force uploading some testing data

Author: nlsschim

diff_viz/data_loading.py

@@ -20,14 +20,14 @@ def check_mpt_data(df, expected_columns):
     Returns
     --------
     columns_present, has_data: bool
-        True if the DataFrame contains at least one row of data and all of the expected columns, False otherwise.
+        True if the DataFrame contains at least one row of data and all of the expected columns, 
+        False otherwise.
     """
     # Check that all of the expected columns are present
     columns_present = all(col in df.columns for col in expected_columns)
-    
     # Check that the DataFrame has at least one row of data
     has_data = not df.empty
-    
+
     # Return True if both the expected columns and data are present
     return columns_present and has_data
 
@@ -142,4 +142,4 @@ def concatenate_csv_files(uploaded_files):
         dfs.append(df)
 
     concatenated_df = pd.concat(dfs, ignore_index=True)
-    return concatenated_df
+    return concatenated_df

diff_viz/tests/hypothesis_util_functions.py

@@ -0,0 +1,73 @@
+from hypothesis.extra.pandas import columns, column, data_frames, range_indexes
+import pandas as pd
+import numpy as np
+from hypothesis import strategies as st, given, settings
+
+def hypothesis_features_dataframe(include_target_col=False):
+    float_without_nan_st = st.floats(min_value=0.0001, max_value=3030, allow_nan=False)
+    float_with_nan_st = st.floats(allow_nan=True, allow_infinity=False)
+    int_st = st.integers(min_value=0, max_value=1000)
+
+    np.random.seed(1234)
+    param = {}
+    df_columns = {'alpha': {'elements': float_with_nan_st, 'unique': True},
+                'D_fit': {'elements': float_with_nan_st, 'unique': True},
+                'kurtosis': {'elements': float_with_nan_st, 'unique': True},
+                'asymmetry1': {'elements': float_with_nan_st, 'unique': True},
+                'asymmetry2': {'elements': float_with_nan_st, 'unique': True},
+                'asymmetry3': {'elements': float_with_nan_st, 'unique': True},
+                'AR': {'elements': float_with_nan_st, 'unique': True},
+                'elongation': {'elements': float_with_nan_st, 'unique': True},
+                'boundedness': {'elements': float_with_nan_st, 'unique': True},
+                'fractal_dim': {'elements': float_with_nan_st, 'unique': True},
+                'trappedness': {'elements': float_with_nan_st, 'unique': True},
+                'efficiency': {'elements': float_with_nan_st, 'unique': True},
+                'straightness': {'elements': float_with_nan_st, 'unique': True},
+                'MSD_ratio': {'elements': float_with_nan_st, 'unique': True},
+                'frames': {'elements': int_st, 'unique': True},
+                'Deff1': {'elements': float_with_nan_st, 'unique': True},
+                'Deff2': {'elements': float_with_nan_st, 'unique': True},
+                # 'angle_mean', 
+                # 'angle_mag_mean',
+                # 'angle_var', 
+                # 'dist_tot', 
+                # 'dist_net', 
+                # 'progression',
+                'Mean alpha': {'elements': float_with_nan_st, 'unique': True},
+                'Mean D_fit': {'elements': float_with_nan_st, 'unique': True},
+                'Mean kurtosis': {'elements': float_with_nan_st, 'unique': True},
+                'Mean asymmetry1': {'elements': float_with_nan_st, 'unique': True},
+                'Mean asymmetry2': {'elements': float_with_nan_st, 'unique': True},
+                'Mean asymmetry3': {'elements': float_with_nan_st, 'unique': True},
+                'Mean AR': {'elements': float_with_nan_st, 'unique': True},
+                'Mean elongation': {'elements': float_with_nan_st, 'unique': True},
+                'Mean boundedness': {'elements': float_with_nan_st, 'unique': True},
+                'Mean fractal_dim': {'elements': float_with_nan_st, 'unique': True},
+                'Mean trappedness': {'elements': float_with_nan_st, 'unique': True},
+                'Mean efficiency': {'elements': float_with_nan_st, 'unique': True},
+                'Mean straightness': {'elements': float_with_nan_st, 'unique': True},
+                'Mean MSD_ratio': {'elements': float_with_nan_st, 'unique': True},
+                'Mean Deff1': {'elements': float_with_nan_st, 'unique': True},
+                'Mean Deff2': {'elements': float_with_nan_st, 'unique': True},
+                }   
+
+
+    test_dfs = data_frames(index=range_indexes(min_size=10), columns=[column(key, **value) for key, value in df_columns.items()])
+
+    return test_dfs
+
+# def features_dataframe(features=categories, include_target_col=True):
+
+#     data_cols = columns(names_or_number=features, dtype=float, elements=st.floats())
+#     position_cols = columns(names_or_number=['X', 'Y'], dtype=float, elements=st.floats(min_value=0.0, max_value=2048.0))
+
+#     df_columns = [data_cols, position_cols]
+#     if include_target_col:
+#         target_col = column(name='target', dtype=int, elements=st.integers(min_value=0, max_value=20)) #up to twenty unique targets
+#         df_columns.append(target_col)
+
+#     df = data_frames(columns=df_columns, index=range_indexes(min_size=10))
+#     return df
+
+dfs = hypothesis_features_dataframe(include_target_col=False)
+print('done')