Merge pull request #3 from ElemarJR/planned_vs_actual

Planned vs actual

.gitignore

@@ -11,6 +11,7 @@ downloads/
 eggs/
 .eggs/
 lib/
+!frontend/src/lib
 lib64/
 parts/
 sdist/

backend/api/datasets/timesheets.py

@@ -1,3 +1,4 @@
+from datetime import datetime
 import pandas as pd
 from typing import Dict, Any, List, Union
 from api.utils.fields import get_requested_fields_from
@@ -16,7 +17,7 @@ def summarize(df: pd.DataFrame) -> Dict[str, Any]:
 
     # Calculate statistics
     total_hours = df["TimeInHs"].sum()
-    average_hours_per_entry = total_hours / len(df)
+    average_hours_per_entry = total_hours / len(df) if len(df) > 0 else 0
 
     return {
         "total_entries": len(df),
@@ -70,13 +71,15 @@ def summarize_by_group(df: pd.DataFrame, group_column: str, name_key: str = "nam
     summaries = []
     for group_value, group_df in df.groupby(group_column):
         summary = summarize(group_df)
-        summary["by_kind"] = summarize_by_kind(group_df)
         summary[name_key] = group_value
 
-        # Add support for squad, consulting, internal, and hands-on total hours
         for kind in ['Squad', 'Consulting', 'Internal', 'HandsOn']:
             summary[f"total_{kind.lower()}_hours"] = group_df[group_df['Kind'] == kind]['TimeInHs'].sum()
-
+
+        summary["by_kind"] = summarize_by_kind(group_df)
+        if group_column != 'Week':
+            summary['by_week'] = summarize_by_week(group_df)
+
         summaries.append(summary)
 
     summaries = sorted(summaries, key=lambda x: x["total_hours"], reverse=True)
@@ -101,7 +104,12 @@ def summarize_by_date(df: pd.DataFrame) -> List[Dict[str, Union[Dict[str, Any],
     return summarize_by_group(df, 'Date', name_key="date")
 
 def summarize_by_week(df: pd.DataFrame) -> List[Dict[str, Union[Dict[str, Any], Any]]]:
-    return summarize_by_group(df, 'Week', name_key="week")
+    summaries = summarize_by_group(df, 'Week', name_key="week")
+
+    # Sort the summaries based on the 'week' key
+    sorted_summaries = sorted(summaries, key=lambda x: datetime.strptime(x['week'].split(' - ')[0], '%d/%m'))
+
+    return sorted_summaries
 
 def summarize_by_offer(df: pd.DataFrame) -> List[Dict[str, Union[Dict[str, Any], Any]]]:
     return summarize_by_group(df, 'ProductsOrServices', name_key="name")

backend/api/domain/clients.py

@@ -1,7 +1,9 @@
 import globals
 
-def resolve_clients(_, info):
+def resolve_clients(_, info, account_manager_name=None):
     all_clients = sorted(globals.omni_models.clients.get_all().values(), key=lambda client: client.name)
+    if account_manager_name:
+        all_clients = [client for client in all_clients if client.account_manager and client.account_manager.name == account_manager_name]
     return all_clients
 
 def resolve_client(_, info, id=None, slug=None):

backend/api/domain/user.py

@@ -1,9 +1,33 @@
 from models.domain import WorkerKind
 import globals
 
-def resolve_user(_, info, email=None):
-    if email is not None:
-        return globals.omni_models.workers.get_by_email(email)
-    else:
+def resolve_user(_, info, email=None, slug=None):
+    if not email and not slug:
         return None
-
+
+    worker = None
+    if email:
+        worker = globals.omni_models.workers.get_by_email(email)
+    elif slug:
+        worker = globals.omni_models.workers.get_by_slug(slug)
+
+    if not worker:
+        return None
+
+    result = {
+        "id": worker.id,
+        "name": worker.name,
+        "email": worker.email,
+        "slug": worker.slug,
+        "kind": worker.kind,
+        "position": worker.position,
+        "photo_url": worker.photo_url,
+        # Add other attributes as needed
+    }
+    kind_map = {
+        WorkerKind.ACCOUNT_MANAGER: "ACCOUNT_MANAGER",
+        WorkerKind.CONSULTANT: "CONSULTANT"
+    }
+    result["kind"] = kind_map.get(result["kind"], result["kind"])
+
+    return result