Allow limiting New-PI credit to partner institutions

`process_report.py` will now use `nerc_rates` to determine if
the New-PI credit is limited to institutions that are MGHPCC
partners for the given invoice month.

The script determines whether an institution's MGHPCC partnership
is active by first seeing if the `mghpcc_partnership_start_date` field
is set in `institute_list.yaml`, then checks if the start date is
within or before the invoice month.

process_report/institute_list.yaml

@@ -4,7 +4,6 @@
 -   display_name: Boston University
     domains:
     - bu.edu
-    - robbaron
 -   display_name: Bentley
     domains:
     - bentley.edu
@@ -17,7 +16,6 @@
 -   display_name: Boston Childrens Hospital
     domains:
     - childrens.harvard.edu
-    - rudolph
 -   display_name: McLean Hospital
     domains:
     - mclean.harvard.edu
@@ -36,9 +34,6 @@
 -   display_name: Harvard University
     domains:
     - harvard.edu
-    - mmsh
-    - kmdalton
-    - francesco.pontiggia
     - chemistry.harvard.edu
 -   display_name: Worcester Polytechnic Institute
     domains:
@@ -49,8 +44,6 @@
 -   display_name: University of Massachusetts Amherst
     domains:
     - umass.edu
-    - gstuart
-    - mzink
 -   display_name: University of Massachusetts Lowell
     domains:
     - uml.edu

process_report/invoices/billable_invoice.py

@@ -1,4 +1,5 @@
 from dataclasses import dataclass
+from datetime import datetime
 import logging
 import sys
 
@@ -23,6 +24,7 @@ class BillableInvoice(discount_invoice.DiscountInvoice):
     nonbillable_pis: list[str]
     nonbillable_projects: list[str]
     old_pi_filepath: str
+    limit_new_pi_credit_to_partners: bool = False
 
     @staticmethod
     def _load_old_pis(old_pi_filepath) -> pandas.DataFrame:
@@ -115,6 +117,31 @@ def export_s3(self, s3_bucket):
         super().export_s3(s3_bucket)
         s3_bucket.upload_file(self.old_pi_filepath, self.PI_S3_FILEPATH)
 
+    def _filter_partners(self, data):
+        active_partnerships = list()
+        institute_list = util.load_institute_list()
+        for institute_info in institute_list:
+            if partnership_start_date := institute_info.get(
+                "mghpcc_partnership_start_date"
+            ):
+                partnership_year_month = "{:%Y-%m}".format(
+                    datetime.strptime(partnership_start_date, "%Y-%m-%d")
+                )
+                if util.get_month_diff(self.invoice_month, partnership_year_month) >= 0:
+                    active_partnerships.append(institute_info["display_name"])
+
+        return data[data[invoice.INSTITUTION_FIELD].isin(active_partnerships)]
+
+    def _filter_excluded_su_types(self, data):
+        return data[~(data[invoice.SU_TYPE_FIELD].isin(self.EXCLUDE_SU_TYPES))]
+
+    def _get_credit_eligible_projects(self, data: pandas.DataFrame):
+        filtered_data = self._filter_excluded_su_types(data)
+        if self.limit_new_pi_credit_to_partners:
+            filtered_data = self._filter_partners(filtered_data)
+
+        return filtered_data
+
     def _apply_credits_new_pi(
         self, data: pandas.DataFrame, old_pi_df: pandas.DataFrame
     ):
@@ -140,19 +167,19 @@ def get_initial_credit_amount(
         )
         print(f"New PI Credit set at {new_pi_credit_amount} for {self.invoice_month}")
 
-        current_pi_set = set(data[invoice.PI_FIELD])
+        credit_eligible_projects = self._get_credit_eligible_projects(data)
+        current_pi_set = set(credit_eligible_projects[invoice.PI_FIELD])
         for pi in current_pi_set:
-            credit_eligible_projects = data[
-                (data[invoice.PI_FIELD] == pi)
-                & ~(data[invoice.SU_TYPE_FIELD].isin(self.EXCLUDE_SU_TYPES))
+            pi_projects = credit_eligible_projects[
+                credit_eligible_projects[invoice.PI_FIELD] == pi
             ]
             pi_age = self._get_pi_age(old_pi_df, pi, self.invoice_month)
             pi_old_pi_entry = old_pi_df.loc[
                 old_pi_df[invoice.PI_PI_FIELD] == pi
             ].squeeze()
 
             if pi_age > 1:
-                for i, row in credit_eligible_projects.iterrows():
+                for i, row in pi_projects.iterrows():
                     data.at[i, invoice.BALANCE_FIELD] = row[invoice.COST_FIELD]
             else:
                 if pi_age == 0:
@@ -180,7 +207,7 @@ def get_initial_credit_amount(
 
                 credits_used = self.apply_flat_discount(
                     data,
-                    credit_eligible_projects,
+                    pi_projects,
                     remaining_credit,
                     invoice.CREDIT_FIELD,
                     invoice.BALANCE_FIELD,

process_report/process_report.py

@@ -4,6 +4,7 @@
 
 import pandas
 import pyarrow
+from nerc_rates import load_from_url
 
 from process_report import util
 from process_report.invoices import (
@@ -215,13 +216,17 @@ def main():
     if args.upload_to_s3:
         backup_to_s3_old_pi_file(old_pi_file)
 
+    rates_info = load_from_url()
     billable_inv = billable_invoice.BillableInvoice(
         name=args.output_file,
         invoice_month=invoice_month,
         data=merged_dataframe.copy(),
         nonbillable_pis=pi,
         nonbillable_projects=projects,
         old_pi_filepath=old_pi_file,
+        limit_new_pi_credit_to_partners=rates_info.get_value_at(
+            "Limit New PI Credit to MGHPCC Partners", invoice_month
+        ),
     )
 
     util.process_and_export_invoices(

process_report/tests/unit_tests.py

@@ -833,3 +833,38 @@ def test_upload_to_s3(self, mock_get_time, mock_get_bucket):
 
         for i, call_args in enumerate(mock_bucket.upload_file.call_args_list):
             self.assertTrue(answers[i] in call_args)
+
+
+class TestNERCRates(TestCase):
+    @mock.patch("process_report.util.load_institute_list")
+    def test_flag_limit_new_pi_credit(self, mock_load_institute_list):
+        mock_load_institute_list.return_value = [
+            {"display_name": "BU", "mghpcc_partnership_start_date": "2024-02-01"},
+            {"display_name": "HU", "mghpcc_partnership_start_date": "2024-06-31"},
+            {"display_name": "NEU", "mghpcc_partnership_start_date": "2024-11-01"},
+        ]
+        sample_df = pandas.DataFrame(
+            {
+                "Institution": ["BU", "HU", "NEU", "MIT", "BC"],
+            }
+        )
+        sample_inv = test_utils.new_billable_invoice(
+            limit_new_pi_credit_to_partners=True
+        )
+
+        # When no partnerships are active
+        sample_inv.invoice_month = "2024-01"
+        output_df = sample_inv._filter_partners(sample_df)
+        self.assertTrue(output_df.empty)
+
+        # When some partnerships are active
+        sample_inv.invoice_month = "2024-06"
+        output_df = sample_inv._filter_partners(sample_df)
+        answer_df = pandas.DataFrame({"Institution": ["BU", "HU"]})
+        self.assertTrue(output_df.equals(answer_df))
+
+        # When all partnerships are active
+        sample_inv.invoice_month = "2024-12"
+        output_df = sample_inv._filter_partners(sample_df)
+        answer_df = pandas.DataFrame({"Institution": ["BU", "HU", "NEU"]})
+        self.assertTrue(output_df.equals(answer_df))

process_report/tests/util.py

@@ -23,6 +23,7 @@ def new_billable_invoice(
     nonbillable_pis=[],
     nonbillable_projects=[],
     old_pi_filepath="",
+    limit_new_pi_credit_to_partners=False,
 ):
     return billable_invoice.BillableInvoice(
         name,
@@ -31,6 +32,7 @@ def new_billable_invoice(
         nonbillable_pis,
         nonbillable_projects,
         old_pi_filepath,
+        limit_new_pi_credit_to_partners,
     )
 
 

process_report/util.py

@@ -44,7 +44,7 @@ def get_institution_from_pi(institute_map, pi_uname):
         institution_domain = institution_domain[institution_domain.find(".") + 1 :]
 
     if institution_name == "":
-        print(f"Warning: PI name {pi_uname} does not match any institution!")
+        logger.warn(f"PI name {pi_uname} does not match any institution!")
 
     return institution_name
 

requirements.txt

@@ -1,3 +1,4 @@
+git+https://github.com/CCI-MOC/nerc-rates@74eb4a7#egg=nerc_rates
 pandas
 pyarrow
 boto3