Implemented Processor class and refactored some preliminary processing

A `Processor` class has been added, subclassing from the `Invoice` class.
This is the first step to refactor invoicing in order to seperate the
processing and filtering/exporting functionalities of our current
Invoice subclasses. Subclasses of `Processor` should only process invoices
and manipulate its internal data, while subclasses of `Invoice` should
only perform filtering and exporting, never changing any data itself.

In addition to implementing `Processor`, two of its subclasses,
`ValidatePIAliasProcessor` and `AddInstitutionProcessor` has been
added to perform some preliminary processing steps.

process_report/process_report.py

@@ -2,7 +2,6 @@
 import sys
 import datetime
 
-import json
 import pandas
 import pyarrow
 
@@ -15,7 +14,10 @@
     bu_internal_invoice,
     pi_specific_invoice,
 )
-
+from process_report.processors import (
+    validate_pi_alias_processor,
+    add_institution_processor,
+)
 
 ### PI file field names
 PI_PI_FIELD = "PI"
@@ -51,26 +53,6 @@
 ALIAS_S3_FILEPATH = "PIs/alias.csv"
 
 
-def get_institution_from_pi(institute_map, pi_uname):
-    institution_domain = pi_uname.split("@")[-1]
-    for i in range(institution_domain.count(".") + 1):
-        if institution_name := institute_map.get(institution_domain, ""):
-            break
-        institution_domain = institution_domain[institution_domain.find(".") + 1 :]
-
-    if institution_name == "":
-        print(f"Warning: PI name {pi_uname} does not match any institution!")
-
-    return institution_name
-
-
-def load_institute_map() -> dict:
-    with open("process_report/institute_map.json", "r") as f:
-        institute_map = json.load(f)
-
-    return institute_map
-
-
 def load_alias(alias_file):
     alias_dict = dict()
 
@@ -220,15 +202,27 @@ def main():
 
     projects = list(set(projects + timed_projects_list))
 
-    merged_dataframe = validate_pi_aliases(merged_dataframe, alias_dict)
-    merged_dataframe = add_institution(merged_dataframe)
+    ### Preliminary processing
+
+    validate_pi_alias_proc = validate_pi_alias_processor.ValidatePIAliasProcessor(
+        "", invoice_month, merged_dataframe, alias_dict
+    )
+    validate_pi_alias_proc.process()
+
+    add_institute_proc = add_institution_processor.AddInstitutionProcessor(
+        "", invoice_month, validate_pi_alias_proc.data
+    )
+    add_institute_proc.process()
+
+    ### Finish preliminary processing
+
     lenovo_inv = lenovo_invoice.LenovoInvoice(
-        name=args.Lenovo_file, invoice_month=invoice_month, data=merged_dataframe.copy()
+        name=args.Lenovo_file, invoice_month=invoice_month, data=add_institute_proc.data
     )
     nonbillable_inv = nonbillable_invoice.NonbillableInvoice(
         name=args.nonbillable_file,
         invoice_month=invoice_month,
-        data=merged_dataframe.copy(),
+        data=add_institute_proc.data,
         nonbillable_pis=pi,
         nonbillable_projects=projects,
     )
@@ -239,7 +233,7 @@ def main():
     billable_inv = billable_invoice.BillableInvoice(
         name=args.output_file,
         invoice_month=invoice_month,
-        data=merged_dataframe.copy(),
+        data=add_institute_proc.data.copy(),
         nonbillable_pis=pi,
         nonbillable_projects=projects,
         old_pi_filepath=old_pi_file,
@@ -330,13 +324,6 @@ def timed_projects(timed_projects_file, invoice_date):
     return dataframe[mask]["Project"].to_list()
 
 
-def validate_pi_aliases(dataframe: pandas.DataFrame, alias_dict: dict):
-    for pi, pi_aliases in alias_dict.items():
-        dataframe.loc[dataframe[PI_FIELD].isin(pi_aliases), PI_FIELD] = pi
-
-    return dataframe
-
-
 def fetch_s3_alias_file():
     local_name = "alias.csv"
     invoice_bucket = get_invoice_bucket()
@@ -356,32 +343,6 @@ def backup_to_s3_old_pi_file(old_pi_file):
     invoice_bucket.upload_file(old_pi_file, f"PIs/Archive/PI {get_iso8601_time()}.csv")
 
 
-def add_institution(dataframe: pandas.DataFrame):
-    """Determine every PI's institution name, logging any PI whose institution cannot be determined
-    This is performed by `get_institution_from_pi()`, which tries to match the PI's username to
-    a list of known institution email domains (i.e bu.edu), or to several edge cases (i.e rudolph) if
-    the username is not an email address.
-
-    Exact matches are then mapped to the corresponding institution name.
-
-    I.e "foo@bu.edu" would match with "bu.edu", which maps to the instition name "Boston University"
-
-    The list of mappings are defined in `institute_map.json`.
-    """
-    institute_map = load_institute_map()
-    dataframe = dataframe.astype({INSTITUTION_FIELD: "str"})
-    for i, row in dataframe.iterrows():
-        pi_name = row[PI_FIELD]
-        if pandas.isna(pi_name):
-            print(f"Project {row[PROJECT_FIELD]} has no PI")
-        else:
-            dataframe.at[i, INSTITUTION_FIELD] = get_institution_from_pi(
-                institute_map, pi_name
-            )
-
-    return dataframe
-
-
 def export_billables(dataframe, output_file):
     dataframe.to_csv(output_file, index=False)
 

process_report/processors/add_institution_processor.py

@@ -0,0 +1,58 @@
+from dataclasses import dataclass
+import json
+
+import pandas
+
+from process_report.invoices import invoice
+from process_report.processors import processor
+
+
+@dataclass
+class AddInstitutionProcessor(processor.Processor):
+    @staticmethod
+    def _load_institute_map() -> dict:
+        with open("process_report/institute_map.json", "r") as f:
+            institute_map = json.load(f)
+
+        return institute_map
+
+    @staticmethod
+    def _get_institution_from_pi(institute_map, pi_uname):
+        institution_domain = pi_uname.split("@")[-1]
+        for i in range(institution_domain.count(".") + 1):
+            if institution_name := institute_map.get(institution_domain, ""):
+                break
+            institution_domain = institution_domain[institution_domain.find(".") + 1 :]
+
+        if institution_name == "":
+            print(f"Warning: PI name {pi_uname} does not match any institution!")
+
+        return institution_name
+
+    def _add_institution(self, dataframe: pandas.DataFrame):
+        """Determine every PI's institution name, logging any PI whose institution cannot be determined
+        This is performed by `get_institution_from_pi()`, which tries to match the PI's username to
+        a list of known institution email domains (i.e bu.edu), or to several edge cases (i.e rudolph) if
+        the username is not an email address.
+
+        Exact matches are then mapped to the corresponding institution name.
+
+        I.e "foo@bu.edu" would match with "bu.edu", which maps to the instition name "Boston University"
+
+        The list of mappings are defined in `institute_map.json`.
+        """
+        institute_map = self._load_institute_map()
+        dataframe = dataframe.astype({invoice.INSTITUTION_FIELD: "str"})
+        for i, row in dataframe.iterrows():
+            pi_name = row[invoice.PI_FIELD]
+            if pandas.isna(pi_name):
+                print(f"Project {row[invoice.PROJECT_FIELD]} has no PI")
+            else:
+                dataframe.at[
+                    i, invoice.INSTITUTION_FIELD
+                ] = self._get_institution_from_pi(institute_map, pi_name)
+
+        return dataframe
+
+    def _process(self):
+        self.data = self._add_institution(self.data)

process_report/processors/processor.py

@@ -0,0 +1,8 @@
+from dataclasses import dataclass
+
+from process_report.invoices import invoice
+
+
+@dataclass
+class Processor(invoice.Invoice):
+    pass

process_report/processors/validate_pi_alias_processor.py

@@ -0,0 +1,23 @@
+from dataclasses import dataclass
+
+import pandas
+
+from process_report.invoices import invoice
+from process_report.processors import processor
+
+
+@dataclass
+class ValidatePIAliasProcessor(processor.Processor):
+    alias_map: dict
+
+    @staticmethod
+    def _validate_pi_aliases(dataframe: pandas.DataFrame, alias_dict: dict):
+        for pi, pi_aliases in alias_dict.items():
+            dataframe.loc[
+                dataframe[invoice.PI_FIELD].isin(pi_aliases), invoice.PI_FIELD
+            ] = pi
+
+        return dataframe
+
+    def _process(self):
+        self.data = self._validate_pi_aliases(self.data, self.alias_map)

process_report/tests/unit_tests.py

@@ -219,7 +219,7 @@ def test_export_pi(self):
         self.assertNotIn("ProjectC", pi_df["Project - Allocation"].tolist())
 
 
-class TestGetInstitute(TestCase):
+class TestAddInstituteProcessor(TestCase):
     def test_get_pi_institution(self):
         institute_map = {
             "harvard.edu": "Harvard University",
@@ -248,31 +248,34 @@ def test_get_pi_institution(self):
             "g@bidmc.harvard.edu": "Beth Israel Deaconess Medical Center",
         }
 
+        add_institute_proc = test_utils.new_add_institution_processor()
+
         for pi_email, answer in answers.items():
             self.assertEqual(
-                process_report.get_institution_from_pi(institute_map, pi_email), answer
+                add_institute_proc._get_institution_from_pi(institute_map, pi_email),
+                answer,
             )
 
 
-class TestAlias(TestCase):
-    def setUp(self):
-        self.alias_dict = {"PI1": ["PI1_1", "PI1_2"], "PI2": ["PI2_1"]}
-
-        self.data = pandas.DataFrame(
+class TestValidateAliasProcessor(TestCase):
+    def test_validate_alias(self):
+        alias_map = {"PI1": ["PI1_1", "PI1_2"], "PI2": ["PI2_1"]}
+        test_data = pandas.DataFrame(
             {
                 "Manager (PI)": ["PI1", "PI1_1", "PI1_2", "PI2_1", "PI2_1"],
             }
         )
-
-        self.answer = pandas.DataFrame(
+        answer_data = pandas.DataFrame(
             {
                 "Manager (PI)": ["PI1", "PI1", "PI1", "PI2", "PI2"],
             }
         )
 
-    def test_validate_alias(self):
-        output = process_report.validate_pi_aliases(self.data, self.alias_dict)
-        self.assertTrue(self.answer.equals(output))
+        validate_pi_alias_proc = test_utils.new_validate_pi_alias_processor(
+            data=test_data, alias_map=alias_map
+        )
+        validate_pi_alias_proc.process()
+        self.assertTrue(answer_data.equals(validate_pi_alias_proc.data))
 
 
 class TestMonthUtils(TestCase):

process_report/tests/util.py

@@ -7,6 +7,11 @@
     pi_specific_invoice,
 )
 
+from process_report.processors import (
+    add_institution_processor,
+    validate_pi_alias_processor,
+)
+
 
 def new_base_invoice(
     name="",
@@ -52,3 +57,19 @@ def new_pi_specific_invoice(
         invoice_month,
         data,
     )
+
+
+def new_add_institution_processor(
+    name="",
+    invoice_month="0000-00",
+    data=pandas.DataFrame(),
+):
+    return add_institution_processor.AddInstitutionProcessor(name, invoice_month, data)
+
+
+def new_validate_pi_alias_processor(
+    name="", invoice_month="0000-00", data=pandas.DataFrame(), alias_map={}
+):
+    return validate_pi_alias_processor.ValidatePIAliasProcessor(
+        name, invoice_month, data, alias_map
+    )

process_report/util.py

@@ -1,6 +1,5 @@
 import os
 import datetime
-import json
 import logging
 import functools
 
@@ -27,23 +26,6 @@ def get_invoice_bucket():
     return s3_resource.Bucket(os.environ.get("S3_BUCKET_NAME", "nerc-invoicing"))
 
 
-def get_institution_from_pi(institute_map, pi_uname):
-    institution_key = pi_uname.split("@")[-1]
-    institution_name = institute_map.get(institution_key, "")
-
-    if institution_name == "":
-        logger.warn(f"PI name {pi_uname} does not match any institution!")
-
-    return institution_name
-
-
-def load_institute_map() -> dict:
-    with open("process_report/institute_map.json", "r") as f:
-        institute_map = json.load(f)
-
-    return institute_map
-
-
 def get_iso8601_time():
     return datetime.datetime.now().strftime("%Y%m%dT%H%M%SZ")