Update with master file including all image metadata

.gitattributes

@@ -53,3 +53,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg 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
+

.gitignore

@@ -0,0 +1,164 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# Mac system
+.DS_Store
+

Jiggins_Zenodo_dorsal_Img_Master.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2034f2b00a4849e80b7707934c67331c6761318d023799c661165c7bf2d00f50
+size 5771003

README.md

@@ -30,8 +30,8 @@ language:
 ### 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 34,000 RGB images 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://doi.org/10.5281/zenodo.4289223).
+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).
 
 Both dorsal and ventral images available. Contains both whole butterfly, and 4 wings separate. Large variation in image content.
 
@@ -48,27 +48,36 @@ This dataset card aims to be a base template for new datasets. It has been gener
 
 ## Dataset Structure
 
-* **Jiggins_Zenodo_Img_Master.csv:** Information for the approximately 34,000 unprocessed image files included in the Jiggins Heliconius Collection.
+* **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_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_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.
+
+
+
+* Note: The notebooks that generated these files and stats are included in the `notebooks` folder.
+
+
 ### Data Instances
 
-Multiple species of Heliconius including erato and melpomene.
+`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 normalised)
+* **Background (color or none):** multiple (needs to be normalized)
 * **Fit in frame:**
 * **Ruler or Scale:** Some with Ruler
 * **Color (ColorChecker, white-balance, None):** None
 
 #### Preprocessing steps (to be done):
-1. Hybrid separation - some images labeled as H.erato and H. melpomene without subspecies names are hybrids and need to be determined what subspecies they are.
+1. Hybrid separation - some images labeled as _H. erato_ and _H. melpomene_ without subspecies names are hybrids and need to be determined what subspecies they are.
 2. Label correction - along with step 1.
 3. Removal of subspecies with no mimic pairs.
 4. Segmentation of four wings from images so we can focus on forewings vs hindwings: [WingSeg](https://github.com/agporto/WingSeg).
@@ -77,7 +86,38 @@ Current preprocessing steps: [WingSeg](https://github.com/agporto/WingSeg) on th
 
 ### Data Fields
 
-[More Information Needed]
+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.
+- `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_).
+- `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.
+- `Other_ID`: Mostly numeric IDs, not unique, please see the associated Zenodo record for more information on the meaning of these designations.
+- `Date`: Please see the associated Zenodo record for more information on the meaning of these designations.
+- `Dataset`: Overall collection the images belong to: `Heliconiine Butterfly Collection Records from University of Cambridge` (largely, but not entirely Heliconius), `Patricio Salazar`, `Nadeau Sheffield`, `Bogota Collection (Camilo Salazar)`, `Cambridge Collection`, `Mallet`, `Merril_Gamboa`, `STRI Collection (Owen)`. Please see the associated Zenodo record for more information on the meaning of these designations.
+- `Store`: Storage location for specimen (`Box ###`).
+- `Brood`: Likely indicator of batch of butterflies. Please see the associated Zenodo record for more information on the meaning of these designations.
+- `Death_Date`: Date of specimen death. Only noted for 318 images.
+- `Cross_Type`: Laboratory cross breeding information. There is a mix of F1 (subspecies x subspecies), F2 (F1 x F1), and backcross (F1 x subspecies) hybrids. Generally, the order of the subspecies listed in the cross corresponds to the sexes of the parents (Maternal x Paternal). There are approximately 5,000 such images; on average, there are both raw and jpg images of the specimens for each view, so this covers 820 unique specimens.
+- `Stage`: Life stage of the specimen. Only 15 specimens have a non-null value for this feature, and they are all labeled as `Adult`.
+- `Sex`: Sex of the specimen: `Male`, `Female`, or `Unknown`.
+- `Unit_Type`: Type of the specimen: `wild`, `reared`, `Mutant`, `Wild`, `e11`, or `e12`. Please see the associated Zenodo record for more information on the meaning of these designations. 
+- `file_type`: Image type: `jpg`, `raw` (.CR2), or `tif`.
+- `record_number`: The number associated with the Zenodo record that the image came from.
+- `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.
+
+**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.
 
 ### Data Splits
 
@@ -91,8 +131,9 @@ Current preprocessing steps: [WingSeg](https://github.com/agporto/WingSeg) on th
 
 ### Source Data
 
-Image subset of the [collection records](https://doi.org/10.5281/zenodo.4289222) from the research group of Chris Jiggins at the University of Cambridge derived from almost 20 years of field studies. 
+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.
 
+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
 
@@ -140,15 +181,15 @@ None
 
 ### Licensing Information
 
-[More Information Needed]
+The data (images and text) are all licensed under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/). Each image and text in this dataset is provided under the least restrictive terms allowed by its licensing requirements as provided to us (i.e, we impose no additional restrictions past those specified by this license on the original source files). Modified images are only restricted by this original license.
+
+Images can be matched to their source record through the `zenodo_link` column in the Master CSVs to the `url` in the licenses.json file.
 
 ### 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.
 
-If you use this dataset, please cite the original dataset (citation below) as well as this curated subset.
-
-Gabriela Montejo-Kovacevich, Eva van der Heijden, Nicola Nadeau, & Chris Jiggins. (2020). Cambridge butterfly wing collection batch 10. Zenodo. https://doi.org/10.5281/zenodo.4289223
+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. 
 
 ### Contributions
 

licenses.json

@@ -0,0 +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. 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": "1247307", "url": "https://zenodo.org/record/1247307", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Jiggins, C., & Warren, I. (2018 , May). Cambridge butterfly wing collection batch 1. Zenodo. https://doi.org/10.5281/zenodo.1247307", "bibtex": "@misc{gabriela_montejo_kovacevich_2018_1247307, author = {Gabriela Montejo-Kovacevich and Chris Jiggins and Ian Warren}, title = {Cambridge butterfly wing collection batch 1}, month = may, year = 2018, publisher = {Zenodo}, doi = {10.5281/zenodo.1247307}, url = {https://doi.org/10.5281/zenodo.1247307} }"}, {"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. 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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. 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Zenodo. https://doi.org/10.5281/zenodo.4291095", "bibtex": "@misc{gabriela_montejo_kovacevich_2020_4291095, author = {Gabriela Montejo-Kovacevich and Eva van der Heijden and Chris Jiggins}, title = {{Cambridge butterfly collection - GMK Broods Ikiam 2018}}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4291095}, url = {https://doi.org/10.5281/zenodo.4291095} }"}, {"record_number": "5731587", "url": "https://zenodo.org/record/5731587", "license": "cc-by-4.0", "citation": "Meier, J., Barker, A., Jiggins, C., Warren, I., & Blow, R. (2021 , November). Cambridge butterfly wing collection - Ecuador, August 2019. 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. 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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/make_Jiggins_Img_Master.ipynb

@@ -0,0 +1,2494 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jiggins = pd.read_csv(\"../Jiggins_Zenodo_Master.csv\", low_memory = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(49956, 25)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jiggins.shape"
+   ]
+  },
+  {
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+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
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+       "\n",
+       "[5 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jiggins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Unnamed: 0', 'CAMID', 'X', 'Image_name', 'Side', 'zenodo_name',\n",
+       "       'zenodo_link', 'Sequence', 'Taxonomic.Name', 'Locality',\n",
+       "       'Sample.accession', 'Collected.by', 'Other.Id', 'Collected.By', 'Date',\n",
+       "       'Dataset', 'Store', 'Eclosion.Date', 'Brood', 'Death.Date',\n",
+       "       'Cross.Type', 'Stage', 'Sex', 'Unit.Type', 'Verbatim.Coordinates'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jiggins.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "411"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jiggins.Image_name.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "28585                  CAM036108_v.JPG\n",
+       "45469                  CAM044420_v.CR2\n",
+       "16097                  CAM011543_v.CR2\n",
+       "22992                  CAM017525_v.JPG\n",
+       "6601                   CAM000930_v.JPG\n",
+       "39261                  CAM043354_v.CR2\n",
+       "2577                     19N2059_d.CR2\n",
+       "21344                  CAM017153_d.JPG\n",
+       "48669                   CS004036_d.JPG\n",
+       "19706                  CAM016699_v.JPG\n",
+       "12020                  CAM009527_v.JPG\n",
+       "47898                   CS000628_d.JPG\n",
+       "7592                   CAM001394_d.JPG\n",
+       "43523                  CAM044173_v.CR2\n",
+       "12596                  CAM010226_v.JPG\n",
+       "33816                  CAM041028_v.JPG\n",
+       "23148    CAM017547_v_whitestandard.JPG\n",
+       "44996                CAM044362_hwv.CR2\n",
+       "3460                     19N2605_v.JPG\n",
+       "24164    CAM017738_d_whitestandard.CR2\n",
+       "Name: Image_name, dtype: object"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jiggins.Image_name.sample(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>22895</th>\n",
+       "      <td>CAM017512</td>\n",
+       "      <td>5226</td>\n",
+       "      <td>CAM017512_d.JPG</td>\n",
+       "      <td>CAM.coll.images.batch1_v2.csv</td>\n",
+       "      <td>https://zenodo.org/record/3082688</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36246</th>\n",
+       "      <td>CAM041681</td>\n",
+       "      <td>39690</td>\n",
+       "      <td>CAM041681_d.CR2</td>\n",
+       "      <td>0.gmk.broods.all.csv</td>\n",
+       "      <td>https://zenodo.org/record/4291095</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38630</th>\n",
+       "      <td>CAM043178</td>\n",
+       "      <td>29793</td>\n",
+       "      <td>CAM043178_fwv.CR2</td>\n",
+       "      <td>batch1.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/3569598</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45984</th>\n",
+       "      <td>CAM044484</td>\n",
+       "      <td>34635</td>\n",
+       "      <td>CAM044484_hwd.CR2</td>\n",
+       "      <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/4287444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15393</th>\n",
+       "      <td>CAM011382</td>\n",
+       "      <td>28038</td>\n",
+       "      <td>CAM011382_d.JPG</td>\n",
+       "      <td>2001_2.broods.batch.2.csv</td>\n",
+       "      <td>https://zenodo.org/record/2550097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44443</th>\n",
+       "      <td>CAM044299</td>\n",
+       "      <td>33864</td>\n",
+       "      <td>CAM044299_d.JPG</td>\n",
+       "      <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/4287444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7117</th>\n",
+       "      <td>CAM001175</td>\n",
+       "      <td>7908</td>\n",
+       "      <td>CAM001175_d.JPG</td>\n",
+       "      <td>CAM.coll.images.batch4.csv</td>\n",
+       "      <td>https://zenodo.org/record/2682669</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X         Image_name  \\\n",
+       "22895  CAM017512   5226    CAM017512_d.JPG   \n",
+       "36246  CAM041681  39690    CAM041681_d.CR2   \n",
+       "38630  CAM043178  29793  CAM043178_fwv.CR2   \n",
+       "45984  CAM044484  34635  CAM044484_hwd.CR2   \n",
+       "15393  CAM011382  28038    CAM011382_d.JPG   \n",
+       "44443  CAM044299  33864    CAM044299_d.JPG   \n",
+       "7117   CAM001175   7908    CAM001175_d.JPG   \n",
+       "\n",
+       "                              zenodo_name                        zenodo_link  \n",
+       "22895       CAM.coll.images.batch1_v2.csv  https://zenodo.org/record/3082688  \n",
+       "36246                0.gmk.broods.all.csv  https://zenodo.org/record/4291095  \n",
+       "38630  batch1.Peru.image.names.Zenodo.csv  https://zenodo.org/record/3569598  \n",
+       "45984  batch2.Peru.image.names.Zenodo.csv  https://zenodo.org/record/4287444  \n",
+       "15393           2001_2.broods.batch.2.csv  https://zenodo.org/record/2550097  \n",
+       "44443  batch2.Peru.image.names.Zenodo.csv  https://zenodo.org/record/4287444  \n",
+       "7117           CAM.coll.images.batch4.csv  https://zenodo.org/record/2682669  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jiggins_imgs = jiggins[['CAMID', 'X', 'Image_name', 'zenodo_name', 'zenodo_link']].dropna()\n",
+    "jiggins_imgs.sample(7)"
+   ]
+  },
+  {
+   "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>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10528</th>\n",
+       "      <td>CAM008821</td>\n",
+       "      <td>9887</td>\n",
+       "      <td>CAM008821_d.JPG</td>\n",
+       "      <td>CAM.coll.images.batch6.csv</td>\n",
+       "      <td>https://zenodo.org/record/2686762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19587</th>\n",
+       "      <td>CAM016660</td>\n",
+       "      <td>3856</td>\n",
+       "      <td>CAM016660_v.JPG</td>\n",
+       "      <td>CAM.coll.images.batch1_v2.csv</td>\n",
+       "      <td>https://zenodo.org/record/3082688</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25858</th>\n",
+       "      <td>CAM017989</td>\n",
+       "      <td>17211</td>\n",
+       "      <td>CAM017989_v_whitestandard.JPG</td>\n",
+       "      <td>CAM.coll.patricio.batch1.csv</td>\n",
+       "      <td>https://zenodo.org/record/1748277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10042</th>\n",
+       "      <td>CAM008668</td>\n",
+       "      <td>46436</td>\n",
+       "      <td>CAM008668_d.JPG</td>\n",
+       "      <td>occurences_and_multimedia.csv</td>\n",
+       "      <td>https://zenodo.org/record/3477891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30646</th>\n",
+       "      <td>CAM040217</td>\n",
+       "      <td>47915</td>\n",
+       "      <td>CAM040217_v.JPG</td>\n",
+       "      <td>occurences_and_multimedia.csv</td>\n",
+       "      <td>https://zenodo.org/record/3477891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42889</th>\n",
+       "      <td>CAM044091</td>\n",
+       "      <td>33084</td>\n",
+       "      <td>CAM044091_fwd.JPG</td>\n",
+       "      <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/4287444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39215</th>\n",
+       "      <td>CAM043344</td>\n",
+       "      <td>30372</td>\n",
+       "      <td>CAM043344_fwd.JPG</td>\n",
+       "      <td>batch1.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/3569598</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X                     Image_name  \\\n",
+       "10528  CAM008821   9887                CAM008821_d.JPG   \n",
+       "19587  CAM016660   3856                CAM016660_v.JPG   \n",
+       "25858  CAM017989  17211  CAM017989_v_whitestandard.JPG   \n",
+       "10042  CAM008668  46436                CAM008668_d.JPG   \n",
+       "30646  CAM040217  47915                CAM040217_v.JPG   \n",
+       "42889  CAM044091  33084              CAM044091_fwd.JPG   \n",
+       "39215  CAM043344  30372              CAM043344_fwd.JPG   \n",
+       "\n",
+       "                              zenodo_name                        zenodo_link  \n",
+       "10528          CAM.coll.images.batch6.csv  https://zenodo.org/record/2686762  \n",
+       "19587       CAM.coll.images.batch1_v2.csv  https://zenodo.org/record/3082688  \n",
+       "25858        CAM.coll.patricio.batch1.csv  https://zenodo.org/record/1748277  \n",
+       "10042       occurences_and_multimedia.csv  https://zenodo.org/record/3477891  \n",
+       "30646       occurences_and_multimedia.csv  https://zenodo.org/record/3477891  \n",
+       "42889  batch2.Peru.image.names.Zenodo.csv  https://zenodo.org/record/4287444  \n",
+       "39215  batch1.Peru.image.names.Zenodo.csv  https://zenodo.org/record/3569598  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jiggins_imgs[jiggins_imgs.Image_name.str.contains('JPG')].sample(7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8296</th>\n",
+       "      <td>CAM008147</td>\n",
+       "      <td>44302</td>\n",
+       "      <td>8147v.jpg</td>\n",
+       "      <td>occurences_and_multimedia.csv</td>\n",
+       "      <td>https://zenodo.org/record/3477891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15626</th>\n",
+       "      <td>CAM011437</td>\n",
+       "      <td>44275</td>\n",
+       "      <td>11437v.jpg</td>\n",
+       "      <td>occurences_and_multimedia.csv</td>\n",
+       "      <td>https://zenodo.org/record/3477891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16975</th>\n",
+       "      <td>CAM012169</td>\n",
+       "      <td>25057</td>\n",
+       "      <td>12169v.jpg</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
+       "      <td>https://zenodo.org/record/2552371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14097</th>\n",
+       "      <td>CAM010773</td>\n",
+       "      <td>23942</td>\n",
+       "      <td>10773v.jpg</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
+       "      <td>https://zenodo.org/record/2552371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15970</th>\n",
+       "      <td>CAM011513</td>\n",
+       "      <td>24565</td>\n",
+       "      <td>11513v.jpg</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
+       "      <td>https://zenodo.org/record/2552371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13349</th>\n",
+       "      <td>CAM010485</td>\n",
+       "      <td>23640</td>\n",
+       "      <td>10485d.jpg</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
+       "      <td>https://zenodo.org/record/2552371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16919</th>\n",
+       "      <td>CAM012124</td>\n",
+       "      <td>25001</td>\n",
+       "      <td>12124v.jpg</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
+       "      <td>https://zenodo.org/record/2552371</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X  Image_name  \\\n",
+       "8296   CAM008147  44302   8147v.jpg   \n",
+       "15626  CAM011437  44275  11437v.jpg   \n",
+       "16975  CAM012169  25057  12169v.jpg   \n",
+       "14097  CAM010773  23942  10773v.jpg   \n",
+       "15970  CAM011513  24565  11513v.jpg   \n",
+       "13349  CAM010485  23640  10485d.jpg   \n",
+       "16919  CAM012124  25001  12124v.jpg   \n",
+       "\n",
+       "                                          zenodo_name  \\\n",
+       "8296                    occurences_and_multimedia.csv   \n",
+       "15626                   occurences_and_multimedia.csv   \n",
+       "16975  Heliconius_wing_old_photos_2001_2019_part1.csv   \n",
+       "14097  Heliconius_wing_old_photos_2001_2019_part1.csv   \n",
+       "15970  Heliconius_wing_old_photos_2001_2019_part1.csv   \n",
+       "13349  Heliconius_wing_old_photos_2001_2019_part1.csv   \n",
+       "16919  Heliconius_wing_old_photos_2001_2019_part1.csv   \n",
+       "\n",
+       "                             zenodo_link  \n",
+       "8296   https://zenodo.org/record/3477891  \n",
+       "15626  https://zenodo.org/record/3477891  \n",
+       "16975  https://zenodo.org/record/2552371  \n",
+       "14097  https://zenodo.org/record/2552371  \n",
+       "15970  https://zenodo.org/record/2552371  \n",
+       "13349  https://zenodo.org/record/2552371  \n",
+       "16919  https://zenodo.org/record/2552371  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jiggins_imgs[jiggins_imgs.Image_name.str.contains('jpg')].sample(7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\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>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>47693</th>\n",
+       "      <td>CAM050052</td>\n",
+       "      <td>26152</td>\n",
+       "      <td>CAM050052_M1_10_Hmr_mut_D_cut.tif</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
+       "      <td>https://zenodo.org/record/2553977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47694</th>\n",
+       "      <td>CAM050063</td>\n",
+       "      <td>26155</td>\n",
+       "      <td>CAM050063_M7_17_Hmr_mut_V_IMG_8293_wb_cut.tif</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
+       "      <td>https://zenodo.org/record/2553977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47712</th>\n",
+       "      <td>CAM050147</td>\n",
+       "      <td>26197</td>\n",
+       "      <td>CAM050147_DS1_HW_IMG_8537_cut_3.tif</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
+       "      <td>https://zenodo.org/record/2553977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47637</th>\n",
+       "      <td>CAM050006</td>\n",
+       "      <td>26126</td>\n",
+       "      <td>CAM050006_S1_17_Hsar_mut_V_8296_wb_cut.tif</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
+       "      <td>https://zenodo.org/record/2553977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47626</th>\n",
+       "      <td>CAM050001</td>\n",
+       "      <td>26118</td>\n",
+       "      <td>CAM050001_S1_5_Hs_mut_D_wb_cut.tif</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
+       "      <td>https://zenodo.org/record/2553977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47692</th>\n",
+       "      <td>CAM050052</td>\n",
+       "      <td>26153</td>\n",
+       "      <td>CAM050052_M1_10_Hmr_mut_V_cut.tif</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
+       "      <td>https://zenodo.org/record/2553977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47673</th>\n",
+       "      <td>CAM050026</td>\n",
+       "      <td>26147</td>\n",
+       "      <td>CAM050026_S4_1_Hs_mut_V_IMG_8453_wb_cut.tif</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
+       "      <td>https://zenodo.org/record/2553977</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X                                     Image_name  \\\n",
+       "47693  CAM050052  26152              CAM050052_M1_10_Hmr_mut_D_cut.tif   \n",
+       "47694  CAM050063  26155  CAM050063_M7_17_Hmr_mut_V_IMG_8293_wb_cut.tif   \n",
+       "47712  CAM050147  26197            CAM050147_DS1_HW_IMG_8537_cut_3.tif   \n",
+       "47637  CAM050006  26126     CAM050006_S1_17_Hsar_mut_V_8296_wb_cut.tif   \n",
+       "47626  CAM050001  26118             CAM050001_S1_5_Hs_mut_D_wb_cut.tif   \n",
+       "47692  CAM050052  26153              CAM050052_M1_10_Hmr_mut_V_cut.tif   \n",
+       "47673  CAM050026  26147    CAM050026_S4_1_Hs_mut_V_IMG_8453_wb_cut.tif   \n",
+       "\n",
+       "                                          zenodo_name  \\\n",
+       "47693  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
+       "47694  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
+       "47712  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
+       "47637  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
+       "47626  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
+       "47692  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
+       "47673  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
+       "\n",
+       "                             zenodo_link  \n",
+       "47693  https://zenodo.org/record/2553977  \n",
+       "47694  https://zenodo.org/record/2553977  \n",
+       "47712  https://zenodo.org/record/2553977  \n",
+       "47637  https://zenodo.org/record/2553977  \n",
+       "47626  https://zenodo.org/record/2553977  \n",
+       "47692  https://zenodo.org/record/2553977  \n",
+       "47673  https://zenodo.org/record/2553977  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jiggins_imgs[jiggins_imgs.Image_name.str.contains('tif')].sample(7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>42543</th>\n",
+       "      <td>CAM044049</td>\n",
+       "      <td>32915</td>\n",
+       "      <td>CAM044049_d.CR2</td>\n",
+       "      <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/4287444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44160</th>\n",
+       "      <td>CAM044261</td>\n",
+       "      <td>36392</td>\n",
+       "      <td>CAM044261_d.JPG</td>\n",
+       "      <td>batch3.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/4288250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14298</th>\n",
+       "      <td>CAM010848</td>\n",
+       "      <td>27458</td>\n",
+       "      <td>CAM010848_d.JPG</td>\n",
+       "      <td>2001_2.broods.batch.1.csv</td>\n",
+       "      <td>https://zenodo.org/record/2549524</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19141</th>\n",
+       "      <td>CAM016524</td>\n",
+       "      <td>759</td>\n",
+       "      <td>CAM016524_d.JPG</td>\n",
+       "      <td>CAM.coll.images.batch1.csv</td>\n",
+       "      <td>https://zenodo.org/record/1247307</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3229</th>\n",
+       "      <td>19N2383</td>\n",
+       "      <td>22175</td>\n",
+       "      <td>19N2383_d.JPG</td>\n",
+       "      <td>0.sheffield.ps.nn.ikiam.batch2.csv</td>\n",
+       "      <td>https://zenodo.org/record/4288311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34948</th>\n",
+       "      <td>CAM041352</td>\n",
+       "      <td>14340</td>\n",
+       "      <td>CAM041352_d.JPG</td>\n",
+       "      <td>CAM.coll.images.batch9.csv</td>\n",
+       "      <td>https://zenodo.org/record/2714333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28072</th>\n",
+       "      <td>CAM021080</td>\n",
+       "      <td>11587</td>\n",
+       "      <td>CAM021080_d.JPG</td>\n",
+       "      <td>CAM.coll.images.batch7.csv</td>\n",
+       "      <td>https://zenodo.org/record/2702457</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X       Image_name                         zenodo_name  \\\n",
+       "42543  CAM044049  32915  CAM044049_d.CR2  batch2.Peru.image.names.Zenodo.csv   \n",
+       "44160  CAM044261  36392  CAM044261_d.JPG  batch3.Peru.image.names.Zenodo.csv   \n",
+       "14298  CAM010848  27458  CAM010848_d.JPG           2001_2.broods.batch.1.csv   \n",
+       "19141  CAM016524    759  CAM016524_d.JPG          CAM.coll.images.batch1.csv   \n",
+       "3229     19N2383  22175    19N2383_d.JPG  0.sheffield.ps.nn.ikiam.batch2.csv   \n",
+       "34948  CAM041352  14340  CAM041352_d.JPG          CAM.coll.images.batch9.csv   \n",
+       "28072  CAM021080  11587  CAM021080_d.JPG          CAM.coll.images.batch7.csv   \n",
+       "\n",
+       "                             zenodo_link  \n",
+       "42543  https://zenodo.org/record/4287444  \n",
+       "44160  https://zenodo.org/record/4288250  \n",
+       "14298  https://zenodo.org/record/2549524  \n",
+       "19141  https://zenodo.org/record/1247307  \n",
+       "3229   https://zenodo.org/record/4288311  \n",
+       "34948  https://zenodo.org/record/2714333  \n",
+       "28072  https://zenodo.org/record/2702457  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jiggins_imgs[jiggins_imgs.Image_name.str.contains('d.')].sample(7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'JPG', 'CR2', 'tif', 'jpeg', 'JPG(1)', 'jpg'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "check_filetypes = []\n",
+    "for img_name in list(jiggins_imgs.Image_name.unique()):\n",
+    "    check_filetypes.append(img_name.split(\".\")[1])\n",
+    "\n",
+    "print(set(check_filetypes))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Note CR2 is raw, may be duplicates, would need conversion\n",
+    "file_types = [\"JPG\", \"jpg\", \"jpeg\", \"tif\", \"JPG(1)\", \"CR2\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "37821"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "images = []\n",
+    "\n",
+    "for img_name in list(jiggins_imgs.Image_name.unique()):\n",
+    "    if img_name.split(\".\")[1] in file_types:\n",
+    "        images.append(img_name)\n",
+    "\n",
+    "len(images)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 49359 entries, 433 to 49791\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column       Non-Null Count  Dtype \n",
+      "---  ------       --------------  ----- \n",
+      " 0   CAMID        49359 non-null  object\n",
+      " 1   X            49359 non-null  int64 \n",
+      " 2   Image_name   49359 non-null  object\n",
+      " 3   zenodo_name  49359 non-null  object\n",
+      " 4   zenodo_link  49359 non-null  object\n",
+      "dtypes: int64(1), object(4)\n",
+      "memory usage: 2.3+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "img = jiggins_imgs[jiggins_imgs.Image_name.isin(images)]\n",
+    "img.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_file_type(filename):\n",
+    "    jpgs = [\"JPG\", \"jpg\", \"jpeg\", \"JPG(1)\"]\n",
+    "    file_type = filename.split(\".\")[1]\n",
+    "    if file_type in jpgs:\n",
+    "        return \"jpg\"\n",
+    "    elif file_type == \"tif\":\n",
+    "        return \"tif\"\n",
+    "    elif file_type == \"CR2\":\n",
+    "        return \"raw\"\n",
+    "    else:\n",
+    "        print(f\"{file_type} does not match known file types\")\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 49359 entries, 433 to 49791\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column       Non-Null Count  Dtype \n",
+      "---  ------       --------------  ----- \n",
+      " 0   CAMID        49359 non-null  object\n",
+      " 1   X            49359 non-null  int64 \n",
+      " 2   Image_name   49359 non-null  object\n",
+      " 3   zenodo_name  49359 non-null  object\n",
+      " 4   zenodo_link  49359 non-null  object\n",
+      " 5   file_type    49359 non-null  object\n",
+      "dtypes: int64(1), object(5)\n",
+      "memory usage: 2.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "img[\"file_type\"] = img[\"Image_name\"].apply(get_file_type)\n",
+    "img.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img.to_csv(\"../zendo_img_master.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unnamed: 0    49956\n",
+      "CAMID         12586\n",
+      "X             49956\n",
+      "Image_name    37843\n",
+      "dtype: int64\n",
+      "\n",
+      "CAMID          12586\n",
+      "X              49359\n",
+      "Image_name     37821\n",
+      "zenodo_name       36\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jiggins[list(jiggins.columns)[:4]].nunique())\n",
+    "print()\n",
+    "print(img[list(img.columns[:4])].nunique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 49956 entries, 0 to 49955\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   Unnamed: 0  49956 non-null  int64 \n",
+      " 1   CAMID       49359 non-null  object\n",
+      " 2   X           49956 non-null  int64 \n",
+      " 3   Image_name  49545 non-null  object\n",
+      "dtypes: int64(2), object(2)\n",
+      "memory usage: 1.5+ MB\n",
+      "None\n",
+      "\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 49359 entries, 433 to 49791\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column       Non-Null Count  Dtype \n",
+      "---  ------       --------------  ----- \n",
+      " 0   CAMID        49359 non-null  object\n",
+      " 1   X            49359 non-null  int64 \n",
+      " 2   Image_name   49359 non-null  object\n",
+      " 3   zenodo_name  49359 non-null  object\n",
+      "dtypes: int64(1), object(3)\n",
+      "memory usage: 1.9+ MB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(jiggins[list(jiggins.columns)[:4]].info())\n",
+    "print()\n",
+    "print(img[list(img.columns[:4])].info())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to match these up on `X`, since all entries have a value and it is unique."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 49359 entries, 433 to 49791\n",
+      "Data columns (total 25 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   Unnamed: 0            49359 non-null  int64  \n",
+      " 1   CAMID                 49359 non-null  object \n",
+      " 2   X                     49359 non-null  int64  \n",
+      " 3   Image_name            49359 non-null  object \n",
+      " 4   Side                  48288 non-null  object \n",
+      " 5   zenodo_name           49359 non-null  object \n",
+      " 6   zenodo_link           49359 non-null  object \n",
+      " 7   Sequence              48424 non-null  object \n",
+      " 8   Taxonomic.Name        45473 non-null  object \n",
+      " 9   Locality              34015 non-null  object \n",
+      " 10  Sample.accession      5884 non-null   object \n",
+      " 11  Collected.by          5280 non-null   object \n",
+      " 12  Other.Id              14382 non-null  object \n",
+      " 13  Collected.By          0 non-null      float64\n",
+      " 14  Date                  33718 non-null  object \n",
+      " 15  Dataset               40405 non-null  object \n",
+      " 16  Store                 39485 non-null  object \n",
+      " 17  Eclosion.Date         97 non-null     object \n",
+      " 18  Brood                 14942 non-null  object \n",
+      " 19  Death.Date            318 non-null    object \n",
+      " 20  Cross.Type            5133 non-null   object \n",
+      " 21  Stage                 15 non-null     object \n",
+      " 22  Sex                   36243 non-null  object \n",
+      " 23  Unit.Type             33890 non-null  object \n",
+      " 24  Verbatim.Coordinates  0 non-null      float64\n",
+      "dtypes: float64(2), int64(2), object(21)\n",
+      "memory usage: 9.8+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_img = jiggins.loc[jiggins.X.isin(list(img.X))]\n",
+    "df_img.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rename columns to have underscore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_66533/3055364590.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\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",
+      "  df_img.rename(columns = {\"Side\": \"View\",\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_img.rename(columns = {\"Side\": \"View\",\n",
+    "                        \"Taxonomic.Name\": \"Taxonomic_Name\",\n",
+    "                        \"Cross.Type\": \"Cross_Type\",\n",
+    "                        \"Sample.accession\": \"Sample_accession\",\n",
+    "                        \"Collected.by\": \"Collected_by\",\n",
+    "                        \"Other.Id\": \"Other_ID\",\n",
+    "                        \"Death.Date\": \"Death_Date\",\n",
+    "                        \"Unit.Type\": \"Unit_Type\"},\n",
+    "                        inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "null_cols = [\"Unnamed: 0\", \"Collected.By\", \"Eclosion.Date\", \"Verbatim.Coordinates\"]\n",
+    "non_null_cols = [col for col in list(df_img.columns) if col not in null_cols]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_img = df_img[non_null_cols]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "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>Other_ID</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Dataset</th>\n",
+       "      <th>Store</th>\n",
+       "      <th>Brood</th>\n",
+       "      <th>Death_Date</th>\n",
+       "      <th>Cross_Type</th>\n",
+       "      <th>Stage</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Unit_Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>433</th>\n",
+       "      <td>14N004</td>\n",
+       "      <td>15131</td>\n",
+       "      <td>14N004_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>0.4.nn.requests2.csv</td>\n",
+       "      <td>https://zenodo.org/record/4289223</td>\n",
+       "      <td>4</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>Nadeau Sheffield</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>434</th>\n",
+       "      <td>14N004</td>\n",
+       "      <td>15132</td>\n",
+       "      <td>14N004_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>0.4.nn.requests2.csv</td>\n",
+       "      <td>https://zenodo.org/record/4289223</td>\n",
+       "      <td>4</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>Nadeau Sheffield</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>435</th>\n",
+       "      <td>14N009</td>\n",
+       "      <td>15134</td>\n",
+       "      <td>14N009_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>0.4.nn.requests2.csv</td>\n",
+       "      <td>https://zenodo.org/record/4289223</td>\n",
+       "      <td>9</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>Nadeau Sheffield</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>436</th>\n",
+       "      <td>14N009</td>\n",
+       "      <td>15133</td>\n",
+       "      <td>14N009_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>0.4.nn.requests2.csv</td>\n",
+       "      <td>https://zenodo.org/record/4289223</td>\n",
+       "      <td>9</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>Nadeau Sheffield</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>14N014</td>\n",
+       "      <td>15136</td>\n",
+       "      <td>14N014_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>0.4.nn.requests2.csv</td>\n",
+       "      <td>https://zenodo.org/record/4289223</td>\n",
+       "      <td>14</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>Nadeau Sheffield</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      CAMID      X    Image_name     View           zenodo_name  \\\n",
+       "433  14N004  15131  14N004_d.JPG   dorsal  0.4.nn.requests2.csv   \n",
+       "434  14N004  15132  14N004_v.JPG  ventral  0.4.nn.requests2.csv   \n",
+       "435  14N009  15134  14N009_v.JPG  ventral  0.4.nn.requests2.csv   \n",
+       "436  14N009  15133  14N009_d.JPG   dorsal  0.4.nn.requests2.csv   \n",
+       "437  14N014  15136  14N014_v.JPG  ventral  0.4.nn.requests2.csv   \n",
+       "\n",
+       "                           zenodo_link Sequence Taxonomic_Name Locality  \\\n",
+       "433  https://zenodo.org/record/4289223        4            NaN      NaN   \n",
+       "434  https://zenodo.org/record/4289223        4            NaN      NaN   \n",
+       "435  https://zenodo.org/record/4289223        9            NaN      NaN   \n",
+       "436  https://zenodo.org/record/4289223        9            NaN      NaN   \n",
+       "437  https://zenodo.org/record/4289223       14            NaN      NaN   \n",
+       "\n",
+       "    Sample_accession  ... Other_ID Date           Dataset Store Brood  \\\n",
+       "433              NaN  ...      NaN  NaN  Nadeau Sheffield   NaN   NaN   \n",
+       "434              NaN  ...      NaN  NaN  Nadeau Sheffield   NaN   NaN   \n",
+       "435              NaN  ...      NaN  NaN  Nadeau Sheffield   NaN   NaN   \n",
+       "436              NaN  ...      NaN  NaN  Nadeau Sheffield   NaN   NaN   \n",
+       "437              NaN  ...      NaN  NaN  Nadeau Sheffield   NaN   NaN   \n",
+       "\n",
+       "    Death_Date Cross_Type Stage  Sex Unit_Type  \n",
+       "433        NaN        NaN   NaN  NaN       NaN  \n",
+       "434        NaN        NaN   NaN  NaN       NaN  \n",
+       "435        NaN        NaN   NaN  NaN       NaN  \n",
+       "436        NaN        NaN   NaN  NaN       NaN  \n",
+       "437        NaN        NaN   NaN  NaN       NaN  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_img.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 49359 entries, 433 to 49791\n",
+      "Data columns (total 22 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             49359 non-null  object\n",
+      " 1   X                 49359 non-null  int64 \n",
+      " 2   Image_name        49359 non-null  object\n",
+      " 3   View              48288 non-null  object\n",
+      " 4   zenodo_name       49359 non-null  object\n",
+      " 5   zenodo_link       49359 non-null  object\n",
+      " 6   Sequence          48424 non-null  object\n",
+      " 7   Taxonomic_Name    45473 non-null  object\n",
+      " 8   Locality          34015 non-null  object\n",
+      " 9   Sample_accession  5884 non-null   object\n",
+      " 10  Collected_by      5280 non-null   object\n",
+      " 11  Other_ID          14382 non-null  object\n",
+      " 12  Date              33718 non-null  object\n",
+      " 13  Dataset           40405 non-null  object\n",
+      " 14  Store             39485 non-null  object\n",
+      " 15  Brood             14942 non-null  object\n",
+      " 16  Death_Date        318 non-null    object\n",
+      " 17  Cross_Type        5133 non-null   object\n",
+      " 18  Stage             15 non-null     object\n",
+      " 19  Sex               36243 non-null  object\n",
+      " 20  Unit_Type         33890 non-null  object\n",
+      " 21  file_type         49359 non-null  object\n",
+      "dtypes: int64(1), object(21)\n",
+      "memory usage: 8.7+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_img[\"file_type\"] = df_img[\"Image_name\"].apply(get_file_type)\n",
+    "df_img.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_img.to_csv(\"../Jiggins_Zenodo_Img_Master.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remaining Question is just if the raw images are duplicated as jpgs or are unique. The `CAMID`'s correspond to samples (as noted in the [zenodo records](https://zenodo.org/record/4289223)), so we can check a single view for this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "View\n",
+       "dorsal                15128\n",
+       "ventral               13424\n",
+       "Dorsal                 8360\n",
+       "Ventral                8090\n",
+       "ventral                1644\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": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_img.View.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['dorsal',\n",
+       " 'Dorsal',\n",
+       " 'Dorsal and Ventral',\n",
+       " 'forewing dorsal',\n",
+       " 'hindwing dorsal']"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_labels = [view for view in list(df_img.View.dropna().unique()) if \"dorsal\" in view.lower()]\n",
+    "dorsal_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 24318 entries, 433 to 49790\n",
+      "Data columns (total 22 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             24318 non-null  object\n",
+      " 1   X                 24318 non-null  int64 \n",
+      " 2   Image_name        24318 non-null  object\n",
+      " 3   View              24318 non-null  object\n",
+      " 4   zenodo_name       24318 non-null  object\n",
+      " 5   zenodo_link       24318 non-null  object\n",
+      " 6   Sequence          23851 non-null  object\n",
+      " 7   Taxonomic_Name    22511 non-null  object\n",
+      " 8   Locality          16773 non-null  object\n",
+      " 9   Sample_accession  2953 non-null   object\n",
+      " 10  Collected_by      2656 non-null   object\n",
+      " 11  Other_ID          6931 non-null   object\n",
+      " 12  Date              16847 non-null  object\n",
+      " 13  Dataset           19935 non-null  object\n",
+      " 14  Store             19894 non-null  object\n",
+      " 15  Brood             7264 non-null   object\n",
+      " 16  Death_Date        107 non-null    object\n",
+      " 17  Cross_Type        2572 non-null   object\n",
+      " 18  Stage             8 non-null      object\n",
+      " 19  Sex               17875 non-null  object\n",
+      " 20  Unit_Type         16693 non-null  object\n",
+      " 21  file_type         24318 non-null  object\n",
+      "dtypes: int64(1), object(21)\n",
+      "memory usage: 4.3+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_img_dorsal = df_img.loc[df_img[\"View\"].isin(dorsal_labels)]\n",
+    "df_img_dorsal.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID               12296\n",
+       "X                   24318\n",
+       "Image_name          18539\n",
+       "View                    5\n",
+       "zenodo_name            36\n",
+       "zenodo_link            32\n",
+       "Sequence            11107\n",
+       "Taxonomic_Name        359\n",
+       "Locality              642\n",
+       "Sample_accession     1564\n",
+       "Collected_by           12\n",
+       "Other_ID             2897\n",
+       "Date                  794\n",
+       "Dataset                 8\n",
+       "Store                 142\n",
+       "Brood                 217\n",
+       "Death_Date             64\n",
+       "Cross_Type             30\n",
+       "Stage                   1\n",
+       "Sex                     3\n",
+       "Unit_Type               6\n",
+       "file_type               3\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_img_dorsal.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We still have repeated `CAMID`s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_66533/4002726096.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",
+      "  df_img_dorsal[\"CAM_Dupe\"] = df_img_dorsal.duplicated(subset = \"CAMID\", keep = False)\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_img_dorsal[\"CAM_Dupe\"] = df_img_dorsal.duplicated(subset = \"CAMID\", keep = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAM_Dupe\n",
+       "True     20765\n",
+       "False     3553\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_img_dorsal[\"CAM_Dupe\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 20765 entries, 709 to 49692\n",
+      "Data columns (total 23 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             20765 non-null  object\n",
+      " 1   X                 20765 non-null  int64 \n",
+      " 2   Image_name        20765 non-null  object\n",
+      " 3   View              20765 non-null  object\n",
+      " 4   zenodo_name       20765 non-null  object\n",
+      " 5   zenodo_link       20765 non-null  object\n",
+      " 6   Sequence          20299 non-null  object\n",
+      " 7   Taxonomic_Name    19368 non-null  object\n",
+      " 8   Locality          15301 non-null  object\n",
+      " 9   Sample_accession  2657 non-null   object\n",
+      " 10  Collected_by      2653 non-null   object\n",
+      " 11  Other_ID          6107 non-null   object\n",
+      " 12  Date              15980 non-null  object\n",
+      " 13  Dataset           16862 non-null  object\n",
+      " 14  Store             17579 non-null  object\n",
+      " 15  Brood             5414 non-null   object\n",
+      " 16  Death_Date        26 non-null     object\n",
+      " 17  Cross_Type        2572 non-null   object\n",
+      " 18  Stage             0 non-null      object\n",
+      " 19  Sex               14897 non-null  object\n",
+      " 20  Unit_Type         13814 non-null  object\n",
+      " 21  file_type         20765 non-null  object\n",
+      " 22  CAM_Dupe          20765 non-null  bool  \n",
+      "dtypes: bool(1), int64(1), object(21)\n",
+      "memory usage: 3.7+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "duplicate_samples = df_img_dorsal.loc[df_img_dorsal[\"CAM_Dupe\"]]\n",
+    "duplicate_samples.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID                8743\n",
+       "X                   20765\n",
+       "Image_name          14986\n",
+       "View                    5\n",
+       "zenodo_name            32\n",
+       "zenodo_link            31\n",
+       "Sequence             8407\n",
+       "Taxonomic_Name        328\n",
+       "Locality              516\n",
+       "Sample_accession     1268\n",
+       "Collected_by           12\n",
+       "Other_ID             2073\n",
+       "Date                  578\n",
+       "Dataset                 4\n",
+       "Store                 130\n",
+       "Brood                 144\n",
+       "Death_Date             12\n",
+       "Cross_Type             30\n",
+       "Stage                   0\n",
+       "Sex                     3\n",
+       "Unit_Type               3\n",
+       "file_type               3\n",
+       "CAM_Dupe                1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duplicate_samples.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All sources are impacted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "file_type\n",
+       "jpg    14788\n",
+       "raw     5956\n",
+       "tif       21\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duplicate_samples.file_type.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will get some added duplication from `forewing dorsal` and `hindwing dorsal`, so we should filter those down to just one for a more accurate assessment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True     680\n",
+       "False    132\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duplicate_samples.loc[duplicate_samples[\"View\"].isin([\"forewing dorsal\", \"hindwing dorsal\"])].duplicated(\"CAMID\", keep = \"first\").value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID               132\n",
+       "X                   812\n",
+       "Image_name          528\n",
+       "View                  2\n",
+       "zenodo_name           3\n",
+       "zenodo_link           3\n",
+       "Sequence            132\n",
+       "Taxonomic_Name        7\n",
+       "Locality             59\n",
+       "Sample_accession      0\n",
+       "Collected_by          0\n",
+       "Other_ID            127\n",
+       "Date                 16\n",
+       "Dataset               1\n",
+       "Store                17\n",
+       "Brood                 0\n",
+       "Death_Date            0\n",
+       "Cross_Type            0\n",
+       "Stage                 0\n",
+       "Sex                   0\n",
+       "Unit_Type             0\n",
+       "file_type             2\n",
+       "CAM_Dupe              1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duplicate_samples.loc[duplicate_samples[\"View\"].isin([\"forewing dorsal\", \"hindwing dorsal\"])].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is only a couple zenodo links, so it is not contributing to the duplication across sources. It also covers two file types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 3553 entries, 433 to 49790\n",
+      "Data columns (total 23 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             3553 non-null   object\n",
+      " 1   X                 3553 non-null   int64 \n",
+      " 2   Image_name        3553 non-null   object\n",
+      " 3   View              3553 non-null   object\n",
+      " 4   zenodo_name       3553 non-null   object\n",
+      " 5   zenodo_link       3553 non-null   object\n",
+      " 6   Sequence          3552 non-null   object\n",
+      " 7   Taxonomic_Name    3143 non-null   object\n",
+      " 8   Locality          1472 non-null   object\n",
+      " 9   Sample_accession  296 non-null    object\n",
+      " 10  Collected_by      3 non-null      object\n",
+      " 11  Other_ID          824 non-null    object\n",
+      " 12  Date              867 non-null    object\n",
+      " 13  Dataset           3073 non-null   object\n",
+      " 14  Store             2315 non-null   object\n",
+      " 15  Brood             1850 non-null   object\n",
+      " 16  Death_Date        81 non-null     object\n",
+      " 17  Cross_Type        0 non-null      object\n",
+      " 18  Stage             8 non-null      object\n",
+      " 19  Sex               2978 non-null   object\n",
+      " 20  Unit_Type         2879 non-null   object\n",
+      " 21  file_type         3553 non-null   object\n",
+      " 22  CAM_Dupe          3553 non-null   bool  \n",
+      "dtypes: bool(1), int64(1), object(21)\n",
+      "memory usage: 641.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "unique_samples = df_img_dorsal.loc[~df_img_dorsal[\"CAM_Dupe\"]]\n",
+    "unique_samples.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID               3553\n",
+       "X                   3553\n",
+       "Image_name          3553\n",
+       "View                   3\n",
+       "zenodo_name           21\n",
+       "zenodo_link           18\n",
+       "Sequence            3016\n",
+       "Taxonomic_Name       109\n",
+       "Locality             160\n",
+       "Sample_accession     296\n",
+       "Collected_by           3\n",
+       "Other_ID             824\n",
+       "Date                 265\n",
+       "Dataset                8\n",
+       "Store                 76\n",
+       "Brood                 95\n",
+       "Death_Date            52\n",
+       "Cross_Type             0\n",
+       "Stage                  1\n",
+       "Sex                    3\n",
+       "Unit_Type              6\n",
+       "file_type              2\n",
+       "CAM_Dupe               1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unique_samples.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "file_type\n",
+       "jpg    3535\n",
+       "tif      18\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unique_samples.file_type.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ah, well all raw images are indeed duplicated. Let's check if they're duplicated among themselves or duplicated to other file types (eg., jpg)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(5753, 23)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "4944"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(duplicate_samples.loc[(duplicate_samples[\"View\"] != \"hindwing dorsal\") & (duplicate_samples[\"file_type\"] == \"raw\")].shape)\n",
+    "duplicate_samples.loc[(duplicate_samples[\"View\"] != \"hindwing dorsal\") & (duplicate_samples[\"file_type\"] == \"raw\"), \"CAMID\"].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Okay, so there are multiple raw images of the same sample. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "View\n",
+       "dorsal             4219\n",
+       "Dorsal             1331\n",
+       "forewing dorsal     203\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duplicate_raw = duplicate_samples.loc[(duplicate_samples[\"View\"] != \"hindwing dorsal\") & (duplicate_samples[\"file_type\"] == \"raw\")]\n",
+    "duplicate_raw.View.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "raw_dupe\n",
+      "False    4240\n",
+      "True     1513\n",
+      "Name: count, dtype: int64\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_66533/3049709573.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",
+      "  duplicate_raw[\"raw_dupe\"] = duplicate_raw.duplicated(\"CAMID\", keep = False)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "View\n",
+       "dorsal             1159\n",
+       "Dorsal              212\n",
+       "forewing dorsal     142\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duplicate_raw[\"raw_dupe\"] = duplicate_raw.duplicated(\"CAMID\", keep = False)\n",
+    "print(duplicate_raw[\"raw_dupe\"].value_counts())\n",
+    "print()\n",
+    "duplicate_raw.loc[duplicate_raw[\"raw_dupe\"], \"View\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There does not seem to be an easy method of filtering these other than just keeping the first instance of a particular `CAMID`.\n",
+    "\n",
+    "It is interesting that non of the Cross Types are unique. Is that just because we have the forewing/hindwing duplication?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2572, 23)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "820"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(duplicate_samples.loc[(duplicate_samples[\"View\"] != \"hindwing dorsal\") & (duplicate_samples[\"Cross_Type\"].notna())].shape)\n",
+    "duplicate_samples.loc[(duplicate_samples[\"View\"] != \"hindwing dorsal\") & (duplicate_samples[\"Cross_Type\"].notna()), \"CAMID\"].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There still seem to be 3 images for each specimen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cross_dupe\n",
+      "True    2572\n",
+      "Name: count, dtype: int64\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_66533/2419376272.py:2: 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",
+      "  duplicate_cross[\"cross_dupe\"] = duplicate_cross.duplicated(\"CAMID\", keep = False)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "View\n",
+       "dorsal    2322\n",
+       "Dorsal     250\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duplicate_cross = duplicate_samples.loc[(duplicate_samples[\"View\"] != \"hindwing dorsal\") & (duplicate_samples[\"Cross_Type\"].notna())]\n",
+    "duplicate_cross[\"cross_dupe\"] = duplicate_cross.duplicated(\"CAMID\", keep = False)\n",
+    "print(duplicate_cross[\"cross_dupe\"].value_counts())\n",
+    "print()\n",
+    "duplicate_cross.View.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Yes, they are all duplicated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "file_type\n",
+       "jpg    1753\n",
+       "raw     819\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duplicate_cross.file_type.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It seems there's one raw image for each sample (other than one) and then everything else is jpg. I wonder if this includes the `JPG(1)` values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "count = 0\n",
+    "for img_name in list(duplicate_cross.Image_name.unique()):\n",
+    "    if \"JPG(1)\" in img_name:\n",
+    "        count = count + 1\n",
+    "count"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No, that's not the issue."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "count = 0\n",
+    "for img_name in list(duplicate_samples.Image_name.unique()):\n",
+    "    if \"JPG(1)\" in img_name:\n",
+    "        count = count + 1\n",
+    "count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "count = 0\n",
+    "for img_name in list(df_img_dorsal.Image_name.unique()):\n",
+    "    if \"JPG(1)\" in img_name:\n",
+    "        count = count + 1\n",
+    "count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "count = 0\n",
+    "for img_name in list(df_img.Image_name.unique()):\n",
+    "    if \"JPG(1)\" in img_name:\n",
+    "        count = count + 1\n",
+    "count"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There's only one instance of this, and it's not a dorsal image, so that's not part of the issue."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's save all the dorsal images as another CSV (with the duplicate `CAMID` indicator)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_img_dorsal.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
+}

notebooks/standardize_taxa.ipynb

@@ -0,0 +1,1464 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "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                   10\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",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "file_type\n",
+       "jpg    37072\n",
+       "raw    12226\n",
+       "tif       61\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.file_type.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "View\n",
+       "dorsal                15128\n",
+       "ventral               13424\n",
+       "Dorsal                 8360\n",
+       "Ventral                8090\n",
+       "ventral                1644\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": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.View.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Not great that `ventral` gets listed twice as lowercase and _again_ as `Ventral`.\n",
+    "\n",
+    "### Standardize `View` Column\n",
+    "Let's standardize `View` so that there isn't a discrepancy based on case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "View\n",
+       "dorsal                23488\n",
+       "ventral               21514\n",
+       "ventral                1644\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": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"View\"] = df.View.str.lower()\n",
+    "df.View.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['dorsal' 'ventral' nan 'dorsal and ventral' 'ventral ' 'forewing dorsal'\n",
+      " 'hindwing dorsal' 'forewing ventral' 'hindwing ventral']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.View.unique())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Yes, one has a space after it, so we'll replace that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "View\n",
+       "dorsal                23488\n",
+       "ventral               23158\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.loc[df[\"View\"] == \"ventral \", \"View\"] = \"ventral\"\n",
+    "df.View.value_counts() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add Record Number Column\n",
+    "We'll add a `record_number` column for easier matching to the license/citation file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_record_number(url):\n",
+    "    num = url.split(sep = \"/\")[-1]\n",
+    "    return num"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "32"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"record_number\"] = df.zenodo_link.apply(get_record_number)\n",
+    "df.record_number.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have 32 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 `species` and `subspecies` Columns\n",
+    "This will make some analysis easier and allow for easy viewing on the [Data Dashboard](https://huggingface.co/spaces/imageomics/dashboard-prototype)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_species(taxa_name):\n",
+    "    if type(taxa_name) != float: #taxa name not null\n",
+    "        species = taxa_name.split(sep = \" ssp\")[0]\n",
+    "        return species\n",
+    "    else:\n",
+    "        return taxa_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_subspecies(taxa_name):\n",
+    "    if type(taxa_name) != float:\n",
+    "        if \"ssp.\" in taxa_name:\n",
+    "            subspecies = taxa_name.split(sep = \"ssp. \")[1]\n",
+    "        elif \"ssp \" in taxa_name:\n",
+    "            subspecies = taxa_name.split(sep = \"ssp \")[1]\n",
+    "        else:\n",
+    "            subspecies = None\n",
+    "    else:\n",
+    "        subspecies = None\n",
+    "    return subspecies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "246"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"species\"] = df.Taxonomic_Name.apply(get_species)\n",
+    "df.species.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"subspecies\"] = df.Taxonomic_Name.apply(get_subspecies)\n",
+    "df.subspecies.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cross Types are labeled differently:\n",
+    "They are all abbreviations, we have `malleti (mal), plesseni (ple), notabilis (not), lativitta (lat)`, and Neil would guess that `latRo` refers to lativitta with a rounded apical band (e.g., a phenotypic variant of lativitta), but he couldn't say for sure without some more digging, so that will have to stay as-is. We will leave the `Test cross...` ones, but there is not much more to do with them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['mal', 'mal x ple', 'ple', 'ple x mal', 'latRo x not',\n",
+       "       '(latRo x not) x not', '(mal x ple) x mal', '(mal x ple) x ple',\n",
+       "       'ple x (mal x ple)', '(ple x mal) x (mal x ple)', 'lat x not',\n",
+       "       '(ple x mal) x ple', '(mal x ple) x (mal x ple)',\n",
+       "       '(ple x mal) x mal', '(ple x mal) x (ple x mal)',\n",
+       "       '(mal x ple) x (ple x mal)', 'hybrid', 'mal x (ple x mal)',\n",
+       "       '(lat x not) x lat', '(lat x not) x not', 'Ac heterozygote',\n",
+       "       'ple x (ple x mal)', '2 banded', 'lat',\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 mal - thin)'], dtype=object)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Cross_Type.dropna().unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def clean_cross_types(cross_type):\n",
+    "    if type(cross_type) != float:\n",
+    "        cross_type = cross_type.replace(\"mal\", \"malleti\")\n",
+    "        cross_type = cross_type.replace(\"ple\", \"plesseni\")\n",
+    "        cross_type = cross_type.replace(\"not\", \"notabilis\")\n",
+    "        if \"latRo\" not in cross_type:\n",
+    "            #latRo does not cross with lativitta, so only apply when latRo isn't present\n",
+    "            cross_type = cross_type.replace(\"lat\", \"lativitta\")\n",
+    "    return cross_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Cross_Type\"] = df[\"Cross_Type\"].apply(clean_cross_types)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can fill these cross types in for the `subspecies` column (all Cross Types are just labeled to the spceies level in `Taxonomic_Name`, so they did not get processed previously)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "156"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cross_type_subspecies = [ct for ct in list(df.Cross_Type.dropna().unique()) if \"Test\" not in ct and \"banded\" not in ct]\n",
+    "cross_type_subspecies.remove(\"hybrid\")\n",
+    "cross_type_subspecies.remove(\"Ac heterozygote\")\n",
+    "\n",
+    "for ct in cross_type_subspecies:\n",
+    "    df.loc[df[\"Cross_Type\"] == ct, \"subspecies\"] = ct\n",
+    "\n",
+    "df.subspecies.nunique()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(cross_type_subspecies)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "subspecies\n",
+       "(malleti x plesseni) x malleti                 1204\n",
+       "plesseni x (malleti x plesseni)                 600\n",
+       "malleti x (plesseni x malleti)                  370\n",
+       "(plesseni x malleti) x plesseni                 363\n",
+       "(plesseni x malleti) x (malleti x plesseni)     354\n",
+       "(plesseni x malleti) x (plesseni x malleti)     286\n",
+       "(malleti x plesseni) x plesseni                 278\n",
+       "plesseni x malleti                              234\n",
+       "malleti x plesseni                              192\n",
+       "lativitta x notabilis                           136\n",
+       "(lativitta x notabilis) x lativitta             110\n",
+       "plesseni x (plesseni x malleti)                 106\n",
+       "(lativitta x notabilis) x notabilis             106\n",
+       "(malleti x plesseni) x (malleti x plesseni)      98\n",
+       "(plesseni x malleti) x malleti                   80\n",
+       "(malleti x plesseni) x (plesseni x malleti)      56\n",
+       "malleti                                          28\n",
+       "plesseni                                         28\n",
+       "(latRo x notabilis) x notabilis                  16\n",
+       "latRo x notabilis                                 4\n",
+       "lativitta                                         4\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df[\"Cross_Type\"].notna(), \"subspecies\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['malleti', 'plesseni', 'plesseni x malleti', 'lativitta']"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "already_present_subspecies = []\n",
+    "\n",
+    "for subspecies in list(df.loc[df[\"Cross_Type\"].notna(), \"subspecies\"].dropna().unique()):\n",
+    "    if subspecies in list(df.loc[~df[\"Cross_Type\"].notna(), \"subspecies\"].dropna().unique()):\n",
+    "        already_present_subspecies.append(subspecies)\n",
+    "\n",
+    "print(len(already_present_subspecies))\n",
+    "already_present_subspecies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Perfect, this adds 17 more subspecies (`lativitta`, `plessani`, `maletti`, and `plesseni x malleti` were already represented). Note, this is based on _exact_ duplicates. `notabilis x lativitta` is also already in the dataset, but the order (where the cross types are concerned) general goes `maternal x paternal`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "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>Brood</th>\n",
+       "      <th>Death_Date</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1986</th>\n",
+       "      <td>19N1989</td>\n",
+       "      <td>21369</td>\n",
+       "      <td>19N1989_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>0.sheffield.ps.nn.ikiam.batch2.csv</td>\n",
+       "      <td>https://zenodo.org/record/4288311</td>\n",
+       "      <td>1,989</td>\n",
+       "      <td>Heliconius melpomene ssp. malleti</td>\n",
+       "      <td>Ikiam Mariposario</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>IKIAM.P44</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>reared</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>4288311</td>\n",
+       "      <td>Heliconius melpomene</td>\n",
+       "      <td>malleti</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45062</th>\n",
+       "      <td>CAM044423</td>\n",
+       "      <td>34391</td>\n",
+       "      <td>CAM044423_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/4287444</td>\n",
+       "      <td>44,423</td>\n",
+       "      <td>Taygetis cleopatra</td>\n",
+       "      <td>B6old6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>4287444</td>\n",
+       "      <td>Taygetis cleopatra</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48534</th>\n",
+       "      <td>E23</td>\n",
+       "      <td>37555</td>\n",
+       "      <td>E23_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Anniina.Matilla.Field.Caught.E.csv</td>\n",
+       "      <td>https://zenodo.org/record/2554218</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>2554218</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45206</th>\n",
+       "      <td>CAM044445</td>\n",
+       "      <td>37132</td>\n",
+       "      <td>CAM044445_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>batch3.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/4288250</td>\n",
+       "      <td>44,445</td>\n",
+       "      <td>Taygetis cleopatra</td>\n",
+       "      <td>B4old2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>4288250</td>\n",
+       "      <td>Taygetis cleopatra</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12212</th>\n",
+       "      <td>CAM010238</td>\n",
+       "      <td>23307</td>\n",
+       "      <td>10238v.jpg</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
+       "      <td>https://zenodo.org/record/2552371</td>\n",
+       "      <td>10,238</td>\n",
+       "      <td>Heliconius sp.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>B043</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>reared</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>2552371</td>\n",
+       "      <td>Heliconius sp.</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39059</th>\n",
+       "      <td>CAM043418</td>\n",
+       "      <td>30654</td>\n",
+       "      <td>CAM043418_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>batch1.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/3569598</td>\n",
+       "      <td>43,418</td>\n",
+       "      <td>Archaeoprepona licomedes</td>\n",
+       "      <td>B6rec6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>3569598</td>\n",
+       "      <td>Archaeoprepona licomedes</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38163</th>\n",
+       "      <td>CAM043170</td>\n",
+       "      <td>29755</td>\n",
+       "      <td>CAM043170_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>batch1.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/3569598</td>\n",
+       "      <td>43,170</td>\n",
+       "      <td>Adelpha mesentina</td>\n",
+       "      <td>F3rec2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>3569598</td>\n",
+       "      <td>Adelpha mesentina</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X       Image_name     View  \\\n",
+       "1986     19N1989  21369    19N1989_v.JPG  ventral   \n",
+       "45062  CAM044423  34391  CAM044423_d.CR2   dorsal   \n",
+       "48534        E23  37555        E23_d.CR2   dorsal   \n",
+       "45206  CAM044445  37132  CAM044445_d.JPG   dorsal   \n",
+       "12212  CAM010238  23307       10238v.jpg  ventral   \n",
+       "39059  CAM043418  30654  CAM043418_v.JPG  ventral   \n",
+       "38163  CAM043170  29755  CAM043170_d.CR2   dorsal   \n",
+       "\n",
+       "                                          zenodo_name  \\\n",
+       "1986               0.sheffield.ps.nn.ikiam.batch2.csv   \n",
+       "45062              batch2.Peru.image.names.Zenodo.csv   \n",
+       "48534              Anniina.Matilla.Field.Caught.E.csv   \n",
+       "45206              batch3.Peru.image.names.Zenodo.csv   \n",
+       "12212  Heliconius_wing_old_photos_2001_2019_part1.csv   \n",
+       "39059              batch1.Peru.image.names.Zenodo.csv   \n",
+       "38163              batch1.Peru.image.names.Zenodo.csv   \n",
+       "\n",
+       "                             zenodo_link Sequence  \\\n",
+       "1986   https://zenodo.org/record/4288311    1,989   \n",
+       "45062  https://zenodo.org/record/4287444   44,423   \n",
+       "48534  https://zenodo.org/record/2554218      NaN   \n",
+       "45206  https://zenodo.org/record/4288250   44,445   \n",
+       "12212  https://zenodo.org/record/2552371   10,238   \n",
+       "39059  https://zenodo.org/record/3569598   43,418   \n",
+       "38163  https://zenodo.org/record/3569598   43,170   \n",
+       "\n",
+       "                          Taxonomic_Name           Locality Sample_accession  \\\n",
+       "1986   Heliconius melpomene ssp. malleti  Ikiam Mariposario              NaN   \n",
+       "45062                 Taygetis cleopatra             B6old6              NaN   \n",
+       "48534                                NaN                NaN              NaN   \n",
+       "45206                 Taygetis cleopatra             B4old2              NaN   \n",
+       "12212                     Heliconius sp.                NaN              NaN   \n",
+       "39059           Archaeoprepona licomedes             B6rec6              NaN   \n",
+       "38163                  Adelpha mesentina             F3rec2              NaN   \n",
+       "\n",
+       "       ...      Brood Death_Date Cross_Type Stage     Sex Unit_Type file_type  \\\n",
+       "1986   ...  IKIAM.P44        NaN        NaN   NaN    Male    reared       jpg   \n",
+       "45062  ...        NaN        NaN        NaN   NaN     NaN       NaN       raw   \n",
+       "48534  ...        NaN        NaN        NaN   NaN     NaN       NaN       raw   \n",
+       "45206  ...        NaN        NaN        NaN   NaN     NaN       NaN       jpg   \n",
+       "12212  ...       B043        NaN        NaN   NaN  Female    reared       jpg   \n",
+       "39059  ...        NaN        NaN        NaN   NaN     NaN       NaN       jpg   \n",
+       "38163  ...        NaN        NaN        NaN   NaN     NaN       NaN       raw   \n",
+       "\n",
+       "      record_number                   species subspecies  \n",
+       "1986        4288311      Heliconius melpomene    malleti  \n",
+       "45062       4287444        Taygetis cleopatra       None  \n",
+       "48534       2554218                       NaN       None  \n",
+       "45206       4288250        Taygetis cleopatra       None  \n",
+       "12212       2552371            Heliconius sp.       None  \n",
+       "39059       3569598  Archaeoprepona licomedes       None  \n",
+       "38163       3569598         Adelpha mesentina       None  \n",
+       "\n",
+       "[7 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sample(7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Add Genus Column\n",
+    "\n",
+    "This willl allow us to easily remove all non Heliconius samples, and make some image stats easier to see."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_genus(species):\n",
+    "    if type(species) != float: #taxa name not null\n",
+    "        return species.split(sep = \" \")[0]\n",
+    "    return species"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"genus\"] = df[\"species\"].apply(get_genus)\n",
+    "df.genus.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Final stats for all data summarized here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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            156\n",
+       "genus                  94\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 49359 entries, 0 to 49358\n",
+      "Data columns (total 26 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             49359 non-null  object\n",
+      " 1   X                 49359 non-null  int64 \n",
+      " 2   Image_name        49359 non-null  object\n",
+      " 3   View              48288 non-null  object\n",
+      " 4   zenodo_name       49359 non-null  object\n",
+      " 5   zenodo_link       49359 non-null  object\n",
+      " 6   Sequence          48424 non-null  object\n",
+      " 7   Taxonomic_Name    45473 non-null  object\n",
+      " 8   Locality          34015 non-null  object\n",
+      " 9   Sample_accession  5884 non-null   object\n",
+      " 10  Collected_by      5280 non-null   object\n",
+      " 11  Other_ID          14382 non-null  object\n",
+      " 12  Date              33718 non-null  object\n",
+      " 13  Dataset           40405 non-null  object\n",
+      " 14  Store             39485 non-null  object\n",
+      " 15  Brood             14942 non-null  object\n",
+      " 16  Death_Date        318 non-null    object\n",
+      " 17  Cross_Type        5133 non-null   object\n",
+      " 18  Stage             15 non-null     object\n",
+      " 19  Sex               36243 non-null  object\n",
+      " 20  Unit_Type         33890 non-null  object\n",
+      " 21  file_type         49359 non-null  object\n",
+      " 22  record_number     49359 non-null  object\n",
+      " 23  species           45473 non-null  object\n",
+      " 24  subspecies        25715 non-null  object\n",
+      " 25  genus             45473 non-null  object\n",
+      "dtypes: int64(1), object(25)\n",
+      "memory usage: 9.8+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observe that not all images have a species label."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "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>Death_Date</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>48538</th>\n",
+       "      <td>E24</td>\n",
+       "      <td>37559</td>\n",
+       "      <td>E24_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Anniina.Matilla.Field.Caught.E.csv</td>\n",
+       "      <td>https://zenodo.org/record/2554218</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>2554218</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37246</th>\n",
+       "      <td>CAM042045</td>\n",
+       "      <td>43973</td>\n",
+       "      <td>CAM042045_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>Collection_August2019.csv</td>\n",
+       "      <td>https://zenodo.org/record/5731587</td>\n",
+       "      <td>42,045</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>5731587</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37484</th>\n",
+       "      <td>CAM042166</td>\n",
+       "      <td>44211</td>\n",
+       "      <td>CAM042166_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>Collection_August2019.csv</td>\n",
+       "      <td>https://zenodo.org/record/5731587</td>\n",
+       "      <td>42,166</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>5731587</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48780</th>\n",
+       "      <td>E83</td>\n",
+       "      <td>37777</td>\n",
+       "      <td>E83_v.CR2</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>Anniina.Matilla.Field.Caught.E.csv</td>\n",
+       "      <td>https://zenodo.org/record/2554218</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>2554218</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3118</th>\n",
+       "      <td>19N2627</td>\n",
+       "      <td>22498</td>\n",
+       "      <td>19N2627_v.CR2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.sheffield.ps.nn.ikiam.batch2.csv</td>\n",
+       "      <td>https://zenodo.org/record/4288311</td>\n",
+       "      <td>0</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>4288311</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46111</th>\n",
+       "      <td>CAM045060</td>\n",
+       "      <td>42806</td>\n",
+       "      <td>CAM045060_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>45,060</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>5526257</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39502</th>\n",
+       "      <td>CAM043576</td>\n",
+       "      <td>31097</td>\n",
+       "      <td>CAM043576_v.CR2</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
+       "      <td>https://zenodo.org/record/4287444</td>\n",
+       "      <td>43,576</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>4287444</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>None</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X       Image_name     View  \\\n",
+       "48538        E24  37559        E24_d.CR2   dorsal   \n",
+       "37246  CAM042045  43973  CAM042045_v.JPG  ventral   \n",
+       "37484  CAM042166  44211  CAM042166_v.JPG  ventral   \n",
+       "48780        E83  37777        E83_v.CR2  ventral   \n",
+       "3118     19N2627  22498    19N2627_v.CR2      NaN   \n",
+       "46111  CAM045060  42806  CAM045060_v.CR2  ventral   \n",
+       "39502  CAM043576  31097  CAM043576_v.CR2  ventral   \n",
+       "\n",
+       "                              zenodo_name                        zenodo_link  \\\n",
+       "48538  Anniina.Matilla.Field.Caught.E.csv  https://zenodo.org/record/2554218   \n",
+       "37246           Collection_August2019.csv  https://zenodo.org/record/5731587   \n",
+       "37484           Collection_August2019.csv  https://zenodo.org/record/5731587   \n",
+       "48780  Anniina.Matilla.Field.Caught.E.csv  https://zenodo.org/record/2554218   \n",
+       "3118   0.sheffield.ps.nn.ikiam.batch2.csv  https://zenodo.org/record/4288311   \n",
+       "46111   image.names.cook.island.erato.csv  https://zenodo.org/record/5526257   \n",
+       "39502  batch2.Peru.image.names.Zenodo.csv  https://zenodo.org/record/4287444   \n",
+       "\n",
+       "      Sequence Taxonomic_Name Locality Sample_accession  ... Death_Date  \\\n",
+       "48538      NaN            NaN      NaN              NaN  ...        NaN   \n",
+       "37246   42,045            NaN      NaN              NaN  ...        NaN   \n",
+       "37484   42,166            NaN      NaN              NaN  ...        NaN   \n",
+       "48780      NaN            NaN      NaN              NaN  ...        NaN   \n",
+       "3118         0            NaN      NaN              NaN  ...        NaN   \n",
+       "46111   45,060            NaN      NaN              NaN  ...        NaN   \n",
+       "39502   43,576            NaN      NaN              NaN  ...        NaN   \n",
+       "\n",
+       "      Cross_Type Stage  Sex Unit_Type file_type record_number species  \\\n",
+       "48538        NaN   NaN  NaN       NaN       raw       2554218     NaN   \n",
+       "37246        NaN   NaN  NaN       NaN       jpg       5731587     NaN   \n",
+       "37484        NaN   NaN  NaN       NaN       jpg       5731587     NaN   \n",
+       "48780        NaN   NaN  NaN       NaN       raw       2554218     NaN   \n",
+       "3118         NaN   NaN  NaN       NaN       raw       4288311     NaN   \n",
+       "46111        NaN   NaN  NaN       NaN       raw       5526257     NaN   \n",
+       "39502        NaN   NaN  NaN       NaN       raw       4287444     NaN   \n",
+       "\n",
+       "      subspecies genus  \n",
+       "48538       None   NaN  \n",
+       "37246       None   NaN  \n",
+       "37484       None   NaN  \n",
+       "48780       None   NaN  \n",
+       "3118        None   NaN  \n",
+       "46111       None   NaN  \n",
+       "39502       None   NaN  \n",
+       "\n",
+       "[7 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df.species.isna()].sample(7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Update Master File with Genus through Subspecies Columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"../Jiggins_Zenodo_Img_Master.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make Heliconius Subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 34929 entries, 6 to 49358\n",
+      "Data columns (total 26 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             34929 non-null  object\n",
+      " 1   X                 34929 non-null  int64 \n",
+      " 2   Image_name        34929 non-null  object\n",
+      " 3   View              34150 non-null  object\n",
+      " 4   zenodo_name       34929 non-null  object\n",
+      " 5   zenodo_link       34929 non-null  object\n",
+      " 6   Sequence          34929 non-null  object\n",
+      " 7   Taxonomic_Name    34929 non-null  object\n",
+      " 8   Locality          23417 non-null  object\n",
+      " 9   Sample_accession  5860 non-null   object\n",
+      " 10  Collected_by      5280 non-null   object\n",
+      " 11  Other_ID          6404 non-null   object\n",
+      " 12  Date              23162 non-null  object\n",
+      " 13  Dataset           32846 non-null  object\n",
+      " 14  Store             29446 non-null  object\n",
+      " 15  Brood             14921 non-null  object\n",
+      " 16  Death_Date        316 non-null    object\n",
+      " 17  Cross_Type        5133 non-null   object\n",
+      " 18  Stage             6 non-null      object\n",
+      " 19  Sex               33880 non-null  object\n",
+      " 20  Unit_Type         31975 non-null  object\n",
+      " 21  file_type         34929 non-null  object\n",
+      " 22  record_number     34929 non-null  object\n",
+      " 23  species           34929 non-null  object\n",
+      " 24  subspecies        24953 non-null  object\n",
+      " 25  genus             34929 non-null  object\n",
+      "dtypes: int64(1), object(25)\n",
+      "memory usage: 7.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "heliconius_subset = df.loc[df.genus.str.lower() == \"heliconius\"]\n",
+    "\n",
+    "heliconius_subset.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID                9546\n",
+       "X                   34929\n",
+       "Image_name          26946\n",
+       "View                    3\n",
+       "zenodo_name            31\n",
+       "zenodo_link            28\n",
+       "Sequence             8701\n",
+       "Taxonomic_Name        129\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          28\n",
+       "species                37\n",
+       "subspecies            110\n",
+       "genus                   1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "heliconius_subset.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "View\n",
+       "dorsal                17218\n",
+       "ventral               16914\n",
+       "dorsal and ventral       18\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "heliconius_subset.View.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that this subset is distributed across 28 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",
+    "We'll drop the `genus` column, since they're all `Heliconius`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heliconius_subset[list(heliconius_subset.columns)[:-1]].to_csv(\"../Jiggins_Heliconius_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
+}