hunk.code_window:             # Replace slashes by dots
               self.module = dir.replace('/', '.') + '.' + SETTINGS_DEFAULT_NAME + '.' + name
               # Create the directory structure
               dir_path = os.path.join(self.settings_root, dir)
               mkdir_p(dir_path)
               # Add the __init__ for the directories
               # This is required for Python 2.7
               basedir = self.settings_root
               for part in dir.split('/'):
  
hunk.labels: keep keep keep keep keep replace keep keep keep keep keep
hunk.after_edit:             pathlib.Path(dir_path).mkdir(parents=True, exist_ok=True)
  
hunk.file_path: tests/test_logging_config.py
hunk.type: replace
file: # -*- coding: utf-8 -*-
  #
  # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you under the Apache License, Version 2.0 (the
  # "License"); you may not use this file except in compliance
  # with the License.  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing,
  # software distributed under the License is distributed on an
  # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  # KIND, either express or implied.  See the License for the
  # specific language governing permissions and limitations
  # under the License.
  #
  """
  This module contains a Salesforce Hook which allows you to connect to your Salesforce instance,
  retrieve data from it, and write that data to a file for other uses.
  
  .. note:: this hook also relies on the simple_salesforce package:
        https://github.com/simple-salesforce/simple-salesforce
  """
  import time
  
  import pandas as pd
  from simple_salesforce import Salesforce
  
  from airflow.hooks.base_hook import BaseHook
  from airflow.utils.log.logging_mixin import LoggingMixin
  
  
  class SalesforceHook(BaseHook):
  
      def __init__(self, conn_id):
          """
          Create new connection to Salesforce and allows you to pull data out of SFDC and save it to a file.
  
          You can then use that file with other Airflow operators to move the data into another data source.
  
          :param conn_id: the name of the connection that has the parameters we need to connect to Salesforce.
              The connection should be type `http` and include a user's security token in the `Extras` field.
          :type conn_id: str
  
          .. note::
              For the HTTP connection type, you can include a
              JSON structure in the `Extras` field.
              We need a user's security token to connect to Salesforce.
              So we define it in the `Extras` field as:
                  `{"security_token":"YOUR_SECURITY_TOKEN"}`
  
          """
          super().__init__(conn_id)
          self.conn_id = conn_id
          self.conn = None
  
      def get_conn(self):
          """
          Sign into Salesforce, only if we are not already signed in.
          """
          if not self.conn:
              connection = self.get_connection(self.conn_id)
              extras = connection.extra_dejson
              self.conn = Salesforce(
                  username=connection.login,
                  password=connection.password,
                  security_token=extras['security_token'],
                  instance_url=connection.host,
                  sandbox=extras.get('sandbox', False)
              )
          return self.conn
  
      def make_query(self, query):
          """
          Make a query to Salesforce.
  
          :param query: The query to make to Salesforce.
          :type query: str
          :return: The query result.
          :rtype: dict
          """
          conn = self.get_conn()
  
          self.log.info("Querying for all objects")
          query_results = conn.query_all(query)
  
          self.log.info("Received results: Total size: %s; Done: %s",
                        query_results['totalSize'], query_results['done'])
  
          return query_results
  
      def describe_object(self, obj):
          """
          Get the description of an object from Salesforce.
          This description is the object's schema and
          some extra metadata that Salesforce stores for each object.
  
          :param obj: The name of the Salesforce object that we are getting a description of.
          :type obj: str
          :return: the description of the Salesforce object.
          :rtype: dict
          """
          conn = self.get_conn()
  
          return conn.__getattr__(obj).describe()
  
      def get_available_fields(self, obj):
          """
          Get a list of all available fields for an object.
  
          :param obj: The name of the Salesforce object that we are getting a description of.
          :type obj: str
          :return: the names of the fields.
          :rtype: list of str
          """
          self.get_conn()
  
          obj_description = self.describe_object(obj)
  
          return [field['name'] for field in obj_description['fields']]
  
      def get_object_from_salesforce(self, obj, fields):
          """
          Get all instances of the `object` from Salesforce.
          For each model, only get the fields specified in fields.
  
          All we really do underneath the hood is run:
              SELECT <fields> FROM <obj>;
  
          :param obj: The object name to get from Salesforce.
          :type obj: str
          :param fields: The fields to get from the object.
          :type fields: iterable
          :return: all instances of the object from Salesforce.
          :rtype: dict
          """
          query = "SELECT {} FROM {}".format(",".join(fields), obj)
  
          self.log.info("Making query to Salesforce: %s",
                        query if len(query) < 30 else " ... ".join([query[:15], query[-15:]]))
  
          return self.make_query(query)
  
      @classmethod
      def _to_timestamp(cls, column):
          """
          Convert a column of a dataframe to UNIX timestamps if applicable
  
          :param column: A Series object representing a column of a dataframe.
          :type column: pd.Series
          :return: a new series that maintains the same index as the original
          :rtype: pd.Series
          """
          # try and convert the column to datetimes
          # the column MUST have a four digit year somewhere in the string
          # there should be a better way to do this,
          # but just letting pandas try and convert every column without a format
          # caused it to convert floats as well
          # For example, a column of integers
          # between 0 and 10 are turned into timestamps
          # if the column cannot be converted,
          # just return the original column untouched
          try:
              column = pd.to_datetime(column)
          except ValueError:
              log = LoggingMixin().log
              log.warning("Could not convert field to timestamps: %s", column.name)
              return column
  
          # now convert the newly created datetimes into timestamps
          # we have to be careful here
          # because NaT cannot be converted to a timestamp
          # so we have to return NaN
          converted = []
          for value in column:
              try:
                  converted.append(value.timestamp())
              except (ValueError, AttributeError):
                  converted.append(pd.np.NaN)
  
          return pd.Series(converted, index=column.index)
  
      def write_object_to_file(self,
                               query_results,
                               filename,
                               fmt="csv",
                               coerce_to_timestamp=False,
                               record_time_added=False):
          """
          Write query results to file.
  
          Acceptable formats are:
              - csv:
                  comma-separated-values file. This is the default format.
              - json:
                  JSON array. Each element in the array is a different row.
              - ndjson:
                  JSON array but each element is new-line delimited instead of comma delimited like in `json`
  
          This requires a significant amount of cleanup.
          Pandas doesn't handle output to CSV and json in a uniform way.
          This is especially painful for datetime types.
          Pandas wants to write them as strings in CSV, but as millisecond Unix timestamps.
  
          By default, this function will try and leave all values as they are represented in Salesforce.
          You use the `coerce_to_timestamp` flag to force all datetimes to become Unix timestamps (UTC).
          This is can be greatly beneficial as it will make all of your datetime fields look the same,
          and makes it easier to work with in other database environments
  
          :param query_results: the results from a SQL query
          :type query_results: list of dict
          :param filename: the name of the file where the data should be dumped to
          :type filename: str
          :param fmt: the format you want the output in. Default:  'csv'
          :type fmt: str
          :param coerce_to_timestamp: True if you want all datetime fields to be converted into Unix timestamps.
              False if you want them to be left in the same format as they were in Salesforce.
              Leaving the value as False will result in datetimes being strings. Default: False
          :type coerce_to_timestamp: bool
          :param record_time_added: True if you want to add a Unix timestamp field
              to the resulting data that marks when the data was fetched from Salesforce. Default: False
          :type record_time_added: bool
          :return: the dataframe that gets written to the file.
          :rtype: pd.Dataframe
          """
          fmt = fmt.lower()
          if fmt not in ['csv', 'json', 'ndjson']:
              raise ValueError("Format value is not recognized: {}".format(fmt))
  
          # this line right here will convert all integers to floats
          # if there are any None/np.nan values in the column
          # that's because None/np.nan cannot exist in an integer column
          # we should write all of our timestamps as FLOATS in our final schema
          df = pd.DataFrame.from_records(query_results, exclude=["attributes"])
  
          df.columns = [column.lower() for column in df.columns]
  
          # convert columns with datetime strings to datetimes
          # not all strings will be datetimes, so we ignore any errors that occur
          # we get the object's definition at this point and only consider
          # features that are DATE or DATETIME
          if coerce_to_timestamp and df.shape[0] > 0:
              # get the object name out of the query results
              # it's stored in the "attributes" dictionary
              # for each returned record
              object_name = query_results[0]['attributes']['type']
  
              self.log.info("Coercing timestamps for: %s", object_name)
  
              schema = self.describe_object(object_name)
  
              # possible columns that can be converted to timestamps
              # are the ones that are either date or datetime types
              # strings are too general and we risk unintentional conversion
              possible_timestamp_cols = [
                  field['name'].lower()
                  for field in schema['fields']
                  if field['type'] in ["date", "datetime"] and field['name'].lower() in df.columns
              ]
              df[possible_timestamp_cols] = df[possible_timestamp_cols].apply(self._to_timestamp)
  
          if record_time_added:
              fetched_time = time.time()
              df["time_fetched_from_salesforce"] = fetched_time
  
          # write the CSV or JSON file depending on the option
          # NOTE:
          #   datetimes here are an issue.
          #   There is no good way to manage the difference
          #   for to_json, the options are an epoch or a ISO string
          #   but for to_csv, it will be a string output by datetime
          #   For JSON we decided to output the epoch timestamp in seconds
          #   (as is fairly standard for JavaScript)
          #   And for csv, we do a string
          if fmt == "csv":
              # there are also a ton of newline objects that mess up our ability to write to csv
              # we remove these newlines so that the output is a valid CSV format
              self.log.info("Cleaning data and writing to CSV")
              possible_strings = df.columns[df.dtypes == "object"]
              df[possible_strings] = df[possible_strings].apply(
                  lambda x: x.str.replace("\r\n", "").str.replace("\n", "")
              )
              # write the dataframe
              df.to_csv(filename, index=False)
          elif fmt == "json":
              df.to_json(filename, "records", date_unit="s")
          elif fmt == "ndjson":
              df.to_json(filename, "records", lines=True, date_unit="s")
  
          return df
  