feat(backend): add opencollective donations data exploration

backend-python/media_impact_monitor/data_loaders/donations/opencollective.py

@@ -0,0 +1,226 @@
+from gql import gql, Client
+from gql.transport.requests import RequestsHTTPTransport
+import os
+from dotenv import load_dotenv
+import pandas as pd
+import matplotlib.pyplot as plt
+from joblib import Memory
+from tqdm.auto import tqdm
+import time
+import re
+
+# Setup caching
+memory = Memory(".cache", verbose=0)
+cache = memory.cache
+wait_time = 1
+
+load_dotenv()
+transport = RequestsHTTPTransport(
+    url="https://api.opencollective.com/graphql/v2",
+    headers={"Personal-Token": os.getenv("OPENCOLLECTIVE_API_TOKEN")},
+)
+client = Client(transport=transport, fetch_schema_from_transport=True)
+
+
+@cache(ignore=["wait_time"])
+def fetch(query, variable_values, wait_time=0.1, **kwargs):
+    time.sleep(wait_time)
+    return client.execute(query, variable_values=variable_values, **kwargs)
+
+
+def fetch_climate_orgs(limit=1000):
+    # Define search terms
+    search_terms = [
+        "climate",
+        "for future",
+        "extinction rebellion",
+        "xr",
+        "fossil",
+        "oil",
+    ]
+
+    query = gql("""
+    query GetAccounts($limit: Int, $offset: Int, $searchTerm: String) {
+        accounts(
+            limit: $limit
+            offset: $offset
+            isActive: true
+            searchTerm: $searchTerm
+            type: COLLECTIVE
+        ) {
+            nodes {
+                slug
+                name
+                legalName
+                description
+                longDescription
+                tags
+                location {
+                    name
+                    address
+                    country
+                }
+                stats {
+                    totalAmountReceived {
+                        value
+                        currency
+                        valueInCents
+                    }
+                    totalAmountReceivedTimeSeries {
+                        dateFrom
+                        dateTo
+                        timeUnit
+                        nodes {
+                            date
+                            amount {
+                                value
+                                currency
+                                valueInCents
+                            }
+                            label
+                        }
+                    }
+                }
+            }
+        }
+    }
+    """)
+
+    all_orgs = []
+    seen_slugs = set()  # To prevent duplicates
+
+    # Fetch orgs for each search term
+    for term in search_terms:
+        response = fetch(
+            query, variable_values={"limit": limit, "offset": 0, "searchTerm": term}
+        )
+
+        # Add only unique organizations
+        for org in response["accounts"]["nodes"]:
+            if org["slug"] not in seen_slugs:
+                all_orgs.append(org)
+                seen_slugs.add(org["slug"])
+
+    print(f"Found {len(all_orgs)} unique organizations")
+    return all_orgs
+
+
+# Fetch transactions for an organization with pagination
+@cache
+def fetch_transactions(org_slug, total_limit=100_000, page_size=1000):
+    query = gql("""
+    query GetAccountTransactions(
+        $account: [AccountReferenceInput!]
+        $limit: Int!
+        $offset: Int!
+        $orderBy: ChronologicalOrderInput!
+    ) {
+        transactions(
+            account: $account
+            limit: $limit
+            offset: $offset
+            orderBy: $orderBy
+        ) {
+            nodes {
+                id
+                createdAt
+                type
+                amount {
+                    value
+                    currency
+                }
+            }
+            totalCount
+        }
+    }
+    """)
+
+    all_transactions = []
+    offset = 0
+    while offset < total_limit:
+        variables = {
+            "account": [{"slug": org_slug}],
+            "limit": min(page_size, total_limit - offset),
+            "offset": offset,
+            "orderBy": {"field": "CREATED_AT", "direction": "DESC"},
+        }
+
+        response = fetch(query, variables, wait_time)
+        transactions = response["transactions"]["nodes"]
+        total_count = response["transactions"]["totalCount"]
+
+        all_transactions.extend(transactions)
+
+        # Break if we've fetched all available transactions
+        if len(transactions) < page_size or offset + page_size >= total_count:
+            break
+
+        offset += page_size
+    print(f"Fetched {len(all_transactions)} transactions for {org_slug}")
+    return all_transactions
+
+def get_transactions_df(orgs):
+    all_transactions = []
+    for org in tqdm(orgs):
+        transactions = fetch_transactions(org["slug"])
+        if transactions:
+            # Convert to DataFrame with just date and amount
+            df = pd.DataFrame(
+                [
+                    {
+                        "date": pd.to_datetime(t["createdAt"]).floor("D"),  # Floor to day
+                        "amount": float(t["amount"]["value"]) if "amount" in t else 0,
+                    }
+                    for t in transactions
+                ]
+            )
+            if not df.empty:
+                df["organization"] = org["name"]
+                all_transactions.append(df)
+    if not all_transactions:
+        return None
+    return pd.concat(all_transactions)
+
+def generalize_group_name(name):
+    if re.search(r"xr|extinction.?rebellion|scientist.?rebellion", name.lower()):
+        return "Extinction Rebellion"
+    elif re.search(r"(4|for).?future|fff|klimatreik", name.lower()):
+        return "Fridays For Future"
+    elif re.search(r"fossil.?free", name.lower()):
+        return "Fossil Free"
+    else:
+        return name
+
+def group_by_wealth(df, top_n=10):
+    # Calculate total donations per organization
+    total_by_org = df.groupby("organization")["amount"].sum().sort_values(ascending=False)
+    # Get top N organizations
+    top_orgs = set(total_by_org.head(top_n).index)
+    # Create a mapping function
+    def map_org(org):
+        return org if org in top_orgs else "Other"
+    return df.assign(organization=df["organization"].apply(map_org))
+
+def get_monthly_dfs(df, pivot=False):
+    monthly = (
+        df.set_index("date")
+        .groupby(["organization", pd.Grouper(freq="W")])["amount"]
+        .sum()
+        .reset_index()
+    )
+
+    # Create separate positive and negative DataFrames
+    positive_df = monthly[monthly["amount"] > 0].copy()
+    negative_df = monthly[monthly["amount"] < 0].copy()
+
+    if pivot:
+        # Pivot to get organizations as columns
+        positive_pivot = positive_df.pivot(
+            index="date", columns="organization", values="amount"
+        ).fillna(0)
+        negative_pivot = negative_df.pivot(
+            index="date", columns="organization", values="amount"
+        ).fillna(0)
+        return positive_pivot, negative_pivot
+    else:
+        return positive_df, negative_df

backend-python/pyproject.toml

@@ -44,6 +44,7 @@ json-repair = "^0.26.0"
 freezegun = "^1.5.1"
 aiolimiter = "^1.1.0"
 pytest-asyncio = "^0.23.8"
+gql = {extras = ["requests"], version = "^3.5.0"}
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.0.2"