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Rename datascience to datascience_modules (microsoft#10525)
* Rename datascience to datascience_modules * Change name to something even harder to get wrong
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56 changes: 28 additions & 28 deletions
56
pythonFiles/datascience/dummyJupyter.py → ...scode_datascience_helpers/dummyJupyter.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -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() |
File renamed without changes.
File renamed without changes.
240 changes: 120 additions & 120 deletions
240
...cience/getJupyterVariableDataFrameInfo.py → ...elpers/getJupyterVariableDataFrameInfo.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -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 |
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