Function bodies 54 total

def plot_difference(data, ax = False):
    pcf_dm = np.median(np.vstack(data['normPCF']),axis=0)
    pcf_dsem = error_median(data['normPCF'])
    
    if ax == False:
        fig, ax = plt.subplots()
        fig.set_size_inches(8,5)

    ax.plot(x_data,  pcf_dm)
    ax.fill_between(x_data, pcf_dsem[0], pcf_dsem[1],  alpha = .4, lw=0)

    ax.set_title(data['Cell_one'].iloc[0] + ' vs. ' + data['Cell_two'].iloc[0], fontsize = 18);
    ax.plot(x_data, np.ones(len(x_data)), 'k--');
    ax.set_xlabel('Radius ($\mu$m)', fontsize = 16);
    ax.set_ylabel('PCF', fontsize = 16);
    ax.tick_params(axis='both', which='major', labelsize=14);

def interaction_subset(df, cell1, cell2, min_count=0):
    pcf_table = df[(((df['Cell_one']==cell1) & (df['Cell_two']==cell2)) | ((df['Cell_one']==cell2) & (df['Cell_two']==cell1)) ) & (df['min_count'] > min_count)]
    #pcf_table = pcf_table[pcf_table['PCF'] != 'NA']
    return pcf_table

def plot_pcf_curves(pcf_df,cell_types_,resolution,label,out_path,radius):
  CELL_TYPES_ALIAS =['All']
  CELL_TYPES_ALIAS.extend(cell_types_)
  
  CELL_TYPES = CELL_TYPES_ALIAS
  COLOR_LIST = sns.color_palette(None, len(CELL_TYPES))
  print(COLOR_LIST)
  
  #x_data = np.arange(0, 125, .25) 
  rmax=radius/resolution
  stepsize=rmax/500
  
  x_data = np.arange(0,rmax,stepsize)
  
  # fig = plt.figure(figsize=(10, 10))
  # #outer = mpl.gridspec.GridSpec(3, 3, wspace=0.2, hspace=0.2)
  # outer = mpl.gridspec.GridSpec(int(np.ceil(np.sqrt(len(CELL_TYPES))))+1, int(np.ceil(np.sqrt(len(CELL_TYPES)))), wspace=0.4, hspace=0.4)
  # print(str(outer.ncols))
  # print(str(outer.nrows))
  # print(len(CELL_TYPES))
  # print(CELL_TYPES)
  
  pcfs_global=[]
  for i in range(len(CELL_TYPES)):
    print('i: '+str(i))
    print(CELL_TYPES[i])
    # inner = mpl.gridspec.GridSpecFromSubplotSpec(2, 1,
    #                 subplot_spec=outer[i], wspace=0.2, hspace=0.2, height_ratios=[1,2])
    # 
    # 
    # 

def pcf_AUC(pcf_df,cell_types_,cell1,label,out_path,resolution):
  CELL_TYPES = ['All']
  CELL_TYPES.extend(cell_types_)
  print(CELL_TYPES)
  CELL_TYPES_ALIAS = CELL_TYPES
 
  CELL2_LIST = CELL_TYPES
  print(CELL_TYPES)
  CELL2_LIST.pop(CELL_TYPES.index(cell1))
  print(CELL_TYPES)
  LABELS = CELL_TYPES
  print(cell1)
  print(LABELS)
  
  print('Cell 2 list:')
  print(CELL2_LIST)
  COLORS = sns.color_palette(None, len(CELL_TYPES))
  STEP_TO_UM = resolution

  def interaction_subset(df, cell1, cell2, min_count=0):
    pcf_table = df[(((df['Cell_one']==cell1) & (df['Cell_two']==cell2)) | ((df['Cell_one']==cell2) & (df['Cell_two']==cell1)) ) & (df['min_count'] > min_count)]
    #pcf_table = pcf_table[pcf_table['PCF'] != 'NA']
    return pcf_table


  def pvalue_text(data1, data2, verbose = False):
      pvalue = stats.mannwhitneyu(data1, data2, alternative='two-sided')[1]
      if verbose:
          print(pvalue)
      if pvalue < 0.001:
          return '***'
      elif pvalue < 0.01: