Merge pull request #94 from WangHong007/main

Prevent NoneType from intensity frame
bigbio · Jan 20, 2024 · 73e97eb · 73e97eb
2 parents 5f3e3e5 + 8d019ed
commit 73e97eb
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Showing 2 changed files with 6 additions and 6 deletions.
diff --git a/pmultiqc/modules/quantms/quantms.py b/pmultiqc/modules/quantms/quantms.py
@@ -1008,6 +1008,7 @@ def CalHeatMapScore(self):
         meta_data = dict(mztab_data.metadata)
         if self.pep_table_exists:
             pep_table = mztab_data.peptide_table
+            pep_table = pep_table.fillna(np.nan)
             pep_table.loc[:, 'stand_spectra_ref'] = pep_table.apply(
                 lambda x: self.file_prefix(meta_data[x.spectra_ref.split(':')[0] + '-location']), axis=1)
             study_variables = list(filter(lambda x: re.match(r'peptide_abundance_study_variable.*?', x) is not None,
@@ -1018,11 +1019,10 @@ def CalHeatMapScore(self):
                     group[group['accession'].str.contains(config.kwargs["contaminant_affix"])][study_variables])) / \
                                                np.sum(np.sum(group[study_variables]))
                 if config.kwargs['remove_decoy']:
-                    T = sum(group[(group['opt_global_cv_MS:1002217_decoy_peptide'] == 0)][study_variables].values. \
-                            tolist(), [])
+                    pep_median = np.nanmedian(group[(group['opt_global_cv_MS:1002217_decoy_peptide'] == 0)][study_variables]. \
+                                              to_numpy())
                 else:
-                    T = sum(group[study_variables].values.tolist(), [])
-                pep_median = np.median([j for j in T if not math.isnan(j) is True])
+                    pep_median = np.nanmedian(group[study_variables].to_numpy())
                 self.heatmap_pep_intensity[name] = np.minimum(1.0, pep_median / (2 ** 23))  # Threshold
 
         #  HeatMapMissedCleavages

diff --git a/pmultiqc/modules/quantms/sparklines.py b/pmultiqc/modules/quantms/sparklines.py
@@ -29,8 +29,8 @@ def plot(data, headers=None, pconfig=None, maxValue=0.0):
         for k, v in config.custom_plot_config[pconfig["id"]].items():
             pconfig[k] = v
 
-    # Make a datatable object
-    dt = table_object.datatable(data, headers, pconfig)
+    # Make a DataTable object
+    dt = table_object.DataTable(data, headers, pconfig)
 
     # Collect unique sample names
     s_names = set()