Rename datascience to datascience_modules

Author: rchiodo

pythonFiles/datascience/__init__.py renamed to pythonFiles/datascience_modules/__init__.py

@@ -1,28 +1,28 @@
-# This file can mimic juypter running. Useful for testing jupyter crash handling
-
-import sys
-import argparse
-import time
-
-
-def main():
-    print("hello from dummy jupyter")
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--version", type=bool, default=False, const=True, nargs="?")
-    parser.add_argument("notebook", type=bool, default=False, const=True, nargs="?")
-    parser.add_argument("--no-browser", type=bool, default=False, const=True, nargs="?")
-    parser.add_argument("--notebook-dir", default="")
-    parser.add_argument("--config", default="")
-    results = parser.parse_args()
-    if results.version:
-        print("1.1.dummy")
-    else:
-        print(
-            "http://localhost:8888/?token=012f08663a68e279fe0a5335e0b5dfe44759ddcccf0b3a56"
-        )
-        time.sleep(5)
-        raise Exception("Dummy is dead")
-
-
-if __name__ == "__main__":
-    main()
+# This file can mimic juypter running. Useful for testing jupyter crash handling
+
+import sys
+import argparse
+import time
+
+
+def main():
+    print("hello from dummy jupyter")
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--version", type=bool, default=False, const=True, nargs="?")
+    parser.add_argument("notebook", type=bool, default=False, const=True, nargs="?")
+    parser.add_argument("--no-browser", type=bool, default=False, const=True, nargs="?")
+    parser.add_argument("--notebook-dir", default="")
+    parser.add_argument("--config", default="")
+    results = parser.parse_args()
+    if results.version:
+        print("1.1.dummy")
+    else:
+        print(
+            "http://localhost:8888/?token=012f08663a68e279fe0a5335e0b5dfe44759ddcccf0b3a56"
+        )
+        time.sleep(5)
+        raise Exception("Dummy is dead")
+
+
+if __name__ == "__main__":
+    main()

pythonFiles/datascience/daemon/README.md renamed to pythonFiles/datascience_modules/daemon/README.md

@@ -1,120 +1,120 @@
-# Query Jupyter server for the info about a dataframe
-import json as _VSCODE_json
-import pandas as _VSCODE_pd
-import pandas.io.json as _VSCODE_pd_json
-import builtins as _VSCODE_builtins
-
-# _VSCode_sub_supportsDataExplorer will contain our list of data explorer supported types
-_VSCode_supportsDataExplorer = "['list', 'Series', 'dict', 'ndarray', 'DataFrame']"
-
-# In IJupyterVariables.getValue this '_VSCode_JupyterTestValue' will be replaced with the json stringified value of the target variable
-# Indexes off of _VSCODE_targetVariable need to index types that are part of IJupyterVariable
-_VSCODE_targetVariable = _VSCODE_json.loads("""_VSCode_JupyterTestValue""")
-
-# Function to compute row count for a value
-def _VSCODE_getRowCount(var):
-    if hasattr(var, "shape"):
-        try:
-            # Get a bit more restrictive with exactly what we want to count as a shape, since anything can define it
-            if isinstance(var.shape, tuple):
-                return var.shape[0]
-        except TypeError:
-            return 0
-    elif hasattr(var, "__len__"):
-        try:
-            return _VSCODE_builtins.len(var)
-        except TypeError:
-            return 0
-
-
-# First check to see if we are a supported type, this prevents us from adding types that are not supported
-# and also keeps our types in sync with what the variable explorer says that we support
-if _VSCODE_targetVariable["type"] not in _VSCode_supportsDataExplorer:
-    del _VSCode_supportsDataExplorer
-    print(_VSCODE_json.dumps(_VSCODE_targetVariable))
-    del _VSCODE_targetVariable
-else:
-    del _VSCode_supportsDataExplorer
-    _VSCODE_evalResult = _VSCODE_builtins.eval(_VSCODE_targetVariable["name"])
-
-    # Figure out shape if not already there. Use the shape to compute the row count
-    _VSCODE_targetVariable["rowCount"] = _VSCODE_getRowCount(_VSCODE_evalResult)
-
-    # Turn the eval result into a df
-    _VSCODE_df = _VSCODE_evalResult
-    if isinstance(_VSCODE_evalResult, list):
-        _VSCODE_df = _VSCODE_pd.DataFrame(_VSCODE_evalResult)
-    elif isinstance(_VSCODE_evalResult, _VSCODE_pd.Series):
-        _VSCODE_df = _VSCODE_pd.Series.to_frame(_VSCODE_evalResult)
-    elif isinstance(_VSCODE_evalResult, dict):
-        _VSCODE_evalResult = _VSCODE_pd.Series(_VSCODE_evalResult)
-        _VSCODE_df = _VSCODE_pd.Series.to_frame(_VSCODE_evalResult)
-    elif _VSCODE_targetVariable["type"] == "ndarray":
-        _VSCODE_df = _VSCODE_pd.DataFrame(_VSCODE_evalResult)
-    elif hasattr(_VSCODE_df, "toPandas"):
-        _VSCODE_df = _VSCODE_df.toPandas()
-        _VSCODE_targetVariable["rowCount"] = _VSCODE_getRowCount(_VSCODE_df)
-
-    # If any rows, use pandas json to convert a single row to json. Extract
-    # the column names and types from the json so we match what we'll fetch when
-    # we ask for all of the rows
-    if (
-        hasattr(_VSCODE_targetVariable, "rowCount")
-        and _VSCODE_targetVariable["rowCount"]
-    ):
-        try:
-            _VSCODE_row = _VSCODE_df.iloc[0:1]
-            _VSCODE_json_row = _VSCODE_pd_json.to_json(
-                None, _VSCODE_row, date_format="iso"
-            )
-            _VSCODE_columnNames = list(_VSCODE_json.loads(_VSCODE_json_row))
-            del _VSCODE_row
-            del _VSCODE_json_row
-        except:
-            _VSCODE_columnNames = list(_VSCODE_df)
-    else:
-        _VSCODE_columnNames = list(_VSCODE_df)
-
-    # Compute the index column. It may have been renamed
-    _VSCODE_indexColumn = _VSCODE_df.index.name if _VSCODE_df.index.name else "index"
-    _VSCODE_columnTypes = _VSCODE_builtins.list(_VSCODE_df.dtypes)
-    del _VSCODE_df
-
-    # Make sure the index column exists
-    if _VSCODE_indexColumn not in _VSCODE_columnNames:
-        _VSCODE_columnNames.insert(0, _VSCODE_indexColumn)
-        _VSCODE_columnTypes.insert(0, "int64")
-
-    # Then loop and generate our output json
-    _VSCODE_columns = []
-    for _VSCODE_n in _VSCODE_builtins.range(
-        0, _VSCODE_builtins.len(_VSCODE_columnNames)
-    ):
-        _VSCODE_column_type = _VSCODE_columnTypes[_VSCODE_n]
-        _VSCODE_column_name = str(_VSCODE_columnNames[_VSCODE_n])
-        _VSCODE_colobj = {}
-        _VSCODE_colobj["key"] = _VSCODE_column_name
-        _VSCODE_colobj["name"] = _VSCODE_column_name
-        _VSCODE_colobj["type"] = str(_VSCODE_column_type)
-        _VSCODE_columns.append(_VSCODE_colobj)
-        del _VSCODE_column_name
-        del _VSCODE_column_type
-
-    del _VSCODE_columnNames
-    del _VSCODE_columnTypes
-
-    # Save this in our target
-    _VSCODE_targetVariable["columns"] = _VSCODE_columns
-    _VSCODE_targetVariable["indexColumn"] = _VSCODE_indexColumn
-    del _VSCODE_columns
-    del _VSCODE_indexColumn
-
-    # Transform this back into a string
-    print(_VSCODE_json.dumps(_VSCODE_targetVariable))
-    del _VSCODE_targetVariable
-
-    # Cleanup imports
-    del _VSCODE_json
-    del _VSCODE_pd
-    del _VSCODE_pd_json
-    del _VSCODE_builtins
+# Query Jupyter server for the info about a dataframe
+import json as _VSCODE_json
+import pandas as _VSCODE_pd
+import pandas.io.json as _VSCODE_pd_json
+import builtins as _VSCODE_builtins
+
+# _VSCode_sub_supportsDataExplorer will contain our list of data explorer supported types
+_VSCode_supportsDataExplorer = "['list', 'Series', 'dict', 'ndarray', 'DataFrame']"
+
+# In IJupyterVariables.getValue this '_VSCode_JupyterTestValue' will be replaced with the json stringified value of the target variable
+# Indexes off of _VSCODE_targetVariable need to index types that are part of IJupyterVariable
+_VSCODE_targetVariable = _VSCODE_json.loads("""_VSCode_JupyterTestValue""")
+
+# Function to compute row count for a value
+def _VSCODE_getRowCount(var):
+    if hasattr(var, "shape"):
+        try:
+            # Get a bit more restrictive with exactly what we want to count as a shape, since anything can define it
+            if isinstance(var.shape, tuple):
+                return var.shape[0]
+        except TypeError:
+            return 0
+    elif hasattr(var, "__len__"):
+        try:
+            return _VSCODE_builtins.len(var)
+        except TypeError:
+            return 0
+
+
+# First check to see if we are a supported type, this prevents us from adding types that are not supported
+# and also keeps our types in sync with what the variable explorer says that we support
+if _VSCODE_targetVariable["type"] not in _VSCode_supportsDataExplorer:
+    del _VSCode_supportsDataExplorer
+    print(_VSCODE_json.dumps(_VSCODE_targetVariable))
+    del _VSCODE_targetVariable
+else:
+    del _VSCode_supportsDataExplorer
+    _VSCODE_evalResult = _VSCODE_builtins.eval(_VSCODE_targetVariable["name"])
+
+    # Figure out shape if not already there. Use the shape to compute the row count
+    _VSCODE_targetVariable["rowCount"] = _VSCODE_getRowCount(_VSCODE_evalResult)
+
+    # Turn the eval result into a df
+    _VSCODE_df = _VSCODE_evalResult
+    if isinstance(_VSCODE_evalResult, list):
+        _VSCODE_df = _VSCODE_pd.DataFrame(_VSCODE_evalResult)
+    elif isinstance(_VSCODE_evalResult, _VSCODE_pd.Series):
+        _VSCODE_df = _VSCODE_pd.Series.to_frame(_VSCODE_evalResult)
+    elif isinstance(_VSCODE_evalResult, dict):
+        _VSCODE_evalResult = _VSCODE_pd.Series(_VSCODE_evalResult)
+        _VSCODE_df = _VSCODE_pd.Series.to_frame(_VSCODE_evalResult)
+    elif _VSCODE_targetVariable["type"] == "ndarray":
+        _VSCODE_df = _VSCODE_pd.DataFrame(_VSCODE_evalResult)
+    elif hasattr(_VSCODE_df, "toPandas"):
+        _VSCODE_df = _VSCODE_df.toPandas()
+        _VSCODE_targetVariable["rowCount"] = _VSCODE_getRowCount(_VSCODE_df)
+
+    # If any rows, use pandas json to convert a single row to json. Extract
+    # the column names and types from the json so we match what we'll fetch when
+    # we ask for all of the rows
+    if (
+        hasattr(_VSCODE_targetVariable, "rowCount")
+        and _VSCODE_targetVariable["rowCount"]
+    ):
+        try:
+            _VSCODE_row = _VSCODE_df.iloc[0:1]
+            _VSCODE_json_row = _VSCODE_pd_json.to_json(
+                None, _VSCODE_row, date_format="iso"
+            )
+            _VSCODE_columnNames = list(_VSCODE_json.loads(_VSCODE_json_row))
+            del _VSCODE_row
+            del _VSCODE_json_row
+        except:
+            _VSCODE_columnNames = list(_VSCODE_df)
+    else:
+        _VSCODE_columnNames = list(_VSCODE_df)
+
+    # Compute the index column. It may have been renamed
+    _VSCODE_indexColumn = _VSCODE_df.index.name if _VSCODE_df.index.name else "index"
+    _VSCODE_columnTypes = _VSCODE_builtins.list(_VSCODE_df.dtypes)
+    del _VSCODE_df
+
+    # Make sure the index column exists
+    if _VSCODE_indexColumn not in _VSCODE_columnNames:
+        _VSCODE_columnNames.insert(0, _VSCODE_indexColumn)
+        _VSCODE_columnTypes.insert(0, "int64")
+
+    # Then loop and generate our output json
+    _VSCODE_columns = []
+    for _VSCODE_n in _VSCODE_builtins.range(
+        0, _VSCODE_builtins.len(_VSCODE_columnNames)
+    ):
+        _VSCODE_column_type = _VSCODE_columnTypes[_VSCODE_n]
+        _VSCODE_column_name = str(_VSCODE_columnNames[_VSCODE_n])
+        _VSCODE_colobj = {}
+        _VSCODE_colobj["key"] = _VSCODE_column_name
+        _VSCODE_colobj["name"] = _VSCODE_column_name
+        _VSCODE_colobj["type"] = str(_VSCODE_column_type)
+        _VSCODE_columns.append(_VSCODE_colobj)
+        del _VSCODE_column_name
+        del _VSCODE_column_type
+
+    del _VSCODE_columnNames
+    del _VSCODE_columnTypes
+
+    # Save this in our target
+    _VSCODE_targetVariable["columns"] = _VSCODE_columns
+    _VSCODE_targetVariable["indexColumn"] = _VSCODE_indexColumn
+    del _VSCODE_columns
+    del _VSCODE_indexColumn
+
+    # Transform this back into a string
+    print(_VSCODE_json.dumps(_VSCODE_targetVariable))
+    del _VSCODE_targetVariable
+
+    # Cleanup imports
+    del _VSCODE_json
+    del _VSCODE_pd
+    del _VSCODE_pd_json
+    del _VSCODE_builtins