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| 1 | +# --- |
| 2 | +# jupyter: |
| 3 | +# jupytext: |
| 4 | +# text_representation: |
| 5 | +# extension: .py |
| 6 | +# format_name: light |
| 7 | +# format_version: '1.5' |
| 8 | +# jupytext_version: 1.16.4 |
| 9 | +# kernelspec: |
| 10 | +# display_name: Python 3 (ipykernel) |
| 11 | +# language: python |
| 12 | +# name: python3 |
| 13 | +# --- |
| 14 | + |
| 15 | +# # CytoDataFrame at a Glance |
| 16 | +# |
| 17 | +# This notebook demonstrates various capabilities of |
| 18 | +# [CytoDataFrame](https://github.com/WayScience/CytoDataFrame) using examples. |
| 19 | +# |
| 20 | +# CytoDataFrame is intended to provide you a Pandas-like |
| 21 | +# DataFrame experience which is enhanced with single-cell |
| 22 | +# visual information which can be viewed directly in a Jupyter notebook. |
| 23 | + |
| 24 | +# + |
| 25 | +from cytodataframe.frame import CytoDataFrame |
| 26 | + |
| 27 | +# create paths for use with CytoDataFrames below |
| 28 | +jump_data_path = "../../../tests/data/cytotable/JUMP_plate_BR00117006" |
| 29 | +nf1_cellpainting_path = "../../../tests/data/cytotable/NF1_cellpainting_data_shrunken/" |
| 30 | +nuclear_speckles_path = "../../../tests/data/cytotable/nuclear_speckles" |
| 31 | +# - |
| 32 | +# %%time |
| 33 | +# view JUMP plate BR00117006 with images |
| 34 | +CytoDataFrame( |
| 35 | + data=f"{jump_data_path}/BR00117006_shrunken.parquet", |
| 36 | + data_context_dir=f"{jump_data_path}/images/orig", |
| 37 | +)[ |
| 38 | + [ |
| 39 | + "Metadata_ImageNumber", |
| 40 | + "Cells_Number_Object_Number", |
| 41 | + "Image_FileName_OrigAGP", |
| 42 | + "Image_FileName_OrigDNA", |
| 43 | + "Image_FileName_OrigRNA", |
| 44 | + ] |
| 45 | +][:3] |
| 46 | + |
| 47 | +# %%time |
| 48 | +# view JUMP plate BR00117006 with images and overlaid outlines for segmentation |
| 49 | +CytoDataFrame( |
| 50 | + data=f"{jump_data_path}/BR00117006_shrunken.parquet", |
| 51 | + data_context_dir=f"{jump_data_path}/images/orig", |
| 52 | + data_outline_context_dir=f"{jump_data_path}/images/outlines", |
| 53 | +)[ |
| 54 | + [ |
| 55 | + "Metadata_ImageNumber", |
| 56 | + "Cells_Number_Object_Number", |
| 57 | + "Image_FileName_OrigAGP", |
| 58 | + "Image_FileName_OrigDNA", |
| 59 | + "Image_FileName_OrigRNA", |
| 60 | + ] |
| 61 | +][:3] |
| 62 | + |
| 63 | + |
| 64 | +# %%time |
| 65 | +# view NF1 Cell Painting data with images |
| 66 | +CytoDataFrame( |
| 67 | + data=f"{nf1_cellpainting_path}/Plate_2_with_image_data_shrunken.parquet", |
| 68 | + data_context_dir=f"{nf1_cellpainting_path}/Plate_2_images", |
| 69 | +)[ |
| 70 | + [ |
| 71 | + "Metadata_ImageNumber", |
| 72 | + "Metadata_Cells_Number_Object_Number", |
| 73 | + "Image_FileName_GFP", |
| 74 | + "Image_FileName_RFP", |
| 75 | + "Image_FileName_DAPI", |
| 76 | + ] |
| 77 | +][:3] |
| 78 | + |
| 79 | +# %%time |
| 80 | +# view NF1 Cell Painting data with images and overlaid outlines from masks |
| 81 | +CytoDataFrame( |
| 82 | + data=f"{nf1_cellpainting_path}/Plate_2_with_image_data_shrunken.parquet", |
| 83 | + data_context_dir=f"{nf1_cellpainting_path}/Plate_2_images", |
| 84 | + data_mask_context_dir=f"{nf1_cellpainting_path}/Plate_2_masks", |
| 85 | +)[ |
| 86 | + [ |
| 87 | + "Metadata_ImageNumber", |
| 88 | + "Metadata_Cells_Number_Object_Number", |
| 89 | + "Image_FileName_GFP", |
| 90 | + "Image_FileName_RFP", |
| 91 | + "Image_FileName_DAPI", |
| 92 | + ] |
| 93 | +][:3] |
| 94 | + |
| 95 | +# %%time |
| 96 | +# view nuclear speckles data with images and overlaid outlines from masks |
| 97 | +CytoDataFrame( |
| 98 | + data=f"{nuclear_speckles_path}/test_slide1_converted.parquet", |
| 99 | + data_context_dir=f"{nuclear_speckles_path}/images/plate1", |
| 100 | + data_mask_context_dir=f"{nuclear_speckles_path}/masks/plate1", |
| 101 | +)[ |
| 102 | + [ |
| 103 | + "Metadata_ImageNumber", |
| 104 | + "Nuclei_Number_Object_Number", |
| 105 | + "Image_FileName_A647", |
| 106 | + "Image_FileName_DAPI", |
| 107 | + "Image_FileName_GOLD", |
| 108 | + ] |
| 109 | +][:3] |
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