Define min and max axes for figures based on the input

Author: RJMW

beams/plots.py

@@ -24,22 +24,10 @@
         except:
             continue
 
-import matplotlib.pyplot as plt
-from matplotlib import gridspec
-import seaborn as sns
-
 
-import sys
 import sqlite3
 import numpy as np
 import pandas as pd
-import matplotlib
-import matplotlib.pyplot as plt
-import matplotlib.gridspec as gridspec
-import seaborn as sns
-from matplotlib.backends.backend_pdf import PdfPages
-
-
 import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
 import seaborn as sns
@@ -64,42 +52,51 @@ def plot_correlations(column_corr, column_pvalue, df):
     ax_x_dist.set_axisbelow(True)
     ax_y_dist.set_axisbelow(True)
 
-    hb = ax_main.hexbin(x=column_corr, y=column_pvalue, data=df, gridsize=(20, 20), mincnt=1, extent=[-1, 1.0, 0, 0.1])
-    ax_main.set(xlabel="Correlation coefficient (R)", ylabel="P-value", xticks=np.arange(-1, 1.1, 0.1), yticks=np.arange(0.0,  0.105, 0.005))
+    max_pvalue = df[column_pvalue].max()
+    bin_size_pvalue = max_pvalue / 10.0
+
+    hb = ax_main.hexbin(x=column_corr, y=column_pvalue, data=df, gridsize=(40, 40), mincnt=1, extent=[-1, 1.0, 0, max_pvalue])
+    ax_main.set(xlabel="Correlation coefficient (R)", ylabel="P-value",
+                xticks=np.arange(-1, 1.1, 0.1), yticks=np.arange(0.0,  max_pvalue * 1.1, bin_size_pvalue))
+    ax_main.ticklabel_format(style='sci', axis='y', scilimits=(0, 0), useMathText=True)
 
-    ax_x_dist.hist(x=column_corr, data=df, bins=40, align='mid', color="lightblue")
-    ax_x_dist.set(ylabel='count', xlim=(-1, 1))
+    bins = np.arange(-1, 1.05, 0.05)
+    ax_x_dist.hist(x=column_corr, data=df, bins=bins, align='mid', color="lightblue")
+    ax_x_dist.set(ylabel='Frequency', xlim=(-1.1, 1.1))
     ax_x_dist.axvline(0, color='k', linestyle='dashed', linewidth=1)
+    ax_x_dist.tick_params(axis="x", labelsize=7.5)
 
     ax_xcum_dist = ax_x_dist.twinx()
-    ax_xcum_dist.hist(x=column_corr, data=df, bins=40, cumulative=True, histtype='step',
+    ax_xcum_dist.hist(x=column_corr, data=df, bins=bins, cumulative=True, histtype='step',
                       density=True, color='darkblue', align='mid')
-    ax_xcum_dist.set(xlim=(-1, 1))
+    ax_xcum_dist.set(xlim=(-1.1, 1.1))
     ax_xcum_dist.tick_params(column_corr, colors='darkblue')
     ax_xcum_dist.set_ylabel('cumulative', color='darkblue')
     ax_xcum_dist.set(yticks=np.arange(0.0, 1.2, 0.2))
 
-    ax_y_dist.hist(x=column_pvalue, data=df, bins=200, orientation='horizontal',
+    bins = np.arange(0, max_pvalue + bin_size_pvalue, bin_size_pvalue)
+    ax_y_dist.hist(x=column_pvalue, data=df, bins=bins, orientation='horizontal',
                    align='mid', color="lightblue")
-    ax_y_dist.set(xlabel='count')
+    ax_y_dist.set(xlabel='Frequency')
     ax_ycum_dist = ax_y_dist.twiny()
-    ax_ycum_dist.hist(x=column_pvalue, data=df, bins=200, cumulative=True, histtype='step',
+    ax_ycum_dist.hist(x=column_pvalue, data=df, bins=bins, cumulative=True, histtype='step',
                       density=True, color='darkblue', align='mid', orientation='horizontal')
     ax_ycum_dist.tick_params(column_pvalue, colors='darkblue')
     ax_ycum_dist.set_xlabel('cumulative', color='darkblue')
-    ax_ycum_dist.set(xticks=np.arange(0.0, 1.2, 0.2), ylim=(0, 0.1))
+    ax_ycum_dist.set(xticks=np.arange(0.0, 1.2, 0.2), ylim=(-bin_size_pvalue, max_pvalue * 1.1))
 
-    plt.setp(ax_x_dist.get_xticklabels(), visible=False)
+    #plt.setp(ax_x_dist.get_xticklabels(), visible=False)
     plt.setp(ax_y_dist.get_yticklabels(), visible=False)
-
+    plt.setp(ax_x_dist.get_xticklabels(), rotation=90)
     plt.setp(ax_main.get_xticklabels(), rotation=90)
     plt.setp(ax_y_dist.get_xticklabels(), rotation=90)
+    plt.setp(ax_ycum_dist.get_xticklabels(), rotation=90)
 
     fig.subplots_adjust(top=0.85, right=0.85)
     cbar_ax = fig.add_axes([0.87, 0.15, 0.03, 0.4])
 
     cb = plt.colorbar(hb, cax=cbar_ax)
-    cb.set_label('counts')
+    cb.set_label('Frequency')
 
     return plt
 
@@ -119,11 +116,13 @@ def plot_annotations(column_ppm_error, column_adducts, df):
     ppm_errors = df[column_ppm_error].dropna()
 
     sns.boxplot(ppm_errors, ax=ax_box)
-    sns.distplot(ppm_errors, ax=ax_hist)
+
+    bin_size = 0.1
+    bins = np.arange(np.floor(ppm_errors.min()) - bin_size, np.ceil(ppm_errors.max()) + bin_size, bin_size).round(3)
+    ax_hist.hist(x=column_ppm_error, data=df, bins=bins, align='mid', color="lightblue")
 
     ax_hist.grid(False)
     ax_box.grid(False)
-    ax_hist.grid(False)
 
     std = ppm_errors.std()
     mean = ppm_errors.mean()
@@ -137,12 +136,13 @@ def plot_annotations(column_ppm_error, column_adducts, df):
     ax_box.set_title("Q1={}; median={}; Q3={}".format(round(Q1, 2), round(median, 2), round(Q3, 2)))
 
     ax_hist.set_title("mean={}; std={}".format(round(mean, 2), round(std, 2)))
-    ax_hist.set(xlabel="ppm error")
+    ax_hist.set(xlabel="Ppm error", ylabel="Frequency")
 
-    sns.set(style="whitegrid")
     sns.countplot(df[column_adducts].dropna(), ax=ax_count)
+    ax_count.set(xlabel="Adduct", ylabel="Frequency")
 
     plt.setp(ax_box.get_xticklabels(), visible=False)
+    plt.setp(ax_count.get_xticklabels(), rotation=90)
 
     return plt
 
@@ -154,14 +154,15 @@ def report(db, pdf_out, column_corr, column_pvalue, column_ppm_error, column_add
         conn = sqlite3.connect(db)
         cursor = conn.cursor()
         cursor.execute("""SELECT name FROM sqlite_master WHERE type='table';""")
-
-        for table in cursor.fetchall():
+        title = "Summary - BEAMS\n\n\n"
+        for i, table in enumerate(cursor.fetchall()):
             if str(table[0]) == "groups":
 
                 df = pd.read_sql_query("SELECT {}, {} FROM groups".format(column_corr, column_pvalue), conn)
 
                 plt = plot_correlations(column_corr, column_pvalue, df)
-                plt.suptitle('Summary - BEAMS\n\n\nGrouping features', fontsize=20)
+                plt.suptitle('{}Grouping features'.format(title), fontsize=20)
+                title = "\n\n\n"
                 pdf.savefig(dpi=300)
                 plt.close()
 
@@ -170,7 +171,18 @@ def report(db, pdf_out, column_corr, column_pvalue, column_ppm_error, column_add
                 df = pd.read_sql_query("SELECT {}, {} FROM {}".format(column_ppm_error, column_adducts, table[0]), conn)
 
                 plt = plot_annotations("ppm_error", "adduct", df)
-                plt.suptitle('Annotation\n\n{}'.format(table[0].replace("compounds_", "")), fontsize=20)
+                plt.suptitle('{}Compound Annotation\nDatabase: {}'.format(title, table[0].replace("compounds_", "")), fontsize=20)
+                title = "\n\n\n"
                 pdf.savefig(dpi=300)
                 plt.close()
         conn.close()
+
+
+# if __name__ == '__main__':
+#
+#     report("../tests/test_results/results_annotation.sqlite", "test_report_01.pdf",
+#            "r_value", "p_value", "ppm_error", "adduct")
+#     statinfo = os.stat("test_report_01.pdf")
+#
+#     report("../tests/test_results/results_pearson_all.sqlite", "test_report_02.pdf",
+#            "r_value", "p_value", "ppm_error", "adduct")