old_contents: import numpy as np
  import matplotlib.pyplot as plt
  
  from tridesclous import download_dataset
  from tridesclous.dataio import DataIO
  from tridesclous.catalogueconstructor import CatalogueConstructor
  from tridesclous.matplotlibplot import *
  
  
  import matplotlib.pyplot as plt
  
  from tridesclous.tests.testingtools import setup_catalogue
  import shutil
  
  def setup_module():
      setup_catalogue('test_matplotlibplot', dataset_name='olfactory_bulb')
  
  def teardown_module():
      shutil.rmtree('test_matplotlibplot')
  
      
  
  def test_plot_probe_geometry():
      dataio = DataIO('test_matplotlibplot')
      plot_probe_geometry(dataio, chan_grp=0) 
  
      
  
  def test_plot_signals():
      dataio = DataIO('test_matplotlibplot')
      catalogueconstructor = CatalogueConstructor(dataio=dataio, chan_grp=0)
      
      plot_signals(dataio, signal_type='initial')
      plot_signals(dataio, signal_type='processed')
      plot_signals(catalogueconstructor, signal_type='processed', with_peaks=True, time_slice=(2., 3))
      plot_signals(catalogueconstructor, signal_type='processed', with_span=True, time_slice=(2., 3))
      
  
  
  def test_plot_waveforms_with_geometry():
      nb_channel = 32
      waveforms = np.random.randn(200, 45, nb_channel)
      channels = np.arange(nb_channel)
      #~ geometry = {c: [np.random.randint(100), np.random.randint(100)] for c in channels}
      geometry = np.random.randint(low=0, high=100, size=(200, 2))
      
      #~ , channels, geometry
      #~ print(geometry)
      
      plot_waveforms_with_geometry(waveforms, channels, geometry) 
      
  def test_plot_waveforms():
      dataio = DataIO('test_matplotlibplot')
      catalogueconstructor = CatalogueConstructor(dataio=dataio, chan_grp=0)
      
      plot_waveforms(catalogueconstructor)
  
  
  
  def test_plot_features_scatter_2d():
      dataio = DataIO('test_matplotlibplot')
      catalogueconstructor = CatalogueConstructor(dataio=dataio, chan_grp=0)
      
      #~ plot_features_scatter_2d(catalogueconstructor)
      plot_features_scatter_2d(catalogueconstructor, labels=[0])
  
      
  if __name__ == '__main__':
      setup_module()
      
      test_plot_probe_geometry()
      test_plot_signals()
      test_plot_waveforms_with_geometry()
      test_plot_waveforms()
      test_plot_features_scatter_2d()
      
  
  
  
      plt.show()
new_contents: import numpy as np
  import matplotlib.pyplot as plt
  
  from tridesclous import download_dataset
  from tridesclous.dataio import DataIO
  from tridesclous.catalogueconstructor import CatalogueConstructor
  from tridesclous.matplotlibplot import *
  
  
  import matplotlib.pyplot as plt
  
  from tridesclous.tests.testingtools import setup_catalogue
  import shutil
  
  from tridesclous.tests.testingtools import ON_CI_CLOUD
  import pytest
  
  def setup_module():
      setup_catalogue('test_matplotlibplot', dataset_name='olfactory_bulb')
  
  def teardown_module():
      shutil.rmtree('test_matplotlibplot')
  
      
  @pytest.mark.skipif(ON_CI_CLOUD, reason='ON_CI_CLOUD')
  def test_plot_probe_geometry():
      dataio = DataIO('test_matplotlibplot')
      plot_probe_geometry(dataio, chan_grp=0) 
  
      
  @pytest.mark.skipif(ON_CI_CLOUD, reason='ON_CI_CLOUD')
  def test_plot_signals():
      dataio = DataIO('test_matplotlibplot')
      catalogueconstructor = CatalogueConstructor(dataio=dataio, chan_grp=0)
      
      plot_signals(dataio, signal_type='initial')
      plot_signals(dataio, signal_type='processed')
      plot_signals(catalogueconstructor, signal_type='processed', with_peaks=True, time_slice=(2., 3))
      plot_signals(catalogueconstructor, signal_type='processed', with_span=True, time_slice=(2., 3))
      
  
  @pytest.mark.skipif(ON_CI_CLOUD, reason='ON_CI_CLOUD')
  def test_plot_waveforms_with_geometry():
      nb_channel = 32
      waveforms = np.random.randn(200, 45, nb_channel)
      channels = np.arange(nb_channel)
      #~ geometry = {c: [np.random.randint(100), np.random.randint(100)] for c in channels}
      geometry = np.random.randint(low=0, high=100, size=(200, 2))
      
      #~ , channels, geometry
      #~ print(geometry)
      
      plot_waveforms_with_geometry(waveforms, channels, geometry) 
  
  @pytest.mark.skipif(ON_CI_CLOUD, reason='ON_CI_CLOUD')
  def test_plot_waveforms():
      dataio = DataIO('test_matplotlibplot')
      catalogueconstructor = CatalogueConstructor(dataio=dataio, chan_grp=0)
      
      plot_waveforms(catalogueconstructor)
  
  
  @pytest.mark.skipif(ON_CI_CLOUD, reason='ON_CI_CLOUD')
  def test_plot_features_scatter_2d():
      dataio = DataIO('test_matplotlibplot')
      catalogueconstructor = CatalogueConstructor(dataio=dataio, chan_grp=0)
      
      #~ plot_features_scatter_2d(catalogueconstructor)
      plot_features_scatter_2d(catalogueconstructor, labels=[0])
  
      
  if __name__ == '__main__':
      setup_module()
      
      test_plot_probe_geometry()
      test_plot_signals()
      test_plot_waveforms_with_geometry()
      test_plot_waveforms()
      test_plot_features_scatter_2d()
      
  
  
  
      plt.show()
subject: Remove matplotilib test when on cloud