Masks for Cloud plots

Import modules and setup font.

In [1]:

import os
from ScanPyImports.plotter import DataPlotter, mask_from_path, get_fontname_list

import os from ScanPyImports.plotter import DataPlotter, mask_from_path, get_fontname_list

In [2]:

# Initailize DataPlotter with directory
plotter = DataPlotter('../../ScanPyImports')

# In this example, we use the Impact font
plotter.settings.set_font('Impact')

# If you are not on Windows, you might not have it.
# Either skip the previous step or choose another font
if 'Impact' in get_fontname_list():
    print('"Impact" is installed on your computer')
else:
    print('"Impact" is not installed!')
    print('Use `get_fontname_list()` to check which fonts are installed on your computer.')

# Initailize DataPlotter with directory plotter = DataPlotter('../../ScanPyImports') # In this example, we use the Impact font plotter.settings.set_font('Impact') # If you are not on Windows, you might not have it. # Either skip the previous step or choose another font if 'Impact' in get_fontname_list(): print('"Impact" is installed on your computer') else: print('"Impact" is not installed!') print('Use `get_fontname_list()` to check which fonts are installed on your computer.')

"Impact" is installed on your computer

You can use any png image as a mask for the WordCloud plot.

Here is an example with an image included in the package. You can use your own image!

In [3]:

# File with the image to use as a mask
path = r'../../ScanPyImports/images'
file_img = 'star.png'

# Get the mask of the image (np.array)
mask = mask_from_path(os.path.join(path, file_img))

# Plot
fig, ax, *_ = plotter.cloud_frequencies(mask=mask)

# File with the image to use as a mask path = r'../../ScanPyImports/images' file_img = 'star.png' # Get the mask of the image (np.array) mask = mask_from_path(os.path.join(path, file_img)) # Plot fig, ax, *_ = plotter.cloud_frequencies(mask=mask)

In the image folder of the package, you can find other images to use as masks.

Here is a showcase of the available images.

In [4]:

import matplotlib.pyplot as plt

# Helper function to get the mask from the file image
mask = lambda file_img: mask_from_path(os.path.join(path, file_img))

# Set the size for the plots
size = 3

# Create a mosaic of subplots for each shape/mask
fig, axs = plt.subplot_mosaic(
    [['default', 'star'], ['circle', 'triangle']],
    figsize=(size * 2, size * 2),
    layout='constrained'
)

# Default shape plot
fig, axs['default'], *_ = plotter.cloud_frequencies(ax=axs['default'])

# Plot for other shapes
for shape in ['star', 'circle', 'triangle']:
    fig, axs[shape], *_=plotter.cloud_frequencies(ax=axs[shape]
                                                  , mask=mask(f'{shape}.png')
                                                  )

import matplotlib.pyplot as plt # Helper function to get the mask from the file image mask = lambda file_img: mask_from_path(os.path.join(path, file_img)) # Set the size for the plots size = 3 # Create a mosaic of subplots for each shape/mask fig, axs = plt.subplot_mosaic( [['default', 'star'], ['circle', 'triangle']], figsize=(size * 2, size * 2), layout='constrained' ) # Default shape plot fig, axs['default'], *_ = plotter.cloud_frequencies(ax=axs['default']) # Plot for other shapes for shape in ['star', 'circle', 'triangle']: fig, axs[shape], *_=plotter.cloud_frequencies(ax=axs[shape] , mask=mask(f'{shape}.png') )