as a non-duplicate, so to have a clear assessment of all duplication (eg., is it just across a couple records, file types, etc).
152
+ - `CAMID`s are necessarily duplicated for the images that are of just a dorsal forewing or hindwing, so we label those as `single_wing`.
153
+ - There are multiple jpg images & multiple raw images of the same specimen. Note that this does not necessarily mean these are duplicates of the same images. There are also jpg copies provided alongside raw images.
154
 
155
+ <!--
156
  ### Data Splits
157
 
158
  [More Information Needed]
159
+ -->
160
 
161
  ## Dataset Creation
162
 
163
  ### Curation Rationale
164
 
165
+ The Butterfly Genetics Group has a large collection of butterfly images distributed across 31 Zenodo records. They do not all have the same information, and it is sometimes only provided in the record, but not the metadata. With this collection, we combine the provided information (metadata) into a shared format that is easily ingested into ML pipelines. We also add some other labels of interest (based on identification determined by the Butterfly Genetics Group), and endeavor to remove duplication, noting potential points of duplication and providing some assessment tools to help prevent data leakage.
 
 
166
 
167
+ Additionally, these datasets are prepared in a format that allows for easy integration with the Imageomics Institute's [Data Dashboard](https://huggingface.co/spaces/imageomics/dashboard-prototype) for distribution statistics and easy sampling of images by taxonomic information and view.
168
 
169
+ ### Source Data
 
 
 
 
 
 
170
 
171
+ These images are a subset of the [Butterfly Genetics Group's Cambridge butterfly collection](https://zenodo.org/communities/butterfly?q=&f=subject%3ACambridge&l=list&p=1&s=10&sort=newest). This collection of butterfly images comes from the research group of Chris Jiggins at the University of Cambridge derived from almost 20 years of field studies.
172
 
173
+ Data is pulled from the Zenodo Records in [`licenses.json`](https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection/blob/main/metadata/licenses.json). This file also contains full citation information for all records.
174
 
 
175
 
176
+ #### Initial Data Collection and Annotation
177
 
178
+ These images are of a mix of wild-caught and lab-bred butterflies, classified by the Butterfly Genetics Group.
179
 
 
180
 
181
  ### Personal and Sensitive Information
182
 
 
184
 
185
  ## Considerations for Using the Data
186
 
187
+ ### Discussion of Biases and Other Known Limitations
188
 
189
+ - This dataset is imbalanced. Even among the Heliconius subset, some subspecies are more heavily represented than others.
190
+ - There are a mix of valid subspecies and hybrids that are labeled as such, but there are also images of butterflies classified only to the genus or species level, for which such a designation may not be clearly made (there are also instances of "_Heliconius <species> hybrid_", where the parent subspecies are not indicated.
191
+ - There may be overlap between images in [record 2548678](https://zenodo.org/records/2548678) and [record 3082688](https://zenodo.org/records/3082688).
192
+ - There are multiple images of the same specimen for many specimens; sometimes this is due to different views (eg., dorsal or ventral side), sometimes it is due to JPG copies of the RAW photos, though it is also sometimes that new photos were taken of the same specimen at a different times.
193
 
 
 
 
 
 
 
 
194
 
195
  ## Additional Information
196
 
 
199
  * Christopher Lawrence (Princeton University) - ORCID: 0000-0002-3846-5968
200
  * Chris Jiggins (University of Cambridge) - ORCID: 0000-0002-7809-062X
201
  * Butterfly Genetics Group (University of Cambridge)
202
+ * Elizabeth G. Campolongo - ORCID: 0000-0003-0846-2413
203
 
204
  ### Licensing Information
205
 
 
209
 
210
  ### Citation Information
211
 
212
+ Christopher Lawrence, Chris Jiggins, Butterfly Genetics Group (University of Cambridge), Elizabeth Campolongo. (2024). Jiggins Heliconius Collection. Hugging Face. https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection.
213
 
214
+ If you use this dataset, please cite the original datasets (citations for all 31 Zenodo records are in licenses.json) as well as this curated subset.
215
 
216
  ### Contributions
217
 
218
+ The [Imageomics Institute](https://imageomics.org) is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
metadata/Missing_taxa_Jiggins_Zenodo_Master.csv ADDED
The diff for this file is too large to render. See raw diff
 
licenses.json → metadata/licenses.json RENAMED
@@ -1 +1 @@
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Zenodo. https://doi.org/10.5281/zenodo.2677821", "bibtex": "@misc{montejo_kovacevich_2019_2677821, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian}, title = {Cambridge butterfly wing collection batch 2}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2677821}, url = {https://doi.org/10.5281/zenodo.2677821} }"}, {"record_number": "3477891", "url": "https://zenodo.org/record/3477891", "license": "cc-by-4.0", "citation": "Jiggins, C., Montejo-Kovacevich, G., Salazar, P., & Warren, I. (2019 , October). Heliconiine Butterfly Collection Records from University of Cambridge. 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Zenodo. https://doi.org/10.5281/zenodo.4291095", "bibtex": "@misc{gabriela_montejo_kovacevich_2020_4291095, author = {Gabriela Montejo-Kovacevich and Eva van der Heijden and Chris Jiggins}, title = {{Cambridge butterfly collection - GMK Broods Ikiam 2018}}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4291095}, url = {https://doi.org/10.5281/zenodo.4291095} }"}, {"record_number": "5731587", "url": "https://zenodo.org/record/5731587", "license": "cc-by-4.0", "citation": "Meier, J., Barker, A., Jiggins, C., Warren, I., & Blow, R. (2021 , November). Cambridge butterfly wing collection - Ecuador, August 2019. 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Edwards and Chris Jiggins}, title = {{Cambridge butterfly collection - Loreto, Peru 2018 batch2}}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4287444}, url = {https://doi.org/10.5281/zenodo.4287444} }"}, {"record_number": "4288250", "url": "https://zenodo.org/record/4288250", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Cookson, L., van der Heijden, E., Warren, I., Edwards, D. P., & Jiggins, C. (2020 , November). Cambridge butterfly collection - Loreto, Peru 2018 batch3. Zenodo. https://doi.org/10.5281/zenodo.4288250", "bibtex": "@misc{gabriela_montejo_kovacevich_2020_4288250, author = {Gabriela Montejo-Kovacevich and Letitia Cookson and Eva van der Heijden and Ian Warren and David P. Edwards and Chris Jiggins}, title = {{Cambridge butterfly collection - Loreto, Peru 2018 batch3}}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4288250}, url = {https://doi.org/10.5281/zenodo.4288250} }"}, {"record_number": "5526257", "url": "https://zenodo.org/record/5526257", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Paynter, Q., & Ghane, A. (2021 , September). Heliconius erato cyrbia, Cook Islands (New Zealand) 2016, 2019, 2021. 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Zenodo. https://doi.org/10.5281/zenodo.2553501", "bibtex": "@misc{warren_2019_2553501, author = {Warren, Ian and Jiggins, Chris}, title = {{Miscellaneous Heliconius wing photographs (2001-2019) Part 2}}, month = feb, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2553501}, url = {https://doi.org/10.5281/zenodo.2553501} }"}, {"record_number": "2735056", "url": "https://zenodo.org/record/2735056", "license": "cc-by-4.0", "citation": "Salazar, C., Montejo-Kovacevich, G., Jiggins, C., Warren, I., & Gavins, I. (2019 , May). Camilo Salazar and Cambridge butterfly wing collection batch 1. Zenodo. https://doi.org/10.5281/zenodo.2735056", "bibtex": "@misc{salazar_2019_2735056, author = {Salazar, Camilo and Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Gavins, Imogen}, title = {{Camilo Salazar and Cambridge butterfly wing collection batch 1}}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2735056}, url = {https://doi.org/10.5281/zenodo.2735056} }"}, {"record_number": "2554218", "url": "https://zenodo.org/record/2554218", "license": "cc-by-4.0", "citation": "Mattila, A., Jiggins, C., & Warren, I. (2019 , February). University of Helsinki butterfly wing collection - Anniina Mattila field caught specimens. Zenodo. https://doi.org/10.5281/zenodo.2554218", "bibtex": "@misc{mattila_2019_2554218, author = {Mattila, Anniina and Jiggins, Chris and Warren, Ian}, title = {{University of Helsinki butterfly wing collection - Anniina Mattila field caught specimens}}, month = feb, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2554218}, url = {https://doi.org/10.5281/zenodo.2554218} }"}, {"record_number": "2555086", "url": "https://zenodo.org/record/2555086", "license": "cc-by-4.0", "citation": "Mattila, A., Jiggins, C., & Warren, I. (2019 , February). University of Helsinki butterfly collection - Anniina Mattila bred specimens. Zenodo. https://doi.org/10.5281/zenodo.2555086", "bibtex": "@misc{mattila_2019_2555086, author = {Mattila, Anniina and Jiggins, Chris and Warren, Ian}, title = {{University of Helsinki butterfly collection - Anniina Mattila bred specimens}}, month = feb, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2555086}, url = {https://doi.org/10.5281/zenodo.2555086} }"}]
 
1
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Zenodo. https://doi.org/10.5281/zenodo.2555086", "bibtex": "@misc{mattila_2019_2555086, author = {Mattila, Anniina and Jiggins, Chris and Warren, Ian}, title = {{University of Helsinki butterfly collection - Anniina Mattila bred specimens}}, month = feb, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2555086}, url = {https://doi.org/10.5281/zenodo.2555086} }"}]
notebooks/standardize_datasets.ipynb ADDED
@@ -0,0 +1,2635 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 1,
6
+ "metadata": {},
7
+ "outputs": [],
8
+ "source": [
9
+ "import pandas as pd"
10
+ ]
11
+ },
12
+ {
13
+ "cell_type": "markdown",
14
+ "metadata": {},
15
+ "source": [
16
+ "It turns out [record 3082688](https://zenodo.org/records/3082688) is version 2 of [record 1247307](https://zenodo.org/records/1247307), where some mislabeling was fixed. Images from record 1247307 should not be used, so we will remove them here.\n",
17
+ "\n",
18
+ "Will also add `hybrid_stat` and `file_url` columns so these can be explored with all functionality of the [data dashboard](https://huggingface.co/spaces/imageomics/dashboard-prototype)."
19
+ ]
20
+ },
21
+ {
22
+ "cell_type": "code",
23
+ "execution_count": 2,
24
+ "metadata": {},
25
+ "outputs": [],
26
+ "source": [
27
+ "df = pd.read_csv(\"../Jiggins_Zenodo_Img_Master.csv\", low_memory=False)"
28
+ ]
29
+ },
30
+ {
31
+ "cell_type": "code",
32
+ "execution_count": 3,
33
+ "metadata": {},
34
+ "outputs": [
35
+ {
36
+ "data": {
37
+ "text/plain": [
38
+ "CAMID 12586\n",
39
+ "X 49359\n",
40
+ "Image_name 37821\n",
41
+ "View 7\n",
42
+ "zenodo_name 36\n",
43
+ "zenodo_link 32\n",
44
+ "Sequence 11301\n",
45
+ "Taxonomic_Name 363\n",
46
+ "Locality 645\n",
47
+ "Sample_accession 1571\n",
48
+ "Collected_by 12\n",
49
+ "Other_ID 3088\n",
50
+ "Date 810\n",
51
+ "Dataset 8\n",
52
+ "Store 142\n",
53
+ "Brood 226\n",
54
+ "Death_Date 82\n",
55
+ "Cross_Type 30\n",
56
+ "Stage 1\n",
57
+ "Sex 3\n",
58
+ "Unit_Type 6\n",
59
+ "file_type 3\n",
60
+ "record_number 32\n",
61
+ "species 246\n",
62
+ "subspecies 155\n",
63
+ "genus 94\n",
64
+ "dtype: int64"
65
+ ]
66
+ },
67
+ "execution_count": 3,
68
+ "metadata": {},
69
+ "output_type": "execute_result"
70
+ }
71
+ ],
72
+ "source": [
73
+ "df.nunique()"
74
+ ]
75
+ },
76
+ {
77
+ "cell_type": "code",
78
+ "execution_count": 4,
79
+ "metadata": {},
80
+ "outputs": [
81
+ {
82
+ "name": "stdout",
83
+ "output_type": "stream",
84
+ "text": [
85
+ "<class 'pandas.core.series.Series'>\n",
86
+ "RangeIndex: 49359 entries, 0 to 49358\n",
87
+ "Series name: record_number\n",
88
+ "Non-Null Count Dtype\n",
89
+ "-------------- -----\n",
90
+ "49359 non-null int64\n",
91
+ "dtypes: int64(1)\n",
92
+ "memory usage: 385.7 KB\n"
93
+ ]
94
+ }
95
+ ],
96
+ "source": [
97
+ "df.record_number.info()"
98
+ ]
99
+ },
100
+ {
101
+ "cell_type": "code",
102
+ "execution_count": 5,
103
+ "metadata": {},
104
+ "outputs": [
105
+ {
106
+ "data": {
107
+ "text/plain": [
108
+ "CAMID 12586\n",
109
+ "X 46439\n",
110
+ "Image_name 37821\n",
111
+ "View 7\n",
112
+ "zenodo_name 35\n",
113
+ "zenodo_link 31\n",
114
+ "Sequence 11301\n",
115
+ "Taxonomic_Name 363\n",
116
+ "Locality 645\n",
117
+ "Sample_accession 1571\n",
118
+ "Collected_by 12\n",
119
+ "Other_ID 3088\n",
120
+ "Date 810\n",
121
+ "Dataset 8\n",
122
+ "Store 142\n",
123
+ "Brood 226\n",
124
+ "Death_Date 82\n",
125
+ "Cross_Type 30\n",
126
+ "Stage 1\n",
127
+ "Sex 3\n",
128
+ "Unit_Type 6\n",
129
+ "file_type 3\n",
130
+ "record_number 31\n",
131
+ "species 246\n",
132
+ "subspecies 155\n",
133
+ "genus 94\n",
134
+ "dtype: int64"
135
+ ]
136
+ },
137
+ "execution_count": 5,
138
+ "metadata": {},
139
+ "output_type": "execute_result"
140
+ }
141
+ ],
142
+ "source": [
143
+ "df = df.loc[df.record_number != 1247307]\n",
144
+ "df.nunique()"
145
+ ]
146
+ },
147
+ {
148
+ "cell_type": "markdown",
149
+ "metadata": {},
150
+ "source": [
151
+ "Checking all records on Zenodo for the [Butterfly Genetics Group](https://zenodo.org/communities/butterfly/records) (the overall source of the Jiggins data), [record 3477891](https://zenodo.org/records/3477891) and [record 2548678](https://zenodo.org/records/2548678) are marked as version 2 of records 3477412 & 1880783, respectively. Let's check that the first versions are not included.\n",
152
+ "\n",
153
+ "It is also noted in [record 2548678](https://zenodo.org/records/2548678):\n",
154
+ ">Some images overlap with 'Cambridge butterfly wing collection batch 1', taken by Eva Whiltshire. Images here differ in having a white reflectance standard for calibration. Information on duplicates can be found in 'CAM.coll.patricio.batch2.csv'.\n",
155
+ "\n",
156
+ "Note that this is a reference to [record 1247307](https://zenodo.org/records/1247307), so the overlap is in fact with Cambridge butterfly wing collection batch 1- version 2 ([record 3082688](https://zenodo.org/records/3082688)); it is unclear as yet if the duplication information will be acurate considering the mislabelings fixed between the versions."
157
+ ]
158
+ },
159
+ {
160
+ "cell_type": "code",
161
+ "execution_count": 6,
162
+ "metadata": {},
163
+ "outputs": [
164
+ {
165
+ "data": {
166
+ "text/plain": [
167
+ "(0, 26)"
168
+ ]
169
+ },
170
+ "execution_count": 6,
171
+ "metadata": {},
172
+ "output_type": "execute_result"
173
+ }
174
+ ],
175
+ "source": [
176
+ "v1s = [3477412, 1880783]\n",
177
+ "df.loc[df[\"record_number\"].isin(v1s)].shape"
178
+ ]
179
+ },
180
+ {
181
+ "cell_type": "markdown",
182
+ "metadata": {},
183
+ "source": [
184
+ "The good news is that neither of these earlier versions seem to have been picked up, so we can move on to assessing this and adding the `hybrid_stat` and `file_url` columns."
185
+ ]
186
+ },
187
+ {
188
+ "cell_type": "code",
189
+ "execution_count": 7,
190
+ "metadata": {},
191
+ "outputs": [
192
+ {
193
+ "data": {
194
+ "text/plain": [
195
+ "file_type\n",
196
+ "jpg 34152\n",
197
+ "raw 12226\n",
198
+ "tif 61\n",
199
+ "Name: count, dtype: int64"
200
+ ]
201
+ },
202
+ "execution_count": 7,
203
+ "metadata": {},
204
+ "output_type": "execute_result"
205
+ }
206
+ ],
207
+ "source": [
208
+ "df.file_type.value_counts()"
209
+ ]
210
+ },
211
+ {
212
+ "cell_type": "code",
213
+ "execution_count": 8,
214
+ "metadata": {},
215
+ "outputs": [
216
+ {
217
+ "data": {
218
+ "text/plain": [
219
+ "View\n",
220
+ "dorsal 22022\n",
221
+ "ventral 21704\n",
222
+ "forewing dorsal 406\n",
223
+ "hindwing dorsal 406\n",
224
+ "forewing ventral 406\n",
225
+ "hindwing ventral 406\n",
226
+ "dorsal and ventral 18\n",
227
+ "Name: count, dtype: int64"
228
+ ]
229
+ },
230
+ "execution_count": 8,
231
+ "metadata": {},
232
+ "output_type": "execute_result"
233
+ }
234
+ ],
235
+ "source": [
236
+ "df.View.value_counts()"
237
+ ]
238
+ },
239
+ {
240
+ "cell_type": "markdown",
241
+ "metadata": {},
242
+ "source": [
243
+ "We have 31 unique records represented in the full dataset. When we reduce down to just the Heliconius images, this will probably be less."
244
+ ]
245
+ },
246
+ {
247
+ "cell_type": "markdown",
248
+ "metadata": {},
249
+ "source": [
250
+ "### Add File URL Column"
251
+ ]
252
+ },
253
+ {
254
+ "cell_type": "code",
255
+ "execution_count": 9,
256
+ "metadata": {},
257
+ "outputs": [
258
+ {
259
+ "data": {
260
+ "text/html": [
261
+ "<div>\n",
262
+ "<style scoped>\n",
263
+ " .dataframe tbody tr th:only-of-type {\n",
264
+ " vertical-align: middle;\n",
265
+ " }\n",
266
+ "\n",
267
+ " .dataframe tbody tr th {\n",
268
+ " vertical-align: top;\n",
269
+ " }\n",
270
+ "\n",
271
+ " .dataframe thead th {\n",
272
+ " text-align: right;\n",
273
+ " }\n",
274
+ "</style>\n",
275
+ "<table border=\"1\" class=\"dataframe\">\n",
276
+ " <thead>\n",
277
+ " <tr style=\"text-align: right;\">\n",
278
+ " <th></th>\n",
279
+ " <th>CAMID</th>\n",
280
+ " <th>X</th>\n",
281
+ " <th>Image_name</th>\n",
282
+ " <th>View</th>\n",
283
+ " <th>zenodo_name</th>\n",
284
+ " <th>zenodo_link</th>\n",
285
+ " <th>Sequence</th>\n",
286
+ " <th>Taxonomic_Name</th>\n",
287
+ " <th>Locality</th>\n",
288
+ " <th>Sample_accession</th>\n",
289
+ " <th>...</th>\n",
290
+ " <th>Cross_Type</th>\n",
291
+ " <th>Stage</th>\n",
292
+ " <th>Sex</th>\n",
293
+ " <th>Unit_Type</th>\n",
294
+ " <th>file_type</th>\n",
295
+ " <th>record_number</th>\n",
296
+ " <th>species</th>\n",
297
+ " <th>subspecies</th>\n",
298
+ " <th>genus</th>\n",
299
+ " <th>file_url</th>\n",
300
+ " </tr>\n",
301
+ " </thead>\n",
302
+ " <tbody>\n",
303
+ " <tr>\n",
304
+ " <th>34925</th>\n",
305
+ " <td>CAM041457</td>\n",
306
+ " <td>38799</td>\n",
307
+ " <td>CAM041457_d.CR2</td>\n",
308
+ " <td>dorsal</td>\n",
309
+ " <td>0.gmk.broods.all.csv</td>\n",
310
+ " <td>https://zenodo.org/record/4291095</td>\n",
311
+ " <td>41,457</td>\n",
312
+ " <td>Heliconius timareta</td>\n",
313
+ " <td>Reventador road 2</td>\n",
314
+ " <td>NaN</td>\n",
315
+ " <td>...</td>\n",
316
+ " <td>NaN</td>\n",
317
+ " <td>NaN</td>\n",
318
+ " <td>Male</td>\n",
319
+ " <td>NaN</td>\n",
320
+ " <td>raw</td>\n",
321
+ " <td>4291095</td>\n",
322
+ " <td>Heliconius timareta</td>\n",
323
+ " <td>NaN</td>\n",
324
+ " <td>Heliconius</td>\n",
325
+ " <td>https://zenodo.org/record/4291095/files/CAM041...</td>\n",
326
+ " </tr>\n",
327
+ " <tr>\n",
328
+ " <th>28286</th>\n",
329
+ " <td>CAM036145</td>\n",
330
+ " <td>41369</td>\n",
331
+ " <td>CAM036145_v.JPG</td>\n",
332
+ " <td>ventral</td>\n",
333
+ " <td>Filelist.csv</td>\n",
334
+ " <td>https://zenodo.org/record/5561246</td>\n",
335
+ " <td>36,145</td>\n",
336
+ " <td>Methona singularis</td>\n",
337
+ " <td>Guaribas - RG16</td>\n",
338
+ " <td>NaN</td>\n",
339
+ " <td>...</td>\n",
340
+ " <td>NaN</td>\n",
341
+ " <td>NaN</td>\n",
342
+ " <td>Male</td>\n",
343
+ " <td>NaN</td>\n",
344
+ " <td>jpg</td>\n",
345
+ " <td>5561246</td>\n",
346
+ " <td>Methona singularis</td>\n",
347
+ " <td>NaN</td>\n",
348
+ " <td>Methona</td>\n",
349
+ " <td>https://zenodo.org/record/5561246/files/CAM036...</td>\n",
350
+ " </tr>\n",
351
+ " <tr>\n",
352
+ " <th>21858</th>\n",
353
+ " <td>CAM017409</td>\n",
354
+ " <td>19047</td>\n",
355
+ " <td>CAM017409_d.CR2</td>\n",
356
+ " <td>dorsal</td>\n",
357
+ " <td>CAM.coll.PS.list.individuals.haplotagging.new....</td>\n",
358
+ " <td>https://zenodo.org/record/4153502</td>\n",
359
+ " <td>17,409</td>\n",
360
+ " <td>Heliconius erato ssp. lativitta</td>\n",
361
+ " <td>San Pedro de Arajuno, Río Arajuno</td>\n",
362
+ " <td>ERS353373</td>\n",
363
+ " <td>...</td>\n",
364
+ " <td>NaN</td>\n",
365
+ " <td>NaN</td>\n",
366
+ " <td>Female</td>\n",
367
+ " <td>wild</td>\n",
368
+ " <td>raw</td>\n",
369
+ " <td>4153502</td>\n",
370
+ " <td>Heliconius erato</td>\n",
371
+ " <td>lativitta</td>\n",
372
+ " <td>Heliconius</td>\n",
373
+ " <td>https://zenodo.org/record/4153502/files/CAM017...</td>\n",
374
+ " </tr>\n",
375
+ " <tr>\n",
376
+ " <th>12708</th>\n",
377
+ " <td>CAM010412</td>\n",
378
+ " <td>26792</td>\n",
379
+ " <td>CAM010412_v.CR2</td>\n",
380
+ " <td>ventral</td>\n",
381
+ " <td>2001_2.broods.batch.1.csv</td>\n",
382
+ " <td>https://zenodo.org/record/2549524</td>\n",
383
+ " <td>10,412</td>\n",
384
+ " <td>Heliconius sp.</td>\n",
385
+ " <td>NaN</td>\n",
386
+ " <td>NaN</td>\n",
387
+ " <td>...</td>\n",
388
+ " <td>NaN</td>\n",
389
+ " <td>NaN</td>\n",
390
+ " <td>Unknown</td>\n",
391
+ " <td>reared</td>\n",
392
+ " <td>raw</td>\n",
393
+ " <td>2549524</td>\n",
394
+ " <td>Heliconius sp.</td>\n",
395
+ " <td>NaN</td>\n",
396
+ " <td>Heliconius</td>\n",
397
+ " <td>https://zenodo.org/record/2549524/files/CAM010...</td>\n",
398
+ " </tr>\n",
399
+ " </tbody>\n",
400
+ "</table>\n",
401
+ "<p>4 rows × 27 columns</p>\n",
402
+ "</div>"
403
+ ],
404
+ "text/plain": [
405
+ " CAMID X Image_name View \\\n",
406
+ "34925 CAM041457 38799 CAM041457_d.CR2 dorsal \n",
407
+ "28286 CAM036145 41369 CAM036145_v.JPG ventral \n",
408
+ "21858 CAM017409 19047 CAM017409_d.CR2 dorsal \n",
409
+ "12708 CAM010412 26792 CAM010412_v.CR2 ventral \n",
410
+ "\n",
411
+ " zenodo_name \\\n",
412
+ "34925 0.gmk.broods.all.csv \n",
413
+ "28286 Filelist.csv \n",
414
+ "21858 CAM.coll.PS.list.individuals.haplotagging.new.... \n",
415
+ "12708 2001_2.broods.batch.1.csv \n",
416
+ "\n",
417
+ " zenodo_link Sequence \\\n",
418
+ "34925 https://zenodo.org/record/4291095 41,457 \n",
419
+ "28286 https://zenodo.org/record/5561246 36,145 \n",
420
+ "21858 https://zenodo.org/record/4153502 17,409 \n",
421
+ "12708 https://zenodo.org/record/2549524 10,412 \n",
422
+ "\n",
423
+ " Taxonomic_Name Locality \\\n",
424
+ "34925 Heliconius timareta Reventador road 2 \n",
425
+ "28286 Methona singularis Guaribas - RG16 \n",
426
+ "21858 Heliconius erato ssp. lativitta San Pedro de Arajuno, Río Arajuno \n",
427
+ "12708 Heliconius sp. NaN \n",
428
+ "\n",
429
+ " Sample_accession ... Cross_Type Stage Sex Unit_Type file_type \\\n",
430
+ "34925 NaN ... NaN NaN Male NaN raw \n",
431
+ "28286 NaN ... NaN NaN Male NaN jpg \n",
432
+ "21858 ERS353373 ... NaN NaN Female wild raw \n",
433
+ "12708 NaN ... NaN NaN Unknown reared raw \n",
434
+ "\n",
435
+ " record_number species subspecies genus \\\n",
436
+ "34925 4291095 Heliconius timareta NaN Heliconius \n",
437
+ "28286 5561246 Methona singularis NaN Methona \n",
438
+ "21858 4153502 Heliconius erato lativitta Heliconius \n",
439
+ "12708 2549524 Heliconius sp. NaN Heliconius \n",
440
+ "\n",
441
+ " file_url \n",
442
+ "34925 https://zenodo.org/record/4291095/files/CAM041... \n",
443
+ "28286 https://zenodo.org/record/5561246/files/CAM036... \n",
444
+ "21858 https://zenodo.org/record/4153502/files/CAM017... \n",
445
+ "12708 https://zenodo.org/record/2549524/files/CAM010... \n",
446
+ "\n",
447
+ "[4 rows x 27 columns]"
448
+ ]
449
+ },
450
+ "execution_count": 9,
451
+ "metadata": {},
452
+ "output_type": "execute_result"
453
+ }
454
+ ],
455
+ "source": [
456
+ "df[\"file_url\"] = df[\"zenodo_link\"] + \"/files/\" + df[\"Image_name\"]\n",
457
+ "df.sample(4)"
458
+ ]
459
+ },
460
+ {
461
+ "cell_type": "markdown",
462
+ "metadata": {},
463
+ "source": [
464
+ "### Add Proper Taxonomic Name for Crosstypes\n",
465
+ "\n",
466
+ "We want the cross types to also have full taxonomic names (`Heliconius <species> <subspecies>`) so this can be used in downloading to appropriate branches and also for easier diversity counts. Cross Types will still be easily filtered using the `Cross_Type` column."
467
+ ]
468
+ },
469
+ {
470
+ "cell_type": "code",
471
+ "execution_count": 10,
472
+ "metadata": {},
473
+ "outputs": [
474
+ {
475
+ "data": {
476
+ "text/plain": [
477
+ "subspecies\n",
478
+ "(malleti x plesseni) x malleti 1007\n",
479
+ "plesseni x (malleti x plesseni) 462\n",
480
+ "(plesseni x malleti) x (malleti x plesseni) 348\n",
481
+ "(plesseni x malleti) x plesseni 345\n",
482
+ "malleti x (plesseni x malleti) 324\n",
483
+ "(plesseni x malleti) x (plesseni x malleti) 228\n",
484
+ "(malleti x plesseni) x plesseni 216\n",
485
+ "plesseni x malleti 212\n",
486
+ "malleti x plesseni 156\n",
487
+ "lativitta x notabilis 111\n",
488
+ "plesseni x (plesseni x malleti) 106\n",
489
+ "(lativitta x notabilis) x notabilis 90\n",
490
+ "(lativitta x notabilis) x lativitta 90\n",
491
+ "(malleti x plesseni) x (malleti x plesseni) 81\n",
492
+ "(plesseni x malleti) x malleti 80\n",
493
+ "(malleti x plesseni) x (plesseni x malleti) 42\n",
494
+ "malleti 28\n",
495
+ "plesseni 28\n",
496
+ "(latRo x notabilis) x notabilis 12\n",
497
+ "latRo x notabilis 4\n",
498
+ "lativitta 4\n",
499
+ "Name: count, dtype: int64"
500
+ ]
501
+ },
502
+ "execution_count": 10,
503
+ "metadata": {},
504
+ "output_type": "execute_result"
505
+ }
506
+ ],
507
+ "source": [
508
+ "df.loc[df[\"Cross_Type\"].notna(), \"subspecies\"].value_counts()"
509
+ ]
510
+ },
511
+ {
512
+ "cell_type": "code",
513
+ "execution_count": 11,
514
+ "metadata": {},
515
+ "outputs": [
516
+ {
517
+ "data": {
518
+ "text/plain": [
519
+ "Cross_Type\n",
520
+ "Test cross (4 spots x 4 spots) 150\n",
521
+ "Test cross (N heterozygocity - NBNN x malleti - thin) 114\n",
522
+ "Test cross (N heterozygozity) 78\n",
523
+ "Test cross (short HW bar) 54\n",
524
+ "Test cross (4 spots x 2 banded) 48\n",
525
+ "2 banded 16\n",
526
+ "hybrid 10\n",
527
+ "Ac heterozygote 4\n",
528
+ "Test cross (2 banded F2 x 2 banded F2) 4\n",
529
+ "Name: count, dtype: int64"
530
+ ]
531
+ },
532
+ "execution_count": 11,
533
+ "metadata": {},
534
+ "output_type": "execute_result"
535
+ }
536
+ ],
537
+ "source": [
538
+ "df.loc[(df[\"Cross_Type\"].notna()) & (df[\"subspecies\"].isna()), \"Cross_Type\"].value_counts()"
539
+ ]
540
+ },
541
+ {
542
+ "cell_type": "markdown",
543
+ "metadata": {},
544
+ "source": [
545
+ "Notice that we have hybrids here which should be indicated as such though they don't all have an ` x ` in their name. We'll remember this for the `hybrid_stat` column and generate using the Cross Type indicator. Our only non-hybrid Cross Types are `malleti`, `plesseni`, and `lativitta`."
546
+ ]
547
+ },
548
+ {
549
+ "cell_type": "code",
550
+ "execution_count": 12,
551
+ "metadata": {},
552
+ "outputs": [
553
+ {
554
+ "data": {
555
+ "text/plain": [
556
+ "array(['hybrid', 'Ac heterozygote', '2 banded',\n",
557
+ " 'Test cross (2 banded F2 x 2 banded F2)',\n",
558
+ " 'Test cross (4 spots x 2 banded)', 'Test cross (N heterozygozity)',\n",
559
+ " 'Test cross (short HW bar)', 'Test cross (4 spots x 4 spots)',\n",
560
+ " 'Test cross (N heterozygocity - NBNN x malleti - thin)'],\n",
561
+ " dtype=object)"
562
+ ]
563
+ },
564
+ "execution_count": 12,
565
+ "metadata": {},
566
+ "output_type": "execute_result"
567
+ }
568
+ ],
569
+ "source": [
570
+ "non_specific_cross_hybrids = df.loc[(df[\"Cross_Type\"].notna()) & (df[\"subspecies\"].isna()), \"Cross_Type\"].unique()\n",
571
+ "non_specific_cross_hybrids"
572
+ ]
573
+ },
574
+ {
575
+ "cell_type": "code",
576
+ "execution_count": 13,
577
+ "metadata": {},
578
+ "outputs": [
579
+ {
580
+ "data": {
581
+ "text/plain": [
582
+ "array(['cythera', 'cyrbia', 'venus', 'venus x chestertonii', 'vulcanus',\n",
583
+ " 'chestertonii', 'vulcanus x melpomene', 'lativitta', 'malleti',\n",
584
+ " 'erato', 'ssp.nov.P', 'melpomene', 'willmotti',\n",
585
+ " 'chestertonii x venus', 'eleuchia', 'cydnides', 'weymeri',\n",
586
+ " 'chioneus', 'demophoon', 'hydara', 'sapho', 'numata', 'iulia',\n",
587
+ " 'melpomene x thelxiope', 'wallacei', 'rosina', 'formosus',\n",
588
+ " 'melpomene x rosina', 'menapis', 'hydara x petiverana', 'decumana',\n",
589
+ " 'relata', 'agna', 'isthmia', 'bicoloratus', 'euphrasius',\n",
590
+ " 'salapia', 'matronalis', 'ethica', 'agnosia', 'andromica',\n",
591
+ " 'hippocrenis', 'saturata', 'evanides', 'lyra', 'pagasa',\n",
592
+ " 'staudingeri', 'valora', 'cassotis', 'chiriquensis', 'alithea',\n",
593
+ " 'timareta', 'plesseni', 'notabilis', 'amaryllis', 'thelxinoe',\n",
594
+ " 'doris', 'amaryllis x aglaope', 'aglaope', 'carbo', 'lycaste',\n",
595
+ " 'idae', 'macrinus', 'abida', 'notilla', 'neustetteri', 'giulia',\n",
596
+ " 'panamensis', 'eleusinus', 'agalla', 'xanthina', 'ecuadorensis',\n",
597
+ " 'congener', 'etylus', 'derasa', 'lenaeus', 'petiverana',\n",
598
+ " 'melicerta', 'thelxiopeia', 'meriana', 'plesseni x malleti',\n",
599
+ " 'notabilis x lativitta', 'sara', 'silvana', 'amalfreda',\n",
600
+ " 'hydara x amalfreda', 'flavescens', 'hydara x erato',\n",
601
+ " 'meriana x melpomene', 'nanna', 'daetina', 'nesaea', 'phyllis',\n",
602
+ " 'laphria', 'paraiya', 'lysimnia', 'daeta', 'yanetta', 'pyrrha',\n",
603
+ " 'casabranca', 'tristero', 'dignus', 'bellula',\n",
604
+ " 'dignus x lativitta', 'malleti x bellula', 'napoensis',\n",
605
+ " 'clysonymus', 'sotericus', 'primularis', 'sprucei', 'bassleri',\n",
606
+ " 'eximius', 'manabiana', 'nigrippus', 'hygiana', 'neildi',\n",
607
+ " 'clysonomus', 'hierax', 'magdalena', 'calathus', 'corena',\n",
608
+ " 'zelinde', 'florencia', 'weymeri f. gustavi', 'weymeri f. weymeri',\n",
609
+ " 'wanningeri', 'cydno', 'cordula', 'linaresi', 'lisethae',\n",
610
+ " 'hermogenes', 'martinae', 'vicina', 'malleti x vicina',\n",
611
+ " 'reductimacula', 'sergestus', 'felix', 'aerotome'], dtype=object)"
612
+ ]
613
+ },
614
+ "execution_count": 13,
615
+ "metadata": {},
616
+ "output_type": "execute_result"
617
+ }
618
+ ],
619
+ "source": [
620
+ "df.loc[df[\"Cross_Type\"].isna(), \"subspecies\"].dropna().unique()"
621
+ ]
622
+ },
623
+ {
624
+ "cell_type": "code",
625
+ "execution_count": 14,
626
+ "metadata": {},
627
+ "outputs": [],
628
+ "source": [
629
+ "def get_cross_taxa_name(species, cross_type):\n",
630
+ " # label unspecified hybrids as such\n",
631
+ " if cross_type in non_specific_cross_hybrids:\n",
632
+ " return species + \" \" + \"cross hybrid\"\n",
633
+ " \n",
634
+ " # separate out hybrids from non-hybrids\n",
635
+ " subsp_cross_list = cross_type.split(\"(\")\n",
636
+ " if len(subsp_cross_list) > 1:\n",
637
+ " subsp_cross = subsp_cross_list[1].split(\")\")[0]\n",
638
+ " else:\n",
639
+ " subsp_cross = cross_type\n",
640
+ " \n",
641
+ " # Ensure order of hybrid names is consistent so they get placed in same folders (using scripts/download_jiggins.py)\n",
642
+ " # And so counts by taxonomic name aren't needlessly skewed\n",
643
+ " if subsp_cross == \"malleti x plesseni\":\n",
644
+ " subsp_cross = \"plesseni x malleti\"\n",
645
+ " elif subsp_cross == \"lativitta x notabilis\":\n",
646
+ " subsp_cross = \"notabilis x lativitta\"\n",
647
+ " \n",
648
+ " # Set taxa_name as with non cross types labeled to subspecies\n",
649
+ " taxa_name = species + \" ssp. \" + subsp_cross\n",
650
+ " return taxa_name"
651
+ ]
652
+ },
653
+ {
654
+ "cell_type": "code",
655
+ "execution_count": 15,
656
+ "metadata": {},
657
+ "outputs": [
658
+ {
659
+ "name": "stdout",
660
+ "output_type": "stream",
661
+ "text": [
662
+ "H.sp. ssp. malleti\n",
663
+ "H.sp. ssp. plesseni x malleti\n",
664
+ "H.sp. ssp. plesseni\n",
665
+ "H.sp. ssp. plesseni x malleti\n",
666
+ "H.sp. ssp. latRo x notabilis\n",
667
+ "H.sp. ssp. latRo x notabilis\n",
668
+ "H.sp. ssp. plesseni x malleti\n",
669
+ "H.sp. ssp. plesseni x malleti\n",
670
+ "H.sp. ssp. plesseni x malleti\n",
671
+ "H.sp. ssp. plesseni x malleti\n",
672
+ "H.sp. ssp. notabilis x lativitta\n",
673
+ "H.sp. ssp. plesseni x malleti\n",
674
+ "H.sp. ssp. plesseni x malleti\n",
675
+ "H.sp. ssp. plesseni x malleti\n",
676
+ "H.sp. ssp. plesseni x malleti\n",
677
+ "H.sp. ssp. plesseni x malleti\n",
678
+ "H.sp. cross hybrid\n",
679
+ "H.sp. ssp. plesseni x malleti\n",
680
+ "H.sp. ssp. notabilis x lativitta\n",
681
+ "H.sp. ssp. notabilis x lativitta\n",
682
+ "H.sp. cross hybrid\n",
683
+ "H.sp. ssp. plesseni x malleti\n",
684
+ "H.sp. cross hybrid\n",
685
+ "H.sp. ssp. lativitta\n",
686
+ "H.sp. cross hybrid\n",
687
+ "H.sp. cross hybrid\n",
688
+ "H.sp. cross hybrid\n",
689
+ "H.sp. cross hybrid\n",
690
+ "H.sp. cross hybrid\n",
691
+ "H.sp. cross hybrid\n"
692
+ ]
693
+ }
694
+ ],
695
+ "source": [
696
+ "for cross_type in list(df[\"Cross_Type\"].dropna().unique()):\n",
697
+ " print(get_cross_taxa_name(\"H.sp.\", cross_type))"
698
+ ]
699
+ },
700
+ {
701
+ "cell_type": "code",
702
+ "execution_count": 16,
703
+ "metadata": {},
704
+ "outputs": [
705
+ {
706
+ "data": {
707
+ "text/plain": [
708
+ "array(['Heliconius melpomene', 'Heliconius erato'], dtype=object)"
709
+ ]
710
+ },
711
+ "execution_count": 16,
712
+ "metadata": {},
713
+ "output_type": "execute_result"
714
+ }
715
+ ],
716
+ "source": [
717
+ "df.loc[df[\"Cross_Type\"].notna(), \"species\"].unique()"
718
+ ]
719
+ },
720
+ {
721
+ "cell_type": "code",
722
+ "execution_count": 17,
723
+ "metadata": {},
724
+ "outputs": [
725
+ {
726
+ "data": {
727
+ "text/plain": [
728
+ "array(['Heliconius melpomene ssp. malleti',\n",
729
+ " 'Heliconius melpomene ssp. plesseni x malleti',\n",
730
+ " 'Heliconius melpomene ssp. plesseni',\n",
731
+ " 'Heliconius erato ssp. latRo x notabilis',\n",
732
+ " 'Heliconius erato ssp. notabilis x lativitta',\n",
733
+ " 'Heliconius erato cross hybrid',\n",
734
+ " 'Heliconius melpomene cross hybrid',\n",
735
+ " 'Heliconius erato ssp. lativitta'], dtype=object)"
736
+ ]
737
+ },
738
+ "execution_count": 17,
739
+ "metadata": {},
740
+ "output_type": "execute_result"
741
+ }
742
+ ],
743
+ "source": [
744
+ "# https://stackoverflow.com/a/52854800\n",
745
+ "df.loc[df[\"Cross_Type\"].notna(), \"Taxonomic_Name\"] = df.loc[df[\"Cross_Type\"].notna()].apply(lambda x: get_cross_taxa_name(x[\"species\"], x[\"Cross_Type\"]), axis = 1)\n",
746
+ "df.loc[df[\"Cross_Type\"].notna(), \"Taxonomic_Name\"].unique()"
747
+ ]
748
+ },
749
+ {
750
+ "cell_type": "code",
751
+ "execution_count": 18,
752
+ "metadata": {},
753
+ "outputs": [
754
+ {
755
+ "data": {
756
+ "text/plain": [
757
+ "366"
758
+ ]
759
+ },
760
+ "execution_count": 18,
761
+ "metadata": {},
762
+ "output_type": "execute_result"
763
+ }
764
+ ],
765
+ "source": [
766
+ "df.Taxonomic_Name.nunique()"
767
+ ]
768
+ },
769
+ {
770
+ "cell_type": "markdown",
771
+ "metadata": {},
772
+ "source": [
773
+ "So we've added 3 more unique values of `Taxonomic_Name`, that checks out: `Heliconius erato ssp. latRo x notabilis`, `Heliconius erato cross hybrid`, and `Heliconius melpomene cross hybrid`."
774
+ ]
775
+ },
776
+ {
777
+ "cell_type": "markdown",
778
+ "metadata": {},
779
+ "source": [
780
+ "### Add Hybrid Status Column"
781
+ ]
782
+ },
783
+ {
784
+ "cell_type": "code",
785
+ "execution_count": 19,
786
+ "metadata": {},
787
+ "outputs": [
788
+ {
789
+ "data": {
790
+ "text/plain": [
791
+ "20"
792
+ ]
793
+ },
794
+ "execution_count": 19,
795
+ "metadata": {},
796
+ "output_type": "execute_result"
797
+ }
798
+ ],
799
+ "source": [
800
+ "hybrids = [taxa_name for taxa_name in list(df[\"Taxonomic_Name\"].dropna().unique()) if (\" x \" in taxa_name) or (\"hybrid\" in taxa_name)]\n",
801
+ "len(hybrids)"
802
+ ]
803
+ },
804
+ {
805
+ "cell_type": "code",
806
+ "execution_count": 20,
807
+ "metadata": {},
808
+ "outputs": [
809
+ {
810
+ "data": {
811
+ "text/plain": [
812
+ "['Heliconius erato ssp. venus x chestertonii',\n",
813
+ " 'Heliconius melpomene ssp. vulcanus x melpomene',\n",
814
+ " 'Heliconius erato ssp. chestertonii x venus',\n",
815
+ " 'Heliconius melpomene ssp. melpomene x thelxiope',\n",
816
+ " 'Heliconius melpomene ssp. melpomene x rosina',\n",
817
+ " 'Heliconius hybrid',\n",
818
+ " 'Heliconius erato ssp. hydara x petiverana',\n",
819
+ " 'Heliconius melpomene ssp. amaryllis x aglaope',\n",
820
+ " 'Anartia fatima x amathea',\n",
821
+ " 'Heliconius melpomene ssp. plesseni x malleti',\n",
822
+ " 'Heliconius erato ssp. notabilis x lativitta',\n",
823
+ " 'Heliconius erato ssp. latRo x notabilis',\n",
824
+ " 'Heliconius erato cross hybrid',\n",
825
+ " 'Heliconius melpomene cross hybrid',\n",
826
+ " 'Heliconius erato ssp. hydara x amalfreda',\n",
827
+ " 'Heliconius erato ssp. hydara x erato',\n",
828
+ " 'Heliconius melpomene ssp. meriana x melpomene',\n",
829
+ " 'Heliconius melpomene ssp. dignus x lativitta',\n",
830
+ " 'Heliconius melpomene ssp. malleti x bellula',\n",
831
+ " 'Heliconius melpomene ssp. malleti x vicina']"
832
+ ]
833
+ },
834
+ "execution_count": 20,
835
+ "metadata": {},
836
+ "output_type": "execute_result"
837
+ }
838
+ ],
839
+ "source": [
840
+ "hybrids"
841
+ ]
842
+ },
843
+ {
844
+ "cell_type": "markdown",
845
+ "metadata": {},
846
+ "source": [
847
+ "Observe that we have one species-level hybrid: `Anartia fatima x amathea`."
848
+ ]
849
+ },
850
+ {
851
+ "cell_type": "code",
852
+ "execution_count": 21,
853
+ "metadata": {},
854
+ "outputs": [],
855
+ "source": [
856
+ "sp_hybrid_parents = [\"Anartia fatima\", \"Anartia amathea\"]"
857
+ ]
858
+ },
859
+ {
860
+ "cell_type": "code",
861
+ "execution_count": 22,
862
+ "metadata": {},
863
+ "outputs": [],
864
+ "source": [
865
+ "df[\"hybrid_stat\"] = None\n",
866
+ "df.loc[df[\"subspecies\"].notna(), \"hybrid_stat\"] = \"non-hybrid\"\n",
867
+ "df.loc[df[\"species\"].isin(sp_hybrid_parents), \"hybrid_stat\"] = \"non-hybrid\"\n",
868
+ "\n",
869
+ "df.loc[df[\"Taxonomic_Name\"].isin(hybrids), \"hybrid_stat\"] = \"hybrid\""
870
+ ]
871
+ },
872
+ {
873
+ "cell_type": "code",
874
+ "execution_count": 23,
875
+ "metadata": {},
876
+ "outputs": [
877
+ {
878
+ "data": {
879
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880
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+ " text-align: right;\n",
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893
+ "</style>\n",
894
+ "<table border=\"1\" class=\"dataframe\">\n",
895
+ " <thead>\n",
896
+ " <tr style=\"text-align: right;\">\n",
897
+ " <th></th>\n",
898
+ " <th>CAMID</th>\n",
899
+ " <th>X</th>\n",
900
+ " <th>Image_name</th>\n",
901
+ " <th>View</th>\n",
902
+ " <th>zenodo_name</th>\n",
903
+ " <th>zenodo_link</th>\n",
904
+ " <th>Sequence</th>\n",
905
+ " <th>Taxonomic_Name</th>\n",
906
+ " <th>Locality</th>\n",
907
+ " <th>Sample_accession</th>\n",
908
+ " <th>...</th>\n",
909
+ " <th>Stage</th>\n",
910
+ " <th>Sex</th>\n",
911
+ " <th>Unit_Type</th>\n",
912
+ " <th>file_type</th>\n",
913
+ " <th>record_number</th>\n",
914
+ " <th>species</th>\n",
915
+ " <th>subspecies</th>\n",
916
+ " <th>genus</th>\n",
917
+ " <th>file_url</th>\n",
918
+ " <th>hybrid_stat</th>\n",
919
+ " </tr>\n",
920
+ " </thead>\n",
921
+ " <tbody>\n",
922
+ " <tr>\n",
923
+ " <th>7326</th>\n",
924
+ " <td>CAM002890</td>\n",
925
+ " <td>8482</td>\n",
926
+ " <td>CAM002890_v.JPG</td>\n",
927
+ " <td>ventral</td>\n",
928
+ " <td>CAM.coll.images.batch5.csv</td>\n",
929
+ " <td>https://zenodo.org/record/2684906</td>\n",
930
+ " <td>2,890</td>\n",
931
+ " <td>Anartia fatima</td>\n",
932
+ " <td>El Tirao,</td>\n",
933
+ " <td>NaN</td>\n",
934
+ " <td>...</td>\n",
935
+ " <td>NaN</td>\n",
936
+ " <td>Female</td>\n",
937
+ " <td>wild</td>\n",
938
+ " <td>jpg</td>\n",
939
+ " <td>2684906</td>\n",
940
+ " <td>Anartia fatima</td>\n",
941
+ " <td>NaN</td>\n",
942
+ " <td>Anartia</td>\n",
943
+ " <td>https://zenodo.org/record/2684906/files/CAM002...</td>\n",
944
+ " <td>non-hybrid</td>\n",
945
+ " </tr>\n",
946
+ " <tr>\n",
947
+ " <th>7327</th>\n",
948
+ " <td>CAM002890</td>\n",
949
+ " <td>45376</td>\n",
950
+ " <td>CAM002890_v.JPG</td>\n",
951
+ " <td>ventral</td>\n",
952
+ " <td>occurences_and_multimedia.csv</td>\n",
953
+ " <td>https://zenodo.org/record/3477891</td>\n",
954
+ " <td>2,890</td>\n",
955
+ " <td>Anartia fatima</td>\n",
956
+ " <td>El Tirao,</td>\n",
957
+ " <td>NaN</td>\n",
958
+ " <td>...</td>\n",
959
+ " <td>NaN</td>\n",
960
+ " <td>Female</td>\n",
961
+ " <td>wild</td>\n",
962
+ " <td>jpg</td>\n",
963
+ " <td>3477891</td>\n",
964
+ " <td>Anartia fatima</td>\n",
965
+ " <td>NaN</td>\n",
966
+ " <td>Anartia</td>\n",
967
+ " <td>https://zenodo.org/record/3477891/files/CAM002...</td>\n",
968
+ " <td>non-hybrid</td>\n",
969
+ " </tr>\n",
970
+ " <tr>\n",
971
+ " <th>7328</th>\n",
972
+ " <td>CAM002890</td>\n",
973
+ " <td>45375</td>\n",
974
+ " <td>CAM002890_d.JPG</td>\n",
975
+ " <td>dorsal</td>\n",
976
+ " <td>occurences_and_multimedia.csv</td>\n",
977
+ " <td>https://zenodo.org/record/3477891</td>\n",
978
+ " <td>2,890</td>\n",
979
+ " <td>Anartia fatima</td>\n",
980
+ " <td>El Tirao,</td>\n",
981
+ " <td>NaN</td>\n",
982
+ " <td>...</td>\n",
983
+ " <td>NaN</td>\n",
984
+ " <td>Female</td>\n",
985
+ " <td>wild</td>\n",
986
+ " <td>jpg</td>\n",
987
+ " <td>3477891</td>\n",
988
+ " <td>Anartia fatima</td>\n",
989
+ " <td>NaN</td>\n",
990
+ " <td>Anartia</td>\n",
991
+ " <td>https://zenodo.org/record/3477891/files/CAM002...</td>\n",
992
+ " <td>non-hybrid</td>\n",
993
+ " </tr>\n",
994
+ " <tr>\n",
995
+ " <th>7329</th>\n",
996
+ " <td>CAM002890</td>\n",
997
+ " <td>8481</td>\n",
998
+ " <td>CAM002890_d.JPG</td>\n",
999
+ " <td>dorsal</td>\n",
1000
+ " <td>CAM.coll.images.batch5.csv</td>\n",
1001
+ " <td>https://zenodo.org/record/2684906</td>\n",
1002
+ " <td>2,890</td>\n",
1003
+ " <td>Anartia fatima</td>\n",
1004
+ " <td>El Tirao,</td>\n",
1005
+ " <td>NaN</td>\n",
1006
+ " <td>...</td>\n",
1007
+ " <td>NaN</td>\n",
1008
+ " <td>Female</td>\n",
1009
+ " <td>wild</td>\n",
1010
+ " <td>jpg</td>\n",
1011
+ " <td>2684906</td>\n",
1012
+ " <td>Anartia fatima</td>\n",
1013
+ " <td>NaN</td>\n",
1014
+ " <td>Anartia</td>\n",
1015
+ " <td>https://zenodo.org/record/2684906/files/CAM002...</td>\n",
1016
+ " <td>non-hybrid</td>\n",
1017
+ " </tr>\n",
1018
+ " </tbody>\n",
1019
+ "</table>\n",
1020
+ "<p>4 rows × 28 columns</p>\n",
1021
+ "</div>"
1022
+ ],
1023
+ "text/plain": [
1024
+ " CAMID X Image_name View \\\n",
1025
+ "7326 CAM002890 8482 CAM002890_v.JPG ventral \n",
1026
+ "7327 CAM002890 45376 CAM002890_v.JPG ventral \n",
1027
+ "7328 CAM002890 45375 CAM002890_d.JPG dorsal \n",
1028
+ "7329 CAM002890 8481 CAM002890_d.JPG dorsal \n",
1029
+ "\n",
1030
+ " zenodo_name zenodo_link \\\n",
1031
+ "7326 CAM.coll.images.batch5.csv https://zenodo.org/record/2684906 \n",
1032
+ "7327 occurences_and_multimedia.csv https://zenodo.org/record/3477891 \n",
1033
+ "7328 occurences_and_multimedia.csv https://zenodo.org/record/3477891 \n",
1034
+ "7329 CAM.coll.images.batch5.csv https://zenodo.org/record/2684906 \n",
1035
+ "\n",
1036
+ " Sequence Taxonomic_Name Locality Sample_accession ... Stage Sex \\\n",
1037
+ "7326 2,890 Anartia fatima El Tirao, NaN ... NaN Female \n",
1038
+ "7327 2,890 Anartia fatima El Tirao, NaN ... NaN Female \n",
1039
+ "7328 2,890 Anartia fatima El Tirao, NaN ... NaN Female \n",
1040
+ "7329 2,890 Anartia fatima El Tirao, NaN ... NaN Female \n",
1041
+ "\n",
1042
+ " Unit_Type file_type record_number species subspecies genus \\\n",
1043
+ "7326 wild jpg 2684906 Anartia fatima NaN Anartia \n",
1044
+ "7327 wild jpg 3477891 Anartia fatima NaN Anartia \n",
1045
+ "7328 wild jpg 3477891 Anartia fatima NaN Anartia \n",
1046
+ "7329 wild jpg 2684906 Anartia fatima NaN Anartia \n",
1047
+ "\n",
1048
+ " file_url hybrid_stat \n",
1049
+ "7326 https://zenodo.org/record/2684906/files/CAM002... non-hybrid \n",
1050
+ "7327 https://zenodo.org/record/3477891/files/CAM002... non-hybrid \n",
1051
+ "7328 https://zenodo.org/record/3477891/files/CAM002... non-hybrid \n",
1052
+ "7329 https://zenodo.org/record/2684906/files/CAM002... non-hybrid \n",
1053
+ "\n",
1054
+ "[4 rows x 28 columns]"
1055
+ ]
1056
+ },
1057
+ "execution_count": 23,
1058
+ "metadata": {},
1059
+ "output_type": "execute_result"
1060
+ }
1061
+ ],
1062
+ "source": [
1063
+ "df.loc[df[\"species\"].isin(sp_hybrid_parents)]"
1064
+ ]
1065
+ },
1066
+ {
1067
+ "cell_type": "code",
1068
+ "execution_count": 24,
1069
+ "metadata": {},
1070
+ "outputs": [
1071
+ {
1072
+ "data": {
1073
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1074
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1075
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1088
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1089
+ " <thead>\n",
1090
+ " <tr style=\"text-align: right;\">\n",
1091
+ " <th></th>\n",
1092
+ " <th>CAMID</th>\n",
1093
+ " <th>X</th>\n",
1094
+ " <th>Image_name</th>\n",
1095
+ " <th>View</th>\n",
1096
+ " <th>zenodo_name</th>\n",
1097
+ " <th>zenodo_link</th>\n",
1098
+ " <th>Sequence</th>\n",
1099
+ " <th>Taxonomic_Name</th>\n",
1100
+ " <th>Locality</th>\n",
1101
+ " <th>Sample_accession</th>\n",
1102
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1103
+ " <th>Stage</th>\n",
1104
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1105
+ " <th>Unit_Type</th>\n",
1106
+ " <th>file_type</th>\n",
1107
+ " <th>record_number</th>\n",
1108
+ " <th>species</th>\n",
1109
+ " <th>subspecies</th>\n",
1110
+ " <th>genus</th>\n",
1111
+ " <th>file_url</th>\n",
1112
+ " <th>hybrid_stat</th>\n",
1113
+ " </tr>\n",
1114
+ " </thead>\n",
1115
+ " <tbody>\n",
1116
+ " </tbody>\n",
1117
+ "</table>\n",
1118
+ "<p>0 rows × 28 columns</p>\n",
1119
+ "</div>"
1120
+ ],
1121
+ "text/plain": [
1122
+ "Empty DataFrame\n",
1123
+ "Columns: [CAMID, X, Image_name, View, zenodo_name, zenodo_link, Sequence, Taxonomic_Name, Locality, Sample_accession, Collected_by, Other_ID, Date, Dataset, Store, Brood, Death_Date, Cross_Type, Stage, Sex, Unit_Type, file_type, record_number, species, subspecies, genus, file_url, hybrid_stat]\n",
1124
+ "Index: []\n",
1125
+ "\n",
1126
+ "[0 rows x 28 columns]"
1127
+ ]
1128
+ },
1129
+ "execution_count": 24,
1130
+ "metadata": {},
1131
+ "output_type": "execute_result"
1132
+ }
1133
+ ],
1134
+ "source": [
1135
+ "df.loc[df[\"species\"].str.lower() == \"amathea\"] #not represented in the dataset"
1136
+ ]
1137
+ },
1138
+ {
1139
+ "cell_type": "code",
1140
+ "execution_count": 25,
1141
+ "metadata": {},
1142
+ "outputs": [
1143
+ {
1144
+ "data": {
1145
+ "text/plain": [
1146
+ "Taxonomic_Name\n",
1147
+ "Anartia fatima x amathea 60\n",
1148
+ "Anartia fatima 4\n",
1149
+ "Name: count, dtype: int64"
1150
+ ]
1151
+ },
1152
+ "execution_count": 25,
1153
+ "metadata": {},
1154
+ "output_type": "execute_result"
1155
+ }
1156
+ ],
1157
+ "source": [
1158
+ "df.loc[df[\"genus\"] == \"Anartia\", \"Taxonomic_Name\"].value_counts()"
1159
+ ]
1160
+ },
1161
+ {
1162
+ "cell_type": "code",
1163
+ "execution_count": 26,
1164
+ "metadata": {},
1165
+ "outputs": [
1166
+ {
1167
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1184
+ " <thead>\n",
1185
+ " <tr style=\"text-align: right;\">\n",
1186
+ " <th></th>\n",
1187
+ " <th>CAMID</th>\n",
1188
+ " <th>X</th>\n",
1189
+ " <th>Image_name</th>\n",
1190
+ " <th>View</th>\n",
1191
+ " <th>zenodo_name</th>\n",
1192
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1193
+ " <th>Sequence</th>\n",
1194
+ " <th>Taxonomic_Name</th>\n",
1195
+ " <th>Locality</th>\n",
1196
+ " <th>Sample_accession</th>\n",
1197
+ " <th>...</th>\n",
1198
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1199
+ " <th>Sex</th>\n",
1200
+ " <th>Unit_Type</th>\n",
1201
+ " <th>file_type</th>\n",
1202
+ " <th>record_number</th>\n",
1203
+ " <th>species</th>\n",
1204
+ " <th>subspecies</th>\n",
1205
+ " <th>genus</th>\n",
1206
+ " <th>file_url</th>\n",
1207
+ " <th>hybrid_stat</th>\n",
1208
+ " </tr>\n",
1209
+ " </thead>\n",
1210
+ " <tbody>\n",
1211
+ " <tr>\n",
1212
+ " <th>40498</th>\n",
1213
+ " <td>CAM043842</td>\n",
1214
+ " <td>32098</td>\n",
1215
+ " <td>CAM043842_hwv.JPG</td>\n",
1216
+ " <td>hindwing ventral</td>\n",
1217
+ " <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
1218
+ " <td>https://zenodo.org/record/4287444</td>\n",
1219
+ " <td>43,842</td>\n",
1220
+ " <td>Catoblepia berecynthia</td>\n",
1221
+ " <td>B1prim3</td>\n",
1222
+ " <td>NaN</td>\n",
1223
+ " <td>...</td>\n",
1224
+ " <td>NaN</td>\n",
1225
+ " <td>NaN</td>\n",
1226
+ " <td>NaN</td>\n",
1227
+ " <td>jpg</td>\n",
1228
+ " <td>4287444</td>\n",
1229
+ " <td>Catoblepia berecynthia</td>\n",
1230
+ " <td>NaN</td>\n",
1231
+ " <td>Catoblepia</td>\n",
1232
+ " <td>https://zenodo.org/record/4287444/files/CAM043...</td>\n",
1233
+ " <td>None</td>\n",
1234
+ " </tr>\n",
1235
+ " <tr>\n",
1236
+ " <th>9192</th>\n",
1237
+ " <td>CAM008567</td>\n",
1238
+ " <td>46249</td>\n",
1239
+ " <td>CAM008567_v.JPG</td>\n",
1240
+ " <td>ventral</td>\n",
1241
+ " <td>occurences_and_multimedia.csv</td>\n",
1242
+ " <td>https://zenodo.org/record/3477891</td>\n",
1243
+ " <td>8,567</td>\n",
1244
+ " <td>Heliconius demeter</td>\n",
1245
+ " <td>Tarapoto-Yurimaguas (Km15), Tunel Cumbre, Fond...</td>\n",
1246
+ " <td>NaN</td>\n",
1247
+ " <td>...</td>\n",
1248
+ " <td>NaN</td>\n",
1249
+ " <td>Male</td>\n",
1250
+ " <td>wild</td>\n",
1251
+ " <td>jpg</td>\n",
1252
+ " <td>3477891</td>\n",
1253
+ " <td>Heliconius demeter</td>\n",
1254
+ " <td>NaN</td>\n",
1255
+ " <td>Heliconius</td>\n",
1256
+ " <td>https://zenodo.org/record/3477891/files/CAM008...</td>\n",
1257
+ " <td>None</td>\n",
1258
+ " </tr>\n",
1259
+ " <tr>\n",
1260
+ " <th>14127</th>\n",
1261
+ " <td>CAM010971</td>\n",
1262
+ " <td>27577</td>\n",
1263
+ " <td>CAM010971_d.CR2</td>\n",
1264
+ " <td>dorsal</td>\n",
1265
+ " <td>2001_2.broods.batch.1.csv</td>\n",
1266
+ " <td>https://zenodo.org/record/2549524</td>\n",
1267
+ " <td>10,971</td>\n",
1268
+ " <td>Heliconius sp.</td>\n",
1269
+ " <td>NaN</td>\n",
1270
+ " <td>NaN</td>\n",
1271
+ " <td>...</td>\n",
1272
+ " <td>NaN</td>\n",
1273
+ " <td>Male</td>\n",
1274
+ " <td>reared</td>\n",
1275
+ " <td>raw</td>\n",
1276
+ " <td>2549524</td>\n",
1277
+ " <td>Heliconius sp.</td>\n",
1278
+ " <td>NaN</td>\n",
1279
+ " <td>Heliconius</td>\n",
1280
+ " <td>https://zenodo.org/record/2549524/files/CAM010...</td>\n",
1281
+ " <td>None</td>\n",
1282
+ " </tr>\n",
1283
+ " <tr>\n",
1284
+ " <th>5315</th>\n",
1285
+ " <td>CAM000537</td>\n",
1286
+ " <td>6676</td>\n",
1287
+ " <td>CAM000537_d.JPG</td>\n",
1288
+ " <td>dorsal</td>\n",
1289
+ " <td>CAM.coll.images.batch3.csv</td>\n",
1290
+ " <td>https://zenodo.org/record/2682458</td>\n",
1291
+ " <td>537</td>\n",
1292
+ " <td>Heliconius melpomene ssp. rosina</td>\n",
1293
+ " <td>Pipeline road 4km,</td>\n",
1294
+ " <td>NaN</td>\n",
1295
+ " <td>...</td>\n",
1296
+ " <td>NaN</td>\n",
1297
+ " <td>Male</td>\n",
1298
+ " <td>wild</td>\n",
1299
+ " <td>jpg</td>\n",
1300
+ " <td>2682458</td>\n",
1301
+ " <td>Heliconius melpomene</td>\n",
1302
+ " <td>rosina</td>\n",
1303
+ " <td>Heliconius</td>\n",
1304
+ " <td>https://zenodo.org/record/2682458/files/CAM000...</td>\n",
1305
+ " <td>non-hybrid</td>\n",
1306
+ " </tr>\n",
1307
+ " <tr>\n",
1308
+ " <th>585</th>\n",
1309
+ " <td>19N0204</td>\n",
1310
+ " <td>19553</td>\n",
1311
+ " <td>19N0204_d.JPG</td>\n",
1312
+ " <td>dorsal</td>\n",
1313
+ " <td>0.sheffield.ps.nn.ikiam.batch1.csv</td>\n",
1314
+ " <td>https://zenodo.org/record/4288311</td>\n",
1315
+ " <td>204</td>\n",
1316
+ " <td>Heliconius erato ssp. lativitta</td>\n",
1317
+ " <td>Ikiam Mariposario</td>\n",
1318
+ " <td>NaN</td>\n",
1319
+ " <td>...</td>\n",
1320
+ " <td>NaN</td>\n",
1321
+ " <td>Male</td>\n",
1322
+ " <td>reared</td>\n",
1323
+ " <td>jpg</td>\n",
1324
+ " <td>4288311</td>\n",
1325
+ " <td>Heliconius erato</td>\n",
1326
+ " <td>lativitta</td>\n",
1327
+ " <td>Heliconius</td>\n",
1328
+ " <td>https://zenodo.org/record/4288311/files/19N020...</td>\n",
1329
+ " <td>non-hybrid</td>\n",
1330
+ " </tr>\n",
1331
+ " <tr>\n",
1332
+ " <th>38875</th>\n",
1333
+ " <td>CAM043366</td>\n",
1334
+ " <td>30469</td>\n",
1335
+ " <td>CAM043366_v.CR2</td>\n",
1336
+ " <td>ventral</td>\n",
1337
+ " <td>batch1.Peru.image.names.Zenodo.csv</td>\n",
1338
+ " <td>https://zenodo.org/record/3569598</td>\n",
1339
+ " <td>43,366</td>\n",
1340
+ " <td>Eunica pusilla</td>\n",
1341
+ " <td>B6rec3</td>\n",
1342
+ " <td>NaN</td>\n",
1343
+ " <td>...</td>\n",
1344
+ " <td>NaN</td>\n",
1345
+ " <td>NaN</td>\n",
1346
+ " <td>NaN</td>\n",
1347
+ " <td>raw</td>\n",
1348
+ " <td>3569598</td>\n",
1349
+ " <td>Eunica pusilla</td>\n",
1350
+ " <td>NaN</td>\n",
1351
+ " <td>Eunica</td>\n",
1352
+ " <td>https://zenodo.org/record/3569598/files/CAM043...</td>\n",
1353
+ " <td>None</td>\n",
1354
+ " </tr>\n",
1355
+ " <tr>\n",
1356
+ " <th>19226</th>\n",
1357
+ " <td>CAM016687</td>\n",
1358
+ " <td>18917</td>\n",
1359
+ " <td>CAM016687_d.CR2</td>\n",
1360
+ " <td>dorsal</td>\n",
1361
+ " <td>CAM.coll.PS.list.individuals.haplotagging.new....</td>\n",
1362
+ " <td>https://zenodo.org/record/4153502</td>\n",
1363
+ " <td>16,687</td>\n",
1364
+ " <td>Heliconius melpomene ssp. plesseni</td>\n",
1365
+ " <td>El Topo, Baños - Puyo road,</td>\n",
1366
+ " <td>SRS7540355</td>\n",
1367
+ " <td>...</td>\n",
1368
+ " <td>NaN</td>\n",
1369
+ " <td>Male</td>\n",
1370
+ " <td>wild</td>\n",
1371
+ " <td>raw</td>\n",
1372
+ " <td>4153502</td>\n",
1373
+ " <td>Heliconius melpomene</td>\n",
1374
+ " <td>plesseni</td>\n",
1375
+ " <td>Heliconius</td>\n",
1376
+ " <td>https://zenodo.org/record/4153502/files/CAM016...</td>\n",
1377
+ " <td>non-hybrid</td>\n",
1378
+ " </tr>\n",
1379
+ " </tbody>\n",
1380
+ "</table>\n",
1381
+ "<p>7 rows × 28 columns</p>\n",
1382
+ "</div>"
1383
+ ],
1384
+ "text/plain": [
1385
+ " CAMID X Image_name View \\\n",
1386
+ "40498 CAM043842 32098 CAM043842_hwv.JPG hindwing ventral \n",
1387
+ "9192 CAM008567 46249 CAM008567_v.JPG ventral \n",
1388
+ "14127 CAM010971 27577 CAM010971_d.CR2 dorsal \n",
1389
+ "5315 CAM000537 6676 CAM000537_d.JPG dorsal \n",
1390
+ "585 19N0204 19553 19N0204_d.JPG dorsal \n",
1391
+ "38875 CAM043366 30469 CAM043366_v.CR2 ventral \n",
1392
+ "19226 CAM016687 18917 CAM016687_d.CR2 dorsal \n",
1393
+ "\n",
1394
+ " zenodo_name \\\n",
1395
+ "40498 batch2.Peru.image.names.Zenodo.csv \n",
1396
+ "9192 occurences_and_multimedia.csv \n",
1397
+ "14127 2001_2.broods.batch.1.csv \n",
1398
+ "5315 CAM.coll.images.batch3.csv \n",
1399
+ "585 0.sheffield.ps.nn.ikiam.batch1.csv \n",
1400
+ "38875 batch1.Peru.image.names.Zenodo.csv \n",
1401
+ "19226 CAM.coll.PS.list.individuals.haplotagging.new.... \n",
1402
+ "\n",
1403
+ " zenodo_link Sequence \\\n",
1404
+ "40498 https://zenodo.org/record/4287444 43,842 \n",
1405
+ "9192 https://zenodo.org/record/3477891 8,567 \n",
1406
+ "14127 https://zenodo.org/record/2549524 10,971 \n",
1407
+ "5315 https://zenodo.org/record/2682458 537 \n",
1408
+ "585 https://zenodo.org/record/4288311 204 \n",
1409
+ "38875 https://zenodo.org/record/3569598 43,366 \n",
1410
+ "19226 https://zenodo.org/record/4153502 16,687 \n",
1411
+ "\n",
1412
+ " Taxonomic_Name \\\n",
1413
+ "40498 Catoblepia berecynthia \n",
1414
+ "9192 Heliconius demeter \n",
1415
+ "14127 Heliconius sp. \n",
1416
+ "5315 Heliconius melpomene ssp. rosina \n",
1417
+ "585 Heliconius erato ssp. lativitta \n",
1418
+ "38875 Eunica pusilla \n",
1419
+ "19226 Heliconius melpomene ssp. plesseni \n",
1420
+ "\n",
1421
+ " Locality Sample_accession \\\n",
1422
+ "40498 B1prim3 NaN \n",
1423
+ "9192 Tarapoto-Yurimaguas (Km15), Tunel Cumbre, Fond... NaN \n",
1424
+ "14127 NaN NaN \n",
1425
+ "5315 Pipeline road 4km, NaN \n",
1426
+ "585 Ikiam Mariposario NaN \n",
1427
+ "38875 B6rec3 NaN \n",
1428
+ "19226 El Topo, Baños - Puyo road, SRS7540355 \n",
1429
+ "\n",
1430
+ " ... Stage Sex Unit_Type file_type record_number \\\n",
1431
+ "40498 ... NaN NaN NaN jpg 4287444 \n",
1432
+ "9192 ... NaN Male wild jpg 3477891 \n",
1433
+ "14127 ... NaN Male reared raw 2549524 \n",
1434
+ "5315 ... NaN Male wild jpg 2682458 \n",
1435
+ "585 ... NaN Male reared jpg 4288311 \n",
1436
+ "38875 ... NaN NaN NaN raw 3569598 \n",
1437
+ "19226 ... NaN Male wild raw 4153502 \n",
1438
+ "\n",
1439
+ " species subspecies genus \\\n",
1440
+ "40498 Catoblepia berecynthia NaN Catoblepia \n",
1441
+ "9192 Heliconius demeter NaN Heliconius \n",
1442
+ "14127 Heliconius sp. NaN Heliconius \n",
1443
+ "5315 Heliconius melpomene rosina Heliconius \n",
1444
+ "585 Heliconius erato lativitta Heliconius \n",
1445
+ "38875 Eunica pusilla NaN Eunica \n",
1446
+ "19226 Heliconius melpomene plesseni Heliconius \n",
1447
+ "\n",
1448
+ " file_url hybrid_stat \n",
1449
+ "40498 https://zenodo.org/record/4287444/files/CAM043... None \n",
1450
+ "9192 https://zenodo.org/record/3477891/files/CAM008... None \n",
1451
+ "14127 https://zenodo.org/record/2549524/files/CAM010... None \n",
1452
+ "5315 https://zenodo.org/record/2682458/files/CAM000... non-hybrid \n",
1453
+ "585 https://zenodo.org/record/4288311/files/19N020... non-hybrid \n",
1454
+ "38875 https://zenodo.org/record/3569598/files/CAM043... None \n",
1455
+ "19226 https://zenodo.org/record/4153502/files/CAM016... non-hybrid \n",
1456
+ "\n",
1457
+ "[7 rows x 28 columns]"
1458
+ ]
1459
+ },
1460
+ "execution_count": 26,
1461
+ "metadata": {},
1462
+ "output_type": "execute_result"
1463
+ }
1464
+ ],
1465
+ "source": [
1466
+ "df.sample(7)"
1467
+ ]
1468
+ },
1469
+ {
1470
+ "cell_type": "markdown",
1471
+ "metadata": {},
1472
+ "source": [
1473
+ "## Investigate entries with no Taxonomic Information"
1474
+ ]
1475
+ },
1476
+ {
1477
+ "cell_type": "code",
1478
+ "execution_count": 27,
1479
+ "metadata": {},
1480
+ "outputs": [
1481
+ {
1482
+ "name": "stdout",
1483
+ "output_type": "stream",
1484
+ "text": [
1485
+ "<class 'pandas.core.frame.DataFrame'>\n",
1486
+ "Index: 3886 entries, 0 to 49350\n",
1487
+ "Data columns (total 28 columns):\n",
1488
+ " # Column Non-Null Count Dtype \n",
1489
+ "--- ------ -------------- ----- \n",
1490
+ " 0 CAMID 3886 non-null object\n",
1491
+ " 1 X 3886 non-null int64 \n",
1492
+ " 2 Image_name 3886 non-null object\n",
1493
+ " 3 View 3594 non-null object\n",
1494
+ " 4 zenodo_name 3886 non-null object\n",
1495
+ " 5 zenodo_link 3886 non-null object\n",
1496
+ " 6 Sequence 2951 non-null object\n",
1497
+ " 7 Taxonomic_Name 0 non-null object\n",
1498
+ " 8 Locality 70 non-null object\n",
1499
+ " 9 Sample_accession 24 non-null object\n",
1500
+ " 10 Collected_by 0 non-null object\n",
1501
+ " 11 Other_ID 30 non-null object\n",
1502
+ " 12 Date 70 non-null object\n",
1503
+ " 13 Dataset 461 non-null object\n",
1504
+ " 14 Store 345 non-null object\n",
1505
+ " 15 Brood 5 non-null object\n",
1506
+ " 16 Death_Date 2 non-null object\n",
1507
+ " 17 Cross_Type 0 non-null object\n",
1508
+ " 18 Stage 9 non-null object\n",
1509
+ " 19 Sex 35 non-null object\n",
1510
+ " 20 Unit_Type 47 non-null object\n",
1511
+ " 21 file_type 3886 non-null object\n",
1512
+ " 22 record_number 3886 non-null int64 \n",
1513
+ " 23 species 0 non-null object\n",
1514
+ " 24 subspecies 0 non-null object\n",
1515
+ " 25 genus 0 non-null object\n",
1516
+ " 26 file_url 3886 non-null object\n",
1517
+ " 27 hybrid_stat 0 non-null object\n",
1518
+ "dtypes: int64(2), object(26)\n",
1519
+ "memory usage: 880.4+ KB\n"
1520
+ ]
1521
+ }
1522
+ ],
1523
+ "source": [
1524
+ "df.loc[df.Taxonomic_Name.isna()].info()"
1525
+ ]
1526
+ },
1527
+ {
1528
+ "cell_type": "markdown",
1529
+ "metadata": {},
1530
+ "source": [
1531
+ "Wow, there are a surprising number of images with no taxonomic information."
1532
+ ]
1533
+ },
1534
+ {
1535
+ "cell_type": "code",
1536
+ "execution_count": 28,
1537
+ "metadata": {},
1538
+ "outputs": [
1539
+ {
1540
+ "data": {
1541
+ "text/plain": [
1542
+ "0"
1543
+ ]
1544
+ },
1545
+ "execution_count": 28,
1546
+ "metadata": {},
1547
+ "output_type": "execute_result"
1548
+ }
1549
+ ],
1550
+ "source": [
1551
+ "no_taxa_cams = list(df.loc[df[\"Taxonomic_Name\"].isna(), \"CAMID\"].unique())\n",
1552
+ "no_taxa_entries = list(df.loc[df[\"Taxonomic_Name\"].isna(), \"X\"])\n",
1553
+ "\n",
1554
+ "no_taxa_cam_match = [camid for camid in list(df.loc[~df[\"X\"].isin(no_taxa_entries), \"CAMID\"]) if camid in no_taxa_cams]\n",
1555
+ "len(no_taxa_cam_match)"
1556
+ ]
1557
+ },
1558
+ {
1559
+ "cell_type": "markdown",
1560
+ "metadata": {},
1561
+ "source": [
1562
+ "There are no CAMIDs that these can match to for taxonomic info recovery. Last option is to check the records they come from, then we'll remove them."
1563
+ ]
1564
+ },
1565
+ {
1566
+ "cell_type": "code",
1567
+ "execution_count": 29,
1568
+ "metadata": {},
1569
+ "outputs": [
1570
+ {
1571
+ "name": "stdout",
1572
+ "output_type": "stream",
1573
+ "text": [
1574
+ "21\n"
1575
+ ]
1576
+ },
1577
+ {
1578
+ "data": {
1579
+ "text/plain": [
1580
+ "zenodo_link\n",
1581
+ "https://zenodo.org/record/5526257 1324\n",
1582
+ "https://zenodo.org/record/2554218 492\n",
1583
+ "https://zenodo.org/record/4288311 486\n",
1584
+ "https://zenodo.org/record/2555086 440\n",
1585
+ "https://zenodo.org/record/5731587 388\n",
1586
+ "Name: count, dtype: int64"
1587
+ ]
1588
+ },
1589
+ "execution_count": 29,
1590
+ "metadata": {},
1591
+ "output_type": "execute_result"
1592
+ }
1593
+ ],
1594
+ "source": [
1595
+ "print(df.loc[df[\"X\"].isin(no_taxa_entries), \"record_number\"].nunique())\n",
1596
+ "df.loc[df[\"X\"].isin(no_taxa_entries), \"zenodo_link\"].value_counts()[:5]"
1597
+ ]
1598
+ },
1599
+ {
1600
+ "cell_type": "markdown",
1601
+ "metadata": {},
1602
+ "source": [
1603
+ "These are across most of the records. We can check the one with the most, but otherwise they should likely be dropped (can save them in a separate CSV in case they are to be assessed at a later point).\n",
1604
+ "\n",
1605
+ "The first appear to be all _Heliconius erato ssp. cyrbia_, so those could be realigned.\n",
1606
+ "\n",
1607
+ "The second indicates all _Heliconius erato_.\n",
1608
+ "\n",
1609
+ "The third is a mix, so not resolvable without an expert.\n",
1610
+ "\n",
1611
+ "It seems record 2555086 is all bred specimens of _Heliconius erato demophoon_.\n",
1612
+ "\n",
1613
+ "The fifth record is a mix, and not all are labeled in the excel file (in fact, there's a lot of red which is probably why they were excluded)."
1614
+ ]
1615
+ },
1616
+ {
1617
+ "cell_type": "code",
1618
+ "execution_count": 30,
1619
+ "metadata": {},
1620
+ "outputs": [
1621
+ {
1622
+ "name": "stdout",
1623
+ "output_type": "stream",
1624
+ "text": [
1625
+ "<class 'pandas.core.series.Series'>\n",
1626
+ "Index: 1324 entries, 45868 to 47191\n",
1627
+ "Series name: Taxonomic_Name\n",
1628
+ "Non-Null Count Dtype \n",
1629
+ "-------------- ----- \n",
1630
+ "0 non-null object\n",
1631
+ "dtypes: object(1)\n",
1632
+ "memory usage: 20.7+ KB\n"
1633
+ ]
1634
+ }
1635
+ ],
1636
+ "source": [
1637
+ "df.loc[df[\"record_number\"] == 5526257, \"Taxonomic_Name\"].info()"
1638
+ ]
1639
+ },
1640
+ {
1641
+ "cell_type": "markdown",
1642
+ "metadata": {},
1643
+ "source": [
1644
+ "Ah, so these are all null, but all are of the same subspecies, so they can be labeled."
1645
+ ]
1646
+ },
1647
+ {
1648
+ "cell_type": "code",
1649
+ "execution_count": 31,
1650
+ "metadata": {},
1651
+ "outputs": [
1652
+ {
1653
+ "data": {
1654
+ "text/plain": [
1655
+ "4"
1656
+ ]
1657
+ },
1658
+ "execution_count": 31,
1659
+ "metadata": {},
1660
+ "output_type": "execute_result"
1661
+ }
1662
+ ],
1663
+ "source": [
1664
+ "pot_single_taxa_records = []\n",
1665
+ "for record_num in (df.loc[df[\"X\"].isin(no_taxa_entries), \"record_number\"].unique()):\n",
1666
+ " temp = df.loc[df[\"record_number\"] == record_num]\n",
1667
+ " if temp.loc[temp[\"Taxonomic_Name\"].notna()].shape[0] == 0:\n",
1668
+ " pot_single_taxa_records.append(record_num)\n",
1669
+ "\n",
1670
+ "len(pot_single_taxa_records)"
1671
+ ]
1672
+ },
1673
+ {
1674
+ "cell_type": "code",
1675
+ "execution_count": 32,
1676
+ "metadata": {},
1677
+ "outputs": [
1678
+ {
1679
+ "data": {
1680
+ "text/plain": [
1681
+ "[5731587, 5526257, 2554218, 2555086]"
1682
+ ]
1683
+ },
1684
+ "execution_count": 32,
1685
+ "metadata": {},
1686
+ "output_type": "execute_result"
1687
+ }
1688
+ ],
1689
+ "source": [
1690
+ "pot_single_taxa_records"
1691
+ ]
1692
+ },
1693
+ {
1694
+ "cell_type": "code",
1695
+ "execution_count": 33,
1696
+ "metadata": {},
1697
+ "outputs": [
1698
+ {
1699
+ "data": {
1700
+ "text/html": [
1701
+ "<div>\n",
1702
+ "<style scoped>\n",
1703
+ " .dataframe tbody tr th:only-of-type {\n",
1704
+ " vertical-align: middle;\n",
1705
+ " }\n",
1706
+ "\n",
1707
+ " .dataframe tbody tr th {\n",
1708
+ " vertical-align: top;\n",
1709
+ " }\n",
1710
+ "\n",
1711
+ " .dataframe thead th {\n",
1712
+ " text-align: right;\n",
1713
+ " }\n",
1714
+ "</style>\n",
1715
+ "<table border=\"1\" class=\"dataframe\">\n",
1716
+ " <thead>\n",
1717
+ " <tr style=\"text-align: right;\">\n",
1718
+ " <th></th>\n",
1719
+ " <th>CAMID</th>\n",
1720
+ " <th>X</th>\n",
1721
+ " <th>Image_name</th>\n",
1722
+ " <th>View</th>\n",
1723
+ " <th>zenodo_name</th>\n",
1724
+ " <th>zenodo_link</th>\n",
1725
+ " <th>Sequence</th>\n",
1726
+ " <th>Taxonomic_Name</th>\n",
1727
+ " <th>Locality</th>\n",
1728
+ " <th>Sample_accession</th>\n",
1729
+ " <th>...</th>\n",
1730
+ " <th>Stage</th>\n",
1731
+ " <th>Sex</th>\n",
1732
+ " <th>Unit_Type</th>\n",
1733
+ " <th>file_type</th>\n",
1734
+ " <th>record_number</th>\n",
1735
+ " <th>species</th>\n",
1736
+ " <th>subspecies</th>\n",
1737
+ " <th>genus</th>\n",
1738
+ " <th>file_url</th>\n",
1739
+ " <th>hybrid_stat</th>\n",
1740
+ " </tr>\n",
1741
+ " </thead>\n",
1742
+ " <tbody>\n",
1743
+ " <tr>\n",
1744
+ " <th>46747</th>\n",
1745
+ " <td>CAM045219</td>\n",
1746
+ " <td>43439</td>\n",
1747
+ " <td>CAM045219_d.JPG</td>\n",
1748
+ " <td>dorsal</td>\n",
1749
+ " <td>image.names.cook.island.erato.csv</td>\n",
1750
+ " <td>https://zenodo.org/record/5526257</td>\n",
1751
+ " <td>45,219</td>\n",
1752
+ " <td>NaN</td>\n",
1753
+ " <td>NaN</td>\n",
1754
+ " <td>NaN</td>\n",
1755
+ " <td>...</td>\n",
1756
+ " <td>NaN</td>\n",
1757
+ " <td>NaN</td>\n",
1758
+ " <td>NaN</td>\n",
1759
+ " <td>jpg</td>\n",
1760
+ " <td>5526257</td>\n",
1761
+ " <td>NaN</td>\n",
1762
+ " <td>NaN</td>\n",
1763
+ " <td>NaN</td>\n",
1764
+ " <td>https://zenodo.org/record/5526257/files/CAM045...</td>\n",
1765
+ " <td>None</td>\n",
1766
+ " </tr>\n",
1767
+ " <tr>\n",
1768
+ " <th>47062</th>\n",
1769
+ " <td>CAM045298</td>\n",
1770
+ " <td>43756</td>\n",
1771
+ " <td>CAM045298_v.CR2</td>\n",
1772
+ " <td>ventral</td>\n",
1773
+ " <td>image.names.cook.island.erato.csv</td>\n",
1774
+ " <td>https://zenodo.org/record/5526257</td>\n",
1775
+ " <td>45,298</td>\n",
1776
+ " <td>NaN</td>\n",
1777
+ " <td>NaN</td>\n",
1778
+ " <td>NaN</td>\n",
1779
+ " <td>...</td>\n",
1780
+ " <td>NaN</td>\n",
1781
+ " <td>NaN</td>\n",
1782
+ " <td>NaN</td>\n",
1783
+ " <td>raw</td>\n",
1784
+ " <td>5526257</td>\n",
1785
+ " <td>NaN</td>\n",
1786
+ " <td>NaN</td>\n",
1787
+ " <td>NaN</td>\n",
1788
+ " <td>https://zenodo.org/record/5526257/files/CAM045...</td>\n",
1789
+ " <td>None</td>\n",
1790
+ " </tr>\n",
1791
+ " <tr>\n",
1792
+ " <th>37255</th>\n",
1793
+ " <td>CAM042050</td>\n",
1794
+ " <td>43982</td>\n",
1795
+ " <td>CAM042050_d.JPG</td>\n",
1796
+ " <td>dorsal</td>\n",
1797
+ " <td>Collection_August2019.csv</td>\n",
1798
+ " <td>https://zenodo.org/record/5731587</td>\n",
1799
+ " <td>42,050</td>\n",
1800
+ " <td>NaN</td>\n",
1801
+ " <td>NaN</td>\n",
1802
+ " <td>NaN</td>\n",
1803
+ " <td>...</td>\n",
1804
+ " <td>NaN</td>\n",
1805
+ " <td>NaN</td>\n",
1806
+ " <td>NaN</td>\n",
1807
+ " <td>jpg</td>\n",
1808
+ " <td>5731587</td>\n",
1809
+ " <td>NaN</td>\n",
1810
+ " <td>NaN</td>\n",
1811
+ " <td>NaN</td>\n",
1812
+ " <td>https://zenodo.org/record/5731587/files/CAM042...</td>\n",
1813
+ " <td>None</td>\n",
1814
+ " </tr>\n",
1815
+ " <tr>\n",
1816
+ " <th>46992</th>\n",
1817
+ " <td>CAM045281</td>\n",
1818
+ " <td>43687</td>\n",
1819
+ " <td>CAM045281_d.JPG</td>\n",
1820
+ " <td>dorsal</td>\n",
1821
+ " <td>image.names.cook.island.erato.csv</td>\n",
1822
+ " <td>https://zenodo.org/record/5526257</td>\n",
1823
+ " <td>45,281</td>\n",
1824
+ " <td>NaN</td>\n",
1825
+ " <td>NaN</td>\n",
1826
+ " <td>NaN</td>\n",
1827
+ " <td>...</td>\n",
1828
+ " <td>NaN</td>\n",
1829
+ " <td>NaN</td>\n",
1830
+ " <td>NaN</td>\n",
1831
+ " <td>jpg</td>\n",
1832
+ " <td>5526257</td>\n",
1833
+ " <td>NaN</td>\n",
1834
+ " <td>NaN</td>\n",
1835
+ " <td>NaN</td>\n",
1836
+ " <td>https://zenodo.org/record/5526257/files/CAM045...</td>\n",
1837
+ " <td>None</td>\n",
1838
+ " </tr>\n",
1839
+ " <tr>\n",
1840
+ " <th>49106</th>\n",
1841
+ " <td>F901</td>\n",
1842
+ " <td>38167</td>\n",
1843
+ " <td>F901_d.CR2</td>\n",
1844
+ " <td>dorsal</td>\n",
1845
+ " <td>Anniina.Mattila.Bred.F.csv</td>\n",
1846
+ " <td>https://zenodo.org/record/2555086</td>\n",
1847
+ " <td>NaN</td>\n",
1848
+ " <td>NaN</td>\n",
1849
+ " <td>NaN</td>\n",
1850
+ " <td>NaN</td>\n",
1851
+ " <td>...</td>\n",
1852
+ " <td>NaN</td>\n",
1853
+ " <td>NaN</td>\n",
1854
+ " <td>NaN</td>\n",
1855
+ " <td>raw</td>\n",
1856
+ " <td>2555086</td>\n",
1857
+ " <td>NaN</td>\n",
1858
+ " <td>NaN</td>\n",
1859
+ " <td>NaN</td>\n",
1860
+ " <td>https://zenodo.org/record/2555086/files/F901_d...</td>\n",
1861
+ " <td>None</td>\n",
1862
+ " </tr>\n",
1863
+ " </tbody>\n",
1864
+ "</table>\n",
1865
+ "<p>5 rows × 28 columns</p>\n",
1866
+ "</div>"
1867
+ ],
1868
+ "text/plain": [
1869
+ " CAMID X Image_name View \\\n",
1870
+ "46747 CAM045219 43439 CAM045219_d.JPG dorsal \n",
1871
+ "47062 CAM045298 43756 CAM045298_v.CR2 ventral \n",
1872
+ "37255 CAM042050 43982 CAM042050_d.JPG dorsal \n",
1873
+ "46992 CAM045281 43687 CAM045281_d.JPG dorsal \n",
1874
+ "49106 F901 38167 F901_d.CR2 dorsal \n",
1875
+ "\n",
1876
+ " zenodo_name zenodo_link \\\n",
1877
+ "46747 image.names.cook.island.erato.csv https://zenodo.org/record/5526257 \n",
1878
+ "47062 image.names.cook.island.erato.csv https://zenodo.org/record/5526257 \n",
1879
+ "37255 Collection_August2019.csv https://zenodo.org/record/5731587 \n",
1880
+ "46992 image.names.cook.island.erato.csv https://zenodo.org/record/5526257 \n",
1881
+ "49106 Anniina.Mattila.Bred.F.csv https://zenodo.org/record/2555086 \n",
1882
+ "\n",
1883
+ " Sequence Taxonomic_Name Locality Sample_accession ... Stage Sex \\\n",
1884
+ "46747 45,219 NaN NaN NaN ... NaN NaN \n",
1885
+ "47062 45,298 NaN NaN NaN ... NaN NaN \n",
1886
+ "37255 42,050 NaN NaN NaN ... NaN NaN \n",
1887
+ "46992 45,281 NaN NaN NaN ... NaN NaN \n",
1888
+ "49106 NaN NaN NaN NaN ... NaN NaN \n",
1889
+ "\n",
1890
+ " Unit_Type file_type record_number species subspecies genus \\\n",
1891
+ "46747 NaN jpg 5526257 NaN NaN NaN \n",
1892
+ "47062 NaN raw 5526257 NaN NaN NaN \n",
1893
+ "37255 NaN jpg 5731587 NaN NaN NaN \n",
1894
+ "46992 NaN jpg 5526257 NaN NaN NaN \n",
1895
+ "49106 NaN raw 2555086 NaN NaN NaN \n",
1896
+ "\n",
1897
+ " file_url hybrid_stat \n",
1898
+ "46747 https://zenodo.org/record/5526257/files/CAM045... None \n",
1899
+ "47062 https://zenodo.org/record/5526257/files/CAM045... None \n",
1900
+ "37255 https://zenodo.org/record/5731587/files/CAM042... None \n",
1901
+ "46992 https://zenodo.org/record/5526257/files/CAM045... None \n",
1902
+ "49106 https://zenodo.org/record/2555086/files/F901_d... None \n",
1903
+ "\n",
1904
+ "[5 rows x 28 columns]"
1905
+ ]
1906
+ },
1907
+ "execution_count": 33,
1908
+ "metadata": {},
1909
+ "output_type": "execute_result"
1910
+ }
1911
+ ],
1912
+ "source": [
1913
+ "df.loc[df.record_number.isin(pot_single_taxa_records)].sample(5)"
1914
+ ]
1915
+ },
1916
+ {
1917
+ "cell_type": "markdown",
1918
+ "metadata": {},
1919
+ "source": [
1920
+ "Christopher agrees that the following is sufficient to label images from the following three records:\n",
1921
+ ">3 records seem to have all of just one indicated species/subspecies: https://zenodo.org/record/5526257, https://zenodo.org/record/2554218, and https://zenodo.org/record/2555086. According to their Zenodo pages, the first appear to be all _Heliconius erato ssp. cyrbia_, the second indicates all _Heliconius erato_, and the third it seems is all bred specimens of _Heliconius erato demophoon_. \n",
1922
+ "\n",
1923
+ "`Unit_Type` and some other details could potentially be realigned at a later date, but we'll stick with taxonomic information for now."
1924
+ ]
1925
+ },
1926
+ {
1927
+ "cell_type": "code",
1928
+ "execution_count": 34,
1929
+ "metadata": {},
1930
+ "outputs": [],
1931
+ "source": [
1932
+ "df.loc[df[\"record_number\"] == 5526257, \"Taxonomic_Name\"] = \"Heliconius erato ssp. cyrbia\"\n",
1933
+ "df.loc[df[\"record_number\"] == 5526257, \"genus\"] = \"Heliconius\"\n",
1934
+ "df.loc[df[\"record_number\"] == 5526257, \"species\"] = \"Heliconius erato\"\n",
1935
+ "df.loc[df[\"record_number\"] == 5526257, \"subspecies\"] = \"cyrbia\"\n",
1936
+ "df.loc[df[\"record_number\"] == 5526257, \"hybrid_stat\"] = \"non-hybrid\""
1937
+ ]
1938
+ },
1939
+ {
1940
+ "cell_type": "code",
1941
+ "execution_count": 35,
1942
+ "metadata": {},
1943
+ "outputs": [],
1944
+ "source": [
1945
+ "df.loc[df[\"record_number\"] == 2554218, \"Taxonomic_Name\"] = \"Heliconius erato\"\n",
1946
+ "df.loc[df[\"record_number\"] == 2554218, \"genus\"] = \"Heliconius\"\n",
1947
+ "df.loc[df[\"record_number\"] == 2554218, \"species\"] = \"Heliconius erato\""
1948
+ ]
1949
+ },
1950
+ {
1951
+ "cell_type": "code",
1952
+ "execution_count": 36,
1953
+ "metadata": {},
1954
+ "outputs": [],
1955
+ "source": [
1956
+ "df.loc[df[\"record_number\"] == 2555086, \"Taxonomic_Name\"] = \"Heliconius erato ssp. demophoon\"\n",
1957
+ "df.loc[df[\"record_number\"] == 2555086, \"genus\"] = \"Heliconius\"\n",
1958
+ "df.loc[df[\"record_number\"] == 2555086, \"species\"] = \"Heliconius erato\"\n",
1959
+ "df.loc[df[\"record_number\"] == 2555086, \"subspecies\"] = \"demophoon\"\n",
1960
+ "df.loc[df[\"record_number\"] == 2555086, \"hybrid_stat\"] = \"non-hybrid\""
1961
+ ]
1962
+ },
1963
+ {
1964
+ "cell_type": "markdown",
1965
+ "metadata": {},
1966
+ "source": [
1967
+ "### Save record of entries with no Taxonomic Info"
1968
+ ]
1969
+ },
1970
+ {
1971
+ "cell_type": "code",
1972
+ "execution_count": 37,
1973
+ "metadata": {},
1974
+ "outputs": [
1975
+ {
1976
+ "data": {
1977
+ "text/plain": [
1978
+ "(1630, 28)"
1979
+ ]
1980
+ },
1981
+ "execution_count": 37,
1982
+ "metadata": {},
1983
+ "output_type": "execute_result"
1984
+ }
1985
+ ],
1986
+ "source": [
1987
+ "missing_taxa_df = df.loc[df.Taxonomic_Name.isna()]\n",
1988
+ "missing_taxa_df.shape"
1989
+ ]
1990
+ },
1991
+ {
1992
+ "cell_type": "code",
1993
+ "execution_count": 39,
1994
+ "metadata": {},
1995
+ "outputs": [],
1996
+ "source": [
1997
+ "missing_taxa_df.to_csv(\"../metadata/Missing_taxa_Jiggins_Zenodo_Master.csv\", index = False)"
1998
+ ]
1999
+ },
2000
+ {
2001
+ "cell_type": "markdown",
2002
+ "metadata": {},
2003
+ "source": [
2004
+ "### Drop Entries with no Taxonomic Information"
2005
+ ]
2006
+ },
2007
+ {
2008
+ "cell_type": "code",
2009
+ "execution_count": 40,
2010
+ "metadata": {},
2011
+ "outputs": [],
2012
+ "source": [
2013
+ "master_df = df.loc[df.Taxonomic_Name.notna()]"
2014
+ ]
2015
+ },
2016
+ {
2017
+ "cell_type": "markdown",
2018
+ "metadata": {},
2019
+ "source": [
2020
+ "## Final stats for all data in master file summarized here."
2021
+ ]
2022
+ },
2023
+ {
2024
+ "cell_type": "code",
2025
+ "execution_count": 41,
2026
+ "metadata": {},
2027
+ "outputs": [
2028
+ {
2029
+ "data": {
2030
+ "text/plain": [
2031
+ "CAMID 11991\n",
2032
+ "X 44809\n",
2033
+ "Image_name 36281\n",
2034
+ "View 7\n",
2035
+ "zenodo_name 33\n",
2036
+ "zenodo_link 30\n",
2037
+ "Sequence 10905\n",
2038
+ "Taxonomic_Name 366\n",
2039
+ "Locality 645\n",
2040
+ "Sample_accession 1559\n",
2041
+ "Collected_by 12\n",
2042
+ "Other_ID 3081\n",
2043
+ "Date 807\n",
2044
+ "Dataset 8\n",
2045
+ "Store 137\n",
2046
+ "Brood 224\n",
2047
+ "Death_Date 81\n",
2048
+ "Cross_Type 30\n",
2049
+ "Stage 1\n",
2050
+ "Sex 3\n",
2051
+ "Unit_Type 4\n",
2052
+ "file_type 3\n",
2053
+ "record_number 30\n",
2054
+ "species 246\n",
2055
+ "subspecies 155\n",
2056
+ "genus 94\n",
2057
+ "file_url 44794\n",
2058
+ "hybrid_stat 2\n",
2059
+ "dtype: int64"
2060
+ ]
2061
+ },
2062
+ "execution_count": 41,
2063
+ "metadata": {},
2064
+ "output_type": "execute_result"
2065
+ }
2066
+ ],
2067
+ "source": [
2068
+ "master_df.nunique()"
2069
+ ]
2070
+ },
2071
+ {
2072
+ "cell_type": "code",
2073
+ "execution_count": 42,
2074
+ "metadata": {},
2075
+ "outputs": [
2076
+ {
2077
+ "name": "stdout",
2078
+ "output_type": "stream",
2079
+ "text": [
2080
+ "<class 'pandas.core.frame.DataFrame'>\n",
2081
+ "Index: 44809 entries, 6 to 49358\n",
2082
+ "Data columns (total 28 columns):\n",
2083
+ " # Column Non-Null Count Dtype \n",
2084
+ "--- ------ -------------- ----- \n",
2085
+ " 0 CAMID 44809 non-null object\n",
2086
+ " 1 X 44809 non-null int64 \n",
2087
+ " 2 Image_name 44809 non-null object\n",
2088
+ " 3 View 44030 non-null object\n",
2089
+ " 4 zenodo_name 44809 non-null object\n",
2090
+ " 5 zenodo_link 44809 non-null object\n",
2091
+ " 6 Sequence 43877 non-null object\n",
2092
+ " 7 Taxonomic_Name 44809 non-null object\n",
2093
+ " 8 Locality 31708 non-null object\n",
2094
+ " 9 Sample_accession 4572 non-null object\n",
2095
+ " 10 Collected_by 3043 non-null object\n",
2096
+ " 11 Other_ID 14352 non-null object\n",
2097
+ " 12 Date 30730 non-null object\n",
2098
+ " 13 Dataset 37024 non-null object\n",
2099
+ " 14 Store 36220 non-null object\n",
2100
+ " 15 Brood 14258 non-null object\n",
2101
+ " 16 Death_Date 316 non-null object\n",
2102
+ " 17 Cross_Type 4452 non-null object\n",
2103
+ " 18 Stage 6 non-null object\n",
2104
+ " 19 Sex 33312 non-null object\n",
2105
+ " 20 Unit_Type 30923 non-null object\n",
2106
+ " 21 file_type 44809 non-null object\n",
2107
+ " 22 record_number 44809 non-null int64 \n",
2108
+ " 23 species 44809 non-null object\n",
2109
+ " 24 subspecies 24559 non-null object\n",
2110
+ " 25 genus 44809 non-null object\n",
2111
+ " 26 file_url 44809 non-null object\n",
2112
+ " 27 hybrid_stat 25139 non-null object\n",
2113
+ "dtypes: int64(2), object(26)\n",
2114
+ "memory usage: 9.9+ MB\n"
2115
+ ]
2116
+ }
2117
+ ],
2118
+ "source": [
2119
+ "master_df.info()"
2120
+ ]
2121
+ },
2122
+ {
2123
+ "cell_type": "markdown",
2124
+ "metadata": {},
2125
+ "source": [
2126
+ "### Update Master File with Hybrid Status and URL Columns (& unique records)"
2127
+ ]
2128
+ },
2129
+ {
2130
+ "cell_type": "code",
2131
+ "execution_count": 44,
2132
+ "metadata": {},
2133
+ "outputs": [],
2134
+ "source": [
2135
+ "master_df.to_csv(\"../Jiggins_Zenodo_Img_Master.csv\", index = False)"
2136
+ ]
2137
+ },
2138
+ {
2139
+ "cell_type": "markdown",
2140
+ "metadata": {},
2141
+ "source": [
2142
+ "## Make Heliconius Subset"
2143
+ ]
2144
+ },
2145
+ {
2146
+ "cell_type": "code",
2147
+ "execution_count": 45,
2148
+ "metadata": {},
2149
+ "outputs": [
2150
+ {
2151
+ "name": "stdout",
2152
+ "output_type": "stream",
2153
+ "text": [
2154
+ "<class 'pandas.core.frame.DataFrame'>\n",
2155
+ "Index: 34265 entries, 6 to 49358\n",
2156
+ "Data columns (total 28 columns):\n",
2157
+ " # Column Non-Null Count Dtype \n",
2158
+ "--- ------ -------------- ----- \n",
2159
+ " 0 CAMID 34265 non-null object\n",
2160
+ " 1 X 34265 non-null int64 \n",
2161
+ " 2 Image_name 34265 non-null object\n",
2162
+ " 3 View 33486 non-null object\n",
2163
+ " 4 zenodo_name 34265 non-null object\n",
2164
+ " 5 zenodo_link 34265 non-null object\n",
2165
+ " 6 Sequence 33333 non-null object\n",
2166
+ " 7 Taxonomic_Name 34265 non-null object\n",
2167
+ " 8 Locality 21180 non-null object\n",
2168
+ " 9 Sample_accession 4572 non-null object\n",
2169
+ " 10 Collected_by 3043 non-null object\n",
2170
+ " 11 Other_ID 6404 non-null object\n",
2171
+ " 12 Date 20244 non-null object\n",
2172
+ " 13 Dataset 29926 non-null object\n",
2173
+ " 14 Store 26526 non-null object\n",
2174
+ " 15 Brood 14242 non-null object\n",
2175
+ " 16 Death_Date 316 non-null object\n",
2176
+ " 17 Cross_Type 4452 non-null object\n",
2177
+ " 18 Stage 6 non-null object\n",
2178
+ " 19 Sex 30984 non-null object\n",
2179
+ " 20 Unit_Type 29055 non-null object\n",
2180
+ " 21 file_type 34265 non-null object\n",
2181
+ " 22 record_number 34265 non-null int64 \n",
2182
+ " 23 species 34265 non-null object\n",
2183
+ " 24 subspecies 23801 non-null object\n",
2184
+ " 25 genus 34265 non-null object\n",
2185
+ " 26 file_url 34265 non-null object\n",
2186
+ " 27 hybrid_stat 24317 non-null object\n",
2187
+ "dtypes: int64(2), object(26)\n",
2188
+ "memory usage: 7.6+ MB\n"
2189
+ ]
2190
+ }
2191
+ ],
2192
+ "source": [
2193
+ "heliconius_subset = master_df.loc[master_df.genus.str.lower() == \"heliconius\"]\n",
2194
+ "\n",
2195
+ "heliconius_subset.info()"
2196
+ ]
2197
+ },
2198
+ {
2199
+ "cell_type": "code",
2200
+ "execution_count": 46,
2201
+ "metadata": {},
2202
+ "outputs": [
2203
+ {
2204
+ "data": {
2205
+ "text/plain": [
2206
+ "CAMID 10109\n",
2207
+ "X 34265\n",
2208
+ "Image_name 29192\n",
2209
+ "View 3\n",
2210
+ "zenodo_name 33\n",
2211
+ "zenodo_link 30\n",
2212
+ "Sequence 9031\n",
2213
+ "Taxonomic_Name 132\n",
2214
+ "Locality 472\n",
2215
+ "Sample_accession 1559\n",
2216
+ "Collected_by 12\n",
2217
+ "Other_ID 1865\n",
2218
+ "Date 776\n",
2219
+ "Dataset 8\n",
2220
+ "Store 121\n",
2221
+ "Brood 224\n",
2222
+ "Death_Date 81\n",
2223
+ "Cross_Type 30\n",
2224
+ "Stage 1\n",
2225
+ "Sex 3\n",
2226
+ "Unit_Type 4\n",
2227
+ "file_type 3\n",
2228
+ "record_number 30\n",
2229
+ "species 37\n",
2230
+ "subspecies 110\n",
2231
+ "genus 1\n",
2232
+ "file_url 34250\n",
2233
+ "hybrid_stat 2\n",
2234
+ "dtype: int64"
2235
+ ]
2236
+ },
2237
+ "execution_count": 46,
2238
+ "metadata": {},
2239
+ "output_type": "execute_result"
2240
+ }
2241
+ ],
2242
+ "source": [
2243
+ "heliconius_subset.nunique()"
2244
+ ]
2245
+ },
2246
+ {
2247
+ "cell_type": "code",
2248
+ "execution_count": 47,
2249
+ "metadata": {},
2250
+ "outputs": [
2251
+ {
2252
+ "data": {
2253
+ "text/plain": [
2254
+ "View\n",
2255
+ "dorsal 16882\n",
2256
+ "ventral 16586\n",
2257
+ "dorsal and ventral 18\n",
2258
+ "Name: count, dtype: int64"
2259
+ ]
2260
+ },
2261
+ "execution_count": 47,
2262
+ "metadata": {},
2263
+ "output_type": "execute_result"
2264
+ }
2265
+ ],
2266
+ "source": [
2267
+ "heliconius_subset.View.value_counts()"
2268
+ ]
2269
+ },
2270
+ {
2271
+ "cell_type": "markdown",
2272
+ "metadata": {},
2273
+ "source": [
2274
+ "Note that this subset is distributed across 30 Zenodo records from the [Butterfly Genetics Group](https://zenodo.org/communities/butterfly?q=&l=list&p=1&s=10&sort=newest)."
2275
+ ]
2276
+ },
2277
+ {
2278
+ "cell_type": "markdown",
2279
+ "metadata": {},
2280
+ "source": [
2281
+ "### Save the Heliconius Subset to CSV\n"
2282
+ ]
2283
+ },
2284
+ {
2285
+ "cell_type": "code",
2286
+ "execution_count": 48,
2287
+ "metadata": {},
2288
+ "outputs": [],
2289
+ "source": [
2290
+ "heliconius_subset.to_csv(\"../Jiggins_Heliconius_Master.csv\", index = False)"
2291
+ ]
2292
+ },
2293
+ {
2294
+ "cell_type": "markdown",
2295
+ "metadata": {},
2296
+ "source": [
2297
+ "## Make Dorsal Subset"
2298
+ ]
2299
+ },
2300
+ {
2301
+ "cell_type": "code",
2302
+ "execution_count": 49,
2303
+ "metadata": {},
2304
+ "outputs": [
2305
+ {
2306
+ "name": "stdout",
2307
+ "output_type": "stream",
2308
+ "text": [
2309
+ "<class 'pandas.core.frame.DataFrame'>\n",
2310
+ "Index: 22175 entries, 7 to 49357\n",
2311
+ "Data columns (total 28 columns):\n",
2312
+ " # Column Non-Null Count Dtype \n",
2313
+ "--- ------ -------------- ----- \n",
2314
+ " 0 CAMID 22175 non-null object\n",
2315
+ " 1 X 22175 non-null int64 \n",
2316
+ " 2 Image_name 22175 non-null object\n",
2317
+ " 3 View 22175 non-null object\n",
2318
+ " 4 zenodo_name 22175 non-null object\n",
2319
+ " 5 zenodo_link 22175 non-null object\n",
2320
+ " 6 Sequence 21709 non-null object\n",
2321
+ " 7 Taxonomic_Name 22175 non-null object\n",
2322
+ " 8 Locality 15615 non-null object\n",
2323
+ " 9 Sample_accession 2294 non-null object\n",
2324
+ " 10 Collected_by 1533 non-null object\n",
2325
+ " 11 Other_ID 6916 non-null object\n",
2326
+ " 12 Date 15347 non-null object\n",
2327
+ " 13 Dataset 18250 non-null object\n",
2328
+ " 14 Store 18254 non-null object\n",
2329
+ " 15 Brood 6920 non-null object\n",
2330
+ " 16 Death_Date 106 non-null object\n",
2331
+ " 17 Cross_Type 2230 non-null object\n",
2332
+ " 18 Stage 3 non-null object\n",
2333
+ " 19 Sex 16403 non-null object\n",
2334
+ " 20 Unit_Type 15203 non-null object\n",
2335
+ " 21 file_type 22175 non-null object\n",
2336
+ " 22 record_number 22175 non-null int64 \n",
2337
+ " 23 species 22175 non-null object\n",
2338
+ " 24 subspecies 12040 non-null object\n",
2339
+ " 25 genus 22175 non-null object\n",
2340
+ " 26 file_url 22175 non-null object\n",
2341
+ " 27 hybrid_stat 12330 non-null object\n",
2342
+ "dtypes: int64(2), object(26)\n",
2343
+ "memory usage: 4.9+ MB\n"
2344
+ ]
2345
+ }
2346
+ ],
2347
+ "source": [
2348
+ "dorsal_views = [view for view in list(master_df.View.dropna().unique()) if \"dorsal\" in view]\n",
2349
+ "\n",
2350
+ "dorsal_subset = master_df.loc[master_df[\"View\"].isin(dorsal_views)]\n",
2351
+ "dorsal_subset.info()"
2352
+ ]
2353
+ },
2354
+ {
2355
+ "cell_type": "code",
2356
+ "execution_count": 50,
2357
+ "metadata": {},
2358
+ "outputs": [
2359
+ {
2360
+ "data": {
2361
+ "text/plain": [
2362
+ "CAMID 11776\n",
2363
+ "X 22175\n",
2364
+ "Image_name 17907\n",
2365
+ "View 4\n",
2366
+ "zenodo_name 33\n",
2367
+ "zenodo_link 30\n",
2368
+ "Sequence 10713\n",
2369
+ "Taxonomic_Name 362\n",
2370
+ "Locality 642\n",
2371
+ "Sample_accession 1552\n",
2372
+ "Collected_by 12\n",
2373
+ "Other_ID 2890\n",
2374
+ "Date 791\n",
2375
+ "Dataset 8\n",
2376
+ "Store 137\n",
2377
+ "Brood 215\n",
2378
+ "Death_Date 63\n",
2379
+ "Cross_Type 30\n",
2380
+ "Stage 1\n",
2381
+ "Sex 3\n",
2382
+ "Unit_Type 4\n",
2383
+ "file_type 3\n",
2384
+ "record_number 30\n",
2385
+ "species 245\n",
2386
+ "subspecies 152\n",
2387
+ "genus 94\n",
2388
+ "file_url 22168\n",
2389
+ "hybrid_stat 2\n",
2390
+ "dtype: int64"
2391
+ ]
2392
+ },
2393
+ "execution_count": 50,
2394
+ "metadata": {},
2395
+ "output_type": "execute_result"
2396
+ }
2397
+ ],
2398
+ "source": [
2399
+ "dorsal_subset.nunique()"
2400
+ ]
2401
+ },
2402
+ {
2403
+ "cell_type": "markdown",
2404
+ "metadata": {},
2405
+ "source": [
2406
+ "Observe that we still have duplicate samples (duplicated `CAMID`), so we'll add a column indicating this (`CAM_Dupe`). We will not leave the first instance as a non-duplicate to have a clear assessment of all duplication (eg., is it just across a couple records).\n",
2407
+ "\n",
2408
+ "Note that they will be duplicated for the images that are of a dorsal forewing or hindwing, so we will label those as `single_wing`."
2409
+ ]
2410
+ },
2411
+ {
2412
+ "cell_type": "code",
2413
+ "execution_count": 51,
2414
+ "metadata": {},
2415
+ "outputs": [
2416
+ {
2417
+ "name": "stderr",
2418
+ "output_type": "stream",
2419
+ "text": [
2420
+ "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_21745/2272441064.py:1: SettingWithCopyWarning: \n",
2421
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
2422
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
2423
+ "\n",
2424
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
2425
+ " dorsal_subset[\"CAM_Dupe\"] = dorsal_subset.duplicated(subset = \"CAMID\", keep = False)\n"
2426
+ ]
2427
+ },
2428
+ {
2429
+ "data": {
2430
+ "text/plain": [
2431
+ "CAM_Dupe\n",
2432
+ "True 17213\n",
2433
+ "False 4170\n",
2434
+ "single-wing 792\n",
2435
+ "Name: count, dtype: int64"
2436
+ ]
2437
+ },
2438
+ "execution_count": 51,
2439
+ "metadata": {},
2440
+ "output_type": "execute_result"
2441
+ }
2442
+ ],
2443
+ "source": [
2444
+ "dorsal_subset[\"CAM_Dupe\"] = dorsal_subset.duplicated(subset = \"CAMID\", keep = False)\n",
2445
+ "dorsal_subset.loc[dorsal_subset[\"View\"].isin([\"forewing dorsal\", \"hindwing dorsal\"]), \"CAM_Dupe\"] = \"single-wing\"\n",
2446
+ "dorsal_subset[\"CAM_Dupe\"].value_counts()"
2447
+ ]
2448
+ },
2449
+ {
2450
+ "cell_type": "code",
2451
+ "execution_count": 52,
2452
+ "metadata": {},
2453
+ "outputs": [
2454
+ {
2455
+ "data": {
2456
+ "text/plain": [
2457
+ "29"
2458
+ ]
2459
+ },
2460
+ "execution_count": 52,
2461
+ "metadata": {},
2462
+ "output_type": "execute_result"
2463
+ }
2464
+ ],
2465
+ "source": [
2466
+ "dorsal_subset.loc[dorsal_subset[\"CAM_Dupe\"] == True, \"record_number\"].nunique()"
2467
+ ]
2468
+ },
2469
+ {
2470
+ "cell_type": "markdown",
2471
+ "metadata": {},
2472
+ "source": [
2473
+ "Okay, nearly all records have duplication even in the dorsal subset. That does make sense when we have just over half as many unique `CAMID`s as number of images."
2474
+ ]
2475
+ },
2476
+ {
2477
+ "cell_type": "code",
2478
+ "execution_count": 53,
2479
+ "metadata": {},
2480
+ "outputs": [
2481
+ {
2482
+ "data": {
2483
+ "text/plain": [
2484
+ "record_number\n",
2485
+ "4287444 396\n",
2486
+ "4288250 284\n",
2487
+ "3569598 112\n",
2488
+ "Name: count, dtype: int64"
2489
+ ]
2490
+ },
2491
+ "execution_count": 53,
2492
+ "metadata": {},
2493
+ "output_type": "execute_result"
2494
+ }
2495
+ ],
2496
+ "source": [
2497
+ "dorsal_subset.loc[dorsal_subset[\"CAM_Dupe\"] == \"single-wing\", \"record_number\"].value_counts()"
2498
+ ]
2499
+ },
2500
+ {
2501
+ "cell_type": "markdown",
2502
+ "metadata": {},
2503
+ "source": [
2504
+ "Single-wing images are constrained to 3 records."
2505
+ ]
2506
+ },
2507
+ {
2508
+ "cell_type": "code",
2509
+ "execution_count": 54,
2510
+ "metadata": {},
2511
+ "outputs": [
2512
+ {
2513
+ "data": {
2514
+ "text/plain": [
2515
+ "file_type\n",
2516
+ "jpg 11752\n",
2517
+ "raw 5440\n",
2518
+ "tif 21\n",
2519
+ "Name: count, dtype: int64"
2520
+ ]
2521
+ },
2522
+ "execution_count": 54,
2523
+ "metadata": {},
2524
+ "output_type": "execute_result"
2525
+ }
2526
+ ],
2527
+ "source": [
2528
+ "dorsal_subset.loc[dorsal_subset[\"CAM_Dupe\"] == True, \"file_type\"].value_counts()"
2529
+ ]
2530
+ },
2531
+ {
2532
+ "cell_type": "markdown",
2533
+ "metadata": {},
2534
+ "source": [
2535
+ "Some of this duplication is by file type."
2536
+ ]
2537
+ },
2538
+ {
2539
+ "cell_type": "code",
2540
+ "execution_count": 55,
2541
+ "metadata": {},
2542
+ "outputs": [
2543
+ {
2544
+ "data": {
2545
+ "text/plain": [
2546
+ "True 8415\n",
2547
+ "False 3337\n",
2548
+ "Name: count, dtype: int64"
2549
+ ]
2550
+ },
2551
+ "execution_count": 55,
2552
+ "metadata": {},
2553
+ "output_type": "execute_result"
2554
+ }
2555
+ ],
2556
+ "source": [
2557
+ "dorsal_subset.loc[(dorsal_subset[\"CAM_Dupe\"] == True) & (dorsal_subset[\"file_type\"] == \"jpg\")].duplicated(subset = \"CAMID\", keep = False).value_counts()"
2558
+ ]
2559
+ },
2560
+ {
2561
+ "cell_type": "code",
2562
+ "execution_count": 56,
2563
+ "metadata": {},
2564
+ "outputs": [
2565
+ {
2566
+ "data": {
2567
+ "text/plain": [
2568
+ "False 4106\n",
2569
+ "True 1334\n",
2570
+ "Name: count, dtype: int64"
2571
+ ]
2572
+ },
2573
+ "execution_count": 56,
2574
+ "metadata": {},
2575
+ "output_type": "execute_result"
2576
+ }
2577
+ ],
2578
+ "source": [
2579
+ "dorsal_subset.loc[(dorsal_subset[\"CAM_Dupe\"] == True) & (dorsal_subset[\"file_type\"] == \"raw\")].duplicated(subset = \"CAMID\", keep = False).value_counts()"
2580
+ ]
2581
+ },
2582
+ {
2583
+ "cell_type": "markdown",
2584
+ "metadata": {},
2585
+ "source": [
2586
+ "We have multiple jpg images & multiple raw images of the same specimen. Note that this does not necessarily mean these are duplicates of the same images. There are also jpg copies provided alongside raw images."
2587
+ ]
2588
+ },
2589
+ {
2590
+ "cell_type": "markdown",
2591
+ "metadata": {},
2592
+ "source": [
2593
+ "### Save Dorsal Subset to CSV"
2594
+ ]
2595
+ },
2596
+ {
2597
+ "cell_type": "code",
2598
+ "execution_count": 57,
2599
+ "metadata": {},
2600
+ "outputs": [],
2601
+ "source": [
2602
+ "dorsal_subset.to_csv(\"../Jiggins_Zenodo_dorsal_Img_Master.csv\", index = False)"
2603
+ ]
2604
+ },
2605
+ {
2606
+ "cell_type": "code",
2607
+ "execution_count": null,
2608
+ "metadata": {},
2609
+ "outputs": [],
2610
+ "source": []
2611
+ }
2612
+ ],
2613
+ "metadata": {
2614
+ "kernelspec": {
2615
+ "display_name": "std",
2616
+ "language": "python",
2617
+ "name": "python3"
2618
+ },
2619
+ "language_info": {
2620
+ "codemirror_mode": {
2621
+ "name": "ipython",
2622
+ "version": 3
2623
+ },
2624
+ "file_extension": ".py",
2625
+ "mimetype": "text/x-python",
2626
+ "name": "python",
2627
+ "nbconvert_exporter": "python",
2628
+ "pygments_lexer": "ipython3",
2629
+ "version": "3.11.3"
2630
+ },
2631
+ "orig_nbformat": 4
2632
+ },
2633
+ "nbformat": 4,
2634
+ "nbformat_minor": 2
2635
+ }
scripts/checksum.py ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import argparse
2
+ import os
3
+ import hashlib
4
+ import csv
5
+ from tqdm import tqdm
6
+
7
+ def md5_checksum(file_path):
8
+ hash_md5 = hashlib.md5()
9
+ with open(file_path, "rb") as f:
10
+ for chunk in iter(lambda: f.read(4096), b""):
11
+ hash_md5.update(chunk)
12
+ return hash_md5.hexdigest()
13
+
14
+ def get_checksums(input_directory, output_filepath):
15
+ with open(output_filepath, 'w', newline='') as csvfile:
16
+ writer = csv.writer(csvfile)
17
+ writer.writerow(["filepath", "filename", "md5"])
18
+ for root, dirs, files in os.walk(input_directory):
19
+ n_files = len(files)
20
+ for name in tqdm(files, total=n_files, desc="MD5ing"):
21
+ file_path = os.path.join(root, name)
22
+ checksum = md5_checksum(file_path)
23
+ writer.writerow([file_path, name, checksum])
24
+ print(f"Checksums written to {output_filepath}")
25
+
26
+ if __name__ == "__main__":
27
+ parser = argparse.ArgumentParser(description="Generate MD5 checksums for files in a directory")
28
+ parser.add_argument("--input-directory", required=True, help="Directory to traverse for files")
29
+ parser.add_argument("--output-filepath", required=True, help="Filepath for the output CSV file")
30
+ args = parser.parse_args()
31
+ get_checksums(args.input_directory, args.output_filepath)
32
+
scripts/download_jiggins_subset.py ADDED
@@ -0,0 +1,98 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Modified code from https://huggingface.co/datasets/imageomics/Comparison-Subset-Jiggins/blob/main/scripts/download_jiggins_subset.py
2
+ # For downloading Jiggins images from any of the master CSV files
3
+ # Generates Checksum file for all images download
4
+ # logs image download in json file
5
+
6
+ import requests
7
+ import shutil
8
+ import json
9
+
10
+ import pandas as pd
11
+ from checksum import get_checksums
12
+
13
+ from tqdm import tqdm
14
+ import os
15
+ import argparse
16
+
17
+
18
+ def parse_args():
19
+ parser = argparse.ArgumentParser()
20
+ parser.add_argument("--csv", required=True, help="Path to CSV file with urls.", nargs="?")
21
+ parser.add_argument("--output", required=True, help="Main directory to download images into.", nargs="?")
22
+
23
+ return parser.parse_args()
24
+
25
+
26
+ def update_log(log_data, index, image, url, response_code):
27
+ # log status
28
+ log_entry = {}
29
+ log_entry["Image"] = image
30
+ log_entry["zenodo_link"] = url
31
+ log_entry["Response_status"] = response_code
32
+ log_data[index] = log_entry
33
+
34
+ return log_data
35
+
36
+
37
+ def download_images(csv_path, image_folder, log_filepath):
38
+ #load csv
39
+ jiggins_data = pd.read_csv(csv_path)
40
+ log_data = {}
41
+
42
+ for i in tqdm(range(0, len(jiggins_data))) :
43
+ species = jiggins_data["Taxonomic_Name"][i]
44
+ image_name = jiggins_data["X"][i].astype(str) + "_" + jiggins_data["Image_name"][i]
45
+
46
+ #download the image from url is not already downloaded
47
+ if os.path.exists(f"{image_folder}/{species}/{image_name}") != True:
48
+ #get image from url
49
+ url = jiggins_data["zenodo_link"][i]
50
+ response = requests.get(url, stream=True)
51
+
52
+ # log status
53
+ log_data = update_log(log_data,
54
+ index = i,
55
+ image = species + "/" + image_name,
56
+ url = url,
57
+ response_code = response.status_code
58
+ )
59
+
60
+ #create the species appropriate folder if necessary
61
+ if os.path.exists(f"{image_folder}/{species}") != True:
62
+ os.makedirs(f"{image_folder}/{species}", exist_ok=False)
63
+
64
+ #download the image
65
+ if response.status_code == 200:
66
+ with open(f"{image_folder}/{species}/{image_name}", "wb") as out_file:
67
+ shutil.copyfileobj(response.raw, out_file)
68
+ del response
69
+
70
+ with open(log_filepath, "w") as log_file:
71
+ json.dump(log_data, log_file, indent = 4)
72
+
73
+ return
74
+
75
+ def main():
76
+
77
+ #get arguments from commandline
78
+ args = parse_args()
79
+ csv_path = args.csv #path to our csv with urls to download images from
80
+ image_folder = args.output #folder where dataset will be downloaded to
81
+
82
+ # log file location
83
+ log_filepath = csv_path.split(".")[0] + "_log.json"
84
+
85
+ #dowload images from urls
86
+ download_images(csv_path, image_folder, log_filepath)
87
+
88
+ # generate checksums and save CSV to same folder as CSV used for download
89
+ checksum_path = csv_path.split(".")[0] + "_checksums.csv"
90
+ get_checksums(image_folder, checksum_path)
91
+
92
+ print(f"Images downloaded from {csv_path} to {image_folder}.")
93
+ print(f"Checksums recorded in {checksum_path} and download log is in {log_filepath}.")
94
+
95
+ return
96
+
97
+ if __name__ == "__main__":
98
+ main()