diff --git a/tests/test_search_aggregations.py b/tests/test_search_aggregations.py
index 152ea9e965d95d05eaa1cbf088b5ea9038b7dbbb..6eb328795a8b48aee5c9d19641621bd656b0c576 100644
--- a/tests/test_search_aggregations.py
+++ b/tests/test_search_aggregations.py
@@ -176,309 +176,581 @@ class TestApps:
)
db.execute("COMMIT")
- def test_search_with_global_attributes(self, client, db):
- response = client.get("/search/a?climatic_zone_year2016=WarmTemperateDry&htap_region_tier1_year2010=HTAPTier1SAF")
+ def test_search_single(self, client, db):
+ response = client.get("/search/a?id=2")
expected_status_code = 200
assert response.status_code == expected_status_code
- expected_resp = [{'id': 2, 'label': 'CMA', 'order': 1,
- 'sampling_frequency': 'hourly', 'aggregation': 'mean', 'data_origin_type': 'measurement',
- 'data_start_date': '2003-09-07T15:30:00+00:00', 'data_end_date': '2016-12-31T14:30:00+00:00', 'coverage': -1.0,
- 'data_origin': 'instrument', 'sampling_height': 7.0,
- 'provider_version': 'N/A',
- 'doi': '',
- 'additional_metadata': {'absorption_cross_section': 'Hearn 1961',
- 'measurement_method': 'uv_abs',
- 'original_units': {'since_19740101000000': 'nmol/mol'},
- 'ebas_metadata_19740101000000_29y': {'Submitter': 'Unknown, Lady, lady.unknown@unknown.com, some long division name, SHORT, , 111 Streetname, , zipcode, Boulder, CO, USA',
- 'Data level': '2',
- 'Frameworks': 'GAW-WDCRG NOAA-ESRL',
- 'Station code': 'XXX',
- 'Station name': 'Secret' } },
- 'roles': [{'id': 1, 'role': 'resource provider', 'status': 'active',
- 'contact': {'id': 5, 'organisation': {'id': 2, 'name': 'FZJ', 'longname': 'Forschungszentrum Jülich',
- 'kind': 'research', 'city': 'Jülich', 'postcode': '52425', 'street_address': 'Wilhelm-Johnen-Straße',
- 'country': 'Germany', 'homepage': 'https://www.fz-juelich.de', 'contact_url': 'mailto:toar-data@fz-juelich.de'}}}],
- 'variable': {'name': 'o3', 'longname': 'ozone', 'displayname': 'Ozone',
- 'cf_standardname': 'mole_fraction_of_ozone_in_air', 'units': 'nmol mol-1',
- 'chemical_formula': 'O3', 'id': 5},
- 'station': {'id': 3, 'codes': ['China_test8'], 'name': 'Test_China',
- 'coordinates': {'alt': 1534.0, 'lat': 36.256, 'lng': 117.106},
- 'coordinate_validation_status': 'not checked',
- 'country': 'China', 'state': 'Shandong Sheng',
- 'type': 'unknown', 'type_of_area': 'unknown',
- 'timezone': 'Asia/Shanghai', 'additional_metadata': {},
- 'roles': [], 'annotations': [],
- 'aux_images': [], 'aux_docs': [], 'aux_urls': [],
- 'globalmeta': {'climatic_zone_year2016': '6 (warm temperate dry)',
- 'distance_to_major_road_year2020': -999.0,
- 'dominant_ecoregion_year2017': '-1 (undefined)',
- 'dominant_landcover_year2012': '10 (Cropland, rainfed)',
- 'ecoregion_description_25km_year2017': '',
- 'htap_region_tier1_year2010': '10 (SAF Sub Saharan/sub Sahel Africa)',
- 'landcover_description_25km_year2012': '',
- 'max_stable_nightlights_25km_year1992': -999.0,
- 'max_stable_nightlights_25km_year2013': -999.0,
- 'max_population_density_25km_year1990': -1.0,
- 'max_population_density_25km_year2015': -1.0,
- 'max_topography_srtm_relative_alt_5km_year1994': -999.0,
- 'mean_stable_nightlights_1km_year2013': -999.0,
- 'mean_stable_nightlights_5km_year2013': -999.0,
- 'mean_nox_emissions_10km_year2000': -999.0,
- 'mean_nox_emissions_10km_year2015': -999.0,
- 'mean_population_density_250m_year1990': -1.0,
- 'mean_population_density_250m_year2015': -1.0,
- 'mean_population_density_5km_year1990': -1.0,
- 'mean_population_density_5km_year2015': -1.0,
- 'mean_topography_srtm_alt_1km_year1994': -999.0,
- 'mean_topography_srtm_alt_90m_year1994': -999.0,
- 'min_topography_srtm_relative_alt_5km_year1994': -999.0,
- 'stddev_topography_srtm_relative_alt_5km_year1994': -999.0,
- 'toar1_category': 'unclassified'},
- 'changelog': [{'datetime': '2023-08-15T21:16:20.596545+00:00',
- 'description': 'station created',
- 'old_value': '',
- 'new_value': '',
- 'station_id': 3,
- 'author_id': 1,
- 'type_of_change': 'created'
- }]},
- 'programme': {'id': 0, 'name': '', 'longname': '', 'homepage': '', 'description': ''}}]
+ expected_resp = [
+ {
+ "id": 2,
+ "label": "CMA",
+ "order": 1,
+ "sampling_frequency": "hourly",
+ "aggregation": "mean",
+ "data_origin_type": "measurement",
+ "data_start_date": "2003-09-07T15:30:00+00:00",
+ "data_end_date": "2016-12-31T14:30:00+00:00",
+ "coverage": -1.0,
+ "data_origin": "instrument",
+ "sampling_height": 7.0,
+ "provider_version": "N/A",
+ "doi": "",
+ "additional_metadata": {
+ "absorption_cross_section": "Hearn 1961",
+ "measurement_method": "uv_abs",
+ "original_units": {"since_19740101000000": "nmol/mol"},
+ "ebas_metadata_19740101000000_29y": {
+ "Submitter": "Unknown, Lady, lady.unknown@unknown.com, some long division name, SHORT, , 111 Streetname, , zipcode, Boulder, CO, USA",
+ "Data level": "2",
+ "Frameworks": "GAW-WDCRG NOAA-ESRL",
+ "Station code": "XXX",
+ "Station name": "Secret",
+ },
+ },
+ "roles": [
+ {
+ "id": 1,
+ "role": "resource provider",
+ "status": "active",
+ "contact": {
+ "id": 5,
+ "organisation": {
+ "id": 2,
+ "name": "FZJ",
+ "longname": "Forschungszentrum Jülich",
+ "kind": "research",
+ "city": "Jülich",
+ "postcode": "52425",
+ "street_address": "Wilhelm-Johnen-Straße",
+ "country": "Germany",
+ "homepage": "https://www.fz-juelich.de",
+ "contact_url": "mailto:toar-data@fz-juelich.de",
+ },
+ },
+ }
+ ],
+ "variable": {
+ "name": "o3",
+ "longname": "ozone",
+ "displayname": "Ozone",
+ "cf_standardname": "mole_fraction_of_ozone_in_air",
+ "units": "nmol mol-1",
+ "chemical_formula": "O3",
+ "id": 5,
+ },
+ "station": {
+ "id": 3,
+ "codes": ["China_test8"],
+ "name": "Test_China",
+ "coordinates": {"alt": 1534.0, "lat": 36.256, "lng": 117.106},
+ "coordinate_validation_status": "not checked",
+ "country": "China",
+ "state": "Shandong Sheng",
+ "type": "unknown",
+ "type_of_area": "unknown",
+ "timezone": "Asia/Shanghai",
+ "additional_metadata": {},
+ "roles": [],
+ "annotations": [],
+ "aux_images": [],
+ "aux_docs": [],
+ "aux_urls": [],
+ "globalmeta": {
+ "climatic_zone_year2016": "6 (warm temperate dry)",
+ "distance_to_major_road_year2020": -999.0,
+ "dominant_ecoregion_year2017": "-1 (undefined)",
+ "dominant_landcover_year2012": "10 (Cropland, rainfed)",
+ "ecoregion_description_25km_year2017": "",
+ "htap_region_tier1_year2010": "10 (SAF Sub Saharan/sub Sahel Africa)",
+ "landcover_description_25km_year2012": "",
+ "max_stable_nightlights_25km_year1992": -999.0,
+ "max_stable_nightlights_25km_year2013": -999.0,
+ "max_population_density_25km_year1990": -1.0,
+ "max_population_density_25km_year2015": -1.0,
+ "max_topography_srtm_relative_alt_5km_year1994": -999.0,
+ "mean_stable_nightlights_1km_year2013": -999.0,
+ "mean_stable_nightlights_5km_year2013": -999.0,
+ "mean_nox_emissions_10km_year2000": -999.0,
+ "mean_nox_emissions_10km_year2015": -999.0,
+ "mean_population_density_250m_year1990": -1.0,
+ "mean_population_density_250m_year2015": -1.0,
+ "mean_population_density_5km_year1990": -1.0,
+ "mean_population_density_5km_year2015": -1.0,
+ "mean_topography_srtm_alt_1km_year1994": -999.0,
+ "mean_topography_srtm_alt_90m_year1994": -999.0,
+ "min_topography_srtm_relative_alt_5km_year1994": -999.0,
+ "stddev_topography_srtm_relative_alt_5km_year1994": -999.0,
+ "toar1_category": "unclassified",
+ },
+ "changelog": [
+ {
+ "datetime": "2023-08-15T21:16:20.596545+00:00",
+ "description": "station created",
+ "old_value": "",
+ "new_value": "",
+ "station_id": 3,
+ "author_id": 1,
+ "type_of_change": "created",
+ }
+ ],
+ },
+ "programme": {"id": 0, "name": "", "longname": "", "homepage": "", "description": ""},
+ }
+ ]
assert response.json() == expected_resp
- def test_search_with_global_attributes2(self, client, db):
- response = client.get("/search/a?climatic_zone_year2016=WarmTemperateDry&htap_region_tier1_year2010=HTAPTier1SAF+dominant_landcover_year2012=CroplandRainfed&dominant_ecoregion_year2017=Undefined")
+ def test_search_plus(self, client, db):
+ response = client.get("/search/a?id=1+id=2")
expected_status_code = 200
assert response.status_code == expected_status_code
expected_resp = [
- {
- "id": 2,
- "label": "CMA",
- "order": 1,
- "sampling_frequency": "hourly",
- "aggregation": "mean",
- "data_origin_type": "measurement",
- "data_start_date": "2003-09-07T15:30:00+00:00",
- "data_end_date": "2016-12-31T14:30:00+00:00",
- "coverage": -1.0,
- "data_origin": "instrument",
- "sampling_height": 7.0,
- "provider_version": "N/A",
- "doi": "",
- "additional_metadata": {
- "absorption_cross_section": "Hearn 1961",
- "measurement_method": "uv_abs",
- "original_units": {"since_19740101000000": "nmol/mol"},
- "ebas_metadata_19740101000000_29y": {
- "Submitter": "Unknown, Lady, lady.unknown@unknown.com, some long division name, SHORT, , 111 Streetname, , zipcode, Boulder, CO, USA",
- "Data level": "2",
- "Frameworks": "GAW-WDCRG NOAA-ESRL",
- "Station code": "XXX",
- "Station name": "Secret",
+ {
+ "id": 1,
+ "label": "CMA",
+ "order": 1,
+ "sampling_frequency": "hourly",
+ "aggregation": "mean",
+ "data_start_date": "2003-09-07T15:30:00+00:00",
+ "data_end_date": "2016-12-31T14:30:00+00:00",
+ "data_origin": "instrument",
+ "data_origin_type": "measurement",
+ "provider_version": "N/A",
+ "sampling_height": 7.0,
+ "additional_metadata": {},
+ "doi": "",
+ "coverage": -1.0,
+ "station": {
+ "id": 2,
+ "codes": ["SDZ54421"],
+ "name": "Shangdianzi",
+ "coordinates": {"lat": 40.65, "lng": 117.106, "alt": 293.9},
+ "coordinate_validation_status": "not checked",
+ "country": "China",
+ "state": "Beijing Shi",
+ "type": "unknown",
+ "type_of_area": "unknown",
+ "timezone": "Asia/Shanghai",
+ "additional_metadata": {"dummy_info": "Here is some more information about the station"},
+ "roles": [],
+ "annotations": [],
+ "aux_images": [],
+ "aux_docs": [],
+ "aux_urls": [],
+ "globalmeta": {
+ "mean_topography_srtm_alt_90m_year1994": -999.0,
+ "mean_topography_srtm_alt_1km_year1994": -999.0,
+ "max_topography_srtm_relative_alt_5km_year1994": -999.0,
+ "min_topography_srtm_relative_alt_5km_year1994": -999.0,
+ "stddev_topography_srtm_relative_alt_5km_year1994": -999.0,
+ "climatic_zone_year2016": "6 (warm temperate dry)",
+ "htap_region_tier1_year2010": "11 (MDE Middle East: S. Arabia, Oman, etc, Iran, Iraq)",
+ "dominant_landcover_year2012": "11 (Cropland, rainfed, herbaceous cover)",
+ "landcover_description_25km_year2012": "",
+ "dominant_ecoregion_year2017": "-1 (undefined)",
+ "ecoregion_description_25km_year2017": "",
+ "distance_to_major_road_year2020": -999.0,
+ "mean_stable_nightlights_1km_year2013": -999.0,
+ "mean_stable_nightlights_5km_year2013": -999.0,
+ "max_stable_nightlights_25km_year2013": -999.0,
+ "max_stable_nightlights_25km_year1992": -999.0,
+ "mean_population_density_250m_year2015": -1.0,
+ "mean_population_density_5km_year2015": -1.0,
+ "max_population_density_25km_year2015": -1.0,
+ "mean_population_density_250m_year1990": -1.0,
+ "mean_population_density_5km_year1990": -1.0,
+ "max_population_density_25km_year1990": -1.0,
+ "mean_nox_emissions_10km_year2015": -999.0,
+ "mean_nox_emissions_10km_year2000": -999.0,
+ "toar1_category": "unclassified",
+ },
+ "changelog": [
+ {
+ "datetime": "2023-07-15T19:27:09.463245+00:00",
+ "description": "station created",
+ "old_value": "",
+ "new_value": "",
+ "station_id": 2,
+ "author_id": 1,
+ "type_of_change": "created",
+ }
+ ],
},
- },
- "roles": [
- {
- "id": 1,
- "role": "resource provider",
- "status": "active",
- "contact": {
- "id": 5,
- "organisation": {
- "id": 2,
- "name": "FZJ",
- "longname": "Forschungszentrum Jülich",
- "kind": "research",
- "city": "Jülich",
- "postcode": "52425",
- "street_address": "Wilhelm-Johnen-Straße",
- "country": "Germany",
- "homepage": "https://www.fz-juelich.de",
- "contact_url": "mailto:toar-data@fz-juelich.de",
+ "variable": {
+ "name": "toluene",
+ "longname": "toluene",
+ "displayname": "Toluene",
+ "cf_standardname": "mole_fraction_of_toluene_in_air",
+ "units": "nmol mol-1",
+ "chemical_formula": "C7H8",
+ "id": 7,
+ },
+ "programme": {"id": 0, "name": "", "longname": "", "homepage": "", "description": ""},
+ "roles": [
+ {
+ "id": 2,
+ "role": "resource provider",
+ "status": "active",
+ "contact": {
+ "id": 4,
+ "organisation": {
+ "id": 1,
+ "name": "UBA",
+ "longname": "Umweltbundesamt",
+ "kind": "government",
+ "city": "Dessau-Roßlau",
+ "postcode": "06844",
+ "street_address": "Wörlitzer Platz 1",
+ "country": "Germany",
+ "homepage": "https://www.umweltbundesamt.de",
+ "contact_url": "mailto:immission@uba.de",
+ },
},
- },
- }
- ],
- "variable": {
- "name": "o3",
- "longname": "ozone",
- "displayname": "Ozone",
- "cf_standardname": "mole_fraction_of_ozone_in_air",
- "units": "nmol mol-1",
- "chemical_formula": "O3",
- "id": 5,
+ }
+ ],
},
- "station": {
- "id": 3,
- "codes": ["China_test8"],
- "name": "Test_China",
- "coordinates": {"alt": 1534.0, "lat": 36.256, "lng": 117.106},
- "coordinate_validation_status": "not checked",
- "country": "China",
- "state": "Shandong Sheng",
- "type": "unknown",
- "type_of_area": "unknown",
- "timezone": "Asia/Shanghai",
- "additional_metadata": {},
- "roles": [],
- "annotations": [],
- "aux_images": [],
- "aux_docs": [],
- "aux_urls": [],
- "globalmeta": {
- "climatic_zone_year2016": "6 (warm temperate dry)",
- "distance_to_major_road_year2020": -999.0,
- "dominant_ecoregion_year2017": "-1 (undefined)",
- "dominant_landcover_year2012": "10 (Cropland, rainfed)",
- "ecoregion_description_25km_year2017": "",
- "htap_region_tier1_year2010": "10 (SAF Sub Saharan/sub Sahel Africa)",
- "landcover_description_25km_year2012": "",
- "max_stable_nightlights_25km_year1992": -999.0,
- "max_stable_nightlights_25km_year2013": -999.0,
- "max_population_density_25km_year1990": -1.0,
- "max_population_density_25km_year2015": -1.0,
- "max_topography_srtm_relative_alt_5km_year1994": -999.0,
- "mean_stable_nightlights_1km_year2013": -999.0,
- "mean_stable_nightlights_5km_year2013": -999.0,
- "mean_nox_emissions_10km_year2000": -999.0,
- "mean_nox_emissions_10km_year2015": -999.0,
- "mean_population_density_250m_year1990": -1.0,
- "mean_population_density_250m_year2015": -1.0,
- "mean_population_density_5km_year1990": -1.0,
- "mean_population_density_5km_year2015": -1.0,
- "mean_topography_srtm_alt_1km_year1994": -999.0,
- "mean_topography_srtm_alt_90m_year1994": -999.0,
- "min_topography_srtm_relative_alt_5km_year1994": -999.0,
- "stddev_topography_srtm_relative_alt_5km_year1994": -999.0,
- "toar1_category": "unclassified",
+ {
+ "id": 2,
+ "label": "CMA",
+ "order": 1,
+ "sampling_frequency": "hourly",
+ "aggregation": "mean",
+ "data_start_date": "2003-09-07T15:30:00+00:00",
+ "data_end_date": "2016-12-31T14:30:00+00:00",
+ "data_origin": "instrument",
+ "data_origin_type": "measurement",
+ "provider_version": "N/A",
+ "sampling_height": 7.0,
+ "additional_metadata": {
+ "original_units": {"since_19740101000000": "nmol/mol"},
+ "measurement_method": "uv_abs",
+ "absorption_cross_section": "Hearn 1961",
+ "ebas_metadata_19740101000000_29y": {
+ "Submitter": "Unknown, Lady, lady.unknown@unknown.com, some long division name, SHORT, , 111 Streetname, , zipcode, Boulder, CO, USA",
+ "Data level": "2",
+ "Frameworks": "GAW-WDCRG NOAA-ESRL",
+ "Station code": "XXX",
+ "Station name": "Secret",
+ },
},
- "changelog": [
+ "doi": "",
+ "coverage": -1.0,
+ "station": {
+ "id": 3,
+ "codes": ["China_test8"],
+ "name": "Test_China",
+ "coordinates": {"lat": 36.256, "lng": 117.106, "alt": 1534.0},
+ "coordinate_validation_status": "not checked",
+ "country": "China",
+ "state": "Shandong Sheng",
+ "type": "unknown",
+ "type_of_area": "unknown",
+ "timezone": "Asia/Shanghai",
+ "additional_metadata": {},
+ "roles": [],
+ "annotations": [],
+ "aux_images": [],
+ "aux_docs": [],
+ "aux_urls": [],
+ "globalmeta": {
+ "mean_topography_srtm_alt_90m_year1994": -999.0,
+ "mean_topography_srtm_alt_1km_year1994": -999.0,
+ "max_topography_srtm_relative_alt_5km_year1994": -999.0,
+ "min_topography_srtm_relative_alt_5km_year1994": -999.0,
+ "stddev_topography_srtm_relative_alt_5km_year1994": -999.0,
+ "climatic_zone_year2016": "6 (warm temperate dry)",
+ "htap_region_tier1_year2010": "10 (SAF Sub Saharan/sub Sahel Africa)",
+ "dominant_landcover_year2012": "10 (Cropland, rainfed)",
+ "landcover_description_25km_year2012": "",
+ "dominant_ecoregion_year2017": "-1 (undefined)",
+ "ecoregion_description_25km_year2017": "",
+ "distance_to_major_road_year2020": -999.0,
+ "mean_stable_nightlights_1km_year2013": -999.0,
+ "mean_stable_nightlights_5km_year2013": -999.0,
+ "max_stable_nightlights_25km_year2013": -999.0,
+ "max_stable_nightlights_25km_year1992": -999.0,
+ "mean_population_density_250m_year2015": -1.0,
+ "mean_population_density_5km_year2015": -1.0,
+ "max_population_density_25km_year2015": -1.0,
+ "mean_population_density_250m_year1990": -1.0,
+ "mean_population_density_5km_year1990": -1.0,
+ "max_population_density_25km_year1990": -1.0,
+ "mean_nox_emissions_10km_year2015": -999.0,
+ "mean_nox_emissions_10km_year2000": -999.0,
+ "toar1_category": "unclassified",
+ },
+ "changelog": [
+ {
+ "datetime": "2023-08-15T21:16:20.596545+00:00",
+ "description": "station created",
+ "old_value": "",
+ "new_value": "",
+ "station_id": 3,
+ "author_id": 1,
+ "type_of_change": "created",
+ }
+ ],
+ },
+ "variable": {
+ "name": "o3",
+ "longname": "ozone",
+ "displayname": "Ozone",
+ "cf_standardname": "mole_fraction_of_ozone_in_air",
+ "units": "nmol mol-1",
+ "chemical_formula": "O3",
+ "id": 5,
+ },
+ "programme": {"id": 0, "name": "", "longname": "", "homepage": "", "description": ""},
+ "roles": [
{
- "datetime": "2023-08-15T21:16:20.596545+00:00",
- "description": "station created",
- "old_value": "",
- "new_value": "",
- "station_id": 3,
- "author_id": 1,
- "type_of_change": "created",
+ "id": 1,
+ "role": "resource provider",
+ "status": "active",
+ "contact": {
+ "id": 5,
+ "organisation": {
+ "id": 2,
+ "name": "FZJ",
+ "longname": "Forschungszentrum Jülich",
+ "kind": "research",
+ "city": "Jülich",
+ "postcode": "52425",
+ "street_address": "Wilhelm-Johnen-Straße",
+ "country": "Germany",
+ "homepage": "https://www.fz-juelich.de",
+ "contact_url": "mailto:toar-data@fz-juelich.de",
+ },
+ },
}
],
},
- "programme": {"id": 0, "name": "", "longname": "", "homepage": "", "description": ""},
- },
- {
- "id": 2,
- "label": "CMA",
- "order": 1,
- "sampling_frequency": "hourly",
- "aggregation": "mean",
- "data_origin_type": "measurement",
- "data_start_date": "2003-09-07T15:30:00+00:00",
- "data_end_date": "2016-12-31T14:30:00+00:00",
- "coverage": -1.0,
- "data_origin": "instrument",
- "sampling_height": 7.0,
- "provider_version": "N/A",
- "doi": "",
- "additional_metadata": {
- "absorption_cross_section": "Hearn 1961",
- "measurement_method": "uv_abs",
- "original_units": {"since_19740101000000": "nmol/mol"},
- "ebas_metadata_19740101000000_29y": {
- "Submitter": "Unknown, Lady, lady.unknown@unknown.com, some long division name, SHORT, , 111 Streetname, , zipcode, Boulder, CO, USA",
- "Data level": "2",
- "Frameworks": "GAW-WDCRG NOAA-ESRL",
- "Station code": "XXX",
- "Station name": "Secret",
+ ]
+ assert response.json() == expected_resp
+
+ def test_search_minus(self, client, db):
+ response = client.get("/search/a?id=2-id=2")
+ expected_status_code = 200
+ assert response.status_code == expected_status_code
+ expected_resp = []
+ assert response.json() == expected_resp
+
+ def test_search_distinct(self, client, db):
+ response = client.get("/search/a?id=1+id=1+id=1+id=1")
+ expected_status_code = 200
+ assert response.status_code == expected_status_code
+ expected_resp = [
+ {
+ "id": 1,
+ "label": "CMA",
+ "order": 1,
+ "sampling_frequency": "hourly",
+ "aggregation": "mean",
+ "data_start_date": "2003-09-07T15:30:00+00:00",
+ "data_end_date": "2016-12-31T14:30:00+00:00",
+ "data_origin": "instrument",
+ "data_origin_type": "measurement",
+ "provider_version": "N/A",
+ "sampling_height": 7.0,
+ "additional_metadata": {},
+ "doi": "",
+ "coverage": -1.0,
+ "station": {
+ "id": 2,
+ "codes": ["SDZ54421"],
+ "name": "Shangdianzi",
+ "coordinates": {"lat": 40.65, "lng": 117.106, "alt": 293.9},
+ "coordinate_validation_status": "not checked",
+ "country": "China",
+ "state": "Beijing Shi",
+ "type": "unknown",
+ "type_of_area": "unknown",
+ "timezone": "Asia/Shanghai",
+ "additional_metadata": {"dummy_info": "Here is some more information about the station"},
+ "roles": [],
+ "annotations": [],
+ "aux_images": [],
+ "aux_docs": [],
+ "aux_urls": [],
+ "globalmeta": {
+ "mean_topography_srtm_alt_90m_year1994": -999.0,
+ "mean_topography_srtm_alt_1km_year1994": -999.0,
+ "max_topography_srtm_relative_alt_5km_year1994": -999.0,
+ "min_topography_srtm_relative_alt_5km_year1994": -999.0,
+ "stddev_topography_srtm_relative_alt_5km_year1994": -999.0,
+ "climatic_zone_year2016": "6 (warm temperate dry)",
+ "htap_region_tier1_year2010": "11 (MDE Middle East: S. Arabia, Oman, etc, Iran, Iraq)",
+ "dominant_landcover_year2012": "11 (Cropland, rainfed, herbaceous cover)",
+ "landcover_description_25km_year2012": "",
+ "dominant_ecoregion_year2017": "-1 (undefined)",
+ "ecoregion_description_25km_year2017": "",
+ "distance_to_major_road_year2020": -999.0,
+ "mean_stable_nightlights_1km_year2013": -999.0,
+ "mean_stable_nightlights_5km_year2013": -999.0,
+ "max_stable_nightlights_25km_year2013": -999.0,
+ "max_stable_nightlights_25km_year1992": -999.0,
+ "mean_population_density_250m_year2015": -1.0,
+ "mean_population_density_5km_year2015": -1.0,
+ "max_population_density_25km_year2015": -1.0,
+ "mean_population_density_250m_year1990": -1.0,
+ "mean_population_density_5km_year1990": -1.0,
+ "max_population_density_25km_year1990": -1.0,
+ "mean_nox_emissions_10km_year2015": -999.0,
+ "mean_nox_emissions_10km_year2000": -999.0,
+ "toar1_category": "unclassified",
+ },
+ "changelog": [
+ {
+ "datetime": "2023-07-15T19:27:09.463245+00:00",
+ "description": "station created",
+ "old_value": "",
+ "new_value": "",
+ "station_id": 2,
+ "author_id": 1,
+ "type_of_change": "created",
+ }
+ ],
},
- },
- "roles": [
- {
- "id": 1,
- "role": "resource provider",
- "status": "active",
- "contact": {
- "id": 5,
- "organisation": {
- "id": 2,
- "name": "FZJ",
- "longname": "Forschungszentrum Jülich",
- "kind": "research",
- "city": "Jülich",
- "postcode": "52425",
- "street_address": "Wilhelm-Johnen-Straße",
- "country": "Germany",
- "homepage": "https://www.fz-juelich.de",
- "contact_url": "mailto:toar-data@fz-juelich.de",
+ "variable": {
+ "name": "toluene",
+ "longname": "toluene",
+ "displayname": "Toluene",
+ "cf_standardname": "mole_fraction_of_toluene_in_air",
+ "units": "nmol mol-1",
+ "chemical_formula": "C7H8",
+ "id": 7,
+ },
+ "programme": {"id": 0, "name": "", "longname": "", "homepage": "", "description": ""},
+ "roles": [
+ {
+ "id": 2,
+ "role": "resource provider",
+ "status": "active",
+ "contact": {
+ "id": 4,
+ "organisation": {
+ "id": 1,
+ "name": "UBA",
+ "longname": "Umweltbundesamt",
+ "kind": "government",
+ "city": "Dessau-Roßlau",
+ "postcode": "06844",
+ "street_address": "Wörlitzer Platz 1",
+ "country": "Germany",
+ "homepage": "https://www.umweltbundesamt.de",
+ "contact_url": "mailto:immission@uba.de",
+ },
},
- },
- }
- ],
- "variable": {
- "name": "o3",
- "longname": "ozone",
- "displayname": "Ozone",
- "cf_standardname": "mole_fraction_of_ozone_in_air",
- "units": "nmol mol-1",
- "chemical_formula": "O3",
- "id": 5,
- },
- "station": {
- "id": 3,
- "codes": ["China_test8"],
- "name": "Test_China",
- "coordinates": {"alt": 1534.0, "lat": 36.256, "lng": 117.106},
- "coordinate_validation_status": "not checked",
- "country": "China",
- "state": "Shandong Sheng",
- "type": "unknown",
- "type_of_area": "unknown",
- "timezone": "Asia/Shanghai",
+ }
+ ],
+ }]
+ assert response.json() == expected_resp
+
+ def test_search_complex(self, client, db):
+ response = client.get("/search/a?id=1+id=2-id=2")
+ expected_status_code = 200
+ assert response.status_code == expected_status_code
+ expected_resp = [
+ {
+ "id": 1,
+ "label": "CMA",
+ "order": 1,
+ "sampling_frequency": "hourly",
+ "aggregation": "mean",
+ "data_start_date": "2003-09-07T15:30:00+00:00",
+ "data_end_date": "2016-12-31T14:30:00+00:00",
+ "data_origin": "instrument",
+ "data_origin_type": "measurement",
+ "provider_version": "N/A",
+ "sampling_height": 7.0,
"additional_metadata": {},
- "roles": [],
- "annotations": [],
- "aux_images": [],
- "aux_docs": [],
- "aux_urls": [],
- "globalmeta": {
- "climatic_zone_year2016": "6 (warm temperate dry)",
- "distance_to_major_road_year2020": -999.0,
- "dominant_ecoregion_year2017": "-1 (undefined)",
- "dominant_landcover_year2012": "10 (Cropland, rainfed)",
- "ecoregion_description_25km_year2017": "",
- "htap_region_tier1_year2010": "10 (SAF Sub Saharan/sub Sahel Africa)",
- "landcover_description_25km_year2012": "",
- "max_stable_nightlights_25km_year1992": -999.0,
- "max_stable_nightlights_25km_year2013": -999.0,
- "max_population_density_25km_year1990": -1.0,
- "max_population_density_25km_year2015": -1.0,
- "max_topography_srtm_relative_alt_5km_year1994": -999.0,
- "mean_stable_nightlights_1km_year2013": -999.0,
- "mean_stable_nightlights_5km_year2013": -999.0,
- "mean_nox_emissions_10km_year2000": -999.0,
- "mean_nox_emissions_10km_year2015": -999.0,
- "mean_population_density_250m_year1990": -1.0,
- "mean_population_density_250m_year2015": -1.0,
- "mean_population_density_5km_year1990": -1.0,
- "mean_population_density_5km_year2015": -1.0,
- "mean_topography_srtm_alt_1km_year1994": -999.0,
- "mean_topography_srtm_alt_90m_year1994": -999.0,
- "min_topography_srtm_relative_alt_5km_year1994": -999.0,
- "stddev_topography_srtm_relative_alt_5km_year1994": -999.0,
- "toar1_category": "unclassified",
+ "doi": "",
+ "coverage": -1.0,
+ "station": {
+ "id": 2,
+ "codes": ["SDZ54421"],
+ "name": "Shangdianzi",
+ "coordinates": {"lat": 40.65, "lng": 117.106, "alt": 293.9},
+ "coordinate_validation_status": "not checked",
+ "country": "China",
+ "state": "Beijing Shi",
+ "type": "unknown",
+ "type_of_area": "unknown",
+ "timezone": "Asia/Shanghai",
+ "additional_metadata": {"dummy_info": "Here is some more information about the station"},
+ "roles": [],
+ "annotations": [],
+ "aux_images": [],
+ "aux_docs": [],
+ "aux_urls": [],
+ "globalmeta": {
+ "mean_topography_srtm_alt_90m_year1994": -999.0,
+ "mean_topography_srtm_alt_1km_year1994": -999.0,
+ "max_topography_srtm_relative_alt_5km_year1994": -999.0,
+ "min_topography_srtm_relative_alt_5km_year1994": -999.0,
+ "stddev_topography_srtm_relative_alt_5km_year1994": -999.0,
+ "climatic_zone_year2016": "6 (warm temperate dry)",
+ "htap_region_tier1_year2010": "11 (MDE Middle East: S. Arabia, Oman, etc, Iran, Iraq)",
+ "dominant_landcover_year2012": "11 (Cropland, rainfed, herbaceous cover)",
+ "landcover_description_25km_year2012": "",
+ "dominant_ecoregion_year2017": "-1 (undefined)",
+ "ecoregion_description_25km_year2017": "",
+ "distance_to_major_road_year2020": -999.0,
+ "mean_stable_nightlights_1km_year2013": -999.0,
+ "mean_stable_nightlights_5km_year2013": -999.0,
+ "max_stable_nightlights_25km_year2013": -999.0,
+ "max_stable_nightlights_25km_year1992": -999.0,
+ "mean_population_density_250m_year2015": -1.0,
+ "mean_population_density_5km_year2015": -1.0,
+ "max_population_density_25km_year2015": -1.0,
+ "mean_population_density_250m_year1990": -1.0,
+ "mean_population_density_5km_year1990": -1.0,
+ "max_population_density_25km_year1990": -1.0,
+ "mean_nox_emissions_10km_year2015": -999.0,
+ "mean_nox_emissions_10km_year2000": -999.0,
+ "toar1_category": "unclassified",
+ },
+ "changelog": [
+ {
+ "datetime": "2023-07-15T19:27:09.463245+00:00",
+ "description": "station created",
+ "old_value": "",
+ "new_value": "",
+ "station_id": 2,
+ "author_id": 1,
+ "type_of_change": "created",
+ }
+ ],
+ },
+ "variable": {
+ "name": "toluene",
+ "longname": "toluene",
+ "displayname": "Toluene",
+ "cf_standardname": "mole_fraction_of_toluene_in_air",
+ "units": "nmol mol-1",
+ "chemical_formula": "C7H8",
+ "id": 7,
},
- "changelog": [
+ "programme": {"id": 0, "name": "", "longname": "", "homepage": "", "description": ""},
+ "roles": [
{
- "datetime": "2023-08-15T21:16:20.596545+00:00",
- "description": "station created",
- "old_value": "",
- "new_value": "",
- "station_id": 3,
- "author_id": 1,
- "type_of_change": "created",
+ "id": 2,
+ "role": "resource provider",
+ "status": "active",
+ "contact": {
+ "id": 4,
+ "organisation": {
+ "id": 1,
+ "name": "UBA",
+ "longname": "Umweltbundesamt",
+ "kind": "government",
+ "city": "Dessau-Roßlau",
+ "postcode": "06844",
+ "street_address": "Wörlitzer Platz 1",
+ "country": "Germany",
+ "homepage": "https://www.umweltbundesamt.de",
+ "contact_url": "mailto:immission@uba.de",
+ },
+ },
}
],
- },
- "programme": {"id": 0, "name": "", "longname": "", "homepage": "", "description": ""},
- },
- ]
+ }]
assert response.json() == expected_resp
\ No newline at end of file
diff --git a/toardb/timeseries/crud.py b/toardb/timeseries/crud.py
index b5bc4e5db2edcddf14772570f07b976d57d5b069..906259861ebf56ebd1f28c166f849080fac3a697 100644
--- a/toardb/timeseries/crud.py
+++ b/toardb/timeseries/crud.py
@@ -139,37 +139,109 @@ def get_citation(db: Session, timeseries_id: int, datetime: dt.datetime = None):
license_txt = "This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). https://creativecommons.org/licenses/by/4.0/"
return {"attribution": attribution, "citation": citation, "license": license_txt}
-def adapt_db_object(fields, fields1, db_object_immut):
- db_object = dict(zip(fields1.split(','),db_object_immut))
- # there is a mismatch with coordinates and additional_metadata
- try:
- db_object['coordinates'] = get_coordinates_from_string(db_object['coordinates'])
- except:
- pass
- try:
- db_object['additional_metadata'] = clean_additional_metadata(db_object['additional_metadata'])
- except:
- pass
- try:
- station_id = { "id": db_object['station_id'] }
- db_object['station'] = station_id
- del db_object['station_id']
- except:
- pass
- try:
- variable_id = { "id": db_object['variable_id'] }
- db_object['variable'] = variable_id
- del db_object['variable_id']
- except:
- pass
- if "changelog" in fields:
- db_object['changelog'] = get_timeseries_changelog(db, db_object['id'])
- if "station_changelog" in fields:
- try:
- db_object['station_changelog'] = get_stationmeta_changelog(db, db_object['station_id'])
- except:
+def adapt_db_object(db_object_immut, fields_str = "", lconstr_roles = False):
+ if fields_str:
+ db_object = dict(zip(fields_str.split(","), db_object_immut))
+ # there is a mismatch with coordinates and additional_metadata
+ if "coordinates" in db_object:
+ db_object["coordinates"] = get_coordinates_from_string(db_object["coordinates"])
+
+ if "additional_metadata" in db_object:
+ db_object["additional_metadata"] = clean_additional_metadata(db_object["additional_metadata"])
+
+ if "station" in db_object:
+ station_id = {"id": db_object["station_id"]}
+ db_object["station"] = station_id
+ del db_object["station_id"]
+
+ if "variable_id" in db_object:
+ variable_id = {"id": db_object["variable_id"]}
+ db_object["variable"] = variable_id
+ del db_object["variable_id"]
+
+ if "changelog" in db_object:
+ db_object["changelog"] = get_timeseries_changelog(db, db_object["id"])
+
+ if "station_changelog" in db_object:
+ db_object["station_changelog"] = get_stationmeta_changelog(db, db_object["station_id"])
+
+ if lconstr_roles:
+ # example, how to put the roles explicitly (not needed at the moment)
+ # organisation = get_contact(db, contact_id=39)
+ # roles_atts["contact"] = {"id": 39, "organisation": organisation.__dict__}
+ roles_atts = {key: value for key, value in db_object.items() if key in roles_params}
+ db_object = {key: value for key, value in db_object.items() if key not in roles_params}
+ db_object["roles"] = TimeseriesRoleFields(**roles_atts)
+
+ return db_object
+
+ else:
+ if isinstance(db_object_immut.station.coordinates, (WKBElement, WKTElement)):
+ db_object_immut.station.coordinates = get_coordinates_from_geom(db_object_immut.station.coordinates)
+ # there is a mismatch with additional_metadata
+ if isinstance(db_object_immut.station.additional_metadata, dict):
+ db_object_immut.station.additional_metadata = json.dumps(db_object_immut.station.additional_metadata)
+ db_object_immut.additional_metadata = clean_additional_metadata(db_object_immut.additional_metadata)
+
+
+ #Internall use
+ try:
+ del db_object_immut.data_license_accepted
+ except AttributeError:
pass
- return db_object
+
+ try:
+ del db_object_immut.dataset_approved_by_provider
+ except AttributeError:
+ pass
+
+ return db_object_immut
+
+class TimeseriesQuery:
+ def __init__(self, sign, query):
+ self.sign = sign
+ self.query = query
+
+ def __add__(self, other):
+ return TimeseriesQuery(True, self.query.union_all(other.query) if other.sign else self.query.except_all(other.query))
+
+ def from_query_params(query_params, db, endpoint = False, sign = True):
+ try:
+ limit, offset, fields, format, filters = create_filter(query_params, endpoint)
+ t_filter = filters["t_filter"]
+ t_r_filter = filters["t_r_filter"]
+ s_c_filter = filters["s_c_filter"]
+ s_g_filter = filters["s_g_filter"]
+ except (KeyError, ValueError) as e:
+ status_code = 400
+ return JSONResponse(status_code=status_code, content=str(e))
+
+ query = (
+ db.query(models.Timeseries)
+ .filter(text(t_filter))
+ .distinct()
+ .join(StationmetaCore)
+ .filter(and_(models.Timeseries.station_id == StationmetaCore.id, text(s_c_filter)))
+ .join(StationmetaGlobal)
+ .filter(and_(StationmetaCore.id == StationmetaGlobal.station_id, text(s_g_filter)))
+ .filter(
+ and_(
+ (models.Timeseries.id == timeseries_timeseries_roles_table.c.timeseries_id),
+ (models.timeseries_timeseries_roles_table.c.role_id == models.TimeseriesRole.id),
+ (models.TimeseriesRole.contact_id == Contact.id),
+ (Contact.organisation_id == Organisation.id),
+ (Contact.person_id == Person.id),
+ (Variable.id == models.Timeseries.variable_id),
+ text(t_r_filter),
+ )
+ )
+ .execution_options(stream_results=True)
+ )
+
+ if limit:
+ query = query.limit(limit).offset(offset)
+
+ return TimeseriesQuery(sign, query)
def search_all(db, path_params, query_params, lts=False):
@@ -183,7 +255,7 @@ def search_all(db, path_params, query_params, lts=False):
except (KeyError, ValueError) as e:
status_code = 400
return JSONResponse(status_code=status_code, content=str(e))
- lnot_role = t_r_filter.find("NOT") > 0
+
if fields:
# sort input fields to be sure to replace station_changelog before changelog
@@ -207,107 +279,70 @@ def search_all(db, path_params, query_params, lts=False):
"station_country": "stationmeta_core.country",
}
- fields1 = ",".join(field for field in fields if field not in ("station_changelog", "changelog"))
- fields2 = [field_map.get(field, field) for field in fields]
+ fields_str = ",".join(field for field in fields if field not in ("station_changelog", "changelog"))
+ fields_lst = [text(field_map.get(field, field)) for field in fields]
+
+ query = db.query(*fields_lst)
# ordering is needed (because of limit/offset-option)
# --> Timeseries.id is added to columns while doing distinct! (see: https://docs.sqlalchemy.org/en/14/changelog/migration_20.html#migration-20-query-distinct)
# (duplicates are being created, which means that limit/offset in the query itself is useless!)
# hot fix to speed up the search
- query = (
- db.query(*map(text, fields2))
- .select_from(models.Timeseries)
- .filter(text(t_filter))
- .distinct()
- .join(StationmetaCore)
- .filter(and_(models.Timeseries.station_id == StationmetaCore.id, text(s_c_filter)))
- .join(StationmetaGlobal)
- .filter(and_(StationmetaCore.id == StationmetaGlobal.station_id, text(s_g_filter)))
- .filter(
- and_(
- (models.Timeseries.id == timeseries_timeseries_roles_table.c.timeseries_id),
- (models.timeseries_timeseries_roles_table.c.role_id == models.TimeseriesRole.id),
- (models.TimeseriesRole.contact_id == Contact.id),
- (Contact.organisation_id == Organisation.id),
- (Contact.person_id == Person.id),
- (Variable.id == models.Timeseries.variable_id),
- text(t_r_filter),
- )
+ else:
+ lconstr_roles = False
+ fields_str = ""
+ query = db.query(models.Timeseries)
+
+ query = (
+ query.select_from(models.Timeseries)
+ .filter(text(t_filter))
+ .distinct()
+ .join(StationmetaCore)
+ .filter(and_(models.Timeseries.station_id == StationmetaCore.id, text(s_c_filter)))
+ .join(StationmetaGlobal)
+ .filter(and_(StationmetaCore.id == StationmetaGlobal.station_id, text(s_g_filter)))
+ .filter(
+ and_(
+ (models.Timeseries.id == timeseries_timeseries_roles_table.c.timeseries_id),
+ (models.timeseries_timeseries_roles_table.c.role_id == models.TimeseriesRole.id),
+ (models.TimeseriesRole.contact_id == Contact.id),
+ (Contact.organisation_id == Organisation.id),
+ (Contact.person_id == Person.id),
+ (Variable.id == models.Timeseries.variable_id),
+ text(t_r_filter),
)
- .execution_options(stream_results=True)
)
- # is required like this due to test and previous implementation seems weird
- if limit:
- query = query.order_by(models.Timeseries.id)
- else:
- query = query.limit(limit).offset(offset)
+ .execution_options(stream_results=True)
+ .order_by(models.Timeseries.id)
+ )
- db_objects = []
- for db_object_immut in query:
- db_object = adapt_db_object(fields, fields1, db_object_immut)
- if lconstr_roles:
- roles_atts = {key: value for key, value in db_object.items() if key in roles_params}
- # example, how to put the roles explicitly (not needed at the moment)
- # organisation = get_contact(db, contact_id=39)
- # roles_atts["contact"] = {"id": 39, "organisation": organisation.__dict__}
- db_object = {key: value for key, value in db_object.items() if key not in roles_params}
- db_object["roles"] = TimeseriesRoleFields(**roles_atts)
- db_objects.append(db_object)
+ # is required like this due to test and previous implementation seems weird
+ # order by defeats purpose of limit limited data retrieval
+ if limit:
+ query = query.limit(limit).offset(offset)
- else:
- db_objects = (
- db.query(models.Timeseries)
- .filter(text(t_filter))
- .distinct()
- .join(StationmetaCore)
- .filter(and_(models.Timeseries.station_id == StationmetaCore.id, text(s_c_filter)))
- .join(StationmetaGlobal)
- .filter(and_(StationmetaCore.id == StationmetaGlobal.station_id, text(s_g_filter)))
- .filter(
- and_(
- (models.Timeseries.id == timeseries_timeseries_roles_table.c.timeseries_id),
- (models.timeseries_timeseries_roles_table.c.role_id == models.TimeseriesRole.id),
- (models.TimeseriesRole.contact_id == Contact.id),
- (Contact.organisation_id == Organisation.id),
- (Contact.person_id == Person.id),
- text(t_r_filter),
- )
- )
- .order_by(models.Timeseries.id)
- .limit(limit)
- .offset(offset)
- .all()
- )
- if lnot_role:
- role_string = query_params.get("has_role")[1:]
- role_ids = get_role_id_from_string(db, role_string)
- db_objects = [
- db_object
- for db_object in db_objects
- if all(role[1] in role_ids for role in get_timeseries_roles(db, db_object.id))
- ]
+ not_in_tr = "NOT" in t_r_filter
+ if not_in_tr:
+ role_string = query_params.get("has_role")[1:]
+ role_ids = get_role_id_from_string(db, role_string)
- for db_object in db_objects:
- # there is also a mismatch with coordinates and additional_metadata from station object
- if isinstance(db_object.station.coordinates, (WKBElement, WKTElement)):
- db_object.station.coordinates = get_coordinates_from_geom(db_object.station.coordinates)
- # there is a mismatch with additional_metadata
- if isinstance(db_object.station.additional_metadata, dict):
- db_object.station.additional_metadata = json.dumps(db_object.station.additional_metadata)
- db_object.additional_metadata = clean_additional_metadata(db_object.additional_metadata)
- # only for internal use!
- try:
- del db_object.data_license_accepted
- del db_object.dataset_approved_by_provider
- except AttributeError:
- pass
+ return [
+ adapt_db_object(db_object_immut, fields_str, lconstr_roles)
+ for db_object_immut in query.all()
+ if not not_in_tr or not all(role[1] in role_ids for role in get_timeseries_roles(db, db_object_immut.id))
+ ]
- return db_objects
+def search_all_aggreagtion(db, path_params, signs, query_params_list, lts=False):
+ endpoint = "timeseries" if lts else "search"
+
+ queries = [
+ TimeseriesQuery.from_query_params(query_params, db, endpoint, sign)
+ for sign, query_params in zip(signs, query_params_list)
+ ]
-def seearch_all_aggreagtion(db, path_params, query_params, lts=False):
- pass
+ return [adapt_db_object(db_object_immut) for db_object_immut in sum(queries[1:], queries[0]).query]
def get_timeseries_by_unique_constraints(db: Session, station_id: int, variable_id: int, resource_provider: str = None,
diff --git a/toardb/timeseries/timeseries.py b/toardb/timeseries/timeseries.py
index 52799cb286d6f6e8f178e2e0d34eeaba2f943bd9..445cefd0ba24427f28169ec0ca7339d278a2cd99 100644
--- a/toardb/timeseries/timeseries.py
+++ b/toardb/timeseries/timeseries.py
@@ -31,31 +31,29 @@ from toardb.utils.utils import (
router = APIRouter()
-# CRUD: create, retrieve, update, delete
-# 1. retrieve
+def get_query_params(raw_query_string):
+ updated_query_string = raw_query_string.replace('+', '%2B')
+ updated_params = dict(urllib.parse.parse_qsl(updated_query_string))
+ return QueryParams(updated_params)
+# CRUD: create, retrieve, update, delete
+# 1. retrieve
#get all entries of table timeseries
@router.get('/search/', response_model=List[schemas.Timeseries] | List[schemas.TimeseriesFields], response_model_exclude_none=True, response_model_exclude_unset=True)
def search_all_timeseries(request: Request, db: Session = Depends(get_db)):
- raw_query_string = request.url.query
- updated_query_string = raw_query_string.replace('+', '%2B')
- updated_params = dict(urllib.parse.parse_qsl(updated_query_string))
- updated_query_params = QueryParams(updated_params)
+ updated_query_params = get_query_params(request.url.query)
return crud.search_all(db, path_params=request.path_params, query_params=updated_query_params)
-# Does not work correctly for request connected with a + or -
+# Does not make sense when only certain fields are selected in diffrent requests needs diffrent syntax
+# order of concatinated statmens is also unclear
@router.get('/search/a', response_model=List[schemas.Timeseries] | List[schemas.TimeseriesFields], response_model_exclude_none=True, response_model_exclude_unset=True)
def search_all_timeseries_aggregations(request: Request, db: Session = Depends(get_db)):
- urls = re.split(r"[+-](?=\D)",request.url.query)
-
- results = []
- for raw_query_string in urls:
- updated_query_string = raw_query_string.replace('+', '%2B')
- updated_params = dict(urllib.parse.parse_qsl(updated_query_string))
- updated_query_params = QueryParams(updated_params)
- results.extend(crud.search_all(db, path_params=request.path_params, query_params=updated_query_params))
- return results
+ urls = re.split(r"(?=[+-]\D)", "+" + request.url.query)[1:]
+
+ signs = [ url.startswith("+") for url in urls]
+ query_params = [get_query_params(url[1:]) for url in urls]
+ return crud.search_all_aggreagtion(db, path_params=request.path_params, signs=signs, query_params_list=query_params)
diff --git a/toardb/utils/utils.py b/toardb/utils/utils.py
index 5bdc4578b7a2ea8dd6af676a2f919477fdee08de..02a330309a855671399096621cc3b606f4bb05a4 100644
--- a/toardb/utils/utils.py
+++ b/toardb/utils/utils.py
@@ -164,25 +164,24 @@ def create_filter(query_params, endpoint):
raise ValueError(f"Wrong value for limit given: {limit}")
# fields and format are no filter options
- fields = query_params.get("fields", None)
+ fields = query_params.get("fields", "")
format = query_params.get("format", 'json')
- allowed_params = allrel_params.copy()
- allowed_params |= profiling_params
- if endpoint in {'stationmeta'}:
- allowed_params |= gis_params | core_params | global_params
- elif endpoint in {'timeseries'}:
- allowed_params |= timeseries_params | roles_params
- elif endpoint in {'search'}:
- allowed_params |= gis_params | core_params | global_params | timeseries_params | roles_params | ambig_params
- elif endpoint in {'data'}:
- allowed_params |= data_params | profiling_params
- elif endpoint in {'variables'}:
- allowed_params |= variable_params
- elif endpoint in {'persons'}:
- allowed_params |= person_params
- else:
+ allowed_params = allrel_params | profiling_params
+ endpoint_params_map = {
+ 'stationmeta': gis_params | core_params | global_params,
+ 'timeseries': timeseries_params | roles_params,
+ 'search': gis_params | core_params | global_params | timeseries_params | roles_params | ambig_params,
+ 'data': data_params | profiling_params,
+ 'variables': variable_params,
+ 'persons': person_params,
+ }
+ try:
+ allowed_params |= endpoint_params_map[endpoint]
+ except KeyError:
raise ValueError(f"Wrong endpoint given: {endpoint}")
+
+
if fields:
for field in fields.split(','):
if field not in allowed_params:
@@ -313,14 +312,15 @@ def create_filter(query_params, endpoint):
d_filter = " AND ".join(d_filter).replace('[','(').replace(']',')')
v_filter = " AND ".join(v_filter).replace('[','(').replace(']',')')
p_filter = " AND ".join(p_filter).replace('[','(').replace(']',')')
- filters = {}
- filters["t_filter"] = t_filter
- filters["t_r_filter"] = t_r_filter
- filters["s_c_filter"] = s_c_filter
- filters["s_g_filter"] = s_g_filter
- filters["d_filter"] = d_filter
- filters["v_filter"] = v_filter
- filters["p_filter"] = p_filter
+ filters = {
+ "t_filter": t_filter,
+ "t_r_filter":t_r_filter,
+ "s_c_filter": s_c_filter,
+ "s_g_filter": s_g_filter,
+ "d_filter": d_filter,
+ "v_filter": v_filter,
+ "p_filter": p_filter,
+ }
return limit, offset, fields, format, filters