Datasets:
Update with master file including all image metadata (#1)
Browse files- # This is a combination of 9 commits. (70efb3e21c36f9d911efe0b0f56a161dee44b627)
- Update readme with more info on multiple zenodo records and duplication of specimens. Add license/citation files. (77c774519af2a83f45a388ae8169e1c89f3d502c)
- Update master file with standardized taxa and view columns. (ee264e4cb905fe716427e90dceb7dc16cc945a5b)
- Add metadata file for all Heliconius images. (fa42db39d85df13d1332a369b200a26a08769211)
- Add notebook that performs taxa and view standardization. (be98f9434534ba11732e6fbe04621b1d7371ef17)
- Update with information about the Heliconius subset and notebooks. (5c70c0403920d6d85b6c244cab3160741a8339c5)
- Add detailed column descriptions. (4c52e57b51f799f785ee694990a8f02f278b0e01)
- .gitattributes +3 -0
- .gitignore +164 -0
- Jiggins_Heliconius_Master.csv +0 -0
- Jiggins_Zenodo_Img_Master.csv +0 -0
- Jiggins_Zenodo_dorsal_Img_Master.csv +3 -0
- README.md +53 -12
- licenses.json +1 -0
- notebooks/make_Jiggins_Img_Master.ipynb +2494 -0
- notebooks/standardize_taxa.ipynb +1464 -0
.gitattributes
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*.jpg filter=lfs diff=lfs merge=lfs -text
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*.jpg filter=lfs diff=lfs merge=lfs -text
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Jiggins_Zenodo_Img_Master.csv filter=lfs diff=lfs merge=lfs -text
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# Byte-compiled / optimized / DLL files
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__pycache__/
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build/
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dist/
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downloads/
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wheels/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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pip-log.txt
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pip-delete-this-directory.txt
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nosetests.xml
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coverage.xml
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cover/
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# Translations
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*.mo
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*.pot
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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instance/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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# Rope project settings
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.dmypy.json
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dmypy.json
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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Jiggins_Heliconius_Master.csv
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The diff for this file is too large to render.
See raw diff
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Jiggins_Zenodo_Img_Master.csv
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Jiggins_Zenodo_dorsal_Img_Master.csv
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version https://git-lfs.github.com/spec/v1
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oid sha256:2034f2b00a4849e80b7707934c67331c6761318d023799c661165c7bf2d00f50
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size 5771003
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README.md
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### Dataset Summary
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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.
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This subset contains approximately
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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://
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Both dorsal and ventral images available. Contains both whole butterfly, and 4 wings separate. Large variation in image content.
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## Dataset Structure
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* **Jiggins_Zenodo_Img_Master.csv:** Information for the approximately
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* To access the images combine columns `zenodo_link` and `Image_name`:
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```
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zenodo_link + '/files/' + Image_name
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```
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### Data Instances
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Detached wings in four quadrants (generally).
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Some subspecies may be photographed differently, needs segmentation preprocessing.
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* **Type:** JPG/jpg/tif(very few)
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* **Size (x pixels by y pixels):** Unknown yet
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* **Background (color or none):** multiple (needs to be
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* **Fit in frame:**
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* **Ruler or Scale:** Some with Ruler
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* **Color (ColorChecker, white-balance, None):** None
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#### Preprocessing steps (to be done):
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1. Hybrid separation - some images labeled as
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2. Label correction - along with step 1.
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3. Removal of subspecies with no mimic pairs.
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4. Segmentation of four wings from images so we can focus on forewings vs hindwings: [WingSeg](https://github.com/agporto/WingSeg).
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### Data Fields
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### Data Splits
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### Source Data
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-
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#### Initial Data Collection and Normalization
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### Licensing Information
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[
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### Citation Information
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Christopher Lawrence, Chris Jiggins, Butterfly Genetics Group (University of Cambridge). (2023). Jiggins Heliconius Collection. Hugging Face. https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection.
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If you use this dataset, please cite the original
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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
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### Contributions
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### Dataset Summary
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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.
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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.
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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).
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Both dorsal and ventral images available. Contains both whole butterfly, and 4 wings separate. Large variation in image content.
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## Dataset Structure
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* **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.
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* To access the images combine columns `zenodo_link` and `Image_name`:
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```
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zenodo_link + '/files/' + Image_name
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```
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* **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`.
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* **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.
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* Note: The notebooks that generated these files and stats are included in the `notebooks` folder.
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### Data Instances
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`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).
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Detached wings in four quadrants (generally).
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Some subspecies may be photographed differently, needs segmentation preprocessing.
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* **Type:** JPG/jpg/tif(very few)
|
| 73 |
* **Size (x pixels by y pixels):** Unknown yet
|
| 74 |
+
* **Background (color or none):** multiple (needs to be normalized)
|
| 75 |
* **Fit in frame:**
|
| 76 |
* **Ruler or Scale:** Some with Ruler
|
| 77 |
* **Color (ColorChecker, white-balance, None):** None
|
| 78 |
|
| 79 |
#### Preprocessing steps (to be done):
|
| 80 |
+
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.
|
| 81 |
2. Label correction - along with step 1.
|
| 82 |
3. Removal of subspecies with no mimic pairs.
|
| 83 |
4. Segmentation of four wings from images so we can focus on forewings vs hindwings: [WingSeg](https://github.com/agporto/WingSeg).
|
|
|
|
| 86 |
|
| 87 |
### Data Fields
|
| 88 |
|
| 89 |
+
CSV Columns are as follows:
|
| 90 |
+
|
| 91 |
+
- `CAMID`: Unique identifier for each specimen that was photographed. Each `CAMID` corresponds to multiple images (based on factors such as `View` and `file_type`).
|
| 92 |
+
- `X`: Unique identifier for each line in the master CSV.
|
| 93 |
+
- `Image_name`: Filename of image.
|
| 94 |
+
- `View`: View of the butterfly in the image: `dorsal`, `ventral`, `forewing dorsal`, `hindwing dorsal`, `forewing ventral`, `hindwing ventral`, `dorsal and ventral`.
|
| 95 |
+
- `zenodo_name`: Name of the CSV file with metadata used to populate this file from the associated Zenodo record.
|
| 96 |
+
- `zenodo_link`: URL for the Zenodo record of the image.
|
| 97 |
+
- `Sequence`: Mostly numeric IDs, not unique, please see the associated Zenodo record for more information on the meaning of these designations.
|
| 98 |
+
- `Taxonomic_Name`: Indication of the Genus, Species, and possibly, subspecies, of the specimen. For Cross Types, this is just the Genus species pair (all _Heliconius erato_ or _Heliconius melpomene_).
|
| 99 |
+
- `Locality`: Likely location of specimen collection, varying levels of specificity. Please see the associated Zenodo record for more information on the meaning of these designations.
|
| 100 |
+
- `Sample_accession`: Some type of ID, not unique, please see the associated Zenodo record for more information on the meaning of these designations.
|
| 101 |
+
- `Collected_by`: Abbreviations (likely for those collecting the specimen), please see the associated Zenodo record for more information on the meaning of these designations.
|
| 102 |
+
- `Other_ID`: Mostly numeric IDs, not unique, please see the associated Zenodo record for more information on the meaning of these designations.
|
| 103 |
+
- `Date`: Please see the associated Zenodo record for more information on the meaning of these designations.
|
| 104 |
+
- `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.
|
| 105 |
+
- `Store`: Storage location for specimen (`Box ###`).
|
| 106 |
+
- `Brood`: Likely indicator of batch of butterflies. Please see the associated Zenodo record for more information on the meaning of these designations.
|
| 107 |
+
- `Death_Date`: Date of specimen death. Only noted for 318 images.
|
| 108 |
+
- `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.
|
| 109 |
+
- `Stage`: Life stage of the specimen. Only 15 specimens have a non-null value for this feature, and they are all labeled as `Adult`.
|
| 110 |
+
- `Sex`: Sex of the specimen: `Male`, `Female`, or `Unknown`.
|
| 111 |
+
- `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.
|
| 112 |
+
- `file_type`: Image type: `jpg`, `raw` (.CR2), or `tif`.
|
| 113 |
+
- `record_number`: The number associated with the Zenodo record that the image came from.
|
| 114 |
+
- `species`: Species of the specimen. There are 246 species represented in the full collection, 37 of these are species of Heliconius.
|
| 115 |
+
- `subspecies`: Subspecies of the specimen (where available, mostly labeled for Heliconius). There are 155 subspecies represented in the full collection, 110 of which are Heliconius subspecies.
|
| 116 |
+
- `genus`: Genus of the specimen. There are 94 unique genera represented in the full collection.
|
| 117 |
+
|
| 118 |
+
**Note:**
|
| 119 |
+
- `Jiggins_Heliconius_Master.csv` has all but the `genus` column, since all images are Heliconius.
|
| 120 |
+
- `Jiggins_Zenodo_dorsal_Img_Master.csv` does not have the last four columns, but they can be added easily with the appropriate functions in `notebooks/standardize_taxa.ipynb`. This file also has a column `CAM_dupe` indicating whether the `CAMID` has multiple images within this subset.
|
| 121 |
|
| 122 |
### Data Splits
|
| 123 |
|
|
|
|
| 131 |
|
| 132 |
### Source Data
|
| 133 |
|
| 134 |
+
These images are a subset of the [Butterfly Genetics Group's Cambridge butterfly collection](https://zenodo.org/communities/butterfly?q=&f=subject%3ACambridge&l=list&p=1&s=10&sort=newest). This collection of butterfly images comes from the research group of Chris Jiggins at the University of Cambridge derived from almost 20 years of field studies.
|
| 135 |
|
| 136 |
+
Data is pulled from the Zenodo Records in [`licenses.json`](https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection/blob/main/licenses.json). This file also contains full citation information for all records.
|
| 137 |
|
| 138 |
#### Initial Data Collection and Normalization
|
| 139 |
|
|
|
|
| 181 |
|
| 182 |
### Licensing Information
|
| 183 |
|
| 184 |
+
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.
|
| 185 |
+
|
| 186 |
+
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.
|
| 187 |
|
| 188 |
### Citation Information
|
| 189 |
|
| 190 |
Christopher Lawrence, Chris Jiggins, Butterfly Genetics Group (University of Cambridge). (2023). Jiggins Heliconius Collection. Hugging Face. https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection.
|
| 191 |
|
| 192 |
+
If you use this dataset, please cite the original datasets (citations for all 32 Zenodo records are in licenses.json) as well as this curated subset.
|
|
|
|
|
|
|
| 193 |
|
| 194 |
### Contributions
|
| 195 |
|
licenses.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
[{"record_number": "4289223", "url": "https://zenodo.org/record/4289223", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., van der Heijden, E., Nadeau, N., & Jiggins, C. (2020 , November). Cambridge butterfly wing collection batch 10. Zenodo. https://doi.org/10.5281/zenodo.4289223", "bibtex": "@misc{gabriela_montejo_kovacevich_2020_4289223, author = {Gabriela Montejo-Kovacevich and Eva van der Heijden and Nicola Nadeau and Chris Jiggins}, title = {Cambridge butterfly wing collection batch 10}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4289223}, url = {https://doi.org/10.5281/zenodo.4289223} }"}, {"record_number": "4288311", "url": "https://zenodo.org/record/4288311", "license": "cc-by-4.0", "citation": "Salazar, P. A., Nadeau, N., Montejo-Kovacevich, G., & Jiggins, C. (2020 , November). Sheffield butterfly wing collection - Patricio Salazar, Nicola Nadeau, Ikiam broods batch 1 and 2. Zenodo. https://doi.org/10.5281/zenodo.4288311", "bibtex": "@misc{patricio_a_salazar_2020_4288311, author = {Patricio A. Salazar and Nicola Nadeau and Gabriela Montejo-Kovacevich and Chris Jiggins}, title = {{Sheffield butterfly wing collection - Patricio Salazar, Nicola Nadeau, Ikiam broods batch 1 and 2}}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4288311}, url = {https://doi.org/10.5281/zenodo.4288311} }"}, {"record_number": "2677821", "url": "https://zenodo.org/record/2677821", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Jiggins, C., & Warren, I. (2019 , May). Cambridge butterfly wing collection batch 2. Zenodo. https://doi.org/10.5281/zenodo.2677821", "bibtex": "@misc{montejo_kovacevich_2019_2677821, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian}, title = {Cambridge butterfly wing collection batch 2}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2677821}, url = {https://doi.org/10.5281/zenodo.2677821} }"}, {"record_number": "3477891", "url": "https://zenodo.org/record/3477891", "license": "cc-by-4.0", "citation": "Jiggins, C., Montejo-Kovacevich, G., Salazar, P., & Warren, I. (2019 , October). Heliconiine Butterfly Collection Records from University of Cambridge. Zenodo. https://doi.org/10.5281/zenodo.3477891", "bibtex": "@misc{chris_jiggins_2019_3477891, author = {Chris Jiggins and Gabriela Montejo-Kovacevich and Patricio Salazar and Ian Warren}, title = {{Heliconiine Butterfly Collection Records from University of Cambridge}}, month = oct, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.3477891}, url = {https://doi.org/10.5281/zenodo.3477891} }"}, {"record_number": "2682458", "url": "https://zenodo.org/record/2682458", "license": "cc-by-4.0", "citation": "Jiggins, C., Montejo-Kovacevich, G., Warren, I., & Wiltshire, E. (2019 , May). Cambridge butterfly wing collection batch 3. Zenodo. https://doi.org/10.5281/zenodo.2682458", "bibtex": "@misc{jiggins_2019_2682458, author = {Jiggins, Chris and Montejo-Kovacevich, Gabriela and Warren, Ian and Wiltshire, Eva}, title = {Cambridge butterfly wing collection batch 3}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2682458}, url = {https://doi.org/10.5281/zenodo.2682458} }"}, {"record_number": "2682669", "url": "https://zenodo.org/record/2682669", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Jiggins, C., & Warren, I. (2019 , May). Cambridge butterfly wing collection batch 4. Zenodo. https://doi.org/10.5281/zenodo.2682669", "bibtex": "@misc{montejo_kovacevich_2019_2682669, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian}, title = {Cambridge butterfly wing collection batch 4}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2682669}, url = {https://doi.org/10.5281/zenodo.2682669} }"}, {"record_number": "2684906", "url": "https://zenodo.org/record/2684906", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Jiggins, C., Warren, I., & Wiltshire, E. (2019 , May). Cambridge butterfly wing collection batch 5. Zenodo. https://doi.org/10.5281/zenodo.2684906", "bibtex": "@misc{montejo_kovacevich_2019_2684906, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva}, title = {Cambridge butterfly wing collection batch 5}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2684906}, url = {https://doi.org/10.5281/zenodo.2684906} }"}, {"record_number": "2552371", "url": "https://zenodo.org/record/2552371", "license": "cc-by-4.0", "citation": "Warren, I. & Jiggins, C. (2019 , February). Miscellaneous Heliconius wing photographs (2001-2019) Part 1. Zenodo. https://doi.org/10.5281/zenodo.2552371", "bibtex": "@misc{warren_2019_2552371, author = {Warren, Ian and Jiggins, Chris}, title = {{Miscellaneous Heliconius wing photographs (2001-2019) Part 1}}, month = feb, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2552371}, url = {https://doi.org/10.5281/zenodo.2552371} }"}, {"record_number": "2553977", "url": "https://zenodo.org/record/2553977", "license": "cc-by-4.0", "citation": "Warren, I. & Jiggins, C. (2019 , February). Miscellaneous Heliconius wing photographs (2001-2019) Part 3. Zenodo. https://doi.org/10.5281/zenodo.2553977", "bibtex": "@misc{warren_2019_2553977, author = {Warren, Ian and Jiggins, Chris}, title = {{Miscellaneous Heliconius wing photographs (2001-2019) Part 3}}, month = feb, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2553977}, url = {https://doi.org/10.5281/zenodo.2553977} }"}, {"record_number": "2686762", "url": "https://zenodo.org/record/2686762", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Jiggins, C., Warren, I., & Wiltshire, E. (2019 , May). Cambridge butterfly wing collection batch 6. Zenodo. https://doi.org/10.5281/zenodo.2686762", "bibtex": "@misc{montejo_kovacevich_2019_2686762, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva}, title = {Cambridge butterfly wing collection batch 6}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2686762}, url = {https://doi.org/10.5281/zenodo.2686762} }"}, {"record_number": "2549524", "url": "https://zenodo.org/record/2549524", "license": "cc-by-4.0", "citation": "Jiggins, C. & Warren, I. (2019 , January). Cambridge butterfly wing collection - Chris Jiggins 2001/2 broods batch 1. Zenodo. https://doi.org/10.5281/zenodo.2549524", "bibtex": "@misc{jiggins_2019_2549524, author = {Jiggins, Chris and Warren, Ian}, title = {{Cambridge butterfly wing collection - Chris Jiggins 2001/2 broods batch 1}}, month = jan, year = 2019, publisher = {Zenodo}, version = 1, doi = {10.5281/zenodo.2549524}, url = {https://doi.org/10.5281/zenodo.2549524} }"}, {"record_number": "2550097", "url": "https://zenodo.org/record/2550097", "license": "cc-by-4.0", "citation": "Jiggins, C. & Warren, I. (2019 , January). Cambridge butterfly wing collection - Chris Jiggins 2001/2 broods batch 2. Zenodo. https://doi.org/10.5281/zenodo.2550097", "bibtex": "@misc{jiggins_2019_2550097, author = {Jiggins, Chris and Warren, Ian}, title = {{Cambridge butterfly wing collection - Chris Jiggins 2001/2 broods batch 2}}, month = jan, year = 2019, publisher = {Zenodo}, version = 1, doi = {10.5281/zenodo.2550097}, url = {https://doi.org/10.5281/zenodo.2550097} }"}, {"record_number": "4153502", "url": "https://zenodo.org/record/4153502", "license": "cc-by-4.0", "citation": "Meier, J. I., Salazar, P., Montejo-Kovacevich, G., Warren, I., & Jggins, C. (2020 , October). Cambridge butterfly wing collection - Patricio Salazar PhD wild specimens batch 3. Zenodo. https://doi.org/10.5281/zenodo.4153502", "bibtex": "@misc{joana_i_meier_2020_4153502, author = {Joana I. 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. Zenodo. https://doi.org/10.5281/zenodo.2813153", "bibtex": "@misc{montejo_kovacevich_2019_2813153, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Salazar, Camilo and Elias, Marianne and Gavins, Imogen and Wiltshire, Eva and Montgomery, Stephen and McMillan, Owen}, title = {{Cambridge and collaborators butterfly wing collection batch 10}}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2813153}, url = {https://doi.org/10.5281/zenodo.2813153} }"}, {"record_number": "1748277", "url": "https://zenodo.org/record/1748277", "license": "cc-by-4.0", "citation": "Salazar, P., Montejo-Kovacevich, G., Warren, I., & Jiggins, C. (2018 , December). Cambridge butterfly wing collection - Patricio Salazar PhD wild and bred specimens batch 1. Zenodo. https://doi.org/10.5281/zenodo.1748277", "bibtex": "@misc{salazar_2018_1748277, author = {Salazar, Patricio and Montejo-Kovacevich, Gabriela and Warren, Ian and Jiggins, Chris}, title = {{Cambridge butterfly wing collection - Patricio Salazar PhD wild and bred specimens batch 1}}, month = dec, year = 2018, publisher = {Zenodo}, doi = {10.5281/zenodo.1748277}, url = {https://doi.org/10.5281/zenodo.1748277} }"}, {"record_number": "2702457", "url": "https://zenodo.org/record/2702457", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Jiggins, C., Warren, I., & Wiltshire, E. (2019 , May). Cambridge butterfly wing collection batch 7. Zenodo. https://doi.org/10.5281/zenodo.2702457", "bibtex": "@misc{montejo_kovacevich_2019_2702457, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva}, title = {Cambridge butterfly wing collection batch 7}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2702457}, url = {https://doi.org/10.5281/zenodo.2702457} }"}, {"record_number": "2548678", "url": "https://zenodo.org/record/2548678", "license": "cc-by-4.0", "citation": "Salazar, P., Montejo-Kovacevich, G., Warren, I., & Jiggins, C. (2019 , January). Cambridge butterfly wing collection - Patricio Salazar PhD wild and bred specimens batch 2. Zenodo. https://doi.org/10.5281/zenodo.2548678", "bibtex": "@misc{salazar_2019_2548678, author = {Salazar, Patricio and Montejo-Kovacevich, Gabriela and Warren, Ian and Jiggins, Chris}, title = {{Cambridge butterfly wing collection - Patricio Salazar PhD wild and bred specimens batch 2}}, month = jan, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2548678}, url = {https://doi.org/10.5281/zenodo.2548678} }"}, {"record_number": "5561246", "url": "https://zenodo.org/record/5561246", "license": "cc-by-4.0", "citation": "Pinheiro de Castro, E., Jiggins, C., Lucas da Silva-Brand\u00e3o, K., Victor Lucci Freitas, A., Zikan Cardoso, M., Van Der Heijden, E., Meier, J., & Warren, I. (2022 , February). Brazilian Butterflies Collected December 2020 to January 2021. Zenodo. https://doi.org/10.5281/zenodo.5561246", "bibtex": "@misc{pinheiro_de_castro_2022_5561246, author = {Pinheiro de Castro, Erika and Jiggins, Christopher and Lucas da Silva-Brand\u00e3o, Karina and Victor Lucci Freitas, Andre and Zikan Cardoso, Marcio and Van Der Heijden, Eva and Meier, Joana and Warren, Ian}, title = {{Brazilian Butterflies Collected December 2020 to January 2021}}, month = feb, year = 2022, publisher = {Zenodo}, doi = {10.5281/zenodo.5561246}, url = {https://doi.org/10.5281/zenodo.5561246} }"}, {"record_number": "2707828", "url": "https://zenodo.org/record/2707828", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Jiggins, C., Warren, I., & Wiltshire, E. (2019 , May). Cambridge butterfly wing collection batch 8. Zenodo. https://doi.org/10.5281/zenodo.2707828", "bibtex": "@misc{montejo_kovacevich_2019_2707828, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva}, title = {Cambridge butterfly wing collection batch 8}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2707828}, url = {https://doi.org/10.5281/zenodo.2707828} }"}, {"record_number": "2714333", "url": "https://zenodo.org/record/2714333", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Jiggins, C., Warren, I., Wiltshire, E., & Gavins, I. (2019 , May). Cambridge butterfly wing collection batch 9. Zenodo. https://doi.org/10.5281/zenodo.2714333", "bibtex": "@misc{montejo_kovacevich_2019_2714333, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva and Gavins, Imogen}, title = {Cambridge butterfly wing collection batch 9}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2714333}, url = {https://doi.org/10.5281/zenodo.2714333} }"}, {"record_number": "4291095", "url": "https://zenodo.org/record/4291095", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., van der Heijden, E., & Jiggins, C. (2020 , November). Cambridge butterfly collection - GMK Broods Ikiam 2018. Zenodo. https://doi.org/10.5281/zenodo.4291095", "bibtex": "@misc{gabriela_montejo_kovacevich_2020_4291095, author = {Gabriela Montejo-Kovacevich and Eva van der Heijden and Chris Jiggins}, title = {{Cambridge butterfly collection - GMK Broods Ikiam 2018}}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4291095}, url = {https://doi.org/10.5281/zenodo.4291095} }"}, {"record_number": "5731587", "url": "https://zenodo.org/record/5731587", "license": "cc-by-4.0", "citation": "Meier, J., Barker, A., Jiggins, C., Warren, I., & Blow, R. (2021 , November). Cambridge butterfly wing collection - Ecuador, August 2019. Zenodo. https://doi.org/10.5281/zenodo.5731587", "bibtex": "@misc{joana_meier_2021_5731587, author = {Joana Meier and Annalie Barker and Chris Jiggins and Ian Warren and Rachel Blow}, title = {{Cambridge butterfly wing collection - Ecuador, August 2019}}, month = nov, year = 2021, publisher = {Zenodo}, version = {version 1}, doi = {10.5281/zenodo.5731587}, url = {https://doi.org/10.5281/zenodo.5731587} }"}, {"record_number": "3569598", "url": "https://zenodo.org/record/3569598", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Cookson, L., van der Heijden, E., Warren, I., Edwards, D. P., & Jiggins, C. (2019 , December). Cambridge butterfly collection - Loreto, Peru 2018. Zenodo. https://doi.org/10.5281/zenodo.3569598", "bibtex": "@misc{gabriela_montejo_kovacevich_2019_3569598, author = {Gabriela Montejo-Kovacevich and Letitia Cookson and Eva van der Heijden and Ian Warren and David P. Edwards and Chris Jiggins}, title = {Cambridge butterfly collection - Loreto, Peru 2018}, month = dec, year = 2019, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.3569598}, url = {https://doi.org/10.5281/zenodo.3569598} }"}, {"record_number": "4287444", "url": "https://zenodo.org/record/4287444", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Cookson, L., van der Heijden, E., Warren, I., Edwards, D. P., & Jiggins, C. (2020 , November). Cambridge butterfly collection - Loreto, Peru 2018 batch2. Zenodo. https://doi.org/10.5281/zenodo.4287444", "bibtex": "@misc{gabriela_montejo_kovacevich_2020_4287444, author = {Gabriela Montejo-Kovacevich and Letitia Cookson and Eva van der Heijden and Ian Warren and David P. Edwards and Chris Jiggins}, title = {{Cambridge butterfly collection - Loreto, Peru 2018 batch2}}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4287444}, url = {https://doi.org/10.5281/zenodo.4287444} }"}, {"record_number": "4288250", "url": "https://zenodo.org/record/4288250", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Cookson, L., van der Heijden, E., Warren, I., Edwards, D. P., & Jiggins, C. (2020 , November). Cambridge butterfly collection - Loreto, Peru 2018 batch3. Zenodo. https://doi.org/10.5281/zenodo.4288250", "bibtex": "@misc{gabriela_montejo_kovacevich_2020_4288250, author = {Gabriela Montejo-Kovacevich and Letitia Cookson and Eva van der Heijden and Ian Warren and David P. Edwards and Chris Jiggins}, title = {{Cambridge butterfly collection - Loreto, Peru 2018 batch3}}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4288250}, url = {https://doi.org/10.5281/zenodo.4288250} }"}, {"record_number": "5526257", "url": "https://zenodo.org/record/5526257", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Paynter, Q., & Ghane, A. (2021 , September). Heliconius erato cyrbia, Cook Islands (New Zealand) 2016, 2019, 2021. Zenodo. https://doi.org/10.5281/zenodo.5526257", "bibtex": "@misc{montejo_kovacevich_2021_5526257, author = {Montejo-Kovacevich, Gabriela and Paynter, Quentin and Ghane, Amin}, title = {{Heliconius erato cyrbia, Cook Islands (New Zealand) 2016, 2019, 2021}}, month = sep, year = 2021, publisher = {Zenodo}, doi = {10.5281/zenodo.5526257}, url = {https://doi.org/10.5281/zenodo.5526257} }"}, {"record_number": "2553501", "url": "https://zenodo.org/record/2553501", "license": "cc-by-4.0", "citation": "Warren, I. & Jiggins, C. (2019 , February). Miscellaneous Heliconius wing photographs (2001-2019) Part 2. Zenodo. https://doi.org/10.5281/zenodo.2553501", "bibtex": "@misc{warren_2019_2553501, author = {Warren, Ian and Jiggins, Chris}, title = {{Miscellaneous Heliconius wing photographs (2001-2019) Part 2}}, month = feb, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2553501}, url = {https://doi.org/10.5281/zenodo.2553501} }"}, {"record_number": "2735056", "url": "https://zenodo.org/record/2735056", "license": "cc-by-4.0", "citation": "Salazar, C., Montejo-Kovacevich, G., Jiggins, C., Warren, I., & Gavins, I. (2019 , May). Camilo Salazar and Cambridge butterfly wing collection batch 1. Zenodo. https://doi.org/10.5281/zenodo.2735056", "bibtex": "@misc{salazar_2019_2735056, author = {Salazar, Camilo and Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Gavins, Imogen}, title = {{Camilo Salazar and Cambridge butterfly wing collection batch 1}}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2735056}, url = {https://doi.org/10.5281/zenodo.2735056} }"}, {"record_number": "2554218", "url": "https://zenodo.org/record/2554218", "license": "cc-by-4.0", "citation": "Mattila, A., Jiggins, C., & Warren, I. (2019 , February). University of Helsinki butterfly wing collection - Anniina Mattila field caught specimens. Zenodo. https://doi.org/10.5281/zenodo.2554218", "bibtex": "@misc{mattila_2019_2554218, author = {Mattila, Anniina and Jiggins, Chris and Warren, Ian}, title = {{University of Helsinki butterfly wing collection - Anniina Mattila field caught specimens}}, month = feb, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2554218}, url = {https://doi.org/10.5281/zenodo.2554218} }"}, {"record_number": "2555086", "url": "https://zenodo.org/record/2555086", "license": "cc-by-4.0", "citation": "Mattila, A., Jiggins, C., & Warren, I. (2019 , February). University of Helsinki butterfly collection - Anniina Mattila bred specimens. Zenodo. https://doi.org/10.5281/zenodo.2555086", "bibtex": "@misc{mattila_2019_2555086, author = {Mattila, Anniina and Jiggins, Chris and Warren, Ian}, title = {{University of Helsinki butterfly collection - Anniina Mattila bred specimens}}, month = feb, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2555086}, url = {https://doi.org/10.5281/zenodo.2555086} }"}]
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notebooks/make_Jiggins_Img_Master.ipynb
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| 356 |
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| 362 |
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| 363 |
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|
| 364 |
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|
| 365 |
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|
| 366 |
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" <tr style=\"text-align: right;\">\n",
|
| 367 |
+
" <th></th>\n",
|
| 368 |
+
" <th>CAMID</th>\n",
|
| 369 |
+
" <th>X</th>\n",
|
| 370 |
+
" <th>Image_name</th>\n",
|
| 371 |
+
" <th>zenodo_name</th>\n",
|
| 372 |
+
" <th>zenodo_link</th>\n",
|
| 373 |
+
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|
| 374 |
+
" </thead>\n",
|
| 375 |
+
" <tbody>\n",
|
| 376 |
+
" <tr>\n",
|
| 377 |
+
" <th>22895</th>\n",
|
| 378 |
+
" <td>CAM017512</td>\n",
|
| 379 |
+
" <td>5226</td>\n",
|
| 380 |
+
" <td>CAM017512_d.JPG</td>\n",
|
| 381 |
+
" <td>CAM.coll.images.batch1_v2.csv</td>\n",
|
| 382 |
+
" <td>https://zenodo.org/record/3082688</td>\n",
|
| 383 |
+
" </tr>\n",
|
| 384 |
+
" <tr>\n",
|
| 385 |
+
" <th>36246</th>\n",
|
| 386 |
+
" <td>CAM041681</td>\n",
|
| 387 |
+
" <td>39690</td>\n",
|
| 388 |
+
" <td>CAM041681_d.CR2</td>\n",
|
| 389 |
+
" <td>0.gmk.broods.all.csv</td>\n",
|
| 390 |
+
" <td>https://zenodo.org/record/4291095</td>\n",
|
| 391 |
+
" </tr>\n",
|
| 392 |
+
" <tr>\n",
|
| 393 |
+
" <th>38630</th>\n",
|
| 394 |
+
" <td>CAM043178</td>\n",
|
| 395 |
+
" <td>29793</td>\n",
|
| 396 |
+
" <td>CAM043178_fwv.CR2</td>\n",
|
| 397 |
+
" <td>batch1.Peru.image.names.Zenodo.csv</td>\n",
|
| 398 |
+
" <td>https://zenodo.org/record/3569598</td>\n",
|
| 399 |
+
" </tr>\n",
|
| 400 |
+
" <tr>\n",
|
| 401 |
+
" <th>45984</th>\n",
|
| 402 |
+
" <td>CAM044484</td>\n",
|
| 403 |
+
" <td>34635</td>\n",
|
| 404 |
+
" <td>CAM044484_hwd.CR2</td>\n",
|
| 405 |
+
" <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
|
| 406 |
+
" <td>https://zenodo.org/record/4287444</td>\n",
|
| 407 |
+
" </tr>\n",
|
| 408 |
+
" <tr>\n",
|
| 409 |
+
" <th>15393</th>\n",
|
| 410 |
+
" <td>CAM011382</td>\n",
|
| 411 |
+
" <td>28038</td>\n",
|
| 412 |
+
" <td>CAM011382_d.JPG</td>\n",
|
| 413 |
+
" <td>2001_2.broods.batch.2.csv</td>\n",
|
| 414 |
+
" <td>https://zenodo.org/record/2550097</td>\n",
|
| 415 |
+
" </tr>\n",
|
| 416 |
+
" <tr>\n",
|
| 417 |
+
" <th>44443</th>\n",
|
| 418 |
+
" <td>CAM044299</td>\n",
|
| 419 |
+
" <td>33864</td>\n",
|
| 420 |
+
" <td>CAM044299_d.JPG</td>\n",
|
| 421 |
+
" <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
|
| 422 |
+
" <td>https://zenodo.org/record/4287444</td>\n",
|
| 423 |
+
" </tr>\n",
|
| 424 |
+
" <tr>\n",
|
| 425 |
+
" <th>7117</th>\n",
|
| 426 |
+
" <td>CAM001175</td>\n",
|
| 427 |
+
" <td>7908</td>\n",
|
| 428 |
+
" <td>CAM001175_d.JPG</td>\n",
|
| 429 |
+
" <td>CAM.coll.images.batch4.csv</td>\n",
|
| 430 |
+
" <td>https://zenodo.org/record/2682669</td>\n",
|
| 431 |
+
" </tr>\n",
|
| 432 |
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" </tbody>\n",
|
| 433 |
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|
| 434 |
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|
| 435 |
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|
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|
| 438 |
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"22895 CAM017512 5226 CAM017512_d.JPG \n",
|
| 439 |
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"36246 CAM041681 39690 CAM041681_d.CR2 \n",
|
| 440 |
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|
| 441 |
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|
| 442 |
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|
| 443 |
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|
| 444 |
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"7117 CAM001175 7908 CAM001175_d.JPG \n",
|
| 445 |
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"\n",
|
| 446 |
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" zenodo_name zenodo_link \n",
|
| 447 |
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|
| 448 |
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"36246 0.gmk.broods.all.csv https://zenodo.org/record/4291095 \n",
|
| 449 |
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"38630 batch1.Peru.image.names.Zenodo.csv https://zenodo.org/record/3569598 \n",
|
| 450 |
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"45984 batch2.Peru.image.names.Zenodo.csv https://zenodo.org/record/4287444 \n",
|
| 451 |
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"15393 2001_2.broods.batch.2.csv https://zenodo.org/record/2550097 \n",
|
| 452 |
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"44443 batch2.Peru.image.names.Zenodo.csv https://zenodo.org/record/4287444 \n",
|
| 453 |
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"7117 CAM.coll.images.batch4.csv https://zenodo.org/record/2682669 "
|
| 454 |
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]
|
| 455 |
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|
| 456 |
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|
| 457 |
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|
| 458 |
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|
| 459 |
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}
|
| 460 |
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],
|
| 461 |
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"source": [
|
| 462 |
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"jiggins_imgs = jiggins[['CAMID', 'X', 'Image_name', 'zenodo_name', 'zenodo_link']].dropna()\n",
|
| 463 |
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"jiggins_imgs.sample(7)"
|
| 464 |
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|
| 465 |
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},
|
| 466 |
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|
| 467 |
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|
| 468 |
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|
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| 490 |
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| 491 |
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|
| 492 |
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|
| 493 |
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| 494 |
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|
| 495 |
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| 496 |
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| 497 |
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| 498 |
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|
| 499 |
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|
| 500 |
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|
| 501 |
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" <th>10528</th>\n",
|
| 502 |
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|
| 503 |
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|
| 504 |
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|
| 505 |
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" <td>CAM.coll.images.batch6.csv</td>\n",
|
| 506 |
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" <td>https://zenodo.org/record/2686762</td>\n",
|
| 507 |
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" </tr>\n",
|
| 508 |
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" <tr>\n",
|
| 509 |
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" <th>19587</th>\n",
|
| 510 |
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" <td>CAM016660</td>\n",
|
| 511 |
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|
| 512 |
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" <td>CAM016660_v.JPG</td>\n",
|
| 513 |
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" <td>CAM.coll.images.batch1_v2.csv</td>\n",
|
| 514 |
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" <td>https://zenodo.org/record/3082688</td>\n",
|
| 515 |
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" </tr>\n",
|
| 516 |
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" <tr>\n",
|
| 517 |
+
" <th>25858</th>\n",
|
| 518 |
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" <td>CAM017989</td>\n",
|
| 519 |
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" <td>17211</td>\n",
|
| 520 |
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" <td>CAM017989_v_whitestandard.JPG</td>\n",
|
| 521 |
+
" <td>CAM.coll.patricio.batch1.csv</td>\n",
|
| 522 |
+
" <td>https://zenodo.org/record/1748277</td>\n",
|
| 523 |
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" </tr>\n",
|
| 524 |
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" <tr>\n",
|
| 525 |
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" <th>10042</th>\n",
|
| 526 |
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" <td>CAM008668</td>\n",
|
| 527 |
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" <td>46436</td>\n",
|
| 528 |
+
" <td>CAM008668_d.JPG</td>\n",
|
| 529 |
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" <td>occurences_and_multimedia.csv</td>\n",
|
| 530 |
+
" <td>https://zenodo.org/record/3477891</td>\n",
|
| 531 |
+
" </tr>\n",
|
| 532 |
+
" <tr>\n",
|
| 533 |
+
" <th>30646</th>\n",
|
| 534 |
+
" <td>CAM040217</td>\n",
|
| 535 |
+
" <td>47915</td>\n",
|
| 536 |
+
" <td>CAM040217_v.JPG</td>\n",
|
| 537 |
+
" <td>occurences_and_multimedia.csv</td>\n",
|
| 538 |
+
" <td>https://zenodo.org/record/3477891</td>\n",
|
| 539 |
+
" </tr>\n",
|
| 540 |
+
" <tr>\n",
|
| 541 |
+
" <th>42889</th>\n",
|
| 542 |
+
" <td>CAM044091</td>\n",
|
| 543 |
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" <td>33084</td>\n",
|
| 544 |
+
" <td>CAM044091_fwd.JPG</td>\n",
|
| 545 |
+
" <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
|
| 546 |
+
" <td>https://zenodo.org/record/4287444</td>\n",
|
| 547 |
+
" </tr>\n",
|
| 548 |
+
" <tr>\n",
|
| 549 |
+
" <th>39215</th>\n",
|
| 550 |
+
" <td>CAM043344</td>\n",
|
| 551 |
+
" <td>30372</td>\n",
|
| 552 |
+
" <td>CAM043344_fwd.JPG</td>\n",
|
| 553 |
+
" <td>batch1.Peru.image.names.Zenodo.csv</td>\n",
|
| 554 |
+
" <td>https://zenodo.org/record/3569598</td>\n",
|
| 555 |
+
" </tr>\n",
|
| 556 |
+
" </tbody>\n",
|
| 557 |
+
"</table>\n",
|
| 558 |
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"</div>"
|
| 559 |
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],
|
| 560 |
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"text/plain": [
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" CAMID X Image_name \\\n",
|
| 562 |
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"10528 CAM008821 9887 CAM008821_d.JPG \n",
|
| 563 |
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"19587 CAM016660 3856 CAM016660_v.JPG \n",
|
| 564 |
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"25858 CAM017989 17211 CAM017989_v_whitestandard.JPG \n",
|
| 565 |
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"10042 CAM008668 46436 CAM008668_d.JPG \n",
|
| 566 |
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"30646 CAM040217 47915 CAM040217_v.JPG \n",
|
| 567 |
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"42889 CAM044091 33084 CAM044091_fwd.JPG \n",
|
| 568 |
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"39215 CAM043344 30372 CAM043344_fwd.JPG \n",
|
| 569 |
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"\n",
|
| 570 |
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" zenodo_name zenodo_link \n",
|
| 571 |
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"10528 CAM.coll.images.batch6.csv https://zenodo.org/record/2686762 \n",
|
| 572 |
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"19587 CAM.coll.images.batch1_v2.csv https://zenodo.org/record/3082688 \n",
|
| 573 |
+
"25858 CAM.coll.patricio.batch1.csv https://zenodo.org/record/1748277 \n",
|
| 574 |
+
"10042 occurences_and_multimedia.csv https://zenodo.org/record/3477891 \n",
|
| 575 |
+
"30646 occurences_and_multimedia.csv https://zenodo.org/record/3477891 \n",
|
| 576 |
+
"42889 batch2.Peru.image.names.Zenodo.csv https://zenodo.org/record/4287444 \n",
|
| 577 |
+
"39215 batch1.Peru.image.names.Zenodo.csv https://zenodo.org/record/3569598 "
|
| 578 |
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]
|
| 579 |
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|
| 580 |
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|
| 581 |
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|
| 582 |
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|
| 583 |
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}
|
| 584 |
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],
|
| 585 |
+
"source": [
|
| 586 |
+
"jiggins_imgs[jiggins_imgs.Image_name.str.contains('JPG')].sample(7)"
|
| 587 |
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]
|
| 588 |
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},
|
| 589 |
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|
| 590 |
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|
| 591 |
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|
| 592 |
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|
| 593 |
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| 594 |
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|
| 595 |
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|
| 613 |
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|
| 614 |
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|
| 615 |
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|
| 616 |
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|
| 617 |
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" <th>Image_name</th>\n",
|
| 618 |
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|
| 619 |
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|
| 620 |
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|
| 621 |
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" </thead>\n",
|
| 622 |
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" <tbody>\n",
|
| 623 |
+
" <tr>\n",
|
| 624 |
+
" <th>8296</th>\n",
|
| 625 |
+
" <td>CAM008147</td>\n",
|
| 626 |
+
" <td>44302</td>\n",
|
| 627 |
+
" <td>8147v.jpg</td>\n",
|
| 628 |
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" <td>occurences_and_multimedia.csv</td>\n",
|
| 629 |
+
" <td>https://zenodo.org/record/3477891</td>\n",
|
| 630 |
+
" </tr>\n",
|
| 631 |
+
" <tr>\n",
|
| 632 |
+
" <th>15626</th>\n",
|
| 633 |
+
" <td>CAM011437</td>\n",
|
| 634 |
+
" <td>44275</td>\n",
|
| 635 |
+
" <td>11437v.jpg</td>\n",
|
| 636 |
+
" <td>occurences_and_multimedia.csv</td>\n",
|
| 637 |
+
" <td>https://zenodo.org/record/3477891</td>\n",
|
| 638 |
+
" </tr>\n",
|
| 639 |
+
" <tr>\n",
|
| 640 |
+
" <th>16975</th>\n",
|
| 641 |
+
" <td>CAM012169</td>\n",
|
| 642 |
+
" <td>25057</td>\n",
|
| 643 |
+
" <td>12169v.jpg</td>\n",
|
| 644 |
+
" <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
|
| 645 |
+
" <td>https://zenodo.org/record/2552371</td>\n",
|
| 646 |
+
" </tr>\n",
|
| 647 |
+
" <tr>\n",
|
| 648 |
+
" <th>14097</th>\n",
|
| 649 |
+
" <td>CAM010773</td>\n",
|
| 650 |
+
" <td>23942</td>\n",
|
| 651 |
+
" <td>10773v.jpg</td>\n",
|
| 652 |
+
" <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
|
| 653 |
+
" <td>https://zenodo.org/record/2552371</td>\n",
|
| 654 |
+
" </tr>\n",
|
| 655 |
+
" <tr>\n",
|
| 656 |
+
" <th>15970</th>\n",
|
| 657 |
+
" <td>CAM011513</td>\n",
|
| 658 |
+
" <td>24565</td>\n",
|
| 659 |
+
" <td>11513v.jpg</td>\n",
|
| 660 |
+
" <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
|
| 661 |
+
" <td>https://zenodo.org/record/2552371</td>\n",
|
| 662 |
+
" </tr>\n",
|
| 663 |
+
" <tr>\n",
|
| 664 |
+
" <th>13349</th>\n",
|
| 665 |
+
" <td>CAM010485</td>\n",
|
| 666 |
+
" <td>23640</td>\n",
|
| 667 |
+
" <td>10485d.jpg</td>\n",
|
| 668 |
+
" <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
|
| 669 |
+
" <td>https://zenodo.org/record/2552371</td>\n",
|
| 670 |
+
" </tr>\n",
|
| 671 |
+
" <tr>\n",
|
| 672 |
+
" <th>16919</th>\n",
|
| 673 |
+
" <td>CAM012124</td>\n",
|
| 674 |
+
" <td>25001</td>\n",
|
| 675 |
+
" <td>12124v.jpg</td>\n",
|
| 676 |
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| 746 |
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| 747 |
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| 748 |
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| 757 |
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|
| 758 |
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|
| 759 |
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| 760 |
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| 765 |
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| 766 |
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| 767 |
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| 768 |
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| 770 |
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|
| 772 |
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|
| 773 |
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|
| 774 |
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|
| 775 |
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|
| 776 |
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|
| 777 |
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|
| 778 |
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|
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| 780 |
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|
| 781 |
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|
| 782 |
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|
| 783 |
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|
| 784 |
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|
| 785 |
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|
| 786 |
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|
| 787 |
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|
| 788 |
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|
| 789 |
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|
| 790 |
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|
| 791 |
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|
| 792 |
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|
| 793 |
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|
| 794 |
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|
| 795 |
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|
| 796 |
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|
| 797 |
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|
| 798 |
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|
| 799 |
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|
| 800 |
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| 801 |
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|
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|
| 803 |
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|
| 804 |
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|
| 805 |
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|
| 806 |
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|
| 807 |
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|
| 808 |
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| 809 |
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|
| 810 |
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| 829 |
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| 832 |
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| 878 |
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| 879 |
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| 880 |
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| 881 |
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| 882 |
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|
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|
| 888 |
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|
| 889 |
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|
| 890 |
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|
| 891 |
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|
| 892 |
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|
| 893 |
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|
| 894 |
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|
| 895 |
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|
| 896 |
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|
| 897 |
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|
| 898 |
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|
| 899 |
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" <td>CAM044261_d.JPG</td>\n",
|
| 900 |
+
" <td>batch3.Peru.image.names.Zenodo.csv</td>\n",
|
| 901 |
+
" <td>https://zenodo.org/record/4288250</td>\n",
|
| 902 |
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" </tr>\n",
|
| 903 |
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|
| 904 |
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" <th>14298</th>\n",
|
| 905 |
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|
| 906 |
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|
| 907 |
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|
| 908 |
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|
| 909 |
+
" <td>https://zenodo.org/record/2549524</td>\n",
|
| 910 |
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|
| 911 |
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|
| 912 |
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|
| 913 |
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|
| 914 |
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|
| 915 |
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" <td>CAM016524_d.JPG</td>\n",
|
| 916 |
+
" <td>CAM.coll.images.batch1.csv</td>\n",
|
| 917 |
+
" <td>https://zenodo.org/record/1247307</td>\n",
|
| 918 |
+
" </tr>\n",
|
| 919 |
+
" <tr>\n",
|
| 920 |
+
" <th>3229</th>\n",
|
| 921 |
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|
| 922 |
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|
| 923 |
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|
| 924 |
+
" <td>0.sheffield.ps.nn.ikiam.batch2.csv</td>\n",
|
| 925 |
+
" <td>https://zenodo.org/record/4288311</td>\n",
|
| 926 |
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" </tr>\n",
|
| 927 |
+
" <tr>\n",
|
| 928 |
+
" <th>34948</th>\n",
|
| 929 |
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" <td>CAM041352</td>\n",
|
| 930 |
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" <td>14340</td>\n",
|
| 931 |
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" <td>CAM041352_d.JPG</td>\n",
|
| 932 |
+
" <td>CAM.coll.images.batch9.csv</td>\n",
|
| 933 |
+
" <td>https://zenodo.org/record/2714333</td>\n",
|
| 934 |
+
" </tr>\n",
|
| 935 |
+
" <tr>\n",
|
| 936 |
+
" <th>28072</th>\n",
|
| 937 |
+
" <td>CAM021080</td>\n",
|
| 938 |
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" <td>11587</td>\n",
|
| 939 |
+
" <td>CAM021080_d.JPG</td>\n",
|
| 940 |
+
" <td>CAM.coll.images.batch7.csv</td>\n",
|
| 941 |
+
" <td>https://zenodo.org/record/2702457</td>\n",
|
| 942 |
+
" </tr>\n",
|
| 943 |
+
" </tbody>\n",
|
| 944 |
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|
| 945 |
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|
| 946 |
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|
| 950 |
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|
| 951 |
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|
| 952 |
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|
| 953 |
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|
| 954 |
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|
| 955 |
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|
| 956 |
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|
| 957 |
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" zenodo_link \n",
|
| 958 |
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|
| 959 |
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|
| 960 |
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|
| 961 |
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|
| 962 |
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|
| 963 |
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|
| 964 |
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|
| 965 |
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|
| 966 |
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| 967 |
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| 968 |
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| 969 |
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|
| 970 |
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| 971 |
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| 979 |
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| 980 |
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| 985 |
+
"{'JPG', 'CR2', 'tif', 'jpeg', 'JPG(1)', 'jpg'}\n"
|
| 986 |
+
]
|
| 987 |
+
}
|
| 988 |
+
],
|
| 989 |
+
"source": [
|
| 990 |
+
"check_filetypes = []\n",
|
| 991 |
+
"for img_name in list(jiggins_imgs.Image_name.unique()):\n",
|
| 992 |
+
" check_filetypes.append(img_name.split(\".\")[1])\n",
|
| 993 |
+
"\n",
|
| 994 |
+
"print(set(check_filetypes))"
|
| 995 |
+
]
|
| 996 |
+
},
|
| 997 |
+
{
|
| 998 |
+
"cell_type": "code",
|
| 999 |
+
"execution_count": 14,
|
| 1000 |
+
"metadata": {},
|
| 1001 |
+
"outputs": [],
|
| 1002 |
+
"source": [
|
| 1003 |
+
"# Note CR2 is raw, may be duplicates, would need conversion\n",
|
| 1004 |
+
"file_types = [\"JPG\", \"jpg\", \"jpeg\", \"tif\", \"JPG(1)\", \"CR2\"]"
|
| 1005 |
+
]
|
| 1006 |
+
},
|
| 1007 |
+
{
|
| 1008 |
+
"cell_type": "code",
|
| 1009 |
+
"execution_count": 15,
|
| 1010 |
+
"metadata": {},
|
| 1011 |
+
"outputs": [
|
| 1012 |
+
{
|
| 1013 |
+
"data": {
|
| 1014 |
+
"text/plain": [
|
| 1015 |
+
"37821"
|
| 1016 |
+
]
|
| 1017 |
+
},
|
| 1018 |
+
"execution_count": 15,
|
| 1019 |
+
"metadata": {},
|
| 1020 |
+
"output_type": "execute_result"
|
| 1021 |
+
}
|
| 1022 |
+
],
|
| 1023 |
+
"source": [
|
| 1024 |
+
"images = []\n",
|
| 1025 |
+
"\n",
|
| 1026 |
+
"for img_name in list(jiggins_imgs.Image_name.unique()):\n",
|
| 1027 |
+
" if img_name.split(\".\")[1] in file_types:\n",
|
| 1028 |
+
" images.append(img_name)\n",
|
| 1029 |
+
"\n",
|
| 1030 |
+
"len(images)"
|
| 1031 |
+
]
|
| 1032 |
+
},
|
| 1033 |
+
{
|
| 1034 |
+
"cell_type": "code",
|
| 1035 |
+
"execution_count": 16,
|
| 1036 |
+
"metadata": {},
|
| 1037 |
+
"outputs": [
|
| 1038 |
+
{
|
| 1039 |
+
"name": "stdout",
|
| 1040 |
+
"output_type": "stream",
|
| 1041 |
+
"text": [
|
| 1042 |
+
"<class 'pandas.core.frame.DataFrame'>\n",
|
| 1043 |
+
"Index: 49359 entries, 433 to 49791\n",
|
| 1044 |
+
"Data columns (total 5 columns):\n",
|
| 1045 |
+
" # Column Non-Null Count Dtype \n",
|
| 1046 |
+
"--- ------ -------------- ----- \n",
|
| 1047 |
+
" 0 CAMID 49359 non-null object\n",
|
| 1048 |
+
" 1 X 49359 non-null int64 \n",
|
| 1049 |
+
" 2 Image_name 49359 non-null object\n",
|
| 1050 |
+
" 3 zenodo_name 49359 non-null object\n",
|
| 1051 |
+
" 4 zenodo_link 49359 non-null object\n",
|
| 1052 |
+
"dtypes: int64(1), object(4)\n",
|
| 1053 |
+
"memory usage: 2.3+ MB\n"
|
| 1054 |
+
]
|
| 1055 |
+
}
|
| 1056 |
+
],
|
| 1057 |
+
"source": [
|
| 1058 |
+
"img = jiggins_imgs[jiggins_imgs.Image_name.isin(images)]\n",
|
| 1059 |
+
"img.info()"
|
| 1060 |
+
]
|
| 1061 |
+
},
|
| 1062 |
+
{
|
| 1063 |
+
"cell_type": "code",
|
| 1064 |
+
"execution_count": 17,
|
| 1065 |
+
"metadata": {},
|
| 1066 |
+
"outputs": [],
|
| 1067 |
+
"source": [
|
| 1068 |
+
"def get_file_type(filename):\n",
|
| 1069 |
+
" jpgs = [\"JPG\", \"jpg\", \"jpeg\", \"JPG(1)\"]\n",
|
| 1070 |
+
" file_type = filename.split(\".\")[1]\n",
|
| 1071 |
+
" if file_type in jpgs:\n",
|
| 1072 |
+
" return \"jpg\"\n",
|
| 1073 |
+
" elif file_type == \"tif\":\n",
|
| 1074 |
+
" return \"tif\"\n",
|
| 1075 |
+
" elif file_type == \"CR2\":\n",
|
| 1076 |
+
" return \"raw\"\n",
|
| 1077 |
+
" else:\n",
|
| 1078 |
+
" print(f\"{file_type} does not match known file types\")\n",
|
| 1079 |
+
" return None"
|
| 1080 |
+
]
|
| 1081 |
+
},
|
| 1082 |
+
{
|
| 1083 |
+
"cell_type": "code",
|
| 1084 |
+
"execution_count": 18,
|
| 1085 |
+
"metadata": {},
|
| 1086 |
+
"outputs": [
|
| 1087 |
+
{
|
| 1088 |
+
"name": "stdout",
|
| 1089 |
+
"output_type": "stream",
|
| 1090 |
+
"text": [
|
| 1091 |
+
"<class 'pandas.core.frame.DataFrame'>\n",
|
| 1092 |
+
"Index: 49359 entries, 433 to 49791\n",
|
| 1093 |
+
"Data columns (total 6 columns):\n",
|
| 1094 |
+
" # Column Non-Null Count Dtype \n",
|
| 1095 |
+
"--- ------ -------------- ----- \n",
|
| 1096 |
+
" 0 CAMID 49359 non-null object\n",
|
| 1097 |
+
" 1 X 49359 non-null int64 \n",
|
| 1098 |
+
" 2 Image_name 49359 non-null object\n",
|
| 1099 |
+
" 3 zenodo_name 49359 non-null object\n",
|
| 1100 |
+
" 4 zenodo_link 49359 non-null object\n",
|
| 1101 |
+
" 5 file_type 49359 non-null object\n",
|
| 1102 |
+
"dtypes: int64(1), object(5)\n",
|
| 1103 |
+
"memory usage: 2.6+ MB\n"
|
| 1104 |
+
]
|
| 1105 |
+
}
|
| 1106 |
+
],
|
| 1107 |
+
"source": [
|
| 1108 |
+
"img[\"file_type\"] = img[\"Image_name\"].apply(get_file_type)\n",
|
| 1109 |
+
"img.info()"
|
| 1110 |
+
]
|
| 1111 |
+
},
|
| 1112 |
+
{
|
| 1113 |
+
"cell_type": "code",
|
| 1114 |
+
"execution_count": 20,
|
| 1115 |
+
"metadata": {},
|
| 1116 |
+
"outputs": [],
|
| 1117 |
+
"source": [
|
| 1118 |
+
"img.to_csv(\"../zendo_img_master.csv\", index = False)"
|
| 1119 |
+
]
|
| 1120 |
+
},
|
| 1121 |
+
{
|
| 1122 |
+
"cell_type": "code",
|
| 1123 |
+
"execution_count": 21,
|
| 1124 |
+
"metadata": {},
|
| 1125 |
+
"outputs": [
|
| 1126 |
+
{
|
| 1127 |
+
"name": "stdout",
|
| 1128 |
+
"output_type": "stream",
|
| 1129 |
+
"text": [
|
| 1130 |
+
"Unnamed: 0 49956\n",
|
| 1131 |
+
"CAMID 12586\n",
|
| 1132 |
+
"X 49956\n",
|
| 1133 |
+
"Image_name 37843\n",
|
| 1134 |
+
"dtype: int64\n",
|
| 1135 |
+
"\n",
|
| 1136 |
+
"CAMID 12586\n",
|
| 1137 |
+
"X 49359\n",
|
| 1138 |
+
"Image_name 37821\n",
|
| 1139 |
+
"zenodo_name 36\n",
|
| 1140 |
+
"dtype: int64\n"
|
| 1141 |
+
]
|
| 1142 |
+
}
|
| 1143 |
+
],
|
| 1144 |
+
"source": [
|
| 1145 |
+
"print(jiggins[list(jiggins.columns)[:4]].nunique())\n",
|
| 1146 |
+
"print()\n",
|
| 1147 |
+
"print(img[list(img.columns[:4])].nunique())"
|
| 1148 |
+
]
|
| 1149 |
+
},
|
| 1150 |
+
{
|
| 1151 |
+
"cell_type": "code",
|
| 1152 |
+
"execution_count": 22,
|
| 1153 |
+
"metadata": {},
|
| 1154 |
+
"outputs": [
|
| 1155 |
+
{
|
| 1156 |
+
"name": "stdout",
|
| 1157 |
+
"output_type": "stream",
|
| 1158 |
+
"text": [
|
| 1159 |
+
"<class 'pandas.core.frame.DataFrame'>\n",
|
| 1160 |
+
"RangeIndex: 49956 entries, 0 to 49955\n",
|
| 1161 |
+
"Data columns (total 4 columns):\n",
|
| 1162 |
+
" # Column Non-Null Count Dtype \n",
|
| 1163 |
+
"--- ------ -------------- ----- \n",
|
| 1164 |
+
" 0 Unnamed: 0 49956 non-null int64 \n",
|
| 1165 |
+
" 1 CAMID 49359 non-null object\n",
|
| 1166 |
+
" 2 X 49956 non-null int64 \n",
|
| 1167 |
+
" 3 Image_name 49545 non-null object\n",
|
| 1168 |
+
"dtypes: int64(2), object(2)\n",
|
| 1169 |
+
"memory usage: 1.5+ MB\n",
|
| 1170 |
+
"None\n",
|
| 1171 |
+
"\n",
|
| 1172 |
+
"<class 'pandas.core.frame.DataFrame'>\n",
|
| 1173 |
+
"Index: 49359 entries, 433 to 49791\n",
|
| 1174 |
+
"Data columns (total 4 columns):\n",
|
| 1175 |
+
" # Column Non-Null Count Dtype \n",
|
| 1176 |
+
"--- ------ -------------- ----- \n",
|
| 1177 |
+
" 0 CAMID 49359 non-null object\n",
|
| 1178 |
+
" 1 X 49359 non-null int64 \n",
|
| 1179 |
+
" 2 Image_name 49359 non-null object\n",
|
| 1180 |
+
" 3 zenodo_name 49359 non-null object\n",
|
| 1181 |
+
"dtypes: int64(1), object(3)\n",
|
| 1182 |
+
"memory usage: 1.9+ MB\n",
|
| 1183 |
+
"None\n"
|
| 1184 |
+
]
|
| 1185 |
+
}
|
| 1186 |
+
],
|
| 1187 |
+
"source": [
|
| 1188 |
+
"print(jiggins[list(jiggins.columns)[:4]].info())\n",
|
| 1189 |
+
"print()\n",
|
| 1190 |
+
"print(img[list(img.columns[:4])].info())"
|
| 1191 |
+
]
|
| 1192 |
+
},
|
| 1193 |
+
{
|
| 1194 |
+
"cell_type": "markdown",
|
| 1195 |
+
"metadata": {},
|
| 1196 |
+
"source": [
|
| 1197 |
+
"We want to match these up on `X`, since all entries have a value and it is unique."
|
| 1198 |
+
]
|
| 1199 |
+
},
|
| 1200 |
+
{
|
| 1201 |
+
"cell_type": "code",
|
| 1202 |
+
"execution_count": 23,
|
| 1203 |
+
"metadata": {},
|
| 1204 |
+
"outputs": [
|
| 1205 |
+
{
|
| 1206 |
+
"name": "stdout",
|
| 1207 |
+
"output_type": "stream",
|
| 1208 |
+
"text": [
|
| 1209 |
+
"<class 'pandas.core.frame.DataFrame'>\n",
|
| 1210 |
+
"Index: 49359 entries, 433 to 49791\n",
|
| 1211 |
+
"Data columns (total 25 columns):\n",
|
| 1212 |
+
" # Column Non-Null Count Dtype \n",
|
| 1213 |
+
"--- ------ -------------- ----- \n",
|
| 1214 |
+
" 0 Unnamed: 0 49359 non-null int64 \n",
|
| 1215 |
+
" 1 CAMID 49359 non-null object \n",
|
| 1216 |
+
" 2 X 49359 non-null int64 \n",
|
| 1217 |
+
" 3 Image_name 49359 non-null object \n",
|
| 1218 |
+
" 4 Side 48288 non-null object \n",
|
| 1219 |
+
" 5 zenodo_name 49359 non-null object \n",
|
| 1220 |
+
" 6 zenodo_link 49359 non-null object \n",
|
| 1221 |
+
" 7 Sequence 48424 non-null object \n",
|
| 1222 |
+
" 8 Taxonomic.Name 45473 non-null object \n",
|
| 1223 |
+
" 9 Locality 34015 non-null object \n",
|
| 1224 |
+
" 10 Sample.accession 5884 non-null object \n",
|
| 1225 |
+
" 11 Collected.by 5280 non-null object \n",
|
| 1226 |
+
" 12 Other.Id 14382 non-null object \n",
|
| 1227 |
+
" 13 Collected.By 0 non-null float64\n",
|
| 1228 |
+
" 14 Date 33718 non-null object \n",
|
| 1229 |
+
" 15 Dataset 40405 non-null object \n",
|
| 1230 |
+
" 16 Store 39485 non-null object \n",
|
| 1231 |
+
" 17 Eclosion.Date 97 non-null object \n",
|
| 1232 |
+
" 18 Brood 14942 non-null object \n",
|
| 1233 |
+
" 19 Death.Date 318 non-null object \n",
|
| 1234 |
+
" 20 Cross.Type 5133 non-null object \n",
|
| 1235 |
+
" 21 Stage 15 non-null object \n",
|
| 1236 |
+
" 22 Sex 36243 non-null object \n",
|
| 1237 |
+
" 23 Unit.Type 33890 non-null object \n",
|
| 1238 |
+
" 24 Verbatim.Coordinates 0 non-null float64\n",
|
| 1239 |
+
"dtypes: float64(2), int64(2), object(21)\n",
|
| 1240 |
+
"memory usage: 9.8+ MB\n"
|
| 1241 |
+
]
|
| 1242 |
+
}
|
| 1243 |
+
],
|
| 1244 |
+
"source": [
|
| 1245 |
+
"df_img = jiggins.loc[jiggins.X.isin(list(img.X))]\n",
|
| 1246 |
+
"df_img.info()"
|
| 1247 |
+
]
|
| 1248 |
+
},
|
| 1249 |
+
{
|
| 1250 |
+
"cell_type": "markdown",
|
| 1251 |
+
"metadata": {},
|
| 1252 |
+
"source": [
|
| 1253 |
+
"Rename columns to have underscore"
|
| 1254 |
+
]
|
| 1255 |
+
},
|
| 1256 |
+
{
|
| 1257 |
+
"cell_type": "code",
|
| 1258 |
+
"execution_count": 24,
|
| 1259 |
+
"metadata": {},
|
| 1260 |
+
"outputs": [
|
| 1261 |
+
{
|
| 1262 |
+
"name": "stderr",
|
| 1263 |
+
"output_type": "stream",
|
| 1264 |
+
"text": [
|
| 1265 |
+
"/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_66533/3055364590.py:1: SettingWithCopyWarning: \n",
|
| 1266 |
+
"A value is trying to be set on a copy of a slice from a DataFrame\n",
|
| 1267 |
+
"\n",
|
| 1268 |
+
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
| 1269 |
+
" df_img.rename(columns = {\"Side\": \"View\",\n"
|
| 1270 |
+
]
|
| 1271 |
+
}
|
| 1272 |
+
],
|
| 1273 |
+
"source": [
|
| 1274 |
+
"df_img.rename(columns = {\"Side\": \"View\",\n",
|
| 1275 |
+
" \"Taxonomic.Name\": \"Taxonomic_Name\",\n",
|
| 1276 |
+
" \"Cross.Type\": \"Cross_Type\",\n",
|
| 1277 |
+
" \"Sample.accession\": \"Sample_accession\",\n",
|
| 1278 |
+
" \"Collected.by\": \"Collected_by\",\n",
|
| 1279 |
+
" \"Other.Id\": \"Other_ID\",\n",
|
| 1280 |
+
" \"Death.Date\": \"Death_Date\",\n",
|
| 1281 |
+
" \"Unit.Type\": \"Unit_Type\"},\n",
|
| 1282 |
+
" inplace = True)"
|
| 1283 |
+
]
|
| 1284 |
+
},
|
| 1285 |
+
{
|
| 1286 |
+
"cell_type": "code",
|
| 1287 |
+
"execution_count": 25,
|
| 1288 |
+
"metadata": {},
|
| 1289 |
+
"outputs": [],
|
| 1290 |
+
"source": [
|
| 1291 |
+
"null_cols = [\"Unnamed: 0\", \"Collected.By\", \"Eclosion.Date\", \"Verbatim.Coordinates\"]\n",
|
| 1292 |
+
"non_null_cols = [col for col in list(df_img.columns) if col not in null_cols]"
|
| 1293 |
+
]
|
| 1294 |
+
},
|
| 1295 |
+
{
|
| 1296 |
+
"cell_type": "code",
|
| 1297 |
+
"execution_count": 26,
|
| 1298 |
+
"metadata": {},
|
| 1299 |
+
"outputs": [],
|
| 1300 |
+
"source": [
|
| 1301 |
+
"df_img = df_img[non_null_cols]"
|
| 1302 |
+
]
|
| 1303 |
+
},
|
| 1304 |
+
{
|
| 1305 |
+
"cell_type": "code",
|
| 1306 |
+
"execution_count": 27,
|
| 1307 |
+
"metadata": {},
|
| 1308 |
+
"outputs": [
|
| 1309 |
+
{
|
| 1310 |
+
"data": {
|
| 1311 |
+
"text/html": [
|
| 1312 |
+
"<div>\n",
|
| 1313 |
+
"<style scoped>\n",
|
| 1314 |
+
" .dataframe tbody tr th:only-of-type {\n",
|
| 1315 |
+
" vertical-align: middle;\n",
|
| 1316 |
+
" }\n",
|
| 1317 |
+
"\n",
|
| 1318 |
+
" .dataframe tbody tr th {\n",
|
| 1319 |
+
" vertical-align: top;\n",
|
| 1320 |
+
" }\n",
|
| 1321 |
+
"\n",
|
| 1322 |
+
" .dataframe thead th {\n",
|
| 1323 |
+
" text-align: right;\n",
|
| 1324 |
+
" }\n",
|
| 1325 |
+
"</style>\n",
|
| 1326 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
| 1327 |
+
" <thead>\n",
|
| 1328 |
+
" <tr style=\"text-align: right;\">\n",
|
| 1329 |
+
" <th></th>\n",
|
| 1330 |
+
" <th>CAMID</th>\n",
|
| 1331 |
+
" <th>X</th>\n",
|
| 1332 |
+
" <th>Image_name</th>\n",
|
| 1333 |
+
" <th>View</th>\n",
|
| 1334 |
+
" <th>zenodo_name</th>\n",
|
| 1335 |
+
" <th>zenodo_link</th>\n",
|
| 1336 |
+
" <th>Sequence</th>\n",
|
| 1337 |
+
" <th>Taxonomic_Name</th>\n",
|
| 1338 |
+
" <th>Locality</th>\n",
|
| 1339 |
+
" <th>Sample_accession</th>\n",
|
| 1340 |
+
" <th>...</th>\n",
|
| 1341 |
+
" <th>Other_ID</th>\n",
|
| 1342 |
+
" <th>Date</th>\n",
|
| 1343 |
+
" <th>Dataset</th>\n",
|
| 1344 |
+
" <th>Store</th>\n",
|
| 1345 |
+
" <th>Brood</th>\n",
|
| 1346 |
+
" <th>Death_Date</th>\n",
|
| 1347 |
+
" <th>Cross_Type</th>\n",
|
| 1348 |
+
" <th>Stage</th>\n",
|
| 1349 |
+
" <th>Sex</th>\n",
|
| 1350 |
+
" <th>Unit_Type</th>\n",
|
| 1351 |
+
" </tr>\n",
|
| 1352 |
+
" </thead>\n",
|
| 1353 |
+
" <tbody>\n",
|
| 1354 |
+
" <tr>\n",
|
| 1355 |
+
" <th>433</th>\n",
|
| 1356 |
+
" <td>14N004</td>\n",
|
| 1357 |
+
" <td>15131</td>\n",
|
| 1358 |
+
" <td>14N004_d.JPG</td>\n",
|
| 1359 |
+
" <td>dorsal</td>\n",
|
| 1360 |
+
" <td>0.4.nn.requests2.csv</td>\n",
|
| 1361 |
+
" <td>https://zenodo.org/record/4289223</td>\n",
|
| 1362 |
+
" <td>4</td>\n",
|
| 1363 |
+
" <td>NaN</td>\n",
|
| 1364 |
+
" <td>NaN</td>\n",
|
| 1365 |
+
" <td>NaN</td>\n",
|
| 1366 |
+
" <td>...</td>\n",
|
| 1367 |
+
" <td>NaN</td>\n",
|
| 1368 |
+
" <td>NaN</td>\n",
|
| 1369 |
+
" <td>Nadeau Sheffield</td>\n",
|
| 1370 |
+
" <td>NaN</td>\n",
|
| 1371 |
+
" <td>NaN</td>\n",
|
| 1372 |
+
" <td>NaN</td>\n",
|
| 1373 |
+
" <td>NaN</td>\n",
|
| 1374 |
+
" <td>NaN</td>\n",
|
| 1375 |
+
" <td>NaN</td>\n",
|
| 1376 |
+
" <td>NaN</td>\n",
|
| 1377 |
+
" </tr>\n",
|
| 1378 |
+
" <tr>\n",
|
| 1379 |
+
" <th>434</th>\n",
|
| 1380 |
+
" <td>14N004</td>\n",
|
| 1381 |
+
" <td>15132</td>\n",
|
| 1382 |
+
" <td>14N004_v.JPG</td>\n",
|
| 1383 |
+
" <td>ventral</td>\n",
|
| 1384 |
+
" <td>0.4.nn.requests2.csv</td>\n",
|
| 1385 |
+
" <td>https://zenodo.org/record/4289223</td>\n",
|
| 1386 |
+
" <td>4</td>\n",
|
| 1387 |
+
" <td>NaN</td>\n",
|
| 1388 |
+
" <td>NaN</td>\n",
|
| 1389 |
+
" <td>NaN</td>\n",
|
| 1390 |
+
" <td>...</td>\n",
|
| 1391 |
+
" <td>NaN</td>\n",
|
| 1392 |
+
" <td>NaN</td>\n",
|
| 1393 |
+
" <td>Nadeau Sheffield</td>\n",
|
| 1394 |
+
" <td>NaN</td>\n",
|
| 1395 |
+
" <td>NaN</td>\n",
|
| 1396 |
+
" <td>NaN</td>\n",
|
| 1397 |
+
" <td>NaN</td>\n",
|
| 1398 |
+
" <td>NaN</td>\n",
|
| 1399 |
+
" <td>NaN</td>\n",
|
| 1400 |
+
" <td>NaN</td>\n",
|
| 1401 |
+
" </tr>\n",
|
| 1402 |
+
" <tr>\n",
|
| 1403 |
+
" <th>435</th>\n",
|
| 1404 |
+
" <td>14N009</td>\n",
|
| 1405 |
+
" <td>15134</td>\n",
|
| 1406 |
+
" <td>14N009_v.JPG</td>\n",
|
| 1407 |
+
" <td>ventral</td>\n",
|
| 1408 |
+
" <td>0.4.nn.requests2.csv</td>\n",
|
| 1409 |
+
" <td>https://zenodo.org/record/4289223</td>\n",
|
| 1410 |
+
" <td>9</td>\n",
|
| 1411 |
+
" <td>NaN</td>\n",
|
| 1412 |
+
" <td>NaN</td>\n",
|
| 1413 |
+
" <td>NaN</td>\n",
|
| 1414 |
+
" <td>...</td>\n",
|
| 1415 |
+
" <td>NaN</td>\n",
|
| 1416 |
+
" <td>NaN</td>\n",
|
| 1417 |
+
" <td>Nadeau Sheffield</td>\n",
|
| 1418 |
+
" <td>NaN</td>\n",
|
| 1419 |
+
" <td>NaN</td>\n",
|
| 1420 |
+
" <td>NaN</td>\n",
|
| 1421 |
+
" <td>NaN</td>\n",
|
| 1422 |
+
" <td>NaN</td>\n",
|
| 1423 |
+
" <td>NaN</td>\n",
|
| 1424 |
+
" <td>NaN</td>\n",
|
| 1425 |
+
" </tr>\n",
|
| 1426 |
+
" <tr>\n",
|
| 1427 |
+
" <th>436</th>\n",
|
| 1428 |
+
" <td>14N009</td>\n",
|
| 1429 |
+
" <td>15133</td>\n",
|
| 1430 |
+
" <td>14N009_d.JPG</td>\n",
|
| 1431 |
+
" <td>dorsal</td>\n",
|
| 1432 |
+
" <td>0.4.nn.requests2.csv</td>\n",
|
| 1433 |
+
" <td>https://zenodo.org/record/4289223</td>\n",
|
| 1434 |
+
" <td>9</td>\n",
|
| 1435 |
+
" <td>NaN</td>\n",
|
| 1436 |
+
" <td>NaN</td>\n",
|
| 1437 |
+
" <td>NaN</td>\n",
|
| 1438 |
+
" <td>...</td>\n",
|
| 1439 |
+
" <td>NaN</td>\n",
|
| 1440 |
+
" <td>NaN</td>\n",
|
| 1441 |
+
" <td>Nadeau Sheffield</td>\n",
|
| 1442 |
+
" <td>NaN</td>\n",
|
| 1443 |
+
" <td>NaN</td>\n",
|
| 1444 |
+
" <td>NaN</td>\n",
|
| 1445 |
+
" <td>NaN</td>\n",
|
| 1446 |
+
" <td>NaN</td>\n",
|
| 1447 |
+
" <td>NaN</td>\n",
|
| 1448 |
+
" <td>NaN</td>\n",
|
| 1449 |
+
" </tr>\n",
|
| 1450 |
+
" <tr>\n",
|
| 1451 |
+
" <th>437</th>\n",
|
| 1452 |
+
" <td>14N014</td>\n",
|
| 1453 |
+
" <td>15136</td>\n",
|
| 1454 |
+
" <td>14N014_v.JPG</td>\n",
|
| 1455 |
+
" <td>ventral</td>\n",
|
| 1456 |
+
" <td>0.4.nn.requests2.csv</td>\n",
|
| 1457 |
+
" <td>https://zenodo.org/record/4289223</td>\n",
|
| 1458 |
+
" <td>14</td>\n",
|
| 1459 |
+
" <td>NaN</td>\n",
|
| 1460 |
+
" <td>NaN</td>\n",
|
| 1461 |
+
" <td>NaN</td>\n",
|
| 1462 |
+
" <td>...</td>\n",
|
| 1463 |
+
" <td>NaN</td>\n",
|
| 1464 |
+
" <td>NaN</td>\n",
|
| 1465 |
+
" <td>Nadeau Sheffield</td>\n",
|
| 1466 |
+
" <td>NaN</td>\n",
|
| 1467 |
+
" <td>NaN</td>\n",
|
| 1468 |
+
" <td>NaN</td>\n",
|
| 1469 |
+
" <td>NaN</td>\n",
|
| 1470 |
+
" <td>NaN</td>\n",
|
| 1471 |
+
" <td>NaN</td>\n",
|
| 1472 |
+
" <td>NaN</td>\n",
|
| 1473 |
+
" </tr>\n",
|
| 1474 |
+
" </tbody>\n",
|
| 1475 |
+
"</table>\n",
|
| 1476 |
+
"<p>5 rows × 21 columns</p>\n",
|
| 1477 |
+
"</div>"
|
| 1478 |
+
],
|
| 1479 |
+
"text/plain": [
|
| 1480 |
+
" CAMID X Image_name View zenodo_name \\\n",
|
| 1481 |
+
"433 14N004 15131 14N004_d.JPG dorsal 0.4.nn.requests2.csv \n",
|
| 1482 |
+
"434 14N004 15132 14N004_v.JPG ventral 0.4.nn.requests2.csv \n",
|
| 1483 |
+
"435 14N009 15134 14N009_v.JPG ventral 0.4.nn.requests2.csv \n",
|
| 1484 |
+
"436 14N009 15133 14N009_d.JPG dorsal 0.4.nn.requests2.csv \n",
|
| 1485 |
+
"437 14N014 15136 14N014_v.JPG ventral 0.4.nn.requests2.csv \n",
|
| 1486 |
+
"\n",
|
| 1487 |
+
" zenodo_link Sequence Taxonomic_Name Locality \\\n",
|
| 1488 |
+
"433 https://zenodo.org/record/4289223 4 NaN NaN \n",
|
| 1489 |
+
"434 https://zenodo.org/record/4289223 4 NaN NaN \n",
|
| 1490 |
+
"435 https://zenodo.org/record/4289223 9 NaN NaN \n",
|
| 1491 |
+
"436 https://zenodo.org/record/4289223 9 NaN NaN \n",
|
| 1492 |
+
"437 https://zenodo.org/record/4289223 14 NaN NaN \n",
|
| 1493 |
+
"\n",
|
| 1494 |
+
" Sample_accession ... Other_ID Date Dataset Store Brood \\\n",
|
| 1495 |
+
"433 NaN ... NaN NaN Nadeau Sheffield NaN NaN \n",
|
| 1496 |
+
"434 NaN ... NaN NaN Nadeau Sheffield NaN NaN \n",
|
| 1497 |
+
"435 NaN ... NaN NaN Nadeau Sheffield NaN NaN \n",
|
| 1498 |
+
"436 NaN ... NaN NaN Nadeau Sheffield NaN NaN \n",
|
| 1499 |
+
"437 NaN ... NaN NaN Nadeau Sheffield NaN NaN \n",
|
| 1500 |
+
"\n",
|
| 1501 |
+
" Death_Date Cross_Type Stage Sex Unit_Type \n",
|
| 1502 |
+
"433 NaN NaN NaN NaN NaN \n",
|
| 1503 |
+
"434 NaN NaN NaN NaN NaN \n",
|
| 1504 |
+
"435 NaN NaN NaN NaN NaN \n",
|
| 1505 |
+
"436 NaN NaN NaN NaN NaN \n",
|
| 1506 |
+
"437 NaN NaN NaN NaN NaN \n",
|
| 1507 |
+
"\n",
|
| 1508 |
+
"[5 rows x 21 columns]"
|
| 1509 |
+
]
|
| 1510 |
+
},
|
| 1511 |
+
"execution_count": 27,
|
| 1512 |
+
"metadata": {},
|
| 1513 |
+
"output_type": "execute_result"
|
| 1514 |
+
}
|
| 1515 |
+
],
|
| 1516 |
+
"source": [
|
| 1517 |
+
"df_img.head()"
|
| 1518 |
+
]
|
| 1519 |
+
},
|
| 1520 |
+
{
|
| 1521 |
+
"cell_type": "code",
|
| 1522 |
+
"execution_count": 28,
|
| 1523 |
+
"metadata": {},
|
| 1524 |
+
"outputs": [
|
| 1525 |
+
{
|
| 1526 |
+
"name": "stdout",
|
| 1527 |
+
"output_type": "stream",
|
| 1528 |
+
"text": [
|
| 1529 |
+
"<class 'pandas.core.frame.DataFrame'>\n",
|
| 1530 |
+
"Index: 49359 entries, 433 to 49791\n",
|
| 1531 |
+
"Data columns (total 22 columns):\n",
|
| 1532 |
+
" # Column Non-Null Count Dtype \n",
|
| 1533 |
+
"--- ------ -------------- ----- \n",
|
| 1534 |
+
" 0 CAMID 49359 non-null object\n",
|
| 1535 |
+
" 1 X 49359 non-null int64 \n",
|
| 1536 |
+
" 2 Image_name 49359 non-null object\n",
|
| 1537 |
+
" 3 View 48288 non-null object\n",
|
| 1538 |
+
" 4 zenodo_name 49359 non-null object\n",
|
| 1539 |
+
" 5 zenodo_link 49359 non-null object\n",
|
| 1540 |
+
" 6 Sequence 48424 non-null object\n",
|
| 1541 |
+
" 7 Taxonomic_Name 45473 non-null object\n",
|
| 1542 |
+
" 8 Locality 34015 non-null object\n",
|
| 1543 |
+
" 9 Sample_accession 5884 non-null object\n",
|
| 1544 |
+
" 10 Collected_by 5280 non-null object\n",
|
| 1545 |
+
" 11 Other_ID 14382 non-null object\n",
|
| 1546 |
+
" 12 Date 33718 non-null object\n",
|
| 1547 |
+
" 13 Dataset 40405 non-null object\n",
|
| 1548 |
+
" 14 Store 39485 non-null object\n",
|
| 1549 |
+
" 15 Brood 14942 non-null object\n",
|
| 1550 |
+
" 16 Death_Date 318 non-null object\n",
|
| 1551 |
+
" 17 Cross_Type 5133 non-null object\n",
|
| 1552 |
+
" 18 Stage 15 non-null object\n",
|
| 1553 |
+
" 19 Sex 36243 non-null object\n",
|
| 1554 |
+
" 20 Unit_Type 33890 non-null object\n",
|
| 1555 |
+
" 21 file_type 49359 non-null object\n",
|
| 1556 |
+
"dtypes: int64(1), object(21)\n",
|
| 1557 |
+
"memory usage: 8.7+ MB\n"
|
| 1558 |
+
]
|
| 1559 |
+
}
|
| 1560 |
+
],
|
| 1561 |
+
"source": [
|
| 1562 |
+
"df_img[\"file_type\"] = df_img[\"Image_name\"].apply(get_file_type)\n",
|
| 1563 |
+
"df_img.info()"
|
| 1564 |
+
]
|
| 1565 |
+
},
|
| 1566 |
+
{
|
| 1567 |
+
"cell_type": "code",
|
| 1568 |
+
"execution_count": 29,
|
| 1569 |
+
"metadata": {},
|
| 1570 |
+
"outputs": [],
|
| 1571 |
+
"source": [
|
| 1572 |
+
"df_img.to_csv(\"../Jiggins_Zenodo_Img_Master.csv\", index = False)"
|
| 1573 |
+
]
|
| 1574 |
+
},
|
| 1575 |
+
{
|
| 1576 |
+
"cell_type": "markdown",
|
| 1577 |
+
"metadata": {},
|
| 1578 |
+
"source": [
|
| 1579 |
+
"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."
|
| 1580 |
+
]
|
| 1581 |
+
},
|
| 1582 |
+
{
|
| 1583 |
+
"cell_type": "code",
|
| 1584 |
+
"execution_count": 30,
|
| 1585 |
+
"metadata": {},
|
| 1586 |
+
"outputs": [
|
| 1587 |
+
{
|
| 1588 |
+
"data": {
|
| 1589 |
+
"text/plain": [
|
| 1590 |
+
"View\n",
|
| 1591 |
+
"dorsal 15128\n",
|
| 1592 |
+
"ventral 13424\n",
|
| 1593 |
+
"Dorsal 8360\n",
|
| 1594 |
+
"Ventral 8090\n",
|
| 1595 |
+
"ventral 1644\n",
|
| 1596 |
+
"forewing dorsal 406\n",
|
| 1597 |
+
"hindwing dorsal 406\n",
|
| 1598 |
+
"forewing ventral 406\n",
|
| 1599 |
+
"hindwing ventral 406\n",
|
| 1600 |
+
"Dorsal and Ventral 18\n",
|
| 1601 |
+
"Name: count, dtype: int64"
|
| 1602 |
+
]
|
| 1603 |
+
},
|
| 1604 |
+
"execution_count": 30,
|
| 1605 |
+
"metadata": {},
|
| 1606 |
+
"output_type": "execute_result"
|
| 1607 |
+
}
|
| 1608 |
+
],
|
| 1609 |
+
"source": [
|
| 1610 |
+
"df_img.View.value_counts()"
|
| 1611 |
+
]
|
| 1612 |
+
},
|
| 1613 |
+
{
|
| 1614 |
+
"cell_type": "code",
|
| 1615 |
+
"execution_count": 31,
|
| 1616 |
+
"metadata": {},
|
| 1617 |
+
"outputs": [
|
| 1618 |
+
{
|
| 1619 |
+
"data": {
|
| 1620 |
+
"text/plain": [
|
| 1621 |
+
"['dorsal',\n",
|
| 1622 |
+
" 'Dorsal',\n",
|
| 1623 |
+
" 'Dorsal and Ventral',\n",
|
| 1624 |
+
" 'forewing dorsal',\n",
|
| 1625 |
+
" 'hindwing dorsal']"
|
| 1626 |
+
]
|
| 1627 |
+
},
|
| 1628 |
+
"execution_count": 31,
|
| 1629 |
+
"metadata": {},
|
| 1630 |
+
"output_type": "execute_result"
|
| 1631 |
+
}
|
| 1632 |
+
],
|
| 1633 |
+
"source": [
|
| 1634 |
+
"dorsal_labels = [view for view in list(df_img.View.dropna().unique()) if \"dorsal\" in view.lower()]\n",
|
| 1635 |
+
"dorsal_labels"
|
| 1636 |
+
]
|
| 1637 |
+
},
|
| 1638 |
+
{
|
| 1639 |
+
"cell_type": "code",
|
| 1640 |
+
"execution_count": 32,
|
| 1641 |
+
"metadata": {},
|
| 1642 |
+
"outputs": [
|
| 1643 |
+
{
|
| 1644 |
+
"name": "stdout",
|
| 1645 |
+
"output_type": "stream",
|
| 1646 |
+
"text": [
|
| 1647 |
+
"<class 'pandas.core.frame.DataFrame'>\n",
|
| 1648 |
+
"Index: 24318 entries, 433 to 49790\n",
|
| 1649 |
+
"Data columns (total 22 columns):\n",
|
| 1650 |
+
" # Column Non-Null Count Dtype \n",
|
| 1651 |
+
"--- ------ -------------- ----- \n",
|
| 1652 |
+
" 0 CAMID 24318 non-null object\n",
|
| 1653 |
+
" 1 X 24318 non-null int64 \n",
|
| 1654 |
+
" 2 Image_name 24318 non-null object\n",
|
| 1655 |
+
" 3 View 24318 non-null object\n",
|
| 1656 |
+
" 4 zenodo_name 24318 non-null object\n",
|
| 1657 |
+
" 5 zenodo_link 24318 non-null object\n",
|
| 1658 |
+
" 6 Sequence 23851 non-null object\n",
|
| 1659 |
+
" 7 Taxonomic_Name 22511 non-null object\n",
|
| 1660 |
+
" 8 Locality 16773 non-null object\n",
|
| 1661 |
+
" 9 Sample_accession 2953 non-null object\n",
|
| 1662 |
+
" 10 Collected_by 2656 non-null object\n",
|
| 1663 |
+
" 11 Other_ID 6931 non-null object\n",
|
| 1664 |
+
" 12 Date 16847 non-null object\n",
|
| 1665 |
+
" 13 Dataset 19935 non-null object\n",
|
| 1666 |
+
" 14 Store 19894 non-null object\n",
|
| 1667 |
+
" 15 Brood 7264 non-null object\n",
|
| 1668 |
+
" 16 Death_Date 107 non-null object\n",
|
| 1669 |
+
" 17 Cross_Type 2572 non-null object\n",
|
| 1670 |
+
" 18 Stage 8 non-null object\n",
|
| 1671 |
+
" 19 Sex 17875 non-null object\n",
|
| 1672 |
+
" 20 Unit_Type 16693 non-null object\n",
|
| 1673 |
+
" 21 file_type 24318 non-null object\n",
|
| 1674 |
+
"dtypes: int64(1), object(21)\n",
|
| 1675 |
+
"memory usage: 4.3+ MB\n"
|
| 1676 |
+
]
|
| 1677 |
+
}
|
| 1678 |
+
],
|
| 1679 |
+
"source": [
|
| 1680 |
+
"df_img_dorsal = df_img.loc[df_img[\"View\"].isin(dorsal_labels)]\n",
|
| 1681 |
+
"df_img_dorsal.info()"
|
| 1682 |
+
]
|
| 1683 |
+
},
|
| 1684 |
+
{
|
| 1685 |
+
"cell_type": "code",
|
| 1686 |
+
"execution_count": 33,
|
| 1687 |
+
"metadata": {},
|
| 1688 |
+
"outputs": [
|
| 1689 |
+
{
|
| 1690 |
+
"data": {
|
| 1691 |
+
"text/plain": [
|
| 1692 |
+
"CAMID 12296\n",
|
| 1693 |
+
"X 24318\n",
|
| 1694 |
+
"Image_name 18539\n",
|
| 1695 |
+
"View 5\n",
|
| 1696 |
+
"zenodo_name 36\n",
|
| 1697 |
+
"zenodo_link 32\n",
|
| 1698 |
+
"Sequence 11107\n",
|
| 1699 |
+
"Taxonomic_Name 359\n",
|
| 1700 |
+
"Locality 642\n",
|
| 1701 |
+
"Sample_accession 1564\n",
|
| 1702 |
+
"Collected_by 12\n",
|
| 1703 |
+
"Other_ID 2897\n",
|
| 1704 |
+
"Date 794\n",
|
| 1705 |
+
"Dataset 8\n",
|
| 1706 |
+
"Store 142\n",
|
| 1707 |
+
"Brood 217\n",
|
| 1708 |
+
"Death_Date 64\n",
|
| 1709 |
+
"Cross_Type 30\n",
|
| 1710 |
+
"Stage 1\n",
|
| 1711 |
+
"Sex 3\n",
|
| 1712 |
+
"Unit_Type 6\n",
|
| 1713 |
+
"file_type 3\n",
|
| 1714 |
+
"dtype: int64"
|
| 1715 |
+
]
|
| 1716 |
+
},
|
| 1717 |
+
"execution_count": 33,
|
| 1718 |
+
"metadata": {},
|
| 1719 |
+
"output_type": "execute_result"
|
| 1720 |
+
}
|
| 1721 |
+
],
|
| 1722 |
+
"source": [
|
| 1723 |
+
"df_img_dorsal.nunique()"
|
| 1724 |
+
]
|
| 1725 |
+
},
|
| 1726 |
+
{
|
| 1727 |
+
"cell_type": "markdown",
|
| 1728 |
+
"metadata": {},
|
| 1729 |
+
"source": [
|
| 1730 |
+
"We still have repeated `CAMID`s."
|
| 1731 |
+
]
|
| 1732 |
+
},
|
| 1733 |
+
{
|
| 1734 |
+
"cell_type": "code",
|
| 1735 |
+
"execution_count": 34,
|
| 1736 |
+
"metadata": {},
|
| 1737 |
+
"outputs": [
|
| 1738 |
+
{
|
| 1739 |
+
"name": "stderr",
|
| 1740 |
+
"output_type": "stream",
|
| 1741 |
+
"text": [
|
| 1742 |
+
"/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_66533/4002726096.py:1: SettingWithCopyWarning: \n",
|
| 1743 |
+
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
| 1744 |
+
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
| 1745 |
+
"\n",
|
| 1746 |
+
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
| 1747 |
+
" df_img_dorsal[\"CAM_Dupe\"] = df_img_dorsal.duplicated(subset = \"CAMID\", keep = False)\n"
|
| 1748 |
+
]
|
| 1749 |
+
}
|
| 1750 |
+
],
|
| 1751 |
+
"source": [
|
| 1752 |
+
"df_img_dorsal[\"CAM_Dupe\"] = df_img_dorsal.duplicated(subset = \"CAMID\", keep = False)"
|
| 1753 |
+
]
|
| 1754 |
+
},
|
| 1755 |
+
{
|
| 1756 |
+
"cell_type": "code",
|
| 1757 |
+
"execution_count": 35,
|
| 1758 |
+
"metadata": {},
|
| 1759 |
+
"outputs": [
|
| 1760 |
+
{
|
| 1761 |
+
"data": {
|
| 1762 |
+
"text/plain": [
|
| 1763 |
+
"CAM_Dupe\n",
|
| 1764 |
+
"True 20765\n",
|
| 1765 |
+
"False 3553\n",
|
| 1766 |
+
"Name: count, dtype: int64"
|
| 1767 |
+
]
|
| 1768 |
+
},
|
| 1769 |
+
"execution_count": 35,
|
| 1770 |
+
"metadata": {},
|
| 1771 |
+
"output_type": "execute_result"
|
| 1772 |
+
}
|
| 1773 |
+
],
|
| 1774 |
+
"source": [
|
| 1775 |
+
"df_img_dorsal[\"CAM_Dupe\"].value_counts()"
|
| 1776 |
+
]
|
| 1777 |
+
},
|
| 1778 |
+
{
|
| 1779 |
+
"cell_type": "code",
|
| 1780 |
+
"execution_count": 36,
|
| 1781 |
+
"metadata": {},
|
| 1782 |
+
"outputs": [
|
| 1783 |
+
{
|
| 1784 |
+
"name": "stdout",
|
| 1785 |
+
"output_type": "stream",
|
| 1786 |
+
"text": [
|
| 1787 |
+
"<class 'pandas.core.frame.DataFrame'>\n",
|
| 1788 |
+
"Index: 20765 entries, 709 to 49692\n",
|
| 1789 |
+
"Data columns (total 23 columns):\n",
|
| 1790 |
+
" # Column Non-Null Count Dtype \n",
|
| 1791 |
+
"--- ------ -------------- ----- \n",
|
| 1792 |
+
" 0 CAMID 20765 non-null object\n",
|
| 1793 |
+
" 1 X 20765 non-null int64 \n",
|
| 1794 |
+
" 2 Image_name 20765 non-null object\n",
|
| 1795 |
+
" 3 View 20765 non-null object\n",
|
| 1796 |
+
" 4 zenodo_name 20765 non-null object\n",
|
| 1797 |
+
" 5 zenodo_link 20765 non-null object\n",
|
| 1798 |
+
" 6 Sequence 20299 non-null object\n",
|
| 1799 |
+
" 7 Taxonomic_Name 19368 non-null object\n",
|
| 1800 |
+
" 8 Locality 15301 non-null object\n",
|
| 1801 |
+
" 9 Sample_accession 2657 non-null object\n",
|
| 1802 |
+
" 10 Collected_by 2653 non-null object\n",
|
| 1803 |
+
" 11 Other_ID 6107 non-null object\n",
|
| 1804 |
+
" 12 Date 15980 non-null object\n",
|
| 1805 |
+
" 13 Dataset 16862 non-null object\n",
|
| 1806 |
+
" 14 Store 17579 non-null object\n",
|
| 1807 |
+
" 15 Brood 5414 non-null object\n",
|
| 1808 |
+
" 16 Death_Date 26 non-null object\n",
|
| 1809 |
+
" 17 Cross_Type 2572 non-null object\n",
|
| 1810 |
+
" 18 Stage 0 non-null object\n",
|
| 1811 |
+
" 19 Sex 14897 non-null object\n",
|
| 1812 |
+
" 20 Unit_Type 13814 non-null object\n",
|
| 1813 |
+
" 21 file_type 20765 non-null object\n",
|
| 1814 |
+
" 22 CAM_Dupe 20765 non-null bool \n",
|
| 1815 |
+
"dtypes: bool(1), int64(1), object(21)\n",
|
| 1816 |
+
"memory usage: 3.7+ MB\n"
|
| 1817 |
+
]
|
| 1818 |
+
}
|
| 1819 |
+
],
|
| 1820 |
+
"source": [
|
| 1821 |
+
"duplicate_samples = df_img_dorsal.loc[df_img_dorsal[\"CAM_Dupe\"]]\n",
|
| 1822 |
+
"duplicate_samples.info()"
|
| 1823 |
+
]
|
| 1824 |
+
},
|
| 1825 |
+
{
|
| 1826 |
+
"cell_type": "code",
|
| 1827 |
+
"execution_count": 37,
|
| 1828 |
+
"metadata": {},
|
| 1829 |
+
"outputs": [
|
| 1830 |
+
{
|
| 1831 |
+
"data": {
|
| 1832 |
+
"text/plain": [
|
| 1833 |
+
"CAMID 8743\n",
|
| 1834 |
+
"X 20765\n",
|
| 1835 |
+
"Image_name 14986\n",
|
| 1836 |
+
"View 5\n",
|
| 1837 |
+
"zenodo_name 32\n",
|
| 1838 |
+
"zenodo_link 31\n",
|
| 1839 |
+
"Sequence 8407\n",
|
| 1840 |
+
"Taxonomic_Name 328\n",
|
| 1841 |
+
"Locality 516\n",
|
| 1842 |
+
"Sample_accession 1268\n",
|
| 1843 |
+
"Collected_by 12\n",
|
| 1844 |
+
"Other_ID 2073\n",
|
| 1845 |
+
"Date 578\n",
|
| 1846 |
+
"Dataset 4\n",
|
| 1847 |
+
"Store 130\n",
|
| 1848 |
+
"Brood 144\n",
|
| 1849 |
+
"Death_Date 12\n",
|
| 1850 |
+
"Cross_Type 30\n",
|
| 1851 |
+
"Stage 0\n",
|
| 1852 |
+
"Sex 3\n",
|
| 1853 |
+
"Unit_Type 3\n",
|
| 1854 |
+
"file_type 3\n",
|
| 1855 |
+
"CAM_Dupe 1\n",
|
| 1856 |
+
"dtype: int64"
|
| 1857 |
+
]
|
| 1858 |
+
},
|
| 1859 |
+
"execution_count": 37,
|
| 1860 |
+
"metadata": {},
|
| 1861 |
+
"output_type": "execute_result"
|
| 1862 |
+
}
|
| 1863 |
+
],
|
| 1864 |
+
"source": [
|
| 1865 |
+
"duplicate_samples.nunique()"
|
| 1866 |
+
]
|
| 1867 |
+
},
|
| 1868 |
+
{
|
| 1869 |
+
"cell_type": "markdown",
|
| 1870 |
+
"metadata": {},
|
| 1871 |
+
"source": [
|
| 1872 |
+
"All sources are impacted."
|
| 1873 |
+
]
|
| 1874 |
+
},
|
| 1875 |
+
{
|
| 1876 |
+
"cell_type": "code",
|
| 1877 |
+
"execution_count": 38,
|
| 1878 |
+
"metadata": {},
|
| 1879 |
+
"outputs": [
|
| 1880 |
+
{
|
| 1881 |
+
"data": {
|
| 1882 |
+
"text/plain": [
|
| 1883 |
+
"file_type\n",
|
| 1884 |
+
"jpg 14788\n",
|
| 1885 |
+
"raw 5956\n",
|
| 1886 |
+
"tif 21\n",
|
| 1887 |
+
"Name: count, dtype: int64"
|
| 1888 |
+
]
|
| 1889 |
+
},
|
| 1890 |
+
"execution_count": 38,
|
| 1891 |
+
"metadata": {},
|
| 1892 |
+
"output_type": "execute_result"
|
| 1893 |
+
}
|
| 1894 |
+
],
|
| 1895 |
+
"source": [
|
| 1896 |
+
"duplicate_samples.file_type.value_counts()"
|
| 1897 |
+
]
|
| 1898 |
+
},
|
| 1899 |
+
{
|
| 1900 |
+
"cell_type": "markdown",
|
| 1901 |
+
"metadata": {},
|
| 1902 |
+
"source": [
|
| 1903 |
+
"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."
|
| 1904 |
+
]
|
| 1905 |
+
},
|
| 1906 |
+
{
|
| 1907 |
+
"cell_type": "code",
|
| 1908 |
+
"execution_count": 39,
|
| 1909 |
+
"metadata": {},
|
| 1910 |
+
"outputs": [
|
| 1911 |
+
{
|
| 1912 |
+
"data": {
|
| 1913 |
+
"text/plain": [
|
| 1914 |
+
"True 680\n",
|
| 1915 |
+
"False 132\n",
|
| 1916 |
+
"Name: count, dtype: int64"
|
| 1917 |
+
]
|
| 1918 |
+
},
|
| 1919 |
+
"execution_count": 39,
|
| 1920 |
+
"metadata": {},
|
| 1921 |
+
"output_type": "execute_result"
|
| 1922 |
+
}
|
| 1923 |
+
],
|
| 1924 |
+
"source": [
|
| 1925 |
+
"duplicate_samples.loc[duplicate_samples[\"View\"].isin([\"forewing dorsal\", \"hindwing dorsal\"])].duplicated(\"CAMID\", keep = \"first\").value_counts()"
|
| 1926 |
+
]
|
| 1927 |
+
},
|
| 1928 |
+
{
|
| 1929 |
+
"cell_type": "code",
|
| 1930 |
+
"execution_count": 40,
|
| 1931 |
+
"metadata": {},
|
| 1932 |
+
"outputs": [
|
| 1933 |
+
{
|
| 1934 |
+
"data": {
|
| 1935 |
+
"text/plain": [
|
| 1936 |
+
"CAMID 132\n",
|
| 1937 |
+
"X 812\n",
|
| 1938 |
+
"Image_name 528\n",
|
| 1939 |
+
"View 2\n",
|
| 1940 |
+
"zenodo_name 3\n",
|
| 1941 |
+
"zenodo_link 3\n",
|
| 1942 |
+
"Sequence 132\n",
|
| 1943 |
+
"Taxonomic_Name 7\n",
|
| 1944 |
+
"Locality 59\n",
|
| 1945 |
+
"Sample_accession 0\n",
|
| 1946 |
+
"Collected_by 0\n",
|
| 1947 |
+
"Other_ID 127\n",
|
| 1948 |
+
"Date 16\n",
|
| 1949 |
+
"Dataset 1\n",
|
| 1950 |
+
"Store 17\n",
|
| 1951 |
+
"Brood 0\n",
|
| 1952 |
+
"Death_Date 0\n",
|
| 1953 |
+
"Cross_Type 0\n",
|
| 1954 |
+
"Stage 0\n",
|
| 1955 |
+
"Sex 0\n",
|
| 1956 |
+
"Unit_Type 0\n",
|
| 1957 |
+
"file_type 2\n",
|
| 1958 |
+
"CAM_Dupe 1\n",
|
| 1959 |
+
"dtype: int64"
|
| 1960 |
+
]
|
| 1961 |
+
},
|
| 1962 |
+
"execution_count": 40,
|
| 1963 |
+
"metadata": {},
|
| 1964 |
+
"output_type": "execute_result"
|
| 1965 |
+
}
|
| 1966 |
+
],
|
| 1967 |
+
"source": [
|
| 1968 |
+
"duplicate_samples.loc[duplicate_samples[\"View\"].isin([\"forewing dorsal\", \"hindwing dorsal\"])].nunique()"
|
| 1969 |
+
]
|
| 1970 |
+
},
|
| 1971 |
+
{
|
| 1972 |
+
"cell_type": "markdown",
|
| 1973 |
+
"metadata": {},
|
| 1974 |
+
"source": [
|
| 1975 |
+
"This is only a couple zenodo links, so it is not contributing to the duplication across sources. It also covers two file types."
|
| 1976 |
+
]
|
| 1977 |
+
},
|
| 1978 |
+
{
|
| 1979 |
+
"cell_type": "code",
|
| 1980 |
+
"execution_count": 41,
|
| 1981 |
+
"metadata": {},
|
| 1982 |
+
"outputs": [
|
| 1983 |
+
{
|
| 1984 |
+
"name": "stdout",
|
| 1985 |
+
"output_type": "stream",
|
| 1986 |
+
"text": [
|
| 1987 |
+
"<class 'pandas.core.frame.DataFrame'>\n",
|
| 1988 |
+
"Index: 3553 entries, 433 to 49790\n",
|
| 1989 |
+
"Data columns (total 23 columns):\n",
|
| 1990 |
+
" # Column Non-Null Count Dtype \n",
|
| 1991 |
+
"--- ------ -------------- ----- \n",
|
| 1992 |
+
" 0 CAMID 3553 non-null object\n",
|
| 1993 |
+
" 1 X 3553 non-null int64 \n",
|
| 1994 |
+
" 2 Image_name 3553 non-null object\n",
|
| 1995 |
+
" 3 View 3553 non-null object\n",
|
| 1996 |
+
" 4 zenodo_name 3553 non-null object\n",
|
| 1997 |
+
" 5 zenodo_link 3553 non-null object\n",
|
| 1998 |
+
" 6 Sequence 3552 non-null object\n",
|
| 1999 |
+
" 7 Taxonomic_Name 3143 non-null object\n",
|
| 2000 |
+
" 8 Locality 1472 non-null object\n",
|
| 2001 |
+
" 9 Sample_accession 296 non-null object\n",
|
| 2002 |
+
" 10 Collected_by 3 non-null object\n",
|
| 2003 |
+
" 11 Other_ID 824 non-null object\n",
|
| 2004 |
+
" 12 Date 867 non-null object\n",
|
| 2005 |
+
" 13 Dataset 3073 non-null object\n",
|
| 2006 |
+
" 14 Store 2315 non-null object\n",
|
| 2007 |
+
" 15 Brood 1850 non-null object\n",
|
| 2008 |
+
" 16 Death_Date 81 non-null object\n",
|
| 2009 |
+
" 17 Cross_Type 0 non-null object\n",
|
| 2010 |
+
" 18 Stage 8 non-null object\n",
|
| 2011 |
+
" 19 Sex 2978 non-null object\n",
|
| 2012 |
+
" 20 Unit_Type 2879 non-null object\n",
|
| 2013 |
+
" 21 file_type 3553 non-null object\n",
|
| 2014 |
+
" 22 CAM_Dupe 3553 non-null bool \n",
|
| 2015 |
+
"dtypes: bool(1), int64(1), object(21)\n",
|
| 2016 |
+
"memory usage: 641.9+ KB\n"
|
| 2017 |
+
]
|
| 2018 |
+
}
|
| 2019 |
+
],
|
| 2020 |
+
"source": [
|
| 2021 |
+
"unique_samples = df_img_dorsal.loc[~df_img_dorsal[\"CAM_Dupe\"]]\n",
|
| 2022 |
+
"unique_samples.info()"
|
| 2023 |
+
]
|
| 2024 |
+
},
|
| 2025 |
+
{
|
| 2026 |
+
"cell_type": "code",
|
| 2027 |
+
"execution_count": 42,
|
| 2028 |
+
"metadata": {},
|
| 2029 |
+
"outputs": [
|
| 2030 |
+
{
|
| 2031 |
+
"data": {
|
| 2032 |
+
"text/plain": [
|
| 2033 |
+
"CAMID 3553\n",
|
| 2034 |
+
"X 3553\n",
|
| 2035 |
+
"Image_name 3553\n",
|
| 2036 |
+
"View 3\n",
|
| 2037 |
+
"zenodo_name 21\n",
|
| 2038 |
+
"zenodo_link 18\n",
|
| 2039 |
+
"Sequence 3016\n",
|
| 2040 |
+
"Taxonomic_Name 109\n",
|
| 2041 |
+
"Locality 160\n",
|
| 2042 |
+
"Sample_accession 296\n",
|
| 2043 |
+
"Collected_by 3\n",
|
| 2044 |
+
"Other_ID 824\n",
|
| 2045 |
+
"Date 265\n",
|
| 2046 |
+
"Dataset 8\n",
|
| 2047 |
+
"Store 76\n",
|
| 2048 |
+
"Brood 95\n",
|
| 2049 |
+
"Death_Date 52\n",
|
| 2050 |
+
"Cross_Type 0\n",
|
| 2051 |
+
"Stage 1\n",
|
| 2052 |
+
"Sex 3\n",
|
| 2053 |
+
"Unit_Type 6\n",
|
| 2054 |
+
"file_type 2\n",
|
| 2055 |
+
"CAM_Dupe 1\n",
|
| 2056 |
+
"dtype: int64"
|
| 2057 |
+
]
|
| 2058 |
+
},
|
| 2059 |
+
"execution_count": 42,
|
| 2060 |
+
"metadata": {},
|
| 2061 |
+
"output_type": "execute_result"
|
| 2062 |
+
}
|
| 2063 |
+
],
|
| 2064 |
+
"source": [
|
| 2065 |
+
"unique_samples.nunique()"
|
| 2066 |
+
]
|
| 2067 |
+
},
|
| 2068 |
+
{
|
| 2069 |
+
"cell_type": "code",
|
| 2070 |
+
"execution_count": 43,
|
| 2071 |
+
"metadata": {},
|
| 2072 |
+
"outputs": [
|
| 2073 |
+
{
|
| 2074 |
+
"data": {
|
| 2075 |
+
"text/plain": [
|
| 2076 |
+
"file_type\n",
|
| 2077 |
+
"jpg 3535\n",
|
| 2078 |
+
"tif 18\n",
|
| 2079 |
+
"Name: count, dtype: int64"
|
| 2080 |
+
]
|
| 2081 |
+
},
|
| 2082 |
+
"execution_count": 43,
|
| 2083 |
+
"metadata": {},
|
| 2084 |
+
"output_type": "execute_result"
|
| 2085 |
+
}
|
| 2086 |
+
],
|
| 2087 |
+
"source": [
|
| 2088 |
+
"unique_samples.file_type.value_counts()"
|
| 2089 |
+
]
|
| 2090 |
+
},
|
| 2091 |
+
{
|
| 2092 |
+
"cell_type": "markdown",
|
| 2093 |
+
"metadata": {},
|
| 2094 |
+
"source": [
|
| 2095 |
+
"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)."
|
| 2096 |
+
]
|
| 2097 |
+
},
|
| 2098 |
+
{
|
| 2099 |
+
"cell_type": "code",
|
| 2100 |
+
"execution_count": 44,
|
| 2101 |
+
"metadata": {},
|
| 2102 |
+
"outputs": [
|
| 2103 |
+
{
|
| 2104 |
+
"name": "stdout",
|
| 2105 |
+
"output_type": "stream",
|
| 2106 |
+
"text": [
|
| 2107 |
+
"(5753, 23)\n"
|
| 2108 |
+
]
|
| 2109 |
+
},
|
| 2110 |
+
{
|
| 2111 |
+
"data": {
|
| 2112 |
+
"text/plain": [
|
| 2113 |
+
"4944"
|
| 2114 |
+
]
|
| 2115 |
+
},
|
| 2116 |
+
"execution_count": 44,
|
| 2117 |
+
"metadata": {},
|
| 2118 |
+
"output_type": "execute_result"
|
| 2119 |
+
}
|
| 2120 |
+
],
|
| 2121 |
+
"source": [
|
| 2122 |
+
"print(duplicate_samples.loc[(duplicate_samples[\"View\"] != \"hindwing dorsal\") & (duplicate_samples[\"file_type\"] == \"raw\")].shape)\n",
|
| 2123 |
+
"duplicate_samples.loc[(duplicate_samples[\"View\"] != \"hindwing dorsal\") & (duplicate_samples[\"file_type\"] == \"raw\"), \"CAMID\"].nunique()"
|
| 2124 |
+
]
|
| 2125 |
+
},
|
| 2126 |
+
{
|
| 2127 |
+
"cell_type": "markdown",
|
| 2128 |
+
"metadata": {},
|
| 2129 |
+
"source": [
|
| 2130 |
+
"Okay, so there are multiple raw images of the same sample. "
|
| 2131 |
+
]
|
| 2132 |
+
},
|
| 2133 |
+
{
|
| 2134 |
+
"cell_type": "code",
|
| 2135 |
+
"execution_count": 45,
|
| 2136 |
+
"metadata": {},
|
| 2137 |
+
"outputs": [
|
| 2138 |
+
{
|
| 2139 |
+
"data": {
|
| 2140 |
+
"text/plain": [
|
| 2141 |
+
"View\n",
|
| 2142 |
+
"dorsal 4219\n",
|
| 2143 |
+
"Dorsal 1331\n",
|
| 2144 |
+
"forewing dorsal 203\n",
|
| 2145 |
+
"Name: count, dtype: int64"
|
| 2146 |
+
]
|
| 2147 |
+
},
|
| 2148 |
+
"execution_count": 45,
|
| 2149 |
+
"metadata": {},
|
| 2150 |
+
"output_type": "execute_result"
|
| 2151 |
+
}
|
| 2152 |
+
],
|
| 2153 |
+
"source": [
|
| 2154 |
+
"duplicate_raw = duplicate_samples.loc[(duplicate_samples[\"View\"] != \"hindwing dorsal\") & (duplicate_samples[\"file_type\"] == \"raw\")]\n",
|
| 2155 |
+
"duplicate_raw.View.value_counts()"
|
| 2156 |
+
]
|
| 2157 |
+
},
|
| 2158 |
+
{
|
| 2159 |
+
"cell_type": "code",
|
| 2160 |
+
"execution_count": 46,
|
| 2161 |
+
"metadata": {},
|
| 2162 |
+
"outputs": [
|
| 2163 |
+
{
|
| 2164 |
+
"name": "stdout",
|
| 2165 |
+
"output_type": "stream",
|
| 2166 |
+
"text": [
|
| 2167 |
+
"raw_dupe\n",
|
| 2168 |
+
"False 4240\n",
|
| 2169 |
+
"True 1513\n",
|
| 2170 |
+
"Name: count, dtype: int64\n",
|
| 2171 |
+
"\n"
|
| 2172 |
+
]
|
| 2173 |
+
},
|
| 2174 |
+
{
|
| 2175 |
+
"name": "stderr",
|
| 2176 |
+
"output_type": "stream",
|
| 2177 |
+
"text": [
|
| 2178 |
+
"/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_66533/3049709573.py:1: SettingWithCopyWarning: \n",
|
| 2179 |
+
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
| 2180 |
+
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
| 2181 |
+
"\n",
|
| 2182 |
+
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
| 2183 |
+
" duplicate_raw[\"raw_dupe\"] = duplicate_raw.duplicated(\"CAMID\", keep = False)\n"
|
| 2184 |
+
]
|
| 2185 |
+
},
|
| 2186 |
+
{
|
| 2187 |
+
"data": {
|
| 2188 |
+
"text/plain": [
|
| 2189 |
+
"View\n",
|
| 2190 |
+
"dorsal 1159\n",
|
| 2191 |
+
"Dorsal 212\n",
|
| 2192 |
+
"forewing dorsal 142\n",
|
| 2193 |
+
"Name: count, dtype: int64"
|
| 2194 |
+
]
|
| 2195 |
+
},
|
| 2196 |
+
"execution_count": 46,
|
| 2197 |
+
"metadata": {},
|
| 2198 |
+
"output_type": "execute_result"
|
| 2199 |
+
}
|
| 2200 |
+
],
|
| 2201 |
+
"source": [
|
| 2202 |
+
"duplicate_raw[\"raw_dupe\"] = duplicate_raw.duplicated(\"CAMID\", keep = False)\n",
|
| 2203 |
+
"print(duplicate_raw[\"raw_dupe\"].value_counts())\n",
|
| 2204 |
+
"print()\n",
|
| 2205 |
+
"duplicate_raw.loc[duplicate_raw[\"raw_dupe\"], \"View\"].value_counts()"
|
| 2206 |
+
]
|
| 2207 |
+
},
|
| 2208 |
+
{
|
| 2209 |
+
"cell_type": "markdown",
|
| 2210 |
+
"metadata": {},
|
| 2211 |
+
"source": [
|
| 2212 |
+
"There does not seem to be an easy method of filtering these other than just keeping the first instance of a particular `CAMID`.\n",
|
| 2213 |
+
"\n",
|
| 2214 |
+
"It is interesting that non of the Cross Types are unique. Is that just because we have the forewing/hindwing duplication?"
|
| 2215 |
+
]
|
| 2216 |
+
},
|
| 2217 |
+
{
|
| 2218 |
+
"cell_type": "code",
|
| 2219 |
+
"execution_count": 47,
|
| 2220 |
+
"metadata": {},
|
| 2221 |
+
"outputs": [
|
| 2222 |
+
{
|
| 2223 |
+
"name": "stdout",
|
| 2224 |
+
"output_type": "stream",
|
| 2225 |
+
"text": [
|
| 2226 |
+
"(2572, 23)\n"
|
| 2227 |
+
]
|
| 2228 |
+
},
|
| 2229 |
+
{
|
| 2230 |
+
"data": {
|
| 2231 |
+
"text/plain": [
|
| 2232 |
+
"820"
|
| 2233 |
+
]
|
| 2234 |
+
},
|
| 2235 |
+
"execution_count": 47,
|
| 2236 |
+
"metadata": {},
|
| 2237 |
+
"output_type": "execute_result"
|
| 2238 |
+
}
|
| 2239 |
+
],
|
| 2240 |
+
"source": [
|
| 2241 |
+
"print(duplicate_samples.loc[(duplicate_samples[\"View\"] != \"hindwing dorsal\") & (duplicate_samples[\"Cross_Type\"].notna())].shape)\n",
|
| 2242 |
+
"duplicate_samples.loc[(duplicate_samples[\"View\"] != \"hindwing dorsal\") & (duplicate_samples[\"Cross_Type\"].notna()), \"CAMID\"].nunique()"
|
| 2243 |
+
]
|
| 2244 |
+
},
|
| 2245 |
+
{
|
| 2246 |
+
"cell_type": "markdown",
|
| 2247 |
+
"metadata": {},
|
| 2248 |
+
"source": [
|
| 2249 |
+
"There still seem to be 3 images for each specimen."
|
| 2250 |
+
]
|
| 2251 |
+
},
|
| 2252 |
+
{
|
| 2253 |
+
"cell_type": "code",
|
| 2254 |
+
"execution_count": 48,
|
| 2255 |
+
"metadata": {},
|
| 2256 |
+
"outputs": [
|
| 2257 |
+
{
|
| 2258 |
+
"name": "stdout",
|
| 2259 |
+
"output_type": "stream",
|
| 2260 |
+
"text": [
|
| 2261 |
+
"cross_dupe\n",
|
| 2262 |
+
"True 2572\n",
|
| 2263 |
+
"Name: count, dtype: int64\n",
|
| 2264 |
+
"\n"
|
| 2265 |
+
]
|
| 2266 |
+
},
|
| 2267 |
+
{
|
| 2268 |
+
"name": "stderr",
|
| 2269 |
+
"output_type": "stream",
|
| 2270 |
+
"text": [
|
| 2271 |
+
"/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_66533/2419376272.py:2: SettingWithCopyWarning: \n",
|
| 2272 |
+
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
| 2273 |
+
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
| 2274 |
+
"\n",
|
| 2275 |
+
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
| 2276 |
+
" duplicate_cross[\"cross_dupe\"] = duplicate_cross.duplicated(\"CAMID\", keep = False)\n"
|
| 2277 |
+
]
|
| 2278 |
+
},
|
| 2279 |
+
{
|
| 2280 |
+
"data": {
|
| 2281 |
+
"text/plain": [
|
| 2282 |
+
"View\n",
|
| 2283 |
+
"dorsal 2322\n",
|
| 2284 |
+
"Dorsal 250\n",
|
| 2285 |
+
"Name: count, dtype: int64"
|
| 2286 |
+
]
|
| 2287 |
+
},
|
| 2288 |
+
"execution_count": 48,
|
| 2289 |
+
"metadata": {},
|
| 2290 |
+
"output_type": "execute_result"
|
| 2291 |
+
}
|
| 2292 |
+
],
|
| 2293 |
+
"source": [
|
| 2294 |
+
"duplicate_cross = duplicate_samples.loc[(duplicate_samples[\"View\"] != \"hindwing dorsal\") & (duplicate_samples[\"Cross_Type\"].notna())]\n",
|
| 2295 |
+
"duplicate_cross[\"cross_dupe\"] = duplicate_cross.duplicated(\"CAMID\", keep = False)\n",
|
| 2296 |
+
"print(duplicate_cross[\"cross_dupe\"].value_counts())\n",
|
| 2297 |
+
"print()\n",
|
| 2298 |
+
"duplicate_cross.View.value_counts()"
|
| 2299 |
+
]
|
| 2300 |
+
},
|
| 2301 |
+
{
|
| 2302 |
+
"cell_type": "markdown",
|
| 2303 |
+
"metadata": {},
|
| 2304 |
+
"source": [
|
| 2305 |
+
"Yes, they are all duplicated."
|
| 2306 |
+
]
|
| 2307 |
+
},
|
| 2308 |
+
{
|
| 2309 |
+
"cell_type": "code",
|
| 2310 |
+
"execution_count": 49,
|
| 2311 |
+
"metadata": {},
|
| 2312 |
+
"outputs": [
|
| 2313 |
+
{
|
| 2314 |
+
"data": {
|
| 2315 |
+
"text/plain": [
|
| 2316 |
+
"file_type\n",
|
| 2317 |
+
"jpg 1753\n",
|
| 2318 |
+
"raw 819\n",
|
| 2319 |
+
"Name: count, dtype: int64"
|
| 2320 |
+
]
|
| 2321 |
+
},
|
| 2322 |
+
"execution_count": 49,
|
| 2323 |
+
"metadata": {},
|
| 2324 |
+
"output_type": "execute_result"
|
| 2325 |
+
}
|
| 2326 |
+
],
|
| 2327 |
+
"source": [
|
| 2328 |
+
"duplicate_cross.file_type.value_counts()"
|
| 2329 |
+
]
|
| 2330 |
+
},
|
| 2331 |
+
{
|
| 2332 |
+
"cell_type": "markdown",
|
| 2333 |
+
"metadata": {},
|
| 2334 |
+
"source": [
|
| 2335 |
+
"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."
|
| 2336 |
+
]
|
| 2337 |
+
},
|
| 2338 |
+
{
|
| 2339 |
+
"cell_type": "code",
|
| 2340 |
+
"execution_count": 50,
|
| 2341 |
+
"metadata": {},
|
| 2342 |
+
"outputs": [
|
| 2343 |
+
{
|
| 2344 |
+
"data": {
|
| 2345 |
+
"text/plain": [
|
| 2346 |
+
"0"
|
| 2347 |
+
]
|
| 2348 |
+
},
|
| 2349 |
+
"execution_count": 50,
|
| 2350 |
+
"metadata": {},
|
| 2351 |
+
"output_type": "execute_result"
|
| 2352 |
+
}
|
| 2353 |
+
],
|
| 2354 |
+
"source": [
|
| 2355 |
+
"count = 0\n",
|
| 2356 |
+
"for img_name in list(duplicate_cross.Image_name.unique()):\n",
|
| 2357 |
+
" if \"JPG(1)\" in img_name:\n",
|
| 2358 |
+
" count = count + 1\n",
|
| 2359 |
+
"count"
|
| 2360 |
+
]
|
| 2361 |
+
},
|
| 2362 |
+
{
|
| 2363 |
+
"cell_type": "markdown",
|
| 2364 |
+
"metadata": {},
|
| 2365 |
+
"source": [
|
| 2366 |
+
"No, that's not the issue."
|
| 2367 |
+
]
|
| 2368 |
+
},
|
| 2369 |
+
{
|
| 2370 |
+
"cell_type": "code",
|
| 2371 |
+
"execution_count": 51,
|
| 2372 |
+
"metadata": {},
|
| 2373 |
+
"outputs": [
|
| 2374 |
+
{
|
| 2375 |
+
"data": {
|
| 2376 |
+
"text/plain": [
|
| 2377 |
+
"0"
|
| 2378 |
+
]
|
| 2379 |
+
},
|
| 2380 |
+
"execution_count": 51,
|
| 2381 |
+
"metadata": {},
|
| 2382 |
+
"output_type": "execute_result"
|
| 2383 |
+
}
|
| 2384 |
+
],
|
| 2385 |
+
"source": [
|
| 2386 |
+
"count = 0\n",
|
| 2387 |
+
"for img_name in list(duplicate_samples.Image_name.unique()):\n",
|
| 2388 |
+
" if \"JPG(1)\" in img_name:\n",
|
| 2389 |
+
" count = count + 1\n",
|
| 2390 |
+
"count"
|
| 2391 |
+
]
|
| 2392 |
+
},
|
| 2393 |
+
{
|
| 2394 |
+
"cell_type": "code",
|
| 2395 |
+
"execution_count": 52,
|
| 2396 |
+
"metadata": {},
|
| 2397 |
+
"outputs": [
|
| 2398 |
+
{
|
| 2399 |
+
"data": {
|
| 2400 |
+
"text/plain": [
|
| 2401 |
+
"0"
|
| 2402 |
+
]
|
| 2403 |
+
},
|
| 2404 |
+
"execution_count": 52,
|
| 2405 |
+
"metadata": {},
|
| 2406 |
+
"output_type": "execute_result"
|
| 2407 |
+
}
|
| 2408 |
+
],
|
| 2409 |
+
"source": [
|
| 2410 |
+
"count = 0\n",
|
| 2411 |
+
"for img_name in list(df_img_dorsal.Image_name.unique()):\n",
|
| 2412 |
+
" if \"JPG(1)\" in img_name:\n",
|
| 2413 |
+
" count = count + 1\n",
|
| 2414 |
+
"count"
|
| 2415 |
+
]
|
| 2416 |
+
},
|
| 2417 |
+
{
|
| 2418 |
+
"cell_type": "code",
|
| 2419 |
+
"execution_count": 53,
|
| 2420 |
+
"metadata": {},
|
| 2421 |
+
"outputs": [
|
| 2422 |
+
{
|
| 2423 |
+
"data": {
|
| 2424 |
+
"text/plain": [
|
| 2425 |
+
"1"
|
| 2426 |
+
]
|
| 2427 |
+
},
|
| 2428 |
+
"execution_count": 53,
|
| 2429 |
+
"metadata": {},
|
| 2430 |
+
"output_type": "execute_result"
|
| 2431 |
+
}
|
| 2432 |
+
],
|
| 2433 |
+
"source": [
|
| 2434 |
+
"count = 0\n",
|
| 2435 |
+
"for img_name in list(df_img.Image_name.unique()):\n",
|
| 2436 |
+
" if \"JPG(1)\" in img_name:\n",
|
| 2437 |
+
" count = count + 1\n",
|
| 2438 |
+
"count"
|
| 2439 |
+
]
|
| 2440 |
+
},
|
| 2441 |
+
{
|
| 2442 |
+
"cell_type": "markdown",
|
| 2443 |
+
"metadata": {},
|
| 2444 |
+
"source": [
|
| 2445 |
+
"There's only one instance of this, and it's not a dorsal image, so that's not part of the issue."
|
| 2446 |
+
]
|
| 2447 |
+
},
|
| 2448 |
+
{
|
| 2449 |
+
"cell_type": "markdown",
|
| 2450 |
+
"metadata": {},
|
| 2451 |
+
"source": [
|
| 2452 |
+
"Let's save all the dorsal images as another CSV (with the duplicate `CAMID` indicator)."
|
| 2453 |
+
]
|
| 2454 |
+
},
|
| 2455 |
+
{
|
| 2456 |
+
"cell_type": "code",
|
| 2457 |
+
"execution_count": 54,
|
| 2458 |
+
"metadata": {},
|
| 2459 |
+
"outputs": [],
|
| 2460 |
+
"source": [
|
| 2461 |
+
"df_img_dorsal.to_csv(\"../Jiggins_Zenodo_dorsal_Img_Master.csv\", index = False)"
|
| 2462 |
+
]
|
| 2463 |
+
},
|
| 2464 |
+
{
|
| 2465 |
+
"cell_type": "code",
|
| 2466 |
+
"execution_count": null,
|
| 2467 |
+
"metadata": {},
|
| 2468 |
+
"outputs": [],
|
| 2469 |
+
"source": []
|
| 2470 |
+
}
|
| 2471 |
+
],
|
| 2472 |
+
"metadata": {
|
| 2473 |
+
"kernelspec": {
|
| 2474 |
+
"display_name": "std",
|
| 2475 |
+
"language": "python",
|
| 2476 |
+
"name": "python3"
|
| 2477 |
+
},
|
| 2478 |
+
"language_info": {
|
| 2479 |
+
"codemirror_mode": {
|
| 2480 |
+
"name": "ipython",
|
| 2481 |
+
"version": 3
|
| 2482 |
+
},
|
| 2483 |
+
"file_extension": ".py",
|
| 2484 |
+
"mimetype": "text/x-python",
|
| 2485 |
+
"name": "python",
|
| 2486 |
+
"nbconvert_exporter": "python",
|
| 2487 |
+
"pygments_lexer": "ipython3",
|
| 2488 |
+
"version": "3.11.3"
|
| 2489 |
+
},
|
| 2490 |
+
"orig_nbformat": 4
|
| 2491 |
+
},
|
| 2492 |
+
"nbformat": 4,
|
| 2493 |
+
"nbformat_minor": 2
|
| 2494 |
+
}
|
notebooks/standardize_taxa.ipynb
ADDED
|
@@ -0,0 +1,1464 @@
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "code",
|
| 5 |
+
"execution_count": 1,
|
| 6 |
+
"metadata": {},
|
| 7 |
+
"outputs": [],
|
| 8 |
+
"source": [
|
| 9 |
+
"import pandas as pd"
|
| 10 |
+
]
|
| 11 |
+
},
|
| 12 |
+
{
|
| 13 |
+
"cell_type": "code",
|
| 14 |
+
"execution_count": 2,
|
| 15 |
+
"metadata": {},
|
| 16 |
+
"outputs": [],
|
| 17 |
+
"source": [
|
| 18 |
+
"df = pd.read_csv(\"../Jiggins_Zenodo_Img_Master.csv\", low_memory=False)"
|
| 19 |
+
]
|
| 20 |
+
},
|
| 21 |
+
{
|
| 22 |
+
"cell_type": "code",
|
| 23 |
+
"execution_count": 3,
|
| 24 |
+
"metadata": {},
|
| 25 |
+
"outputs": [
|
| 26 |
+
{
|
| 27 |
+
"data": {
|
| 28 |
+
"text/plain": [
|
| 29 |
+
"CAMID 12586\n",
|
| 30 |
+
"X 49359\n",
|
| 31 |
+
"Image_name 37821\n",
|
| 32 |
+
"View 10\n",
|
| 33 |
+
"zenodo_name 36\n",
|
| 34 |
+
"zenodo_link 32\n",
|
| 35 |
+
"Sequence 11301\n",
|
| 36 |
+
"Taxonomic_Name 363\n",
|
| 37 |
+
"Locality 645\n",
|
| 38 |
+
"Sample_accession 1571\n",
|
| 39 |
+
"Collected_by 12\n",
|
| 40 |
+
"Other_ID 3088\n",
|
| 41 |
+
"Date 810\n",
|
| 42 |
+
"Dataset 8\n",
|
| 43 |
+
"Store 142\n",
|
| 44 |
+
"Brood 226\n",
|
| 45 |
+
"Death_Date 82\n",
|
| 46 |
+
"Cross_Type 30\n",
|
| 47 |
+
"Stage 1\n",
|
| 48 |
+
"Sex 3\n",
|
| 49 |
+
"Unit_Type 6\n",
|
| 50 |
+
"file_type 3\n",
|
| 51 |
+
"dtype: int64"
|
| 52 |
+
]
|
| 53 |
+
},
|
| 54 |
+
"execution_count": 3,
|
| 55 |
+
"metadata": {},
|
| 56 |
+
"output_type": "execute_result"
|
| 57 |
+
}
|
| 58 |
+
],
|
| 59 |
+
"source": [
|
| 60 |
+
"df.nunique()"
|
| 61 |
+
]
|
| 62 |
+
},
|
| 63 |
+
{
|
| 64 |
+
"cell_type": "code",
|
| 65 |
+
"execution_count": 4,
|
| 66 |
+
"metadata": {},
|
| 67 |
+
"outputs": [
|
| 68 |
+
{
|
| 69 |
+
"data": {
|
| 70 |
+
"text/plain": [
|
| 71 |
+
"file_type\n",
|
| 72 |
+
"jpg 37072\n",
|
| 73 |
+
"raw 12226\n",
|
| 74 |
+
"tif 61\n",
|
| 75 |
+
"Name: count, dtype: int64"
|
| 76 |
+
]
|
| 77 |
+
},
|
| 78 |
+
"execution_count": 4,
|
| 79 |
+
"metadata": {},
|
| 80 |
+
"output_type": "execute_result"
|
| 81 |
+
}
|
| 82 |
+
],
|
| 83 |
+
"source": [
|
| 84 |
+
"df.file_type.value_counts()"
|
| 85 |
+
]
|
| 86 |
+
},
|
| 87 |
+
{
|
| 88 |
+
"cell_type": "code",
|
| 89 |
+
"execution_count": 5,
|
| 90 |
+
"metadata": {},
|
| 91 |
+
"outputs": [
|
| 92 |
+
{
|
| 93 |
+
"data": {
|
| 94 |
+
"text/plain": [
|
| 95 |
+
"View\n",
|
| 96 |
+
"dorsal 15128\n",
|
| 97 |
+
"ventral 13424\n",
|
| 98 |
+
"Dorsal 8360\n",
|
| 99 |
+
"Ventral 8090\n",
|
| 100 |
+
"ventral 1644\n",
|
| 101 |
+
"forewing dorsal 406\n",
|
| 102 |
+
"hindwing dorsal 406\n",
|
| 103 |
+
"forewing ventral 406\n",
|
| 104 |
+
"hindwing ventral 406\n",
|
| 105 |
+
"Dorsal and Ventral 18\n",
|
| 106 |
+
"Name: count, dtype: int64"
|
| 107 |
+
]
|
| 108 |
+
},
|
| 109 |
+
"execution_count": 5,
|
| 110 |
+
"metadata": {},
|
| 111 |
+
"output_type": "execute_result"
|
| 112 |
+
}
|
| 113 |
+
],
|
| 114 |
+
"source": [
|
| 115 |
+
"df.View.value_counts()"
|
| 116 |
+
]
|
| 117 |
+
},
|
| 118 |
+
{
|
| 119 |
+
"cell_type": "markdown",
|
| 120 |
+
"metadata": {},
|
| 121 |
+
"source": [
|
| 122 |
+
"Not great that `ventral` gets listed twice as lowercase and _again_ as `Ventral`.\n",
|
| 123 |
+
"\n",
|
| 124 |
+
"### Standardize `View` Column\n",
|
| 125 |
+
"Let's standardize `View` so that there isn't a discrepancy based on case."
|
| 126 |
+
]
|
| 127 |
+
},
|
| 128 |
+
{
|
| 129 |
+
"cell_type": "code",
|
| 130 |
+
"execution_count": 6,
|
| 131 |
+
"metadata": {},
|
| 132 |
+
"outputs": [
|
| 133 |
+
{
|
| 134 |
+
"data": {
|
| 135 |
+
"text/plain": [
|
| 136 |
+
"View\n",
|
| 137 |
+
"dorsal 23488\n",
|
| 138 |
+
"ventral 21514\n",
|
| 139 |
+
"ventral 1644\n",
|
| 140 |
+
"forewing dorsal 406\n",
|
| 141 |
+
"hindwing dorsal 406\n",
|
| 142 |
+
"forewing ventral 406\n",
|
| 143 |
+
"hindwing ventral 406\n",
|
| 144 |
+
"dorsal and ventral 18\n",
|
| 145 |
+
"Name: count, dtype: int64"
|
| 146 |
+
]
|
| 147 |
+
},
|
| 148 |
+
"execution_count": 6,
|
| 149 |
+
"metadata": {},
|
| 150 |
+
"output_type": "execute_result"
|
| 151 |
+
}
|
| 152 |
+
],
|
| 153 |
+
"source": [
|
| 154 |
+
"df[\"View\"] = df.View.str.lower()\n",
|
| 155 |
+
"df.View.value_counts()"
|
| 156 |
+
]
|
| 157 |
+
},
|
| 158 |
+
{
|
| 159 |
+
"cell_type": "code",
|
| 160 |
+
"execution_count": 7,
|
| 161 |
+
"metadata": {},
|
| 162 |
+
"outputs": [
|
| 163 |
+
{
|
| 164 |
+
"name": "stdout",
|
| 165 |
+
"output_type": "stream",
|
| 166 |
+
"text": [
|
| 167 |
+
"['dorsal' 'ventral' nan 'dorsal and ventral' 'ventral ' 'forewing dorsal'\n",
|
| 168 |
+
" 'hindwing dorsal' 'forewing ventral' 'hindwing ventral']\n"
|
| 169 |
+
]
|
| 170 |
+
}
|
| 171 |
+
],
|
| 172 |
+
"source": [
|
| 173 |
+
"print(df.View.unique())"
|
| 174 |
+
]
|
| 175 |
+
},
|
| 176 |
+
{
|
| 177 |
+
"cell_type": "markdown",
|
| 178 |
+
"metadata": {},
|
| 179 |
+
"source": [
|
| 180 |
+
"Yes, one has a space after it, so we'll replace that."
|
| 181 |
+
]
|
| 182 |
+
},
|
| 183 |
+
{
|
| 184 |
+
"cell_type": "code",
|
| 185 |
+
"execution_count": 8,
|
| 186 |
+
"metadata": {},
|
| 187 |
+
"outputs": [
|
| 188 |
+
{
|
| 189 |
+
"data": {
|
| 190 |
+
"text/plain": [
|
| 191 |
+
"View\n",
|
| 192 |
+
"dorsal 23488\n",
|
| 193 |
+
"ventral 23158\n",
|
| 194 |
+
"forewing dorsal 406\n",
|
| 195 |
+
"hindwing dorsal 406\n",
|
| 196 |
+
"forewing ventral 406\n",
|
| 197 |
+
"hindwing ventral 406\n",
|
| 198 |
+
"dorsal and ventral 18\n",
|
| 199 |
+
"Name: count, dtype: int64"
|
| 200 |
+
]
|
| 201 |
+
},
|
| 202 |
+
"execution_count": 8,
|
| 203 |
+
"metadata": {},
|
| 204 |
+
"output_type": "execute_result"
|
| 205 |
+
}
|
| 206 |
+
],
|
| 207 |
+
"source": [
|
| 208 |
+
"df.loc[df[\"View\"] == \"ventral \", \"View\"] = \"ventral\"\n",
|
| 209 |
+
"df.View.value_counts() "
|
| 210 |
+
]
|
| 211 |
+
},
|
| 212 |
+
{
|
| 213 |
+
"cell_type": "markdown",
|
| 214 |
+
"metadata": {},
|
| 215 |
+
"source": [
|
| 216 |
+
"### Add Record Number Column\n",
|
| 217 |
+
"We'll add a `record_number` column for easier matching to the license/citation file."
|
| 218 |
+
]
|
| 219 |
+
},
|
| 220 |
+
{
|
| 221 |
+
"cell_type": "code",
|
| 222 |
+
"execution_count": 9,
|
| 223 |
+
"metadata": {},
|
| 224 |
+
"outputs": [],
|
| 225 |
+
"source": [
|
| 226 |
+
"def get_record_number(url):\n",
|
| 227 |
+
" num = url.split(sep = \"/\")[-1]\n",
|
| 228 |
+
" return num"
|
| 229 |
+
]
|
| 230 |
+
},
|
| 231 |
+
{
|
| 232 |
+
"cell_type": "code",
|
| 233 |
+
"execution_count": 10,
|
| 234 |
+
"metadata": {},
|
| 235 |
+
"outputs": [
|
| 236 |
+
{
|
| 237 |
+
"data": {
|
| 238 |
+
"text/plain": [
|
| 239 |
+
"32"
|
| 240 |
+
]
|
| 241 |
+
},
|
| 242 |
+
"execution_count": 10,
|
| 243 |
+
"metadata": {},
|
| 244 |
+
"output_type": "execute_result"
|
| 245 |
+
}
|
| 246 |
+
],
|
| 247 |
+
"source": [
|
| 248 |
+
"df[\"record_number\"] = df.zenodo_link.apply(get_record_number)\n",
|
| 249 |
+
"df.record_number.nunique()"
|
| 250 |
+
]
|
| 251 |
+
},
|
| 252 |
+
{
|
| 253 |
+
"cell_type": "markdown",
|
| 254 |
+
"metadata": {},
|
| 255 |
+
"source": [
|
| 256 |
+
"We have 32 unique records represented in the full dataset. When we reduce down to just the Heliconius images, this will probably be less."
|
| 257 |
+
]
|
| 258 |
+
},
|
| 259 |
+
{
|
| 260 |
+
"cell_type": "markdown",
|
| 261 |
+
"metadata": {},
|
| 262 |
+
"source": [
|
| 263 |
+
"### Add `species` and `subspecies` Columns\n",
|
| 264 |
+
"This will make some analysis easier and allow for easy viewing on the [Data Dashboard](https://huggingface.co/spaces/imageomics/dashboard-prototype)."
|
| 265 |
+
]
|
| 266 |
+
},
|
| 267 |
+
{
|
| 268 |
+
"cell_type": "code",
|
| 269 |
+
"execution_count": 11,
|
| 270 |
+
"metadata": {},
|
| 271 |
+
"outputs": [],
|
| 272 |
+
"source": [
|
| 273 |
+
"def get_species(taxa_name):\n",
|
| 274 |
+
" if type(taxa_name) != float: #taxa name not null\n",
|
| 275 |
+
" species = taxa_name.split(sep = \" ssp\")[0]\n",
|
| 276 |
+
" return species\n",
|
| 277 |
+
" else:\n",
|
| 278 |
+
" return taxa_name"
|
| 279 |
+
]
|
| 280 |
+
},
|
| 281 |
+
{
|
| 282 |
+
"cell_type": "code",
|
| 283 |
+
"execution_count": 12,
|
| 284 |
+
"metadata": {},
|
| 285 |
+
"outputs": [],
|
| 286 |
+
"source": [
|
| 287 |
+
"def get_subspecies(taxa_name):\n",
|
| 288 |
+
" if type(taxa_name) != float:\n",
|
| 289 |
+
" if \"ssp.\" in taxa_name:\n",
|
| 290 |
+
" subspecies = taxa_name.split(sep = \"ssp. \")[1]\n",
|
| 291 |
+
" elif \"ssp \" in taxa_name:\n",
|
| 292 |
+
" subspecies = taxa_name.split(sep = \"ssp \")[1]\n",
|
| 293 |
+
" else:\n",
|
| 294 |
+
" subspecies = None\n",
|
| 295 |
+
" else:\n",
|
| 296 |
+
" subspecies = None\n",
|
| 297 |
+
" return subspecies"
|
| 298 |
+
]
|
| 299 |
+
},
|
| 300 |
+
{
|
| 301 |
+
"cell_type": "code",
|
| 302 |
+
"execution_count": 13,
|
| 303 |
+
"metadata": {},
|
| 304 |
+
"outputs": [
|
| 305 |
+
{
|
| 306 |
+
"data": {
|
| 307 |
+
"text/plain": [
|
| 308 |
+
"246"
|
| 309 |
+
]
|
| 310 |
+
},
|
| 311 |
+
"execution_count": 13,
|
| 312 |
+
"metadata": {},
|
| 313 |
+
"output_type": "execute_result"
|
| 314 |
+
}
|
| 315 |
+
],
|
| 316 |
+
"source": [
|
| 317 |
+
"df[\"species\"] = df.Taxonomic_Name.apply(get_species)\n",
|
| 318 |
+
"df.species.nunique()"
|
| 319 |
+
]
|
| 320 |
+
},
|
| 321 |
+
{
|
| 322 |
+
"cell_type": "code",
|
| 323 |
+
"execution_count": 14,
|
| 324 |
+
"metadata": {},
|
| 325 |
+
"outputs": [
|
| 326 |
+
{
|
| 327 |
+
"data": {
|
| 328 |
+
"text/plain": [
|
| 329 |
+
"139"
|
| 330 |
+
]
|
| 331 |
+
},
|
| 332 |
+
"execution_count": 14,
|
| 333 |
+
"metadata": {},
|
| 334 |
+
"output_type": "execute_result"
|
| 335 |
+
}
|
| 336 |
+
],
|
| 337 |
+
"source": [
|
| 338 |
+
"df[\"subspecies\"] = df.Taxonomic_Name.apply(get_subspecies)\n",
|
| 339 |
+
"df.subspecies.nunique()"
|
| 340 |
+
]
|
| 341 |
+
},
|
| 342 |
+
{
|
| 343 |
+
"cell_type": "markdown",
|
| 344 |
+
"metadata": {},
|
| 345 |
+
"source": [
|
| 346 |
+
"Cross Types are labeled differently:\n",
|
| 347 |
+
"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."
|
| 348 |
+
]
|
| 349 |
+
},
|
| 350 |
+
{
|
| 351 |
+
"cell_type": "code",
|
| 352 |
+
"execution_count": 15,
|
| 353 |
+
"metadata": {},
|
| 354 |
+
"outputs": [
|
| 355 |
+
{
|
| 356 |
+
"data": {
|
| 357 |
+
"text/plain": [
|
| 358 |
+
"array(['mal', 'mal x ple', 'ple', 'ple x mal', 'latRo x not',\n",
|
| 359 |
+
" '(latRo x not) x not', '(mal x ple) x mal', '(mal x ple) x ple',\n",
|
| 360 |
+
" 'ple x (mal x ple)', '(ple x mal) x (mal x ple)', 'lat x not',\n",
|
| 361 |
+
" '(ple x mal) x ple', '(mal x ple) x (mal x ple)',\n",
|
| 362 |
+
" '(ple x mal) x mal', '(ple x mal) x (ple x mal)',\n",
|
| 363 |
+
" '(mal x ple) x (ple x mal)', 'hybrid', 'mal x (ple x mal)',\n",
|
| 364 |
+
" '(lat x not) x lat', '(lat x not) x not', 'Ac heterozygote',\n",
|
| 365 |
+
" 'ple x (ple x mal)', '2 banded', 'lat',\n",
|
| 366 |
+
" 'Test cross (2 banded F2 x 2 banded F2)',\n",
|
| 367 |
+
" 'Test cross (4 spots x 2 banded)', 'Test cross (N heterozygozity)',\n",
|
| 368 |
+
" 'Test cross (short HW bar)', 'Test cross (4 spots x 4 spots)',\n",
|
| 369 |
+
" 'Test cross (N heterozygocity - NBNN x mal - thin)'], dtype=object)"
|
| 370 |
+
]
|
| 371 |
+
},
|
| 372 |
+
"execution_count": 15,
|
| 373 |
+
"metadata": {},
|
| 374 |
+
"output_type": "execute_result"
|
| 375 |
+
}
|
| 376 |
+
],
|
| 377 |
+
"source": [
|
| 378 |
+
"df.Cross_Type.dropna().unique()"
|
| 379 |
+
]
|
| 380 |
+
},
|
| 381 |
+
{
|
| 382 |
+
"cell_type": "code",
|
| 383 |
+
"execution_count": 16,
|
| 384 |
+
"metadata": {},
|
| 385 |
+
"outputs": [],
|
| 386 |
+
"source": [
|
| 387 |
+
"def clean_cross_types(cross_type):\n",
|
| 388 |
+
" if type(cross_type) != float:\n",
|
| 389 |
+
" cross_type = cross_type.replace(\"mal\", \"malleti\")\n",
|
| 390 |
+
" cross_type = cross_type.replace(\"ple\", \"plesseni\")\n",
|
| 391 |
+
" cross_type = cross_type.replace(\"not\", \"notabilis\")\n",
|
| 392 |
+
" if \"latRo\" not in cross_type:\n",
|
| 393 |
+
" #latRo does not cross with lativitta, so only apply when latRo isn't present\n",
|
| 394 |
+
" cross_type = cross_type.replace(\"lat\", \"lativitta\")\n",
|
| 395 |
+
" return cross_type"
|
| 396 |
+
]
|
| 397 |
+
},
|
| 398 |
+
{
|
| 399 |
+
"cell_type": "code",
|
| 400 |
+
"execution_count": 17,
|
| 401 |
+
"metadata": {},
|
| 402 |
+
"outputs": [],
|
| 403 |
+
"source": [
|
| 404 |
+
"df[\"Cross_Type\"] = df[\"Cross_Type\"].apply(clean_cross_types)"
|
| 405 |
+
]
|
| 406 |
+
},
|
| 407 |
+
{
|
| 408 |
+
"cell_type": "markdown",
|
| 409 |
+
"metadata": {},
|
| 410 |
+
"source": [
|
| 411 |
+
"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)."
|
| 412 |
+
]
|
| 413 |
+
},
|
| 414 |
+
{
|
| 415 |
+
"cell_type": "code",
|
| 416 |
+
"execution_count": 18,
|
| 417 |
+
"metadata": {},
|
| 418 |
+
"outputs": [
|
| 419 |
+
{
|
| 420 |
+
"data": {
|
| 421 |
+
"text/plain": [
|
| 422 |
+
"156"
|
| 423 |
+
]
|
| 424 |
+
},
|
| 425 |
+
"execution_count": 18,
|
| 426 |
+
"metadata": {},
|
| 427 |
+
"output_type": "execute_result"
|
| 428 |
+
}
|
| 429 |
+
],
|
| 430 |
+
"source": [
|
| 431 |
+
"cross_type_subspecies = [ct for ct in list(df.Cross_Type.dropna().unique()) if \"Test\" not in ct and \"banded\" not in ct]\n",
|
| 432 |
+
"cross_type_subspecies.remove(\"hybrid\")\n",
|
| 433 |
+
"cross_type_subspecies.remove(\"Ac heterozygote\")\n",
|
| 434 |
+
"\n",
|
| 435 |
+
"for ct in cross_type_subspecies:\n",
|
| 436 |
+
" df.loc[df[\"Cross_Type\"] == ct, \"subspecies\"] = ct\n",
|
| 437 |
+
"\n",
|
| 438 |
+
"df.subspecies.nunique()\n"
|
| 439 |
+
]
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"cell_type": "code",
|
| 443 |
+
"execution_count": 19,
|
| 444 |
+
"metadata": {},
|
| 445 |
+
"outputs": [
|
| 446 |
+
{
|
| 447 |
+
"data": {
|
| 448 |
+
"text/plain": [
|
| 449 |
+
"21"
|
| 450 |
+
]
|
| 451 |
+
},
|
| 452 |
+
"execution_count": 19,
|
| 453 |
+
"metadata": {},
|
| 454 |
+
"output_type": "execute_result"
|
| 455 |
+
}
|
| 456 |
+
],
|
| 457 |
+
"source": [
|
| 458 |
+
"len(cross_type_subspecies)"
|
| 459 |
+
]
|
| 460 |
+
},
|
| 461 |
+
{
|
| 462 |
+
"cell_type": "code",
|
| 463 |
+
"execution_count": 20,
|
| 464 |
+
"metadata": {},
|
| 465 |
+
"outputs": [
|
| 466 |
+
{
|
| 467 |
+
"data": {
|
| 468 |
+
"text/plain": [
|
| 469 |
+
"subspecies\n",
|
| 470 |
+
"(malleti x plesseni) x malleti 1204\n",
|
| 471 |
+
"plesseni x (malleti x plesseni) 600\n",
|
| 472 |
+
"malleti x (plesseni x malleti) 370\n",
|
| 473 |
+
"(plesseni x malleti) x plesseni 363\n",
|
| 474 |
+
"(plesseni x malleti) x (malleti x plesseni) 354\n",
|
| 475 |
+
"(plesseni x malleti) x (plesseni x malleti) 286\n",
|
| 476 |
+
"(malleti x plesseni) x plesseni 278\n",
|
| 477 |
+
"plesseni x malleti 234\n",
|
| 478 |
+
"malleti x plesseni 192\n",
|
| 479 |
+
"lativitta x notabilis 136\n",
|
| 480 |
+
"(lativitta x notabilis) x lativitta 110\n",
|
| 481 |
+
"plesseni x (plesseni x malleti) 106\n",
|
| 482 |
+
"(lativitta x notabilis) x notabilis 106\n",
|
| 483 |
+
"(malleti x plesseni) x (malleti x plesseni) 98\n",
|
| 484 |
+
"(plesseni x malleti) x malleti 80\n",
|
| 485 |
+
"(malleti x plesseni) x (plesseni x malleti) 56\n",
|
| 486 |
+
"malleti 28\n",
|
| 487 |
+
"plesseni 28\n",
|
| 488 |
+
"(latRo x notabilis) x notabilis 16\n",
|
| 489 |
+
"latRo x notabilis 4\n",
|
| 490 |
+
"lativitta 4\n",
|
| 491 |
+
"Name: count, dtype: int64"
|
| 492 |
+
]
|
| 493 |
+
},
|
| 494 |
+
"execution_count": 20,
|
| 495 |
+
"metadata": {},
|
| 496 |
+
"output_type": "execute_result"
|
| 497 |
+
}
|
| 498 |
+
],
|
| 499 |
+
"source": [
|
| 500 |
+
"df.loc[df[\"Cross_Type\"].notna(), \"subspecies\"].value_counts()"
|
| 501 |
+
]
|
| 502 |
+
},
|
| 503 |
+
{
|
| 504 |
+
"cell_type": "code",
|
| 505 |
+
"execution_count": 21,
|
| 506 |
+
"metadata": {},
|
| 507 |
+
"outputs": [
|
| 508 |
+
{
|
| 509 |
+
"name": "stdout",
|
| 510 |
+
"output_type": "stream",
|
| 511 |
+
"text": [
|
| 512 |
+
"4\n"
|
| 513 |
+
]
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"data": {
|
| 517 |
+
"text/plain": [
|
| 518 |
+
"['malleti', 'plesseni', 'plesseni x malleti', 'lativitta']"
|
| 519 |
+
]
|
| 520 |
+
},
|
| 521 |
+
"execution_count": 21,
|
| 522 |
+
"metadata": {},
|
| 523 |
+
"output_type": "execute_result"
|
| 524 |
+
}
|
| 525 |
+
],
|
| 526 |
+
"source": [
|
| 527 |
+
"already_present_subspecies = []\n",
|
| 528 |
+
"\n",
|
| 529 |
+
"for subspecies in list(df.loc[df[\"Cross_Type\"].notna(), \"subspecies\"].dropna().unique()):\n",
|
| 530 |
+
" if subspecies in list(df.loc[~df[\"Cross_Type\"].notna(), \"subspecies\"].dropna().unique()):\n",
|
| 531 |
+
" already_present_subspecies.append(subspecies)\n",
|
| 532 |
+
"\n",
|
| 533 |
+
"print(len(already_present_subspecies))\n",
|
| 534 |
+
"already_present_subspecies"
|
| 535 |
+
]
|
| 536 |
+
},
|
| 537 |
+
{
|
| 538 |
+
"cell_type": "markdown",
|
| 539 |
+
"metadata": {},
|
| 540 |
+
"source": [
|
| 541 |
+
"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`."
|
| 542 |
+
]
|
| 543 |
+
},
|
| 544 |
+
{
|
| 545 |
+
"cell_type": "code",
|
| 546 |
+
"execution_count": 22,
|
| 547 |
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"metadata": {},
|
| 548 |
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"outputs": [
|
| 549 |
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{
|
| 550 |
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"data": {
|
| 551 |
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| 555 |
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| 557 |
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| 558 |
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| 559 |
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" vertical-align: top;\n",
|
| 560 |
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" }\n",
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| 561 |
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" .dataframe thead th {\n",
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| 564 |
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|
| 565 |
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"</style>\n",
|
| 566 |
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"<table border=\"1\" class=\"dataframe\">\n",
|
| 567 |
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" <thead>\n",
|
| 568 |
+
" <tr style=\"text-align: right;\">\n",
|
| 569 |
+
" <th></th>\n",
|
| 570 |
+
" <th>CAMID</th>\n",
|
| 571 |
+
" <th>X</th>\n",
|
| 572 |
+
" <th>Image_name</th>\n",
|
| 573 |
+
" <th>View</th>\n",
|
| 574 |
+
" <th>zenodo_name</th>\n",
|
| 575 |
+
" <th>zenodo_link</th>\n",
|
| 576 |
+
" <th>Sequence</th>\n",
|
| 577 |
+
" <th>Taxonomic_Name</th>\n",
|
| 578 |
+
" <th>Locality</th>\n",
|
| 579 |
+
" <th>Sample_accession</th>\n",
|
| 580 |
+
" <th>...</th>\n",
|
| 581 |
+
" <th>Brood</th>\n",
|
| 582 |
+
" <th>Death_Date</th>\n",
|
| 583 |
+
" <th>Cross_Type</th>\n",
|
| 584 |
+
" <th>Stage</th>\n",
|
| 585 |
+
" <th>Sex</th>\n",
|
| 586 |
+
" <th>Unit_Type</th>\n",
|
| 587 |
+
" <th>file_type</th>\n",
|
| 588 |
+
" <th>record_number</th>\n",
|
| 589 |
+
" <th>species</th>\n",
|
| 590 |
+
" <th>subspecies</th>\n",
|
| 591 |
+
" </tr>\n",
|
| 592 |
+
" </thead>\n",
|
| 593 |
+
" <tbody>\n",
|
| 594 |
+
" <tr>\n",
|
| 595 |
+
" <th>1986</th>\n",
|
| 596 |
+
" <td>19N1989</td>\n",
|
| 597 |
+
" <td>21369</td>\n",
|
| 598 |
+
" <td>19N1989_v.JPG</td>\n",
|
| 599 |
+
" <td>ventral</td>\n",
|
| 600 |
+
" <td>0.sheffield.ps.nn.ikiam.batch2.csv</td>\n",
|
| 601 |
+
" <td>https://zenodo.org/record/4288311</td>\n",
|
| 602 |
+
" <td>1,989</td>\n",
|
| 603 |
+
" <td>Heliconius melpomene ssp. malleti</td>\n",
|
| 604 |
+
" <td>Ikiam Mariposario</td>\n",
|
| 605 |
+
" <td>NaN</td>\n",
|
| 606 |
+
" <td>...</td>\n",
|
| 607 |
+
" <td>IKIAM.P44</td>\n",
|
| 608 |
+
" <td>NaN</td>\n",
|
| 609 |
+
" <td>NaN</td>\n",
|
| 610 |
+
" <td>NaN</td>\n",
|
| 611 |
+
" <td>Male</td>\n",
|
| 612 |
+
" <td>reared</td>\n",
|
| 613 |
+
" <td>jpg</td>\n",
|
| 614 |
+
" <td>4288311</td>\n",
|
| 615 |
+
" <td>Heliconius melpomene</td>\n",
|
| 616 |
+
" <td>malleti</td>\n",
|
| 617 |
+
" </tr>\n",
|
| 618 |
+
" <tr>\n",
|
| 619 |
+
" <th>45062</th>\n",
|
| 620 |
+
" <td>CAM044423</td>\n",
|
| 621 |
+
" <td>34391</td>\n",
|
| 622 |
+
" <td>CAM044423_d.CR2</td>\n",
|
| 623 |
+
" <td>dorsal</td>\n",
|
| 624 |
+
" <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
|
| 625 |
+
" <td>https://zenodo.org/record/4287444</td>\n",
|
| 626 |
+
" <td>44,423</td>\n",
|
| 627 |
+
" <td>Taygetis cleopatra</td>\n",
|
| 628 |
+
" <td>B6old6</td>\n",
|
| 629 |
+
" <td>NaN</td>\n",
|
| 630 |
+
" <td>...</td>\n",
|
| 631 |
+
" <td>NaN</td>\n",
|
| 632 |
+
" <td>NaN</td>\n",
|
| 633 |
+
" <td>NaN</td>\n",
|
| 634 |
+
" <td>NaN</td>\n",
|
| 635 |
+
" <td>NaN</td>\n",
|
| 636 |
+
" <td>NaN</td>\n",
|
| 637 |
+
" <td>raw</td>\n",
|
| 638 |
+
" <td>4287444</td>\n",
|
| 639 |
+
" <td>Taygetis cleopatra</td>\n",
|
| 640 |
+
" <td>None</td>\n",
|
| 641 |
+
" </tr>\n",
|
| 642 |
+
" <tr>\n",
|
| 643 |
+
" <th>48534</th>\n",
|
| 644 |
+
" <td>E23</td>\n",
|
| 645 |
+
" <td>37555</td>\n",
|
| 646 |
+
" <td>E23_d.CR2</td>\n",
|
| 647 |
+
" <td>dorsal</td>\n",
|
| 648 |
+
" <td>Anniina.Matilla.Field.Caught.E.csv</td>\n",
|
| 649 |
+
" <td>https://zenodo.org/record/2554218</td>\n",
|
| 650 |
+
" <td>NaN</td>\n",
|
| 651 |
+
" <td>NaN</td>\n",
|
| 652 |
+
" <td>NaN</td>\n",
|
| 653 |
+
" <td>NaN</td>\n",
|
| 654 |
+
" <td>...</td>\n",
|
| 655 |
+
" <td>NaN</td>\n",
|
| 656 |
+
" <td>NaN</td>\n",
|
| 657 |
+
" <td>NaN</td>\n",
|
| 658 |
+
" <td>NaN</td>\n",
|
| 659 |
+
" <td>NaN</td>\n",
|
| 660 |
+
" <td>NaN</td>\n",
|
| 661 |
+
" <td>raw</td>\n",
|
| 662 |
+
" <td>2554218</td>\n",
|
| 663 |
+
" <td>NaN</td>\n",
|
| 664 |
+
" <td>None</td>\n",
|
| 665 |
+
" </tr>\n",
|
| 666 |
+
" <tr>\n",
|
| 667 |
+
" <th>45206</th>\n",
|
| 668 |
+
" <td>CAM044445</td>\n",
|
| 669 |
+
" <td>37132</td>\n",
|
| 670 |
+
" <td>CAM044445_d.JPG</td>\n",
|
| 671 |
+
" <td>dorsal</td>\n",
|
| 672 |
+
" <td>batch3.Peru.image.names.Zenodo.csv</td>\n",
|
| 673 |
+
" <td>https://zenodo.org/record/4288250</td>\n",
|
| 674 |
+
" <td>44,445</td>\n",
|
| 675 |
+
" <td>Taygetis cleopatra</td>\n",
|
| 676 |
+
" <td>B4old2</td>\n",
|
| 677 |
+
" <td>NaN</td>\n",
|
| 678 |
+
" <td>...</td>\n",
|
| 679 |
+
" <td>NaN</td>\n",
|
| 680 |
+
" <td>NaN</td>\n",
|
| 681 |
+
" <td>NaN</td>\n",
|
| 682 |
+
" <td>NaN</td>\n",
|
| 683 |
+
" <td>NaN</td>\n",
|
| 684 |
+
" <td>NaN</td>\n",
|
| 685 |
+
" <td>jpg</td>\n",
|
| 686 |
+
" <td>4288250</td>\n",
|
| 687 |
+
" <td>Taygetis cleopatra</td>\n",
|
| 688 |
+
" <td>None</td>\n",
|
| 689 |
+
" </tr>\n",
|
| 690 |
+
" <tr>\n",
|
| 691 |
+
" <th>12212</th>\n",
|
| 692 |
+
" <td>CAM010238</td>\n",
|
| 693 |
+
" <td>23307</td>\n",
|
| 694 |
+
" <td>10238v.jpg</td>\n",
|
| 695 |
+
" <td>ventral</td>\n",
|
| 696 |
+
" <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
|
| 697 |
+
" <td>https://zenodo.org/record/2552371</td>\n",
|
| 698 |
+
" <td>10,238</td>\n",
|
| 699 |
+
" <td>Heliconius sp.</td>\n",
|
| 700 |
+
" <td>NaN</td>\n",
|
| 701 |
+
" <td>NaN</td>\n",
|
| 702 |
+
" <td>...</td>\n",
|
| 703 |
+
" <td>B043</td>\n",
|
| 704 |
+
" <td>NaN</td>\n",
|
| 705 |
+
" <td>NaN</td>\n",
|
| 706 |
+
" <td>NaN</td>\n",
|
| 707 |
+
" <td>Female</td>\n",
|
| 708 |
+
" <td>reared</td>\n",
|
| 709 |
+
" <td>jpg</td>\n",
|
| 710 |
+
" <td>2552371</td>\n",
|
| 711 |
+
" <td>Heliconius sp.</td>\n",
|
| 712 |
+
" <td>None</td>\n",
|
| 713 |
+
" </tr>\n",
|
| 714 |
+
" <tr>\n",
|
| 715 |
+
" <th>39059</th>\n",
|
| 716 |
+
" <td>CAM043418</td>\n",
|
| 717 |
+
" <td>30654</td>\n",
|
| 718 |
+
" <td>CAM043418_v.JPG</td>\n",
|
| 719 |
+
" <td>ventral</td>\n",
|
| 720 |
+
" <td>batch1.Peru.image.names.Zenodo.csv</td>\n",
|
| 721 |
+
" <td>https://zenodo.org/record/3569598</td>\n",
|
| 722 |
+
" <td>43,418</td>\n",
|
| 723 |
+
" <td>Archaeoprepona licomedes</td>\n",
|
| 724 |
+
" <td>B6rec6</td>\n",
|
| 725 |
+
" <td>NaN</td>\n",
|
| 726 |
+
" <td>...</td>\n",
|
| 727 |
+
" <td>NaN</td>\n",
|
| 728 |
+
" <td>NaN</td>\n",
|
| 729 |
+
" <td>NaN</td>\n",
|
| 730 |
+
" <td>NaN</td>\n",
|
| 731 |
+
" <td>NaN</td>\n",
|
| 732 |
+
" <td>NaN</td>\n",
|
| 733 |
+
" <td>jpg</td>\n",
|
| 734 |
+
" <td>3569598</td>\n",
|
| 735 |
+
" <td>Archaeoprepona licomedes</td>\n",
|
| 736 |
+
" <td>None</td>\n",
|
| 737 |
+
" </tr>\n",
|
| 738 |
+
" <tr>\n",
|
| 739 |
+
" <th>38163</th>\n",
|
| 740 |
+
" <td>CAM043170</td>\n",
|
| 741 |
+
" <td>29755</td>\n",
|
| 742 |
+
" <td>CAM043170_d.CR2</td>\n",
|
| 743 |
+
" <td>dorsal</td>\n",
|
| 744 |
+
" <td>batch1.Peru.image.names.Zenodo.csv</td>\n",
|
| 745 |
+
" <td>https://zenodo.org/record/3569598</td>\n",
|
| 746 |
+
" <td>43,170</td>\n",
|
| 747 |
+
" <td>Adelpha mesentina</td>\n",
|
| 748 |
+
" <td>F3rec2</td>\n",
|
| 749 |
+
" <td>NaN</td>\n",
|
| 750 |
+
" <td>...</td>\n",
|
| 751 |
+
" <td>NaN</td>\n",
|
| 752 |
+
" <td>NaN</td>\n",
|
| 753 |
+
" <td>NaN</td>\n",
|
| 754 |
+
" <td>NaN</td>\n",
|
| 755 |
+
" <td>NaN</td>\n",
|
| 756 |
+
" <td>NaN</td>\n",
|
| 757 |
+
" <td>raw</td>\n",
|
| 758 |
+
" <td>3569598</td>\n",
|
| 759 |
+
" <td>Adelpha mesentina</td>\n",
|
| 760 |
+
" <td>None</td>\n",
|
| 761 |
+
" </tr>\n",
|
| 762 |
+
" </tbody>\n",
|
| 763 |
+
"</table>\n",
|
| 764 |
+
"<p>7 rows × 25 columns</p>\n",
|
| 765 |
+
"</div>"
|
| 766 |
+
],
|
| 767 |
+
"text/plain": [
|
| 768 |
+
" CAMID X Image_name View \\\n",
|
| 769 |
+
"1986 19N1989 21369 19N1989_v.JPG ventral \n",
|
| 770 |
+
"45062 CAM044423 34391 CAM044423_d.CR2 dorsal \n",
|
| 771 |
+
"48534 E23 37555 E23_d.CR2 dorsal \n",
|
| 772 |
+
"45206 CAM044445 37132 CAM044445_d.JPG dorsal \n",
|
| 773 |
+
"12212 CAM010238 23307 10238v.jpg ventral \n",
|
| 774 |
+
"39059 CAM043418 30654 CAM043418_v.JPG ventral \n",
|
| 775 |
+
"38163 CAM043170 29755 CAM043170_d.CR2 dorsal \n",
|
| 776 |
+
"\n",
|
| 777 |
+
" zenodo_name \\\n",
|
| 778 |
+
"1986 0.sheffield.ps.nn.ikiam.batch2.csv \n",
|
| 779 |
+
"45062 batch2.Peru.image.names.Zenodo.csv \n",
|
| 780 |
+
"48534 Anniina.Matilla.Field.Caught.E.csv \n",
|
| 781 |
+
"45206 batch3.Peru.image.names.Zenodo.csv \n",
|
| 782 |
+
"12212 Heliconius_wing_old_photos_2001_2019_part1.csv \n",
|
| 783 |
+
"39059 batch1.Peru.image.names.Zenodo.csv \n",
|
| 784 |
+
"38163 batch1.Peru.image.names.Zenodo.csv \n",
|
| 785 |
+
"\n",
|
| 786 |
+
" zenodo_link Sequence \\\n",
|
| 787 |
+
"1986 https://zenodo.org/record/4288311 1,989 \n",
|
| 788 |
+
"45062 https://zenodo.org/record/4287444 44,423 \n",
|
| 789 |
+
"48534 https://zenodo.org/record/2554218 NaN \n",
|
| 790 |
+
"45206 https://zenodo.org/record/4288250 44,445 \n",
|
| 791 |
+
"12212 https://zenodo.org/record/2552371 10,238 \n",
|
| 792 |
+
"39059 https://zenodo.org/record/3569598 43,418 \n",
|
| 793 |
+
"38163 https://zenodo.org/record/3569598 43,170 \n",
|
| 794 |
+
"\n",
|
| 795 |
+
" Taxonomic_Name Locality Sample_accession \\\n",
|
| 796 |
+
"1986 Heliconius melpomene ssp. malleti Ikiam Mariposario NaN \n",
|
| 797 |
+
"45062 Taygetis cleopatra B6old6 NaN \n",
|
| 798 |
+
"48534 NaN NaN NaN \n",
|
| 799 |
+
"45206 Taygetis cleopatra B4old2 NaN \n",
|
| 800 |
+
"12212 Heliconius sp. NaN NaN \n",
|
| 801 |
+
"39059 Archaeoprepona licomedes B6rec6 NaN \n",
|
| 802 |
+
"38163 Adelpha mesentina F3rec2 NaN \n",
|
| 803 |
+
"\n",
|
| 804 |
+
" ... Brood Death_Date Cross_Type Stage Sex Unit_Type file_type \\\n",
|
| 805 |
+
"1986 ... IKIAM.P44 NaN NaN NaN Male reared jpg \n",
|
| 806 |
+
"45062 ... NaN NaN NaN NaN NaN NaN raw \n",
|
| 807 |
+
"48534 ... NaN NaN NaN NaN NaN NaN raw \n",
|
| 808 |
+
"45206 ... NaN NaN NaN NaN NaN NaN jpg \n",
|
| 809 |
+
"12212 ... B043 NaN NaN NaN Female reared jpg \n",
|
| 810 |
+
"39059 ... NaN NaN NaN NaN NaN NaN jpg \n",
|
| 811 |
+
"38163 ... NaN NaN NaN NaN NaN NaN raw \n",
|
| 812 |
+
"\n",
|
| 813 |
+
" record_number species subspecies \n",
|
| 814 |
+
"1986 4288311 Heliconius melpomene malleti \n",
|
| 815 |
+
"45062 4287444 Taygetis cleopatra None \n",
|
| 816 |
+
"48534 2554218 NaN None \n",
|
| 817 |
+
"45206 4288250 Taygetis cleopatra None \n",
|
| 818 |
+
"12212 2552371 Heliconius sp. None \n",
|
| 819 |
+
"39059 3569598 Archaeoprepona licomedes None \n",
|
| 820 |
+
"38163 3569598 Adelpha mesentina None \n",
|
| 821 |
+
"\n",
|
| 822 |
+
"[7 rows x 25 columns]"
|
| 823 |
+
]
|
| 824 |
+
},
|
| 825 |
+
"execution_count": 22,
|
| 826 |
+
"metadata": {},
|
| 827 |
+
"output_type": "execute_result"
|
| 828 |
+
}
|
| 829 |
+
],
|
| 830 |
+
"source": [
|
| 831 |
+
"df.sample(7)"
|
| 832 |
+
]
|
| 833 |
+
},
|
| 834 |
+
{
|
| 835 |
+
"cell_type": "markdown",
|
| 836 |
+
"metadata": {},
|
| 837 |
+
"source": [
|
| 838 |
+
"### Add Genus Column\n",
|
| 839 |
+
"\n",
|
| 840 |
+
"This willl allow us to easily remove all non Heliconius samples, and make some image stats easier to see."
|
| 841 |
+
]
|
| 842 |
+
},
|
| 843 |
+
{
|
| 844 |
+
"cell_type": "code",
|
| 845 |
+
"execution_count": 23,
|
| 846 |
+
"metadata": {},
|
| 847 |
+
"outputs": [],
|
| 848 |
+
"source": [
|
| 849 |
+
"def get_genus(species):\n",
|
| 850 |
+
" if type(species) != float: #taxa name not null\n",
|
| 851 |
+
" return species.split(sep = \" \")[0]\n",
|
| 852 |
+
" return species"
|
| 853 |
+
]
|
| 854 |
+
},
|
| 855 |
+
{
|
| 856 |
+
"cell_type": "code",
|
| 857 |
+
"execution_count": 24,
|
| 858 |
+
"metadata": {},
|
| 859 |
+
"outputs": [
|
| 860 |
+
{
|
| 861 |
+
"data": {
|
| 862 |
+
"text/plain": [
|
| 863 |
+
"94"
|
| 864 |
+
]
|
| 865 |
+
},
|
| 866 |
+
"execution_count": 24,
|
| 867 |
+
"metadata": {},
|
| 868 |
+
"output_type": "execute_result"
|
| 869 |
+
}
|
| 870 |
+
],
|
| 871 |
+
"source": [
|
| 872 |
+
"df[\"genus\"] = df[\"species\"].apply(get_genus)\n",
|
| 873 |
+
"df.genus.nunique()"
|
| 874 |
+
]
|
| 875 |
+
},
|
| 876 |
+
{
|
| 877 |
+
"cell_type": "markdown",
|
| 878 |
+
"metadata": {},
|
| 879 |
+
"source": [
|
| 880 |
+
"Final stats for all data summarized here."
|
| 881 |
+
]
|
| 882 |
+
},
|
| 883 |
+
{
|
| 884 |
+
"cell_type": "code",
|
| 885 |
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"execution_count": 25,
|
| 886 |
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"metadata": {},
|
| 887 |
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"outputs": [
|
| 888 |
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{
|
| 889 |
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"data": {
|
| 890 |
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"text/plain": [
|
| 891 |
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"CAMID 12586\n",
|
| 892 |
+
"X 49359\n",
|
| 893 |
+
"Image_name 37821\n",
|
| 894 |
+
"View 7\n",
|
| 895 |
+
"zenodo_name 36\n",
|
| 896 |
+
"zenodo_link 32\n",
|
| 897 |
+
"Sequence 11301\n",
|
| 898 |
+
"Taxonomic_Name 363\n",
|
| 899 |
+
"Locality 645\n",
|
| 900 |
+
"Sample_accession 1571\n",
|
| 901 |
+
"Collected_by 12\n",
|
| 902 |
+
"Other_ID 3088\n",
|
| 903 |
+
"Date 810\n",
|
| 904 |
+
"Dataset 8\n",
|
| 905 |
+
"Store 142\n",
|
| 906 |
+
"Brood 226\n",
|
| 907 |
+
"Death_Date 82\n",
|
| 908 |
+
"Cross_Type 30\n",
|
| 909 |
+
"Stage 1\n",
|
| 910 |
+
"Sex 3\n",
|
| 911 |
+
"Unit_Type 6\n",
|
| 912 |
+
"file_type 3\n",
|
| 913 |
+
"record_number 32\n",
|
| 914 |
+
"species 246\n",
|
| 915 |
+
"subspecies 156\n",
|
| 916 |
+
"genus 94\n",
|
| 917 |
+
"dtype: int64"
|
| 918 |
+
]
|
| 919 |
+
},
|
| 920 |
+
"execution_count": 25,
|
| 921 |
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"metadata": {},
|
| 922 |
+
"output_type": "execute_result"
|
| 923 |
+
}
|
| 924 |
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],
|
| 925 |
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"source": [
|
| 926 |
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"df.nunique()"
|
| 927 |
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]
|
| 928 |
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},
|
| 929 |
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{
|
| 930 |
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"cell_type": "code",
|
| 931 |
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"execution_count": 26,
|
| 932 |
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"metadata": {},
|
| 933 |
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"outputs": [
|
| 934 |
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{
|
| 935 |
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"name": "stdout",
|
| 936 |
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"output_type": "stream",
|
| 937 |
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"text": [
|
| 938 |
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"<class 'pandas.core.frame.DataFrame'>\n",
|
| 939 |
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"RangeIndex: 49359 entries, 0 to 49358\n",
|
| 940 |
+
"Data columns (total 26 columns):\n",
|
| 941 |
+
" # Column Non-Null Count Dtype \n",
|
| 942 |
+
"--- ------ -------------- ----- \n",
|
| 943 |
+
" 0 CAMID 49359 non-null object\n",
|
| 944 |
+
" 1 X 49359 non-null int64 \n",
|
| 945 |
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" 2 Image_name 49359 non-null object\n",
|
| 946 |
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" 3 View 48288 non-null object\n",
|
| 947 |
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" 4 zenodo_name 49359 non-null object\n",
|
| 948 |
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" 5 zenodo_link 49359 non-null object\n",
|
| 949 |
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" 6 Sequence 48424 non-null object\n",
|
| 950 |
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" 7 Taxonomic_Name 45473 non-null object\n",
|
| 951 |
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" 8 Locality 34015 non-null object\n",
|
| 952 |
+
" 9 Sample_accession 5884 non-null object\n",
|
| 953 |
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" 10 Collected_by 5280 non-null object\n",
|
| 954 |
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" 11 Other_ID 14382 non-null object\n",
|
| 955 |
+
" 12 Date 33718 non-null object\n",
|
| 956 |
+
" 13 Dataset 40405 non-null object\n",
|
| 957 |
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" 14 Store 39485 non-null object\n",
|
| 958 |
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" 15 Brood 14942 non-null object\n",
|
| 959 |
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" 16 Death_Date 318 non-null object\n",
|
| 960 |
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" 17 Cross_Type 5133 non-null object\n",
|
| 961 |
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" 18 Stage 15 non-null object\n",
|
| 962 |
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" 19 Sex 36243 non-null object\n",
|
| 963 |
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" 20 Unit_Type 33890 non-null object\n",
|
| 964 |
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" 21 file_type 49359 non-null object\n",
|
| 965 |
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" 22 record_number 49359 non-null object\n",
|
| 966 |
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" 23 species 45473 non-null object\n",
|
| 967 |
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" 24 subspecies 25715 non-null object\n",
|
| 968 |
+
" 25 genus 45473 non-null object\n",
|
| 969 |
+
"dtypes: int64(1), object(25)\n",
|
| 970 |
+
"memory usage: 9.8+ MB\n"
|
| 971 |
+
]
|
| 972 |
+
}
|
| 973 |
+
],
|
| 974 |
+
"source": [
|
| 975 |
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"df.info()"
|
| 976 |
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]
|
| 977 |
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},
|
| 978 |
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{
|
| 979 |
+
"cell_type": "markdown",
|
| 980 |
+
"metadata": {},
|
| 981 |
+
"source": [
|
| 982 |
+
"Observe that not all images have a species label."
|
| 983 |
+
]
|
| 984 |
+
},
|
| 985 |
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{
|
| 986 |
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"cell_type": "code",
|
| 987 |
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"execution_count": 27,
|
| 988 |
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"metadata": {},
|
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{
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| 991 |
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|
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|
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|
| 1009 |
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" <tr style=\"text-align: right;\">\n",
|
| 1010 |
+
" <th></th>\n",
|
| 1011 |
+
" <th>CAMID</th>\n",
|
| 1012 |
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" <th>X</th>\n",
|
| 1013 |
+
" <th>Image_name</th>\n",
|
| 1014 |
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|
| 1015 |
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|
| 1016 |
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|
| 1017 |
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" <th>Sequence</th>\n",
|
| 1018 |
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|
| 1019 |
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" <th>Locality</th>\n",
|
| 1020 |
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" <th>Sample_accession</th>\n",
|
| 1021 |
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" <th>...</th>\n",
|
| 1022 |
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" <th>Death_Date</th>\n",
|
| 1023 |
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" <th>Cross_Type</th>\n",
|
| 1024 |
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" <th>Stage</th>\n",
|
| 1025 |
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" <th>Sex</th>\n",
|
| 1026 |
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|
| 1027 |
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" <th>file_type</th>\n",
|
| 1028 |
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|
| 1029 |
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|
| 1030 |
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|
| 1031 |
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|
| 1032 |
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|
| 1033 |
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" </thead>\n",
|
| 1034 |
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" <tbody>\n",
|
| 1035 |
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" <tr>\n",
|
| 1036 |
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" <th>48538</th>\n",
|
| 1037 |
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" <td>E24</td>\n",
|
| 1038 |
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" <td>37559</td>\n",
|
| 1039 |
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" <td>E24_d.CR2</td>\n",
|
| 1040 |
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" <td>dorsal</td>\n",
|
| 1041 |
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|
| 1042 |
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|
| 1043 |
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" <td>NaN</td>\n",
|
| 1044 |
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" <td>NaN</td>\n",
|
| 1045 |
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" <td>NaN</td>\n",
|
| 1046 |
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" <td>NaN</td>\n",
|
| 1047 |
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" <td>...</td>\n",
|
| 1048 |
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" <td>NaN</td>\n",
|
| 1049 |
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" <td>NaN</td>\n",
|
| 1050 |
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" <td>NaN</td>\n",
|
| 1051 |
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" <td>NaN</td>\n",
|
| 1052 |
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" <td>NaN</td>\n",
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| 1053 |
+
" <td>raw</td>\n",
|
| 1054 |
+
" <td>2554218</td>\n",
|
| 1055 |
+
" <td>NaN</td>\n",
|
| 1056 |
+
" <td>None</td>\n",
|
| 1057 |
+
" <td>NaN</td>\n",
|
| 1058 |
+
" </tr>\n",
|
| 1059 |
+
" <tr>\n",
|
| 1060 |
+
" <th>37246</th>\n",
|
| 1061 |
+
" <td>CAM042045</td>\n",
|
| 1062 |
+
" <td>43973</td>\n",
|
| 1063 |
+
" <td>CAM042045_v.JPG</td>\n",
|
| 1064 |
+
" <td>ventral</td>\n",
|
| 1065 |
+
" <td>Collection_August2019.csv</td>\n",
|
| 1066 |
+
" <td>https://zenodo.org/record/5731587</td>\n",
|
| 1067 |
+
" <td>42,045</td>\n",
|
| 1068 |
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" <td>NaN</td>\n",
|
| 1069 |
+
" <td>NaN</td>\n",
|
| 1070 |
+
" <td>NaN</td>\n",
|
| 1071 |
+
" <td>...</td>\n",
|
| 1072 |
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" <td>NaN</td>\n",
|
| 1073 |
+
" <td>NaN</td>\n",
|
| 1074 |
+
" <td>NaN</td>\n",
|
| 1075 |
+
" <td>NaN</td>\n",
|
| 1076 |
+
" <td>NaN</td>\n",
|
| 1077 |
+
" <td>jpg</td>\n",
|
| 1078 |
+
" <td>5731587</td>\n",
|
| 1079 |
+
" <td>NaN</td>\n",
|
| 1080 |
+
" <td>None</td>\n",
|
| 1081 |
+
" <td>NaN</td>\n",
|
| 1082 |
+
" </tr>\n",
|
| 1083 |
+
" <tr>\n",
|
| 1084 |
+
" <th>37484</th>\n",
|
| 1085 |
+
" <td>CAM042166</td>\n",
|
| 1086 |
+
" <td>44211</td>\n",
|
| 1087 |
+
" <td>CAM042166_v.JPG</td>\n",
|
| 1088 |
+
" <td>ventral</td>\n",
|
| 1089 |
+
" <td>Collection_August2019.csv</td>\n",
|
| 1090 |
+
" <td>https://zenodo.org/record/5731587</td>\n",
|
| 1091 |
+
" <td>42,166</td>\n",
|
| 1092 |
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" <td>NaN</td>\n",
|
| 1093 |
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" <td>NaN</td>\n",
|
| 1094 |
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" <td>NaN</td>\n",
|
| 1095 |
+
" <td>...</td>\n",
|
| 1096 |
+
" <td>NaN</td>\n",
|
| 1097 |
+
" <td>NaN</td>\n",
|
| 1098 |
+
" <td>NaN</td>\n",
|
| 1099 |
+
" <td>NaN</td>\n",
|
| 1100 |
+
" <td>NaN</td>\n",
|
| 1101 |
+
" <td>jpg</td>\n",
|
| 1102 |
+
" <td>5731587</td>\n",
|
| 1103 |
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" <td>NaN</td>\n",
|
| 1104 |
+
" <td>None</td>\n",
|
| 1105 |
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" <td>NaN</td>\n",
|
| 1106 |
+
" </tr>\n",
|
| 1107 |
+
" <tr>\n",
|
| 1108 |
+
" <th>48780</th>\n",
|
| 1109 |
+
" <td>E83</td>\n",
|
| 1110 |
+
" <td>37777</td>\n",
|
| 1111 |
+
" <td>E83_v.CR2</td>\n",
|
| 1112 |
+
" <td>ventral</td>\n",
|
| 1113 |
+
" <td>Anniina.Matilla.Field.Caught.E.csv</td>\n",
|
| 1114 |
+
" <td>https://zenodo.org/record/2554218</td>\n",
|
| 1115 |
+
" <td>NaN</td>\n",
|
| 1116 |
+
" <td>NaN</td>\n",
|
| 1117 |
+
" <td>NaN</td>\n",
|
| 1118 |
+
" <td>NaN</td>\n",
|
| 1119 |
+
" <td>...</td>\n",
|
| 1120 |
+
" <td>NaN</td>\n",
|
| 1121 |
+
" <td>NaN</td>\n",
|
| 1122 |
+
" <td>NaN</td>\n",
|
| 1123 |
+
" <td>NaN</td>\n",
|
| 1124 |
+
" <td>NaN</td>\n",
|
| 1125 |
+
" <td>raw</td>\n",
|
| 1126 |
+
" <td>2554218</td>\n",
|
| 1127 |
+
" <td>NaN</td>\n",
|
| 1128 |
+
" <td>None</td>\n",
|
| 1129 |
+
" <td>NaN</td>\n",
|
| 1130 |
+
" </tr>\n",
|
| 1131 |
+
" <tr>\n",
|
| 1132 |
+
" <th>3118</th>\n",
|
| 1133 |
+
" <td>19N2627</td>\n",
|
| 1134 |
+
" <td>22498</td>\n",
|
| 1135 |
+
" <td>19N2627_v.CR2</td>\n",
|
| 1136 |
+
" <td>NaN</td>\n",
|
| 1137 |
+
" <td>0.sheffield.ps.nn.ikiam.batch2.csv</td>\n",
|
| 1138 |
+
" <td>https://zenodo.org/record/4288311</td>\n",
|
| 1139 |
+
" <td>0</td>\n",
|
| 1140 |
+
" <td>NaN</td>\n",
|
| 1141 |
+
" <td>NaN</td>\n",
|
| 1142 |
+
" <td>NaN</td>\n",
|
| 1143 |
+
" <td>...</td>\n",
|
| 1144 |
+
" <td>NaN</td>\n",
|
| 1145 |
+
" <td>NaN</td>\n",
|
| 1146 |
+
" <td>NaN</td>\n",
|
| 1147 |
+
" <td>NaN</td>\n",
|
| 1148 |
+
" <td>NaN</td>\n",
|
| 1149 |
+
" <td>raw</td>\n",
|
| 1150 |
+
" <td>4288311</td>\n",
|
| 1151 |
+
" <td>NaN</td>\n",
|
| 1152 |
+
" <td>None</td>\n",
|
| 1153 |
+
" <td>NaN</td>\n",
|
| 1154 |
+
" </tr>\n",
|
| 1155 |
+
" <tr>\n",
|
| 1156 |
+
" <th>46111</th>\n",
|
| 1157 |
+
" <td>CAM045060</td>\n",
|
| 1158 |
+
" <td>42806</td>\n",
|
| 1159 |
+
" <td>CAM045060_v.CR2</td>\n",
|
| 1160 |
+
" <td>ventral</td>\n",
|
| 1161 |
+
" <td>image.names.cook.island.erato.csv</td>\n",
|
| 1162 |
+
" <td>https://zenodo.org/record/5526257</td>\n",
|
| 1163 |
+
" <td>45,060</td>\n",
|
| 1164 |
+
" <td>NaN</td>\n",
|
| 1165 |
+
" <td>NaN</td>\n",
|
| 1166 |
+
" <td>NaN</td>\n",
|
| 1167 |
+
" <td>...</td>\n",
|
| 1168 |
+
" <td>NaN</td>\n",
|
| 1169 |
+
" <td>NaN</td>\n",
|
| 1170 |
+
" <td>NaN</td>\n",
|
| 1171 |
+
" <td>NaN</td>\n",
|
| 1172 |
+
" <td>NaN</td>\n",
|
| 1173 |
+
" <td>raw</td>\n",
|
| 1174 |
+
" <td>5526257</td>\n",
|
| 1175 |
+
" <td>NaN</td>\n",
|
| 1176 |
+
" <td>None</td>\n",
|
| 1177 |
+
" <td>NaN</td>\n",
|
| 1178 |
+
" </tr>\n",
|
| 1179 |
+
" <tr>\n",
|
| 1180 |
+
" <th>39502</th>\n",
|
| 1181 |
+
" <td>CAM043576</td>\n",
|
| 1182 |
+
" <td>31097</td>\n",
|
| 1183 |
+
" <td>CAM043576_v.CR2</td>\n",
|
| 1184 |
+
" <td>ventral</td>\n",
|
| 1185 |
+
" <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
|
| 1186 |
+
" <td>https://zenodo.org/record/4287444</td>\n",
|
| 1187 |
+
" <td>43,576</td>\n",
|
| 1188 |
+
" <td>NaN</td>\n",
|
| 1189 |
+
" <td>NaN</td>\n",
|
| 1190 |
+
" <td>NaN</td>\n",
|
| 1191 |
+
" <td>...</td>\n",
|
| 1192 |
+
" <td>NaN</td>\n",
|
| 1193 |
+
" <td>NaN</td>\n",
|
| 1194 |
+
" <td>NaN</td>\n",
|
| 1195 |
+
" <td>NaN</td>\n",
|
| 1196 |
+
" <td>NaN</td>\n",
|
| 1197 |
+
" <td>raw</td>\n",
|
| 1198 |
+
" <td>4287444</td>\n",
|
| 1199 |
+
" <td>NaN</td>\n",
|
| 1200 |
+
" <td>None</td>\n",
|
| 1201 |
+
" <td>NaN</td>\n",
|
| 1202 |
+
" </tr>\n",
|
| 1203 |
+
" </tbody>\n",
|
| 1204 |
+
"</table>\n",
|
| 1205 |
+
"<p>7 rows × 26 columns</p>\n",
|
| 1206 |
+
"</div>"
|
| 1207 |
+
],
|
| 1208 |
+
"text/plain": [
|
| 1209 |
+
" CAMID X Image_name View \\\n",
|
| 1210 |
+
"48538 E24 37559 E24_d.CR2 dorsal \n",
|
| 1211 |
+
"37246 CAM042045 43973 CAM042045_v.JPG ventral \n",
|
| 1212 |
+
"37484 CAM042166 44211 CAM042166_v.JPG ventral \n",
|
| 1213 |
+
"48780 E83 37777 E83_v.CR2 ventral \n",
|
| 1214 |
+
"3118 19N2627 22498 19N2627_v.CR2 NaN \n",
|
| 1215 |
+
"46111 CAM045060 42806 CAM045060_v.CR2 ventral \n",
|
| 1216 |
+
"39502 CAM043576 31097 CAM043576_v.CR2 ventral \n",
|
| 1217 |
+
"\n",
|
| 1218 |
+
" zenodo_name zenodo_link \\\n",
|
| 1219 |
+
"48538 Anniina.Matilla.Field.Caught.E.csv https://zenodo.org/record/2554218 \n",
|
| 1220 |
+
"37246 Collection_August2019.csv https://zenodo.org/record/5731587 \n",
|
| 1221 |
+
"37484 Collection_August2019.csv https://zenodo.org/record/5731587 \n",
|
| 1222 |
+
"48780 Anniina.Matilla.Field.Caught.E.csv https://zenodo.org/record/2554218 \n",
|
| 1223 |
+
"3118 0.sheffield.ps.nn.ikiam.batch2.csv https://zenodo.org/record/4288311 \n",
|
| 1224 |
+
"46111 image.names.cook.island.erato.csv https://zenodo.org/record/5526257 \n",
|
| 1225 |
+
"39502 batch2.Peru.image.names.Zenodo.csv https://zenodo.org/record/4287444 \n",
|
| 1226 |
+
"\n",
|
| 1227 |
+
" Sequence Taxonomic_Name Locality Sample_accession ... Death_Date \\\n",
|
| 1228 |
+
"48538 NaN NaN NaN NaN ... NaN \n",
|
| 1229 |
+
"37246 42,045 NaN NaN NaN ... NaN \n",
|
| 1230 |
+
"37484 42,166 NaN NaN NaN ... NaN \n",
|
| 1231 |
+
"48780 NaN NaN NaN NaN ... NaN \n",
|
| 1232 |
+
"3118 0 NaN NaN NaN ... NaN \n",
|
| 1233 |
+
"46111 45,060 NaN NaN NaN ... NaN \n",
|
| 1234 |
+
"39502 43,576 NaN NaN NaN ... NaN \n",
|
| 1235 |
+
"\n",
|
| 1236 |
+
" Cross_Type Stage Sex Unit_Type file_type record_number species \\\n",
|
| 1237 |
+
"48538 NaN NaN NaN NaN raw 2554218 NaN \n",
|
| 1238 |
+
"37246 NaN NaN NaN NaN jpg 5731587 NaN \n",
|
| 1239 |
+
"37484 NaN NaN NaN NaN jpg 5731587 NaN \n",
|
| 1240 |
+
"48780 NaN NaN NaN NaN raw 2554218 NaN \n",
|
| 1241 |
+
"3118 NaN NaN NaN NaN raw 4288311 NaN \n",
|
| 1242 |
+
"46111 NaN NaN NaN NaN raw 5526257 NaN \n",
|
| 1243 |
+
"39502 NaN NaN NaN NaN raw 4287444 NaN \n",
|
| 1244 |
+
"\n",
|
| 1245 |
+
" subspecies genus \n",
|
| 1246 |
+
"48538 None NaN \n",
|
| 1247 |
+
"37246 None NaN \n",
|
| 1248 |
+
"37484 None NaN \n",
|
| 1249 |
+
"48780 None NaN \n",
|
| 1250 |
+
"3118 None NaN \n",
|
| 1251 |
+
"46111 None NaN \n",
|
| 1252 |
+
"39502 None NaN \n",
|
| 1253 |
+
"\n",
|
| 1254 |
+
"[7 rows x 26 columns]"
|
| 1255 |
+
]
|
| 1256 |
+
},
|
| 1257 |
+
"execution_count": 27,
|
| 1258 |
+
"metadata": {},
|
| 1259 |
+
"output_type": "execute_result"
|
| 1260 |
+
}
|
| 1261 |
+
],
|
| 1262 |
+
"source": [
|
| 1263 |
+
"df.loc[df.species.isna()].sample(7)"
|
| 1264 |
+
]
|
| 1265 |
+
},
|
| 1266 |
+
{
|
| 1267 |
+
"cell_type": "markdown",
|
| 1268 |
+
"metadata": {},
|
| 1269 |
+
"source": [
|
| 1270 |
+
"### Update Master File with Genus through Subspecies Columns"
|
| 1271 |
+
]
|
| 1272 |
+
},
|
| 1273 |
+
{
|
| 1274 |
+
"cell_type": "code",
|
| 1275 |
+
"execution_count": 28,
|
| 1276 |
+
"metadata": {},
|
| 1277 |
+
"outputs": [],
|
| 1278 |
+
"source": [
|
| 1279 |
+
"df.to_csv(\"../Jiggins_Zenodo_Img_Master.csv\", index = False)"
|
| 1280 |
+
]
|
| 1281 |
+
},
|
| 1282 |
+
{
|
| 1283 |
+
"cell_type": "markdown",
|
| 1284 |
+
"metadata": {},
|
| 1285 |
+
"source": [
|
| 1286 |
+
"### Make Heliconius Subset"
|
| 1287 |
+
]
|
| 1288 |
+
},
|
| 1289 |
+
{
|
| 1290 |
+
"cell_type": "code",
|
| 1291 |
+
"execution_count": 29,
|
| 1292 |
+
"metadata": {},
|
| 1293 |
+
"outputs": [
|
| 1294 |
+
{
|
| 1295 |
+
"name": "stdout",
|
| 1296 |
+
"output_type": "stream",
|
| 1297 |
+
"text": [
|
| 1298 |
+
"<class 'pandas.core.frame.DataFrame'>\n",
|
| 1299 |
+
"Index: 34929 entries, 6 to 49358\n",
|
| 1300 |
+
"Data columns (total 26 columns):\n",
|
| 1301 |
+
" # Column Non-Null Count Dtype \n",
|
| 1302 |
+
"--- ------ -------------- ----- \n",
|
| 1303 |
+
" 0 CAMID 34929 non-null object\n",
|
| 1304 |
+
" 1 X 34929 non-null int64 \n",
|
| 1305 |
+
" 2 Image_name 34929 non-null object\n",
|
| 1306 |
+
" 3 View 34150 non-null object\n",
|
| 1307 |
+
" 4 zenodo_name 34929 non-null object\n",
|
| 1308 |
+
" 5 zenodo_link 34929 non-null object\n",
|
| 1309 |
+
" 6 Sequence 34929 non-null object\n",
|
| 1310 |
+
" 7 Taxonomic_Name 34929 non-null object\n",
|
| 1311 |
+
" 8 Locality 23417 non-null object\n",
|
| 1312 |
+
" 9 Sample_accession 5860 non-null object\n",
|
| 1313 |
+
" 10 Collected_by 5280 non-null object\n",
|
| 1314 |
+
" 11 Other_ID 6404 non-null object\n",
|
| 1315 |
+
" 12 Date 23162 non-null object\n",
|
| 1316 |
+
" 13 Dataset 32846 non-null object\n",
|
| 1317 |
+
" 14 Store 29446 non-null object\n",
|
| 1318 |
+
" 15 Brood 14921 non-null object\n",
|
| 1319 |
+
" 16 Death_Date 316 non-null object\n",
|
| 1320 |
+
" 17 Cross_Type 5133 non-null object\n",
|
| 1321 |
+
" 18 Stage 6 non-null object\n",
|
| 1322 |
+
" 19 Sex 33880 non-null object\n",
|
| 1323 |
+
" 20 Unit_Type 31975 non-null object\n",
|
| 1324 |
+
" 21 file_type 34929 non-null object\n",
|
| 1325 |
+
" 22 record_number 34929 non-null object\n",
|
| 1326 |
+
" 23 species 34929 non-null object\n",
|
| 1327 |
+
" 24 subspecies 24953 non-null object\n",
|
| 1328 |
+
" 25 genus 34929 non-null object\n",
|
| 1329 |
+
"dtypes: int64(1), object(25)\n",
|
| 1330 |
+
"memory usage: 7.2+ MB\n"
|
| 1331 |
+
]
|
| 1332 |
+
}
|
| 1333 |
+
],
|
| 1334 |
+
"source": [
|
| 1335 |
+
"heliconius_subset = df.loc[df.genus.str.lower() == \"heliconius\"]\n",
|
| 1336 |
+
"\n",
|
| 1337 |
+
"heliconius_subset.info()"
|
| 1338 |
+
]
|
| 1339 |
+
},
|
| 1340 |
+
{
|
| 1341 |
+
"cell_type": "code",
|
| 1342 |
+
"execution_count": 30,
|
| 1343 |
+
"metadata": {},
|
| 1344 |
+
"outputs": [
|
| 1345 |
+
{
|
| 1346 |
+
"data": {
|
| 1347 |
+
"text/plain": [
|
| 1348 |
+
"CAMID 9546\n",
|
| 1349 |
+
"X 34929\n",
|
| 1350 |
+
"Image_name 26946\n",
|
| 1351 |
+
"View 3\n",
|
| 1352 |
+
"zenodo_name 31\n",
|
| 1353 |
+
"zenodo_link 28\n",
|
| 1354 |
+
"Sequence 8701\n",
|
| 1355 |
+
"Taxonomic_Name 129\n",
|
| 1356 |
+
"Locality 472\n",
|
| 1357 |
+
"Sample_accession 1559\n",
|
| 1358 |
+
"Collected_by 12\n",
|
| 1359 |
+
"Other_ID 1865\n",
|
| 1360 |
+
"Date 776\n",
|
| 1361 |
+
"Dataset 8\n",
|
| 1362 |
+
"Store 121\n",
|
| 1363 |
+
"Brood 224\n",
|
| 1364 |
+
"Death_Date 81\n",
|
| 1365 |
+
"Cross_Type 30\n",
|
| 1366 |
+
"Stage 1\n",
|
| 1367 |
+
"Sex 3\n",
|
| 1368 |
+
"Unit_Type 4\n",
|
| 1369 |
+
"file_type 3\n",
|
| 1370 |
+
"record_number 28\n",
|
| 1371 |
+
"species 37\n",
|
| 1372 |
+
"subspecies 110\n",
|
| 1373 |
+
"genus 1\n",
|
| 1374 |
+
"dtype: int64"
|
| 1375 |
+
]
|
| 1376 |
+
},
|
| 1377 |
+
"execution_count": 30,
|
| 1378 |
+
"metadata": {},
|
| 1379 |
+
"output_type": "execute_result"
|
| 1380 |
+
}
|
| 1381 |
+
],
|
| 1382 |
+
"source": [
|
| 1383 |
+
"heliconius_subset.nunique()"
|
| 1384 |
+
]
|
| 1385 |
+
},
|
| 1386 |
+
{
|
| 1387 |
+
"cell_type": "code",
|
| 1388 |
+
"execution_count": 31,
|
| 1389 |
+
"metadata": {},
|
| 1390 |
+
"outputs": [
|
| 1391 |
+
{
|
| 1392 |
+
"data": {
|
| 1393 |
+
"text/plain": [
|
| 1394 |
+
"View\n",
|
| 1395 |
+
"dorsal 17218\n",
|
| 1396 |
+
"ventral 16914\n",
|
| 1397 |
+
"dorsal and ventral 18\n",
|
| 1398 |
+
"Name: count, dtype: int64"
|
| 1399 |
+
]
|
| 1400 |
+
},
|
| 1401 |
+
"execution_count": 31,
|
| 1402 |
+
"metadata": {},
|
| 1403 |
+
"output_type": "execute_result"
|
| 1404 |
+
}
|
| 1405 |
+
],
|
| 1406 |
+
"source": [
|
| 1407 |
+
"heliconius_subset.View.value_counts()"
|
| 1408 |
+
]
|
| 1409 |
+
},
|
| 1410 |
+
{
|
| 1411 |
+
"cell_type": "markdown",
|
| 1412 |
+
"metadata": {},
|
| 1413 |
+
"source": [
|
| 1414 |
+
"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)."
|
| 1415 |
+
]
|
| 1416 |
+
},
|
| 1417 |
+
{
|
| 1418 |
+
"cell_type": "markdown",
|
| 1419 |
+
"metadata": {},
|
| 1420 |
+
"source": [
|
| 1421 |
+
"### Save the Heliconius Subset to CSV\n",
|
| 1422 |
+
"We'll drop the `genus` column, since they're all `Heliconius`."
|
| 1423 |
+
]
|
| 1424 |
+
},
|
| 1425 |
+
{
|
| 1426 |
+
"cell_type": "code",
|
| 1427 |
+
"execution_count": 32,
|
| 1428 |
+
"metadata": {},
|
| 1429 |
+
"outputs": [],
|
| 1430 |
+
"source": [
|
| 1431 |
+
"heliconius_subset[list(heliconius_subset.columns)[:-1]].to_csv(\"../Jiggins_Heliconius_Master.csv\", index = False)"
|
| 1432 |
+
]
|
| 1433 |
+
},
|
| 1434 |
+
{
|
| 1435 |
+
"cell_type": "code",
|
| 1436 |
+
"execution_count": null,
|
| 1437 |
+
"metadata": {},
|
| 1438 |
+
"outputs": [],
|
| 1439 |
+
"source": []
|
| 1440 |
+
}
|
| 1441 |
+
],
|
| 1442 |
+
"metadata": {
|
| 1443 |
+
"kernelspec": {
|
| 1444 |
+
"display_name": "std",
|
| 1445 |
+
"language": "python",
|
| 1446 |
+
"name": "python3"
|
| 1447 |
+
},
|
| 1448 |
+
"language_info": {
|
| 1449 |
+
"codemirror_mode": {
|
| 1450 |
+
"name": "ipython",
|
| 1451 |
+
"version": 3
|
| 1452 |
+
},
|
| 1453 |
+
"file_extension": ".py",
|
| 1454 |
+
"mimetype": "text/x-python",
|
| 1455 |
+
"name": "python",
|
| 1456 |
+
"nbconvert_exporter": "python",
|
| 1457 |
+
"pygments_lexer": "ipython3",
|
| 1458 |
+
"version": "3.11.3"
|
| 1459 |
+
},
|
| 1460 |
+
"orig_nbformat": 4
|
| 1461 |
+
},
|
| 1462 |
+
"nbformat": 4,
|
| 1463 |
+
"nbformat_minor": 2
|
| 1464 |
+
}
|