Merge pull request #186 from MichaelTiemannOSC/20230503-cleanup

Clean up release issues (and will clean up pip-audit when new point release is available).

.github/workflows/pip-audit.yml

@@ -26,4 +26,4 @@ jobs:
     - name: Install
       run: python -m pip install .
     - name: Run pip-audit
-      uses: pypa/gh-action-pip-audit@v1.0.3
+      uses: pypa/gh-action-pip-audit@ww/pin-requests

ITR/data/base_providers.py

@@ -774,9 +774,9 @@ def _extract_historic_df(self, companies: List[ICompanyData]) -> pd.DataFrame:
             raise ValueError("No historic data anywhere")
         df = pd.DataFrame.from_records(data).set_index(
             [ColumnsConfig.COMPANY_ID, ColumnsConfig.VARIABLE, ColumnsConfig.SCOPE])
+
         # Note that the first valid index may well be Quantity with a NaN value--that's fine
         # We just need to fill in the pure NaNs that arise from very ragged data
-
         with warnings.catch_warnings():
             # TODO: need to investigate whether there is a more sane way to avoid unit warnings
             warnings.simplefilter("ignore")
@@ -815,6 +815,8 @@ def _historic_ei_to_dicts(self, id: str, intensities_scopes: IHistoricEIScopes)
     def _compute_missing_historic_ei(self, companies: List[ICompanyData], historic_df: pd.DataFrame):
         scopes = [EScope[scope_name] for scope_name in EScope.get_scopes()]
         missing_data = []
+        # https://github.com/pandas-dev/pandas/issues/53053
+        index_names = historic_df.index.names
         for company in companies:
             # Create keys to index historic_df DataFrame for readability
             production_key = (company.company_id, VariablesConfig.PRODUCTIONS, 'Production')
@@ -839,6 +841,8 @@ def _compute_missing_historic_ei(self, companies: List[ICompanyData], historic_d
             # This only happens if ALL scope data is missing.  If ANY scope data is present, we'll work with what we get.
             if this_missing_data and append_this_missing_data:
                 missing_data.extend(this_missing_data)
+        # https://github.com/pandas-dev/pandas/issues/53053
+        historic_df.index.names = index_names
         if missing_data:
             error_message = f"Provide either historic emissions intensity data, or historic emission and " \
                             f"production data for these company - scope combinations: {missing_data}"

README.md

@@ -2,14 +2,13 @@
 This Python module implements the ITR methodology.
 
 ## Getting started with the user interface:
-If you use Anaconda environments, open an Anaconda prompt window, navigate to the 'examples' directory and run:
+If you use Anaconda environments, open an Anaconda prompt window, navigate to the root of the ITR release (ITR-develop) and run:
 ```
 conda env create -f environment.yml
 conda activate itr_ui
-python ITR_UI.py
 ```
 
-For virtual environments, open a command prompt/terminal window, navigate to the 'examples' directory and run:
+For virtual environments, open a command prompt/terminal window, navigate to the root of the ITR release and run
 ```
 python3 -m venv itr_ui
 ```
@@ -25,9 +24,17 @@ Next, run:
 ```
 python3 -m pip install --upgrade pip
 pip install -r requirements.txt
+pip install -e .
+```
+
+Now you are ready to change to the examples directory and run the tool:
+```
+cd examples
 python3 ITR_UI.py
+python3 ITR_UI.py "data/20230106 ITR V2 Sample Data.xlsx"
 ```
-Note the python commands are ```python``` for windows and ``python3`` for linux/mac.
+
+Note the python commands are ```python``` for windows and ``python3`` for linux/mac.  If no file is specified, the tool uses a default, small ITR dataset.  With a filename given, the ITR tool will load data from that dataset.  The 20230106 data template has over 120 companies across nearly a dozen sectors.
 
 Finally, open a browser window and navigate to `http://127.0.0.1:8050/` to access the user interface.
 

requirements.txt

@@ -1,24 +1,25 @@
-python-dotenv==0.21.0
-dash==2.7.1
-dash_bootstrap_components==1.2.1
+python-dotenv==1.0.0
+dash==2.9.3
+dash_bootstrap_components==1.4.1
 multiprocess==0.70.14
-diskcache==5.4.0
+diskcache==5.6.1
 iam-units==2022.10.27
 jupyter==1.0.0
-matplotlib==3.6.1
+matplotlib==3.7.1
+multiprocess==0.70.14
 osc-ingest-tools==0.4.3
 openpyxl==3.0.10
-openscm-units==0.5.0
+openscm-units==0.5.1
 pandas==1.5.3
 Pint==0.20.1
 Pint-Pandas==0.3
-pydantic==1.10.2
+pydantic==1.10.7
 pygithub==1.55
-pytest==7.2.0
+pytest==7.3.1
 Sphinx==5.1.1
 sphinx-autoapi==1.9.0
 sphinx-autodoc-typehints==1.19.1
 sphinx-rtd-theme==1.0.0
-SQLAlchemy==1.4.42
-trino==0.322.0
+SQLAlchemy==1.4.48
+trino==0.323.0
 xlrd==2.0.1