Workflow tutorial

Import the subsetmask package

import pandas as pd
import subsetmask as sm

Load the metadata as a pandas data frame

# Load the metadata CSV file
meta = pd.read_csv("test/metadata.csv", index_col=0, header=0)

We use the Image class

image = sm.Image(metadata_df=meta,
                  images_col='slide',
                  image_name='slide_1',
                  x_col='x_slide_mm',
                  y_col='y_slide_mm')

# addd store image method
image.store_image(group_col=None, figsize=(30, 50), dpi=300, dot_size=5, cmap='tab20')

# launches napari with the generated image
image.draw_labels()

# without closing napari we run this line to save the annotations
image.save_annotations(ann_col_name="new_col")

# we change our annotations for something more meaningful
map = {
    "label_1": "sample_1",
    "label_2": "sample_2"
}

image.rename_annotations(ann_col_name="new_col", mapping_dict=map)

# we extract the annotated df from the object
annotated_meta = image.annotated_metadata_df

# we verify the value counts of the new column
print(annotated_meta[["new_col"]].value_counts())

new_col
not_assigned    205211
sample_2         75815
sample_1         64059
Name: count, dtype: int64

# now just save the annotated metadata
annotated_meta.to_csv("test/metadata_annotated.csv")