Visualizing Imported Modules

For this demo, let's analyze the modules imported by this package - ScanPyImports.

Import the DataPlotter class.

In [1]:

from ScanPyImports.plotter import DataPlotter

from ScanPyImports.plotter import DataPlotter

Let's inspect first the frequencies of imported modules by the ScanPyImports package.

In [2]:

plotter = DataPlotter('../../ScanPyImports')
plotter.get_frequencies()

plotter = DataPlotter('../../ScanPyImports') plotter.get_frequencies()

Out[2]:

Imports
typing           14
matplotlib        8
ScanPyImports     4
os                3
PIL               1
nbformat          1
numpy             1
pandas            1
re                1
wordcloud         1
dtype: int64

Spiral plot¶

In [3]:

fig, ax, bars, texts = plotter.spiral_frequencies()

fig, ax, bars, texts = plotter.spiral_frequencies()

WordCloud plot¶

In [4]:

fig, ax, wc, im = plotter.cloud_frequencies()

fig, ax, wc, im = plotter.cloud_frequencies()

Parameters¶

The spiral_frequencies() and cloud_frequencies() methods have three types of parameters:

1. Parameters relating to the data processing of the imported modules:
   • The exclude and process_own_modules parameters, explained in Data.
2. Parameters passed as an argument to external APIs:
   • The **kwargs parameters explained in this tutorial.
   • In particular:
     • Spiral plots call the bar plot method from Matplotlib.
     • Cloud plots are instances of the WordCloud class.
3. Parameters relating to visualization options of the plots implemented in this package:
   • These are showcased below.

Spiral Parameters¶

Signature:

• spiral_frequencies(exclude=True, process_own_modules=True, top=25, ax=None, zero_at='NE', defaults=True, label_padding=2, **kwargs)

Let's showcase some paramterers changes.

In [5]:

import matplotlib.pyplot as plt

size = 10
fig, axs = plt.subplot_mosaic(
    [["defaults", 'zero_at'], ['top', 'label_padding']],
    figsize=(size, size),
    subplot_kw = dict(projection = 'polar'),
    layout='constrained'
)

parameters = "defaults", 'zero_at' , 'top', 'label_padding'
values = False, 'SW', 3, 10

for p,val in zip(parameters, values) :
    parms={p:val}
    fig, axs[p], *_=plotter.spiral_frequencies(ax=axs[p], **parms) 
    title = axs[p].set_title(f'{p}={val}'
                             , bbox=dict(boxstyle='round'
                             , edgecolor='cyan'
                             , facecolor='cyan'
                             )
                            )

import matplotlib.pyplot as plt size = 10 fig, axs = plt.subplot_mosaic( [["defaults", 'zero_at'], ['top', 'label_padding']], figsize=(size, size), subplot_kw = dict(projection = 'polar'), layout='constrained' ) parameters = "defaults", 'zero_at' , 'top', 'label_padding' values = False, 'SW', 3, 10 for p,val in zip(parameters, values) : parms={p:val} fig, axs[p], *_=plotter.spiral_frequencies(ax=axs[p], **parms) title = axs[p].set_title(f'{p}={val}' , bbox=dict(boxstyle='round' , edgecolor='cyan' , facecolor='cyan' ) )

Cloud Parameters¶

Signature:

• cloud_frequencies(exclude=True, process_own_modules=True, defaults=True, ax=None, imshow=None, **kwargs)

Setting defaults to False.

In [6]:

_ = plotter.cloud_frequencies(defaults=False)

_ = plotter.cloud_frequencies(defaults=False)

Modifying the imshow parameter.

In [7]:

_ = plotter.cloud_frequencies(imshow=dict(alpha=0.3))

_ = plotter.cloud_frequencies(imshow=dict(alpha=0.3))