0.1.3 flake8

.flake8

@@ -0,0 +1,2 @@
+[flake8]
+ignore = E501, E203, W503, F401

.github/workflows/flake8.yml

@@ -0,0 +1,31 @@
+# This is a basic workflow to help you get started with Actions
+
+name: flake8
+
+# Controls when the workflow will run
+on:
+  pull_request:
+    branches: [ master ]
+  push:
+    branches: [ master ]
+
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+# A workflow run is made up of one or more jobs that can run sequentially or in parallel
+jobs:
+  # This workflow contains a single job called "build"
+  build:
+    # The type of runner that the job will run on
+    runs-on: ubuntu-latest
+
+    # Steps represent a sequence of tasks that will be executed as part of the job
+    steps:
+      # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+      - uses: actions/setup-python@v2
+      - run: pip install --upgrade pip
+      - run: pip install flake8
+      - run: python3 -m flake8 --count

pytabular/__init__.py

@@ -1,44 +1,57 @@
+# flake8: noqa
 import logging
 import os
 import sys
 import platform
 
-logging.basicConfig(level=logging.DEBUG,
-	format='%(asctime)s::%(module)s::%(funcName)s::%(levelname)s::%(message)s',
-	datefmt='%y/%m/%d %H:%M:%S %z')
-logger = logging.getLogger('PyTabular')
+logging.basicConfig(
+    level=logging.DEBUG,
+    format="%(asctime)s::%(module)s::%(funcName)s::%(levelname)s::%(message)s",
+    datefmt="%y/%m/%d %H:%M:%S %z",
+)
+logger = logging.getLogger("PyTabular")
 logger.setLevel(logging.DEBUG)
-logger.info('Logging configured...')
-logger.info(f'To update PyTabular logger...')
-logger.info(f'>>> import logging')
-logger.info(f'>>> pytabular.logger.setLevel(level=logging.INFO)')
-logger.info(f'See https://docs.python.org/3/library/logging.html#logging-levels')
+logger.info("Logging configured...")
+logger.info(f"To update PyTabular logger...")
+logger.info(f">>> import logging")
+logger.info(f">>> pytabular.logger.setLevel(level=logging.INFO)")
+logger.info(f"See https://docs.python.org/3/library/logging.html#logging-levels")
 
 
-logger.debug(f'Python Version::{sys.version}')
-logger.debug(f'Python Location::{sys.exec_prefix}')
-logger.debug(f'Package Location::{__file__}')
-logger.debug(f'Working Directory::{os.getcwd()}')
-logger.debug(f'Platform::{sys.platform}-{platform.release()}')
+logger.debug(f"Python Version::{sys.version}")
+logger.debug(f"Python Location::{sys.exec_prefix}")
+logger.debug(f"Package Location::{__file__}")
+logger.debug(f"Working Directory::{os.getcwd()}")
+logger.debug(f"Platform::{sys.platform}-{platform.release()}")
 
-dll = os.path.join(os.path.dirname(__file__),"dll")
+dll = os.path.join(os.path.dirname(__file__), "dll")
 sys.path.append(dll)
 sys.path.append(os.path.dirname(__file__))
 
-logger.debug(f'Beginning CLR references...')
+logger.debug(f"Beginning CLR references...")
 import clr
-logger.debug('Adding Reference Microsoft.AnalysisServices.AdomdClient')
-clr.AddReference('Microsoft.AnalysisServices.AdomdClient')
-logger.debug('Adding Reference Microsoft.AnalysisServices.Tabular')
-clr.AddReference('Microsoft.AnalysisServices.Tabular')
-logger.debug('Adding Reference Microsoft.AnalysisServices')
-clr.AddReference('Microsoft.AnalysisServices')
+
+logger.debug("Adding Reference Microsoft.AnalysisServices.AdomdClient")
+clr.AddReference("Microsoft.AnalysisServices.AdomdClient")
+logger.debug("Adding Reference Microsoft.AnalysisServices.Tabular")
+clr.AddReference("Microsoft.AnalysisServices.Tabular")
+logger.debug("Adding Reference Microsoft.AnalysisServices")
+clr.AddReference("Microsoft.AnalysisServices")
 
 logger.debug(f"Importing specifics in module...")
-from . pytabular import Tabular
-from . basic_checks import Return_Zero_Row_Tables, Table_Last_Refresh_Times, BPA_Violations_To_DF, Last_X_Interval
-from . logic_utils import pd_dataframe_to_m_expression, pandas_datatype_to_tabular_datatype
-from . tabular_tracing import Base_Trace, Refresh_Trace
-from . tabular_editor import Tabular_Editor
-from . best_practice_analyzer import BPA
-logger.info(f'Import successful...')
+from .pytabular import Tabular
+from .basic_checks import (
+    Return_Zero_Row_Tables,
+    Table_Last_Refresh_Times,
+    BPA_Violations_To_DF,
+    Last_X_Interval,
+)
+from .logic_utils import (
+    pd_dataframe_to_m_expression,
+    pandas_datatype_to_tabular_datatype,
+)
+from .tabular_tracing import Base_Trace, Refresh_Trace
+from .tabular_editor import Tabular_Editor
+from .best_practice_analyzer import BPA
+
+logger.info(f"Import successful...")

pytabular/basic_checks.py

@@ -1,108 +1,133 @@
 import logging
-logger = logging.getLogger('PyTabular')
 from typing import List, Union
 import pytabular
 from logic_utils import ticks_to_datetime
 import sys
 import pandas as pd
 
-def Return_Zero_Row_Tables(model:pytabular.Tabular) -> List[str]:
-	'''	Returns list of table names of those that are returning isna()
-
-	Args:
-		model (pytabular.Tabular): Tabular Model
-
-	Returns:
-		List[str]: List of table names where DAX COUNTROWS('Table Name') is nan or 0.
-	'''	
-	logger.info(f'Executing Basic Function {sys._getframe(0).f_code.co_name}')
-	query_function: str = 'COUNTROWS(_)'
-	df: pd.DataFrame = model.Query_Every_Table(query_function)
-	return df[df[f'[{query_function}]'].isna()]['[Table]'].to_list()
-
-def Table_Last_Refresh_Times(model:pytabular.Tabular, group_partition:bool = True) -> pd.DataFrame:
-	'''	Returns pd.DataFrame of tables with their latest refresh time. 
-	Optional 'group_partition' variable, default is True.
-	If False an extra column will be include to have the last refresh time to the grain of the partition
-	Example to add to model model.Create_Table(p.Table_Last_Refresh_Times(model),'RefreshTimes')
-
-	Args:
-		model (pytabular.Tabular): Tabular Model
-		group_partition (bool, optional): Whether or not you want the grain of the dataframe to be by table or by partition. Defaults to True.
-
-	Returns:
-		pd.DataFrame: pd dataframe with the RefreshedTime property: https://docs.microsoft.com/en-us/dotnet/api/microsoft.analysisservices.tabular.partition.refreshedtime?view=analysisservices-dotnet#microsoft-analysisservices-tabular-partition-refreshedtime
-		If group_partition == True and the table has multiple partitions, then df.groupby(by["tables"]).max()
-	'''	
-	logger.info(f'Executing Basic Function {sys._getframe(0).f_code.co_name}')
-	data = {\
-		"Tables":[partition.Table.Name for partition in model.Partitions],\
-		"Partitions":[partition.Name for partition in model.Partitions],\
-		"RefreshedTime":[ticks_to_datetime(partition.RefreshedTime.Ticks).strftime('%Y-%m-%dT%H:%M:%S.%fZ')[:-3] for partition in model.Partitions]\
-	}
-	df = pd.DataFrame(data)
-	if group_partition:
-		logger.debug('Grouping together to grain of Table')
-		return df[["Tables","RefreshedTime"]].groupby(by=["Tables"]).max().reset_index(drop=False)
-	else:
-		logger.debug('Returning DF')
-		return df
-
-def BPA_Violations_To_DF(model:pytabular.Tabular,te2:str, bpa:str) -> pd.DataFrame:
-	'''Runs BPA Analyzer from TE2 and outputs result into a DF.
-
-	Args:
-		model (pytabular.Tabular): Tabular Model Class
-		te2 (str): TE2 Exe File Path (Can use TE2().EXE_path)
-		bpa (str): BPA File Location (Can use BPA().Location)
-
-	Returns:
-		pd.DataFrame: Super simple right now. Just splits into two columns.. The object in violation and the rule.
-	'''	
-	results = model.Analyze_BPA(te2,bpa)
-	data = [rule.replace(' violates rule ','^').replace('\"','').split('^') for rule in results]
-	columns = ["Object","Violation"]
-	return pd.DataFrame(data,columns=columns)
+logger = logging.getLogger("PyTabular")
+
+
+def Return_Zero_Row_Tables(model: pytabular.Tabular) -> List[str]:
+    """Returns list of table names of those that are returning isna()
+
+    Args:
+            model (pytabular.Tabular): Tabular Model
+
+    Returns:
+            List[str]: List of table names where DAX COUNTROWS('Table Name') is nan or 0.
+    """
+    logger.info(f"Executing Basic Function {sys._getframe(0).f_code.co_name}")
+    query_function: str = "COUNTROWS(_)"
+    df: pd.DataFrame = model.Query_Every_Table(query_function)
+    return df[df[f"[{query_function}]"].isna()]["[Table]"].to_list()
+
+
+def Table_Last_Refresh_Times(
+    model: pytabular.Tabular, group_partition: bool = True
+) -> pd.DataFrame:
+    """Returns pd.DataFrame of tables with their latest refresh time.
+    Optional 'group_partition' variable, default is True.
+    If False an extra column will be include to have the last refresh time to the grain of the partition
+    Example to add to model model.Create_Table(p.Table_Last_Refresh_Times(model),'RefreshTimes')
+
+    Args:
+            model (pytabular.Tabular): Tabular Model
+            group_partition (bool, optional): Whether or not you want the grain of the dataframe to be by table or by partition. Defaults to True.
+
+    Returns:
+            pd.DataFrame: pd dataframe with the RefreshedTime property: https://docs.microsoft.com/en-us/dotnet/api/microsoft.analysisservices.tabular.partition.refreshedtime?view=analysisservices-dotnet#microsoft-analysisservices-tabular-partition-refreshedtime
+            If group_partition == True and the table has multiple partitions, then df.groupby(by["tables"]).max()
+    """
+    logger.info(f"Executing Basic Function {sys._getframe(0).f_code.co_name}")
+    data = {
+        "Tables": [partition.Table.Name for partition in model.Partitions],
+        "Partitions": [partition.Name for partition in model.Partitions],
+        "RefreshedTime": [
+            ticks_to_datetime(partition.RefreshedTime.Ticks).strftime(
+                "%Y-%m-%dT%H:%M:%S.%fZ"
+            )[:-3]
+            for partition in model.Partitions
+        ],
+    }
+    df = pd.DataFrame(data)
+    if group_partition:
+        logger.debug("Grouping together to grain of Table")
+        return (
+            df[["Tables", "RefreshedTime"]]
+            .groupby(by=["Tables"])
+            .max()
+            .reset_index(drop=False)
+        )
+    else:
+        logger.debug("Returning DF")
+        return df
+
+
+def BPA_Violations_To_DF(model: pytabular.Tabular, te2: str, bpa: str) -> pd.DataFrame:
+    """Runs BPA Analyzer from TE2 and outputs result into a DF.
+
+    Args:
+            model (pytabular.Tabular): Tabular Model Class
+            te2 (str): TE2 Exe File Path (Can use TE2().EXE_path)
+            bpa (str): BPA File Location (Can use BPA().Location)
+
+    Returns:
+            pd.DataFrame: Super simple right now. Just splits into two columns.. The object in violation and the rule.
+    """
+    results = model.Analyze_BPA(te2, bpa)
+    data = [
+        rule.replace(" violates rule ", "^").replace('"', "").split("^")
+        for rule in results
+    ]
+    columns = ["Object", "Violation"]
+    return pd.DataFrame(data, columns=columns)
+
 
 def Last_X_Interval(
-	Model:pytabular.Tabular,
-	Measure:Union[str,pytabular.pytabular.Measure],
-	Column_Name:Union[str,None] = None,
-	Date_Column_Identifier:str = "'Date'[DATE_DTE_KEY]",
-	Number_Of_Intervals:int = 90,
-	Interval:str = "DAY") -> pd.DataFrame:
-	'''Pulls the Last X Interval (Ex Last 90 Days) of a specific measure.
-
-	Args:
-		Model (pytabular.Tabular): Tabular Model to perform query on.
-		Measure (Union[str,pytabular.pytabular.Measure]): Measure to query. If string, will first check for a measure in the model with that name, otherwise will assume it is a DAX Expression (Ex SUM(FactTable[ColumnValue]) ) and perform that as expression
-		Column_Name (Union[str,None], optional): Column Name to be outputted in DataFrame. You can provide your own otherwise will take from the Measure Name. Defaults to "Result".
-		Date_Column_Identifier (str, optional): Date column dax identifier. Defaults to "'Date'[DATE_DTE_KEY]".
-		Number_Of_Intervals (int, optional): This is used to plug in the variables for [DATESINPERIOD](https://docs.microsoft.com/en-us/dax/datesinperiod-function-dax). Defaults to 90.
-		Interval (str, optional): Sames as Number_Of_Intervals. Used to plug in parameters of DAX function [DATESINPERIOD](https://docs.microsoft.com/en-us/dax/datesinperiod-function-dax). Defaults to "DAY". Possible options are "DAY", "MONTH", "QUARTER", and "YEAR"
-
-	Returns:
-		pd.DataFrame: Pandas DataFrame of results.
-	'''	
-	if isinstance(Measure,str):
-		try:
-			Measure = [measure for measure in Model.Measures if measure.Name == Measure][-1]
-			Column_Name = Measure.Name if Column_Name is None else Column_Name
-			Expression = f"[{Measure.Name}]"
-		except:
-			logging.debug(f'Measure is string but unable to find Measure...')
-			Column_Name = "Result" if Column_Name is None else Column_Name
-			Expression = Measure
-	else:
-		Column_Name = Measure.Name if Column_Name is None else Column_Name
-		Expression = f'[{Column_Name}]'
-	Query_Str = f'''
-	EVALUATE
-		SUMMARIZECOLUMNS(
-		{Date_Column_Identifier},
-		KEEPFILTERS( DATESINPERIOD ( {Date_Column_Identifier}, UTCTODAY(), -{Number_Of_Intervals}, {Interval} ) ),
-		"{Column_Name}", {Expression}
-		)
-	'''
-	logging.info(f'Running query for {Column_Name} in the last {Number_Of_Intervals} {Interval}s...')
-	return Model.Query(Query_Str)
+    Model: pytabular.Tabular,
+    Measure: Union[str, pytabular.pytabular.Measure],
+    Column_Name: Union[str, None] = None,
+    Date_Column_Identifier: str = "'Date'[DATE_DTE_KEY]",
+    Number_Of_Intervals: int = 90,
+    Interval: str = "DAY",
+) -> pd.DataFrame:
+    """Pulls the Last X Interval (Ex Last 90 Days) of a specific measure.
+
+    Args:
+            Model (pytabular.Tabular): Tabular Model to perform query on.
+            Measure (Union[str,pytabular.pytabular.Measure]): Measure to query. If string, will first check for a measure in the model with that name, otherwise will assume it is a DAX Expression (Ex SUM(FactTable[ColumnValue]) ) and perform that as expression
+            Column_Name (Union[str,None], optional): Column Name to be outputted in DataFrame. You can provide your own otherwise will take from the Measure Name. Defaults to "Result".
+            Date_Column_Identifier (str, optional): Date column dax identifier. Defaults to "'Date'[DATE_DTE_KEY]".
+            Number_Of_Intervals (int, optional): This is used to plug in the variables for [DATESINPERIOD](https://docs.microsoft.com/en-us/dax/datesinperiod-function-dax). Defaults to 90.
+            Interval (str, optional): Sames as Number_Of_Intervals. Used to plug in parameters of DAX function [DATESINPERIOD](https://docs.microsoft.com/en-us/dax/datesinperiod-function-dax). Defaults to "DAY". Possible options are "DAY", "MONTH", "QUARTER", and "YEAR"
+
+    Returns:
+            pd.DataFrame: Pandas DataFrame of results.
+    """
+    if isinstance(Measure, str):
+        try:
+            Measure = [
+                measure for measure in Model.Measures if measure.Name == Measure
+            ][-1]
+            Column_Name = Measure.Name if Column_Name is None else Column_Name
+            Expression = f"[{Measure.Name}]"
+        except Exception:
+            logging.debug("Measure is string but unable to find Measure...")
+            Column_Name = "Result" if Column_Name is None else Column_Name
+            Expression = Measure
+    else:
+        Column_Name = Measure.Name if Column_Name is None else Column_Name
+        Expression = f"[{Column_Name}]"
+    Query_Str = f"""
+    EVALUATE
+        SUMMARIZECOLUMNS(
+        {Date_Column_Identifier},
+        KEEPFILTERS( DATESINPERIOD ( {Date_Column_Identifier}, UTCTODAY(), -{Number_Of_Intervals}, {Interval} ) ),
+        "{Column_Name}", {Expression}
+        )
+    """
+    logging.info(
+        f"Running query for {Column_Name} in the last {Number_Of_Intervals} {Interval}s..."
+    )
+    return Model.Query(Query_Str)