Figure2_compact.py 5.8 KB

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  1. #!/usr/bin/env python
  2. # coding: utf-8
  3. # ### Link to the file with meta information on recordings
  4. # In[14]:
  5. #import matplotlib.pyplot as plt
  6. #plt.rcParams["figure.figsize"] = (20,3)
  7. database_path = '/media/andrey/My Passport/GIN/Anesthesia_CA1/meta_data/meta_recordings_transition_state.xlsx'
  8. # ### Select the range of recordings for the analysis (see "Number" row in the meta data file)
  9. # In[4]:
  10. #rec = [x for x in range(0,198+1)]
  11. rec = [127,128]
  12. # In[1]:
  13. import numpy as np
  14. import numpy.ma as ma
  15. import matplotlib.pyplot as plt
  16. import matplotlib.ticker as ticker
  17. import pandas as pd
  18. import seaborn as sns
  19. import pickle
  20. import os
  21. sns.set()
  22. sns.set_style("whitegrid")
  23. from scipy.signal import medfilt
  24. from scipy.stats import skew, kurtosis, zscore
  25. from scipy import signal
  26. from sklearn.linear_model import LinearRegression, TheilSenRegressor
  27. plt.rcParams['figure.figsize'] = [16, 8]
  28. color_awake = (0,191/255,255/255)
  29. color_mmf = (245/255,143/255,32/255)
  30. color_keta = (181./255,34./255,48./255)
  31. color_iso = (143./255,39./255,143./255)
  32. custom_palette ={'keta':color_keta, 'iso':color_iso,'mmf':color_mmf,'awake':color_awake}
  33. # In[2]:
  34. from capipeline import *
  35. df_estimators = pd.read_pickle("./transition_state_calcium_imaging_stability_validation.pkl")
  36. df_estimators['neuronID'] = df_estimators.index
  37. #df_estimators["animal"] = df_estimators["animal"]
  38. print(np.unique(df_estimators["condition"]))
  39. df_estimators["CONDITION"] = df_estimators["condition"]
  40. df_estimators.loc[:,"CONDITION"] = 'awake'
  41. df_estimators.loc[(df_estimators.condition == 'iso'),"CONDITION"] = 'iso'
  42. df_estimators.loc[(df_estimators.condition == 'keta'),"CONDITION"] = 'keta'
  43. df_estimators.loc[(df_estimators.condition == 'mmf'),"CONDITION"] = 'mmf'
  44. print(np.unique(df_estimators["CONDITION"]))
  45. #df_estimators["multihue"] = df_estimators["animal"].astype("string") + df_estimators["CONDITION"]
  46. df_estimators["batch"] = (df_estimators["recording"] > 101).astype("int") + 1
  47. #df_estimators["multihue"] = df_estimators["animal"].astype("string") + df_estimators["CONDITION"]
  48. df_estimators["multihue"] = "batch" + df_estimators["batch"].astype("string") + df_estimators["CONDITION"]
  49. #print(np.unique(df_estimators["batch"]))
  50. #print()
  51. # ### Plot
  52. # In[9]:
  53. parameters = ['number.neurons','traces.median','traces.skewness','decay','median.stability']
  54. labels = ['Extracted \n ROIs','Median, \n A.U.','Skewness','Decay time, \n s','1st/2nd \n ratio, %']
  55. number_subplots = len(parameters)
  56. recordings_ranges = [[0,198]]
  57. #sns.stripplot(x='multihue', y='number.neurons', data=df_estimators, jitter=True)
  58. #sns.scatterplot(x='multihue', y='number.neurons', data=df_estimators)
  59. #plt.show()
  60. #sns.swarmplot(x='multihue', y='number.neurons', data=df_estimators)
  61. for rmin,rmax in recordings_ranges:
  62. f, axes = plt.subplots(number_subplots, 1, figsize=(2, 5)) # sharex=Truerex=True
  63. #plt.subplots_adjust(left=None, bottom=0.1, right=None, top=0.9, wspace=None, hspace=0.2)
  64. #f.tight_layout()
  65. sns.despine(left=True)
  66. for i, param in enumerate(parameters):
  67. lw = 0.8
  68. #else:
  69. df_nneurons = df_estimators.groupby(['recording','multihue','CONDITION'], as_index=False)['number.neurons'].median()
  70. if (i == 0):
  71. sns.scatterplot(x='multihue', y='number.neurons', data=df_nneurons[(df_nneurons.recording>=rmin)&(df_nneurons.recording<=rmax)], hue = "CONDITION", palette = custom_palette, ax=axes[i], marker = '.',edgecolor="black", linewidth = lw*0.5) #
  72. else:
  73. sns.boxplot(x='multihue', y=param, data=df_estimators[(df_estimators.recording>=rmin)&(df_estimators.recording<=rmax)], hue = "CONDITION", palette = custom_palette, dodge=False, showfliers = False,ax=axes[i],linewidth=lw)
  74. # param = "animal"
  75. # print(np.unique(df_estimators[param]))
  76. # axes[i].set_yticks(np.unique(df_estimators[param]))
  77. # sns.swarmplot(x='recording', y=param, data=df_estimators[(df_estimators.recording>=rmin)&(df_estimators.recording<=rmax)&(df_estimators['neuronID'] == 0)],dodge=False, s=1, edgecolor='black', linewidth=1, ax=axes[i])
  78. #ax.set(ylabel="")
  79. if i == 0:
  80. axes[i].set_ylim([0.0,1200.0])
  81. if i > 0:
  82. axes[i].set_ylim([0.0,1000.0])
  83. if i > 1:
  84. axes[i].set_ylim([0.0,10.0])
  85. if i > 2:
  86. axes[i].set_ylim([0.0,1.0])
  87. if i > 3:
  88. axes[i].set_ylim([80,120])
  89. axes[i].get_xaxis().set_visible(True)
  90. else:
  91. axes[i].get_xaxis().set_visible(False)
  92. #if i < number_subplots-1:
  93. axes[i].xaxis.label.set_visible(False)
  94. #if i==0:
  95. # axes[i].set_title("Validation: stability check (recordings #%d-#%d)" % (rmin,rmax), fontsize=9, pad=30) #45
  96. axes[i].set_ylabel(labels[i], fontsize=9,labelpad=5) #40
  97. #axes[i].set_xlabel("Recording", fontsize=5,labelpad=5) #40
  98. #axes[i].axis('off')
  99. axes[i].xaxis.set_tick_params(labelsize=9) #35
  100. axes[i].yaxis.set_tick_params(labelsize=9) #30
  101. axes[i].get_legend().remove()
  102. #axes[i].xaxis.set_major_locator(ticker.MultipleLocator(10))
  103. #axes[i].xaxis.set_major_formatter(ticker.ScalarFormatter())
  104. plt.xticks(rotation=90)
  105. #plt.legend(bbox_to_anchor=(1.01, 1), loc=2, borderaxespad=0.,fontsize=25)
  106. plt.savefig("Validation_stability_check_figure2.png",dpi=400)
  107. plt.savefig("Validation_stability_check_figure2.svg")
  108. #plt.show()
  109. # In[13]:
  110. sns.displot(data=df_estimators, x="median.stability", kind="kde", hue = "animal")
  111. plt.xlim([80,120])
  112. plt.xlabel("Stability, %", fontsize = 15)
  113. plt.title("Validation: summary on stability (recordings #%d-#%d)" % (min(rec),max(rec)), fontsize = 20, pad=20)
  114. plt.grid(False)
  115. plt.savefig("Validation_summary_stability_recordings_#%d-#%d)" % (min(rec),max(rec)))
  116. #plt.show()
  117. # In[62]: