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Make violin plots more robust to empty array inputs. #348

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merged 1 commit into from Jan 12, 2021
Merged

Make violin plots more robust to empty array inputs. #348

merged 1 commit into from Jan 12, 2021

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JS3xton
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@JS3xton JS3xton commented Nov 19, 2020 •
edited

Make violin plots more robust to empty array inputs.

  • _plot_single_violin() was modified to handle empty violin_data inputs.
  • The logic in the plot violin functions that understands the data input was made more flexible to detect and correctly label empty lists.
  • Calculation of the default axes limits was updated to tolerate empty lists.

All unit tests pass in Python 3.8 + Anaconda 2020.07 and Python 2.7 + Anaconda 5.2.0.

Test scripts, detailed below, also now run without error and produce the expected outputs in Python 3.8 + Anaconda 2020.07 and Python 2.7 + Anaconda 5.2.0. (Some tests already ran successfully before, as noted in the scripts.) Test scripts previously used to test other violin plot functionality also still produce expected outputs.

test_plot_violin_empty_lists.py 

import FlowCal
import numpy as np
import matplotlib.pyplot as plt

###
# 1D arrays
###
plt.figure()
FlowCal.plot.violin(data=np.random.random((9,)),  # <-- worked previously
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[],
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[[],[],[]],
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=np.empty(shape=(0,)),
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[np.empty(shape=(0,))],
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[np.random.random((9,))],  # <-- worked previously
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[np.empty(shape=(0,)),
                          np.random.random((9,)),
                          np.random.random((9,))],
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[[],
                          np.random.random((9,)),
                          np.random.random((9,))],
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[np.random.random((9,)),
                          np.random.random((9,)),
                          np.empty(shape=(0,))],
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[np.random.random((9,)),
                          np.random.random((9,)),
                          []],
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[np.random.random((9,)),  # <-- worked previously
                          np.random.random((9,)),
                          np.random.random((9,))],
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[[],
                          np.random.random((9,)),
                          np.random.random((9,))],
                    positions=[0,1,2],
                    xscale='log',
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[np.random.random((9,)),
                          [],
                          np.random.random((9,))],
                    positions=[0,1,2],
                    xscale='log',
                    yscale='linear')

###
# ND arrays
###
plt.figure()
FlowCal.plot.violin(data=np.random.random((9,3)),  # <-- worked previously
                    channel=0,
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[],
                    channel=0,
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=np.empty(shape=(0,3)),
                    channel=0,
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[np.empty(shape=(0,3))],
                    channel=0,
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[np.random.random((9,3))],  # <-- worked previously
                    channel=0,
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[np.empty(shape=(0,3)),
                          np.random.random((9,3)),
                          np.random.random((9,3))],
                    channel=0,
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[[],
                          np.random.random((9,3)),
                          np.random.random((9,3))],
                    channel=0,
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[np.random.random((9,3)),
                          np.random.random((9,3)),
                          np.empty(shape=(0,3))],
                    channel=0,
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[np.random.random((9,3)),
                          np.random.random((9,3)),
                          []],
                    channel=0,
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[np.random.random((9,3)),  # <-- worked previously
                          np.random.random((9,3)),
                          np.random.random((9,3))],
                    channel=0,
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[[],
                          np.random.random((9,3)),
                          np.random.random((9,3))],
                    channel=0,
                    positions=[0,1,2],
                    xscale='log',
                    yscale='linear')
plt.figure()
FlowCal.plot.violin(data=[np.random.random((9,3)),
                          [],
                          np.random.random((9,3))],
                    channel=0,
                    positions=[0,1,2],
                    xscale='log',
                    yscale='linear')

plt.show()

test_plot_violin_dose_response_empty_lists_1D.py 

import FlowCal
import numpy as np
import matplotlib.pyplot as plt

###
# 1D arrays
###
plt.figure()
FlowCal.plot.violin_dose_response(data=np.random.random((9,)),  # <-- worked previously
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[],
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[[],[],[]],
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=np.empty(shape=(0,)),
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.empty(shape=(0,))],
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,))],  # <-- worked previously
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.empty(shape=(0,)),
                                        np.random.random((9,)),
                                        np.random.random((9,))],
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[[],
                                        np.random.random((9,)),
                                        np.random.random((9,))],
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,)),
                                        np.random.random((9,)),
                                        np.empty(shape=(0,))],
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,)),
                                        np.random.random((9,)),
                                        []],
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,)),  # <-- worked previously
                                        np.random.random((9,)),
                                        np.random.random((9,))],
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[[],
                                        np.random.random((9,)),
                                        np.random.random((9,))],
                                  positions=[0,1,2],
                                  xscale='log',
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,)),
                                        [],
                                        np.random.random((9,))],
                                  positions=[0,1,2],
                                  xscale='log',
                                  yscale='linear')
# test min and max
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,)),  # <-- worked previously
                                        np.random.random((9,)),
                                        np.random.random((9,))],
                                  min_data=np.random.random((9,)),
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,)),
                                        np.random.random((9,)),
                                        np.random.random((9,))],
                                  min_data=[],
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,)),
                                        np.random.random((9,)),
                                        np.random.random((9,))],
                                  min_data=np.empty(shape=(0,)),
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,)),  # <-- worked previously
                                        np.random.random((9,)),
                                        np.random.random((9,))],
                                  max_data=np.random.random((9,)),
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,)),
                                        np.random.random((9,)),
                                        np.random.random((9,))],
                                  max_data=[],
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,)),
                                        np.random.random((9,)),
                                        np.random.random((9,))],
                                  max_data=np.empty(shape=(0,)),
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,)),  # <-- worked previously
                                        np.random.random((9,)),
                                        np.random.random((9,))],
                                  min_data=np.random.random((9,)),
                                  max_data=np.random.random((9,)),
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,)),
                                        np.random.random((9,)),
                                        np.random.random((9,))],
                                  min_data=[],
                                  max_data=[],
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,)),  # <-- worked previously
                                        np.random.random((9,)),
                                        np.random.random((9,))],
                                  positions=[0,1,2],
                                  xscale='log',
                                  min_data=np.random.random((9,)),
                                  max_data=np.random.random((9,)),
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[[],
                                        np.random.random((9,)),
                                        np.random.random((9,))],
                                  positions=[0,1,2],
                                  xscale='log',
                                  min_data=[],
                                  max_data=[],
                                  yscale='linear')

plt.show()

test_plot_violin_dose_response_empty_lists_ND.py 

import FlowCal
import numpy as np
import matplotlib.pyplot as plt

###
# ND arrays
###
plt.figure()
FlowCal.plot.violin_dose_response(data=np.random.random((9,3)),  # <-- worked previously
                                  channel=0,
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[],
                                  channel=0,
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=np.empty(shape=(0,3)),
                                  channel=0,
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.empty(shape=(0,3))],
                                  channel=0,
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,3))],  # <-- worked previously
                                  channel=0,
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.empty(shape=(0,3)),
                                        np.random.random((9,3)),
                                        np.random.random((9,3))],
                                  channel=0,
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[[],
                                        np.random.random((9,3)),
                                        np.random.random((9,3))],
                                  channel=0,
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,3)),
                                        np.random.random((9,3)),
                                        np.empty(shape=(0,3))],
                                  channel=0,
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,3)),
                                        np.random.random((9,3)),
                                        []],
                                  channel=0,
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,3)),  # <-- worked previously
                                        np.random.random((9,3)),
                                        np.random.random((9,3))],
                                  channel=0,
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[[],
                                        np.random.random((9,3)),
                                        np.random.random((9,3))],
                                  channel=0,
                                  positions=[0,1,2],
                                  xscale='log',
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,3)),
                                        [],
                                        np.random.random((9,3))],
                                  channel=0,
                                  positions=[0,1,2],
                                  xscale='log',
                                  yscale='linear')

# test min and max
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,3)),  # <-- worked previously
                                        np.random.random((9,3)),
                                        np.random.random((9,3))],
                                  channel=0,
                                  min_data=np.random.random((9,3)),
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,3)),
                                        np.random.random((9,3)),
                                        np.random.random((9,3))],
                                  channel=0,
                                  min_data=[],
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,3)),
                                        np.random.random((9,3)),
                                        np.random.random((9,3))],
                                  channel=0,
                                  min_data=np.empty(shape=(0,3)),
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,3)),  # <-- worked previously
                                        np.random.random((9,3)),
                                        np.random.random((9,3))],
                                  channel=0,
                                  max_data=np.random.random((9,3)),
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,3)),
                                        np.random.random((9,3)),
                                        np.random.random((9,3))],
                                  channel=0,
                                  max_data=[],
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,3)),
                                        np.random.random((9,3)),
                                        np.random.random((9,3))],
                                  channel=0,
                                  max_data=np.empty(shape=(0,3)),
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,3)),  # <-- worked previously
                                        np.random.random((9,3)),
                                        np.random.random((9,3))],
                                  channel=0,
                                  min_data=np.random.random((9,3)),
                                  max_data=np.random.random((9,3)),
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,3)),
                                        np.random.random((9,3)),
                                        np.random.random((9,3))],
                                  channel=0,
                                  min_data=[],
                                  max_data=[],
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[np.random.random((9,3)),  # <-- worked previously
                                        np.random.random((9,3)),
                                        np.random.random((9,3))],
                                  channel=0,
                                  positions=[0,1,2],
                                  xscale='log',
                                  min_data=np.random.random((9,3)),
                                  max_data=np.random.random((9,3)),
                                  yscale='linear')
plt.figure()
FlowCal.plot.violin_dose_response(data=[[],
                                        np.random.random((9,3)),
                                        np.random.random((9,3))],
                                  channel=0,
                                  positions=[0,1,2],
                                  xscale='log',
                                  min_data=[],
                                  max_data=[],
                                  yscale='linear')

plt.show()

@JS3xton
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JS3xton commented Nov 19, 2020

Fixes #342 and #343.

This was linked to issues Nov 19, 2020
Violin plots fail when empty array is included among violin data #342
Open
Violin plots fail when called with empty array #343
Open
@JS3xton JS3xton merged commit 804c675 into taborlab:develop Jan 12, 2021
@JS3xton JS3xton deleted the violin-robust-empty-lists branch January 12, 2021 01:27
@JS3xton JS3xton mentioned this pull request Jan 12, 2021
Merged
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