standardize datasets (#2)

- 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 <[email protected]>

.gitattributes

@@ -55,4 +55,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
 
 Jiggins_Zenodo_Img_Master.csv filter=lfs diff=lfs merge=lfs -text
-
+Jiggins_Heliconius_Master.csv filter=lfs diff=lfs merge=lfs -text

Jiggins_Zenodo_Img_Master.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:75f4effe4a36aaf206c6047537ab75e4037bba7b56dbd376209983f52822b7c6
-size 13583175
+oid sha256:fe79fdb62909be50eff6f7016f02301c56ce09c9c0b164dce680810ec4149b2a
+size 15528648

README.md

@@ -11,67 +11,96 @@ tags:
 - separated wings
 - mimicry
 - CV
+- erato
+- melpomene
+- hybrids
+- cross types
+- wild
+- lab-bred
 pretty_name: Jiggins Heliconius Collection
 size_categories:
 - 10K<n<100K
 language:
 - en
+configs:
+- config_name: full_master
+  data_files:
+  - split: train
+    path: "Jiggins_Zenodo_Img_Master.csv"
+- config_name: heliconius
+  data_files:
+  - split: train
+    path: "Jiggins_Heliconius_Master.csv"
+- config_name: dorsal
+  data_files:
+  - split: train
+    path: "Jiggins_Zenodo_dorsal_Img_Master.csv"
 ---
 # Dataset Card for Jiggins Heliconius Collection
 
 ## Dataset Description
 
+<!--
 - **Homepage:** 
 - **Repository:** 
 - **Paper:** 
 - **Leaderboard:** 
 - **Point of Contact:** 
+-->
 
 ### Dataset Summary
 
-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. 
-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. 
-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).
+Subset of the collection records from Chris Jiggins' research group at the University of Cambridge, collection covers nearly 20 years of field studies. 
+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. 
+
+Most images were photographed with a DSLR camera with a 100 mm macro-lens in standardized conditions. 
+More information can be found at the individual Zenodo record pages.
+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).
 
 Both dorsal and ventral images available. Contains both whole butterfly, and 4 wings separate. Large variation in image content.
 
 <!---
 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).
 --->
+<!--
 ### Supported Tasks and Leaderboards
 
-[More Information Needed]
+[More Information Needed] -->
 
 ### Languages
 
-[More Information Needed]
+English, Latin
 
 ## Dataset Structure
 
-* **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.
-     * To access the images combine columns `zenodo_link` and `Image_name`:
-       ```
-       zenodo_link + '/files/' + Image_name
-       ```
+* **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.
 
-* **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`.
+* **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.
 
-* **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.
+* **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.
 
 
-
-* Note: The notebooks that generated these files and stats are included in the `notebooks` folder.
-
+**Notes:**
+- The notebooks that generated these files and stats are included in the `notebooks` folder.
+    - 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.
+    - 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. 
+    - 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).
+- The `scripts` folder has a download and checksum script. 
+    - Images are downloaded to the provided images directory with subfolders labeled by the `Taxonomic_Name`, with filenames `<X>_<Image_name>`.
+    - 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.
+    - A log of the download is also generated in the same folder as the source CSV (named `<source CSV>_log.json`).
+- `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.
 
 ### Data Instances
 
 `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).
+
 Detached wings in four quadrants (generally).
 Some subspecies may be photographed differently, needs segmentation preprocessing.
 
 * **Type:** JPG/jpg/tif(very few)
 * **Size (x pixels by y pixels):** Unknown yet
-* **Background (color or none):** multiple (needs to be normalized)
+* **Background (color or none):** multiple (needs to be normalized, often grey or lime green)
 * **Fit in frame:**
 * **Ruler or Scale:** Some with Ruler
 * **Color (ColorChecker, white-balance, None):** None
@@ -90,12 +119,12 @@ CSV Columns are as follows:
 
 - `CAMID`: Unique identifier for each specimen that was photographed. Each `CAMID` corresponds to multiple images (based on factors such as `View` and `file_type`).
 - `X`: Unique identifier for each line in the master CSV.
-- `Image_name`: Filename of image.
+- `Image_name`: Filename of image (not unique, often `CAM<CAMID>_<v or d>`).
 - `View`: View of the butterfly in the image: `dorsal`, `ventral`, `forewing dorsal`, `hindwing dorsal`, `forewing ventral`, `hindwing ventral`, `dorsal and ventral`.
 - `zenodo_name`: Name of the CSV file with metadata used to populate this file from the associated Zenodo record.
 - `zenodo_link`: URL for the Zenodo record of the image.
 - `Sequence`: Mostly numeric IDs, not unique, please see the associated Zenodo record for more information on the meaning of these designations.
-- `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_).
+- `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`.
 - `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.
 - `Sample_accession`: Some type of ID, not unique, please see the associated Zenodo record for more information on the meaning of these designations.
 - `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:
 - `species`: Species of the specimen. There are 246 species represented in the full collection, 37 of these are species of Heliconius.
 - `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.
 - `genus`: Genus of the specimen. There are 94 unique genera represented in the full collection.
+- `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).
+- `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).
 
 **Note:**
- - `Jiggins_Heliconius_Master.csv` has all but the `genus` column, since all images are Heliconius.
- - `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.
+ - `Jiggins_Zenodo_dorsal_Img_Master.csv` also has a column `CAM_dupe` indicating whether the `CAMID` has multiple images within this subset.
+    - 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).
+    - `CAMID`s are necessarily duplicated for the images that are of just a dorsal forewing or hindwing, so we label those as `single_wing`.
+    - 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.
 
+<!--
 ### Data Splits
 
 [More Information Needed]
+-->
       
 ## Dataset Creation
 
 ### Curation Rationale
 
-[More Information Needed]
-
-### Source Data
+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. 
 
-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.
+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.
 
-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.
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
+### Source Data
 
-[More Information Needed]
+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. 
 
-### Annotations
+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.
 
-#### Annotation process
 
-[More Information Needed]
+#### Initial Data Collection and Annotation
 
-#### Who are the annotators?
+These images are of a mix of wild-caught and lab-bred butterflies, classified by the Butterfly Genetics Group.
 
-[More Information Needed]
 
 ### Personal and Sensitive Information
 
@@ -159,17 +184,13 @@ None
 
 ## Considerations for Using the Data
 
-### Social Impact of Dataset
+### Discussion of Biases and Other Known Limitations
 
-[More Information Needed]
+- This dataset is imbalanced. Even among the Heliconius subset, some subspecies are more heavily represented than others. 
+- 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. 
+- There may be overlap between images in [record 2548678](https://zenodo.org/records/2548678) and [record 3082688](https://zenodo.org/records/3082688).
+- 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.
 
-### Discussion of Biases
-
-[More Information Needed]
-
-### Other Known Limitations
-
-[More Information Needed]
 
 ## Additional Information
 
@@ -178,6 +199,7 @@ None
 * Christopher Lawrence (Princeton University) - ORCID: 0000-0002-3846-5968
 * Chris Jiggins (University of Cambridge) - ORCID: 0000-0002-7809-062X
 * Butterfly Genetics Group (University of Cambridge)
+* Elizabeth G. Campolongo - ORCID: 0000-0003-0846-2413
 
 ### Licensing Information
 
@@ -187,10 +209,10 @@ Images can be matched to their source record through the `zenodo_link` column in
 
 ### Citation Information
 
-Christopher Lawrence, Chris Jiggins, Butterfly Genetics Group (University of Cambridge). (2023). Jiggins Heliconius Collection. Hugging Face. https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection.
+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.
 
-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. 
+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. 
 
 ### Contributions
 
-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).
+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.

licenses.json → metadata/licenses.json

@@ -1 +1 @@
-[{"record_number": "4289223", "url": "https://zenodo.org/record/4289223", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., van der Heijden, E., Nadeau, N., & Jiggins, C. (2020 , November). Cambridge butterfly wing collection batch 10. Zenodo. https://doi.org/10.5281/zenodo.4289223", "bibtex": "@misc{gabriela_montejo_kovacevich_2020_4289223, author = {Gabriela Montejo-Kovacevich and Eva van der Heijden and Nicola Nadeau and Chris Jiggins}, title = {Cambridge butterfly wing collection batch 10}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4289223}, url = {https://doi.org/10.5281/zenodo.4289223} }"}, {"record_number": "4288311", "url": "https://zenodo.org/record/4288311", "license": "cc-by-4.0", "citation": "Salazar, P. A., Nadeau, N., Montejo-Kovacevich, G., & Jiggins, C. (2020 , November). Sheffield butterfly wing collection - Patricio Salazar, Nicola Nadeau, Ikiam broods batch 1 and 2. Zenodo. https://doi.org/10.5281/zenodo.4288311", "bibtex": "@misc{patricio_a_salazar_2020_4288311, author = {Patricio A. Salazar and Nicola Nadeau and Gabriela Montejo-Kovacevich and Chris Jiggins}, title = {{Sheffield butterfly wing collection - Patricio Salazar, Nicola Nadeau, Ikiam broods batch 1 and 2}}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4288311}, url = {https://doi.org/10.5281/zenodo.4288311} }"}, {"record_number": "2677821", "url": "https://zenodo.org/record/2677821", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Jiggins, C., & Warren, I. (2019 , May). Cambridge butterfly wing collection batch 2. 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. Zenodo. https://doi.org/10.5281/zenodo.3477891", "bibtex": "@misc{chris_jiggins_2019_3477891, author = {Chris Jiggins and Gabriela Montejo-Kovacevich and Patricio Salazar and Ian Warren}, title = {{Heliconiine Butterfly Collection Records from University of Cambridge}}, month = oct, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.3477891}, url = {https://doi.org/10.5281/zenodo.3477891} }"}, {"record_number": "2682458", "url": "https://zenodo.org/record/2682458", "license": "cc-by-4.0", "citation": "Jiggins, C., Montejo-Kovacevich, G., Warren, I., & Wiltshire, E. (2019 , May). Cambridge butterfly wing collection batch 3. Zenodo. https://doi.org/10.5281/zenodo.2682458", "bibtex": "@misc{jiggins_2019_2682458, author = {Jiggins, Chris and Montejo-Kovacevich, Gabriela and Warren, Ian and Wiltshire, Eva}, title = {Cambridge butterfly wing collection batch 3}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2682458}, url = {https://doi.org/10.5281/zenodo.2682458} }"}, {"record_number": "2682669", "url": "https://zenodo.org/record/2682669", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Jiggins, C., & Warren, I. (2019 , May). Cambridge butterfly wing collection batch 4. Zenodo. https://doi.org/10.5281/zenodo.2682669", "bibtex": "@misc{montejo_kovacevich_2019_2682669, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian}, title = {Cambridge butterfly wing collection batch 4}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2682669}, url = {https://doi.org/10.5281/zenodo.2682669} }"}, {"record_number": "2684906", "url": "https://zenodo.org/record/2684906", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Jiggins, C., Warren, I., & Wiltshire, E. (2019 , May). Cambridge butterfly wing collection batch 5. Zenodo. https://doi.org/10.5281/zenodo.2684906", "bibtex": "@misc{montejo_kovacevich_2019_2684906, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva}, title = {Cambridge butterfly wing collection batch 5}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2684906}, url = {https://doi.org/10.5281/zenodo.2684906} }"}, {"record_number": "2552371", "url": "https://zenodo.org/record/2552371", "license": "cc-by-4.0", "citation": "Warren, I. & Jiggins, C. (2019 , February). Miscellaneous Heliconius wing photographs (2001-2019) Part 1. Zenodo. https://doi.org/10.5281/zenodo.2552371", "bibtex": "@misc{warren_2019_2552371, author = {Warren, Ian and Jiggins, Chris}, title = {{Miscellaneous Heliconius wing photographs (2001-2019) Part 1}}, month = feb, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2552371}, url = {https://doi.org/10.5281/zenodo.2552371} }"}, {"record_number": "2553977", "url": "https://zenodo.org/record/2553977", "license": "cc-by-4.0", "citation": "Warren, I. & Jiggins, C. (2019 , February). Miscellaneous Heliconius wing photographs (2001-2019) Part 3. Zenodo. https://doi.org/10.5281/zenodo.2553977", "bibtex": "@misc{warren_2019_2553977, author = {Warren, Ian and Jiggins, Chris}, title = {{Miscellaneous Heliconius wing photographs (2001-2019) Part 3}}, month = feb, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2553977}, url = {https://doi.org/10.5281/zenodo.2553977} }"}, {"record_number": "2686762", "url": "https://zenodo.org/record/2686762", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Jiggins, C., Warren, I., & Wiltshire, E. (2019 , May). Cambridge butterfly wing collection batch 6. Zenodo. https://doi.org/10.5281/zenodo.2686762", "bibtex": "@misc{montejo_kovacevich_2019_2686762, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva}, title = {Cambridge butterfly wing collection batch 6}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2686762}, url = {https://doi.org/10.5281/zenodo.2686762} }"}, {"record_number": "2549524", "url": "https://zenodo.org/record/2549524", "license": "cc-by-4.0", "citation": "Jiggins, C. & Warren, I. (2019 , January). Cambridge butterfly wing collection - Chris Jiggins 2001/2 broods batch 1. Zenodo. https://doi.org/10.5281/zenodo.2549524", "bibtex": "@misc{jiggins_2019_2549524, author = {Jiggins, Chris and Warren, Ian}, title = {{Cambridge butterfly wing collection - Chris Jiggins 2001/2 broods batch 1}}, month = jan, year = 2019, publisher = {Zenodo}, version = 1, doi = {10.5281/zenodo.2549524}, url = {https://doi.org/10.5281/zenodo.2549524} }"}, {"record_number": "2550097", "url": "https://zenodo.org/record/2550097", "license": "cc-by-4.0", "citation": "Jiggins, C. & Warren, I. (2019 , January). Cambridge butterfly wing collection - Chris Jiggins 2001/2 broods batch 2. Zenodo. https://doi.org/10.5281/zenodo.2550097", "bibtex": "@misc{jiggins_2019_2550097, author = {Jiggins, Chris and Warren, Ian}, title = {{Cambridge butterfly wing collection - Chris Jiggins 2001/2 broods batch 2}}, month = jan, year = 2019, publisher = {Zenodo}, version = 1, doi = {10.5281/zenodo.2550097}, url = {https://doi.org/10.5281/zenodo.2550097} }"}, {"record_number": "4153502", "url": "https://zenodo.org/record/4153502", "license": "cc-by-4.0", "citation": "Meier, J. I., Salazar, P., Montejo-Kovacevich, G., Warren, I., & Jggins, C. (2020 , October). Cambridge butterfly wing collection - Patricio Salazar PhD wild specimens batch 3. Zenodo. https://doi.org/10.5281/zenodo.4153502", "bibtex": "@misc{joana_i_meier_2020_4153502, author = {Joana I. 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Zenodo. https://doi.org/10.5281/zenodo.2550097", "bibtex": "@misc{jiggins_2019_2550097, author = {Jiggins, Chris and Warren, Ian}, title = {{Cambridge butterfly wing collection - Chris Jiggins 2001/2 broods batch 2}}, month = jan, year = 2019, publisher = {Zenodo}, version = 1, doi = {10.5281/zenodo.2550097}, url = {https://doi.org/10.5281/zenodo.2550097} }"}, {"record_number": "4153502", "url": "https://zenodo.org/record/4153502", "license": "cc-by-4.0", "citation": "Meier, J. I., Salazar, P., Montejo-Kovacevich, G., Warren, I., & Jggins, C. (2020 , October). Cambridge butterfly wing collection - Patricio Salazar PhD wild specimens batch 3. Zenodo. https://doi.org/10.5281/zenodo.4153502", "bibtex": "@misc{joana_i_meier_2020_4153502, author = {Joana I. Meier and Patricio Salazar and Gabriela Montejo-Kovacevich and Ian Warren and Chris Jggins}, title = {{Cambridge butterfly wing collection - Patricio Salazar PhD wild specimens batch 3}}, month = oct, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4153502}, url = {https://doi.org/10.5281/zenodo.4153502} }"}, {"record_number": "3082688", "url": "https://zenodo.org/record/3082688", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Jiggins, C., & Warren, I. (2019 , May). Cambridge butterfly wing collection batch 1- version 2. Zenodo. https://doi.org/10.5281/zenodo.3082688", "bibtex": "@misc{montejo_kovacevich_2019_3082688, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian}, title = {{Cambridge butterfly wing collection batch 1- version 2}}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.3082688}, url = {https://doi.org/10.5281/zenodo.3082688} }"}, {"record_number": "2813153", "url": "https://zenodo.org/record/2813153", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Jiggins, C., Warren, I., Salazar, C., Elias, M., Gavins, I., Wiltshire, E., Montgomery, S., & McMillan, O. (2019 , May). Cambridge and collaborators butterfly wing collection batch 10. Zenodo. https://doi.org/10.5281/zenodo.2813153", "bibtex": "@misc{montejo_kovacevich_2019_2813153, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Salazar, Camilo and Elias, Marianne and Gavins, Imogen and Wiltshire, Eva and Montgomery, Stephen and McMillan, Owen}, title = {{Cambridge and collaborators butterfly wing collection batch 10}}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2813153}, url = {https://doi.org/10.5281/zenodo.2813153} }"}, {"record_number": "1748277", "url": "https://zenodo.org/record/1748277", "license": "cc-by-4.0", "citation": "Salazar, P., Montejo-Kovacevich, G., Warren, I., & Jiggins, C. (2018 , December). Cambridge butterfly wing collection - Patricio Salazar PhD wild and bred specimens batch 1. Zenodo. https://doi.org/10.5281/zenodo.1748277", "bibtex": "@misc{salazar_2018_1748277, author = {Salazar, Patricio and Montejo-Kovacevich, Gabriela and Warren, Ian and Jiggins, Chris}, title = {{Cambridge butterfly wing collection - Patricio Salazar PhD wild and bred specimens batch 1}}, month = dec, year = 2018, publisher = {Zenodo}, doi = {10.5281/zenodo.1748277}, url = {https://doi.org/10.5281/zenodo.1748277} }"}, {"record_number": "2702457", "url": "https://zenodo.org/record/2702457", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Jiggins, C., Warren, I., & Wiltshire, E. (2019 , May). Cambridge butterfly wing collection batch 7. Zenodo. https://doi.org/10.5281/zenodo.2702457", "bibtex": "@misc{montejo_kovacevich_2019_2702457, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva}, title = {Cambridge butterfly wing collection batch 7}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2702457}, url = {https://doi.org/10.5281/zenodo.2702457} }"}, {"record_number": "2548678", "url": "https://zenodo.org/record/2548678", "license": "cc-by-4.0", "citation": "Salazar, P., Montejo-Kovacevich, G., Warren, I., & Jiggins, C. (2019 , January). Cambridge butterfly wing collection - Patricio Salazar PhD wild and bred specimens batch 2. Zenodo. https://doi.org/10.5281/zenodo.2548678", "bibtex": "@misc{salazar_2019_2548678, author = {Salazar, Patricio and Montejo-Kovacevich, Gabriela and Warren, Ian and Jiggins, Chris}, title = {{Cambridge butterfly wing collection - Patricio Salazar PhD wild and bred specimens batch 2}}, month = jan, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2548678}, url = {https://doi.org/10.5281/zenodo.2548678} }"}, {"record_number": "5561246", "url": "https://zenodo.org/record/5561246", "license": "cc-by-4.0", "citation": "Pinheiro de Castro, E., Jiggins, C., Lucas da Silva-Brand\u00e3o, K., Victor Lucci Freitas, A., Zikan Cardoso, M., Van Der Heijden, E., Meier, J., & Warren, I. (2022 , February). Brazilian Butterflies Collected December 2020 to January 2021. Zenodo. https://doi.org/10.5281/zenodo.5561246", "bibtex": "@misc{pinheiro_de_castro_2022_5561246, author = {Pinheiro de Castro, Erika and Jiggins, Christopher and Lucas da Silva-Brand\u00e3o, Karina and Victor Lucci Freitas, Andre and Zikan Cardoso, Marcio and Van Der Heijden, Eva and Meier, Joana and Warren, Ian}, title = {{Brazilian Butterflies Collected December 2020 to January 2021}}, month = feb, year = 2022, publisher = {Zenodo}, doi = {10.5281/zenodo.5561246}, url = {https://doi.org/10.5281/zenodo.5561246} }"}, {"record_number": "2707828", "url": "https://zenodo.org/record/2707828", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Jiggins, C., Warren, I., & Wiltshire, E. (2019 , May). Cambridge butterfly wing collection batch 8. Zenodo. https://doi.org/10.5281/zenodo.2707828", "bibtex": "@misc{montejo_kovacevich_2019_2707828, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva}, title = {Cambridge butterfly wing collection batch 8}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2707828}, url = {https://doi.org/10.5281/zenodo.2707828} }"}, {"record_number": "2714333", "url": "https://zenodo.org/record/2714333", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Jiggins, C., Warren, I., Wiltshire, E., & Gavins, I. (2019 , May). Cambridge butterfly wing collection batch 9. Zenodo. https://doi.org/10.5281/zenodo.2714333", "bibtex": "@misc{montejo_kovacevich_2019_2714333, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva and Gavins, Imogen}, title = {Cambridge butterfly wing collection batch 9}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2714333}, url = {https://doi.org/10.5281/zenodo.2714333} }"}, {"record_number": "4291095", "url": "https://zenodo.org/record/4291095", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., van der Heijden, E., & Jiggins, C. (2020 , November). Cambridge butterfly collection - GMK Broods Ikiam 2018. 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. Zenodo. https://doi.org/10.5281/zenodo.5731587", "bibtex": "@misc{joana_meier_2021_5731587, author = {Joana Meier and Annalie Barker and Chris Jiggins and Ian Warren and Rachel Blow}, title = {{Cambridge butterfly wing collection - Ecuador, August 2019}}, month = nov, year = 2021, publisher = {Zenodo}, version = {version 1}, doi = {10.5281/zenodo.5731587}, url = {https://doi.org/10.5281/zenodo.5731587} }"}, {"record_number": "3569598", "url": "https://zenodo.org/record/3569598", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Cookson, L., van der Heijden, E., Warren, I., Edwards, D. P., & Jiggins, C. (2019 , December). Cambridge butterfly collection - Loreto, Peru 2018. Zenodo. https://doi.org/10.5281/zenodo.3569598", "bibtex": "@misc{gabriela_montejo_kovacevich_2019_3569598, 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}, month = dec, year = 2019, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.3569598}, url = {https://doi.org/10.5281/zenodo.3569598} }"}, {"record_number": "4287444", "url": "https://zenodo.org/record/4287444", "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 batch2. Zenodo. https://doi.org/10.5281/zenodo.4287444", "bibtex": "@misc{gabriela_montejo_kovacevich_2020_4287444, 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 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. Zenodo. https://doi.org/10.5281/zenodo.5526257", "bibtex": "@misc{montejo_kovacevich_2021_5526257, author = {Montejo-Kovacevich, Gabriela and Paynter, Quentin and Ghane, Amin}, title = {{Heliconius erato cyrbia, Cook Islands (New Zealand) 2016, 2019, 2021}}, month = sep, year = 2021, publisher = {Zenodo}, doi = {10.5281/zenodo.5526257}, url = {https://doi.org/10.5281/zenodo.5526257} }"}, {"record_number": "2553501", "url": "https://zenodo.org/record/2553501", "license": "cc-by-4.0", "citation": "Warren, I. & Jiggins, C. (2019 , February). Miscellaneous Heliconius wing photographs (2001-2019) Part 2. 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} }"}]

notebooks/standardize_datasets.ipynb

@@ -0,0 +1,2635 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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",
+    "\n",
+    "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)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"../Jiggins_Zenodo_Img_Master.csv\", low_memory=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID               12586\n",
+       "X                   49359\n",
+       "Image_name          37821\n",
+       "View                    7\n",
+       "zenodo_name            36\n",
+       "zenodo_link            32\n",
+       "Sequence            11301\n",
+       "Taxonomic_Name        363\n",
+       "Locality              645\n",
+       "Sample_accession     1571\n",
+       "Collected_by           12\n",
+       "Other_ID             3088\n",
+       "Date                  810\n",
+       "Dataset                 8\n",
+       "Store                 142\n",
+       "Brood                 226\n",
+       "Death_Date             82\n",
+       "Cross_Type             30\n",
+       "Stage                   1\n",
+       "Sex                     3\n",
+       "Unit_Type               6\n",
+       "file_type               3\n",
+       "record_number          32\n",
+       "species               246\n",
+       "subspecies            155\n",
+       "genus                  94\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n",
+      "RangeIndex: 49359 entries, 0 to 49358\n",
+      "Series name: record_number\n",
+      "Non-Null Count  Dtype\n",
+      "--------------  -----\n",
+      "49359 non-null  int64\n",
+      "dtypes: int64(1)\n",
+      "memory usage: 385.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.record_number.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID               12586\n",
+       "X                   46439\n",
+       "Image_name          37821\n",
+       "View                    7\n",
+       "zenodo_name            35\n",
+       "zenodo_link            31\n",
+       "Sequence            11301\n",
+       "Taxonomic_Name        363\n",
+       "Locality              645\n",
+       "Sample_accession     1571\n",
+       "Collected_by           12\n",
+       "Other_ID             3088\n",
+       "Date                  810\n",
+       "Dataset                 8\n",
+       "Store                 142\n",
+       "Brood                 226\n",
+       "Death_Date             82\n",
+       "Cross_Type             30\n",
+       "Stage                   1\n",
+       "Sex                     3\n",
+       "Unit_Type               6\n",
+       "file_type               3\n",
+       "record_number          31\n",
+       "species               246\n",
+       "subspecies            155\n",
+       "genus                  94\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.loc[df.record_number != 1247307]\n",
+    "df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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",
+    "\n",
+    "It is also noted in [record 2548678](https://zenodo.org/records/2548678):\n",
+    ">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",
+    "\n",
+    "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 26)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v1s = [3477412, 1880783]\n",
+    "df.loc[df[\"record_number\"].isin(v1s)].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "file_type\n",
+       "jpg    34152\n",
+       "raw    12226\n",
+       "tif       61\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.file_type.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "View\n",
+       "dorsal                22022\n",
+       "ventral               21704\n",
+       "forewing dorsal         406\n",
+       "hindwing dorsal         406\n",
+       "forewing ventral        406\n",
+       "hindwing ventral        406\n",
+       "dorsal and ventral       18\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.View.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have 31 unique records represented in the full dataset. When we reduce down to just the Heliconius images, this will probably be less."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add File URL Column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CAMID</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>View</th>\n",
+       "      <th>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "      <th>Sequence</th>\n",
+       "      <th>Taxonomic_Name</th>\n",
+       "      <th>Locality</th>\n",
+       "      <th>Sample_accession</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Cross_Type</th>\n",
+       "      <th>Stage</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Unit_Type</th>\n",
+       "      <th>file_type</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>file_url</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>34925</th>\n",
+       "      <td>CAM041457</td>\n",
+       "      <td>38799</td>\n",
+       "      <td>CAM041457_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>0.gmk.broods.all.csv</td>\n",
+       "      <td>https://zenodo.org/record/4291095</td>\n",
+       "      <td>41,457</td>\n",
+       "      <td>Heliconius timareta</td>\n",
+       "      <td>Reventador road 2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>4291095</td>\n",
+       "      <td>Heliconius timareta</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/4291095/files/CAM041...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28286</th>\n",
+       "      <td>CAM036145</td>\n",
+       "      <td>41369</td>\n",
+       "      <td>CAM036145_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>Filelist.csv</td>\n",
+       "      <td>https://zenodo.org/record/5561246</td>\n",
+       "      <td>36,145</td>\n",
+       "      <td>Methona singularis</td>\n",
+       "      <td>Guaribas - RG16</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>5561246</td>\n",
+       "      <td>Methona singularis</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Methona</td>\n",
+       "      <td>https://zenodo.org/record/5561246/files/CAM036...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21858</th>\n",
+       "      <td>CAM017409</td>\n",
+       "      <td>19047</td>\n",
+       "      <td>CAM017409_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>CAM.coll.PS.list.individuals.haplotagging.new....</td>\n",
+       "      <td>https://zenodo.org/record/4153502</td>\n",
+       "      <td>17,409</td>\n",
+       "      <td>Heliconius erato ssp. lativitta</td>\n",
+       "      <td>San Pedro de Arajuno, Río Arajuno</td>\n",
+       "      <td>ERS353373</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>wild</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>4153502</td>\n",
+       "      <td>Heliconius erato</td>\n",
+       "      <td>lativitta</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/4153502/files/CAM017...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12708</th>\n",
+       "      <td>CAM010412</td>\n",
+       "      <td>26792</td>\n",
+       "      <td>CAM010412_v.CR2</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>2001_2.broods.batch.1.csv</td>\n",
+       "      <td>https://zenodo.org/record/2549524</td>\n",
+       "      <td>10,412</td>\n",
+       "      <td>Heliconius sp.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>reared</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>2549524</td>\n",
+       "      <td>Heliconius sp.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/2549524/files/CAM010...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X       Image_name     View  \\\n",
+       "34925  CAM041457  38799  CAM041457_d.CR2   dorsal   \n",
+       "28286  CAM036145  41369  CAM036145_v.JPG  ventral   \n",
+       "21858  CAM017409  19047  CAM017409_d.CR2   dorsal   \n",
+       "12708  CAM010412  26792  CAM010412_v.CR2  ventral   \n",
+       "\n",
+       "                                             zenodo_name  \\\n",
+       "34925                               0.gmk.broods.all.csv   \n",
+       "28286                                       Filelist.csv   \n",
+       "21858  CAM.coll.PS.list.individuals.haplotagging.new....   \n",
+       "12708                          2001_2.broods.batch.1.csv   \n",
+       "\n",
+       "                             zenodo_link Sequence  \\\n",
+       "34925  https://zenodo.org/record/4291095   41,457   \n",
+       "28286  https://zenodo.org/record/5561246   36,145   \n",
+       "21858  https://zenodo.org/record/4153502   17,409   \n",
+       "12708  https://zenodo.org/record/2549524   10,412   \n",
+       "\n",
+       "                        Taxonomic_Name                           Locality  \\\n",
+       "34925              Heliconius timareta                  Reventador road 2   \n",
+       "28286               Methona singularis                    Guaribas - RG16   \n",
+       "21858  Heliconius erato ssp. lativitta  San Pedro de Arajuno, Río Arajuno   \n",
+       "12708                   Heliconius sp.                                NaN   \n",
+       "\n",
+       "      Sample_accession  ... Cross_Type Stage      Sex Unit_Type file_type  \\\n",
+       "34925              NaN  ...        NaN   NaN     Male       NaN       raw   \n",
+       "28286              NaN  ...        NaN   NaN     Male       NaN       jpg   \n",
+       "21858        ERS353373  ...        NaN   NaN   Female      wild       raw   \n",
+       "12708              NaN  ...        NaN   NaN  Unknown    reared       raw   \n",
+       "\n",
+       "      record_number              species subspecies       genus  \\\n",
+       "34925       4291095  Heliconius timareta        NaN  Heliconius   \n",
+       "28286       5561246   Methona singularis        NaN     Methona   \n",
+       "21858       4153502     Heliconius erato  lativitta  Heliconius   \n",
+       "12708       2549524       Heliconius sp.        NaN  Heliconius   \n",
+       "\n",
+       "                                                file_url  \n",
+       "34925  https://zenodo.org/record/4291095/files/CAM041...  \n",
+       "28286  https://zenodo.org/record/5561246/files/CAM036...  \n",
+       "21858  https://zenodo.org/record/4153502/files/CAM017...  \n",
+       "12708  https://zenodo.org/record/2549524/files/CAM010...  \n",
+       "\n",
+       "[4 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"file_url\"] = df[\"zenodo_link\"] + \"/files/\" + df[\"Image_name\"]\n",
+    "df.sample(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add Proper Taxonomic Name for Crosstypes\n",
+    "\n",
+    "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "subspecies\n",
+       "(malleti x plesseni) x malleti                 1007\n",
+       "plesseni x (malleti x plesseni)                 462\n",
+       "(plesseni x malleti) x (malleti x plesseni)     348\n",
+       "(plesseni x malleti) x plesseni                 345\n",
+       "malleti x (plesseni x malleti)                  324\n",
+       "(plesseni x malleti) x (plesseni x malleti)     228\n",
+       "(malleti x plesseni) x plesseni                 216\n",
+       "plesseni x malleti                              212\n",
+       "malleti x plesseni                              156\n",
+       "lativitta x notabilis                           111\n",
+       "plesseni x (plesseni x malleti)                 106\n",
+       "(lativitta x notabilis) x notabilis              90\n",
+       "(lativitta x notabilis) x lativitta              90\n",
+       "(malleti x plesseni) x (malleti x plesseni)      81\n",
+       "(plesseni x malleti) x malleti                   80\n",
+       "(malleti x plesseni) x (plesseni x malleti)      42\n",
+       "malleti                                          28\n",
+       "plesseni                                         28\n",
+       "(latRo x notabilis) x notabilis                  12\n",
+       "latRo x notabilis                                 4\n",
+       "lativitta                                         4\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df[\"Cross_Type\"].notna(), \"subspecies\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Cross_Type\n",
+       "Test cross (4 spots x 4 spots)                           150\n",
+       "Test cross (N heterozygocity - NBNN x malleti - thin)    114\n",
+       "Test cross (N heterozygozity)                             78\n",
+       "Test cross (short HW bar)                                 54\n",
+       "Test cross (4 spots x 2 banded)                           48\n",
+       "2 banded                                                  16\n",
+       "hybrid                                                    10\n",
+       "Ac heterozygote                                            4\n",
+       "Test cross (2 banded F2 x 2 banded F2)                     4\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[(df[\"Cross_Type\"].notna()) & (df[\"subspecies\"].isna()), \"Cross_Type\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['hybrid', 'Ac heterozygote', '2 banded',\n",
+       "       'Test cross (2 banded F2 x 2 banded F2)',\n",
+       "       'Test cross (4 spots x 2 banded)', 'Test cross (N heterozygozity)',\n",
+       "       'Test cross (short HW bar)', 'Test cross (4 spots x 4 spots)',\n",
+       "       'Test cross (N heterozygocity - NBNN x malleti - thin)'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "non_specific_cross_hybrids = df.loc[(df[\"Cross_Type\"].notna()) & (df[\"subspecies\"].isna()), \"Cross_Type\"].unique()\n",
+    "non_specific_cross_hybrids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['cythera', 'cyrbia', 'venus', 'venus x chestertonii', 'vulcanus',\n",
+       "       'chestertonii', 'vulcanus x melpomene', 'lativitta', 'malleti',\n",
+       "       'erato', 'ssp.nov.P', 'melpomene', 'willmotti',\n",
+       "       'chestertonii x venus', 'eleuchia', 'cydnides', 'weymeri',\n",
+       "       'chioneus', 'demophoon', 'hydara', 'sapho', 'numata', 'iulia',\n",
+       "       'melpomene x thelxiope', 'wallacei', 'rosina', 'formosus',\n",
+       "       'melpomene x rosina', 'menapis', 'hydara x petiverana', 'decumana',\n",
+       "       'relata', 'agna', 'isthmia', 'bicoloratus', 'euphrasius',\n",
+       "       'salapia', 'matronalis', 'ethica', 'agnosia', 'andromica',\n",
+       "       'hippocrenis', 'saturata', 'evanides', 'lyra', 'pagasa',\n",
+       "       'staudingeri', 'valora', 'cassotis', 'chiriquensis', 'alithea',\n",
+       "       'timareta', 'plesseni', 'notabilis', 'amaryllis', 'thelxinoe',\n",
+       "       'doris', 'amaryllis x aglaope', 'aglaope', 'carbo', 'lycaste',\n",
+       "       'idae', 'macrinus', 'abida', 'notilla', 'neustetteri', 'giulia',\n",
+       "       'panamensis', 'eleusinus', 'agalla', 'xanthina', 'ecuadorensis',\n",
+       "       'congener', 'etylus', 'derasa', 'lenaeus', 'petiverana',\n",
+       "       'melicerta', 'thelxiopeia', 'meriana', 'plesseni x malleti',\n",
+       "       'notabilis x lativitta', 'sara', 'silvana', 'amalfreda',\n",
+       "       'hydara x amalfreda', 'flavescens', 'hydara x erato',\n",
+       "       'meriana x melpomene', 'nanna', 'daetina', 'nesaea', 'phyllis',\n",
+       "       'laphria', 'paraiya', 'lysimnia', 'daeta', 'yanetta', 'pyrrha',\n",
+       "       'casabranca', 'tristero', 'dignus', 'bellula',\n",
+       "       'dignus x lativitta', 'malleti x bellula', 'napoensis',\n",
+       "       'clysonymus', 'sotericus', 'primularis', 'sprucei', 'bassleri',\n",
+       "       'eximius', 'manabiana', 'nigrippus', 'hygiana', 'neildi',\n",
+       "       'clysonomus', 'hierax', 'magdalena', 'calathus', 'corena',\n",
+       "       'zelinde', 'florencia', 'weymeri f. gustavi', 'weymeri f. weymeri',\n",
+       "       'wanningeri', 'cydno', 'cordula', 'linaresi', 'lisethae',\n",
+       "       'hermogenes', 'martinae', 'vicina', 'malleti x vicina',\n",
+       "       'reductimacula', 'sergestus', 'felix', 'aerotome'], dtype=object)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df[\"Cross_Type\"].isna(), \"subspecies\"].dropna().unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_cross_taxa_name(species, cross_type):\n",
+    "    # label unspecified hybrids as such\n",
+    "    if cross_type in non_specific_cross_hybrids:\n",
+    "        return species + \" \" + \"cross hybrid\"\n",
+    "    \n",
+    "    # separate out hybrids from non-hybrids\n",
+    "    subsp_cross_list = cross_type.split(\"(\")\n",
+    "    if len(subsp_cross_list) > 1:\n",
+    "        subsp_cross = subsp_cross_list[1].split(\")\")[0]\n",
+    "    else:\n",
+    "        subsp_cross = cross_type\n",
+    "    \n",
+    "    # Ensure order of hybrid names is consistent so they get placed in same folders (using scripts/download_jiggins.py)\n",
+    "    # And so counts by taxonomic name aren't needlessly skewed\n",
+    "    if subsp_cross == \"malleti x plesseni\":\n",
+    "        subsp_cross = \"plesseni x malleti\"\n",
+    "    elif subsp_cross == \"lativitta x notabilis\":\n",
+    "        subsp_cross = \"notabilis x lativitta\"\n",
+    "    \n",
+    "    # Set taxa_name as with non cross types labeled to subspecies\n",
+    "    taxa_name = species + \" ssp. \" + subsp_cross\n",
+    "    return taxa_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "H.sp. ssp. malleti\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. plesseni\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. latRo x notabilis\n",
+      "H.sp. ssp. latRo x notabilis\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. notabilis x lativitta\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. cross hybrid\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. ssp. notabilis x lativitta\n",
+      "H.sp. ssp. notabilis x lativitta\n",
+      "H.sp. cross hybrid\n",
+      "H.sp. ssp. plesseni x malleti\n",
+      "H.sp. cross hybrid\n",
+      "H.sp. ssp. lativitta\n",
+      "H.sp. cross hybrid\n",
+      "H.sp. cross hybrid\n",
+      "H.sp. cross hybrid\n",
+      "H.sp. cross hybrid\n",
+      "H.sp. cross hybrid\n",
+      "H.sp. cross hybrid\n"
+     ]
+    }
+   ],
+   "source": [
+    "for cross_type in list(df[\"Cross_Type\"].dropna().unique()):\n",
+    "    print(get_cross_taxa_name(\"H.sp.\", cross_type))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Heliconius melpomene', 'Heliconius erato'], dtype=object)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df[\"Cross_Type\"].notna(), \"species\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Heliconius melpomene ssp. malleti',\n",
+       "       'Heliconius melpomene ssp. plesseni x malleti',\n",
+       "       'Heliconius melpomene ssp. plesseni',\n",
+       "       'Heliconius erato ssp. latRo x notabilis',\n",
+       "       'Heliconius erato ssp. notabilis x lativitta',\n",
+       "       'Heliconius erato cross hybrid',\n",
+       "       'Heliconius melpomene cross hybrid',\n",
+       "       'Heliconius erato ssp. lativitta'], dtype=object)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# https://stackoverflow.com/a/52854800\n",
+    "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",
+    "df.loc[df[\"Cross_Type\"].notna(), \"Taxonomic_Name\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "366"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Taxonomic_Name.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add Hybrid Status Column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hybrids = [taxa_name for taxa_name in list(df[\"Taxonomic_Name\"].dropna().unique()) if (\" x \" in taxa_name) or (\"hybrid\" in taxa_name)]\n",
+    "len(hybrids)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Heliconius erato ssp. venus x chestertonii',\n",
+       " 'Heliconius melpomene ssp. vulcanus x melpomene',\n",
+       " 'Heliconius erato ssp. chestertonii x venus',\n",
+       " 'Heliconius melpomene ssp. melpomene x thelxiope',\n",
+       " 'Heliconius melpomene ssp. melpomene x rosina',\n",
+       " 'Heliconius hybrid',\n",
+       " 'Heliconius erato ssp. hydara x petiverana',\n",
+       " 'Heliconius melpomene ssp. amaryllis x aglaope',\n",
+       " 'Anartia fatima x amathea',\n",
+       " 'Heliconius melpomene ssp. plesseni x malleti',\n",
+       " 'Heliconius erato ssp. notabilis x lativitta',\n",
+       " 'Heliconius erato ssp. latRo x notabilis',\n",
+       " 'Heliconius erato cross hybrid',\n",
+       " 'Heliconius melpomene cross hybrid',\n",
+       " 'Heliconius erato ssp. hydara x amalfreda',\n",
+       " 'Heliconius erato ssp. hydara x erato',\n",
+       " 'Heliconius melpomene ssp. meriana x melpomene',\n",
+       " 'Heliconius melpomene ssp. dignus x lativitta',\n",
+       " 'Heliconius melpomene ssp. malleti x bellula',\n",
+       " 'Heliconius melpomene ssp. malleti x vicina']"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hybrids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observe that we have one species-level hybrid: `Anartia fatima x amathea`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sp_hybrid_parents = [\"Anartia fatima\", \"Anartia amathea\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"hybrid_stat\"] = None\n",
+    "df.loc[df[\"subspecies\"].notna(), \"hybrid_stat\"] = \"non-hybrid\"\n",
+    "df.loc[df[\"species\"].isin(sp_hybrid_parents), \"hybrid_stat\"] = \"non-hybrid\"\n",
+    "\n",
+    "df.loc[df[\"Taxonomic_Name\"].isin(hybrids), \"hybrid_stat\"] = \"hybrid\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CAMID</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>View</th>\n",
+       "      <th>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "      <th>Sequence</th>\n",
+       "      <th>Taxonomic_Name</th>\n",
+       "      <th>Locality</th>\n",
+       "      <th>Sample_accession</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Stage</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Unit_Type</th>\n",
+       "      <th>file_type</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>file_url</th>\n",
+       "      <th>hybrid_stat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7326</th>\n",
+       "      <td>CAM002890</td>\n",
+       "      <td>8482</td>\n",
+       "      <td>CAM002890_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>CAM.coll.images.batch5.csv</td>\n",
+       "      <td>https://zenodo.org/record/2684906</td>\n",
+       "      <td>2,890</td>\n",
+       "      <td>Anartia fatima</td>\n",
+       "      <td>El Tirao,</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>wild</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>2684906</td>\n",
+       "      <td>Anartia fatima</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Anartia</td>\n",
+       "      <td>https://zenodo.org/record/2684906/files/CAM002...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7327</th>\n",
+       "      <td>CAM002890</td>\n",
+       "      <td>45376</td>\n",
+       "      <td>CAM002890_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>occurences_and_multimedia.csv</td>\n",
+       "      <td>https://zenodo.org/record/3477891</td>\n",
+       "      <td>2,890</td>\n",
+       "      <td>Anartia fatima</td>\n",
+       "      <td>El Tirao,</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>wild</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>3477891</td>\n",
+       "      <td>Anartia fatima</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Anartia</td>\n",
+       "      <td>https://zenodo.org/record/3477891/files/CAM002...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7328</th>\n",
+       "      <td>CAM002890</td>\n",
+       "      <td>45375</td>\n",
+       "      <td>CAM002890_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>occurences_and_multimedia.csv</td>\n",
+       "      <td>https://zenodo.org/record/3477891</td>\n",
+       "      <td>2,890</td>\n",
+       "      <td>Anartia fatima</td>\n",
+       "      <td>El Tirao,</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>wild</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>3477891</td>\n",
+       "      <td>Anartia fatima</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Anartia</td>\n",
+       "      <td>https://zenodo.org/record/3477891/files/CAM002...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7329</th>\n",
+       "      <td>CAM002890</td>\n",
+       "      <td>8481</td>\n",
+       "      <td>CAM002890_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>CAM.coll.images.batch5.csv</td>\n",
+       "      <td>https://zenodo.org/record/2684906</td>\n",
+       "      <td>2,890</td>\n",
+       "      <td>Anartia fatima</td>\n",
+       "      <td>El Tirao,</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>wild</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>2684906</td>\n",
+       "      <td>Anartia fatima</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Anartia</td>\n",
+       "      <td>https://zenodo.org/record/2684906/files/CAM002...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          CAMID      X       Image_name     View  \\\n",
+       "7326  CAM002890   8482  CAM002890_v.JPG  ventral   \n",
+       "7327  CAM002890  45376  CAM002890_v.JPG  ventral   \n",
+       "7328  CAM002890  45375  CAM002890_d.JPG   dorsal   \n",
+       "7329  CAM002890   8481  CAM002890_d.JPG   dorsal   \n",
+       "\n",
+       "                        zenodo_name                        zenodo_link  \\\n",
+       "7326     CAM.coll.images.batch5.csv  https://zenodo.org/record/2684906   \n",
+       "7327  occurences_and_multimedia.csv  https://zenodo.org/record/3477891   \n",
+       "7328  occurences_and_multimedia.csv  https://zenodo.org/record/3477891   \n",
+       "7329     CAM.coll.images.batch5.csv  https://zenodo.org/record/2684906   \n",
+       "\n",
+       "     Sequence  Taxonomic_Name   Locality Sample_accession  ... Stage     Sex  \\\n",
+       "7326    2,890  Anartia fatima  El Tirao,              NaN  ...   NaN  Female   \n",
+       "7327    2,890  Anartia fatima  El Tirao,              NaN  ...   NaN  Female   \n",
+       "7328    2,890  Anartia fatima  El Tirao,              NaN  ...   NaN  Female   \n",
+       "7329    2,890  Anartia fatima  El Tirao,              NaN  ...   NaN  Female   \n",
+       "\n",
+       "     Unit_Type file_type record_number         species subspecies    genus  \\\n",
+       "7326      wild       jpg       2684906  Anartia fatima        NaN  Anartia   \n",
+       "7327      wild       jpg       3477891  Anartia fatima        NaN  Anartia   \n",
+       "7328      wild       jpg       3477891  Anartia fatima        NaN  Anartia   \n",
+       "7329      wild       jpg       2684906  Anartia fatima        NaN  Anartia   \n",
+       "\n",
+       "                                               file_url hybrid_stat  \n",
+       "7326  https://zenodo.org/record/2684906/files/CAM002...  non-hybrid  \n",
+       "7327  https://zenodo.org/record/3477891/files/CAM002...  non-hybrid  \n",
+       "7328  https://zenodo.org/record/3477891/files/CAM002...  non-hybrid  \n",
+       "7329  https://zenodo.org/record/2684906/files/CAM002...  non-hybrid  \n",
+       "\n",
+       "[4 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df[\"species\"].isin(sp_hybrid_parents)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CAMID</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>View</th>\n",
+       "      <th>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "      <th>Sequence</th>\n",
+       "      <th>Taxonomic_Name</th>\n",
+       "      <th>Locality</th>\n",
+       "      <th>Sample_accession</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Stage</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Unit_Type</th>\n",
+       "      <th>file_type</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>file_url</th>\n",
+       "      <th>hybrid_stat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>0 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "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",
+       "Index: []\n",
+       "\n",
+       "[0 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df[\"species\"].str.lower() == \"amathea\"] #not represented in the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Taxonomic_Name\n",
+       "Anartia fatima x amathea    60\n",
+       "Anartia fatima               4\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df[\"genus\"] == \"Anartia\", \"Taxonomic_Name\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CAMID</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>View</th>\n",
+       "      <th>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "      <th>Sequence</th>\n",
+       "      <th>Taxonomic_Name</th>\n",
+       "      <th>Locality</th>\n",
+       "      <th>Sample_accession</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Stage</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Unit_Type</th>\n",
+       "      <th>file_type</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>file_url</th>\n",
+       "      <th>hybrid_stat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>40498</th>\n",
+       "      <td>CAM043842</td>\n",
+       "      <td>32098</td>\n",
+       "      <td>CAM043842_hwv.JPG</td>\n",
+       "      <td>hindwing ventral</td>\n",
+       "      <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/4287444</td>\n",
+       "      <td>43,842</td>\n",
+       "      <td>Catoblepia berecynthia</td>\n",
+       "      <td>B1prim3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>4287444</td>\n",
+       "      <td>Catoblepia berecynthia</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Catoblepia</td>\n",
+       "      <td>https://zenodo.org/record/4287444/files/CAM043...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9192</th>\n",
+       "      <td>CAM008567</td>\n",
+       "      <td>46249</td>\n",
+       "      <td>CAM008567_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>occurences_and_multimedia.csv</td>\n",
+       "      <td>https://zenodo.org/record/3477891</td>\n",
+       "      <td>8,567</td>\n",
+       "      <td>Heliconius demeter</td>\n",
+       "      <td>Tarapoto-Yurimaguas (Km15), Tunel Cumbre, Fond...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>wild</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>3477891</td>\n",
+       "      <td>Heliconius demeter</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/3477891/files/CAM008...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14127</th>\n",
+       "      <td>CAM010971</td>\n",
+       "      <td>27577</td>\n",
+       "      <td>CAM010971_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>2001_2.broods.batch.1.csv</td>\n",
+       "      <td>https://zenodo.org/record/2549524</td>\n",
+       "      <td>10,971</td>\n",
+       "      <td>Heliconius sp.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>reared</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>2549524</td>\n",
+       "      <td>Heliconius sp.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/2549524/files/CAM010...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5315</th>\n",
+       "      <td>CAM000537</td>\n",
+       "      <td>6676</td>\n",
+       "      <td>CAM000537_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>CAM.coll.images.batch3.csv</td>\n",
+       "      <td>https://zenodo.org/record/2682458</td>\n",
+       "      <td>537</td>\n",
+       "      <td>Heliconius melpomene ssp. rosina</td>\n",
+       "      <td>Pipeline road 4km,</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>wild</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>2682458</td>\n",
+       "      <td>Heliconius melpomene</td>\n",
+       "      <td>rosina</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/2682458/files/CAM000...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>585</th>\n",
+       "      <td>19N0204</td>\n",
+       "      <td>19553</td>\n",
+       "      <td>19N0204_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>0.sheffield.ps.nn.ikiam.batch1.csv</td>\n",
+       "      <td>https://zenodo.org/record/4288311</td>\n",
+       "      <td>204</td>\n",
+       "      <td>Heliconius erato ssp. lativitta</td>\n",
+       "      <td>Ikiam Mariposario</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>reared</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>4288311</td>\n",
+       "      <td>Heliconius erato</td>\n",
+       "      <td>lativitta</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/4288311/files/19N020...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38875</th>\n",
+       "      <td>CAM043366</td>\n",
+       "      <td>30469</td>\n",
+       "      <td>CAM043366_v.CR2</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>batch1.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/3569598</td>\n",
+       "      <td>43,366</td>\n",
+       "      <td>Eunica pusilla</td>\n",
+       "      <td>B6rec3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>3569598</td>\n",
+       "      <td>Eunica pusilla</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Eunica</td>\n",
+       "      <td>https://zenodo.org/record/3569598/files/CAM043...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19226</th>\n",
+       "      <td>CAM016687</td>\n",
+       "      <td>18917</td>\n",
+       "      <td>CAM016687_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>CAM.coll.PS.list.individuals.haplotagging.new....</td>\n",
+       "      <td>https://zenodo.org/record/4153502</td>\n",
+       "      <td>16,687</td>\n",
+       "      <td>Heliconius melpomene ssp. plesseni</td>\n",
+       "      <td>El Topo, Baños - Puyo road,</td>\n",
+       "      <td>SRS7540355</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>wild</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>4153502</td>\n",
+       "      <td>Heliconius melpomene</td>\n",
+       "      <td>plesseni</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/4153502/files/CAM016...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X         Image_name              View  \\\n",
+       "40498  CAM043842  32098  CAM043842_hwv.JPG  hindwing ventral   \n",
+       "9192   CAM008567  46249    CAM008567_v.JPG           ventral   \n",
+       "14127  CAM010971  27577    CAM010971_d.CR2            dorsal   \n",
+       "5315   CAM000537   6676    CAM000537_d.JPG            dorsal   \n",
+       "585      19N0204  19553      19N0204_d.JPG            dorsal   \n",
+       "38875  CAM043366  30469    CAM043366_v.CR2           ventral   \n",
+       "19226  CAM016687  18917    CAM016687_d.CR2            dorsal   \n",
+       "\n",
+       "                                             zenodo_name  \\\n",
+       "40498                 batch2.Peru.image.names.Zenodo.csv   \n",
+       "9192                       occurences_and_multimedia.csv   \n",
+       "14127                          2001_2.broods.batch.1.csv   \n",
+       "5315                          CAM.coll.images.batch3.csv   \n",
+       "585                   0.sheffield.ps.nn.ikiam.batch1.csv   \n",
+       "38875                 batch1.Peru.image.names.Zenodo.csv   \n",
+       "19226  CAM.coll.PS.list.individuals.haplotagging.new....   \n",
+       "\n",
+       "                             zenodo_link Sequence  \\\n",
+       "40498  https://zenodo.org/record/4287444   43,842   \n",
+       "9192   https://zenodo.org/record/3477891    8,567   \n",
+       "14127  https://zenodo.org/record/2549524   10,971   \n",
+       "5315   https://zenodo.org/record/2682458      537   \n",
+       "585    https://zenodo.org/record/4288311      204   \n",
+       "38875  https://zenodo.org/record/3569598   43,366   \n",
+       "19226  https://zenodo.org/record/4153502   16,687   \n",
+       "\n",
+       "                           Taxonomic_Name  \\\n",
+       "40498              Catoblepia berecynthia   \n",
+       "9192                   Heliconius demeter   \n",
+       "14127                      Heliconius sp.   \n",
+       "5315     Heliconius melpomene ssp. rosina   \n",
+       "585       Heliconius erato ssp. lativitta   \n",
+       "38875                      Eunica pusilla   \n",
+       "19226  Heliconius melpomene ssp. plesseni   \n",
+       "\n",
+       "                                                Locality Sample_accession  \\\n",
+       "40498                                            B1prim3              NaN   \n",
+       "9192   Tarapoto-Yurimaguas (Km15), Tunel Cumbre, Fond...              NaN   \n",
+       "14127                                                NaN              NaN   \n",
+       "5315                                  Pipeline road 4km,              NaN   \n",
+       "585                                    Ikiam Mariposario              NaN   \n",
+       "38875                                             B6rec3              NaN   \n",
+       "19226                        El Topo, Baños - Puyo road,       SRS7540355   \n",
+       "\n",
+       "       ... Stage   Sex Unit_Type file_type record_number  \\\n",
+       "40498  ...   NaN   NaN       NaN       jpg       4287444   \n",
+       "9192   ...   NaN  Male      wild       jpg       3477891   \n",
+       "14127  ...   NaN  Male    reared       raw       2549524   \n",
+       "5315   ...   NaN  Male      wild       jpg       2682458   \n",
+       "585    ...   NaN  Male    reared       jpg       4288311   \n",
+       "38875  ...   NaN   NaN       NaN       raw       3569598   \n",
+       "19226  ...   NaN  Male      wild       raw       4153502   \n",
+       "\n",
+       "                      species subspecies       genus  \\\n",
+       "40498  Catoblepia berecynthia        NaN  Catoblepia   \n",
+       "9192       Heliconius demeter        NaN  Heliconius   \n",
+       "14127          Heliconius sp.        NaN  Heliconius   \n",
+       "5315     Heliconius melpomene     rosina  Heliconius   \n",
+       "585          Heliconius erato  lativitta  Heliconius   \n",
+       "38875          Eunica pusilla        NaN      Eunica   \n",
+       "19226    Heliconius melpomene   plesseni  Heliconius   \n",
+       "\n",
+       "                                                file_url hybrid_stat  \n",
+       "40498  https://zenodo.org/record/4287444/files/CAM043...        None  \n",
+       "9192   https://zenodo.org/record/3477891/files/CAM008...        None  \n",
+       "14127  https://zenodo.org/record/2549524/files/CAM010...        None  \n",
+       "5315   https://zenodo.org/record/2682458/files/CAM000...  non-hybrid  \n",
+       "585    https://zenodo.org/record/4288311/files/19N020...  non-hybrid  \n",
+       "38875  https://zenodo.org/record/3569598/files/CAM043...        None  \n",
+       "19226  https://zenodo.org/record/4153502/files/CAM016...  non-hybrid  \n",
+       "\n",
+       "[7 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sample(7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Investigate entries with no Taxonomic Information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 3886 entries, 0 to 49350\n",
+      "Data columns (total 28 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             3886 non-null   object\n",
+      " 1   X                 3886 non-null   int64 \n",
+      " 2   Image_name        3886 non-null   object\n",
+      " 3   View              3594 non-null   object\n",
+      " 4   zenodo_name       3886 non-null   object\n",
+      " 5   zenodo_link       3886 non-null   object\n",
+      " 6   Sequence          2951 non-null   object\n",
+      " 7   Taxonomic_Name    0 non-null      object\n",
+      " 8   Locality          70 non-null     object\n",
+      " 9   Sample_accession  24 non-null     object\n",
+      " 10  Collected_by      0 non-null      object\n",
+      " 11  Other_ID          30 non-null     object\n",
+      " 12  Date              70 non-null     object\n",
+      " 13  Dataset           461 non-null    object\n",
+      " 14  Store             345 non-null    object\n",
+      " 15  Brood             5 non-null      object\n",
+      " 16  Death_Date        2 non-null      object\n",
+      " 17  Cross_Type        0 non-null      object\n",
+      " 18  Stage             9 non-null      object\n",
+      " 19  Sex               35 non-null     object\n",
+      " 20  Unit_Type         47 non-null     object\n",
+      " 21  file_type         3886 non-null   object\n",
+      " 22  record_number     3886 non-null   int64 \n",
+      " 23  species           0 non-null      object\n",
+      " 24  subspecies        0 non-null      object\n",
+      " 25  genus             0 non-null      object\n",
+      " 26  file_url          3886 non-null   object\n",
+      " 27  hybrid_stat       0 non-null      object\n",
+      "dtypes: int64(2), object(26)\n",
+      "memory usage: 880.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.loc[df.Taxonomic_Name.isna()].info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wow, there are a surprising number of images with no taxonomic information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "no_taxa_cams = list(df.loc[df[\"Taxonomic_Name\"].isna(), \"CAMID\"].unique())\n",
+    "no_taxa_entries = list(df.loc[df[\"Taxonomic_Name\"].isna(), \"X\"])\n",
+    "\n",
+    "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",
+    "len(no_taxa_cam_match)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "21\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "zenodo_link\n",
+       "https://zenodo.org/record/5526257    1324\n",
+       "https://zenodo.org/record/2554218     492\n",
+       "https://zenodo.org/record/4288311     486\n",
+       "https://zenodo.org/record/2555086     440\n",
+       "https://zenodo.org/record/5731587     388\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(df.loc[df[\"X\"].isin(no_taxa_entries), \"record_number\"].nunique())\n",
+    "df.loc[df[\"X\"].isin(no_taxa_entries), \"zenodo_link\"].value_counts()[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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",
+    "\n",
+    "The first appear to be all _Heliconius erato ssp. cyrbia_, so those could be realigned.\n",
+    "\n",
+    "The second indicates all _Heliconius erato_.\n",
+    "\n",
+    "The third is a mix, so not resolvable without an expert.\n",
+    "\n",
+    "It seems record 2555086 is all bred specimens of _Heliconius erato demophoon_.\n",
+    "\n",
+    "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)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.series.Series'>\n",
+      "Index: 1324 entries, 45868 to 47191\n",
+      "Series name: Taxonomic_Name\n",
+      "Non-Null Count  Dtype \n",
+      "--------------  ----- \n",
+      "0 non-null      object\n",
+      "dtypes: object(1)\n",
+      "memory usage: 20.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.loc[df[\"record_number\"] == 5526257, \"Taxonomic_Name\"].info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ah, so these are all null, but all are of the same subspecies, so they can be labeled."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pot_single_taxa_records = []\n",
+    "for record_num in (df.loc[df[\"X\"].isin(no_taxa_entries), \"record_number\"].unique()):\n",
+    "    temp = df.loc[df[\"record_number\"] == record_num]\n",
+    "    if temp.loc[temp[\"Taxonomic_Name\"].notna()].shape[0] == 0:\n",
+    "        pot_single_taxa_records.append(record_num)\n",
+    "\n",
+    "len(pot_single_taxa_records)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[5731587, 5526257, 2554218, 2555086]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pot_single_taxa_records"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CAMID</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>View</th>\n",
+       "      <th>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "      <th>Sequence</th>\n",
+       "      <th>Taxonomic_Name</th>\n",
+       "      <th>Locality</th>\n",
+       "      <th>Sample_accession</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Stage</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Unit_Type</th>\n",
+       "      <th>file_type</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>file_url</th>\n",
+       "      <th>hybrid_stat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>46747</th>\n",
+       "      <td>CAM045219</td>\n",
+       "      <td>43439</td>\n",
+       "      <td>CAM045219_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>image.names.cook.island.erato.csv</td>\n",
+       "      <td>https://zenodo.org/record/5526257</td>\n",
+       "      <td>45,219</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>5526257</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/5526257/files/CAM045...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47062</th>\n",
+       "      <td>CAM045298</td>\n",
+       "      <td>43756</td>\n",
+       "      <td>CAM045298_v.CR2</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>image.names.cook.island.erato.csv</td>\n",
+       "      <td>https://zenodo.org/record/5526257</td>\n",
+       "      <td>45,298</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>5526257</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/5526257/files/CAM045...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37255</th>\n",
+       "      <td>CAM042050</td>\n",
+       "      <td>43982</td>\n",
+       "      <td>CAM042050_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Collection_August2019.csv</td>\n",
+       "      <td>https://zenodo.org/record/5731587</td>\n",
+       "      <td>42,050</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>5731587</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/5731587/files/CAM042...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46992</th>\n",
+       "      <td>CAM045281</td>\n",
+       "      <td>43687</td>\n",
+       "      <td>CAM045281_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>image.names.cook.island.erato.csv</td>\n",
+       "      <td>https://zenodo.org/record/5526257</td>\n",
+       "      <td>45,281</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>5526257</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/5526257/files/CAM045...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49106</th>\n",
+       "      <td>F901</td>\n",
+       "      <td>38167</td>\n",
+       "      <td>F901_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Anniina.Mattila.Bred.F.csv</td>\n",
+       "      <td>https://zenodo.org/record/2555086</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>2555086</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2555086/files/F901_d...</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X       Image_name     View  \\\n",
+       "46747  CAM045219  43439  CAM045219_d.JPG   dorsal   \n",
+       "47062  CAM045298  43756  CAM045298_v.CR2  ventral   \n",
+       "37255  CAM042050  43982  CAM042050_d.JPG   dorsal   \n",
+       "46992  CAM045281  43687  CAM045281_d.JPG   dorsal   \n",
+       "49106       F901  38167       F901_d.CR2   dorsal   \n",
+       "\n",
+       "                             zenodo_name                        zenodo_link  \\\n",
+       "46747  image.names.cook.island.erato.csv  https://zenodo.org/record/5526257   \n",
+       "47062  image.names.cook.island.erato.csv  https://zenodo.org/record/5526257   \n",
+       "37255          Collection_August2019.csv  https://zenodo.org/record/5731587   \n",
+       "46992  image.names.cook.island.erato.csv  https://zenodo.org/record/5526257   \n",
+       "49106         Anniina.Mattila.Bred.F.csv  https://zenodo.org/record/2555086   \n",
+       "\n",
+       "      Sequence Taxonomic_Name Locality Sample_accession  ... Stage  Sex  \\\n",
+       "46747   45,219            NaN      NaN              NaN  ...   NaN  NaN   \n",
+       "47062   45,298            NaN      NaN              NaN  ...   NaN  NaN   \n",
+       "37255   42,050            NaN      NaN              NaN  ...   NaN  NaN   \n",
+       "46992   45,281            NaN      NaN              NaN  ...   NaN  NaN   \n",
+       "49106      NaN            NaN      NaN              NaN  ...   NaN  NaN   \n",
+       "\n",
+       "      Unit_Type file_type record_number species subspecies genus  \\\n",
+       "46747       NaN       jpg       5526257     NaN        NaN   NaN   \n",
+       "47062       NaN       raw       5526257     NaN        NaN   NaN   \n",
+       "37255       NaN       jpg       5731587     NaN        NaN   NaN   \n",
+       "46992       NaN       jpg       5526257     NaN        NaN   NaN   \n",
+       "49106       NaN       raw       2555086     NaN        NaN   NaN   \n",
+       "\n",
+       "                                                file_url hybrid_stat  \n",
+       "46747  https://zenodo.org/record/5526257/files/CAM045...        None  \n",
+       "47062  https://zenodo.org/record/5526257/files/CAM045...        None  \n",
+       "37255  https://zenodo.org/record/5731587/files/CAM042...        None  \n",
+       "46992  https://zenodo.org/record/5526257/files/CAM045...        None  \n",
+       "49106  https://zenodo.org/record/2555086/files/F901_d...        None  \n",
+       "\n",
+       "[5 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df.record_number.isin(pot_single_taxa_records)].sample(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Christopher agrees that the following is sufficient to label images from the following three records:\n",
+    ">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",
+    "\n",
+    "`Unit_Type` and some other details could potentially be realigned at a later date, but we'll stick with taxonomic information for now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[df[\"record_number\"] == 5526257, \"Taxonomic_Name\"] = \"Heliconius erato ssp. cyrbia\"\n",
+    "df.loc[df[\"record_number\"] == 5526257, \"genus\"] = \"Heliconius\"\n",
+    "df.loc[df[\"record_number\"] == 5526257, \"species\"] = \"Heliconius erato\"\n",
+    "df.loc[df[\"record_number\"] == 5526257, \"subspecies\"] = \"cyrbia\"\n",
+    "df.loc[df[\"record_number\"] == 5526257, \"hybrid_stat\"] = \"non-hybrid\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[df[\"record_number\"] == 2554218, \"Taxonomic_Name\"] = \"Heliconius erato\"\n",
+    "df.loc[df[\"record_number\"] == 2554218, \"genus\"] = \"Heliconius\"\n",
+    "df.loc[df[\"record_number\"] == 2554218, \"species\"] = \"Heliconius erato\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[df[\"record_number\"] == 2555086, \"Taxonomic_Name\"] = \"Heliconius erato ssp. demophoon\"\n",
+    "df.loc[df[\"record_number\"] == 2555086, \"genus\"] = \"Heliconius\"\n",
+    "df.loc[df[\"record_number\"] == 2555086, \"species\"] = \"Heliconius erato\"\n",
+    "df.loc[df[\"record_number\"] == 2555086, \"subspecies\"] = \"demophoon\"\n",
+    "df.loc[df[\"record_number\"] == 2555086, \"hybrid_stat\"] = \"non-hybrid\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save record of entries with no Taxonomic Info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1630, 28)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_taxa_df = df.loc[df.Taxonomic_Name.isna()]\n",
+    "missing_taxa_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "missing_taxa_df.to_csv(\"../metadata/Missing_taxa_Jiggins_Zenodo_Master.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Drop Entries with no Taxonomic Information"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "master_df = df.loc[df.Taxonomic_Name.notna()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Final stats for all data in master file summarized here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID               11991\n",
+       "X                   44809\n",
+       "Image_name          36281\n",
+       "View                    7\n",
+       "zenodo_name            33\n",
+       "zenodo_link            30\n",
+       "Sequence            10905\n",
+       "Taxonomic_Name        366\n",
+       "Locality              645\n",
+       "Sample_accession     1559\n",
+       "Collected_by           12\n",
+       "Other_ID             3081\n",
+       "Date                  807\n",
+       "Dataset                 8\n",
+       "Store                 137\n",
+       "Brood                 224\n",
+       "Death_Date             81\n",
+       "Cross_Type             30\n",
+       "Stage                   1\n",
+       "Sex                     3\n",
+       "Unit_Type               4\n",
+       "file_type               3\n",
+       "record_number          30\n",
+       "species               246\n",
+       "subspecies            155\n",
+       "genus                  94\n",
+       "file_url            44794\n",
+       "hybrid_stat             2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "master_df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 44809 entries, 6 to 49358\n",
+      "Data columns (total 28 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             44809 non-null  object\n",
+      " 1   X                 44809 non-null  int64 \n",
+      " 2   Image_name        44809 non-null  object\n",
+      " 3   View              44030 non-null  object\n",
+      " 4   zenodo_name       44809 non-null  object\n",
+      " 5   zenodo_link       44809 non-null  object\n",
+      " 6   Sequence          43877 non-null  object\n",
+      " 7   Taxonomic_Name    44809 non-null  object\n",
+      " 8   Locality          31708 non-null  object\n",
+      " 9   Sample_accession  4572 non-null   object\n",
+      " 10  Collected_by      3043 non-null   object\n",
+      " 11  Other_ID          14352 non-null  object\n",
+      " 12  Date              30730 non-null  object\n",
+      " 13  Dataset           37024 non-null  object\n",
+      " 14  Store             36220 non-null  object\n",
+      " 15  Brood             14258 non-null  object\n",
+      " 16  Death_Date        316 non-null    object\n",
+      " 17  Cross_Type        4452 non-null   object\n",
+      " 18  Stage             6 non-null      object\n",
+      " 19  Sex               33312 non-null  object\n",
+      " 20  Unit_Type         30923 non-null  object\n",
+      " 21  file_type         44809 non-null  object\n",
+      " 22  record_number     44809 non-null  int64 \n",
+      " 23  species           44809 non-null  object\n",
+      " 24  subspecies        24559 non-null  object\n",
+      " 25  genus             44809 non-null  object\n",
+      " 26  file_url          44809 non-null  object\n",
+      " 27  hybrid_stat       25139 non-null  object\n",
+      "dtypes: int64(2), object(26)\n",
+      "memory usage: 9.9+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "master_df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Update Master File with Hybrid Status and URL Columns (& unique records)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "master_df.to_csv(\"../Jiggins_Zenodo_Img_Master.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Make Heliconius Subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 34265 entries, 6 to 49358\n",
+      "Data columns (total 28 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             34265 non-null  object\n",
+      " 1   X                 34265 non-null  int64 \n",
+      " 2   Image_name        34265 non-null  object\n",
+      " 3   View              33486 non-null  object\n",
+      " 4   zenodo_name       34265 non-null  object\n",
+      " 5   zenodo_link       34265 non-null  object\n",
+      " 6   Sequence          33333 non-null  object\n",
+      " 7   Taxonomic_Name    34265 non-null  object\n",
+      " 8   Locality          21180 non-null  object\n",
+      " 9   Sample_accession  4572 non-null   object\n",
+      " 10  Collected_by      3043 non-null   object\n",
+      " 11  Other_ID          6404 non-null   object\n",
+      " 12  Date              20244 non-null  object\n",
+      " 13  Dataset           29926 non-null  object\n",
+      " 14  Store             26526 non-null  object\n",
+      " 15  Brood             14242 non-null  object\n",
+      " 16  Death_Date        316 non-null    object\n",
+      " 17  Cross_Type        4452 non-null   object\n",
+      " 18  Stage             6 non-null      object\n",
+      " 19  Sex               30984 non-null  object\n",
+      " 20  Unit_Type         29055 non-null  object\n",
+      " 21  file_type         34265 non-null  object\n",
+      " 22  record_number     34265 non-null  int64 \n",
+      " 23  species           34265 non-null  object\n",
+      " 24  subspecies        23801 non-null  object\n",
+      " 25  genus             34265 non-null  object\n",
+      " 26  file_url          34265 non-null  object\n",
+      " 27  hybrid_stat       24317 non-null  object\n",
+      "dtypes: int64(2), object(26)\n",
+      "memory usage: 7.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "heliconius_subset = master_df.loc[master_df.genus.str.lower() == \"heliconius\"]\n",
+    "\n",
+    "heliconius_subset.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID               10109\n",
+       "X                   34265\n",
+       "Image_name          29192\n",
+       "View                    3\n",
+       "zenodo_name            33\n",
+       "zenodo_link            30\n",
+       "Sequence             9031\n",
+       "Taxonomic_Name        132\n",
+       "Locality              472\n",
+       "Sample_accession     1559\n",
+       "Collected_by           12\n",
+       "Other_ID             1865\n",
+       "Date                  776\n",
+       "Dataset                 8\n",
+       "Store                 121\n",
+       "Brood                 224\n",
+       "Death_Date             81\n",
+       "Cross_Type             30\n",
+       "Stage                   1\n",
+       "Sex                     3\n",
+       "Unit_Type               4\n",
+       "file_type               3\n",
+       "record_number          30\n",
+       "species                37\n",
+       "subspecies            110\n",
+       "genus                   1\n",
+       "file_url            34250\n",
+       "hybrid_stat             2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "heliconius_subset.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "View\n",
+       "dorsal                16882\n",
+       "ventral               16586\n",
+       "dorsal and ventral       18\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "heliconius_subset.View.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save the Heliconius Subset to CSV\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heliconius_subset.to_csv(\"../Jiggins_Heliconius_Master.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Make Dorsal Subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 22175 entries, 7 to 49357\n",
+      "Data columns (total 28 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             22175 non-null  object\n",
+      " 1   X                 22175 non-null  int64 \n",
+      " 2   Image_name        22175 non-null  object\n",
+      " 3   View              22175 non-null  object\n",
+      " 4   zenodo_name       22175 non-null  object\n",
+      " 5   zenodo_link       22175 non-null  object\n",
+      " 6   Sequence          21709 non-null  object\n",
+      " 7   Taxonomic_Name    22175 non-null  object\n",
+      " 8   Locality          15615 non-null  object\n",
+      " 9   Sample_accession  2294 non-null   object\n",
+      " 10  Collected_by      1533 non-null   object\n",
+      " 11  Other_ID          6916 non-null   object\n",
+      " 12  Date              15347 non-null  object\n",
+      " 13  Dataset           18250 non-null  object\n",
+      " 14  Store             18254 non-null  object\n",
+      " 15  Brood             6920 non-null   object\n",
+      " 16  Death_Date        106 non-null    object\n",
+      " 17  Cross_Type        2230 non-null   object\n",
+      " 18  Stage             3 non-null      object\n",
+      " 19  Sex               16403 non-null  object\n",
+      " 20  Unit_Type         15203 non-null  object\n",
+      " 21  file_type         22175 non-null  object\n",
+      " 22  record_number     22175 non-null  int64 \n",
+      " 23  species           22175 non-null  object\n",
+      " 24  subspecies        12040 non-null  object\n",
+      " 25  genus             22175 non-null  object\n",
+      " 26  file_url          22175 non-null  object\n",
+      " 27  hybrid_stat       12330 non-null  object\n",
+      "dtypes: int64(2), object(26)\n",
+      "memory usage: 4.9+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "dorsal_views = [view for view in list(master_df.View.dropna().unique()) if \"dorsal\" in view]\n",
+    "\n",
+    "dorsal_subset = master_df.loc[master_df[\"View\"].isin(dorsal_views)]\n",
+    "dorsal_subset.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID               11776\n",
+       "X                   22175\n",
+       "Image_name          17907\n",
+       "View                    4\n",
+       "zenodo_name            33\n",
+       "zenodo_link            30\n",
+       "Sequence            10713\n",
+       "Taxonomic_Name        362\n",
+       "Locality              642\n",
+       "Sample_accession     1552\n",
+       "Collected_by           12\n",
+       "Other_ID             2890\n",
+       "Date                  791\n",
+       "Dataset                 8\n",
+       "Store                 137\n",
+       "Brood                 215\n",
+       "Death_Date             63\n",
+       "Cross_Type             30\n",
+       "Stage                   1\n",
+       "Sex                     3\n",
+       "Unit_Type               4\n",
+       "file_type               3\n",
+       "record_number          30\n",
+       "species               245\n",
+       "subspecies            152\n",
+       "genus                  94\n",
+       "file_url            22168\n",
+       "hybrid_stat             2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_subset.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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",
+    "\n",
+    "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`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_21745/2272441064.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  dorsal_subset[\"CAM_Dupe\"] = dorsal_subset.duplicated(subset = \"CAMID\", keep = False)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "CAM_Dupe\n",
+       "True           17213\n",
+       "False           4170\n",
+       "single-wing      792\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_subset[\"CAM_Dupe\"] = dorsal_subset.duplicated(subset = \"CAMID\", keep = False)\n",
+    "dorsal_subset.loc[dorsal_subset[\"View\"].isin([\"forewing dorsal\", \"hindwing dorsal\"]), \"CAM_Dupe\"] = \"single-wing\"\n",
+    "dorsal_subset[\"CAM_Dupe\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "29"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_subset.loc[dorsal_subset[\"CAM_Dupe\"] == True, \"record_number\"].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "record_number\n",
+       "4287444    396\n",
+       "4288250    284\n",
+       "3569598    112\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_subset.loc[dorsal_subset[\"CAM_Dupe\"] == \"single-wing\", \"record_number\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Single-wing images are constrained to 3 records."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "file_type\n",
+       "jpg    11752\n",
+       "raw     5440\n",
+       "tif       21\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_subset.loc[dorsal_subset[\"CAM_Dupe\"] == True, \"file_type\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some of this duplication is by file type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True     8415\n",
+       "False    3337\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_subset.loc[(dorsal_subset[\"CAM_Dupe\"] == True) & (dorsal_subset[\"file_type\"] == \"jpg\")].duplicated(subset = \"CAMID\", keep = False).value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False    4106\n",
+       "True     1334\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_subset.loc[(dorsal_subset[\"CAM_Dupe\"] == True) & (dorsal_subset[\"file_type\"] == \"raw\")].duplicated(subset = \"CAMID\", keep = False).value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "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."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save Dorsal Subset to CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dorsal_subset.to_csv(\"../Jiggins_Zenodo_dorsal_Img_Master.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "std",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

scripts/checksum.py

@@ -0,0 +1,32 @@
+import argparse
+import os
+import hashlib
+import csv
+from tqdm import tqdm
+
+def md5_checksum(file_path):
+    hash_md5 = hashlib.md5()
+    with open(file_path, "rb") as f:
+        for chunk in iter(lambda: f.read(4096), b""):
+            hash_md5.update(chunk)
+    return hash_md5.hexdigest()
+
+def get_checksums(input_directory, output_filepath):
+    with open(output_filepath, 'w', newline='') as csvfile:
+        writer = csv.writer(csvfile)
+        writer.writerow(["filepath", "filename", "md5"])
+        for root, dirs, files in os.walk(input_directory):
+            n_files = len(files)
+            for name in tqdm(files, total=n_files, desc="MD5ing"):
+                file_path = os.path.join(root, name)
+                checksum = md5_checksum(file_path)
+                writer.writerow([file_path, name, checksum])
+        print(f"Checksums written to {output_filepath}")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Generate MD5 checksums for files in a directory")
+    parser.add_argument("--input-directory", required=True, help="Directory to traverse for files")
+    parser.add_argument("--output-filepath", required=True, help="Filepath for the output CSV file")
+    args = parser.parse_args()
+    get_checksums(args.input_directory, args.output_filepath)
+

scripts/download_jiggins_subset.py

@@ -0,0 +1,98 @@
+# Modified code from https://huggingface.co/datasets/imageomics/Comparison-Subset-Jiggins/blob/main/scripts/download_jiggins_subset.py
+# For downloading Jiggins images from any of the master CSV files
+# Generates Checksum file for all images download
+# logs image download in json file
+
+import requests
+import shutil
+import json
+
+import pandas as pd
+from checksum import get_checksums
+
+from tqdm import tqdm
+import os
+import argparse
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--csv", required=True, help="Path to CSV file with urls.", nargs="?")
+    parser.add_argument("--output", required=True, help="Main directory to download images into.", nargs="?")
+
+    return parser.parse_args()
+
+
+def update_log(log_data, index, image, url, response_code):
+    # log status
+    log_entry = {}
+    log_entry["Image"] = image
+    log_entry["zenodo_link"] = url
+    log_entry["Response_status"] = response_code
+    log_data[index] = log_entry
+
+    return log_data
+
+
+def download_images(csv_path, image_folder, log_filepath):
+    #load csv 
+    jiggins_data = pd.read_csv(csv_path)
+    log_data = {}
+
+    for i in tqdm(range(0, len(jiggins_data))) : 
+        species = jiggins_data["Taxonomic_Name"][i]
+        image_name = jiggins_data["X"][i].astype(str) + "_" + jiggins_data["Image_name"][i]
+
+        #download the image from url is not already downloaded
+        if os.path.exists(f"{image_folder}/{species}/{image_name}") != True:
+            #get image from url
+            url = jiggins_data["zenodo_link"][i]
+            response = requests.get(url, stream=True)
+
+            # log status
+            log_data = update_log(log_data,
+                                  index = i,
+                                  image = species + "/" + image_name,
+                                  url = url,
+                                  response_code = response.status_code
+                                  )
+            
+            #create the species appropriate folder if necessary
+            if os.path.exists(f"{image_folder}/{species}") != True:
+                os.makedirs(f"{image_folder}/{species}", exist_ok=False)
+
+            #download the image
+            if response.status_code == 200:
+                with open(f"{image_folder}/{species}/{image_name}", "wb") as out_file:
+                    shutil.copyfileobj(response.raw, out_file)
+            del response
+    
+    with open(log_filepath, "w") as log_file:
+        json.dump(log_data, log_file, indent = 4)
+
+    return
+
+def main():
+
+    #get arguments from commandline
+    args = parse_args() 
+    csv_path = args.csv #path to our csv with urls to download images from
+    image_folder = args.output  #folder where dataset will be downloaded to
+
+    # log file location
+    log_filepath = csv_path.split(".")[0] + "_log.json"
+
+    #dowload images from urls
+    download_images(csv_path, image_folder, log_filepath)
+
+    # generate checksums and save CSV to same folder as CSV used for download
+    checksum_path = csv_path.split(".")[0] + "_checksums.csv"
+    get_checksums(image_folder, checksum_path)
+
+    print(f"Images downloaded from {csv_path} to {image_folder}.")
+    print(f"Checksums recorded in {checksum_path} and download log is in {log_filepath}.")
+
+    return
+
+if __name__ == "__main__":
+    main()