UPDATE

2026-05-10 22:49:07 +03:30
parent 2d1f7da89e
commit 2a6321a263
15 changed files with 2667 additions and 162 deletions
@@ -43,7 +43,9 @@ PROXYCHAINS_CHAIN_MODE=strict_chain
 # openEO remote sensing auth
 OPENEO_BACKEND_URL=https://openeofed.dataspace.copernicus.eu
-OPENEO_TIMEOUT_SECONDS=60
+OPENEO_TIMEOUT_SECONDS=600
 OPENEO_HTTP_RETRY_TOTAL=5
 OPENEO_HTTP_RETRY_BACKOFF_FACTOR=2.0
 # Use `password` when filling OPENEO_USERNAME/OPENEO_PASSWORD instead of client credentials.
 OPENEO_AUTH_METHOD=client_credentials
 OPENEO_AUTH_CLIENT_ID=
@@ -0,0 +1,121 @@
 # Local Test Workflow
 This project can be tested in a local virtualenv that mirrors the Python package source used by the Docker images.
 ## 1. Create the virtualenv
 ```bash
 python3 -m venv .venv-test
 source .venv-test/bin/activate
 ```
 ## 2. Install dependencies with the Docker mirror
 ### Option A: full project install
 Use this when your local Python version matches Docker (`3.10`).
 The development Docker image installs packages from `https://mirror-pypi.runflare.com/simple`.
 ```bash
 source .venv-test/bin/activate
 pip --isolated install \
  --prefer-binary \
  --index-url https://mirror-pypi.runflare.com/simple \
  --trusted-host mirror-pypi.runflare.com \
  -c constraints.txt \
  -r requirements.txt
 ```
 ### Option B: focused openEO test install
 I used this path successfully on a host with Python `3.14`, where the pinned full requirements are not Docker-compatible because the project pins older `numpy` for Python `3.10`.
 ```bash
 source .venv-test/bin/activate
 pip --isolated install \
  --prefer-binary \
  --index-url https://mirror-pypi.runflare.com/simple \
  --trusted-host mirror-pypi.runflare.com \
  Django \
  requests \
  PySocks \
  openeo \
  python-dotenv \
  Pillow
 ```
 If the mirror is temporarily slow in your environment, retry the same command once more before changing indexes.
 ## 3. Run the focused openEO test file
 ### Standard Django command
 ```bash
 source .venv-test/bin/activate
 python manage.py test location_data.test_openeo_service
 ```
 ### Minimal runner used on a non-Docker local setup
 This avoids loading the whole project URL tree and only boots the apps needed by `location_data.test_openeo_service`.
 ```bash
 source .venv-test/bin/activate
 python - <<'PY'
 import sys, types
 import django
 from django.conf import settings
 from django.urls import path
 url_module = types.ModuleType("local_test_urls")
 url_module.urlpatterns = [path("__test__/", lambda request: None)]
 sys.modules["local_test_urls"] = url_module
 settings.configure(
    SECRET_KEY="test-secret",
    USE_TZ=True,
    DATABASES={"default": {"ENGINE": "django.db.backends.sqlite3", "NAME": ":memory:"}},
    INSTALLED_APPS=[
        "django.contrib.auth",
        "django.contrib.contenttypes",
        "location_data",
    ],
    MIDDLEWARE=[],
    ROOT_URLCONF="local_test_urls",
    DEFAULT_AUTO_FIELD="django.db.models.BigAutoField",
 )
 django.setup()
 from django.test.utils import get_runner
 runner = get_runner(settings)()
 failures = runner.run_tests(["location_data.test_openeo_service"])
 raise SystemExit(bool(failures))
 PY
 ```
 ## 4. Run the broader location-data suite
 ```bash
 source .venv-test/bin/activate
 python manage.py test location_data
 ```
 ## 5. Run a syntax-only validation
 This is useful when dependencies are still being installed or when you want a very fast sanity check.
 ```bash
 python3 -m py_compile \
  location_data/openeo_service.py \
  location_data/tasks.py \
  location_data/test_openeo_service.py \
  config/proxy.py
 ```
 ## Notes
 - The openEO request timeout is configured through `OPENEO_TIMEOUT_SECONDS`.
 - The current default in code and `.env.example` is `600` seconds.
 - openEO request logging is emitted from `location_data.openeo_service`, including request payloads, process graphs, downloaded batch result files, and parse failures.
@@ -0,0 +1,446 @@
 # گزارش تحلیل خطای `location_data` برای Remote Sensing Run
 این سند بر اساس payload دریافتی از endpoint زیر تهیه شده است:
 - `GET /api/location-data/remote-sensing/runs/{task_id}/status/`
 نمونه run بررسی‌شده:
 - `task_id`: `02959179-63b2-4388-bb2e-a4d79176eca2`
 - `run.id`: `6`
 - بازه زمانی: `2026-04-09` تا `2026-05-09`
 - وضعیت API: `failed`
 - وضعیت Celery: `RETRY`
 ---
 ## 1) خلاصه خیلی کوتاه
 این run تا مرحله واکشی metricها و ذخیره observationها جلو رفته، اما در مرحله ساخت subdivision داده‌محور شکست خورده است.
 خطای نهایی:
 - `هیچ observation قابل استفاده‌ای برای خوشه‌بندی باقی نماند.`
 این خطا در کد دقیقاً زمانی رخ می‌دهد که سیستم بعد از بارگذاری observationها، هیچ ردیفی را برای clustering قابل استفاده تشخیص ندهد.
 مسیر کد:
 - `location_data/tasks.py`
 - `location_data/data_driven_subdivision.py`
 ---
 ## 2) جدول مدل‌ها / جدول‌های درگیر
 | مدل | جدول Django | نقش |
 |---|---|---|
 | `SoilLocation` | `location_data_soillocation` | اطلاعات location و farm boundary |
 | `BlockSubdivision` | `location_data_blocksubdivision` | تقسیم‌بندی بلوک/مزرعه |
 | `RemoteSensingRun` | `location_data_remotesensingrun` | وضعیت اجرای pipeline سنجش‌ازدور |
 | `AnalysisGridCell` | `location_data_analysisgridcell` | سلول‌های شبکه تحلیلی |
 | `AnalysisGridObservation` | `location_data_analysisgridobservation` | مقادیر metricها برای هر cell |
 | `RemoteSensingSubdivisionResult` | `location_data_remotesensingsubdivisionresult` | نتیجه clustering و subdivision نهایی |
 | `RemoteSensingClusterAssignment` | `location_data_remotesensingclusterassignment` | نسبت هر cell به cluster |
 ---
 ## 3) جدول معنی فیلدهای سطح اول response
 | فیلد | مقدار نمونه | توضیح |
 |---|---|---|
 | `code` | `200` | پاسخ HTTP موفق از API؛ لزوماً به معنای موفق بودن task نیست |
 | `msg` | `success` | پیام wrapper API |
 | `data.status` | `failed` | وضعیت client-facing run |
 | `data.source` | `database` | payload از داده‌های ذخیره‌شده در DB ساخته شده |
 | `data.task_id` | UUID | شناسه Celery task |
 | `data.run` | object | snapshot اصلی run |
 | `data.task` | object | جزئیات stageها، timestamps و وضعیت Celery |
 ---
 ## 4) جدول معنی فیلدهای `run`
 | فیلد | مقدار نمونه | توضیح |
 |---|---|---|
 | `id` | `6` | شناسه رکورد run در DB |
 | `block_code` | `""` | خالی یعنی کل مزرعه، نه یک بلوک خاص |
 | `chunk_size_sqm` | `900` | اندازه هر cell به متر مربع |
 | `temporal_start` | `2026-04-09` | شروع بازه تحلیل |
 | `temporal_end` | `2026-05-09` | پایان بازه تحلیل |
 | `status` | `failure` | مقدار خام DB |
 | `status_label` | `failed` | نسخه نرمال‌شده برای client |
 | `pipeline_status` | `failed` | تکرار client-facing status |
 | `stage` | `observations_persisted` | آخرین stage ذخیره‌شده در metadata |
 | `selected_features` | `ndvi`, `ndwi`, `lst_c`, `soil_vv_db`, `dem_m`, `slope_deg` | featureهایی که pipeline فکر می‌کند برای clustering لازم‌اند |
 | `requested_cluster_count` | `null` | تعداد cluster صریح از کاربر نیامده و الگوریتم باید تصمیم بگیرد |
 | `error_message` | متن فارسی خطا | خطای نهایی ثبت‌شده روی run |
 | `started_at` | timestamp | زمان شروع واقعی run |
 | `finished_at` | timestamp | زمان ثبت failure روی run |
 | `created_at` | timestamp | زمان ایجاد رکورد run |
 | `updated_at` | timestamp | آخرین به‌روزرسانی رکورد run |
 ---
 ## 5) جدول معنی فیلدهای `run.metadata`
 | فیلد | توضیح |
 |---|---|
 | `scope` | scope اجرای تحلیل؛ در این نمونه `all_blocks` |
 | `stage` | stage فعلی/آخر ذخیره‌شده |
 | `task_id` | شناسه Celery task |
 | `pipeline.name` | نام pipeline |
 | `pipeline.version` | نسخه pipeline |
 | `farm_uuid` | شناسه مزرعه |
 | `timestamps` | زمان ورود به هر stage |
 | `status_label` | وضعیت client-facing ذخیره‌شده در metadata |
 | `requested_via` | مبدا درخواست؛ در این نمونه `api` |
 | `stage_details` | جزئیات هر stage |
 | `failure_reason` | دلیل نهایی ثبت failure |
 | `selected_features` | featureهای مورد استفاده برای clustering |
 | `requested_cluster_count` | cluster count درخواستی، اگر وجود داشته باشد |
 ---
 ## 6) جدول معنی stageها در این run
 | stage | وضعیت مشاهده‌شده | معنی |
 |---|---|---|
 | `queued` | completed | task در صف قرار گرفته |
 | `preparing_analysis_grid` | completed | آماده‌سازی grid تحلیلی |
 | `analysis_grid_ready` | completed | grid سلول‌ها آماده بوده |
 | `analysis_cells_selected` | completed | 12 سلول برای پردازش انتخاب شده‌اند |
 | `fetching_remote_metrics` | completed | metricها از openEO درخواست شده‌اند |
 | `remote_metrics_fetched` | completed | jobهای openEO تمام شده‌اند |
 | `observations_persisted` | failed | observationها ذخیره شده‌اند ولی pipeline بعد از این مرحله fail شده |
 | `failed` | completed | stage شکست نهایی ثبت شده |
 نکته مهم:
 - در payload فعلی، `run.stage` هنوز `observations_persisted` است اما `data.status` برابر `failed` است.
 - این یعنی status و stage با هم perfectly sync نیستند.
 ---
 ## 7) جدول معنی `stage_details`
 ### 7.1) `preparing_analysis_grid`
 | فیلد | توضیح |
 |---|---|
 | `block_code` | بلوک هدف |
 | `temporal_extent.start_date` | شروع بازه |
 | `temporal_extent.end_date` | پایان بازه |
 ### 7.2) `analysis_grid_ready`
 | فیلد | توضیح |
 |---|---|
 | `grid_summary.created` | آیا grid همین run ساخته شده یا از قبل وجود داشته |
 | `grid_summary.block_code` | کد بلوک |
 | `grid_summary.total_count` | تعداد کل cellها |
 | `grid_summary.created_count` | تعداد cell تازه ایجادشده |
 | `grid_summary.chunk_size_sqm` | اندازه cell |
 | `grid_summary.existing_count` | تعداد cellهای موجود از قبل |
 ### 7.3) `analysis_cells_selected`
 | فیلد | توضیح |
 |---|---|
 | `force_refresh` | آیا cache نادیده گرفته شده |
 | `total_cell_count` | تعداد کل cellها |
 | `existing_cell_count` | تعداد cellهایی که داده از قبل داشته‌اند |
 | `cell_count_to_process` | تعداد cellهایی که باید پردازش شوند |
 ### 7.4) `fetching_remote_metrics`
 | فیلد | توضیح |
 |---|---|
 | `target_cells` | لیست cellهای هدف |
 | `requested_cell_count` | تعداد cellهای هدف |
 | `metric_progress.total_metrics` | تعداد metricهایی که pipeline انتظار دارد |
 | `metric_progress.completed_metric_count` | تعداد metricهای کامل‌شده |
 | `metric_progress.completed_metrics` | metricهای کامل‌شده واقعی |
 | `metric_progress.failed_metrics` | metricهای fail شده |
 | `metric_progress.states` | وضعیت هر metric از نگاه progress tracker |
 ### 7.5) `remote_metrics_fetched`
 | فیلد | توضیح |
 |---|---|
 | `failed_metric_count` | تعداد metric fail شده |
 | `service_metadata.backend` | backend مورد استفاده |
 | `service_metadata.job_refs` | job id هر metric در openEO |
 | `service_metadata.backend_url` | آدرس backend |
 | `service_metadata.failed_metrics` | خطاهای metric-level |
 | `service_metadata.collections_used` | collectionهای مصرف‌شده |
 ### 7.6) `observations_persisted`
 | فیلد | توضیح |
 |---|---|
 | `created_count` | تعداد observation ساخته‌شده |
 | `updated_count` | تعداد observation به‌روزشده |
 ---
 ## 8) جدول معنی `task`
 | فیلد | مقدار نمونه | توضیح |
 |---|---|---|
 | `current_stage` | `observations_persisted` | stage فعلی/آخرین stage ثبت‌شده |
 | `current_stage_details` | `created_count=12`, `updated_count=0` | جزئیات stage جاری |
 | `timestamps` | object | timeline کامل stageها |
 | `stages` | list | نسخه ترتیبی از stageها |
 | `metric_progress` | object | وضعیت پیشرفت metricها |
 | `failure_reason` | متن فارسی | خطای ثبت‌شده |
 | `celery.state` | `RETRY` | Celery task هنوز final نشده و در retry است |
 | `celery.ready` | `false` | نتیجه نهایی هنوز آماده نیست |
 | `celery.successful` | `false` | task هنوز success نشده |
 | `celery.failed` | `false` | task هنوز fail نهایی نشده |
 | `celery.info` | متن خطا | پیام retry فعلی Celery |
 ---
 ## 9) دلیل دقیق خطا
 دلیل مستقیم خطا از خود کد:
 - در `location_data/data_driven_subdivision.py` اگر بعد از ساخت dataset، هیچ observation قابل استفاده برای clustering باقی نماند، این exception پرتاب می‌شود:
  - `هیچ observation قابل استفاده‌ای برای خوشه‌بندی باقی نماند.`
 این یعنی pipeline به این نقطه رسیده:
 1. cellها ساخته شده‌اند
 2. metricها از openEO درخواست شده‌اند
 3. observationها در DB ذخیره شده‌اند
 4. اما در زمان ساخت dataset برای clustering، ردیف قابل مصرفی پیدا نشده است
 ### معنی عملی این خطا
 یعنی برای همه observationهای این run، سیستم نتوانسته feature vector قابل استفاده بسازد.
 در کد فعلی، observation وقتی از clustering حذف می‌شود که:
 - همه featureهای انتخاب‌شده برای آن observation برابر `None` باشند
 پس این خطا معمولاً یعنی:
 - یا تمام metricهای لازم برای همه cellها `null` شده‌اند
 - یا feature set انتخابی با داده‌هایی که واقعاً ذخیره شده‌اند mismatch دارد
 - یا persistence / selection طوری انجام شده که clustering به داده قابل استفاده دسترسی پیدا نکرده است
 ---
 ## 10) محتمل‌ترین علت‌ها برای همین run
 ### علت 1: featureهای حذف‌شده هنوز در pipeline زنده هستند
 در payload می‌بینیم:
 - `selected_features` هنوز شامل `dem_m` و `slope_deg` است
 - اما در `location_data/openeo_service.py` این metricها از محاسبه حذف شده‌اند
 نتیجه:
 - API و metadata هنوز فکر می‌کنند این دو feature بخشی از clustering هستند
 - ولی openEO دیگر آن‌ها را تولید نمی‌کند
 - بنابراین در observationها این فیلدها عملاً `None` می‌مانند
 نکته مهم:
 - فقط `None` بودن `dem_m` و `slope_deg` به‌تنهایی برای تولید این خطا کافی نیست
 - اما این mismatch یکی از مهم‌ترین نشانه‌های ناسازگاری pipeline است
 ### علت 2: metricهای اصلی احتمالاً برای همه cellها مقدار قابل استفاده نداشته‌اند
 چون این خطا فقط وقتی رخ می‌دهد که هیچ observation usable باقی نماند، محتمل است که برای همه 12 cell:
 - `ndvi`
 - `ndwi`
 - `lst_c`
 - `soil_vv_db`
 هم عملاً `None` شده باشند یا آن‌طور که clustering انتظار دارد قابل مصرف نبوده باشند.
 این حالت ممکن است از اینجا آمده باشد:
 - داده خام provider برای این محدوده/بازه تهی یا نامعتبر بوده
 - masking یا aggregation مقدار usable تولید نکرده
 - یا در persistence، featureهای محاسبه‌شده به‌درستی به ستون‌های clustering نرسیده‌اند
 ### علت 3: progress tracker با metricهای واقعی sync نیست
 در payload:
 - `completed_metrics` شامل `soil_vv` است
 - ولی `states` شامل `soil_vv_db` به حالت `pending` است
 یعنی tracker با metric واقعی اجراشده sync نیست:
 - metric واقعی remote: `soil_vv`
 - metric مشتق‌شده محلی: `soil_vv_db`
 این باعث می‌شود progress ظاهراً ناقص دیده شود، حتی وقتی داده remote کامل شده است.
 ### علت 4: وضعیت DB و Celery با هم متناقض‌اند
 در payload:
 - `data.status = failed`
 - ولی `task.celery.state = RETRY`
 یعنی:
 - از نظر DB، run شکست خورده ثبت شده
 - از نظر Celery، task هنوز در حال retry است و failure نهایی نشده
 این برای client گیج‌کننده است، چون معلوم نیست باید run را تمام‌شده و fail شده بداند یا منتظر retry بماند.
 ---
 ## 11) مشکلات فعلی `location_data` که از کد مشخص هستند
 ### مشکل 1: `DEFAULT_CLUSTER_FEATURES` هنوز metricهای حذف‌شده را نگه داشته
 فایل:
 - `location_data/data_driven_subdivision.py`
 وضعیت فعلی:
 - `dem_m`
 - `slope_deg`
 هنوز داخل `DEFAULT_CLUSTER_FEATURES` هستند.
 اثر:
 - `selected_features` اشتباه در run metadata
 - progress tracker اشتباه
 - clustering contract قدیمی باقی می‌ماند
 ### مشکل 2: serializer و summary هنوز `dem_m` و `slope_deg` را به API برمی‌گردانند
 فایل‌ها:
 - `location_data/serializers.py`
 - `location_data/views.py`
 اثر:
 - response API هنوز metricهای حذف‌شده را نمایش می‌دهد
 - summary میانگین `dem_m_mean` و `slope_deg_mean` می‌سازد
 - مصرف‌کننده API فکر می‌کند این metricها هنوز پشتیبانی می‌شوند
 ### مشکل 3: `_upsert_grid_observations` هنوز فیلدهای حذف‌شده را persist می‌کند
 فایل:
 - `location_data/tasks.py`
 اثر:
 - ستون‌های `dem_m` و `slope_deg` همچنان نوشته می‌شوند، اما عملاً با `None`
 - metadata هنوز `slope_supported` را ذخیره می‌کند، در حالی که openEO service دیگر این مفهوم را برنمی‌گرداند
 ### مشکل 4: progress metricها با metricهای واقعی اجراشده mismatch دارد
 فایل:
 - `location_data/tasks.py`
 نمونه mismatch:
 - remote metric واقعی: `soil_vv`
 - feature مورد انتظار برای clustering: `soil_vv_db`
 اثر:
 - `completed_metrics` و `states` با هم ناسازگار می‌شوند
 - `soil_vv_db` در response به شکل `pending` دیده می‌شود، با اینکه از `soil_vv` مشتق می‌شود
 ### مشکل 5: stage و status همیشه هماهنگ نیستند
 در payload فعلی:
 - `run.status_label = failed`
 - `run.stage = observations_persisted`
 اثر:
 - client نمی‌فهمد خطا دقیقاً در کدام stage رخ داده
 - `task.current_stage` هم گمراه‌کننده می‌شود
 ### مشکل 6: DB failure و Celery retry همزمان به client داده می‌شود
 اثر:
 - API برای یک run همزمان پیام `failed` و `RETRY` می‌دهد
 - از نگاه UX، کاربر نمی‌داند باید retry خودکار را صبر کند یا خطا را final بداند
 ---
 ## 12) جمع‌بندی عملی برای همین خطا
 بر اساس payload و کد، نتیجه عملی این است:
 1. openEO jobها برای `ndvi`, `ndwi`, `lst_c`, `soil_vv` اجرا شده‌اند
 2. 12 observation در DB ساخته شده‌اند
 3. pipeline وارد مرحله clustering شده
 4. clustering هیچ observation usable پیدا نکرده
 5. run در DB به حالت `failed` رفته
 6. اما Celery هنوز task را در حالت `RETRY` نگه داشته
 ---
 ## 13) پیشنهادهای اصلاح
 ### اصلاح ضروری
 1. `dem_m` و `slope_deg` را از این نقاط هم حذف کنید:
   - `location_data/data_driven_subdivision.py`
   - `location_data/serializers.py`
   - `location_data/views.py`
   - `location_data/tasks.py`
 2. `DEFAULT_CLUSTER_FEATURES` را با metricهای واقعی sync کنید:
   - `ndvi`
   - `ndwi`
   - `lst_c`
   - `soil_vv_db`
 3. progress tracker را طوری اصلاح کنید که:
   - completion `soil_vv` به completion `soil_vv_db` هم map شود
   - metricهای حذف‌شده اصلاً در state دیده نشوند
 4. منطق status endpoint را طوری تنظیم کنید که:
   - اگر Celery در `RETRY` است، status client نهایی `failed` نباشد
   - یا حداقل یک فیلد صریح مثل `final_status=false` برگردد
 5. قبل از clustering، یک diagnostic بهتر ذخیره شود:
   - تعداد observationهای usable
   - تعداد observationهای all-null
   - تعداد null برای هر feature
 ### اصلاح پیشنهادی برای debug بهتر
 در `stage_details` این موارد اضافه شود:
 - `usable_observation_count`
 - `all_features_missing_cell_codes`
 - `feature_null_counts`
 - `selected_features_effective`
 ---
 ## 14) نتیجه نهایی
 این run به احتمال زیاد به خاطر ناسازگاری بین featureهای مورد انتظار pipeline و featureهای واقعاً تولید/ذخیره‌شده شکست خورده است، و علاوه بر آن چند inconsistency مهم در `location_data` وجود دارد:
 - metricهای حذف‌شده هنوز در feature contract حضور دارند
 - progress report با metricهای واقعی sync نیست
 - DB status و Celery status با هم تناقض دارند
 - stage نهایی برای failure به شکل واضح و قابل اتکا به client نمایش داده نمی‌شود
 اگر بخواهید، مرحله بعدی می‌تواند این باشد که همین موارد را در کد هم اصلاح کنیم، نه فقط مستندسازی.
@@ -0,0 +1,6 @@
 from django.urls import include, path
 urlpatterns = [
    path("api/location-data/", include("location_data.urls")),
 ]
@@ -0,0 +1,98 @@
 from __future__ import annotations
 from datetime import date
 from types import SimpleNamespace
 from unittest.mock import patch
 import uuid
 from django.test import override_settings
 from farm_data.models import SensorData
 from integration_tests.base import IntegrationAPITestCase
 from location_data.models import AnalysisGridCell, AnalysisGridObservation, RemoteSensingRun
@override_settings(ROOT_URLCONF="integration_tests.location_data_urls")
 class RemoteSensingApiFlowTests(IntegrationAPITestCase):
    def setUp(self) -> None:
        super().setUp()
        self.farm_uuid = uuid.UUID("11111111-1111-1111-1111-111111111111")
        self.farm = SensorData.objects.create(
            farm_uuid=self.farm_uuid,
            center_location=self.primary_location,
            sensor_payload={},
        )
        self.temporal_end = date(2026, 5, 9)
        self.temporal_start = date(2026, 4, 9)
    @patch("location_data.views.timezone.localdate", return_value=date(2026, 5, 10))
    @patch("location_data.views.run_remote_sensing_analysis_task.delay")
    def test_remote_sensing_post_and_status_flow(self, mock_delay, _mock_localdate) -> None:
        task_id = uuid.UUID("9e7f3aaa-6d34-4428-a196-478af7a2d7f6")
        mock_delay.return_value = SimpleNamespace(id=str(task_id))
        enqueue_response = self.client.post(
            "/api/location-data/remote-sensing/",
            data={
                "farm_uuid": str(self.farm_uuid),
                "force_refresh": False,
            },
            format="json",
        )
        self.assertEqual(enqueue_response.status_code, 202, enqueue_response.json())
        enqueue_payload = enqueue_response.json()["data"]
        self.assertEqual(enqueue_payload["status"], "processing")
        self.assertEqual(enqueue_payload["source"], "processing")
        self.assertEqual(enqueue_payload["task_id"], str(task_id))
        self.assertEqual(enqueue_payload["temporal_extent"]["start_date"], "2026-04-09")
        self.assertEqual(enqueue_payload["temporal_extent"]["end_date"], "2026-05-09")
        run = RemoteSensingRun.objects.get(pk=enqueue_payload["run"]["id"])
        self.assertEqual(run.status, RemoteSensingRun.STATUS_PENDING)
        self.assertEqual(run.temporal_start, self.temporal_start)
        self.assertEqual(run.temporal_end, self.temporal_end)
        self.assertEqual(run.metadata["task_id"], str(task_id))
        mock_delay.assert_called_once()
        status_response = self.client.get(f"/api/location-data/remote-sensing/runs/{task_id}/status/")
        self.assertEqual(status_response.status_code, 200, status_response.json())
        status_payload = status_response.json()["data"]
        self.assertEqual(status_payload["status"], "pending")
        self.assertEqual(status_payload["run"]["pipeline_status"], "pending")
        cell = AnalysisGridCell.objects.create(
            soil_location=self.primary_location,
            block_code="",
            cell_code="cell-1",
            chunk_size_sqm=900,
            geometry=self.primary_boundary,
            centroid_lat=f"{self.primary_lat:.6f}",
            centroid_lon=f"{self.primary_lon:.6f}",
        )
        AnalysisGridObservation.objects.create(
            cell=cell,
            run=run,
            temporal_start=self.temporal_start,
            temporal_end=self.temporal_end,
            ndvi=0.61,
            ndwi=0.22,
            lst_c=24.5,
            soil_vv=0.13,
            soil_vv_db=-8.860566,
            dem_m=1550.0,
            slope_deg=4.2,
            metadata={"backend_name": "openeo"},
        )
        run.status = RemoteSensingRun.STATUS_SUCCESS
        run.metadata = {**(run.metadata or {}), "stage": "completed", "selected_features": ["ndvi", "ndwi"]}
        run.save(update_fields=["status", "metadata", "updated_at"])
        completed_response = self.client.get(f"/api/location-data/remote-sensing/runs/{task_id}/status/")
        self.assertEqual(completed_response.status_code, 200, completed_response.json())
        completed_payload = completed_response.json()["data"]
        self.assertEqual(completed_payload["status"], "completed")
        self.assertEqual(completed_payload["summary"]["cell_count"], 1)
        self.assertEqual(completed_payload["summary"]["ndvi_mean"], 0.61)
        self.assertEqual(len(completed_payload["cells"]), 1)
        self.assertEqual(completed_payload["cells"][0]["cell_code"], "cell-1")
@@ -1,6 +1,8 @@
 from __future__ import annotations
 from dataclasses import dataclass
 import json
 import logging
 from typing import Any
 from django.db import transaction
@@ -21,18 +23,22 @@ DEFAULT_CLUSTER_FEATURES = [
    "ndwi",
    "lst_c",
    "soil_vv_db",
    "dem_m",
    "slope_deg",
 ]
 SUPPORTED_CLUSTER_FEATURES = tuple(DEFAULT_CLUSTER_FEATURES)
 DEFAULT_RANDOM_STATE = 42
 DEFAULT_MAX_K = 10
 logger = logging.getLogger(__name__)
 class DataDrivenSubdivisionError(Exception):
    """Raised when remote-sensing-driven subdivision can not be computed."""
 class EmptyObservationDatasetError(DataDrivenSubdivisionError):
    """Raised when upstream persistence completes without usable clustering features."""
@dataclass
 class ClusteringDataset:
    observations: list[AnalysisGridObservation]
@@ -70,6 +76,8 @@ def create_remote_sensing_subdivision_result(
    dataset = build_clustering_dataset(
        observations=observations,
        selected_features=selected_features,
        run=run,
        location=location,
    )
    if not dataset.observations:
        raise DataDrivenSubdivisionError("هیچ observation قابل استفاده‌ای برای خوشه‌بندی باقی نماند.")
@@ -164,6 +172,8 @@ def build_clustering_dataset(
    *,
    observations: list[AnalysisGridObservation],
    selected_features: list[str] | None = None,
    run: RemoteSensingRun | None = None,
    location: SoilLocation | None = None,
 ) -> ClusteringDataset:
    selected_features = list(selected_features or DEFAULT_CLUSTER_FEATURES)
    invalid_features = [
@@ -176,6 +186,22 @@ def build_clustering_dataset(
            "ویژگی‌های نامعتبر برای خوشه‌بندی: "
            + ", ".join(sorted(invalid_features))
        )
    log_context = _build_clustering_log_context(
        observations=observations,
        selected_features=selected_features,
        run=run,
        location=location,
    )
    logger.info(
        "Preparing clustering dataset: %s",
        _serialize_log_payload(
            {
                **log_context,
                "total_observations": len(observations),
                "non_null_counts": _count_non_null_features(observations),
            }
        ),
    )
    raw_rows: list[list[float | None]] = []
    raw_maps: list[dict[str, float | None]] = []
    usable_observations: list[AnalysisGridObservation] = []
@@ -193,6 +219,11 @@ def build_clustering_dataset(
            if value is None:
                missing_value_counts[feature_name] += 1
        if all(value is None for value in feature_map.values()):
            logger.debug(
                "Skipping observation cell=%s: all clustering features are null | context=%s",
                observation.cell.cell_code,
                _serialize_log_payload(log_context),
            )
            skipped_cell_codes.append(observation.cell.cell_code)
            skipped_reasons["all_features_missing"].append(observation.cell.cell_code)
            continue
@@ -201,21 +232,42 @@ def build_clustering_dataset(
        raw_maps.append(feature_map)
        raw_rows.append([feature_map[feature_name] for feature_name in selected_features])
    logger.info(
        "Clustering dataset filtered observations: %s",
        _serialize_log_payload(
            {
                **log_context,
                "remaining_observations": len(usable_observations),
                "removed_observations": len(observations) - len(usable_observations),
            }
        )
    )
    zero_usable_feature_names = [
        feature_name for feature_name, missing_count in missing_value_counts.items() if missing_count == len(observations)
    ]
    if zero_usable_feature_names and len(zero_usable_feature_names) < len(selected_features):
        for feature_name in zero_usable_feature_names:
            logger.warning(
                "Feature %s has zero usable values in dataset | context=%s",
                feature_name,
                _serialize_log_payload(log_context),
            )
    if not usable_observations:
-        return ClusteringDataset(
+        error_context = {
-            observations=[],
+            **log_context,
-            selected_features=selected_features,
+            "total_observations": len(observations),
-            raw_feature_rows=[],
+            "removed_observations": len(observations),
-            raw_feature_maps=[],
+            "null_counts_per_feature": missing_value_counts,
-            skipped_cell_codes=skipped_cell_codes,
+            "selected_features": selected_features,
-            used_cell_codes=[],
+        }
-            imputed_matrix=[],
+        logger.error(
-            scaled_matrix=[],
+            "No usable observations available for clustering: %s",
-            imputer_statistics={feature_name: None for feature_name in selected_features},
+            _serialize_log_payload(error_context),
-            scaler_means={feature_name: 0.0 for feature_name in selected_features},
+        )
-            scaler_scales={feature_name: 1.0 for feature_name in selected_features},
+        raise EmptyObservationDatasetError(
-            missing_value_counts=missing_value_counts,
+            "Upstream processing completed but no usable feature values were persisted."
            skipped_reasons=skipped_reasons,
        )
    try:
@@ -487,3 +539,41 @@ def _coerce_float(value: Any) -> float | None:
        return float(value)
    except (TypeError, ValueError):
        return None
 def _count_non_null_features(observations: list[AnalysisGridObservation]) -> dict[str, int]:
    counts = {feature_name: 0 for feature_name in DEFAULT_CLUSTER_FEATURES}
    for observation in observations:
        for feature_name in DEFAULT_CLUSTER_FEATURES:
            if _coerce_float(getattr(observation, feature_name, None)) is not None:
                counts[feature_name] += 1
    return counts
 def _build_clustering_log_context(
    *,
    observations: list[AnalysisGridObservation],
    selected_features: list[str],
    run: RemoteSensingRun | None,
    location: SoilLocation | None,
 ) -> dict[str, Any]:
    first_observation = observations[0] if observations else None
    observation_metadata = dict(getattr(first_observation, "metadata", {}) or {})
    resolved_run = run or getattr(first_observation, "run", None)
    resolved_location = location or getattr(getattr(first_observation, "cell", None), "soil_location", None)
    temporal_start = getattr(resolved_run, "temporal_start", None) or getattr(first_observation, "temporal_start", None)
    temporal_end = getattr(resolved_run, "temporal_end", None) or getattr(first_observation, "temporal_end", None)
    return {
        "run_id": getattr(resolved_run, "id", None),
        "job_ref": observation_metadata.get("job_refs", {}),
        "region_id": getattr(resolved_location, "id", None),
        "date_range": {
            "temporal_start": temporal_start.isoformat() if hasattr(temporal_start, "isoformat") else temporal_start,
            "temporal_end": temporal_end.isoformat() if hasattr(temporal_end, "isoformat") else temporal_end,
        },
        "selected_features": selected_features,
    }
 def _serialize_log_payload(payload: dict[str, Any]) -> str:
    return json.dumps(payload, ensure_ascii=True, default=str, sort_keys=True)
@@ -1,27 +1,38 @@
 from __future__ import annotations
 import json
 import logging
 import math
 import os
 import time
 from dataclasses import dataclass
 from datetime import date
 from decimal import Decimal
 from pathlib import Path
 from tempfile import TemporaryDirectory
 from typing import Any
 import requests
 from requests.adapters import HTTPAdapter
 from urllib3.util.retry import Retry
 from config.proxy import apply_requests_proxy, build_proxy_url_from_proxychains_env
 from .models import AnalysisGridCell
 logger = logging.getLogger(__name__)
 DEFAULT_OPENEO_BACKEND_URL = "https://openeofed.dataspace.copernicus.eu"
 DEFAULT_OPENEO_PROVIDER = "openeo"
 DEFAULT_OPENEO_PROXY_URL = "socks5h://host.docker.internal:10808"
 DEFAULT_OPENEO_TIMEOUT_SECONDS = 600.0
 DEFAULT_OPENEO_HTTP_RETRY_TOTAL = 5
 DEFAULT_OPENEO_HTTP_RETRY_BACKOFF_FACTOR = 2.0
 SENTINEL2_COLLECTION = "SENTINEL2_L2A"
 SENTINEL3_LST_COLLECTION = "SENTINEL3_SLSTR_L2_LST"
 SENTINEL1_COLLECTION = "SENTINEL1_GRD"
 COPERNICUS_DEM_COLLECTION = "COPERNICUS_30"
 VALID_SCL_CLASSES = (4, 5, 6)
 METRIC_NAMES = (
@@ -30,8 +41,12 @@ METRIC_NAMES = (
    "lst_c",
    "soil_vv",
    "soil_vv_db",
-    "dem_m",
+)
-    "slope_deg",
+CLUSTER_METRIC_NAMES = (
    "ndvi",
    "ndwi",
    "lst_c",
    "soil_vv_db",
 )
@@ -53,19 +68,67 @@ class TimeoutOverrideSession(requests.Session):
    def __init__(self, timeout_seconds: float):
        super().__init__()
        self.timeout_seconds = timeout_seconds
        self.last_response_preview = ""
        self.last_response_content_type = ""
        self.last_response_url = ""
    def request(self, method, url, **kwargs):
        timeout = kwargs.get("timeout")
        if timeout is None or timeout < self.timeout_seconds:
            kwargs["timeout"] = self.timeout_seconds
-        return super().request(method, url, **kwargs)
+
        request_log = {
            "method": str(method).upper(),
            "url": url,
            "timeout": kwargs.get("timeout"),
            "params": kwargs.get("params"),
            "json": kwargs.get("json"),
            "data": kwargs.get("data"),
            "headers": _sanitize_headers(kwargs.get("headers")),
            "proxy_url": _sanitize_proxy_url(self.proxies.get("https") or self.proxies.get("http")),
        }
        logger.info("openEO request payload: %s", _serialize_for_log(request_log))
        started_at = time.monotonic()
        try:
            response = super().request(method, url, **kwargs)
        except Exception as exc:
            logger.exception(
                "openEO request failed after %.3fs: %s",
                time.monotonic() - started_at,
                _serialize_for_log(
                    {
                        "method": str(method).upper(),
                        "url": url,
                        "error": repr(exc),
                    }
                ),
            )
            raise
        logger.info(
            "openEO response received after %.3fs: %s",
            time.monotonic() - started_at,
            _serialize_for_log(
                {
                    "method": str(method).upper(),
                    "url": url,
                    "status_code": response.status_code,
                    "headers": _sanitize_headers(dict(response.headers)),
                }
            ),
        )
        self.last_response_url = str(response.url)
        self.last_response_content_type = str(response.headers.get("Content-Type", ""))
        self.last_response_preview = response.text[:1000] if response.text else ""
        return response
@dataclass(frozen=True)
 class OpenEOConnectionSettings:
    backend_url: str = DEFAULT_OPENEO_BACKEND_URL
    auth_method: str = "client_credentials"
-    timeout_seconds: float = 60.0
+    timeout_seconds: float = DEFAULT_OPENEO_TIMEOUT_SECONDS
    client_id: str = ""
    client_secret: str = ""
    provider_id: str = ""
@@ -73,13 +136,18 @@ class OpenEOConnectionSettings:
    password: str = ""
    allow_interactive_oidc: bool = False
    proxy_url: str = ""
    http_retry_total: int = DEFAULT_OPENEO_HTTP_RETRY_TOTAL
    http_retry_backoff_factor: float = DEFAULT_OPENEO_HTTP_RETRY_BACKOFF_FACTOR
    @classmethod
    def from_env(cls) -> "OpenEOConnectionSettings":
        return cls(
            backend_url=os.environ.get("OPENEO_BACKEND_URL", DEFAULT_OPENEO_BACKEND_URL).strip(),
            auth_method=os.environ.get("OPENEO_AUTH_METHOD", "client_credentials").strip().lower(),
-            timeout_seconds=float(os.environ.get("OPENEO_TIMEOUT_SECONDS", "60").strip() or "60"),
+            timeout_seconds=float(
                os.environ.get("OPENEO_TIMEOUT_SECONDS", str(int(DEFAULT_OPENEO_TIMEOUT_SECONDS))).strip()
                or str(int(DEFAULT_OPENEO_TIMEOUT_SECONDS))
            ),
            client_id=os.environ.get("OPENEO_AUTH_CLIENT_ID", "").strip(),
            client_secret=os.environ.get("OPENEO_AUTH_CLIENT_SECRET", "").strip(),
            provider_id=os.environ.get("OPENEO_AUTH_PROVIDER_ID", "").strip(),
@@ -88,6 +156,17 @@ class OpenEOConnectionSettings:
            allow_interactive_oidc=os.environ.get("OPENEO_ALLOW_INTERACTIVE_OIDC", "0").strip().lower()
            in {"1", "true", "yes", "on"},
            proxy_url=_resolve_openeo_proxy_url_from_env(),
            http_retry_total=int(
                os.environ.get("OPENEO_HTTP_RETRY_TOTAL", str(DEFAULT_OPENEO_HTTP_RETRY_TOTAL)).strip()
                or str(DEFAULT_OPENEO_HTTP_RETRY_TOTAL)
            ),
            http_retry_backoff_factor=float(
                os.environ.get(
                    "OPENEO_HTTP_RETRY_BACKOFF_FACTOR",
                    str(DEFAULT_OPENEO_HTTP_RETRY_BACKOFF_FACTOR),
                ).strip()
                or str(DEFAULT_OPENEO_HTTP_RETRY_BACKOFF_FACTOR)
            ),
        )
@@ -104,6 +183,46 @@ def _resolve_openeo_proxy_url_from_env() -> str:
    return configured_proxy_url
 def _sanitize_headers(headers: dict[str, Any] | None) -> dict[str, Any] | None:
    if not headers:
        return headers
    return {key: _mask_sensitive_value(key, value) for key, value in headers.items()}
 def _sanitize_proxy_url(proxy_url: str | None) -> str | None:
    if not proxy_url:
        return proxy_url
    return proxy_url
 def _serialize_for_log(payload: Any) -> str:
    return json.dumps(_mask_sensitive_payload(payload), ensure_ascii=True, default=str, sort_keys=True)
 def _mask_sensitive_payload(value: Any, parent_key: str = "") -> Any:
    if isinstance(value, dict):
        return {str(key): _mask_sensitive_payload(item, str(key)) for key, item in value.items()}
    if isinstance(value, list):
        return [_mask_sensitive_payload(item, parent_key) for item in value]
    if isinstance(value, tuple):
        return [_mask_sensitive_payload(item, parent_key) for item in value]
    return _mask_sensitive_value(parent_key, value)
 def _mask_sensitive_value(key: str, value: Any) -> Any:
    normalized_key = (key or "").lower()
    if normalized_key in {
        "authorization",
        "access_token",
        "refresh_token",
        "id_token",
        "client_secret",
        "password",
    }:
        return "***redacted***"
    return value
 def is_openeo_auth_configured(settings: OpenEOConnectionSettings | None = None) -> bool:
    settings = settings or OpenEOConnectionSettings.from_env()
@@ -118,9 +237,26 @@ def is_openeo_auth_configured(settings: OpenEOConnectionSettings | None = None)
 def build_openeo_requests_session(settings: OpenEOConnectionSettings) -> requests.Session:
    session = TimeoutOverrideSession(settings.timeout_seconds)
    session.headers.setdefault("Accept", "application/json")
    adapter = HTTPAdapter(max_retries=_build_openeo_http_retry(settings))
    session.mount("http://", adapter)
    session.mount("https://", adapter)
    return apply_requests_proxy(session, settings.proxy_url)
 def _build_openeo_http_retry(settings: OpenEOConnectionSettings) -> Retry:
    return Retry(
        total=settings.http_retry_total,
        connect=settings.http_retry_total,
        read=settings.http_retry_total,
        status=settings.http_retry_total,
        backoff_factor=settings.http_retry_backoff_factor,
        allowed_methods=None,
        status_forcelist=(429, 500, 502, 503, 504),
        raise_on_status=False,
    )
 def connect_openeo(settings: OpenEOConnectionSettings | None = None):
    """
    Build an authenticated openEO connection using environment-driven configuration.
@@ -140,11 +276,21 @@ def connect_openeo(settings: OpenEOConnectionSettings | None = None):
        raise OpenEOServiceError("The `openeo` Python client is required for remote sensing jobs.") from exc
    session = build_openeo_requests_session(settings)
    try:
        connection = openeo.connect(
            settings.backend_url,
            session=session,
            default_timeout=settings.timeout_seconds,
        )
    except requests.exceptions.JSONDecodeError as exc:
        preview = (session.last_response_preview or "").strip()
        content_type = session.last_response_content_type or "unknown"
        response_url = session.last_response_url or settings.backend_url
        raise OpenEOServiceError(
            "openEO endpoint returned a non-JSON response while loading capabilities. "
            f"url={response_url!r} content_type={content_type!r} preview={preview[:300]!r}. "
            "This usually means the proxy returned an HTML page instead of the API response."
        ) from exc
    def resolve_oidc_context(
        provider_id: str | None,
@@ -295,6 +441,8 @@ def compute_remote_sensing_metrics(
    *,
    temporal_start: date | str,
    temporal_end: date | str,
    selected_features: list[str] | None = None,
    progress_callback=None,
    connection=None,
 ) -> dict[str, Any]:
    """
@@ -309,7 +457,6 @@ def compute_remote_sensing_metrics(
            "metadata": {
                "backend": DEFAULT_OPENEO_PROVIDER,
                "collections_used": [],
                "slope_supported": False,
                "job_refs": {},
                "failed_metrics": [],
            },
@@ -318,6 +465,14 @@ def compute_remote_sensing_metrics(
    connection = connection or connect_openeo()
    feature_collection = build_feature_collection(cells)
    spatial_extent = build_spatial_extent(cells)
    log_openeo_request_summary(
        cells=cells,
        temporal_start=temporal_start,
        temporal_end=temporal_end,
        spatial_extent=spatial_extent,
        selected_features=selected_features or list(METRIC_NAMES),
    )
    expected_feature_ids = [cell.cell_code for cell in cells]
    results = initialize_metric_result_map(cells)
    metadata = {
        "backend": DEFAULT_OPENEO_PROVIDER,
@@ -326,11 +481,10 @@ def compute_remote_sensing_metrics(
            SENTINEL2_COLLECTION,
            SENTINEL3_LST_COLLECTION,
            SENTINEL1_COLLECTION,
            COPERNICUS_DEM_COLLECTION,
        ],
        "slope_supported": True,
        "job_refs": {},
        "failed_metrics": [],
        "payload_diagnostics": {},
    }
    metric_runners = [
@@ -338,29 +492,32 @@ def compute_remote_sensing_metrics(
        ("ndwi", compute_ndwi),
        ("lst_c", compute_lst_c),
        ("soil_vv", compute_soil_vv),
        ("dem_m", compute_dem_m),
        ("slope_deg", compute_slope_deg),
    ]
    for metric_name, runner in metric_runners:
        try:
            if progress_callback is not None:
                progress_callback(metric_name=metric_name, state="running", metadata=metadata)
            metric_payload = runner(
                connection=connection,
                feature_collection=feature_collection,
                spatial_extent=spatial_extent,
                temporal_start=temporal_start,
                temporal_end=temporal_end,
                expected_feature_ids=expected_feature_ids,
            )
            merge_metric_results(results, metric_payload["results"])
            metadata["job_refs"][metric_name] = metric_payload.get("job_ref")
-            if metric_name == "slope_deg" and not metric_payload.get("supported", True):
+            metadata["payload_diagnostics"][metric_name] = metric_payload.get("payload_diagnostics", {})
-                metadata["slope_supported"] = False
+            if progress_callback is not None:
-        except Exception as exc:
+                progress_callback(
-            if metric_name == "slope_deg":
+                    metric_name=metric_name,
-                metadata["slope_supported"] = False
+                    state="completed",
-                metadata["failed_metrics"].append(
+                    metadata=metadata,
-                    {"metric": metric_name, "error": str(exc), "non_fatal": True}
+                    metric_payload=metric_payload,
                )
-                continue
+        except Exception as exc:
            if progress_callback is not None:
                progress_callback(metric_name=metric_name, state="failed", metadata=metadata, error=str(exc))
            raise OpenEOExecutionError(f"Failed to compute metric `{metric_name}`: {exc}") from exc
    for cell_code, payload in results.items():
@@ -370,7 +527,54 @@ def compute_remote_sensing_metrics(
    return {"results": results, "metadata": metadata}
-def compute_ndvi(*, connection, feature_collection, spatial_extent, temporal_start, temporal_end) -> dict[str, Any]:
+def log_openeo_request_summary(
    *,
    cells: list[AnalysisGridCell],
    temporal_start: date | str,
    temporal_end: date | str,
    spatial_extent: dict[str, float],
    selected_features: list[str],
 ) -> None:
    start_date = _parse_date_value(temporal_start)
    end_date = _parse_date_value(temporal_end)
    logger.info(
        "openEO request summary: %s",
        _serialize_for_log(
            {
                "cell_count": len(cells),
                "date_range_days": max((end_date - start_date).days, 0) + 1,
                "area_m2": round(_estimate_extent_area_m2(spatial_extent), 2),
                "metrics": selected_features,
                "spatial_extent": spatial_extent,
                "temporal_start": start_date.isoformat(),
                "temporal_end": end_date.isoformat(),
            }
        ),
    )
 def _estimate_extent_area_m2(spatial_extent: dict[str, float]) -> float:
    west = float(spatial_extent["west"])
    east = float(spatial_extent["east"])
    south = float(spatial_extent["south"])
    north = float(spatial_extent["north"])
    mean_lat_rad = math.radians((south + north) / 2.0)
    meters_per_degree_lat = 111_320.0
    meters_per_degree_lon = 111_320.0 * math.cos(mean_lat_rad)
    width_m = max(east - west, 0.0) * meters_per_degree_lon
    height_m = max(north - south, 0.0) * meters_per_degree_lat
    return max(width_m, 0.0) * max(height_m, 0.0)
 def compute_ndvi(
    *,
    connection,
    feature_collection,
    spatial_extent,
    temporal_start,
    temporal_end,
    expected_feature_ids: list[str] | None = None,
 ) -> dict[str, Any]:
    cube = connection.load_collection(
        SENTINEL2_COLLECTION,
        spatial_extent=spatial_extent,
@@ -382,11 +586,32 @@ def compute_ndvi(*, connection, feature_collection, spatial_extent, temporal_sta
    red = cube.band("B04") * 0.0001
    nir = cube.band("B08") * 0.0001
    ndvi = ((nir - red) / (nir + red)).mask(invalid_mask.resample_cube_spatial(red))
-    aggregated = ndvi.mean_time().aggregate_spatial(geometries=feature_collection, reducer="mean").execute()
+    aggregated, job_ref = _run_aggregate_spatial_job(
-    return {"results": parse_aggregate_spatial_response(aggregated, "ndvi")}
+        ndvi.mean_time().aggregate_spatial(geometries=feature_collection, reducer="mean"),
        metric_name="ndvi",
    )
    payload_diagnostics = _log_raw_payload_summary(aggregated, metric_name="ndvi", job_ref=job_ref)
    return {
        "results": parse_aggregate_spatial_response(
            aggregated,
            "ndvi",
            job_ref=job_ref,
            expected_feature_ids=expected_feature_ids,
        ),
        "job_ref": job_ref,
        "payload_diagnostics": payload_diagnostics,
    }
-def compute_ndwi(*, connection, feature_collection, spatial_extent, temporal_start, temporal_end) -> dict[str, Any]:
+def compute_ndwi(
    *,
    connection,
    feature_collection,
    spatial_extent,
    temporal_start,
    temporal_end,
    expected_feature_ids: list[str] | None = None,
 ) -> dict[str, Any]:
    cube = connection.load_collection(
        SENTINEL2_COLLECTION,
        spatial_extent=spatial_extent,
@@ -398,11 +623,32 @@ def compute_ndwi(*, connection, feature_collection, spatial_extent, temporal_sta
    green = cube.band("B03") * 0.0001
    nir = cube.band("B08") * 0.0001
    ndwi = ((green - nir) / (green + nir)).mask(invalid_mask.resample_cube_spatial(green))
-    aggregated = ndwi.mean_time().aggregate_spatial(geometries=feature_collection, reducer="mean").execute()
+    aggregated, job_ref = _run_aggregate_spatial_job(
-    return {"results": parse_aggregate_spatial_response(aggregated, "ndwi")}
+        ndwi.mean_time().aggregate_spatial(geometries=feature_collection, reducer="mean"),
        metric_name="ndwi",
    )
    payload_diagnostics = _log_raw_payload_summary(aggregated, metric_name="ndwi", job_ref=job_ref)
    return {
        "results": parse_aggregate_spatial_response(
            aggregated,
            "ndwi",
            job_ref=job_ref,
            expected_feature_ids=expected_feature_ids,
        ),
        "job_ref": job_ref,
        "payload_diagnostics": payload_diagnostics,
    }
-def compute_lst_c(*, connection, feature_collection, spatial_extent, temporal_start, temporal_end) -> dict[str, Any]:
+def compute_lst_c(
    *,
    connection,
    feature_collection,
    spatial_extent,
    temporal_start,
    temporal_end,
    expected_feature_ids: list[str] | None = None,
 ) -> dict[str, Any]:
    cube = connection.load_collection(
        SENTINEL3_LST_COLLECTION,
        spatial_extent=spatial_extent,
@@ -411,11 +657,32 @@ def compute_lst_c(*, connection, feature_collection, spatial_extent, temporal_st
    band_name = infer_band_name(cube, preferred=("LST", "LST_in", "LST", "band_0"))
    lst_k = cube.band(band_name) if band_name else cube
    lst_c = lst_k - 273.15
-    aggregated = lst_c.mean_time().aggregate_spatial(geometries=feature_collection, reducer="mean").execute()
+    aggregated, job_ref = _run_aggregate_spatial_job(
-    return {"results": parse_aggregate_spatial_response(aggregated, "lst_c")}
+        lst_c.mean_time().aggregate_spatial(geometries=feature_collection, reducer="mean"),
        metric_name="lst_c",
    )
    payload_diagnostics = _log_raw_payload_summary(aggregated, metric_name="lst_c", job_ref=job_ref)
    return {
        "results": parse_aggregate_spatial_response(
            aggregated,
            "lst_c",
            job_ref=job_ref,
            expected_feature_ids=expected_feature_ids,
        ),
        "job_ref": job_ref,
        "payload_diagnostics": payload_diagnostics,
    }
-def compute_soil_vv(*, connection, feature_collection, spatial_extent, temporal_start, temporal_end) -> dict[str, Any]:
+def compute_soil_vv(
    *,
    connection,
    feature_collection,
    spatial_extent,
    temporal_start,
    temporal_end,
    expected_feature_ids: list[str] | None = None,
 ) -> dict[str, Any]:
    cube = connection.load_collection(
        SENTINEL1_COLLECTION,
        spatial_extent=spatial_extent,
@@ -423,46 +690,216 @@ def compute_soil_vv(*, connection, feature_collection, spatial_extent, temporal_
        bands=["VV"],
    )
    vv = cube.band("VV")
-    aggregated = vv.mean_time().aggregate_spatial(geometries=feature_collection, reducer="mean").execute()
+    aggregated, job_ref = _run_aggregate_spatial_job(
-    return {"results": parse_aggregate_spatial_response(aggregated, "soil_vv")}
+        vv.mean_time().aggregate_spatial(geometries=feature_collection, reducer="mean"),
-
+        metric_name="soil_vv",
 def compute_dem_m(*, connection, feature_collection, spatial_extent, temporal_start, temporal_end) -> dict[str, Any]:
    cube = connection.load_collection(
        COPERNICUS_DEM_COLLECTION,
        spatial_extent=spatial_extent,
        temporal_extent=[_normalize_date(temporal_start), _normalize_date(temporal_end)],
    )
-    band_name = infer_band_name(cube, preferred=("DEM", "elevation", "band_0"))
+    payload_diagnostics = _log_raw_payload_summary(aggregated, metric_name="soil_vv", job_ref=job_ref)
-    dem = cube.band(band_name) if band_name else cube
+    return {
-    aggregated = dem.aggregate_spatial(geometries=feature_collection, reducer="mean").execute()
+        "results": parse_aggregate_spatial_response(
-    return {"results": parse_aggregate_spatial_response(aggregated, "dem_m")}
+            aggregated,
            "soil_vv",
            job_ref=job_ref,
            expected_feature_ids=expected_feature_ids,
        ),
        "job_ref": job_ref,
        "payload_diagnostics": payload_diagnostics,
    }
-def compute_slope_deg(*, connection, feature_collection, spatial_extent, temporal_start, temporal_end) -> dict[str, Any]:
+def _run_aggregate_spatial_job(process: Any, *, metric_name: str) -> tuple[Any, str | None]:
-    cube = connection.load_collection(
+    title = f"crop-logic-{metric_name}"
-        COPERNICUS_DEM_COLLECTION,
+    description = f"Remote sensing aggregate_spatial execution for metric `{metric_name}`."
-        spatial_extent=spatial_extent,
+    logger.info(
-        temporal_extent=[_normalize_date(temporal_start), _normalize_date(temporal_end)],
+        "openEO process graph prepared: %s",
        _serialize_for_log(
            {
                "metric_name": metric_name,
                "title": title,
                "description": description,
                "process_graph": process.flat_graph() if hasattr(process, "flat_graph") else None,
            }
        ),
    )
-    band_name = infer_band_name(cube, preferred=("DEM", "elevation", "band_0"))
+
-    dem = cube.band(band_name) if band_name else cube
+    if hasattr(process, "create_job"):
        job = process.create_job(
            title=title,
            description=description,
            out_format="JSON",
        )
        logger.info(
            "openEO batch job created: %s",
            _serialize_for_log({"metric_name": metric_name, "job_ref": _extract_job_ref(job)}),
        )
        started_job = job.start_and_wait()
        if started_job is not None:
            job = started_job
        logger.info(
            "openEO batch job finished: %s",
            _serialize_for_log({"metric_name": metric_name, "job_ref": _extract_job_ref(job)}),
        )
        return _load_job_result_payload(job), _extract_job_ref(job)
    logger.info("openEO process uses synchronous execution fallback for metric `%s`.", metric_name)
    return process.execute(), None
 def _load_job_result_payload(job: Any) -> Any:
    results = job.get_results()
    if hasattr(results, "download_files"):
        with TemporaryDirectory(prefix="openeo-job-") as temp_dir:
            results.download_files(temp_dir)
            downloaded_files = sorted(str(path.relative_to(temp_dir)) for path in Path(temp_dir).rglob("*") if path.is_file())
            logger.info(
                "openEO batch job files downloaded: %s",
                _serialize_for_log({"job_ref": _extract_job_ref(job), "files": downloaded_files}),
            )
            payload = _load_first_json_payload(Path(temp_dir), job_ref=_extract_job_ref(job))
            if payload is not None:
                return payload
    if hasattr(results, "get_metadata"):
        metadata = results.get_metadata()
        if isinstance(metadata, dict) and metadata.get("data") is not None:
            return metadata["data"]
    raise OpenEOExecutionError(
        f"openEO batch job `{_extract_job_ref(job) or 'unknown'}` completed but no JSON result payload could be loaded."
    )
 def _load_first_json_payload(directory: Path, *, job_ref: str | None = None) -> Any | None:
    asset_payload = _load_stac_asset_payload(directory, job_ref=job_ref)
    if asset_payload is not None:
        return asset_payload
    for candidate in sorted(directory.rglob("*.json")):
        payload = _read_json_file(candidate, job_ref=job_ref)
        if payload is None:
            continue
        if _looks_like_stac_metadata_payload(payload):
            continue
        return payload
    return None
 def _load_stac_asset_payload(directory: Path, *, job_ref: str | None = None) -> Any | None:
    for candidate in sorted(directory.rglob("*.json")):
        payload = _read_json_file(candidate, job_ref=job_ref)
        if not _looks_like_stac_metadata_payload(payload):
            continue
        for asset_name, asset_path in _iter_stac_asset_paths(payload, directory):
            if asset_path.suffix.lower() != ".json":
                continue
            if not asset_path.exists():
                logger.warning(
                    "openEO STAC asset file is missing: %s",
                    _serialize_for_log(
                        {
                            "job_ref": job_ref,
                            "stac_path": str(candidate),
                            "asset_name": asset_name,
                            "asset_path": str(asset_path),
                        }
                    ),
                )
                continue
            logger.info(
                "openEO batch job selecting STAC asset payload: %s",
                _serialize_for_log(
                    {
                        "job_ref": job_ref,
                        "stac_path": str(candidate),
                        "asset_name": asset_name,
                        "asset_path": str(asset_path),
                    }
                ),
            )
            return _read_json_file(asset_path, job_ref=job_ref)
    return None
 def _iter_stac_asset_paths(payload: Any, directory: Path) -> list[tuple[str, Path]]:
    if not isinstance(payload, dict):
        return []
    assets = payload.get("assets")
    if not isinstance(assets, dict):
        return []
    resolved_paths: list[tuple[str, Path]] = []
    for asset_name, asset_details in assets.items():
        if not isinstance(asset_details, dict):
            continue
        href = asset_details.get("href")
        if not href:
            continue
        raw_path = Path(str(href))
        if raw_path.is_absolute():
            resolved = directory / raw_path.name
        else:
            resolved = directory / raw_path
        resolved_paths.append((str(asset_name), resolved))
    return resolved_paths
 def _looks_like_stac_metadata_path(path: Path) -> bool:
    name = path.name.lower()
    return name in {"item.json", "collection.json"} or name.endswith(".stac-item.json")
 def _looks_like_stac_metadata_payload(payload: Any) -> bool:
    return isinstance(payload, dict) and "assets" in payload and any(
        key in payload for key in ("stac_version", "stac_extensions", "extent", "summaries")
    )
 def _read_json_file(path: Path, *, job_ref: str | None = None) -> Any:
    raw_text = path.read_text(encoding="utf-8", errors="replace")
    if not raw_text.strip():
        logger.warning(
            "openEO batch job JSON file is empty: %s",
            _serialize_for_log({"job_ref": job_ref, "path": str(path), "preview": raw_text[:500]}),
        )
        return None
    try:
-        slope_rad = dem.slope()
+        return json.loads(raw_text)
-        slope_deg = slope_rad * (180.0 / math.pi)
+    except json.JSONDecodeError as exc:
-        aggregated = slope_deg.aggregate_spatial(geometries=feature_collection, reducer="mean").execute()
+        logger.exception(
-        return {
+            "openEO batch job JSON parsing failed: %s",
-            "results": parse_aggregate_spatial_response(aggregated, "slope_deg"),
+            _serialize_for_log(
-            "supported": True,
+                {
-        }
+                    "job_ref": job_ref,
-    except Exception:
+                    "path": str(path),
-        return {
+                    "error": str(exc),
-            "results": {feature["id"]: {"slope_deg": None} for feature in feature_collection.get("features", [])},
+                    "preview": raw_text[:1000],
            "supported": False,
                }
            ),
        )
        raise OpenEOExecutionError(
            f"Failed to parse openEO batch result file `{path.name}` for job `{job_ref or 'unknown'}`: {exc}"
        ) from exc
-def parse_aggregate_spatial_response(payload: Any, metric_name: str) -> dict[str, dict[str, Any]]:
+def _extract_job_ref(job: Any) -> str | None:
    for attribute_name in ("job_id", "id"):
        value = getattr(job, attribute_name, None)
        if value:
            return str(value)
    if hasattr(job, "describe_job"):
        metadata = job.describe_job()
        if isinstance(metadata, dict) and metadata.get("id"):
            return str(metadata["id"])
    return None
 def parse_aggregate_spatial_response(
    payload: Any,
    metric_name: str,
    *,
    job_ref: str | None = None,
    expected_feature_ids: list[str] | None = None,
 ) -> dict[str, dict[str, Any]]:
    """
    Parse different JSON shapes returned by openEO aggregate_spatial executions.
    """
@@ -476,10 +913,20 @@ def parse_aggregate_spatial_response(payload: Any, metric_name: str) -> dict[str
        return _parse_feature_collection_results(payload, metric_name)
    if isinstance(payload, dict):
-        return _parse_mapping_results(payload, metric_name)
+        return _parse_mapping_results(
            payload,
            metric_name,
            job_ref=job_ref,
            expected_feature_ids=expected_feature_ids,
        )
    if isinstance(payload, list):
-        return _parse_list_results(payload, metric_name)
+        return _parse_list_results(
            payload,
            metric_name,
            job_ref=job_ref,
            expected_feature_ids=expected_feature_ids,
        )
    raise OpenEOExecutionError(f"Unsupported openEO aggregate_spatial response type: {type(payload)!r}")
@@ -495,36 +942,174 @@ def _parse_feature_collection_results(payload: dict[str, Any], metric_name: str)
        if not feature_id:
            continue
        properties = feature.get("properties") or {}
        _log_feature_mismatch(feature_id, properties, metric_name)
        value = _extract_aggregate_value(properties)
        results[feature_id] = {metric_name: _coerce_float(value)}
    return results
-def _parse_mapping_results(payload: dict[str, Any], metric_name: str) -> dict[str, dict[str, Any]]:
+def _parse_mapping_results(
    payload: dict[str, Any],
    metric_name: str,
    *,
    job_ref: str | None = None,
    expected_feature_ids: list[str] | None = None,
 ) -> dict[str, dict[str, Any]]:
    if "data" in payload and isinstance(payload["data"], (dict, list)):
-        return parse_aggregate_spatial_response(payload["data"], metric_name)
+        return parse_aggregate_spatial_response(
            payload["data"],
            metric_name,
            job_ref=job_ref,
            expected_feature_ids=expected_feature_ids,
        )
    results: dict[str, dict[str, Any]] = {}
    for feature_id, value in payload.items():
        if feature_id in {"type", "links", "meta"}:
            continue
-        results[str(feature_id)] = {metric_name: _coerce_float(_extract_aggregate_value(value))}
+        normalized_feature_id = _normalize_feature_id(
            feature_id,
            expected_feature_ids=expected_feature_ids,
        )
        if isinstance(value, dict):
            _log_feature_mismatch(str(normalized_feature_id), value, metric_name)
        results[str(normalized_feature_id)] = {metric_name: _coerce_float(_extract_aggregate_value(value))}
    return results
-def _parse_list_results(payload: list[Any], metric_name: str) -> dict[str, dict[str, Any]]:
+def _parse_list_results(
    payload: list[Any],
    metric_name: str,
    *,
    job_ref: str | None = None,
    expected_feature_ids: list[str] | None = None,
 ) -> dict[str, dict[str, Any]]:
    results: dict[str, dict[str, Any]] = {}
    for index, item in enumerate(payload):
        if isinstance(item, dict):
-            feature_id = str(item.get("id") or item.get("cell_code") or item.get("feature_id") or index)
+            feature_id = str(
                item.get("id")
                or item.get("cell_code")
                or item.get("feature_id")
                or _normalize_feature_id(index, expected_feature_ids=expected_feature_ids)
            )
            _log_feature_mismatch(feature_id, item, metric_name)
            value = _extract_aggregate_value(item)
        else:
-            feature_id = str(index)
+            feature_id = str(_normalize_feature_id(index, expected_feature_ids=expected_feature_ids))
            value = item
        results[feature_id] = {metric_name: _coerce_float(value)}
    return results
 def _normalize_feature_id(
    raw_feature_id: Any,
    *,
    expected_feature_ids: list[str] | None = None,
 ) -> str:
    feature_id = str(raw_feature_id)
    if not expected_feature_ids:
        return feature_id
    try:
        index = int(feature_id)
    except (TypeError, ValueError):
        return feature_id
    if index < 0 or index >= len(expected_feature_ids):
        return feature_id
    return str(expected_feature_ids[index])
 def _log_raw_payload_summary(payload: Any, *, metric_name: str, job_ref: str | None = None) -> dict[str, Any]:
    payload_cells = _extract_payload_cells(payload)
    payload_keys_sample = [cell_code for cell_code, _raw in payload_cells[:5]]
    available_features = sorted(_collect_payload_feature_names(payload))
    returned_cell_count = len(payload_cells)
    is_empty = returned_cell_count == 0
    if is_empty:
        logger.warning("openEO payload is empty for job_ref=%s", job_ref)
    logger.info(
        "openEO payload summary: %s",
        _serialize_for_log(
            {
                "metric_name": metric_name,
                "job_ref": job_ref,
                "returned_cell_count": returned_cell_count,
                "payload_keys_sample": payload_keys_sample,
                "available_features": available_features,
            }
        ),
    )
    return {
        "returned_cell_count": returned_cell_count,
        "payload_keys_sample": payload_keys_sample,
        "available_features": available_features,
    }
 def _extract_payload_cells(payload: Any) -> list[tuple[str, Any]]:
    if payload is None:
        return []
    if isinstance(payload, dict) and payload.get("type") == "FeatureCollection":
        cells = []
        for feature in payload.get("features", []):
            feature_id = str(
                feature.get("id")
                or (feature.get("properties") or {}).get("cell_code")
                or (feature.get("properties") or {}).get("id")
                or ""
            )
            if feature_id:
                cells.append((feature_id, feature.get("properties") or {}))
        return cells
    if isinstance(payload, dict) and "features" in payload and isinstance(payload["features"], list):
        return _extract_payload_cells({"type": "FeatureCollection", "features": payload["features"]})
    if isinstance(payload, dict) and "data" in payload and isinstance(payload["data"], (dict, list)):
        return _extract_payload_cells(payload["data"])
    if isinstance(payload, dict):
        return [
            (str(feature_id), value)
            for feature_id, value in payload.items()
            if feature_id not in {"type", "links", "meta", "data"}
        ]
    if isinstance(payload, list):
        cells = []
        for index, item in enumerate(payload):
            if isinstance(item, dict):
                feature_id = str(item.get("id") or item.get("cell_code") or item.get("feature_id") or index)
            else:
                feature_id = str(index)
            cells.append((feature_id, item))
        return cells
    return []
 def _collect_payload_feature_names(payload: Any) -> set[str]:
    names: set[str] = set()
    for _cell_code, raw_value in _extract_payload_cells(payload):
        if isinstance(raw_value, dict):
            names.update(str(key) for key in raw_value.keys())
    return names
 def _log_feature_mismatch(cell_code: str, raw_value: dict[str, Any], metric_name: str) -> None:
    available_keys = sorted(str(key) for key in raw_value.keys())
    if not available_keys:
        return
    recognized_keys = set(CLUSTER_METRIC_NAMES) | {
        metric_name,
        "mean",
        "value",
        "result",
        "average",
        "id",
        "cell_code",
    }
    if not any(key in recognized_keys for key in available_keys):
        logger.warning("Feature mismatch for cell=%s, available_keys=%s", cell_code, available_keys)
 def _extract_aggregate_value(value: Any) -> Any:
    if isinstance(value, dict):
        for key in ("mean", "value", "result", "average"):
@@ -589,3 +1174,9 @@ def _normalize_date(value: date | str) -> str:
    if isinstance(value, date):
        return value.isoformat()
    return str(value)
 def _parse_date_value(value: date | str) -> date:
    if isinstance(value, date):
        return value
    return date.fromisoformat(str(value))
@@ -165,8 +165,6 @@ class RemoteSensingCellObservationSerializer(serializers.ModelSerializer):
            "lst_c",
            "soil_vv",
            "soil_vv_db",
            "dem_m",
            "slope_deg",
            "metadata",
        ]
@@ -177,8 +175,6 @@ class RemoteSensingSummarySerializer(serializers.Serializer):
    ndwi_mean = serializers.FloatField(allow_null=True)
    lst_c_mean = serializers.FloatField(allow_null=True)
    soil_vv_db_mean = serializers.FloatField(allow_null=True)
    dem_m_mean = serializers.FloatField(allow_null=True)
    slope_deg_mean = serializers.FloatField(allow_null=True)
 class RemoteSensingRunSerializer(serializers.ModelSerializer):
@@ -189,10 +185,12 @@ class RemoteSensingRunSerializer(serializers.ModelSerializer):
    requested_cluster_count = serializers.SerializerMethodField()
    def get_status_label(self, obj):
-        return obj.normalized_status
+        metadata_status = (obj.metadata or {}).get("status_label")
        return metadata_status or obj.normalized_status
    def get_pipeline_status(self, obj):
-        return obj.normalized_status
+        metadata_status = (obj.metadata or {}).get("status_label")
        return metadata_status or obj.normalized_status
    def get_stage(self, obj):
        return (obj.metadata or {}).get("stage")
@@ -290,6 +288,7 @@ class RemoteSensingRunStatusResponseSerializer(serializers.Serializer):
    source = serializers.CharField()
    run = RemoteSensingRunSerializer()
    task_id = serializers.UUIDField(allow_null=True, required=False)
    task = serializers.JSONField(required=False)
    location = SoilLocationResponseSerializer(required=False)
    block_code = serializers.CharField(allow_blank=True, required=False)
    chunk_size_sqm = serializers.IntegerField(allow_null=True, required=False)
@@ -42,6 +42,17 @@ else:
 logger = logging.getLogger(__name__)
 REMOTE_SENSING_TASK_MAX_RETRIES = 5
 REMOTE_SENSING_TASK_RETRY_DELAY_SECONDS = 60
 REMOTE_SENSING_TASK_RETRY_BACKOFF_MAX_SECONDS = 600
 PERSISTED_OBSERVATION_FEATURES = (
    "ndvi",
    "ndwi",
    "lst_c",
    "soil_vv",
    "soil_vv_db",
 )
 def run_remote_sensing_analysis(
    *,
@@ -122,17 +133,32 @@ def run_remote_sensing_analysis(
        )
        if not cells_to_process:
            _record_run_stage(
                run,
                "using_cached_observations",
                {"source": "database"},
            )
            observations = _load_observations(
                location=location,
                block_code=resolved_block_code,
                temporal_start=start_date,
                temporal_end=end_date,
            )
            if not _has_usable_observations(
                observations=observations,
                selected_features=selected_features or list(DEFAULT_CLUSTER_FEATURES),
            ):
                logger.warning(
                    "Cached observations are fully null, refetching remote metrics for run_id=%s",
                    run.id,
                )
                _record_run_stage(
                    run,
                    "using_cached_observations",
                    {"source": "database", "usable": False, "refetching": True},
                )
                cells_to_process = all_cells
            else:
                _record_run_stage(
                    run,
                    "using_cached_observations",
                    {"source": "database", "usable": True, "refetching": False},
                )
                subdivision_result = _ensure_subdivision_result(
                    location=location,
                    run=run,
@@ -168,12 +194,22 @@ def run_remote_sensing_analysis(
        _record_run_stage(
            run,
            "fetching_remote_metrics",
-            {"requested_cell_count": len(cells_to_process)},
+            _build_remote_metric_stage_details(
                cells=cells_to_process,
                selected_features=selected_features,
            ),
        )
        progress_callback = _build_remote_metric_progress_callback(
            run=run,
            cells=cells_to_process,
            selected_features=selected_features,
        )
        remote_payload = compute_remote_sensing_metrics(
            cells_to_process,
            temporal_start=start_date,
            temporal_end=end_date,
            selected_features=selected_features or list(DEFAULT_CLUSTER_FEATURES),
            progress_callback=progress_callback,
        )
        _record_run_stage(
            run,
@@ -242,7 +278,11 @@ def run_remote_sensing_analysis(
        raise
-@app.task(bind=True, max_retries=3, default_retry_delay=60)
+@app.task(
    bind=True,
    max_retries=REMOTE_SENSING_TASK_MAX_RETRIES,
    default_retry_delay=REMOTE_SENSING_TASK_RETRY_DELAY_SECONDS,
 )
 def run_remote_sensing_analysis_task(
    self,
    soil_location_id: int,
@@ -287,17 +327,30 @@ def run_remote_sensing_analysis_task(
        )
        raise
    except (OpenEOExecutionError, OpenEOServiceError, RequestException, DataDrivenSubdivisionError) as exc:
        retry_count = self.request.retries + 1
        countdown = min(
            REMOTE_SENSING_TASK_RETRY_DELAY_SECONDS * (2 ** self.request.retries),
            REMOTE_SENSING_TASK_RETRY_BACKOFF_MAX_SECONDS,
        )
        _mark_run_retrying(
            run_id=run_id,
            task_id=self.request.id,
            error_message=str(exc),
            retry_count=retry_count,
            retry_delay_seconds=countdown,
        )
        logger.warning(
            "Transient remote sensing failure, retrying task",
            extra={
                "task_id": self.request.id,
                "soil_location_id": soil_location_id,
                "block_code": block_code,
-                "retry_count": self.request.retries,
+                "retry_count": retry_count,
                "retry_delay_seconds": countdown,
                "error": str(exc),
            },
        )
-        raise self.retry(exc=exc)
+        raise self.retry(exc=exc, countdown=countdown)
 def _normalize_temporal_date(value: Any, field_name: str):
@@ -442,8 +495,20 @@ def _mark_run_success(
 def _mark_run_failure(run: RemoteSensingRun, error_message: str) -> None:
    metadata = dict(run.metadata or {})
    failed_stage = str(metadata.get("stage") or "").strip() or None
    stage_details = dict(metadata.get("stage_details") or {})
    metadata["status_label"] = "failed"
    metadata["stage"] = "failed"
    metadata["failed_stage"] = failed_stage
    metadata["failure_reason"] = error_message[:4000]
    metadata["stage_details"] = {
        **stage_details,
        "failed": {
            "failed_stage": failed_stage,
            "error_message": error_message[:4000],
            "failed_stage_details": stage_details.get(failed_stage, {}) if failed_stage else {},
        },
    }
    metadata["timestamps"] = {
        **dict(metadata.get("timestamps") or {}),
        "failed_at": timezone.now().isoformat(),
@@ -467,6 +532,51 @@ def _mark_run_failure(run: RemoteSensingRun, error_message: str) -> None:
    )
 def _mark_run_retrying(
    *,
    run_id: int | None,
    task_id: str,
    error_message: str,
    retry_count: int,
    retry_delay_seconds: int,
 ) -> None:
    run = None
    if run_id is not None:
        run = RemoteSensingRun.objects.filter(pk=run_id).first()
    if run is None and task_id:
        run = RemoteSensingRun.objects.filter(metadata__task_id=str(task_id)).first()
    if run is None:
        return
    metadata = dict(run.metadata or {})
    stage_details = dict(metadata.get("stage_details") or {})
    failed_stage = (
        str(metadata.get("failed_stage") or metadata.get("stage") or "").strip() or None
    )
    metadata["status_label"] = "retrying"
    metadata["stage"] = "retrying"
    metadata["failed_stage"] = failed_stage
    metadata.pop("failure_reason", None)
    metadata["stage_details"] = {
        **stage_details,
        "retrying": {
            "retry_count": retry_count,
            "retry_delay_seconds": retry_delay_seconds,
            "last_error": error_message[:4000],
            "failed_stage": failed_stage,
            "failed_stage_details": stage_details.get(failed_stage, {}) if failed_stage else {},
        },
    }
    metadata["timestamps"] = {
        **dict(metadata.get("timestamps") or {}),
        "retrying_at": timezone.now().isoformat(),
    }
    run.status = RemoteSensingRun.STATUS_RUNNING
    run.error_message = ""
    run.metadata = metadata
    run.save(update_fields=["status", "error_message", "metadata", "updated_at"])
 def _load_grid_cells(location: SoilLocation, block_code: str) -> list[AnalysisGridCell]:
    queryset = AnalysisGridCell.objects.filter(soil_location=location)
    queryset = queryset.filter(block_code=block_code or "")
@@ -513,6 +623,17 @@ def _select_cells_for_processing(
    return [cell for cell in all_cells if cell.id not in existing_ids]
 def _has_usable_observations(
    *,
    observations: list[AnalysisGridObservation],
    selected_features: list[str],
 ) -> bool:
    for observation in observations:
        if any(getattr(observation, feature_name, None) is not None for feature_name in selected_features):
            return True
    return False
 def _upsert_grid_observations(
    *,
    cells: list[AnalysisGridCell],
@@ -521,19 +642,47 @@ def _upsert_grid_observations(
    temporal_end,
    metric_payload: dict[str, Any],
 ) -> dict[str, int]:
    result_by_cell = metric_payload.get("results", {})
    payload_diagnostics = metric_payload["metadata"].get("payload_diagnostics", {})
    payload_cell_codes = sorted(str(cell_code) for cell_code in result_by_cell.keys())
    db_cell_codes = [cell.cell_code for cell in cells]
    matched_cell_codes = sorted(set(db_cell_codes) & set(payload_cell_codes))
    unmatched_db_cell_codes = sorted(set(db_cell_codes) - set(payload_cell_codes))
    unmatched_payload_cell_codes = sorted(set(payload_cell_codes) - set(db_cell_codes))
    available_features = _collect_available_features(
        result_by_cell=result_by_cell,
        payload_diagnostics=payload_diagnostics,
    )
    payload_keys_sample = payload_cell_codes[:5]
    metadata_template = {
        "backend_name": metric_payload["metadata"].get("backend"),
        "backend_url": metric_payload["metadata"].get("backend_url"),
        "collections_used": metric_payload["metadata"].get("collections_used", []),
        "slope_supported": metric_payload["metadata"].get("slope_supported", False),
        "job_refs": metric_payload["metadata"].get("job_refs", {}),
        "failed_metrics": metric_payload["metadata"].get("failed_metrics", []),
        "payload_diagnostics": payload_diagnostics,
        "run_id": run.id,
    }
-    result_by_cell = metric_payload.get("results", {})
+
    logger.info(
        "Remote sensing payload/DB cell comparison: %s",
        {
            "run_id": run.id,
            "db_cell_count": len(db_cell_codes),
            "payload_cell_count": len(payload_cell_codes),
            "matched_cell_count": len(matched_cell_codes),
            "unmatched_db_cell_codes": unmatched_db_cell_codes,
            "unmatched_payload_cell_codes": unmatched_payload_cell_codes,
        },
    )
    if not matched_cell_codes:
        logger.error("No payload cells matched DB cell_codes for run_id=%s", run.id)
    created_count = 0
    updated_count = 0
    usable_observation_count = 0
    fully_null_observation_count = 0
    with transaction.atomic():
        for cell in cells:
            values = result_by_cell.get(cell.cell_code, {})
@@ -544,10 +693,19 @@ def _upsert_grid_observations(
                "lst_c": values.get("lst_c"),
                "soil_vv": values.get("soil_vv"),
                "soil_vv_db": values.get("soil_vv_db"),
                "dem_m": values.get("dem_m"),
                "slope_deg": values.get("slope_deg"),
                "metadata": metadata_template,
            }
            persisted_values = [defaults[feature_name] for feature_name in PERSISTED_OBSERVATION_FEATURES]
            usable_values = [defaults[feature_name] for feature_name in DEFAULT_CLUSTER_FEATURES]
            if all(value is None for value in persisted_values):
                fully_null_observation_count += 1
                logger.warning(
                    "Persisting empty observation for cell=%s, run_id=%s",
                    cell.cell_code,
                    run.id,
                )
            if any(value is not None for value in usable_values):
                usable_observation_count += 1
            observation, created = AnalysisGridObservation.objects.update_or_create(
                cell=cell,
                temporal_start=temporal_start,
@@ -558,7 +716,179 @@ def _upsert_grid_observations(
                created_count += 1
            else:
                updated_count += 1
-    return {"created_count": created_count, "updated_count": updated_count}
+
    summary = {
        "created_count": created_count,
        "updated_count": updated_count,
        "total_observation_count": len(cells),
        "usable_observation_count": usable_observation_count,
        "fully_null_observation_count": fully_null_observation_count,
        "matched_cell_count": len(matched_cell_codes),
        "matched_cell_codes": matched_cell_codes,
        "unmatched_db_cell_codes": unmatched_db_cell_codes,
        "unmatched_payload_cell_codes": unmatched_payload_cell_codes,
        "payload_keys_sample": payload_keys_sample,
        "available_features": available_features,
    }
    logger.info("Grid observation upsert summary: %s", summary)
    if usable_observation_count == 0:
        diagnostics = {
            "job_ref": metadata_template["job_refs"],
            "total_cells": len(cells),
            "matched_cells": len(matched_cell_codes),
            "payload_keys_sample": payload_keys_sample,
            "available_features": available_features,
        }
        logger.error("All persisted observations are empty for run_id=%s", run.id)
        _store_empty_observation_diagnostics(run=run, diagnostics=diagnostics)
        summary["empty_observation_diagnostics"] = diagnostics
    return summary
 def _collect_available_features(
    *,
    result_by_cell: dict[str, dict[str, Any]],
    payload_diagnostics: dict[str, Any],
 ) -> list[str]:
    available = {
        feature_name
        for values in result_by_cell.values()
        for feature_name, value in (values or {}).items()
        if value is not None
    }
    for metric_diagnostics in payload_diagnostics.values():
        available.update(metric_diagnostics.get("available_features", []))
    return sorted(str(feature_name) for feature_name in available)
 def _store_empty_observation_diagnostics(*, run: RemoteSensingRun, diagnostics: dict[str, Any]) -> None:
    metadata = dict(run.metadata or {})
    metadata["diagnostics"] = {
        **dict(metadata.get("diagnostics") or {}),
        "empty_observations": diagnostics,
    }
    run.metadata = metadata
    run.save(update_fields=["metadata", "updated_at"])
 def _build_remote_metric_stage_details(
    *,
    cells: list[AnalysisGridCell],
    selected_features: list[str] | None,
    active_metric: str | None = None,
    completed_metrics: list[str] | None = None,
    failed_metrics: list[dict[str, Any]] | None = None,
    metric_states: list[dict[str, Any]] | None = None,
 ) -> dict[str, Any]:
    features = list(selected_features or DEFAULT_CLUSTER_FEATURES)
    completed = list(completed_metrics or [])
    failed = list(failed_metrics or [])
    states = metric_states or [
        {
            "metric": metric_name,
            "status": (
                "completed"
                if metric_name in completed
                else "failed"
                if any(item.get("metric") == metric_name for item in failed)
                else "running"
                if metric_name == active_metric
                else "pending"
            ),
        }
        for metric_name in features
    ]
    return {
        "requested_cell_count": len(cells),
        "target_cells": [
            {
                "cell_code": cell.cell_code,
                "block_code": cell.block_code,
                "centroid_lat": str(cell.centroid_lat),
                "centroid_lon": str(cell.centroid_lon),
                "chunk_size_sqm": cell.chunk_size_sqm,
            }
            for cell in cells
        ],
        "metric_progress": {
            "total_metrics": len(features),
            "completed_metric_count": len(completed),
            "active_metric": active_metric,
            "completed_metrics": completed,
            "failed_metrics": failed,
            "states": states,
        },
    }
 def _normalize_progress_metric_name(metric_name: str, features: list[str]) -> str:
    derived_metric_map = {
        "soil_vv": "soil_vv_db",
    }
    normalized = derived_metric_map.get(metric_name, metric_name)
    if normalized in features:
        return normalized
    return metric_name
 def _resolve_progress_job_ref(candidate: str, job_refs: dict[str, Any]) -> Any:
    if candidate in job_refs:
        return job_refs.get(candidate)
    source_metric_map = {
        "soil_vv_db": "soil_vv",
    }
    return job_refs.get(source_metric_map.get(candidate, candidate))
 def _build_remote_metric_progress_callback(
    *,
    run: RemoteSensingRun,
    cells: list[AnalysisGridCell],
    selected_features: list[str] | None,
 ):
    features = list(selected_features or DEFAULT_CLUSTER_FEATURES)
    completed_metrics: list[str] = []
    failed_metrics: list[dict[str, Any]] = []
    def callback(*, metric_name: str, state: str, metadata: dict[str, Any], metric_payload=None, error: str = "") -> None:
        progress_metric_name = _normalize_progress_metric_name(metric_name, features)
        if state == "completed" and progress_metric_name not in completed_metrics:
            completed_metrics.append(progress_metric_name)
        if state == "failed":
            failed_entry = {"metric": progress_metric_name, "error": error}
            if not any(
                item.get("metric") == progress_metric_name and item.get("error") == error
                for item in failed_metrics
            ):
                failed_metrics.append(failed_entry)
        stage_details = _build_remote_metric_stage_details(
            cells=cells,
            selected_features=features,
            active_metric=progress_metric_name if state == "running" else None,
            completed_metrics=completed_metrics,
            failed_metrics=failed_metrics,
            metric_states=[
                {
                    "metric": candidate,
                    "status": (
                        "completed"
                        if candidate in completed_metrics
                        else "failed"
                        if any(item.get("metric") == candidate for item in failed_metrics)
                        else "running"
                        if candidate == progress_metric_name and state == "running"
                        else "pending"
                    ),
                    "job_ref": _resolve_progress_job_ref(candidate, metadata.get("job_refs", {})),
                }
                for candidate in features
            ],
        )
        _record_run_stage(run, "fetching_remote_metrics", stage_details)
    return callback
 def _ensure_subdivision_result(
@@ -2,7 +2,11 @@ from datetime import date
 from django.test import TestCase
-from location_data.data_driven_subdivision import sync_block_subdivision_with_result
+from location_data.data_driven_subdivision import (
    EmptyObservationDatasetError,
    build_clustering_dataset,
    sync_block_subdivision_with_result,
 )
 from location_data.models import (
    AnalysisGridCell,
    AnalysisGridObservation,
@@ -133,3 +137,39 @@ class DataDrivenSubdivisionSyncTests(TestCase):
            self.subdivision.metadata["data_driven_subdivision"]["cluster_count"],
            2,
        )
    def test_build_clustering_dataset_raises_clear_error_when_all_selected_features_are_null(self):
        cell = AnalysisGridCell.objects.create(
            soil_location=self.location,
            block_subdivision=self.subdivision,
            block_code="block-1",
            cell_code="cell-null",
            chunk_size_sqm=900,
            geometry=self.boundary,
            centroid_lat="35.689200",
            centroid_lon="51.389200",
        )
        observation = AnalysisGridObservation.objects.create(
            cell=cell,
            run=self.run,
            temporal_start=date(2025, 1, 1),
            temporal_end=date(2025, 1, 31),
            metadata={"job_refs": {"ndvi": "job-1"}},
        )
        with self.assertLogs("location_data.data_driven_subdivision", level="ERROR") as captured:
            with self.assertRaisesRegex(
                EmptyObservationDatasetError,
                "Upstream processing completed but no usable feature values were persisted.",
            ):
                build_clustering_dataset(
                    observations=[observation],
                    selected_features=["ndvi", "ndwi", "lst_c", "soil_vv_db"],
                    run=self.run,
                    location=self.location,
                )
        joined = "\n".join(captured.output)
        self.assertIn("No usable observations available for clustering", joined)
        self.assertIn('"run_id": {}'.format(self.run.id), joined)
        self.assertIn('"region_id": {}'.format(self.location.id), joined)
@@ -1,15 +1,25 @@
 from decimal import Decimal
 from io import StringIO
 import os
 from pathlib import Path
 from tempfile import TemporaryDirectory
 from unittest.mock import Mock, patch
 from django.core.management import call_command
 from django.test import SimpleTestCase
 import requests
 from config.proxy import resolve_requests_proxy_url
 from location_data.openeo_service import (
    OpenEOConnectionSettings,
    OpenEOServiceError,
    OpenEOExecutionError,
    _log_raw_payload_summary,
    _load_first_json_payload,
    _resolve_openeo_proxy_url_from_env,
    _run_aggregate_spatial_job,
    log_openeo_request_summary,
    build_openeo_requests_session,
    build_empty_metric_payload,
    connect_openeo,
    is_openeo_auth_configured,
@@ -53,6 +63,49 @@ class OpenEOServiceParsingTests(SimpleTestCase):
        self.assertEqual(result["cell-1"]["lst_c"], 12.4)
        self.assertEqual(result["cell-2"]["lst_c"], 15.1)
    def test_parse_mapping_results_maps_numeric_keys_to_expected_feature_ids(self):
        payload = {
            "0": {"mean": 12.4},
            "1": {"mean": 15.1},
        }
        result = parse_aggregate_spatial_response(
            payload,
            "lst_c",
            expected_feature_ids=["cell-1", "cell-2"],
        )
        self.assertEqual(result["cell-1"]["lst_c"], 12.4)
        self.assertEqual(result["cell-2"]["lst_c"], 15.1)
    def test_parse_list_results_maps_positional_payload_to_expected_feature_ids(self):
        payload = [{"mean": 0.61}, {"mean": 0.47}]
        result = parse_aggregate_spatial_response(
            payload,
            "ndvi",
            expected_feature_ids=["cell-1", "cell-2"],
        )
        self.assertEqual(result["cell-1"]["ndvi"], 0.61)
        self.assertEqual(result["cell-2"]["ndvi"], 0.47)
    def test_log_raw_payload_summary_warns_for_empty_payload(self):
        with self.assertLogs("location_data.openeo_service", level="WARNING") as captured:
            summary = _log_raw_payload_summary({}, metric_name="ndvi", job_ref="job-1")
        self.assertEqual(summary["returned_cell_count"], 0)
        self.assertIn("openEO payload is empty for job_ref=job-1", "\n".join(captured.output))
    def test_parse_logs_feature_mismatch_for_unexpected_keys(self):
        payload = {"cell-1": {"foo": 12.4}}
        with self.assertLogs("location_data.openeo_service", level="WARNING") as captured:
            result = parse_aggregate_spatial_response(payload, "lst_c", job_ref="job-2")
        self.assertEqual(result["cell-1"]["lst_c"], 12.4)
        self.assertIn("Feature mismatch for cell=cell-1, available_keys=['foo']", "\n".join(captured.output))
    def test_linear_to_db(self):
        self.assertEqual(linear_to_db(10.0), 10.0)
        self.assertEqual(linear_to_db(Decimal("1.0")), 0.0)
@@ -74,8 +127,32 @@ class OpenEOServiceParsingTests(SimpleTestCase):
        self.assertEqual(target["cell-2"]["ndwi"], 0.2)
        self.assertIn("soil_vv_db", target["cell-2"])
    def test_log_openeo_request_summary_logs_expected_fields(self):
        cell = Mock()
        cell.cell_code = "cell-1"
        with self.assertLogs("location_data.openeo_service", level="INFO") as captured:
            log_openeo_request_summary(
                cells=[cell],
                temporal_start="2026-04-08",
                temporal_end="2026-05-08",
                spatial_extent={"west": 49.9995, "south": 49.9995, "east": 50.0005, "north": 50.0005},
                selected_features=["ndvi", "ndwi"],
            )
        joined = "\n".join(captured.output)
        self.assertIn("openEO request summary", joined)
        self.assertIn('"cell_count": 1', joined)
        self.assertIn('"date_range_days": 31', joined)
        self.assertIn('"metrics": ["ndvi", "ndwi"]', joined)
 class OpenEOConnectionTests(SimpleTestCase):
    def test_default_openeo_timeout_is_ten_minutes(self):
        with patch.dict(os.environ, {}, clear=True):
            settings = OpenEOConnectionSettings.from_env()
        self.assertEqual(settings.timeout_seconds, 600.0)
    def test_default_openeo_proxy_url_uses_proxychains_endpoint_without_wrapping_process(self):
        with patch.dict(
            os.environ,
@@ -140,22 +217,154 @@ class OpenEOConnectionTests(SimpleTestCase):
    def test_connect_openeo_applies_proxy_to_session(self):
        connection = Mock()
-        connection.authenticate_oidc_resource_owner_password_credentials.return_value = connection
+        connection._get_oidc_provider.return_value = ("provider-1", None)
        connection.get.return_value.json.return_value = {
            "providers": [
                {
                    "id": "provider-1",
                    "title": "Provider 1",
                    "issuer": "https://issuer.example.com",
                    "scopes": ["openid"],
                    "default_clients": [],
                }
            ]
        }
        openeo_module = Mock()
        openeo_module.connect.return_value = connection
        oidc_module = Mock()
        oidc_module.OidcClientCredentialsAuthenticator.return_value = Mock()
        oidc_module.OidcClientInfo.side_effect = lambda **kwargs: kwargs
        oidc_module.OidcProviderInfo.side_effect = lambda **kwargs: kwargs
        oidc_module.OidcResourceOwnerPasswordAuthenticator.return_value = Mock()
        connection._authenticate_oidc.return_value = connection
        settings = OpenEOConnectionSettings(
            backend_url="https://openeofed.dataspace.copernicus.eu",
            auth_method="password",
            timeout_seconds=123,
            client_id="client-id",
            username="user@example.com",
            password="secret",
            proxy_url="socks5h://127.0.0.1:10808",
        )
-        with patch.dict("sys.modules", {"openeo": openeo_module}):
+        with patch.dict(
            "sys.modules",
            {
                "openeo": openeo_module,
                "openeo.rest": Mock(),
                "openeo.rest.auth": Mock(),
                "openeo.rest.auth.oidc": oidc_module,
            },
        ):
            connect_openeo(settings)
        self.assertEqual(openeo_module.connect.call_args.kwargs["default_timeout"], 123)
        session = openeo_module.connect.call_args.kwargs["session"]
        self.assertEqual(session.proxies["https"], "socks5h://127.0.0.1:10808")
        self.assertFalse(session.trust_env)
    def test_timeout_override_session_logs_request_payload_before_dispatch(self):
        response = Mock(status_code=200, headers={"Content-Type": "application/json"})
        response.text = "{}"
        response.url = "https://openeofed.dataspace.copernicus.eu/result"
        with patch.object(requests.Session, "request", return_value=response) as request_mock:
            with self.assertLogs("location_data.openeo_service", level="INFO") as captured:
                session = build_openeo_requests_session(OpenEOConnectionSettings(proxy_url="socks5h://127.0.0.1:10808"))
                session.request(
                    "post",
                    "https://openeofed.dataspace.copernicus.eu/result",
                    json={"process": {"foo": "bar"}},
                    headers={"Authorization": "Bearer secret"},
                )
        request_mock.assert_called_once()
        self.assertTrue(any("openEO request payload" in line for line in captured.output))
        self.assertTrue(any("***redacted***" in line for line in captured.output))
    def test_connect_openeo_raises_clear_error_for_html_capabilities_response(self):
        settings = OpenEOConnectionSettings(
            backend_url="https://openeofed.dataspace.copernicus.eu",
            timeout_seconds=600,
        )
        bad_response = Mock()
        bad_response.url = "https://openeofed.dataspace.copernicus.eu/"
        bad_response.headers = {"Content-Type": "text/html"}
        bad_response.text = "<html>proxy page</html>"
        session = build_openeo_requests_session(settings)
        session.last_response_url = bad_response.url
        session.last_response_content_type = "text/html"
        session.last_response_preview = bad_response.text
        openeo_module = Mock()
        openeo_module.connect.side_effect = requests.exceptions.JSONDecodeError("Expecting value", "", 0)
        oidc_module = Mock()
        with patch("location_data.openeo_service.build_openeo_requests_session", return_value=session):
            with patch.dict(
                "sys.modules",
                {
                    "openeo": openeo_module,
                    "openeo.rest": Mock(),
                    "openeo.rest.auth": Mock(),
                    "openeo.rest.auth.oidc": oidc_module,
                },
            ):
                with self.assertRaisesRegex(OpenEOServiceError, "non-JSON response"):
                    connect_openeo(settings)
    def test_build_openeo_requests_session_mounts_retrying_adapters(self):
        session = build_openeo_requests_session(
            OpenEOConnectionSettings(
                timeout_seconds=120,
                http_retry_total=5,
                http_retry_backoff_factor=2.0,
            )
        )
        https_adapter = session.get_adapter("https://openeofed.dataspace.copernicus.eu")
        self.assertEqual(https_adapter.max_retries.total, 5)
        self.assertEqual(https_adapter.max_retries.connect, 5)
        self.assertEqual(https_adapter.max_retries.backoff_factor, 2.0)
        self.assertIsNone(https_adapter.max_retries.allowed_methods)
    def test_run_aggregate_spatial_job_prefers_batch_job_results(self):
        process = Mock()
        job = Mock(job_id="job-123")
        process.create_job.return_value = job
        job.start_and_wait.return_value = job
        results = Mock()
        job.get_results.return_value = results
        def write_json(target_dir):
            Path(target_dir, "result.json").write_text('{"cell-1": {"mean": 0.5}}', encoding="utf-8")
        results.download_files.side_effect = write_json
        payload, job_ref = _run_aggregate_spatial_job(process, metric_name="ndvi")
        self.assertEqual(payload, {"cell-1": {"mean": 0.5}})
        self.assertEqual(job_ref, "job-123")
        process.execute.assert_not_called()
    def test_load_first_json_payload_prefers_stac_asset_data_over_metadata(self):
        with TemporaryDirectory() as temp_dir:
            Path(temp_dir, "item.json").write_text(
                (
                    '{"stac_version":"1.0.0","assets":{"timeseries.json":{"href":"timeseries.json"}},'
                    '"extent":{"spatial":{},"temporal":{}}}'
                ),
                encoding="utf-8",
            )
            Path(temp_dir, "timeseries.json").write_text('{"cell-1": {"mean": 0.5}}', encoding="utf-8")
            payload = _load_first_json_payload(Path(temp_dir), job_ref="job-asset")
        self.assertEqual(payload, {"cell-1": {"mean": 0.5}})
    def test_load_first_json_payload_raises_clear_error_for_invalid_json(self):
        with TemporaryDirectory() as temp_dir:
            Path(temp_dir, "result.json").write_text("not-json", encoding="utf-8")
            with self.assertRaises(OpenEOExecutionError):
                with self.assertLogs("location_data.openeo_service", level="ERROR"):
                    _load_first_json_payload(Path(temp_dir), job_ref="job-123")
@@ -201,6 +201,210 @@ class RemoteSensingApiTests(TestCase):
        self.assertEqual(payload["run"]["stage"], "completed")
        self.assertEqual(payload["run"]["selected_features"], ["ndvi"])
    @patch("location_data.views._get_remote_sensing_async_result")
    def test_run_status_endpoint_returns_detailed_task_progress(self, mock_async_result):
        mock_async_result.return_value = SimpleNamespace(
            state="STARTED",
            result=None,
            info={"message": "fetching_remote_metrics"},
            ready=lambda: False,
            successful=lambda: False,
            failed=lambda: False,
        )
        task_id = "e723ba3e-c53c-401b-b3a0-5f7013c7b401"
        run = RemoteSensingRun.objects.create(
            soil_location=self.location,
            block_subdivision=self.subdivision,
            block_code="",
            chunk_size_sqm=900,
            temporal_start=self.temporal_start,
            temporal_end=self.temporal_end,
            status=RemoteSensingRun.STATUS_RUNNING,
            metadata={
                "task_id": task_id,
                "stage": "fetching_remote_metrics",
                "selected_features": ["ndvi", "ndwi"],
                "timestamps": {
                    "queued_at": "2026-05-10T08:00:00Z",
                    "started_at": "2026-05-10T08:00:03Z",
                    "fetching_remote_metrics_at": "2026-05-10T08:00:12Z",
                },
                "stage_details": {
                    "fetching_remote_metrics": {
                        "requested_cell_count": 2,
                        "metric_progress": {
                            "total_metrics": 2,
                            "completed_metric_count": 1,
                            "active_metric": "ndwi",
                            "completed_metrics": ["ndvi"],
                            "failed_metrics": [],
                            "states": [
                                {"metric": "ndvi", "status": "completed"},
                                {"metric": "ndwi", "status": "running"},
                            ],
                        },
                    }
                },
            },
        )
        response = self.client.get(f"/remote-sensing/runs/{task_id}/status/")
        self.assertEqual(response.status_code, 200)
        payload = response.json()["data"]
        self.assertEqual(payload["status"], "running")
        self.assertEqual(payload["task"]["current_stage"], "fetching_remote_metrics")
        self.assertEqual(payload["task"]["metric_progress"]["active_metric"], "ndwi")
        self.assertEqual(payload["task"]["stages"][-1]["status"], "running")
        self.assertEqual(payload["task"]["celery"]["state"], "STARTED")
        self.assertEqual(payload["task"]["celery"]["info"]["message"], "fetching_remote_metrics")
        self.assertEqual(payload["run"]["id"], run.id)
    @patch("location_data.views._get_remote_sensing_async_result")
    def test_run_status_endpoint_returns_retrying_status_when_celery_is_retrying(self, mock_async_result):
        mock_async_result.return_value = SimpleNamespace(
            state="RETRY",
            result="temporary openEO timeout",
            info="temporary openEO timeout",
            ready=lambda: False,
            successful=lambda: False,
            failed=lambda: False,
        )
        task_id = "e723ba3e-c53c-401b-b3a0-5f7013c7b401"
        run = RemoteSensingRun.objects.create(
            soil_location=self.location,
            block_subdivision=self.subdivision,
            block_code="",
            chunk_size_sqm=900,
            temporal_start=self.temporal_start,
            temporal_end=self.temporal_end,
            status=RemoteSensingRun.STATUS_RUNNING,
            metadata={
                "task_id": task_id,
                "stage": "retrying",
                "status_label": "retrying",
                "failed_stage": "observations_persisted",
                "timestamps": {
                    "failed_at": "2026-05-10T08:10:00Z",
                    "retrying_at": "2026-05-10T08:11:00Z",
                },
                "stage_details": {
                    "retrying": {
                        "retry_count": 2,
                        "retry_delay_seconds": 120,
                        "last_error": "temporary openEO timeout",
                        "failed_stage": "observations_persisted",
                        "failed_stage_details": {"created_count": 12, "updated_count": 0},
                    }
                },
            },
        )
        response = self.client.get(f"/remote-sensing/runs/{task_id}/status/")
        self.assertEqual(response.status_code, 200)
        payload = response.json()["data"]
        self.assertEqual(payload["status"], "retrying")
        self.assertEqual(payload["run"]["pipeline_status"], "retrying")
        self.assertEqual(payload["task"]["current_stage"], "retrying")
        self.assertEqual(payload["task"]["retry"]["retry_count"], 2)
        self.assertEqual(payload["task"]["last_error"], "temporary openEO timeout")
        self.assertNotIn("failure_reason", payload["task"])
        self.assertEqual(payload["task"]["celery"]["state"], "RETRY")
    @patch("location_data.views._get_remote_sensing_async_result")
    def test_run_status_endpoint_overrides_stale_failed_db_state_when_celery_is_retrying(self, mock_async_result):
        mock_async_result.return_value = SimpleNamespace(
            state="RETRY",
            result="temporary openEO timeout",
            info="temporary openEO timeout",
            ready=lambda: False,
            successful=lambda: False,
            failed=lambda: False,
        )
        task_id = "e723ba3e-c53c-401b-b3a0-5f7013c7b401"
        run = RemoteSensingRun.objects.create(
            soil_location=self.location,
            block_subdivision=self.subdivision,
            block_code="",
            chunk_size_sqm=900,
            temporal_start=self.temporal_start,
            temporal_end=self.temporal_end,
            status=RemoteSensingRun.STATUS_FAILURE,
            error_message="temporary openEO timeout",
            metadata={
                "task_id": task_id,
                "stage": "failed",
                "status_label": "failed",
                "failed_stage": "observations_persisted",
                "failure_reason": "temporary openEO timeout",
                "timestamps": {"failed_at": "2026-05-10T08:10:00Z"},
                "stage_details": {
                    "failed": {
                        "failed_stage": "observations_persisted",
                        "error_message": "temporary openEO timeout",
                        "failed_stage_details": {"created_count": 12, "updated_count": 0},
                    }
                },
            },
        )
        response = self.client.get(f"/remote-sensing/runs/{task_id}/status/")
        self.assertEqual(response.status_code, 200)
        payload = response.json()["data"]
        self.assertEqual(payload["status"], "retrying")
        self.assertEqual(payload["run"]["status"], RemoteSensingRun.STATUS_FAILURE)
        self.assertEqual(payload["run"]["status_label"], "retrying")
        self.assertEqual(payload["run"]["pipeline_status"], "retrying")
        self.assertEqual(payload["run"]["stage"], "retrying")
        self.assertEqual(payload["task"]["current_stage"], "retrying")
        self.assertEqual(payload["task"]["retry"]["failed_stage"], "observations_persisted")
        self.assertEqual(payload["task"]["stages"][-1]["name"], "retrying")
        self.assertEqual(payload["task"]["stages"][-1]["status"], "running")
        self.assertNotIn("failure_reason", payload["task"])
        self.assertEqual(payload["task"]["celery"]["state"], "RETRY")
        self.assertEqual(payload["run"]["id"], run.id)
    def test_run_status_endpoint_returns_failed_task_details(self):
        task_id = "e723ba3e-c53c-401b-b3a0-5f7013c7b401"
        run = RemoteSensingRun.objects.create(
            soil_location=self.location,
            block_subdivision=self.subdivision,
            block_code="",
            chunk_size_sqm=900,
            temporal_start=self.temporal_start,
            temporal_end=self.temporal_end,
            status=RemoteSensingRun.STATUS_FAILURE,
            error_message="openEO timeout",
            metadata={
                "task_id": task_id,
                "stage": "failed",
                "failed_stage": "observations_persisted",
                "failure_reason": "openEO timeout",
                "timestamps": {"failed_at": "2026-05-10T08:10:00Z"},
                "stage_details": {
                    "failed": {
                        "failed_stage": "observations_persisted",
                        "error_message": "openEO timeout",
                        "failed_stage_details": {"created_count": 12, "updated_count": 0},
                    }
                },
            },
        )
        response = self.client.get(f"/remote-sensing/runs/{task_id}/status/")
        self.assertEqual(response.status_code, 200)
        payload = response.json()["data"]
        self.assertEqual(payload["status"], "failed")
        self.assertEqual(payload["task"]["current_stage"], "failed")
        self.assertEqual(payload["task"]["failed_stage"], "observations_persisted")
        self.assertEqual(payload["task"]["failure_reason"], "openEO timeout")
        self.assertEqual(payload["task"]["current_stage_details"]["failed_stage"], "observations_persisted")
        self.assertEqual(payload["task"]["stages"][-1]["status"], "failed")
        self.assertEqual(payload["run"]["id"], run.id)
    def test_run_result_endpoint_returns_paginated_assignments(self):
        run = RemoteSensingRun.objects.create(
            soil_location=self.location,
@@ -0,0 +1,183 @@
 from datetime import date
 from unittest.mock import Mock, patch
 from django.test import TestCase
 from location_data.models import AnalysisGridCell, AnalysisGridObservation, RemoteSensingRun, SoilLocation
 from location_data.tasks import _upsert_grid_observations, run_remote_sensing_analysis
 class RemoteSensingTaskDiagnosticsTests(TestCase):
    def setUp(self):
        self.boundary = {
            "type": "Polygon",
            "coordinates": [
                [
                    [51.3890, 35.6890],
                    [51.3900, 35.6890],
                    [51.3900, 35.6900],
                    [51.3890, 35.6900],
                    [51.3890, 35.6890],
                ]
            ],
        }
        self.location = SoilLocation.objects.create(
            latitude="35.689200",
            longitude="51.389000",
            farm_boundary=self.boundary,
        )
        self.run = RemoteSensingRun.objects.create(
            soil_location=self.location,
            block_code="",
            chunk_size_sqm=900,
            temporal_start=date(2026, 4, 9),
            temporal_end=date(2026, 5, 9),
            status=RemoteSensingRun.STATUS_PENDING,
            metadata={},
        )
        self.cell = AnalysisGridCell.objects.create(
            soil_location=self.location,
            block_code="",
            cell_code="cell-1",
            chunk_size_sqm=900,
            geometry=self.boundary,
            centroid_lat="35.689200",
            centroid_lon="51.389200",
        )
    def test_upsert_logs_and_stores_diagnostics_for_empty_observations(self):
        metric_payload = {
            "results": {},
            "metadata": {
                "backend": "openeo",
                "backend_url": "https://openeofed.dataspace.copernicus.eu",
                "collections_used": ["SENTINEL2_L2A"],
                "job_refs": {"ndvi": "job-1"},
                "failed_metrics": [],
                "payload_diagnostics": {
                    "ndvi": {
                        "returned_cell_count": 0,
                        "payload_keys_sample": [],
                        "available_features": ["mean"],
                    }
                },
            },
        }
        with self.assertLogs("location_data.tasks", level="WARNING") as captured:
            summary = _upsert_grid_observations(
                cells=[self.cell],
                run=self.run,
                temporal_start=date(2026, 4, 9),
                temporal_end=date(2026, 5, 9),
                metric_payload=metric_payload,
            )
        log_output = "\n".join(captured.output)
        self.assertIn("Persisting empty observation for cell=cell-1, run_id=", log_output)
        self.assertIn("No payload cells matched DB cell_codes for run_id=", log_output)
        self.assertIn("All persisted observations are empty for run_id=", log_output)
        self.assertEqual(summary["total_observation_count"], 1)
        self.assertEqual(summary["usable_observation_count"], 0)
        self.assertEqual(summary["fully_null_observation_count"], 1)
        self.assertEqual(summary["matched_cell_count"], 0)
        self.assertEqual(summary["payload_keys_sample"], [])
        self.assertEqual(summary["available_features"], ["mean"])
        observation = AnalysisGridObservation.objects.get(cell=self.cell)
        self.assertIsNone(observation.ndvi)
        self.assertIsNone(observation.ndwi)
        self.assertIsNone(observation.lst_c)
        self.assertIsNone(observation.soil_vv)
        self.assertIsNone(observation.soil_vv_db)
        self.run.refresh_from_db()
        diagnostics = self.run.metadata["diagnostics"]["empty_observations"]
        self.assertEqual(diagnostics["job_ref"], {"ndvi": "job-1"})
        self.assertEqual(diagnostics["total_cells"], 1)
        self.assertEqual(diagnostics["matched_cells"], 0)
        self.assertEqual(diagnostics["payload_keys_sample"], [])
        self.assertEqual(diagnostics["available_features"], ["mean"])
    def test_run_remote_sensing_analysis_refetches_when_cached_observations_are_empty(self):
        AnalysisGridObservation.objects.create(
            cell=self.cell,
            run=self.run,
            temporal_start=date(2026, 4, 9),
            temporal_end=date(2026, 5, 9),
            metadata={},
        )
        subdivision_result = Mock(
            id=99,
            cluster_count=1,
            selected_features=["ndvi", "ndwi", "lst_c", "soil_vv_db"],
            metadata={"used_cell_count": 1, "skipped_cell_count": 0, "kmeans_params": {}},
            skipped_cell_codes=[],
        )
        remote_payload = {
            "results": {
                "cell-1": {
                    "ndvi": 0.52,
                    "ndwi": 0.21,
                    "lst_c": None,
                    "soil_vv": 10.0,
                    "soil_vv_db": 10.0,
                }
            },
            "metadata": {
                "backend": "openeo",
                "backend_url": "https://openeofed.dataspace.copernicus.eu",
                "collections_used": ["SENTINEL2_L2A", "SENTINEL1_GRD"],
                "job_refs": {"ndvi": "job-1"},
                "failed_metrics": [],
                "payload_diagnostics": {
                    "ndvi": {
                        "returned_cell_count": 1,
                        "payload_keys_sample": ["0"],
                        "available_features": ["mean"],
                    }
                },
            },
        }
        with patch(
            "location_data.tasks.create_or_get_analysis_grid_cells",
            return_value={
                "created": False,
                "block_code": "",
                "total_count": 1,
                "created_count": 0,
                "chunk_size_sqm": 900,
                "existing_count": 1,
            },
        ), patch(
            "location_data.tasks.compute_remote_sensing_metrics",
            return_value=remote_payload,
        ) as compute_mock, patch(
            "location_data.tasks._ensure_subdivision_result",
            return_value=subdivision_result,
        ):
            summary = run_remote_sensing_analysis(
                soil_location_id=self.location.id,
                block_code="",
                temporal_start=date(2026, 4, 9),
                temporal_end=date(2026, 5, 9),
                run_id=self.run.id,
            )
        compute_mock.assert_called_once()
        self.assertEqual(summary["source"], "openeo")
        self.assertEqual(summary["processed_cell_count"], 1)
        observation = AnalysisGridObservation.objects.get(cell=self.cell)
        self.assertEqual(observation.ndvi, 0.52)
        self.assertEqual(observation.ndwi, 0.21)
        self.assertEqual(observation.soil_vv, 10.0)
        self.assertEqual(observation.soil_vv_db, 10.0)
        self.run.refresh_from_db()
        cached_details = self.run.metadata["stage_details"]["using_cached_observations"]
        self.assertEqual(cached_details["source"], "database")
        self.assertFalse(cached_details["usable"])
        self.assertTrue(cached_details["refetching"])
@@ -1,4 +1,5 @@
 from datetime import timedelta
 from typing import Any
 from django.apps import apps
 from django.core.paginator import EmptyPage, Paginator
@@ -49,6 +50,21 @@ from .serializers import (
 from .tasks import run_remote_sensing_analysis_task
 MAX_REMOTE_SENSING_PAGE_SIZE = 200
 REMOTE_SENSING_RUN_STAGE_ORDER = (
    "queued",
    "running",
    "preparing_analysis_grid",
    "analysis_grid_ready",
    "analysis_cells_selected",
    "using_cached_observations",
    "fetching_remote_metrics",
    "remote_metrics_fetched",
    "observations_persisted",
    "clustering_completed",
    "completed",
    "failed",
    "retrying",
 )
 SoilLocationPayloadSerializer = inline_serializer(
    name="SoilLocationPayloadSerializer",
@@ -636,13 +652,19 @@ class RemoteSensingRunStatusView(APIView):
 def _build_remote_sensing_run_status_payload(run: RemoteSensingRun, *, page: int, page_size: int) -> dict:
    run_data = RemoteSensingRunSerializer(run).data
    task_id = (run.metadata or {}).get("task_id")
-    if run.status in {RemoteSensingRun.STATUS_PENDING, RemoteSensingRun.STATUS_RUNNING}:
+    task_data = _build_remote_sensing_task_payload(run)
-        return {
+    effective_status = _apply_live_retry_state_override(run_data, task_data)
-            "status": run_data["status_label"],
+    status_payload = {
        "status": effective_status or run_data["status_label"],
        "source": "database",
        "run": run_data,
        "task_id": task_id,
        "task": task_data,
    }
    if run.status in {RemoteSensingRun.STATUS_PENDING, RemoteSensingRun.STATUS_RUNNING}:
        return status_payload
    if run.status == RemoteSensingRun.STATUS_FAILURE:
        return status_payload
    location = _get_location_by_lat_lon(run.soil_location.latitude, run.soil_location.longitude, prefetch=True)
    observations = _get_remote_sensing_observations(
@@ -654,10 +676,7 @@ def _build_remote_sensing_run_status_payload(run: RemoteSensingRun, *, page: int
    subdivision_result = getattr(run, "subdivision_result", None)
    response_payload = {
-        "status": run_data["status_label"],
+        **status_payload,
        "source": "database",
        "run": run_data,
        "task_id": task_id,
        "location": SoilLocationResponseSerializer(location).data,
        "block_code": run.block_code,
        "chunk_size_sqm": run.chunk_size_sqm,
@@ -705,6 +724,180 @@ def _build_remote_sensing_run_status_payload(run: RemoteSensingRun, *, page: int
    return response_payload
 def _get_remote_sensing_async_result(task_id: str):
    try:
        from celery.result import AsyncResult
    except ImportError:  # pragma: no cover - fallback when Celery is absent
        return None
    try:
        return AsyncResult(task_id)
    except Exception:  # pragma: no cover - depends on Celery backend configuration
        return None
 def _serialize_task_value(value):
    if value is None or isinstance(value, (str, int, float, bool)):
        return value
    if isinstance(value, dict):
        return {str(key): _serialize_task_value(item) for key, item in value.items()}
    if isinstance(value, (list, tuple)):
        return [_serialize_task_value(item) for item in value]
    return str(value)
 def _build_remote_sensing_task_payload(run: RemoteSensingRun) -> dict:
    metadata = dict(run.metadata or {})
    timestamps = dict(metadata.get("timestamps") or {})
    stage_details = dict(metadata.get("stage_details") or {})
    current_stage = metadata.get("stage")
    failed_stage = metadata.get("failed_stage")
    task_payload = {
        "current_stage": current_stage,
        "current_stage_details": stage_details.get(current_stage, {}),
        "timestamps": timestamps,
        "stages": _build_remote_sensing_stage_entries(
            current_stage=current_stage,
            stage_details=stage_details,
            timestamps=timestamps,
            run_status=run.status,
        ),
    }
    if failed_stage:
        task_payload["failed_stage"] = failed_stage
    metric_progress = (stage_details.get("fetching_remote_metrics") or {}).get("metric_progress")
    if metric_progress:
        task_payload["metric_progress"] = metric_progress
    retry_context = stage_details.get("retrying")
    if retry_context:
        task_payload["retry"] = retry_context
        task_payload["last_error"] = retry_context.get("last_error")
    failure_reason = None
    if metadata.get("stage") == "failed" or run.status == RemoteSensingRun.STATUS_FAILURE:
        failure_reason = metadata.get("failure_reason") or run.error_message
    if failure_reason:
        task_payload["failure_reason"] = failure_reason
    task_id = metadata.get("task_id")
    celery_payload = _build_remote_sensing_celery_payload(str(task_id)) if task_id else None
    if celery_payload is not None:
        task_payload["celery"] = celery_payload
    return task_payload
 def _apply_live_retry_state_override(run_data: dict[str, Any], task_data: dict[str, Any]) -> str | None:
    celery_payload = task_data.get("celery") or {}
    if celery_payload.get("state") != "RETRY":
        return None
    retry_context = dict(task_data.get("retry") or {})
    if not retry_context:
        retry_context = {
            "retry_count": None,
            "retry_delay_seconds": None,
            "last_error": task_data.get("failure_reason") or celery_payload.get("info"),
            "failed_stage": task_data.get("failed_stage"),
            "failed_stage_details": (
                task_data.get("current_stage_details", {})
                if task_data.get("current_stage") == "failed"
                else {}
            ),
        }
    task_data["retry"] = retry_context
    task_data["last_error"] = retry_context.get("last_error") or celery_payload.get("info")
    task_data["current_stage"] = "retrying"
    task_data["current_stage_details"] = retry_context
    task_data.pop("failure_reason", None)
    _upsert_retrying_stage_entry(task_data, retry_context)
    run_data["status_label"] = "retrying"
    run_data["pipeline_status"] = "retrying"
    run_data["stage"] = "retrying"
    return "retrying"
 def _upsert_retrying_stage_entry(task_data: dict[str, Any], retry_context: dict[str, Any]) -> None:
    stages = list(task_data.get("stages") or [])
    retrying_entry = {
        "name": "retrying",
        "status": "running",
        "entered_at": (task_data.get("timestamps") or {}).get("retrying_at"),
        "details": retry_context,
    }
    for index, entry in enumerate(stages):
        if entry.get("name") == "retrying":
            stages[index] = retrying_entry
            task_data["stages"] = stages
            return
    stages.append(retrying_entry)
    task_data["stages"] = stages
 def _build_remote_sensing_stage_entries(
    *,
    current_stage: str | None,
    stage_details: dict,
    timestamps: dict,
    run_status: str,
 ) -> list[dict]:
    stage_names = []
    for stage_name in REMOTE_SENSING_RUN_STAGE_ORDER:
        if stage_name == current_stage or stage_name in stage_details or f"{stage_name}_at" in timestamps:
            stage_names.append(stage_name)
    if current_stage and current_stage not in stage_names:
        stage_names.append(current_stage)
    entries = []
    for stage_name in stage_names:
        if run_status == RemoteSensingRun.STATUS_FAILURE and stage_name == current_stage:
            stage_status = "failed"
        elif stage_name == current_stage and run_status == RemoteSensingRun.STATUS_PENDING:
            stage_status = "pending"
        elif stage_name == current_stage and run_status == RemoteSensingRun.STATUS_RUNNING:
            stage_status = "running"
        else:
            stage_status = "completed"
        entries.append(
            {
                "name": stage_name,
                "status": stage_status,
                "entered_at": timestamps.get(f"{stage_name}_at"),
                "details": stage_details.get(stage_name, {}),
            }
        )
    return entries
 def _build_remote_sensing_celery_payload(task_id: str) -> dict | None:
    async_result = _get_remote_sensing_async_result(task_id)
    if async_result is None:
        return None
    try:
        payload = {
            "state": str(async_result.state),
            "ready": bool(async_result.ready()),
            "successful": bool(async_result.successful()) if async_result.ready() else False,
            "failed": bool(async_result.failed()) if async_result.ready() else False,
        }
    except Exception:  # pragma: no cover - depends on Celery backend configuration
        return None
    info = getattr(async_result, "info", None)
    if info not in (None, {}):
        payload["info"] = _serialize_task_value(info)
    if async_result.failed():
        payload["error"] = _serialize_task_value(async_result.result)
    return payload
 def _get_location_by_lat_lon(lat, lon, *, prefetch: bool = False):
    lat_rounded = round(lat, 6)
    lon_rounded = round(lon, 6)
@@ -894,8 +1087,6 @@ def _build_remote_sensing_summary(observations):
        ndwi_mean=Avg("ndwi"),
        lst_c_mean=Avg("lst_c"),
        soil_vv_db_mean=Avg("soil_vv_db"),
        dem_m_mean=Avg("dem_m"),
        slope_deg_mean=Avg("slope_deg"),
    )
    summary = {
        "cell_count": observations.count(),
@@ -903,8 +1094,6 @@ def _build_remote_sensing_summary(observations):
        "ndwi_mean": _round_or_none(aggregates.get("ndwi_mean")),
        "lst_c_mean": _round_or_none(aggregates.get("lst_c_mean")),
        "soil_vv_db_mean": _round_or_none(aggregates.get("soil_vv_db_mean")),
        "dem_m_mean": _round_or_none(aggregates.get("dem_m_mean")),
        "slope_deg_mean": _round_or_none(aggregates.get("slope_deg_mean")),
    }
    return summary
@@ -916,8 +1105,6 @@ def _empty_remote_sensing_summary():
        "ndwi_mean": None,
        "lst_c_mean": None,
        "soil_vv_db_mean": None,
        "dem_m_mean": None,
        "slope_deg_mean": None,
    }
@@ -1 +0,0 @@
 2026-04-07 07:48:04,983 [INFO] django.utils.autoreload: Watching for file changes with StatReloader
		`@@ -1 +0,0 @@`
			`2026-04-07 07:48:04,983 [INFO] django.utils.autoreload: Watching for file changes with StatReloader`