Merge pull request #269 from herbertludowieg/output-parsing

Increase output parsing

Author: avmarchenko

cicd-matrix.py

@@ -12,6 +12,8 @@
 versions = [
     "3.7",
     "3.8",
+    "3.9",
+    "3.10",
 ]
 
 print(

exatomic/core/gradient.py

@@ -4,7 +4,7 @@
 
 from exatomic.exa import DataFrame
 import numpy as np
-#import pandas as pd
+import pandas as pd
 
 class Gradient(DataFrame):
     """
@@ -14,3 +14,40 @@ class Gradient(DataFrame):
     _index = 'gradient'
     _columns = ['Z', 'atom', 'fx', 'fy', 'fz', 'symbol', 'frame']
     _categories = {'frame': np.int64, 'atom': np.int64, 'symbol': str}
+
+    @property
+    def stats(self):
+        cols = ['fx', 'fy', 'fz']
+        srs = []
+        for _, data in self.groupby('frame'):
+            norms = np.linalg.norm(data[cols].values, axis=1)
+            rms = np.sqrt(np.mean(np.square(norms)))
+            avg = np.mean(norms)
+            max = norms.max()
+            min = norms.min()
+            sr = pd.Series([rms, avg, max, min], index=['rms', 'mean',
+                                                        'max', 'min'])
+            srs.append(sr)
+        df = pd.concat(srs, axis=1, ignore_index=True).T
+        return df
+
+    @property
+    def rms_grad(self):
+        cols = ['fx', 'fy', 'fz']
+        arr = []
+        for _, data in self.groupby('frame'):
+            val = np.sqrt(np.mean(np.square(data[cols].values)))
+            arr.append(val)
+        rms = pd.Series(arr)
+        return rms
+
+    @property
+    def avg_grad(self):
+        cols = ['fx', 'fy', 'fz']
+        arr = []
+        for _, data in self.groupby('frame'):
+            val = np.mean(data[cols].values)
+            arr.append(val)
+        avg = pd.Series(arr)
+        return avg
+

exatomic/exa/core/editor.py

@@ -297,7 +297,7 @@ def pandas_dataframe(self, start, stop, ncol, **kwargs):
         Returns:
             pd.DataFrame: structured data
         """
-        if isinstance(ncol, (int, np.int, np.int64, np.int32)):
+        if isinstance(ncol, (int, np.int64, np.int32)):
             return pd.read_csv(io.StringIO('\n'.join(self[start:stop])), delim_whitespace=True, names=range(ncol), **kwargs)
         else:
             return pd.read_csv(io.StringIO('\n'.join(self[start:stop])), delim_whitespace=True, names=ncol, **kwargs)