Pn/new cta23 (#293)

* First commit for new CTA file

* Updated filtering

* Suppress warning in file reading during test phase

* Update in poetry to P3.9

* Updated CTA filtering and LEI, ISIN code check

* Fixed error in notebook 4b

* Update dependencies

* Update to released URL

* fix logical error in sbti.py

* Final cleanup 1.0.7

* More cleanup

* docs update

* Update getting_started.rst

* Cleanup sbti.py

* Update install in NB#1

.vscode/settings.json

@@ -7,8 +7,17 @@
  "jupyter.notebookFileRoot": "${workspaceFolder}",
  "python.testing.cwd": "${cwd}",
  "python.defaultInterpreterPath": ".venv/bin/python",
- "python.formatting.provider": "black",
- "python.testing.unittestArgs": ["-v", "-s", "./test", "-p", "*test*.py"],
+ "python.formatting.provider": "none",
+ "python.testing.unittestArgs": [
+ "-v",
+ "-s",
+ "./test",
+ "-p",
+ "*test*.py"
+ ],
  "restructuredtext.confPath": "${workspaceFolder}/docs",
- "esbonio.sphinx.confDir": "${workspaceFolder}/docs"
-}
+ "esbonio.sphinx.confDir": "${workspaceFolder}/docs",
+ "[python]": {
+ "editor.defaultFormatter": "ms-python.black-formatter"
+ }
+}

SBTi/configs.py

@@ -159,23 +159,26 @@ class PortfolioCoverageTVPConfig(PortfolioAggregationConfig):
  FILE_TARGETS = os.path.join(
  os.path.dirname(os.path.realpath(__file__)),
  "inputs",
- "current-Companies-Taking-Action-191.xlsx",
+ "current-Companies-Taking-Action.xlsx",
  )
- CTA_FILE_URL = "https://sciencebasedtargets.org/download/excel"
+ # Temporary URL until the SBTi website is updated
+ CTA_FILE_URL = "https://sciencebasedtargets.org/download/target-dashboard"
  OUTPUT_TARGET_STATUS = "sbti_target_status"
  OUTPUT_WEIGHTED_TARGET_STATUS = "weighted_sbti_target_status"
  VALUE_TARGET_NO = "No target"
- VALUE_TARGET_COMMITTED = "Committed"
- VALUE_TARGET_SET = "Targets Set"
+ VALUE_TARGET_SET = "Near-term"
+ VALUE_ACTION_COMMITTED = "Commitment"
+ VALUE_ACTION_TARGET = "Target"
 
  TARGET_SCORE_MAP = {
  VALUE_TARGET_NO: 0,
- VALUE_TARGET_COMMITTED: 0,
+ VALUE_ACTION_COMMITTED: 0,
  VALUE_TARGET_SET: 100,
  }
 
  # SBTi targets overview (TVP coverage)
  COL_COMPANY_NAME = "Company Name"
  COL_COMPANY_ISIN = "ISIN"
  COL_COMPANY_LEI = "LEI"
- COL_TARGET_STATUS = "Near term - Target Status"
+ COL_ACTION = "Action"
+ COL_TARGET = "Target"

SBTi/data/sbti.py

@@ -1,6 +1,8 @@
 from typing import List, Type
 import requests
 import pandas as pd
+import warnings
+
 
 from SBTi.configs import PortfolioCoverageTVPConfig
 from SBTi.interfaces import IDataProviderCompany
@@ -22,30 +24,56 @@ def __init__(
  output.write(resp.content)
  print(f'Status code from fetching the CTA file: {resp.status_code}, 200 = OK')
  # Read CTA file into pandas dataframe
+ # Suppress warning about openpyxl - check if this is still needed in the released version.
+ warnings.filterwarnings('ignore', category=UserWarning, module='openpyxl')
  self.targets = pd.read_excel(self.c.FILE_TARGETS)
+
+
+ def filter_cta_file(self, targets):
+ """
+ Filter the CTA file to create a datafram that has on row per company 
+ with the columns "Action" and "Target".
+ If Action = Target then only keep the rows where Target = Near-term.
+ """
 
- # def get_sbti_targets(
- # self, companies: List[IDataProviderCompany], isin_map: dict
- # ) -> List[IDataProviderCompany]:
- # """
- # Check for each company if they have an SBTi validated target.
-
- # :param companies: A list of IDataProviderCompany instances
- # :param isin_map: A map from company id to ISIN
- # :return: A list of IDataProviderCompany instances, supplemented with the SBTi information
- # """
- # for company in companies:
- # targets = self.targets[
- # self.targets[self.c.COL_COMPANY_ISIN]
- # == isin_map.get(company.company_id)
- # ]
- # if len(targets) > 0:
- # company.sbti_validated = (
- # self.c.VALUE_TARGET_SET in targets[self.c.COL_TARGET_STATUS].values
- # )
-
- # return companies
+ # Create a new dataframe with only the columns "Action" and "Target"
+ # and the columns that are needed for identifying the company
+ targets = targets[
+ [
+ self.c.COL_COMPANY_NAME, 
+ self.c.COL_COMPANY_ISIN, 
+ self.c.COL_COMPANY_LEI, 
+ self.c.COL_ACTION, 
+ self.c.COL_TARGET
+ ]
+ ]
+
+ # Keep rows where Action = Target and Target = Near-term 
+ df_nt_targets = targets[
+ (targets[self.c.COL_ACTION] == self.c.VALUE_ACTION_TARGET) & 
+ (targets[self.c.COL_TARGET] == self.c.VALUE_TARGET_SET)]
+
+ # Drop duplicates in the dataframe by waterfall. 
+ # Do company name last due to risk of misspelled names
+ # First drop duplicates on LEI, then on ISIN, then on company name
+ df_nt_targets = pd.concat([
+ df_nt_targets[~df_nt_targets[self.c.COL_COMPANY_LEI].isnull()].drop_duplicates(
+ subset=self.c.COL_COMPANY_LEI, keep='first'
+ ), 
+ df_nt_targets[df_nt_targets[self.c.COL_COMPANY_LEI].isnull()]
+ ])
+
+ df_nt_targets = pd.concat([
+ df_nt_targets[~df_nt_targets[self.c.COL_COMPANY_ISIN].isnull()].drop_duplicates(
+ subset=self.c.COL_COMPANY_ISIN, keep='first'
+ ),
+ df_nt_targets[df_nt_targets[self.c.COL_COMPANY_ISIN].isnull()]
+ ])
 
+ df_nt_targets.drop_duplicates(subset=self.c.COL_COMPANY_NAME, inplace=True)
+
+ return df_nt_targets
+
  def get_sbti_targets(
  self, companies: List[IDataProviderCompany], id_map: dict
  ) -> List[IDataProviderCompany]:
@@ -57,6 +85,9 @@ def get_sbti_targets(
  :param id_map: A map from company id to a tuple of (ISIN, LEI)
  :return: A list of IDataProviderCompany instances, supplemented with the SBTi information
  """
+ # Filter out information about targets
+ self.targets = self.filter_cta_file(self.targets)
+
  for company in companies:
  isin, lei = id_map.get(company.company_id)
  # Check lei and length of lei to avoid zeros 
@@ -72,6 +103,8 @@ def get_sbti_targets(
  continue
  if len(targets) > 0:
  company.sbti_validated = (
- self.c.VALUE_TARGET_SET in targets[self.c.COL_TARGET_STATUS].values
+ self.c.VALUE_TARGET_SET in targets[self.c.COL_TARGET].values
  )
  return companies
+
+

docs/getting_started.rst

@@ -21,9 +21,11 @@ methodology <https://sciencebasedtargets.org/wp-content/uploads/2020/09/Temperat
 1. `Analysis example (with abbreviated methodology) <https://colab.research.google.com/github/ScienceBasedTargets/SBTi-finance-tool/blob/main/examples/1_analysis_example.ipynb>`__
 2. `Quick temperature calculation <https://colab.research.google.com/github/ScienceBasedTargets/SBTi-finance-tool/blob/main/examples/2_quick_temp_score_calculation.ipynb>`__
 3. `What-if analysis <https://colab.research.google.com/github/ScienceBasedTargets/SBTi-finance-tool/blob/main/examples/3_what-if-analysis.ipynb>`__
-4. `Portfolio aggregation <https://colab.research.google.com/github/ScienceBasedTargets/SBTi-finance-tool/blob/main/examples/4_portfolio_aggregations.ipynb>`__
-5. `Aggregation of ready made scores <https://colab.research.google.com/github/ScienceBasedTargets/SBTi-finance-tool/blob/main/examples/4b_portfolio_agg_readymade_TS.ipynb>`__
-6. `Reporting <https://colab.research.google.com/github/ScienceBasedTargets/SBTi-finance-tool/blob/main/examples/5_reporting.ipynb>`__
+4. Portfolio aggregation 
+ - 4a `Portfolio aggregation of scores generated by user <https://colab.research.google.com/github/ScienceBasedTargets/SBTi-finance-tool/blob/main/examples/4_portfolio_aggregations.ipynb>`__
+ - 4b `Aggregation of ready made scores from data providers <https://colab.research.google.com/github/ScienceBasedTargets/SBTi-finance-tool/blob/main/examples/4b_portfolio_agg_readymade_TS.ipynb>`__
+5. `Reporting <https://colab.research.google.com/github/ScienceBasedTargets/SBTi-finance-tool/blob/main/examples/5_reporting.ipynb>`__
+
 
 Jupyter Notebooks
 -----------------

examples/1_analysis_example.ipynb

@@ -252,11 +252,7 @@
  },
  "outputs": [],
  "source": [
- "%pip install sbti-finance-tool\n",
- "\n",
- "# Install other Python modules required to use this notebook\n",
- "%pip install matplotlib==3.2.2\n",
- "%pip install openpyxl==3.0.9"
+ "%pip install sbti-finance-tool "
  ]
  },
  {
@@ -1144,7 +1140,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.9.6"
  },
  "pycharm": {
  "stem_cell": {

examples/4b_portfolio_agg_readymade_TS.ipynb

@@ -69,7 +69,7 @@
  "outputs": [],
  "source": [
  "# Create a temperature score instance with the desired time frames and scopes\n",
- "ts = TemperatureScore(time_frames=list(ETimeFrames.SHORT, ETimeFrames.MID, ETimeFrames.LONG), \n",
+ "ts = TemperatureScore(time_frames=[ETimeFrames.SHORT, ETimeFrames.MID, ETimeFrames.LONG], \n",
  " scopes=[EScope.S1S2, EScope.S3, EScope.S1S2S3])"
  ]
  },