diff --git a/tests/fixtures/timeseries/timeseries.json b/tests/fixtures/timeseries/timeseries.json
index bd7c59b1699ffe7f33baa9d0e139714d5b77412a..47e89337793c5427ee7e9d1ae27003434910d324 100644
--- a/tests/fixtures/timeseries/timeseries.json
+++ b/tests/fixtures/timeseries/timeseries.json
@@ -11,7 +11,7 @@
"data_origin": 0,
"data_origin_type": 0,
"sampling_height": 7,
- "additional_metadata": {},
+ "additional_metadata": {"original_units": "ppb"},
"programme_id": 0
},
{
diff --git a/tests/test_data.py b/tests/test_data.py
index b49a45623abb72268326abaa074baa2e51e8bddf..da4e3acfd73820e8920291e25cb9457bd934f0e9 100644
--- a/tests/test_data.py
+++ b/tests/test_data.py
@@ -206,7 +206,7 @@ class TestApps:
'doi': '',
'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', 'data_origin_type': 'measurement', 'provider_version': 'N/A',
- 'sampling_height': 7.0, 'additional_metadata': {},
+ 'sampling_height': 7.0, 'additional_metadata': {'original_units': 'ppb'},
'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',
@@ -270,7 +270,7 @@ class TestApps:
'data_origin_type': 'measurement',
'provider_version': 'N/A',
'sampling_height': 7.0,
- 'additional_metadata': {},
+ 'additional_metadata': {'original_units': 'ppb'},
'doi': '',
'coverage': -1.0,
'station': {'id': 2,
@@ -354,7 +354,7 @@ class TestApps:
'data_origin_type': 'measurement',
'provider_version': 'N/A',
'sampling_height': 7.0,
- 'additional_metadata': {},
+ 'additional_metadata': {'original_units': 'ppb'},
'doi': '',
'coverage': -1.0,
'station': {'id': 2,
@@ -433,7 +433,9 @@ class TestApps:
'# "data_origin_type": "measurement",\n',
'# "provider_version": "N/A",\n',
'# "sampling_height": 7.0,\n',
- '# "additional_metadata": {},\n',
+ '# "additional_metadata": {\n',
+ '# "original_units": "ppb"\n',
+ '# },\n',
'# "data_license_accepted": null,\n',
'# "dataset_approved_by_provider": null,\n',
'# "doi": "",\n',
@@ -624,7 +626,7 @@ class TestApps:
'license': 'This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). https://creativecommons.org/licenses/by/4.0/',
'doi': '',
'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',
- 'data_origin_type': 'measurement', 'provider_version': 'N/A', 'sampling_height': 7.0, 'additional_metadata': {},
+ 'data_origin_type': 'measurement', 'provider_version': 'N/A', 'sampling_height': 7.0, 'additional_metadata': {'original_units': 'ppb'},
'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',
@@ -1003,7 +1005,7 @@ class TestApps:
'data_origin_type': 'measurement',
'provider_version': 'N/A',
'sampling_height': 7.0,
- 'additional_metadata': {},
+ 'additional_metadata': {'original_units': 'ppb'},
'station': {'id': 2,
'codes': ['SDZ54421'],
'name': 'Shangdianzi',
diff --git a/tests/test_search.py b/tests/test_search.py
index f1298459c496e5b10262e6c0478ff99489f2347a..b3c32a3ca01e063402208c2b7a5bb1f9c1175c2f 100644
--- a/tests/test_search.py
+++ b/tests/test_search.py
@@ -187,7 +187,7 @@ class TestApps:
'data_origin_type': 'measurement',
'provider_version': 'N/A',
'sampling_height': 7.0,
- 'additional_metadata': {},
+ 'additional_metadata': {"original_units": "ppb"},
'doi': '',
'coverage': -1.0,
'station': {'id': 2,
@@ -675,7 +675,7 @@ class TestApps:
assert response.status_code == expected_status_code
expected_resp = [{'id': 1,
'order': 1,
- 'additional_metadata': {},
+ 'additional_metadata': {"original_units": "ppb"},
'roles': {'role': 'resource provider', 'status': 'active', 'contact_id': 4}
},
{'id': 2,
@@ -999,6 +999,93 @@ class TestApps:
assert response.json() == expected_response
+ def test_search_with_additional_metadata3(self, client, db):
+ response = client.get("/search/?additional_metadata->'original_units'=ppb")
+ expected_status_code = 200
+ assert response.status_code == expected_status_code
+ expected_response = [{'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': {"original_units": "ppb"},
+ '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': {'add_type': 'nature reservation'},
+ '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},
+ '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'}}}]}]
+ assert response.json() == expected_response
+
+
def test_search_with_additional_metadata_unknown(self, client, db):
response = client.get("/search/?additional_metadata->'not_yet_defined'=42")
expected_status_code = 200
@@ -1022,7 +1109,7 @@ class TestApps:
'data_origin_type': 'measurement',
'provider_version': 'N/A',
'sampling_height': 7.0,
- 'additional_metadata': {},
+ 'additional_metadata': {"original_units": "ppb"},
'doi': '',
'coverage': -1.0,
'station': {'id': 2,
diff --git a/tests/test_timeseries.py b/tests/test_timeseries.py
index 672d3b9e83ec0e01b8fe566579fd9a9762fe1722..6f9c5278d8d76d086fa0ea675538c5bd6adfa5ba 100644
--- a/tests/test_timeseries.py
+++ b/tests/test_timeseries.py
@@ -189,7 +189,7 @@ class TestApps:
'data_origin_type': 'measurement',
'provider_version': 'N/A',
'sampling_height': 7.0,
- 'additional_metadata': {},
+ 'additional_metadata': {'original_units': 'ppb'},
'doi': '',
'coverage': -1.0,
'station': {'id': 2,
@@ -380,7 +380,7 @@ class TestApps:
'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': {},
+ 'provider_version': 'N/A', 'doi': '', 'additional_metadata': {'original_units': 'ppb'},
'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',
@@ -603,7 +603,7 @@ class TestApps:
'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', 'coverage': -1.0, 'data_origin': 'instrument', 'data_origin_type': 'measurement',
- 'provider_version': 'N/A', 'doi': '', 'sampling_height': 7.0, 'additional_metadata': {},
+ 'provider_version': 'N/A', 'doi': '', 'sampling_height': 7.0, 'additional_metadata': {'original_units': 'ppb'},
'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',
@@ -653,7 +653,7 @@ class TestApps:
response = client.get("/timeseries/1?fields=additional_metadata")
expected_status_code = 200
assert response.status_code == expected_status_code
- expected_response = {'additional_metadata': {} }
+ expected_response = {'additional_metadata': {'original_units': 'ppb'} }
assert response.json() == expected_response
@@ -662,7 +662,7 @@ class TestApps:
expected_status_code = 200
assert response.status_code == expected_status_code
expected_response = [
- {'additional_metadata': {}},
+ {'additional_metadata': {'original_units': 'ppb'}},
{'additional_metadata':
{'absorption_cross_section': 'Hearn 1961',
'ebas_metadata_19740101000000_29y':
@@ -696,7 +696,7 @@ class TestApps:
'data_origin_type': 'measurement',
'provider_version': 'N/A',
'sampling_height': 7.0,
- 'additional_metadata': {},
+ 'additional_metadata': {'original_units': 'ppb'},
'doi': '',
'coverage': -1.0,
'station': {'id': 2,
diff --git a/toardb/utils/utils.py b/toardb/utils/utils.py
index d2da351446b21c74b68c475b6c675d928ba78506..b41ab05628822d61102b012bf8e15218d1aae5a6 100644
--- a/toardb/utils/utils.py
+++ b/toardb/utils/utils.py
@@ -289,12 +289,19 @@ def create_filter(query_params, endpoint):
if param == "additional_metadata->'absorption_cross_section'":
trlist = translate_convoc_list(values, toardb.toardb.CS_vocabulary, "absorption_cross_section")
values = [ str(val) for val in trlist ]
- if param == "additional_metadata->'sampling_type'":
+ param = f"timeseries.{param}"
+ elif param == "additional_metadata->'sampling_type'":
trlist = translate_convoc_list(values, toardb.toardb.KS_vocabulary, "sampling_type")
values = [ str(val) for val in trlist ]
- if param == "additional_metadata->'calibration_type'":
+ param = f"timeseries.{param}"
+ elif param == "additional_metadata->'calibration_type'":
trlist = translate_convoc_list(values, toardb.toardb.CT_vocabulary, "calibration_type")
values = [ str(val) for val in trlist ]
+ param = f"timeseries.{param}"
+ else:
+ val_mod = [ f"'\"{val}\"'::text" for val in values ]
+ values = "(" + ",".join(val_mod) + ")"
+ param = f"to_json(timeseries.{param})::text"
if param == "has_role":
operator = "IN"
join_operator = "OR"
@@ -311,6 +318,8 @@ def create_filter(query_params, endpoint):
t_r_filter.append(f"persons.name {operator} {values}")
t_r_filter.append(f"persons.orcid {operator} {values}")
t_r_filter = f" {join_operator} ".join(t_r_filter)
+ elif param_long.split('>')[0] == "additional_metadata-":
+ t_filter.append(f"{param} IN {values}")
else:
t_filter.append(f"timeseries.{param} IN {values}")
elif param in data_params: