From aecb52f502134971653d843650655ae4040a7d41 Mon Sep 17 00:00:00 2001 From: Ricardo Garcia Silva Date: Thu, 25 Jul 2024 12:36:18 +0100 Subject: [PATCH] fix incorrect generation of related coverage identifiers (#180) * Adding integration test case for coverage time series endpoint * Fixing incorrect determination of related coverages --- arpav_ppcv/database.py | 75 + arpav_ppcv/operations.py | 50 +- arpav_ppcv/schemas/coverages.py | 9 + arpav_ppcv/webapp/api_v2/routers/coverages.py | 1 + pyproject.toml | 2 +- tests/conftest.py | 49 + tests/notebooks/generic.ipynb | 232 +- .../notebooks/timeseries-smoothing-demo.ipynb | 3590 ++++++++++++++++- tests/test_webapp_v2_routers_coverages.py | 45 + 9 files changed, 3794 insertions(+), 259 deletions(-) diff --git a/arpav_ppcv/database.py b/arpav_ppcv/database.py index 8db1d2f9..9fe42ffc 100644 --- a/arpav_ppcv/database.py +++ b/arpav_ppcv/database.py @@ -777,6 +777,19 @@ def collect_all_configuration_parameters( return result +def create_configuration_parameter_value( + session: sqlmodel.Session, + parameter_value: coverages.ConfigurationParameterValueCreate, +) -> coverages.ConfigurationParameterValue: + db_param_value = coverages.ConfigurationParameterValue( + **parameter_value.model_dump() + ) + session.add(db_param_value) + session.commit() + session.refresh(db_param_value) + return db_param_value + + def create_configuration_parameter( session: sqlmodel.Session, configuration_parameter_create: coverages.ConfigurationParameterCreate, @@ -1307,6 +1320,68 @@ def collect_all_coverage_identifiers( return cov_ids +def ensure_uncertainty_type_configuration_parameters_exist( + session: sqlmodel.Session, +) -> tuple[ + coverages.ConfigurationParameterValue, coverages.ConfigurationParameterValue +]: + """Ensure that the `uncertainty_type` configuration parameter exists. + + Because internally we make use of the `uncertainty_type` configuration parameter, + we must ensure it and its respective values exist. This can happen if an admin + user deletes them by accident. + """ + param_name = "uncertainty_type" + lower_bound_name = "lower_bound" + upper_bound_name = "upper_bound" + param = get_configuration_parameter_by_name(session, param_name) + if param is None: + create_configuration_parameter( + session, + coverages.ConfigurationParameterCreate( + name=param_name, + allowed_values=[ + coverages.ConfigurationParameterValueCreateEmbeddedInConfigurationParameter( + name=lower_bound_name, + ), + coverages.ConfigurationParameterValueCreateEmbeddedInConfigurationParameter( + name=upper_bound_name, + ), + ], + ), + ) + lower_bound_value = get_configuration_parameter_value_by_names( + session, param_name, lower_bound_name + ) + upper_bound_value = get_configuration_parameter_value_by_names( + session, param_name, upper_bound_name + ) + else: + lower_bound_value = get_configuration_parameter_value_by_names( + session, param_name, lower_bound_name + ) + upper_bound_value = get_configuration_parameter_value_by_names( + session, param_name, upper_bound_name + ) + if lower_bound_value is None: + lower_bound_value = create_configuration_parameter_value( + session, + parameter_value=coverages.ConfigurationParameterValueCreate( + name="lower_bound", + configuration_parameter_id=param.id, + ), + ) + if upper_bound_value is None: + upper_bound_value = create_configuration_parameter_value( + session, + parameter_value=coverages.ConfigurationParameterValueCreate( + name="upper_bound", + configuration_parameter_id=param.id, + ), + ) + return lower_bound_value, upper_bound_value + + def _get_total_num_records(session: sqlmodel.Session, statement): return session.exec( sqlmodel.select(sqlmodel.func.count()).select_from(statement) diff --git a/arpav_ppcv/operations.py b/arpav_ppcv/operations.py index e1bf1be4..d1848084 100644 --- a/arpav_ppcv/operations.py +++ b/arpav_ppcv/operations.py @@ -38,6 +38,7 @@ def get_climate_barometer_time_series( settings: ArpavPpcvSettings, + session: sqlmodel.Session, coverage: coverages.CoverageInternal, smoothing_strategies: list[base.CoverageDataSmoothingStrategy] = [ # noqa base.CoverageDataSmoothingStrategy.NO_SMOOTHING @@ -50,7 +51,9 @@ def get_climate_barometer_time_series( df = _get_climate_barometer_data(settings, coverage) dfs.append((coverage, df)) if include_uncertainty: - lower_cov, upper_cov = get_related_uncertainty_coverage_configurations(coverage) + lower_cov, upper_cov = get_related_uncertainty_coverage_configurations( + session, coverage + ) if lower_cov is not None: lower_df = _get_climate_barometer_data(settings, lower_cov) dfs.append((lower_cov, lower_df)) @@ -478,16 +481,21 @@ def process_station_data_smoothing_strategy( def get_related_uncertainty_coverage_configurations( + session: sqlmodel.Session, coverage: coverages.CoverageInternal, ) -> tuple[coverages.CoverageInternal | None, coverages.CoverageInternal | None]: - used_values = coverage.configuration.retrieve_used_values(coverage.identifier) + used_values = [ + pv.configuration_parameter_value + for pv in coverage.configuration.retrieve_used_values(coverage.identifier) + ] + lower_, upper_ = database.ensure_uncertainty_type_configuration_parameters_exist( + session + ) if ( lower_cov_conf := coverage.configuration.uncertainty_lower_bounds_coverage_configuration ): - lower_cov_id = lower_cov_conf.build_coverage_identifier( - [pv.configuration_parameter_value for pv in used_values] - ) + lower_cov_id = lower_cov_conf.build_coverage_identifier(used_values + [lower_]) lower_cov = coverages.CoverageInternal( configuration=lower_cov_conf, identifier=lower_cov_id ) @@ -497,9 +505,7 @@ def get_related_uncertainty_coverage_configurations( upper_cov_conf := coverage.configuration.uncertainty_upper_bounds_coverage_configuration ): - upper_cov_id = upper_cov_conf.build_coverage_identifier( - [pv.configuration_parameter_value for pv in used_values] - ) + upper_cov_id = upper_cov_conf.build_coverage_identifier(used_values + [upper_]) upper_cov = coverages.CoverageInternal( configuration=upper_cov_conf, identifier=upper_cov_id ) @@ -515,9 +521,27 @@ def get_related_coverages( used_values = coverage.configuration.retrieve_used_values(coverage.identifier) for related_ in coverage.configuration.secondary_coverage_configurations: related_cov_conf = related_.secondary_coverage_configuration - related_id = related_cov_conf.build_coverage_identifier( - [pv.configuration_parameter_value for pv in used_values] - ) + possible_used = [ + pv.configuration_parameter_value for pv in related_cov_conf.possible_values + ] + values_to_use = [] + for used_value in used_values: + if used_value in possible_used: + values_to_use.append(used_value) + else: + used_param_id = ( + used_value.configuration_parameter_value.configuration_parameter_id + ) + try: + possible = [ + cp + for cp in possible_used + if cp.configuration_parameter_id == used_param_id + ][0] + values_to_use.append(possible) + except IndexError: + logger.warning(f"Could not find a usable value for {used_value}") + related_id = related_cov_conf.build_coverage_identifier(values_to_use) related_covs.append( coverages.CoverageInternal( configuration=related_cov_conf, identifier=related_id @@ -553,7 +577,9 @@ def get_coverage_time_series( start, end = _parse_temporal_range(temporal_range) to_retrieve_from_ncss = [coverage] if include_coverage_uncertainty: - lower_cov, upper_cov = get_related_uncertainty_coverage_configurations(coverage) + lower_cov, upper_cov = get_related_uncertainty_coverage_configurations( + session, coverage + ) if lower_cov is not None: to_retrieve_from_ncss.append(lower_cov) if upper_cov is not None: diff --git a/arpav_ppcv/schemas/coverages.py b/arpav_ppcv/schemas/coverages.py index 3739365c..3675230b 100644 --- a/arpav_ppcv/schemas/coverages.py +++ b/arpav_ppcv/schemas/coverages.py @@ -57,6 +57,15 @@ class ConfigurationParameterValue(sqlmodel.SQLModel, table=True): ) +class ConfigurationParameterValueCreate(sqlmodel.SQLModel): + name: str + configuration_parameter_id: uuid.UUID + display_name_english: Optional[str] = None + display_name_italian: Optional[str] = None + description_english: Optional[str] = None + description_italian: Optional[str] = None + + class ConfigurationParameter(sqlmodel.SQLModel, table=True): id: uuid.UUID = sqlmodel.Field(default_factory=uuid.uuid4, primary_key=True) name: str = sqlmodel.Field(unique=True, index=True) diff --git a/arpav_ppcv/webapp/api_v2/routers/coverages.py b/arpav_ppcv/webapp/api_v2/routers/coverages.py index 80a3d39d..150c2a43 100644 --- a/arpav_ppcv/webapp/api_v2/routers/coverages.py +++ b/arpav_ppcv/webapp/api_v2/routers/coverages.py @@ -417,6 +417,7 @@ def get_climate_barometer_time_series( try: time_series = operations.get_climate_barometer_time_series( settings, + db_session, coverage, smoothing_strategies=data_smoothing, include_uncertainty=include_uncertainty, diff --git a/pyproject.toml b/pyproject.toml index b05ef727..22a2212f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -73,8 +73,8 @@ pytest-cov = "^4.1.0" pytest-django = "^4.8.0" dagger-io = "^0.9.10" ruff = "^0.2.2" -pytest-httpx = "^0.30.0" pre-commit = "^3.7.1" +pytest-httpx = "^0.30.0" [tool.poetry.group.jupyter] diff --git a/tests/conftest.py b/tests/conftest.py index f583093e..83c81a2e 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -253,6 +253,55 @@ def sample_coverage_configurations( return db_cov_confs +@pytest.fixture() +def sample_tas_csv_data(): + return """ + time,station,latitude[unit="degrees_north"],longitude[unit="degrees_east"],tas[unit="degC"] + 1976-02-15T12:00:00Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,2.640222 + 1977-02-14T17:57:04.390Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,4.131799 + 1978-02-14T23:54:08.780Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,2.9139953 + 1979-02-15T05:51:13.171Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,3.9587035 + 1980-02-15T11:48:17.561Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,3.5937133 + 1981-02-14T17:45:21.951Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,3.7524657 + 1982-02-14T23:42:26.341Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,3.8758483 + 1983-02-15T05:39:30.732Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,3.5044188 + 1984-02-15T11:36:35.122Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,2.284906 + 1985-02-14T17:33:39.512Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,4.2877746 + 1986-02-14T23:30:43.902Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,3.3630004 + 1987-02-15T05:27:48.293Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,2.611383 + 1988-02-15T11:24:52.683Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,3.5216613 + 1989-02-14T17:21:57.073Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,3.7202392 + 1990-02-14T23:19:01.463Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,4.1510253 + 1991-02-15T05:16:05.854Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,3.5604796 + 1992-02-15T11:13:10.244Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,2.830011 + 1993-02-14T17:10:14.634Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,3.3071227 + 1994-02-14T23:07:19.024Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,4.4500365 + 1995-02-15T05:04:23.415Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,4.8746276 + 1996-02-15T11:01:27.805Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,4.0703063 + 1997-02-14T16:58:32.195Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,4.0519347 + 1998-02-14T22:55:36.585Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,3.9186034 + 1999-02-15T04:52:40.976Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,4.3369384 + 2000-02-15T10:49:45.366Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,4.413568 + 2001-02-14T16:46:49.756Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,3.7551513 + 2002-02-14T22:43:54.146Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,3.6977477 + 2003-02-15T04:40:58.537Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,4.3922668 + 2004-02-15T10:38:02.927Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,4.298364 + 2005-02-14T16:35:07.317Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,3.7203918 + 2006-02-14T22:32:11.707Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,5.3815246 + 2007-02-15T04:29:16.098Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,3.568109 + 2008-02-15T10:26:20.488Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,4.08172 + 2009-02-14T16:23:24.878Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,4.7300353 + 2010-02-14T22:20:29.268Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,4.7169127 + 2011-02-15T04:17:33.659Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,3.357843 + 2012-02-15T10:14:38.049Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,2.469293 + 2013-02-14T16:11:42.439Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,3.4914489 + 2014-02-14T22:08:46.829Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,4.1174865 + 2015-02-15T04:05:51.220Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,4.338098 + 2016-02-15T10:02:55.610Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,5.111444 + 2017-02-14T16:00:00Z,GridPointRequestedAt[44.952N_11.547E],44.953,11.547,3.911859 + """.strip() + + def _override_get_settings(): standard_settings = config.get_settings() return standard_settings diff --git a/tests/notebooks/generic.ipynb b/tests/notebooks/generic.ipynb index 11c99d45..822cbec5 100644 --- a/tests/notebooks/generic.ipynb +++ b/tests/notebooks/generic.ipynb @@ -55,7 +55,7 @@ "metadata": {}, "outputs": [], "source": [ - "coverage_identifier = \"tas_seasonal_absolute_model_ensemble-model_ensemble-absolute-rcp26-DJF\"\n", + "coverage_identifier = \"tas_seasonal_absolute_model_ensemble-annual-model_ensemble-tas-absolute-rcp26-DJF\"\n", "point_coords = \"POINT(11.5469 44.9524)\"\n", "date_range = \"../..\"\n", "\n", @@ -65,263 +65,71 @@ ")" ] }, - { - "cell_type": "code", - "execution_count": 2, - "id": "d2de401b-4028-45d2-bf01-4c0cbb806d80", - "metadata": {}, - "outputs": [], - "source": [ - "param_values = [\n", - " db.get_configuration_parameter_value_by_names(session, \"climatological_model\", \"model_ensemble\"),\n", - " db.get_configuration_parameter_value_by_names(session, \"scenario\", \"rcp26\")\n", - "]" - ] - }, { "cell_type": "code", "execution_count": 3, - "id": "f379b854-b318-475f-95c7-82b0a2e48f6b", - "metadata": {}, - "outputs": [], - "source": [ - "cov_conf = db.get_coverage_configuration_by_name(session, \"tas_seasonal_absolute_model_ensemble\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "46f23579-47c7-4123-a5a0-7368dcaee640", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'rcp26'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cov_conf.possible_values[0].configuration_parameter_value.name" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d505e1b4-14e9-459a-b942-2226c6bfc82e", + "id": "974c6dfa-7d87-4388-9ef5-dd29fb855919", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:arpav_ppcv.database:locals()={'session': , 'coverage_configuration': CoverageConfiguration(netcdf_main_dataset_name='tas', observation_variable_aggregation_type=, thredds_url_pattern='ensymbc/clipped/tas_avg_{scenario}_{year_period}_ts19762100_ls_VFVG.nc', uncertainty_lower_bounds_coverage_configuration_id=UUID('9c2bfa7a-d108-4d2d-8c99-f26ae9329e79'), name='tas_seasonal_absolute_model_ensemble', wms_main_layer_name='tas', uncertainty_upper_bounds_coverage_configuration_id=UUID('d33d6b43-0379-447a-ad9d-b2f36f7dce3e'), id=UUID('06c104d2-a502-400b-9728-b8e2d9ee65d9'), unit='ºC', display_name_english='TAS seasonal absolute model ensemble', palette='default/seq-YlOrRd', display_name_italian='TAS valore assoluto di stagione media ensemble', color_scale_min=-3.0, description_english='TAS seasonal absolute model ensemble', color_scale_max=32.0, description_italian='TAS valore assoluto di stagione media ensemble', observation_variable_id=UUID('32706ce0-0134-4122-91a0-9d6f237350b3'), coverage_id_pattern='{name}-{climatological_model}-{measure}-{scenario}-{year_period}'), 'configuration_parameter_values_filter': [ConfigurationParameterValue(id=UUID('c5381cd2-ca57-48a9-a096-71568e812366'), display_name_italian='Insieme di 5 modelli', description_italian='Insieme di cinque modelli climatologici: EC-EARTH CCLM4-8-17, EC-EARTH RACMO22E, EC-EARTH RCA4, HadGEM RACMO22E, MPI-ESM-LR-REMO2009', display_name_english='5 Model ensemble', name='model_ensemble', description_english='Ensemble of five climatological models: EC-EARTH CCLM4-8-17, EC-EARTH RACMO22E, EC-EARTH RCA4, HadGEM RACMO22E, MPI-ESM-LR-REMO2009', configuration_parameter_id=UUID('d6cfbf61-7f87-43a9-8e04-2c9d275eacc2')), ConfigurationParameterValue(id=UUID('70c82437-5e90-4181-96c7-262a097d18ba'), display_name_italian='RCP2.6', description_italian='Scenario Representation Concentration Pathway (RCP) che presuppone valori di forzante climatica di 2,6 W/m2', display_name_english='RCP2.6', name='rcp26', description_english='Representation Concentration Pathway (RCP) scenario that assumes climate forcing values of 2.6 W/m2', configuration_parameter_id=UUID('92bbb178-334f-4f30-bf2c-fb7cba7cd7e0'))]}\n", - "DEBUG:arpav_ppcv.database:pattern_parts=['climatological_model', 'measure', 'scenario', 'year_period']\n" - ] - }, { "data": { "text/plain": [ - "['tas_seasonal_absolute_model_ensemble-model_ensemble-measure-rcp26-year_period']" + "'tas_seasonal_absolute_model_ensemble_upper_uncertainty-annual-model_ensemble-tas-absolute-rcp26-upper_bound-DJF'" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "db.generate_coverage_identifiers(session, coverage_configuration=cov_conf, configuration_parameter_values_filter=param_values)" + "operations.get_related_uncertainty_coverage_configurations(session, cov)[1].identifier" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "978549f0-e1d6-415c-833b-3169b7448920", + "execution_count": 6, + "id": "33991a12-8e81-4de0-8812-057b25a6296f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "([CoverageConfiguration(netcdf_main_dataset_name='tas', observation_variable_aggregation_type=, thredds_url_pattern='ensymbc/clipped/tas_avg_{scenario}_{year_period}_ts19762100_ls_VFVG.nc', uncertainty_lower_bounds_coverage_configuration_id=UUID('9c2bfa7a-d108-4d2d-8c99-f26ae9329e79'), name='tas_seasonal_absolute_model_ensemble', wms_main_layer_name='tas', uncertainty_upper_bounds_coverage_configuration_id=UUID('d33d6b43-0379-447a-ad9d-b2f36f7dce3e'), id=UUID('06c104d2-a502-400b-9728-b8e2d9ee65d9'), unit='ºC', display_name_english='TAS seasonal absolute model ensemble', palette='default/seq-YlOrRd', display_name_italian='TAS valore assoluto di stagione media ensemble', color_scale_min=-3.0, description_english='TAS seasonal absolute model ensemble', color_scale_max=32.0, description_italian='TAS valore assoluto di stagione media ensemble', observation_variable_id=UUID('32706ce0-0134-4122-91a0-9d6f237350b3'), coverage_id_pattern='{name}-{climatological_model}-{measure}-{scenario}-{year_period}')],\n", - " None)" + "CoverageConfiguration(description_english='Average daily air temperature near the ground', color_scale_max=32.0, description_italian=\"Temperatura media giornaliera dell'aria vicino al suolo\", observation_variable_id=None, netcdf_main_dataset_name='tas_stddown', observation_variable_aggregation_type=None, thredds_url_pattern='ensymbc/std/clipped/tas_stddown_{scenario}_{year_period}_ts19762100_ls_VFVG.nc', uncertainty_lower_bounds_coverage_configuration_id=None, name='tas_seasonal_absolute_model_ensemble_lower_uncertainty', wms_main_layer_name='tas_stddown', uncertainty_upper_bounds_coverage_configuration_id=None, id=UUID('37ec5342-7eba-4d99-831e-b8e027577ec0'), unit='ºC', display_name_english='Mean temperature', palette='default/seq-YlOrRd', display_name_italian='Temperatura media', color_scale_min=-3.0, coverage_id_pattern='{name}-{aggregation_period}-{climatological_model}-{climatological_variable}-{measure}-{scenario}-{uncertainty_type}-{year_period}')" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "db.list_coverage_configurations(session, configuration_parameter_value_filter=param_values)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "d15772a0-a1c1-4258-9f2f-4402d074cbee", - "metadata": {}, - "outputs": [], - "source": [ - "pv_cte = (\n", - " sqlmodel\n", - " .select(\n", - " coverages.CoverageConfiguration.id,\n", - " func.jsonb_agg(\n", - " func.json_build_object(\n", - " coverages.ConfigurationParameter.name,\n", - " coverages.ConfigurationParameterValue.name,\n", - " )\n", - " ).label(\"possible_values\")\n", - " )\n", - " .join(\n", - " coverages.ConfigurationParameterPossibleValue,\n", - " coverages.CoverageConfiguration.id ==\n", - " coverages.ConfigurationParameterPossibleValue.coverage_configuration_id\n", - " )\n", - " .join(\n", - " coverages.ConfigurationParameterValue,\n", - " coverages.ConfigurationParameterValue.id ==\n", - " coverages.ConfigurationParameterPossibleValue.configuration_parameter_value_id\n", - " )\n", - " .join(\n", - " coverages.ConfigurationParameter,\n", - " coverages.ConfigurationParameter.id ==\n", - " coverages.ConfigurationParameterValue.configuration_parameter_id\n", - " ).group_by(coverages.CoverageConfiguration.id)\n", - ").cte(\"cov_conf_possible_values\")" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "74f05366-c299-4964-8efe-de36cfe61de4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SELECT coverageconfiguration.id, jsonb_agg(json_build_object(configurationparameter.name, configurationparametervalue.name)) AS possible_values \n", - "FROM coverageconfiguration JOIN configurationparameterpossiblevalue ON coverageconfiguration.id = configurationparameterpossiblevalue.coverage_configuration_id JOIN configurationparametervalue ON configurationparametervalue.id = configurationparameterpossiblevalue.configuration_parameter_value_id JOIN configurationparameter ON configurationparameter.id = configurationparametervalue.configuration_parameter_id GROUP BY coverageconfiguration.id\n" - ] - } - ], - "source": [ - "print(pv_cte)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "f7966b1d-9d38-4b05-a5ae-ab21a5c0f8b9", - "metadata": {}, - "outputs": [], - "source": [ - "pvs = {\n", - " \"climatological_model\": \"model_ensemble\",\n", - " \"scenario\": \"rcp26\"\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "8288a6f8-257d-49b6-a7a6-04b429b15615", - "metadata": {}, - "outputs": [], - "source": [ - "statement = (\n", - " sqlmodel.select(coverages.CoverageConfiguration)\n", - " .join(pv_cte, pv_cte.c.id == coverages.CoverageConfiguration.id)\n", - " .where(func.jsonb_path_exists(pv_cte.c.possible_values, '$[*] ? (@.climatological_model == \"model_ensemble\")'))\n", - " .where(func.jsonb_path_exists(pv_cte.c.possible_values, '$[*] ? (@.scenario == \"rcp26\")'))\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "d1446147-b649-4fdf-b0e6-da0c0a23611e", - "metadata": {}, - "outputs": [], - "source": [ - "statement = (\n", - " sqlmodel.select(coverages.CoverageConfiguration)\n", - " .join(pv_cte, pv_cte.c.id == coverages.CoverageConfiguration.id)\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "035e71d5-3729-4f3f-9c5b-b94e18afae82", - "metadata": {}, - "outputs": [], - "source": [ - "for value_name, value in pvs.items():\n", - " statement = (\n", - " statement\n", - " .where(func.jsonb_path_exists(pv_cte.c.possible_values, f'$[*] ? (@.{value_name} == \"{value}\")'))\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "72889317-a72d-4417-92d1-65694d953fd9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WITH cov_conf_possible_values AS \n", - "(SELECT coverageconfiguration.id AS id, jsonb_agg(json_build_object(configurationparameter.name, configurationparametervalue.name)) AS possible_values \n", - "FROM coverageconfiguration JOIN configurationparameterpossiblevalue ON coverageconfiguration.id = configurationparameterpossiblevalue.coverage_configuration_id JOIN configurationparametervalue ON configurationparametervalue.id = configurationparameterpossiblevalue.configuration_parameter_value_id JOIN configurationparameter ON configurationparameter.id = configurationparametervalue.configuration_parameter_id GROUP BY coverageconfiguration.id)\n", - " SELECT coverageconfiguration.id, coverageconfiguration.name, coverageconfiguration.display_name_english, coverageconfiguration.display_name_italian, coverageconfiguration.description_english, coverageconfiguration.description_italian, coverageconfiguration.netcdf_main_dataset_name, coverageconfiguration.thredds_url_pattern, coverageconfiguration.wms_main_layer_name, coverageconfiguration.unit, coverageconfiguration.palette, coverageconfiguration.color_scale_min, coverageconfiguration.color_scale_max, coverageconfiguration.observation_variable_id, coverageconfiguration.observation_variable_aggregation_type, coverageconfiguration.uncertainty_lower_bounds_coverage_configuration_id, coverageconfiguration.uncertainty_upper_bounds_coverage_configuration_id \n", - "FROM coverageconfiguration JOIN cov_conf_possible_values ON cov_conf_possible_values.id = coverageconfiguration.id \n", - "WHERE jsonb_path_exists(cov_conf_possible_values.possible_values, '$[*] ? (@.climatological_model == \"model_ensemble\")') AND jsonb_path_exists(cov_conf_possible_values.possible_values, '$[*] ? (@.scenario == \"rcp26\")')\n" - ] - } - ], - "source": [ - "print(statement.compile(db.get_engine(settings), compile_kwargs={\"literal_binds\": True}))" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "867b4a0f-232f-4c93-b7e6-ebccdb36af9d", - "metadata": {}, - "outputs": [], - "source": [ - "session.rollback()" + "cov.configuration.uncertainty_lower_bounds_coverage_configuration" ] }, { "cell_type": "code", - "execution_count": 44, - "id": "878c6ea7-8946-4779-925a-c89db88703f1", + "execution_count": 4, + "id": "e2b7ec11-5168-42dd-8992-cb98f1f4def3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[CoverageConfiguration(description_english='TAS seasonal absolute model ensemble', color_scale_max=32.0, description_italian='TAS valore assoluto di stagione media ensemble', observation_variable_id=UUID('32706ce0-0134-4122-91a0-9d6f237350b3'), netcdf_main_dataset_name='tas', observation_variable_aggregation_type=, thredds_url_pattern='ensymbc/clipped/tas_avg_{scenario}_{year_period}_ts19762100_ls_VFVG.nc', uncertainty_lower_bounds_coverage_configuration_id=UUID('9c2bfa7a-d108-4d2d-8c99-f26ae9329e79'), name='tas_seasonal_absolute_model_ensemble', wms_main_layer_name='tas', uncertainty_upper_bounds_coverage_configuration_id=UUID('d33d6b43-0379-447a-ad9d-b2f36f7dce3e'), id=UUID('06c104d2-a502-400b-9728-b8e2d9ee65d9'), unit='ºC', display_name_english='TAS seasonal absolute model ensemble', palette='default/seq-YlOrRd', display_name_italian='TAS valore assoluto di stagione media ensemble', color_scale_min=-3.0, coverage_id_pattern='{name}-{climatological_model}-{measure}-{scenario}-{year_period}')]" + "['tas_seasonal_absolute_model_ec_earth_cclm4_8_17-annual-ec_earth_cclm_4_8_17-tas-absolute-rcp26-DJF',\n", + " 'tas_seasonal_absolute_model_ec_earth_racmo22e-annual-ec_earth_racmo22e-tas-absolute-rcp26-DJF',\n", + " 'tas_seasonal_absolute_model_ec_earth_rca4-annual-ec_earth_rca4-tas-absolute-rcp26-DJF',\n", + " 'tas_seasonal_absolute_model_hadgem2_es_racmo22e-annual-hadgem2_racmo22e-tas-absolute-rcp26-DJF',\n", + " 'tas_seasonal_absolute_model_mpi_esm_lr_remo2009-annual-mpi_esm_lr_remo2009-tas-absolute-rcp26-DJF']" ] }, - "execution_count": 44, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "session.exec(statement).all()" + "[c.identifier for c in operations.get_related_coverages(cov)]" ] } ], @@ -341,7 +149,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.10.14" } }, "nbformat": 4, diff --git a/tests/notebooks/timeseries-smoothing-demo.ipynb b/tests/notebooks/timeseries-smoothing-demo.ipynb index 47b245f2..e8e37c00 100644 --- a/tests/notebooks/timeseries-smoothing-demo.ipynb +++ b/tests/notebooks/timeseries-smoothing-demo.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "id": "df4b0596-aef5-4211-829c-20cbab0ed146", "metadata": {}, "outputs": [], @@ -44,12 +44,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "id": "803425ad-95a3-44cb-aecc-2dee787d2e7e", "metadata": {}, "outputs": [], "source": [ - "coverage_identifier = \"tas_seasonal_absolute_model_ensemble-rcp26-DJF\"\n", + "coverage_identifier = \"tas_seasonal_absolute_model_ensemble-annual-model_ensemble-tas-absolute-rcp26-DJF\"\n", "point_coords = \"POINT(11.5469 44.9524)\"\n", "date_range = \"../..\"\n", "\n", @@ -68,7 +68,8 @@ " ],\n", " \"include_coverage_uncertainty\": True,\n", " \"include_coverage_related_data\": True,\n", - " }\n", + " },\n", + " timeout=20\n", ")\n", "try:\n", " raw_response.raise_for_status()\n", @@ -98,7 +99,7 @@ { "data": { "text/plain": [ - "[{'name': 'tas_seasonal_absolute_model_ensemble-rcp26-DJF',\n", + "[{'name': 'tas_seasonal_absolute_model_ensemble-annual-model_ensemble-tas-absolute-rcp26-DJF',\n", " 'values': [{'value': 2.640222, 'datetime': '1976-02-15T12:00:00Z'},\n", " {'value': 4.131799, 'datetime': '1977-02-14T17:57:04.390000Z'},\n", " {'value': 2.9139953, 'datetime': '1978-02-14T23:54:08.780000Z'},\n", @@ -224,8 +225,12 @@ " {'value': 4.646112, 'datetime': '2098-02-14T18:02:55.610000Z'},\n", " {'value': 4.863336, 'datetime': '2099-02-15T00:00:00Z'}],\n", " 'info': {'processing_method': 'NO_SMOOTHING',\n", - " 'coverage_identifier': 'tas_seasonal_absolute_model_ensemble-rcp26-DJF',\n", + " 'coverage_identifier': 'tas_seasonal_absolute_model_ensemble-annual-model_ensemble-tas-absolute-rcp26-DJF',\n", " 'coverage_configuration': 'tas_seasonal_absolute_model_ensemble',\n", + " 'aggregation_period': 'annual',\n", + " 'climatological_model': 'model_ensemble',\n", + " 'climatological_variable': 'tas',\n", + " 'measure': 'absolute',\n", " 'scenario': 'rcp26',\n", " 'year_period': 'DJF'},\n", " 'translations': {'parameter_names': {'series_name': {'en': 'series name',\n", @@ -236,18 +241,29 @@ " 'it': 'Identificatore di copertura'},\n", " 'coverage_configuration': {'en': 'coverage configuration',\n", " 'it': 'Configurazione della copertura'},\n", + " 'aggregation_period': {'en': 'Temporal aggregation period',\n", + " 'it': 'Periodo di aggregazione temporale'},\n", + " 'climatological_model': {'en': 'Forecast model',\n", + " 'it': 'Modello di previsione'},\n", + " 'climatological_variable': {'en': 'Variable', 'it': 'Variabile'},\n", + " 'measure': {'en': 'Measurement type', 'it': 'Tipo di misurazione'},\n", " 'scenario': {'en': 'Scenario', 'it': 'Scenario'},\n", " 'year_period': {'en': 'Year period', 'it': \"Periodo dell'anno\"}},\n", - " 'parameter_values': {'series_name': {'en': 'TAS seasonal absolute model ensemble',\n", - " 'it': 'TAS valore assoluto di stagione media ensemble'},\n", + " 'parameter_values': {'series_name': {'en': 'Mean temperature',\n", + " 'it': 'Temperatura media'},\n", " 'processing_method': {'en': 'no processing', 'it': 'nessuna elaborazione'},\n", - " 'coverage_identifier': {'en': 'tas_seasonal_absolute_model_ensemble-rcp26-DJF',\n", - " 'it': 'tas_seasonal_absolute_model_ensemble-rcp26-DJF'},\n", - " 'coverage_configuration': {'en': 'TAS seasonal absolute model ensemble',\n", - " 'it': 'TAS valore assoluto di stagione media ensemble'},\n", + " 'coverage_identifier': {'en': 'tas_seasonal_absolute_model_ensemble-annual-model_ensemble-tas-absolute-rcp26-DJF',\n", + " 'it': 'tas_seasonal_absolute_model_ensemble-annual-model_ensemble-tas-absolute-rcp26-DJF'},\n", + " 'coverage_configuration': {'en': 'Mean temperature',\n", + " 'it': 'Temperatura media'},\n", + " 'aggregation_period': {'en': 'Annual', 'it': 'Annuale'},\n", + " 'climatological_model': {'en': '5 Model ensemble',\n", + " 'it': 'Insieme di 5 modelli'},\n", + " 'climatological_variable': {'en': 'TAS', 'it': 'TAS'},\n", + " 'measure': {'en': 'Absolute value', 'it': 'Valore assoluto'},\n", " 'scenario': {'en': 'RCP2.6', 'it': 'RCP2.6'},\n", " 'year_period': {'en': 'Winter', 'it': 'Inverno'}}}},\n", - " {'name': 'tas_seasonal_absolute_model_ensemble-rcp26-DJF__MOVING_AVERAGE_11_YEARS',\n", + " {'name': 'tas_seasonal_absolute_model_ensemble-annual-model_ensemble-tas-absolute-rcp26-DJF__MOVING_AVERAGE_11_YEARS',\n", " 'values': [{'value': 3.482440627272727,\n", " 'datetime': '1981-02-14T17:45:21.951000Z'},\n", " {'value': 3.4798188999999997, 'datetime': '1982-02-14T23:42:26.341000Z'},\n", @@ -364,8 +380,12 @@ " {'value': 4.848634945454545, 'datetime': '2093-02-14T12:17:33.659000Z'},\n", " {'value': 4.840300854545454, 'datetime': '2094-02-14T18:14:38.049000Z'}],\n", " 'info': {'processing_method': 'MOVING_AVERAGE_11_YEARS',\n", - " 'coverage_identifier': 'tas_seasonal_absolute_model_ensemble-rcp26-DJF',\n", + " 'coverage_identifier': 'tas_seasonal_absolute_model_ensemble-annual-model_ensemble-tas-absolute-rcp26-DJF',\n", " 'coverage_configuration': 'tas_seasonal_absolute_model_ensemble',\n", + " 'aggregation_period': 'annual',\n", + " 'climatological_model': 'model_ensemble',\n", + " 'climatological_variable': 'tas',\n", + " 'measure': 'absolute',\n", " 'scenario': 'rcp26',\n", " 'year_period': 'DJF'},\n", " 'translations': {'parameter_names': {'series_name': {'en': 'series name',\n", @@ -376,19 +396,30 @@ " 'it': 'Identificatore di copertura'},\n", " 'coverage_configuration': {'en': 'coverage configuration',\n", " 'it': 'Configurazione della copertura'},\n", + " 'aggregation_period': {'en': 'Temporal aggregation period',\n", + " 'it': 'Periodo di aggregazione temporale'},\n", + " 'climatological_model': {'en': 'Forecast model',\n", + " 'it': 'Modello di previsione'},\n", + " 'climatological_variable': {'en': 'Variable', 'it': 'Variabile'},\n", + " 'measure': {'en': 'Measurement type', 'it': 'Tipo di misurazione'},\n", " 'scenario': {'en': 'Scenario', 'it': 'Scenario'},\n", " 'year_period': {'en': 'Year period', 'it': \"Periodo dell'anno\"}},\n", - " 'parameter_values': {'series_name': {'en': 'TAS seasonal absolute model ensemble',\n", - " 'it': 'TAS valore assoluto di stagione media ensemble'},\n", + " 'parameter_values': {'series_name': {'en': 'Mean temperature',\n", + " 'it': 'Temperatura media'},\n", " 'processing_method': {'en': 'centered 11-year moving average',\n", " 'it': 'media mobile centrata a 11 anni'},\n", - " 'coverage_identifier': {'en': 'tas_seasonal_absolute_model_ensemble-rcp26-DJF',\n", - " 'it': 'tas_seasonal_absolute_model_ensemble-rcp26-DJF'},\n", - " 'coverage_configuration': {'en': 'TAS seasonal absolute model ensemble',\n", - " 'it': 'TAS valore assoluto di stagione media ensemble'},\n", + " 'coverage_identifier': {'en': 'tas_seasonal_absolute_model_ensemble-annual-model_ensemble-tas-absolute-rcp26-DJF',\n", + " 'it': 'tas_seasonal_absolute_model_ensemble-annual-model_ensemble-tas-absolute-rcp26-DJF'},\n", + " 'coverage_configuration': {'en': 'Mean temperature',\n", + " 'it': 'Temperatura media'},\n", + " 'aggregation_period': {'en': 'Annual', 'it': 'Annuale'},\n", + " 'climatological_model': {'en': '5 Model ensemble',\n", + " 'it': 'Insieme di 5 modelli'},\n", + " 'climatological_variable': {'en': 'TAS', 'it': 'TAS'},\n", + " 'measure': {'en': 'Absolute value', 'it': 'Valore assoluto'},\n", " 'scenario': {'en': 'RCP2.6', 'it': 'RCP2.6'},\n", " 'year_period': {'en': 'Winter', 'it': 'Inverno'}}}},\n", - " {'name': 'tas_seasonal_absolute_model_ensemble-rcp26-DJF__LOESS_SMOOTHING',\n", + " {'name': 'tas_seasonal_absolute_model_ensemble-annual-model_ensemble-tas-absolute-rcp26-DJF__LOESS_SMOOTHING',\n", " 'values': [{'value': 3.390450916107852, 'datetime': '1976-02-15T12:00:00Z'},\n", " {'value': 3.4152036707620823, 'datetime': '1977-02-14T17:57:04.390000Z'},\n", " {'value': 3.4395144785386265, 'datetime': '1978-02-14T23:54:08.780000Z'},\n", @@ -514,8 +545,981 @@ " {'value': 4.804508602179396, 'datetime': '2098-02-14T18:02:55.610000Z'},\n", " {'value': 4.804744469271952, 'datetime': '2099-02-15T00:00:00Z'}],\n", " 'info': {'processing_method': 'LOESS_SMOOTHING',\n", - " 'coverage_identifier': 'tas_seasonal_absolute_model_ensemble-rcp26-DJF',\n", + " 'coverage_identifier': 'tas_seasonal_absolute_model_ensemble-annual-model_ensemble-tas-absolute-rcp26-DJF',\n", " 'coverage_configuration': 'tas_seasonal_absolute_model_ensemble',\n", + " 'aggregation_period': 'annual',\n", + " 'climatological_model': 'model_ensemble',\n", + " 'climatological_variable': 'tas',\n", + " 'measure': 'absolute',\n", + " 'scenario': 'rcp26',\n", + " 'year_period': 'DJF'},\n", + " 'translations': {'parameter_names': {'series_name': {'en': 'series name',\n", + " 'it': 'Nome della serie'},\n", + " 'processing_method': {'en': 'processing method',\n", + " 'it': 'Metodo di elaborazione'},\n", + " 'coverage_identifier': {'en': 'coverage identifier',\n", + " 'it': 'Identificatore di copertura'},\n", + " 'coverage_configuration': {'en': 'coverage configuration',\n", + " 'it': 'Configurazione della copertura'},\n", + " 'aggregation_period': {'en': 'Temporal aggregation period',\n", + " 'it': 'Periodo di aggregazione temporale'},\n", + " 'climatological_model': {'en': 'Forecast model',\n", + " 'it': 'Modello di previsione'},\n", + " 'climatological_variable': {'en': 'Variable', 'it': 'Variabile'},\n", + " 'measure': {'en': 'Measurement type', 'it': 'Tipo di misurazione'},\n", + " 'scenario': {'en': 'Scenario', 'it': 'Scenario'},\n", + " 'year_period': {'en': 'Year period', 'it': \"Periodo dell'anno\"}},\n", + " 'parameter_values': {'series_name': {'en': 'Mean temperature',\n", + " 'it': 'Temperatura media'},\n", + " 'processing_method': {'en': 'LOESS', 'it': 'LOESS'},\n", + " 'coverage_identifier': {'en': 'tas_seasonal_absolute_model_ensemble-annual-model_ensemble-tas-absolute-rcp26-DJF',\n", + " 'it': 'tas_seasonal_absolute_model_ensemble-annual-model_ensemble-tas-absolute-rcp26-DJF'},\n", + " 'coverage_configuration': {'en': 'Mean temperature',\n", + " 'it': 'Temperatura media'},\n", + " 'aggregation_period': {'en': 'Annual', 'it': 'Annuale'},\n", + " 'climatological_model': {'en': '5 Model ensemble',\n", + " 'it': 'Insieme di 5 modelli'},\n", + " 'climatological_variable': {'en': 'TAS', 'it': 'TAS'},\n", + " 'measure': {'en': 'Absolute value', 'it': 'Valore assoluto'},\n", + " 'scenario': {'en': 'RCP2.6', 'it': 'RCP2.6'},\n", + " 'year_period': {'en': 'Winter', 'it': 'Inverno'}}}},\n", + " {'name': 'tas_seasonal_absolute_model_ec_earth_racmo22e-annual-ec_earth_racmo22e-tas-absolute-rcp26-DJF',\n", + " 'values': [{'value': 3.1276793, 'datetime': '1976-02-15T12:00:00Z'},\n", + " {'value': 3.5997558, 'datetime': '1977-02-14T17:54:11.613000Z'},\n", + " {'value': 1.8662354, 'datetime': '1978-02-14T23:48:23.226000Z'},\n", + " {'value': 3.4549499, 'datetime': '1979-02-15T05:42:34.839000Z'},\n", + " {'value': 2.607141, 'datetime': '1980-02-15T11:36:46.452000Z'},\n", + " {'value': 2.7058654, 'datetime': '1981-02-14T17:30:58.065000Z'},\n", + " {'value': 1.0867554, 'datetime': '1982-02-14T23:25:09.677000Z'},\n", + " {'value': 3.425653, 'datetime': '1983-02-15T05:19:21.290000Z'},\n", + " {'value': 3.6214843, 'datetime': '1984-02-15T11:13:32.903000Z'},\n", + " {'value': 4.8619323, 'datetime': '1985-02-14T17:07:44.516000Z'},\n", + " {'value': 2.356317, 'datetime': '1986-02-14T23:01:56.129000Z'},\n", + " {'value': 2.859613, 'datetime': '1987-02-15T04:56:07.742000Z'},\n", + " {'value': 3.69552, 'datetime': '1988-02-15T10:50:19.355000Z'},\n", + " {'value': 3.5425048, 'datetime': '1989-02-14T16:44:30.968000Z'},\n", + " {'value': 2.9872375, 'datetime': '1990-02-14T22:38:42.581000Z'},\n", + " {'value': 4.085138, 'datetime': '1991-02-15T04:32:54.194000Z'},\n", + " {'value': 3.6747375, 'datetime': '1992-02-15T10:27:05.806000Z'},\n", + " {'value': 3.3122802, 'datetime': '1993-02-14T16:21:17.419000Z'},\n", + " {'value': 4.7436767, 'datetime': '1994-02-14T22:15:29.032000Z'},\n", + " {'value': 6.795343, 'datetime': 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'tas_seasonal_absolute_model_hadgem2_es_racmo22e',\n", + " 'aggregation_period': 'annual',\n", + " 'climatological_model': 'hadgem2_racmo22e',\n", + " 'climatological_variable': 'tas',\n", + " 'measure': 'absolute',\n", " 'scenario': 'rcp26',\n", " 'year_period': 'DJF'},\n", " 'translations': {'parameter_names': {'series_name': {'en': 'series name',\n", @@ -526,17 +1530,2535 @@ " 'it': 'Identificatore di copertura'},\n", " 'coverage_configuration': {'en': 'coverage configuration',\n", " 'it': 'Configurazione della copertura'},\n", + " 'aggregation_period': {'en': 'Temporal aggregation period',\n", + " 'it': 'Periodo di aggregazione temporale'},\n", + " 'climatological_model': {'en': 'Forecast model',\n", + " 'it': 'Modello di previsione'},\n", + " 'climatological_variable': {'en': 'Variable', 'it': 'Variabile'},\n", + " 'measure': {'en': 'Measurement type', 'it': 'Tipo di misurazione'},\n", " 'scenario': {'en': 'Scenario', 'it': 'Scenario'},\n", " 'year_period': {'en': 'Year period', 'it': \"Periodo dell'anno\"}},\n", - " 'parameter_values': {'series_name': {'en': 'TAS seasonal absolute model ensemble',\n", - " 'it': 'TAS valore assoluto di stagione media ensemble'},\n", + " 'parameter_values': {'series_name': {'en': 'Mean temperature',\n", + " 'it': 'Temperatura media'},\n", " 'processing_method': {'en': 'LOESS', 'it': 'LOESS'},\n", - " 'coverage_identifier': {'en': 'tas_seasonal_absolute_model_ensemble-rcp26-DJF',\n", - " 'it': 'tas_seasonal_absolute_model_ensemble-rcp26-DJF'},\n", - " 'coverage_configuration': {'en': 'TAS seasonal absolute model ensemble',\n", - " 'it': 'TAS valore assoluto di stagione media ensemble'},\n", + " 'coverage_identifier': {'en': 'tas_seasonal_absolute_model_hadgem2_es_racmo22e-annual-hadgem2_racmo22e-tas-absolute-rcp26-DJF',\n", + " 'it': 'tas_seasonal_absolute_model_hadgem2_es_racmo22e-annual-hadgem2_racmo22e-tas-absolute-rcp26-DJF'},\n", + " 'coverage_configuration': {'en': 'Mean temperature',\n", + " 'it': 'Temperatura media'},\n", + " 'aggregation_period': {'en': 'Annual', 'it': 'Annuale'},\n", + " 'climatological_model': {'en': 'HadGEM RACMO22E', 'it': 'HadGEM RACMO22E'},\n", + " 'climatological_variable': {'en': 'TAS', 'it': 'TAS'},\n", + " 'measure': {'en': 'Absolute value', 'it': 'Valore assoluto'},\n", " 'scenario': {'en': 'RCP2.6', 'it': 'RCP2.6'},\n", - " 'year_period': {'en': 'Winter', 'it': 'Inverno'}}}}]" + " 'year_period': {'en': 'Winter', 'it': 'Inverno'}}}},\n", + " {'name': 'tas_seasonal_absolute_model_ensemble_upper_uncertainty-annual-model_ensemble-tas-absolute-rcp26-upper_bound-DJF',\n", + " 'values': [{'value': 3.206551, 'datetime': '1976-02-15T12:00:00Z'},\n", + " {'value': 4.8771133, 'datetime': '1977-02-14T17:57:04.390000Z'},\n", + " {'value': 3.683614, 'datetime': '1978-02-14T23:54:08.780000Z'},\n", + " {'value': 5.198719, 'datetime': '1979-02-15T05:51:13.171000Z'},\n", + " {'value': 5.8943624, 'datetime': '1980-02-15T11:48:17.561000Z'},\n", + " {'value': 4.286587, 'datetime': 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{'en': 'coverage identifier',\n", + " 'it': 'Identificatore di copertura'},\n", + " 'coverage_configuration': {'en': 'coverage configuration',\n", + " 'it': 'Configurazione della copertura'},\n", + " 'aggregation_period': {'en': 'Temporal aggregation period',\n", + " 'it': 'Periodo di aggregazione temporale'},\n", + " 'climatological_model': {'en': 'Forecast model',\n", + " 'it': 'Modello di previsione'},\n", + " 'climatological_variable': {'en': 'Variable', 'it': 'Variabile'},\n", + " 'measure': {'en': 'Measurement type', 'it': 'Tipo di misurazione'},\n", + " 'scenario': {'en': 'Scenario', 'it': 'Scenario'},\n", + " 'uncertainty_type': {'en': 'Uncertainty type',\n", + " 'it': 'Tipologia dei limiti di incertezza'},\n", + " 'year_period': {'en': 'Year period', 'it': \"Periodo dell'anno\"}},\n", + " 'parameter_values': {'series_name': {'en': 'Mean temperature',\n", + " 'it': 'Temperatura media'},\n", + " 'processing_method': {'en': 'LOESS', 'it': 'LOESS'},\n", + " 'coverage_identifier': {'en': 'tas_seasonal_absolute_model_ensemble_lower_uncertainty-annual-model_ensemble-tas-absolute-rcp26-lower_bound-DJF',\n", + " 'it': 'tas_seasonal_absolute_model_ensemble_lower_uncertainty-annual-model_ensemble-tas-absolute-rcp26-lower_bound-DJF'},\n", + " 'coverage_configuration': {'en': 'Mean temperature',\n", + " 'it': 'Temperatura media'},\n", + " 'aggregation_period': {'en': 'Annual', 'it': 'Annuale'},\n", + " 'climatological_model': {'en': '5 Model ensemble',\n", + " 'it': 'Insieme di 5 modelli'},\n", + " 'climatological_variable': {'en': 'TAS', 'it': 'TAS'},\n", + " 'measure': {'en': 'Absolute value', 'it': 'Valore assoluto'},\n", + " 'scenario': {'en': 'RCP2.6', 'it': 'RCP2.6'},\n", + " 'uncertainty_type': {'en': 'Uncertainty lower bounds',\n", + " 'it': \"Limiti inferiori dell'incertezza\"},\n", + " 'year_period': {'en': 'Winter', 'it': 'Inverno'}}}},\n", + " {'name': 'TDd',\n", + " 'values': [{'value': -0.436, 'datetime': '1991-01-01T00:00:00Z'},\n", + " {'value': 2.239, 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'datetime': '2009-01-01T00:00:00Z'},\n", + " {'value': 2.741, 'datetime': '2010-01-01T00:00:00Z'},\n", + " {'value': 2.819, 'datetime': '2011-01-01T00:00:00Z'},\n", + " {'value': 1.791, 'datetime': '2012-01-01T00:00:00Z'},\n", + " {'value': 2.632, 'datetime': '2013-01-01T00:00:00Z'},\n", + " {'value': 5.808, 'datetime': '2014-01-01T00:00:00Z'},\n", + " {'value': 4.941, 'datetime': '2015-01-01T00:00:00Z'},\n", + " {'value': 4.716, 'datetime': '2016-01-01T00:00:00Z'},\n", + " {'value': 3.355, 'datetime': '2017-01-01T00:00:00Z'},\n", + " {'value': 3.797, 'datetime': '2018-01-01T00:00:00Z'},\n", + " {'value': 3.213, 'datetime': '2019-01-01T00:00:00Z'},\n", + " {'value': 5.193, 'datetime': '2020-01-01T00:00:00Z'},\n", + " {'value': 4.847, 'datetime': '2021-01-01T00:00:00Z'},\n", + " {'value': 3.97, 'datetime': '2022-01-01T00:00:00Z'},\n", + " {'value': 5.892, 'datetime': '2023-01-01T00:00:00Z'},\n", + " {'value': 5.56, 'datetime': '2024-01-01T00:00:00Z'}],\n", + " 'info': {'processing_method': 'NO_SMOOTHING',\n", + " 'variable': 'TDd',\n", + " 'series_elaboration': 'ORIGINAL',\n", + " 'derived_series': ''},\n", + " 'translations': {'parameter_names': {'series_name': {'en': 'series name',\n", + " 'it': 'Nome della serie'},\n", + " 'processing_method': {'en': 'processing method',\n", + " 'it': 'Metodo di elaborazione'},\n", + " 'variable': {'en': 'variable', 'it': 'Variabile'},\n", + " 'series_elaboration': {'en': 'series elaboration',\n", + " 'it': 'elaborazione in serie'},\n", + " 'derived_series': {'en': 'derived series', 'it': 'Serie derivate'}},\n", + " 'parameter_values': {'series_name': {'en': 'Mean temperature',\n", + " 'it': 'Temperatura media'},\n", + " 'processing_method': {'en': 'no processing', 'it': 'nessuna elaborazione'},\n", + " 'variable': {'en': 'Mean temperature', 'it': 'Temperatura media'},\n", + " 'series_elaboration': {'en': 'original', 'it': 'Originale'},\n", + " 'derived_series': {'en': '', 'it': ''}}}}]" ] }, "execution_count": 6, @@ -565,7 +4087,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -575,18 +4097,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3933cb3985e740048d09ffd8831be756", + "model_id": "1b849cb2b9b44897910c9519980eb94b", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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qe6sCu2g3XUa0YhGpAhZCCJ8j79zCr1kqs10VHg4ALXbXNYBNmYqt9zG0DKCuxm2XVU4DL27CdjDVi0CgqsvIGacAsMxNoCiEEMI3SAAo/Joj8HMu2vAEa7WA0rm/rscew+7YBqbmn/HFaW3R6WD70QKXdXsN4a7Dh5YBdDMFHCwBoBBC+BwJAIVfszTDFLDNrlC9qLi8GdYBVljdrwEEaBseRP/kKACW7m1cFtBdh4/Y0JoZQLNkAIUQwmdJACj8miOIsngwA+gumGyOQhBtGxh9zSlggEsru4L8tjenUeetqu6tentwZADz3KwBlE4gQgjhe+SdW/i15igCsTql/4yV2bnyZtgKxvE4RoP7P+OeiWoLtxOFTZwCdq4CdrMGsNxNtbAQQgjfIAGg8GuODGD1NXtnw+K03i+8cv+85qgEttQxBQwQalIfu8TcuMd2t71L1RpAi9Nx0glECCF8lQSAwq9VrQH04BRwZR9gna4qCGuWKWB73VPAoSY1MHPu3tEQZjdTuzGhgUBVBlBRFKcqYHkbEUIIXyPv3MKvOQI/i93usa1aHBXFgQF6LThqjiIQx9hrmwIO0zKAjQsA3XX4iK42BVxhs+N4uYKMkgEUQghfIwGg8GuOKWBFwWP9gLX9+fQ6bYsUTweAdruijbf2DKAaAJZW2LA34rm53Qewcgq4yGylwmp3WdMoU8BCCOF7JAAUfs15A2irxwLAqg4djiyap6eAHdO/AIH1ZAABSioangXUOnw4BXYRQYEEVAaa+aUV2jSxXud+I2ohhBCtmwSAwq9VOBVseKobiNVe1aEjuHJ61NNFIM4bVwfq3f8Zmwx6LWhrTCGIu+1d9Hod0SHqOsC8kgqnLGEAOp0EgEII4WskABR+zXn7F091A3FZA2honilg53HXloHT6XSEGhtfCOLoWmKqtr2L8zpAaQMnhBC+TQJA4decM4Ce2gvQkUk0OGUAPb0PoHO2MqCWNYDQtEKQ2rZ3cW4H5zhG2sAJIYRvkgBQ+DXnoM9TAaCWAdQ33xpAx2MYA/R1TsGGNikArFkEAq6bQWvrBGULGCGE8Eny7i38mtklA+ipKeCqDZqDm6sIxFa1zrAujgCwqFFTwO6nd7UMYImlqguIVAALIYRPkgBQ+DXXNYCenwJ2ZNE8XQTiXGlcl6ZNAbtv8aZtBu00BSybQAshhG+Sd2/h15qlCtgpOHNkAB1ZNU+xOGUZ6+LoBtKkNYDVgjtHEUheSYXbfsFCCCF8hwSAwq85T/t6rArY7rQRdDNvA9PQKeDiBm4DY7XZtf0Qq0/vOjaDPiMBoBBC+DwJAMU5R1EUvtl8lEOnSuo9tjmqgC0ureCaZw1gRQMzgI2dAi53ej1qTgE71gBW7QMoVcBCCOGbJAAU55zVB/J49MttPP3tznqPda0C9mwrOHUNoCMA9Ow2MM6PUZeqDGADA0CnQNVUrcOIlgEslSpgIYTwdfLuLc45+04WAepatfqYmyED6LwRdHP1AnbeBqYujc4AVo7TaNCjr7a/oGMN4GnZCFoIIXyeBIDinJN1uhRoWOGFSxWw3UNTwC6t4NQ/sebqBNLgKeAG9gJ2TO1Wz/5BVQbQbLVzpjK4lm1ghBDCN0kAKM45R/LUtX/mBky7Olf+Vlg9NAVsdUzPOq0B9Pg2MI2dAm7Y49dV3BFiDMBYGRgeLyivPE7eQoQQwhfJu7c452RqGcD6A0CL1fMZQEcVbaBe12xFIM6FJnUJa+Q2MI7XzF1gp9PptG4g2QVlgBSBCCGEr5IAUJxT7HaFrDNqcNKQKeCKZmgFZ3G7BtCzRSBWp2nmujS2FZy5lj7ADo5uIMfzHRlACQCFEMIXSQAozikni8q1rV3qywAqiuJS+ev5KuDmKwJxPMf6N4JuZBVwLW3gHBzdQE471gDKFLAQQvgkefcW55TMvFLt5wqrHUWpPair3vnDc1XAzkUgVVPAdY2l0Y9ROc1s0Hu6Crj2KWCAmFCTy2WTZACFEMInSQAozilHTpe6XK4rC1g94+epTiAWp+DMMZVqsyseyzBCVbBqNDR0Cvjsi0AAYkICXS7LFLAQQvgmCQDFOSWrWgBYV39fi7WZM4AGHUHGqj+xcg/2A3ZMAdebATSqAWCFze7S9aQ2VdvA1L0G0CHIzXYxQgghWj959xbnlMzqGcA6ii9qTgF7ag2gowpYjzFAj2M/5XIPbgWjVRrXuwawKpBryDRwVQawtilg1wDQMcUthBDCt0gAKM4pR/KqTwHXHnRVz4h5rgq4ao8+nU6nFYJ4cisYR/ayvilgQ4Be29S5IYUg9RWBOLqBOMgUsBBC+CYJAMU5pfoUcF1rAKtnAK3N0AoOaJa9AC0NLAKBxnUDqb8IpPoUsASAQgjhiyQAFOeMYrNV6/8bUjk1WdcUcPWMX4WHt4Fx7NEX1Ax7ATa0FRw0bi/AevcBrJEBlLcQIYTwRfLuLc4Zji1gokMCiQ1z9K1t+BSwpzKA1bNz2lYwnlwDWC3IrEtj2sE51gCaGpoBlClgIYTwSRIAinOGowCkQ2woxsrMWN3bwDT/PoBAs2wG3dBWcNC4dnDaFHCtVcCyDYwQQpwLJAAU54zM0yUAdIgJ0bYxqSsArH6bI3N3tqoHZ45pUk+uAaxwKjSpT2O6gZjrKQIxGQK0NYXqcfIWIoQQvkjevcU5w5EBTIkJ0aYwzXUEXdW3fam+L2BTObeCA+c1gM0xBezZNYD1FYGAaxZQMoBCCOGbJACs9Morr6DT6Xj44Ye9PRTRRI4tYNQMYP1TwDXWAHooA2i1u58C9mgVcGXwamzIFLCxEQGg1bEGsPbALqayECRAr2tQACqEEKL1kXdvYMOGDbz33nv07dvX20MRZyFLWwNYNQVcV/eLmlXAnsoAuk4BN0cRiKVJU8ANLwKpK7Pn6AYiXUCEEMJ3+f07eHFxMZMnT+aDDz4gOjra28MRTWS12Tl6pgw4iwygpzeCrmwB4iio8GwRSMOngJtWBFL7eR0ZQJn+FUII3+X3AeADDzzAlVdeyaWXXurtoYizkF1QjtWuYAzQEx8RpE1h1rkNTDO1gqu+EbQjA+jJfQCrHqPhGcDGtYJrQAZQAkAhhPBZhvoPOXfNmzePzZs3s2HDhgYdbzabMZvN2uXCwsLmGppoJMf0b/uYYAL0uiZlAJujFRw0TyeQiiYUgTSsCthRBFLHGkAtAPT7/z+K5pa1HnL3Qf9bQF/L76TVDAeWwq4FoNPB+LcgMLhlxymED/LbADArK4uHHnqIxYsXExQU1KD7zJw5kxkzZjTzyERTHDldVQACVAWADegEotOBong+ANQygM0QADoygIYGTQE3phWcIwNYxxSwZABFS7BZ4YubofQU7P4WrvsQgp2W6ZzcDetmwZ7/QXlB1fXtBsHQe1t8uEL4Gr/9L/ymTZvIyclh4MCBGAwGDAYDy5cv5+2338ZgMGCz1fywfuKJJygoKNC+srKyvDBy4Y7zFjCA0z6A9XcCCa2skrV6agrY7jo96wimypuhCMTohSKQdlFqdiU2zFTv+YRosqPr1eAP4MAS+OBiyNkLBUfh2/th9gjY8h81+AtPhM4Xq8eufhusFd4btxA+wm8zgJdccgk7duxwue4Pf/gDPXr04PHHHycgoOYHoMlkwmSSD73WyNEGLrkyADQ2YArYEUSFGAMoNls92AnEfSu48jqC0cZqXC9gz3UCATi/Sxv+cX1fhqTGNGSoQjTNvh/V7ykjIT8TTh+EDy8BuxWs5eptaVer2b4OI8BWAW/2gcKjsOO/MGCy98buq7K3qwF1m24Q1ladHhHnLL8NAMPDw+ndu7fLdaGhocTGxta4XrR+WgYwNhRwmgJuSAbQZIAis8eKQKoHZ9oaQI9mAJswBVxPAKgoihak1jUFrNfruHFwckOHKkTT7Fukfj/vHkg9H768HY6sUq9LGQmXPQ/tB1cdrw+CEdNg8TOw6g3od1Pt6waFK0WBpc/DqterrjNFqIFg97HQ5waITvXa8ETz8NsAUJxbHAFgcow6PVnVCaSOIpDKICqkMkPn+TWAzbkRtOtj1KWhRSAVNjtKZQxsqiMDKESzy02HvAMQYIQul4ApHKZ8C5s/gagU9Tp32anBd8DKf0Leftj7A/Sc0OJD9zl2O/w4HTb+n3o5qoM6zW4uhGMb1a9fX4TkoWog2OsaCG3j3TELj5AA0MmyZcu8PQTRBOUWGwVlFgASIyoDwAb0Aq6xBtBTnUCqZeeqWsF5cBsYu+tWM3VxzgAqioKulmkd59fKJBW+wpsc07+pF6jBH0BAIAy5s+77mcLhvHthxT/UQDDtapnGrIvNAgvug51fATq46g0Y/Ae1svr0ITi6QZ1OP7QCsn5XvxY9Dp1GQ5/roedEMIZ4+UmIppIAUPi8vBJ1wbcxQE9EsPor7ZgCbkgnkBBT/V1DGsNSSys4T24E7RhrY7aBsStqEOpYk1idY3w6XdXrJxrBXARf3QGJ/eHiJ709Gt/mmP7tPq7x9x16H6z9N2Rvg4xf1WxhfUpPgzEMDMbGP54vKS+E7fPh1H51TWXuXijIAr0Brn0fel+nHmcwQdse6tfA26AwG3Z+rQaD2VshY6n6tf59uOOXc/91O0fJu7zweXnF6t6MsWFGLbvVqDWAWgawuVrBqd89ug2M3bXbSF1CnCp665oGdkyXmwz6WrOEog5bP4f9v6jZp70LvT0a31VySs00QdMCwNBYGDRV/Xnho3Bwee3HHt0I82+Df3RSC0zKzjT+8XzJby+p073r34MDi9XgLzAUbp5XFfy5E5Gorq+8dzn8aTNc9CQERcHxLeo5hU+SAFD4vFNOAaBDVSeQhlUBq5fPfgpYURRsdkcVcLWNoJuhCMTYgEydXq8j1Fh/JXBDtoARtVAU2DS36vL/HlazSqLx0n8GFEjoC5Htm3aOEQ9CWAKcOQyfXA3/nQoFx6AkD46sgY1zYM6VatC353v18U5sh88nQUWJ555La6IosOcH9ef+t6obZt/+P3hkJ3S9rOHnie0Mo/4CE/6tXl79Fhxe5XrM0Y2w7BXPjFs0G5kCFj7vVLE6BdzGaV+6BnUCsTlVAeOZIhDnILLmGkAPBoDWhmcAQX2OJRU2lwzgvhNFlFRYGdghunJ89W8BI2qRtR5ydoMhWA1a8varmZbrP/L2yHyPY/1f9yuafo6IRHhgHfz2Mmz4UO0SsutboNp/8vSB0PdG9bG+u1/NPM6/Tc2InWvTmid3qVvkGILhytfOvltK2ngYcBts+VRdR3jfKnXq+LeXYO0sUOxqlXYXabPaWkkGUPg8LQMY6i4ArH8K2JNVwM7TyMZqnUA8WQRStc6wYX/C1beCsdsVbv5gHTe9t478UjWAbsgWMKIWjuxf72vh2vdAF6Cumdr1rTdH5Xss5eq6PWja9K+z4Gi44lW4ZzkkD0MN/nRqlWuXS+HCP8ND22DiO5B2FUz+CgJD1LVtC+4Bu+f+w9YqpP+kfu802nOt8sa+AtEd1ankr+6A2SNhzb/U4K/vJEga6JnHEc1CMoDC550qqswAhjtNATuqgBvQCs6RAfREJxCL1TkD6FoEUmGzY7XZG7R3X72P04gpYKi5Fcyx/DJOVxbPHM4rpX+IUaaAm6rsDOz6Rv150B/UVmTnPwIrX1PXoLUbqAYdon6HVoClFMKTILGfZ86Z2Bfu+End2iQktvaq1eTzYNJ/1GngXQsgvpcaJJ4r0n9Wv3cb47lzmsLg2g/gozFq4AxqV5ar3lT3DxStmvxXX/i8vBI1A9jGOQMY2PApYEeAZrUrKMrZBYEWpwygY3rWueq23AOVxnZ7zXWG9XF0A3EEgBm5xdptx86UqWNzFIFIANg4279UO1O07VW1MfGoxyG+N5TmwdsD1SmyEzvqPo+Avf9Tv3cf69ntW3Q6iEquf8uSLpfA+DfVn1e+AUUnPTcGbyo5pW7pAp4NAAGSh8Clz6qVxANug/vXSfDnIyQDKHyeYwrYOQPomH6tawrYka1zBEegZtaMhqZ/8Fi1CmBdjYpkUNcBOqZjm8o5yAxsYAawagpYfT0ycqsWuh/PdwSAlRlA2QKm4RRFLSgAdf80R9BiMMKNn8B30yBzDWz7Qv3qfIm6+D5KOqnUYK2APZUBoDc3cO4/Wf03PbYRlr2s/ntVstltmG1mKmwV6nd7BRabBYvdgtVu1b7bFBs2uw2rYsWu2LEpNrVATLGhoNRYiuig0+nQ6/To0aErziEgPJGAgED0Oj0GnYEAfQAGvQGD3kCgPtDlyxRgwhhgJDAgEIPO4FrJv38xWmFNRJLnX7ORD8Gw+9W9GoXPkABQ+Lw8N0UgQQ3IAJq1KuCqPwOLzd7gaVV3HNPKjj7AoL6pBwXqKbfYPVIJ7DxVHahv3BRwibsMYPUAUDKADZe1HnL3qAvr+9zgeltsZ7hjERzbpC6K3/WtOk323oVw3QeyOL66g8vU6fTQtuoG0B5gsVsoriimsKKQoooiiiqKKLYUU1xRrH63FFNSUUKJtYRSSyml1lLKLGWUxYZSSgLlJ3+hfN4FlNutlNvKsdrr76fdGujRYTIEERQQhMlgIqj0DMFJCQSFGwj65R5CAkMINgQTbAgmxBBCaGAoIYEhhASGEBYYRmhgKKGBoYQFhhFmDFO/B4YRUFdrPQn+fI4EgMLnuS8CacAaQK0XsHMG8OymaLUAsFqLtuDAAMotdo9UAjuPsSGt4KDmGsCMnKoAUMsAVr4eUgTSCFrxx3UQHOX+mHaD1Grgi56Er/6gblD8n+vVrTRGPS79ah0c6yh7TXT7mpRaSjldfpoz5Wc4Yz5DvjmfM+Xq93xzPgXmgqqvigIKzYWUWkubPh5j5YyCOd/tzQG6AIwBRjXrpg/UsnIBOjVLF6ALUDN3eoOa1av80qHODuhQ/3aVynSgoigoKChnjmAvPIYdsOvAhg5bSAy20DZYUVwyjRa7BautggqbGed3FjsKZdYyyqxlYK680mSEilOQfarJL0loYCgRxgjCjeGEG8OJMEYQaYrUvkcaI4kMiiTKFEWUKYpoUzRRQVGYAkz1n1y0OAkAhU+z2RWtmMG1CKSyE0gdAZ3jNueM19nuBVhbi7bgwADOYPHIZtDOzymggWsAq1cBO08BOzKA5sqxSR/gBrKaK/eQAwbdXv/xsZ3Vrgk/PwEbP4Llf4czR9SqYT+kKArFlmJOlZ3iVNFx8g7/zKmIcPKCdeStfoa88jxOl53mdLn6VW4rb/JjhQaGEm4MJywwTAteHBkuR/Yr1FCZBTNUZsfKzhC04H6CbVaCJszC1HEUpgCT9lVnNqyptnwG6+5Xf77seXXd6I7/AifUzOiU7yC+Z9XxxTnw/kVQeBQbUGEwUhHTBXPePsx6PeVXvo5ZB+WLplMeHEX5hH9TZjNrwWGptVTNfFpKKbGWUGJRM6HFlmJKLCVqxrSimAq7+h5bYlGPyS7JbtTTCjGEEB0UTUxQDNFB0USbookNjiUmKIbY4Fhig2JpE9yGNsFtiDRFotfJf0JbggSAwqedKa3ArqhLr2JCalYB2+xKrZW3jkyayaDHGKCnwmY/6wxgVYs218DMk/2AHVPAxoCGd+xwdDspqbBSUGrRsqZQlQE0SwawcQ6vhIpiteqx/ZCG3ScwSO23mjwMFtwL2+fBhdOhTdfmHWsLstltnDGf4VTZKXJLcyu/55Bbdoq88jxyS3PJLcslryzPNaiLCVO/Zy6q9dxGvZGY4BiiTdFEB0UTaYpUs0ymKCJNkdp3R1YqwhhBmDEMg76JH3X91sPvs2Hlv6Dn9c2brT24DP73oPrzBY+p6+pA7Wryw6Nwah98MgH+sAjadFH/AzJvsrq3X2wXAq7+N8FJAwg2mOD7abDlP7DoSUgZCeVm6HEZpF7epKFV2Cq06fOiiiIKzYUUVjh9mQu1LKzj+xnzGQrMBdgUmxpoFpdyrPhYvY9l0BmICY4hLjiOuOA42oS0YXT70YxKHtWksYvaSQAofJojkIkOMboEeSanIMZsdR8AOoI1Y0AAhgAdFbaz3wrGqlXnuj6e1g3Eg1PADZ3+BecqYBsZp9Tp3/AgA0XlVs6UWiitsMoawMbaV7mvWrcxja9Y7Ve51Uj6InWj4nF/9/z4PERRFMpt5eqHevkZzpSf4bRZnYo9VXaKvLI88srzyCvL41TZKU6Xn8amNPz3PCwwjDZWC7GlBbSJ7kRs6mgtKxQTFENMcIz6PSiGEENIy7YpHPUX2PY5nNwBO7+BvjfUf5+mOLRC3YDaboXe18NFT1Xdlno+3PkzzB2vjuOTq9UgcNlMOLoegiLh5vlqUOhw5RuQd1AtQEqvDKjPovrXGGBU/02CYxt1P0eW90z5GZfp+9Plp7Xfm9Nlp9Xfo/I88s35WBUrOaU55JTmaOdpG9xWAsBmIAGg8GmOApDYUNdd+40BrgFgqJslKFogZdBVTtna6pwybghrLcGZYysYTxSBOKapG7OfoPMUsGP9X9/2kWzPKqDIbOV4frkEgI2hKFUb63Zr4obF592tfjhv+QwufgpM4Z4bXy3sip28sjyyS7LJLsnmRMkJCswFlFnLtOk9R3FEcYWa7SkwF2hTgA2lQ0dMUIw6rZd7gLiyAuJsNtpYbcTZbMTZ7LQZ/SSxQ+4h2G6HV7uo+/+Nn6/um9hahMTAiD/Bry+qAVevayDAwx+b6z+ARY+DYlOzdRPfgerFXcHRcNsCmHsFnEqHdy8Ac4G64fgNc12DP1Cr0Cd9qk4PF2SqHU86X+TZcTeATqfTptw7RNS/F6bFblEDw7I8ckpzyC1TM8hDEhqYYReNIgGg8GnaFjBhrhGeXq/TpnVr2wrGrGUA9VrA5tzJoyksttrXAIJn2sFVZQAbHgA6F4E41v91jgsjr7iCvSeKOJ5f5tQK7hyfAs7PhP9cpwZxKSPUD93U8yGyXcPPcXKX2v3AEAQdL2zaODpdBLFdIO8AbJunBoQeUmYt42DBQQ6cOUBGfgZHCo+QWZTJ0aKjTV5LZ9AZiAqKUtdymdS1XLHB6tqt2KBY7ec2wW2ICYpRp12Pb4H3R0OACW78WH3dDq2AQ8vh52eAQAiPV4O/6FRIGuCx18Bjht4Ha9+B0xmwfT4MmOyZ89ossOgv6npQgD43wtVvq+3U3AmLU9cAzhmn9jgGGDsTOl/s/vjQNnDLPHVj625jW+Q/GGcrUB9IQmgCCaEJ9KKXt4dzzpMAUPg0Rx/g2LCafTtNhsoAsJZ1d86BlCOYcu7k0RRVVcDVp4DVy54IAJ33Gmwolwxg5RYwnePCOHqmjL0nijiWX6aN7ZzeCNpqhi+nqFkUUHv2bv5Y/XnibOh/S8PO45hW6zS6/s2Fa6PXw3n3qEHA+g9gyF11TyXb7WrgtOU/6qa+V78NnUZTYilhd95uduftZtepXew+vZvMwkyturTGw+r0tA1pS0KI+kEbHRStbfsRbAh2KZYICwzT1tQ1afp16+fq97Sr1NZu3cep69sWPwNr3lYLYsLi1WN6X+fZzZ89xRQO5z+sjnn539XewZ7Y8uTrO2H3d4AOLn1OXfNX3/OPSIIp38P/HoIOw9Xfn7rE94KHd7TO11V4nQSAwqfVlgEEdR1gkbn2vQAdawBNBr22bYvlLDOAVnvdRSB5JY2bSnOn4iwygNUDwP05RYBaCFK1Dcw5HAD+9ISalQqOhiteg+ytcGAp5OxW1+I1NADU1v+dZceDfjfD0ufVBf6HlqsBZXU2i9pfddMcyM8kJyCAjUEmtix/nK27OpB+Jh27UvP3NtoUTdfornSO6kxKRAopESl0CO9AYlgigfpGBjCKAvt/gZjONacba2M1V1aw4vq66nRqhasxTN1oubiy20bv6xo3ppY05G5Y82/IP6IG4IP/cHbny8tQgz+dHm76onGdM6JTYMq3DT9egj9RCwkAhU/LqwwA48JrBoB1dQOx2RUq6zWqZQA9MwVcvUXbkNQYftiezfwNWdx7Yaez6gdsrWWvwbo4ikDyyywUl6tbwXRuG0rSMbUpvHMG8JytAt42Hzb+H6CDaz+ErpdCn+th+J/g9R7qhs1njqgfsHUpzlGPhbMPAIMi1CBwwwdqFrB6AFh6moL/3sq6k5tZHxzE+uR2HNa26SmF03sBSAhNoHdsb3rG9qRXbC+6xXSjTXCbsxubs40fqX2NjeFw12Jom1b/fdJ/Ujd2Dk9Up7ud6XQw+nEIDIbFT6t9f9v2dH+e1sAYAhc8Cj/9FVa8pga0tU3VNsS2L9TvnS+RtmnCayQAFD7tVC1FIFA1lekuA1jhdJ3RoNc6ajiqeJuqtvV5Nwxuz5tL0sk8XcpPu05wVd+mt2OyOG0D01COKeD8UgsAIcYAEiKCaBelBoDH88u0MQedi/sAntwNPzys/jzqL2rw5xAer64DPLxSrcw9/+G6z5X+M6BAYn+ISDz7sZ13txoA7vsRTuzAFpfGjrydrD7wP9bs+S87A+zY4+O0w3Xo6IGJQQU59E+5hP6XziQ+NP7sx1Gb3H3w85PqzxVF8MVNcPdvaoFEXRzTv/1uqn37lJEPqv13wxJaf6Zq0B9g9dvqtiur34LzH21aQYjdrq75BOh/s2fHKEQjSAAofFqdU8CVxQzu1gBWuHTT0BNY2f/37KuA3ReBhBgNTBmeyltL9/Pu8gyu7JPY5O0saus2UpfQav2HO8eFodPptADwWH4ZiRHqz+fUFHD2djW42v5fsJapC+ZHPV7zuN7XVgaA3zQgAKyc/u3exOrf6uK6U9DxAlbnbGLF1xNYHRJCviODbADQ0Tm0HcM6jOa8hPMYFD+IyIMrYf5kMK+Eq+oJxM6G1ayuVbOWqe3Z8jPVAoQvp6hVqbWthSs6Wdl/FuhXz7R6vI8s9g8Mggsfg4WPwW8vwe/vqVXBfSdBciOqVI+sUguITJHQ/YrmG68Q9ZAAUPi0vHqKQMD9FLBzBjAwQKft23e2+wDWFZzdPiKV91ZksPNYIasP5HF+16ZN0TWlCjisRgAYCkBSZQB4oqCcyGD1w/ycmALOy4Bv74esdVXXtRsE137gPhuVNgEWTlfbtOVlqF073LGUQ8av6s9nOf2bVZjFb1m/sezoMjbrsrC1rfp9CLfZGV5WxvnBSQy/6l0S2vZ2vXO3MRAaByU56tq8Hlee1Vhq9euLajeK4Bj1tSvPhw8vVYPlRY/DVa+7v9+OL9VtTdoPgbhuzTM2bxg4FQqOwuZPoPSU+p+LDR/Alf9Ui3gaYmvl9G+vieoUuBBeIgGg8FmKopBbZwZQ/aCvcDMF7AiiHN00HNOpZ9sJpLZWcAAxoUZuGtKBuWsO8+7yjLMIAGt/jNqYDHoC9DpslePrHKd2XWgbbiJAr8NiU8g6rXYE8fkMoKLAt3+ErN9Bb4C0q9VqyQ7Dap9mDI2FTqPU4G7XArU7R/Vz5meqGUJLKYQnqevWGjUshT2n97A0cym/Zv7KgfwDLrd3juzMhUkjuDCwDf0LczGYItUuEIFBNU8WEKhOra75F2z+tO4A0GZt2lTlweXq+QGu/pc63R2RCNd9CF/crK6nTB6qbmrt+kTVvQ2h4UU1viLAoFbsXvSk+vpsngt7/ge/vqRu4xIUUff9zcWVlb+ce6+N8DkSAAqfVWy2asFdbVXAUPcaQGNlllCrAj7LALC+Lh13nt+RT9cdYdWBU+w4WkCf9pGNfozaKo3rotPpCDUGUKgVgKgBoCFAT0JEEMfyyygoU9cHmnx9H8Dd36rBX2AI/HE1xHRq2P16XVMzACw9rW7TkvGbmvFx6D62QWvWbHYbW3O3suTIEpZmLnXpoRqgC2BQ/CBGJ49mdPvRJEckN+JJAgOmqAHa/l+g6ASEJ6jXF+eqFcWHlqt77p05AjfMUZ9fQ506oE79osDA29VtXBy6j4PRf1U3Rl7+ilpI45xVzVwHuXvUvf96Xdu45+QrAgLVdaSdRsM7w9TthNbNVgtb6rLnf2ApUX8nk4e2yFCFqI0EgMJnOQpAQo0BWqcNZ3VNAVcP1LQq4LOeAnbfCs4hOSaE8X0T+Xbrcd5bkcG/b2l814OqfsONC9TCTIaqALAyAwjQLiqYY5X9gMHHM4CWclj8rPrzyIcaHvwB9LgKfngETu6E3HQIjoJPr1Evg5pNjO+lTmteML3W01jsFjae2MjiI4v5NfNX8srztNuCDcGMTBrJxR0u5sL2FxJpavx/ADRx3dQgIut3taq06+VqkcLOr9SWYs42/F/DA8CCo/DpRCjJhYQ+MOblmscMnwa/vwunD8Ke713P/dtL6vd+k9TX8FwWYICLnoCv7oC1/1YLeuoqjtnmKIy5ufUXvYhzngSAwmc5toBp42YLGKiaAnabAbS5ZgC1TiAeawVXe3B276jOfLv1OD/uyOZMSQXRbiqY63yMWvoN18dRCKLXQUps1ebFSVGuU4w+vQZw/XvqXm3hiWoLr8YIiVGLRPb/AutmweFVapeOsHh1/VvyUPfTsYDFZmFt9lqWHFnCr1m/UmAu0G4LN4ZzUfJFXNzhYkYkjSDY4MF1XwNuVQPAZX+HJc9VXR/fR53SbtsTvrsfjqxRs5n1Ve6WnIJPJqpFCrFd4NYFYAqreZwpDM67V80ArnoDek5UA5qDy9X1gQFGuPAvnnuerVnPayD+DbVP75q31Slid/Kz4NBK9ee+k9wfI0QLkgBQ+CxHBbC7LWCgnirgalm0QA+tAaxvChggLTGC+AgTJwvNHD1T1ugAUFu/aGhcBsERACbHhLhk+RyFIA4mX90GpuSUukcbwCXPgDG08efoda0aAG6aq16OTFbbb7kpCjHbzKw5tobFRxazLGsZRZYi7baYoBguSr6Iy1Iu47zE8xq/8XKDx3sNLPqrOq2o06vrHUc+5NpPd+2/1Y2u9y+uuV7PWXmBmvHM2w8R7eG2b9X2Y7U57x51O5TsbXBwmTod+usL6m2D/gBRjZzS9lV6PVz8pLo9zu/vwdA/qlsLndylroXM2a3+pyQ/C1DUaur69poUogVIACh8Vm7lFLC79X/gvAbQ3RRw5V562hpAD08B17M+Lz4iiJOFZk4WltOHxk0DOoLXxmYAHZXAztO/UDMA9Nkp4GUzwVyoFmf0valp5+hxhZq9slWoXS+mfOcSyJRaSll9fDWLDy9m+dHllFpLtdvaBLfh0g6XclnKZQyMH6j2wm1upnB1fd/RDeq0orvq5e5XqEHIvoW1B4B2G3x1J5zYDiFtajxvt0JjYdDt6lTwqjfU1+zoBjAEq+3e/Em3sdBuMBzbCN9PA3MRZK6teVxgiBqgC9EKSAAofJZjCji2lgCwqhNIHUUgAa5TwGdfBdyw4KxteBBQwMmi8iY8RuOrgKGqG4hjCxiHdjUCQB+bAlYUtZ/vxjnq5ctfUrMyTREUqU7hHVkDV74O4fEUVRSx/Ohylh5Zyqpjqyi3Vf2bxYfEc1nKZVyWchn92/ZHr/PCa9dtjPpVm+5XwMrX1JZ3VrP7DhbLZsKBxWrwdutXDW/3NvwBtYXeoeXqdDnA0HvUDJg/0engkqfhkwlqBhlAF6AWz3Qdo2b8ojqo1eNNqcgWohnIb6LwWY4p4Dg3ewBC3Z1ALNXXAHqsE4hrZrE28RHqh/DJQnPjH8PatCng/snR/LzrJCO7uG4/0y7ahzOAlnL48TG1Pyuoa+I6XnB25xz+AKf6T+K3rN9Y+vtSfs/+HatTUUW7sHZa0Ne7TW/vBH2NkTRA7bRRfEJdg+bcBQVgzw+w4lX156vfVo9vqKgO0OcGtQil8JjaKm7kwx4buk/pOAr6T1Yzf30nqdXTnugUI0QzkQBQ+KyqTaBrKwJxrAG0qftvBQRq2Q9z9TWAjk4gjekFfGilWvHYbhCMUSsftY2g9fVPAQPkFDY+A2hpYhHIH0d35pahHbQNnx0SI6sKGwL0ukZnFr0mPxPm3wbZW9X1bxc/Dec/0qRTKYrCgfwDLD+6nGVZy9ieux2Fqv8MdIzsqE3v9ojp0eQuLl6h16tbt2yao04DOweAuemw4D7156F/hL43Nv78Ix+q6m07/P76C03OVTodTHzH26MQosEkABQ+q642cFAVABrLcuDNqyC+N0z9AahZrKF1ArE3IAAsPa02sHdknTLXQs8JkHye1knEUE8QVZUBbEIA2IROIA7Vgz+A8KBAIoLULWKCfGUPQEWBzyepa9uCY+D6j6DzRY06RamllA0nNrDq2CpWHlvJseJjLrf3adOHiztczMUdLqZTZCO2k2mNelxZGQAugiv+qQaFpafVdnIVRZByPlz+QtPO3TZNXfN3fKu6PYwQwidIACh8Vp5WBFJbFbA6lZl2ZhmUnVG3p8jZA23TnDaCDqj83sAikPSf1RZjjk2BYzqpe6H9+iLc/r0WQBrrKQJpW5kBbMoUsLUBlcaNlRQVTOGJIt+Z/j28Sg3+jGFw73J1KrIeNruNvaf3si57Heuy17Hp5CYsdot2u1FvZGjiUEYnj2ZU+1HEh55D69hSL4DAUCjKVjOmUR3U7V5Opavr0m6YU3tf34a45BlPjVQI0UIkABSt1snCckrMVjrFudmHDLQ2cLVOAVcWM6QVrqq6cuc3cPGTTq3gHBnABhSBnDkC/52qtgKL6wHj34KIJHh7YGXXhZVUWNVWUPVmAMMrp4CbUATSlFZw9WkfHcxeXwoAHdu09Lm+1uDParey78w+Np3YxKaTm9h4ciOFFYUux7QLa8f57c5nZNJIhiYOJSQwxO25fF5gEHS5RN20efMn6t6BObshtC3ctgDC2np7hEKIFiYBoGi1bnh3LTlF5az96yU19sozW20UVXa1iKtjCjiCErqWba26ctcCuOhvbjaCrmcfQEWBhY+qwV+HEeo2GYbKMQ26Xa2E/O0lrMFq14T61wCqYz5VXIHFZm9UMKetM/RwBhCqguZWrSRPDWRA7ZVbKb88n+2ntrMtdxvbc7ez49QOSiwlLncNCwxjSMIQhiYOZXjScDpGdPSt9Xxno8eV6uu2qbJaOixBXRLRpqt3xyWE8AoJAEWrZLHZyTyt7rG2/VgBo7q5bkjrmP4NDNAREez+19hkCGC0fhsGbBCdCoXZ6ia3J3dSYQ2tvH/1TiC1TAHv/BoOLFH3iBv/VlXwB2pbsC3/gcy1dE1aDyTXG9BFhxgJDNBhsSnkFplr7MVXl7NZA1gbLQD0gU2gla2fkYON9HY92XtqI7v3fcLuvN0cLzle49jwwHAGxA9gUPwgBsUPoldsr5bZn6816nq5ujWJYlOnfaf+4H7fQCGEX/DTd0LR2hWUVa3N2pNdWCMArOoCYqo1g2My6LksYKN6odc1cGo/7P0Bdn5DhWEy4LwPoPq9wl0GsPQ0LKps8n7hn9UerM4iEmHIXbD231yV9xGv8Uy9wZler6NteBDH8ss4WVjeyABQcRm7JyRHq1Of4abW85agKAp55XkczD9IRkGG+j0/g/3ZG8jv0A4ohi1vu9wnNSKVvnF96RfXj35x/egS1YUAfesPaltESAwM+6NatHTdh43rkyyEOOe0nnd7IZw4B4B7swtr3F61BUztbdSC9FYG6bepF7pfCfFH1ABw1wIsPW8GINCgh/SfGX7wB0brE9BZYmue6Jen1aKPuB6173E28mHYOIdU8z4u02/CENCv3ufYNsJUGQBWKwQxF6kbysb3Vvdlq8ajU8DmIjCGcUlaW24fnsKYXglnf85GKqooIrMwkyOFR9SvoiMcLjjMkcIjFFuKa95BDwGKQmpkJ7rF9KBnbE96xvakR2wPIowRLT5+n1K5XZEQQkgAKFql/FLnDGBRjdtz69kCBiAubxMRujJO66KJaTdI3a7CEAxnDhFduAsw0qNkE8x7jL52K3ONUHHgLfhsNIQnQNlpKD0DRyqLSMa/7Tr16ywsDobeC6te5yHDN+zX31Hvc6y1EGT/Yji2Sf1Ku7rGxr0emwLeNh8W3AMpIwka8xIzJjRiA+BGKrWUklmUyeHCwy7BXlZRFqfLT9d6Px062oe3p3NkZzpFdaJT+m90O7SGTr0mYZowq9nGK4QQ5zoJAEWrVOiUAczILcZstbmsT2tIBjDu+BIA1gQM4Sq9Hkxh0O1y2P0dXU8toYuuLzceeh7sVvIie1GRn00ip2H/zzVPNvSP0GFo3YMe8SfKV79Db/1hSnNXAJPrPLzWvQAPraj6+ZenoNNol/ZRVq0K+CwzgBs+UL8fWQ3vj1Z7yV78NES2a9LprHYrx4qPcbjgMIcLD2tB3uGCw+SU5dR539igWFIiUugQ0YGUiBQ6RnQkJSKF5IhkTAGVQX5JHvz4Mtgs6pS7EEKIJpMAULRK+WUV2s9Wu8KBnGJ6JUVq11W1gaslA6goRGctBWAZg7nKcX2va2H3d/Q89QsfBf5MkK0YkoexvM87PPrNHm5NLebFPifVnqnB0epXZHtIrif4AwiJYVHweK4p/S/d9s6Gy29RuwPUota9ALUAUAe5e2Drf1yqXSs8kQEszIajG9Sfe06E3d+q3Rz2LYI/rtGCQLtip9RSSrGlmBJLCaWWUsqsZZRZy8grz+NwwWEOFR7icMFhjhYdxapYa33IaFO0FuA5gr0O4epXmNH9Vj8utn0BtgpI7Ne4dmVCCCFqkABQtEoFTlPAoE4DOweAjgrhuPBaAsDsbRhLjlOqmFhj71V1fdfLITCUSEsOkXrID2pH1E2fEbDfDOg4ZEiFkTc1edwLgiYypuR7os7sgANLa/ZddRKvBYBOGcCCo3A6Q21tNuqvsOxl+PUl6H0dmMIBGtxtpDZ2xc6pnV+SbTJyIq4bp/pcTm58Irn7F5FnKaHgx8nkG4PJN+e7X4NXB1OASQvwUiNS6RjZUbscaYqs/wS1URR1/zpQe6wKIYQ4KxIAilYpv8w1AHQuBLHa7KzLyANgSGotfUf3/QjACntfinDqcGAMge5jYefXFCghLOrzNjeHtiEwIBsAi7WeTiD1OKVE8h/bpdxjWAjLX1E3360lC+h2CtiR/UsaoPa13T5P7TSy+m24cDrs+IqXc17DZMono+QTIMntucusZWQWZpJZlElmYSZHi49ytEj9OlF6AqvdCkkJQCGsf0W9UyAQGAwVeVDhej6DzkCoMZRQQyjBhmCCDcFEmCK0QC81MpXUiFQSQhPQ65phL8Gs9XBqn7qGs8/1nj+/EEL4Gb8OAGfPns3s2bM5fPgwAL169eKZZ55h3Lhx3h2Y0KqA24SZOFVsZs+JqgBw29F8isxWokIC6d3OTVbJblc3fAYW2wZhrt7fd9TjbDucw4y8S7giQt0KQ9sIuiG9gOtgsdn5wHold5qWEHB0AxxcVmuP2gR3U8COALDjhWrByaUz4MvbYM2/YPPHUHySjgA6iFwzjdNp35JhPsXB/IMcKjykfT9RcqLOcQYoCm1tNhLiehMX0YE2wW2ICwwndtk/iLJWED3+X0QmDSbCGEGYMQyj3ujdDZO3VGb/ek2EoLPIJAohhAD8PABs3749r7zyCl27dkVRFD7++GMmTJjAli1b6NWrV/0nEM3GMQU8tFMMC7dnsye7CEVR0Ol0rEhX+/CO7NKGAHcdN3YvgFPp2E0R/FI+mArs2O0Kesexcd15N2EGm0+d4JrKTiCOLVXqbAXXAFa7Qi5R5Ha7hYQ9c2D539UiDjfBk2MNYEGZhXKLjSCD3ikAHKV+TxsPHYZTkrWO/RYb+9sk8T99B2z6LI4aLZz+/upaxxJhjCA1IpXkiGSSw5NpH9aedmHtaH90C21+mI6hTXe4+r+udzqwHnZ/B5lbIe2Gs3otPMZcBDvVgJ6BU7w7FiGEOEf4dQA4fvx4l8svvfQSs2fPZt26dRIAepkjA3heagyLdmRzuqSC3CIzbUP0rNyfC8CFXdvUvKPdBsv+DoD1vPspWqxucFxhsxPktCFw9a1UHJsq19oJpIEc583tdx8J6Z+rm+5mroWUETWOjQgyEBSop9xiJ6fQTAeyUQqPcdwYzF5dOelbZ7PvzD72hds4mprsdM/jQNVzaR8QSqfEQXSK7ESnyE6kRqbSMaIjUUFR7ge5/C31e9pVNW/rc6MaAO78Gi57HlrDJso7vwFLCcR2gQ7DvT0aIYQ4J/h1AOjMZrPx3//+l5KSEoYPlw8Zb3OsAYyPMNGxTSjWUxkEz70Ee9lxSgr+CrTn/K5xNe+4a4G6ViwoEv2w+2DxGgDMFjtBgVXBTIWjm4YjA1iZHXTbCaQRHAGgLjwR+t2kTtuue8dtAGhTbMRGn+Zk+UHe3LKJ/DNr2duhPUUBelj5lxrHxwXH0TW6K1sOBHE6P5ZZA4xc8PsrhCgKJF4JIV0gKAmMsYBODYarB3CWMrWlHUAPNwFg18vUKdaibDi8CjqNOqvXwyO2fKp+H3BbnVXVQgghGs7vA8AdO3YwfPhwysvLCQsLY8GCBfTs2dPtsWazGbO5ar1WYWHNDhXCMxwZwMhgI5PCt3NT4cuEny4DYIZhDk9GzKRd9fZpdps65Qow/E8YQqMJ0Ouw2RXMVhs4FYNUWG2AUy9gg2cygFV79Olh2P1qALh3IZa8/RzAyp7Te9idt5s9eXvYd2Yf5lgzwcDiY5UnCNBjQE+XmG50i+5G9+judI/pTrfobkQHRQMwctOvWAvKiO81khB9Cax5G1a+5n5AgSFqy6/LX4DOF6trEi2lENHO/VYqBpO6Lczmj2HHly0fABYcgzOH1EyfPgBy9qjb1egC1H0KhRBCeITfB4Ddu3dn69atFBQU8NVXX3H77bezfPlyt0HgzJkzmTFjhhdG6X/ySy0EYKPrtn8w/Pi7oINDQT1pZ85gmH4P97fZClQrrtj5NZxKV/fuG3ovoPYDLq2wYba6ZvYs1TKAgfrKIhCPZABtZJbsZ6ftELs79mGXOYf0H67HQs1zBxCEuTSBwQm9uTbra9KKT9P5lm8JTD2/1sewVhaqGPQ6uPQ5CIuHo+vVvf2KsqHoBNgrq6gtpXByJ3x6DfS/FSoqu6r0uLL2bFrfG9UAcPf3cMU/ITDoLF6RRlAU+Ox6yNmtBq0j/gQnd6m3dR8H4fEtMw4hhPADfh8AGo1GunTpAsCgQYPYsGEDb731Fu+9916NY5944gkeffRR7XJhYSHJyck1jhNnR1EUCsssTDd8SZvt/wPgA+sVfGO8i4nWr7nX+hlXnZgF5XdCUGXvV5vVKfs3TbveqAWANpfHqKgMCI1aBtBRBNK4DKDNbuNQwSF25e1i56md2BPXEGY8zqOrnDZENpkAO+GB4Vrf2rTYNNJi0vhkRTEf7TnCqDgb15x+BwJDof15dT6mS/CqD4AR06q/gOpG1hXFUF4A69+H399TN5R2cDf969BhhJohLDymdkXpOaFRr0mTndqvBn+gbn3zwyNVtw24rWXGIIQQfsLvA8Dq7Ha7yzSvM5PJhMlUe+9Z4RllFhud7Ie527gQgDOXv81L37eBnHIyuJzLTUvpWH4Clr0CY1+G4hxYMgPyDrhk/0DNAAKUW6pnACsDQG0NYO0ZQEf1saIoHC85zo5TO9h1ahc7Tu1gd95uyqxl2rG6ymRZqCGM3nG96BnTk54b/0OvU4dpf8mL6Ibf73LuhMgMAGJz1qlXpAyvvd9wtbEb3FVAg5rZCwxSv0LbwLi/qx1Qvp+mZkhDYiFlZO0PoNerG0+veRs2zVWDxbMpBsnPhPwsSK3jMaGqBV/qBWqGcs2/ofAoRHaALrVvqC2EEKLx/DoAfOKJJxg3bhwdOnSgqKiIzz//nGXLlvHzz256wYoWU1Bq5uXADzHo7ChpVxM1fAqRixdTUGahgkDmxf6JJ04/Cb+/C4oNNn+qVokCXPSk1jED0PoHV58CdmQAa1YBVx2XX57P3fO/4VjZPnp3KmTfmd2cLj9dY7zBhmB6xvakV2wv3ltcga2sPUv+fCPxEY41ilFqNuv3d9Xg1BFM2W10VQ5zW8AvnHdabVtHxwvrfX2qVzA3SIehcO9KdZq8bZpLb2G3+t8Ca/8NGb/CvMlw3Qcur2uDKQp8dgPk7lX7KY95qfZgMr3y767HVTDsPrXf78FlavVvfeMVQgjRKH79rpqTk8OUKVPIzs4mMjKSvn378vPPP3PZZZd5e2j+bdPHDNQfoJhgwsb9HXQ60hLDWXdQDb4i+46Fk7/Dnu/VoAogaSBcNqNGAOXIAFZUDwCrZQDtWNAHZWELPcpfVy5jR+4OMosy1YODYa3aKASD3kC36G70adOHXrG96NOmDx0jOxKgD8BqszPr60WVj+sU5PS9CZY+D/lHYN1sQFErbI+s5SJzARcFAo4Z466X1/vyVF+/2GCBQTBgcsOObZsG134A394P6Yvgo7Fw8zyIauSSh7wMNfgD+H22mg287gMwhroeV16gbpcD0K3yNQgIVKuShRBCeJxfB4D/93//5+0hiOqKThL3+0wA5pgm86cItdVZj4QILQC8sGscDJgJRzeqgcTFT6nr1NwUNZgC1SDJeQ2goiiUK6cwhB/g8wObeXXHXvbk7SG0o1o4sfBg1f3t5jbYytsTqnTi/UnX0DsuDVOA+2UAzusHXbJzxhAYfAes/Cf88qTLfeyBoawq78Q2fRp/uuePauBVB7tdwWav7AVc2xSwp/S5HqJSYN4taiHJBxfD1B8grnvDz+HYciaqAxSdhH0LYe6VcPN816KOjN/AboXYrmoBiBBCiGbl1wGgaIV+/huGikK22zuyPGICf6q8umeiWtQRE2pUf9ZHwiO71PVqdTAZAkBnYe+Z7RzamcW23G1sz92OOTGXYGBRZtWxijUEW3ky00ZcTP+2/YgzduHyf24EoBw4dKwNgxJqXwPq3EbO0VlEc9496lS1tVzdEzD1fEg9n9LoNKbMUKd//9CmL2H1vDzOjxHY2AxgUyQPgbt/hS9uUoPAlf+Ea99v+P0PLFa/D7kbkofCvJvh+Bb4dCLcu0LN8gHs/0X93m2MR4cvhBDCPQkARetxYAns/Ao7ev5muZO4kKrtRy7vFc83W2K4sk9iVUu3WoK/EyUn2Jqzla25W8kKWk1Y9yxm7XOtAlYUPfbyRK7uMZyRyQPpEtmTca/tAXTc2XssQYEBHMgpcrnP+ysyuHZAu6rHr8Z5D8HA6mMLT4DH9gGKyxq4MCDMZKDYbCWnsJywuLpDwDofo7lEJauFJHOvVP+N3G0w7Y6lTJ3qBnUqt20a3LUEPrxUrfbd+JG6JtJurwoAGzAFLoQQ4uxJAChaB3Mx/E/d9mNX+0nsPNCJa0KqqmGjQozMu6dmhxab3Ub6mXS25GxhS84WtuZu5UTJiaoD9KADwgzRDE0aSL+4fvSL68dN/8qiwmrgwZsuJikquHKKWF2rVmFTu4YUlasL82JCjVRY7aSfLGZZeg4X93C/H52jgCRAr3MfJNYSsLWNMFGca+VkoZlOcWHkFplZnp7LVX0TXbqXgGuVcmD1LGNzSh4KpggozVMzeO0H13+fw6vVjGdEO4jroV4X00mdsv/hEfjtZehzg7rxc0kuGMOl1ZsQQrQQCQBF6/Dby1CQCZEdWJp0DxzIJjI4sMZhpZZSdpzaweaczWw5uYVtudsotZa6HKPX6eke3Z1+cf3YnB7J5v2R/OWKC7hteCqgrgGssKpVHdU3goaqLFuJWc0axoWZGNU9jvdXHOS95QdrDQAr6tuepRbx4UEczC0hp6icvGIzN7y7hsN5pdjsdiYN6eByrPM6w4DmXgPoLCAQOl+k9gne/0vDAkDH+r8ul7quzxx4O2z4P3VK+beX1W1pADqPrncLHCGEEJ4hAaDwvmOb1ApRgKteJ3enGvhFBgeSX57P5pzNbD65mU0nN7Hn9B5siut0blhgGP3i+tG/bX8GtB1AnzZ9CAkMAeChw1vYZDmu9f4F98Uaer1OaxvnyLIVm9WikLAgA38YmcpHqw7x+6HTbMk8w4AO0TWehksbuEaIj1DXFR4+Vcqc1Rs5nKcGtMfyy2scq+1fGKBH19J9cbteXhUAXvS3+o93rP+rvoefPgDGzoSPx6vTwJWFPnSV9X9CCNFSJAAUzctaAT8+pvZyHfEniO3servNAt8/CIod+txIbrv+7P/9K0wJm/ku9zgfzc+sccqE0AQGtB3AwLYDGdB2AF2iuhBQy5o0x/5+zlXAFU7TqCanQorAgOoBoHqfUJOBxMhgJvRvx9ebj/LR6sP8y10A6GjR1sip2fgIda3jrN8OuIytsLIfsjNtE+iWnP51cARyx7eom2+Hta392NOH1I259Qb3/YQ7Xghp42HP/6AgS71O1v8JIUSLkQBQNK/9P8PmT9SfN3+sdpgY8Sd1y4+cPZxMX8iG4oNsjE9kI0c58t+LATBGQ15l/NMpshOD4gcxMH4gA9sOJCksqcEPr20D49QJxGJ1XkfnFADq9ZRj1zKExeXqAMJN6p/JNQPUAHDXsQK3j1VhbVoGsG1lAFhhs2My6Lm4R1sW7TxBgdsAsGmP4RHhCZDYD7K3wYGl0P/m2o91TP8mD4WgSPfHXPaCuvmzrQIS+0uvXyGEaEESAIrmtUft5Ut4IhRlc2r316w/uJD1QUFsDDZxJDAQ2rZRjyk+ig4dgbb2FOd34L6hl3H7oIuICYpp8sO76wRS4VSs4byOLtCgB3NVMUdJhSMDqJ4jOUbt7HEsv0xrD+fMkQEMbOTavMRINQDU6eCtm/pTWGZl0c4T5JdW1Di2SV1APKnr5WoAuP+XhgWAdbVwi+kIFzwGy2ZC3xs9O04hhBB1kgBQNB9rBQXpP7EhJJjf+13Ohvx0MkqOuRyiB9JC2zMk9VIGxw9mQPwArnpzE3mnS7mw/fCzCv7AfSeQqjZwroGao3jDESA6qoDDTOqaxITIIHQ6NZg8XVJBbJjrnoCO7JyhkcHZ6O5xXDugHaN7tGVs70R+2qlWMbvLAFatM/TCFDBAl8tgxauQsRRsVrVFW8FR+OoOdVPugVPUoO/QCvX4+jp5jHocel8vmz8LIUQLkwBQeJTZZmbzyc2sy17H74cWszshAkWng2O/AaBDR/fobpyXOJTzEs5jYPxAwo2uPWYdgY+7KuDGqsoA1lwDaKwWqAVq/YArp4CdikAc54oLM5FTZOZ4frmbANB9YFmfEKOB1yf11y47nre7ALDC2xnA9oMhOBrKzsDRDWqf3k8mQt5+9faMX9XtXCylEJYA8b3rPp9OB226NPuwhRBCuJIAUJwVu2In/Uw6a46vYe3xtWzJ2YLZZq46QKejU0Ao53UZz7DEYQxOGEykqZY1YaitzgrLHQHg2W8JUtUKzmkNYLU+wA6OwM1xu2MbmDBTVYFJUlQwOUVmjuWX0ae96/NoahVwdVUBoLXGbVZvFoGAWsHb+RLY+ZX6lbVeDf4i2kPfG2DrF1BcuQ9j9e1fhBBCtBoSAIqGKclTP9jje3Gq7BRrj6/Vgr688jyXQ9sGt2VY4nkM2/wV5xXmET/5G+g0ukEPU1RuRancpcUzGcCaAaBjCri2DKBjKrf6FDBAu6hgtmblczy/rMZjWZpYBVxdZIj6eIVllhprDR1jqz72FtX1cjX42/Chejk0DqZ8p2byLnpSLezIXAvDp3lvjEIIIeokAaCol8VqZtt/rmR1eTar2/diT3GWy+3BhmCGJAxhRNIIhiUOo1NkJ3SHVsDSdyA4BlLOb/Bj5ZephQ8hxoAaGbqm0KaALVVTwNpUbbXzG7QA0JEBVAPAUJcMoFqwccxNAOipDGBUZeBbYbNTZrERYqz6M/VUkHlWulyC2l9FAVMk3PpN1TRuQCCkXaV+CSGEaLUkABRu5ZbmsurYKlYeW8naoysoDjJDUARUBn9pMWmMSBrByHYj6R/Xn8CAatm6Pd+r33tcoRYKNJAn1/+B+wyguZYMoLEyqHJU8xZXBoDhQVXjbxelVgK7zQA6Asuz7NEbYgzAoNdhtSsUlFlcAkDHdjZezQCGtlEDvEMr4JYvIbGv98YihBCiSSQAFIC6lm/nqZ2sOLqCFUdXsOf0Hpfbo202hpeVc35pGcOv/oA23evI8NjtsOcH9ee0CY0ah8cDQG0NoHMG0H2mzpEBdOznp2UAnQKwpAYEgGebndPpdEQGB5JXUkFBmYXEyGDttjOVW8NEh3i5ZdqNn6r79xlM9R8rhBCi1ZEA0I+VWkpZm72W5VnLWXF0RY21fL1ie3FhWEfOX/shvaw6AnpcAbsWwJp3oNuVtS/wP7peXS9oinDfBaIO+aWeDQCrOoHU3Ai6viKQosoAMCyoZgDork2btYnbwLijBYClrpXAp0vUADAm1MsBoE4nwZ8QQvgwCQD9TE5pDsuylrEsaxm/Z/9Ohb1qs+HQwFBGJI3gwvYXcn6782kT3AY+uwHMFTDoDzDqL7B3IRxZDYdXqu283NldOf3bbWyjgwRHBjAqxFMZQMcawJobQde6DYxjCriyCCS8WhEIwKliM+UWG0GBVesDq/r0nv36vIhatoLJK64MAMO8HAAKIYTwaRIAnuMUReFQwSF+zfqVXzN/ZcepHS63tw9rz+jk0YxKHsWgtoNc1/Kd3KV2fNDp1fZtEUkw8HbY8AEsewVSL6iZBbSaYefX6s89r270eJtvDWDNIpCaGcDKIhCrgs2uUGZx7QQCamAaHBhAmcXGiYJyUtuEVp3XXpkBPMs1gI7HAcgvq54BVLfYifV2BlAIIYRPkwDwHKQoCrvydrHkyBKWZi7lcOFhl9v7tunLRR0uYnT70XSO6lyjpZlm9Vvq954TILaz+vP5j6g9fWvLAu74Sp3+DU+ErmMaPfaqDKBnAhytE4itZhFIbZ1ALHa7VgACrlPAOp2OpKggMnJLOJZf5hIAenKPPkcAXFg9A6hNAcv0qxBCiKaTAPAcYbPb2Jq7lSVHlrAkcwknSk5otwXqAxmaOJSLO1zM6PajiQuJq/+EZw6rwRzAyIerro9sV5UF/PVF+MNP4Mh4KQqs+Zf689D7wND4IM7R/9ZzGcCaU8C1ZgANjgygXSsACQzQaedwaBcdogWAzqwe3KOvtm4gjilgyQAKIYQ4GxIA+jCb3camk5v45cgvLM1cyqmyU9ptwYZgLmh3AZemXMoF7S4gzBjW8BMfXgXf3AuKTd3AOam/6+3nPwJbP4Os32HTHBhyp3r9gSWQuweMYTBoapOeU/NVAbvrBVwtANQ7toFRtAxgmKnmn0i7yr0Aq1cCVzRDBrB6ANhqikCEEEL4NAkAfYzNbmNzzmZ+PvwzS44scancDQ8M56IOF3Fph0sZnjScIEMQrHgNDG3UHq71teWyWWDZTFj5OqBATGe44rWax0W2g0uehZ8eh8XPQNfLIKoDrHlbvX3QVAiOatLz83QVcFPWAFbYqqaAQ90EgEmR7reC8XQVMLgGgIqiSAAohBDCIyQA9AF2xc723O38dPgnfj78s0umL8IYwcUdLubylMsZljjMtYgjZw/8+gLwgtqrtecE6DUR2g+pGQyWF8J/roWjG9TLA26Dsa+AqZbM4Xn3wO5v1ZZf3z8Ilz6rbgysC1Cnf5vI41XAjilgq11rq1ZbKzhH4Ga1KVoFsLsMYNVegK5bwTiqhx2ZxLPhrgq42GzVsoyxUgUshBDiLEgA2EopikL6mXR+PPQjiw4tIrskW7st3BjOxckXM7bjWIYmDK3ZhUOjgz43wL5FUHgU1s1Svwb9Aa56oyoItNvhm3vU4C8oEsa/rQaKddHrYcIsmD0CDv4G8w+o1/e+DqKSm/y8m2sKWFHUDaCNBh0VjrV61TKARqd9AN11AXGobTNoR3B2tq3goKodXL7TPoCO7F9wYIBLdxAhhBCiseRTpBVasH8BH+/6mIyCDO26EEMIF3W4iHGp4xiRNKKOoM9J2x5w3YdgKYeMpeomzju/VtftBYbAmJfUIHD5K5C+CAJMcNu30G5gwwYa2xkufgp+eQoKKvsDj/hT45+wEy0DGOzZKmBQp4GNBn2tawCregErdU4Bt492bAZdpmUVoXmmgJ2rgPNk+lcIIYSHSADYCp0qO0VGQQZGvZEL2l/AuI7jGNV+lLqmrykCg6DHlepXp9Hw3QNqJtAUDgm9Yfnf1ePGv9Xw4M9h2P2w+zs1e9hp9Fn1ha2w2imtUNfqeboTCKjTwOE49eytZSNoi81e5xRwfEQQOp16vrySCtqEqVuyWG3ut5dpisiQmlPApx0VwDL9K4QQ4ixJANgKje88nriQOC7pcAnhxnDPnnzArVBRAov+omb+AiqDiWH3Q/+bG38+fQBcP0ctADmLtX9QFezodO6nXptCp9NpWT9HJXBFPa3grLaqbWDcBYBGg5624SZOFpo5nl+mBYAVtfQYbgrnIhBHllEKQIQQQnjK2X9SCY9LCE1gYpeJng/+HIbeCxc/rf5sq1A3c77shaafLyoZrni1arPoJiooUwOciKBA9B4opHDQKoErO3vU1rKtqgq47m1gwP06wObYCNpqV7SsqEwBCyGE8BTJAPqrCx4DYygc3Qjj/gEB3v9V8HQFsIPJEEARVRW0tWUADU4ZwLrWAIIaAG7JzOeYUyWwtbIVXKAHWsEFBwYQGKDDYlPIL7MQajJIGzghhBAe4/1PfeEdOh0M+6O3R+HC03sAOlRlACsDwFrWABqd1gA6jqltKrq9mwygxYNrAHU6HZHBRk4VmykotdAuKljawAkhhPAYmQIWrYant4BxqN4NpNYMoNYLWKlzDSBUTQEfO1MzAPREFTBAZLD62I7XxbEGUDKAQgghzpYEgKLVaL4MoGMzaNc1gDWqgJ16AReV1z8FDHC8wHkNoKMIxDPrF6t3A5EiECGEEJ4iAaBoNZpvDaD7KWBT9SrgyrV7Lr2Aa5kCTnLTD9hSuQbQ4IE1gFBzL8C8ym1gYmQbGCGEEGdJAkDRajTbFLDBdQrYYnW/XUugoaoTSH1TwO0qM4Cniisod1QXOzaYNng2AMyvrI6WKWAhhBCeIgGgaDUcma6IIE+vAXSdAjbbaukFrHfaCLqeADAyOJBQo3re7AK1EtiTvYABokLUQK+gzEJZhY2yykBTpoCFEEKcLQkARatRVM+0a1M5Ar2qDKD7TF2gm1ZwtQWAOp2uxl6AFg+2ggOIcFoDmFe5BYwxQF/rmIQQQoiGkgBQtBr1Tbs2laMKuKyi+kbQ7juBlFtslFeuF6xrLI4A8OiZUsApA+jxIhBr1fq/UKPWe1gIIYRoKgkARavRXAFg+2g1UJuz5hAFpRatCMRocN8JxFGNDLVXAQOkJUYA8O2W40DtawubyrkKWCqAhRBCeJIEgKLVqK/7RlPdP6oLHWJCyDpdxsPzt2jVwMaAAJfjHJ1AzpSqwZbRoK+xV6CzKcNTCAzQsfZgHhsPn8ZSmQE0eGgNYKTLFHBlAYhUAAshhPAACQBFq1HfurumigwJZPatAzEZ9Py2L5cThWrRRmC1DKBjStjReze8nnEkRQVz3cD2APz7twPaPoCe2wi6MgAsrdDawEkGUAghhCdIAChajRKzGnh5OgMI0Cspkhcm9na5rkYVcLXLDRnHfaM6o9fBsn25nKisBq5+3qZy7IfokgGUNnBCCCE8QAJA0SooikJJRfNkAB1uHJzMTUOStcs19gGsVrzRkHGktgnl6n5JQNUG0wYPF4EUllcVgcgUsBBCCE+QAFC0CqUVNhR1BrVZtzl57upejOoWx8AOUSRGBrncVj0gbOg47r+oS7XzeDYAtNkVsk6rlcYyBSyEEMIT/DoAnDlzJkOGDCE8PJy2bdsyceJE9u3b5+1h+SXH+j+9DoICm+/XMigwgI/vOI9v7h9ZY8q3RgDYwP0Iu8WHM7ZXQq3naaqgwACtCOXgqRJAAkAhhBCe4dcB4PLly3nggQdYt24dixcvxmKxcPnll1NSUuLtofkd5wpgb+1zV716tzGZyAecsoBBgQF1HNk4jixgbpFaBCJt4IQQQniCX7cU+Omnn1wuz507l7Zt27Jp0yYuvPBCL43KPzn2AKyv8rY5Vd/ypTHFKH3aR/LUlWnkl1qIjwiq/w4NFBUcqAV/IBlAIYQQnuHXAWB1BQUFAMTExLi93Ww2YzZXfRgXFha2yLj8QXPtAdgY1aduwxvZku6uCzp5cjhAVQbQQaqAhRBCeIJfTwE7s9vtPPzww4wcOZLevXu7PWbmzJlERkZqX8nJyW6PE43XnFvANFT16t1Qo/f/f+QcABr0OiKCvT8mIYQQvk8CwEoPPPAAO3fuZN68ebUe88QTT1BQUKB9ZWVlteAIz23FZrX9WnNWANen+v59DS0CaU7OAWC09AEWQgjhId7/hGsFpk2bxg8//MCKFSto3759rceZTCZMJpmCaw7FWgbQcwUUjVWzCMR7Y3GIcAoApQBECCGEp/h1AKgoCn/6059YsGABy5Yto2PHjt4ekt8q0drABdZzZPMJ0OvQ6XDaj9B7Y3FwzgBKAYgQQghP8esA8IEHHuDzzz/nu+++Izw8nBMnTgAQGRlJcHCwl0fnX6oCQO9l3XQ6HYF6vdbRw5vZSAdHOziQAFAIIYTn+PUawNmzZ1NQUMDo0aNJTEzUvubPn+/tofmdonLvVwGDaxePxlYBN4dImQIWQgjRDLz/CedFimOuT3hdSSvYBgao7A6irkdsfVPAsv5UCCGEZ/h1BlC0HiUVlRtBeznr5rwXYGuYAnYJAMMkAyiEEMIzJAAUrYJWBezlvfdcpoBbWQZQpoCFEEJ4igSAolVoLVPArToDKAGgEEIID5EAULQKxeWOKmBvrwFUM4BBgfrK9YDeJfsACiGEaA7e/4QTAudewN7Nujm6gXg7EHUICgygW3wYMaFG2keHeHs4QgghzhGt41NO+L3WUgTiyAC2lgAQ4LsHzqfCZifY6P0paSGEEOeG1vMpJ/yWoiitbg1ga+gD7BBsDCAYCf6EEEJ4jkwBC68zW+1YbOqejF4PAPXqn4S3q5GFEEKI5iQBoPA6R/YPvB94BRrUKWBvT0ULIYQQzUkCQOF1JZV7AIYYAwjQ6+o5unkZHBnAVrQGUAghhPA0CQCF1xW3kvV/4LQGsBWMRQghhGguEgAKr3NUALeGoCuwFVYBCyGEEJ4mAaDwOscm0N7eAxAkAyiEEMI/SAAovM4xBdwagq6eSREA9G4X6eWRCCGEEM3H+5+4wu+VtKIA8L5RnblpSDJRIdJ2TQghxLlLMoDC61pTEQggwZ8QQohzngSAwutaWwAohBBCnOskABRe15qmgIUQQgh/IAGg8Lriyo2gJQAUQgghWoYEgMLrSmQKWAghhGhREgAKr6uaAvb+PoBCCCGEP5AAUHhdkWQAhRBCiBYlAaDwOikCEUIIIVqWBIDC6yQAFEIIIVqWBIDC6xxVwDIFLIQQQrQMCQCF1xWbLYBkAIUQQoiWIgGg8CqrzU65xQ5IBlAIIYRoKRIACq8qqbBpP4fKNjBCCCFEi5AAUHiVowDEGKDHZJAAUAghhGgJEgAKr6rqAiLBnxBCCNFSJAAUXiWbQAshhBAtTwJA4VWyB6AQQgjR8iQAFF4lAaAQQgjR8iQAFF4lm0ALIYQQLU8CQOFVxeWyCbQQQgjR0iQAFF7l2AdQqoCFEEKIliMBoPCqYm0NYKCXRyKEEEL4DwkAhVdVFYFIBlAIIYRoKRIACq8qln0AhRBCiBYnAaDwquJyCQCFEEKIliYBoPCqkgo1AAwPkgBQCCGEaCkSAAqv0vYBNEoAKIQQQrQUvw4AV6xYwfjx40lKSkKn0/Htt996e0h+p0TWAAohhBAtzq8DwJKSEvr168esWbO8PRS/5VgDKBtBCyGEEC3Hrz91x40bx7hx47w9DL9WlQGUbWCEEEKIluLXAWBjmc1mzGazdrmwsNCLo/F9iqJoRSBhUgQihBBCtBi/ngJurJkzZxIZGal9JScne3tIPq3MYsOuqD/LFLAQQgjRciQAbIQnnniCgoIC7SsrK8vbQ/Jpjk2g9ToIDpQpYCGEEKKlSNqlEUwmEyaTydvDOGdom0AbDeh0Oi+PRgghhPAfkgEUXlNSuQegrP8TQgghWpZff/IWFxdz4MAB7fKhQ4fYunUrMTExdOjQwYsj8w/SB1gIIYTwDr/+5N24cSMXXXSRdvnRRx8F4Pbbb2fu3LleGpX/kE2ghRBCCO/w60/e0aNHoyiKt4fhtxwZwDDZA1AIIYRoUbIGUHiNNgUsfYCFEEKIFiUBoPAaxxSwFIEIIYQQLUsCQOE1WgAoawCFEEKIFiUBoPCa4sptYKQIRAghhGhZEgAKryk2WwDJAAohhBAtTQJA4TXaRtASAAohhBAtSgJA4TWyEbQQQgjhHRIACq8pkX0AhRBCCK+QAFB4jWQAhRBCCO+QAFB4TbFsAyOEEEJ4hQSAwmtkH0AhhBDCO+STtwXYbDYsFou3h9HqRAZCSHgAJp2N8vJybw9HCCH8jtFoRK+XXJA/kgCwGSmKwokTJ8jPz/f2UFodRVF4clQbAIpOHackT+flEQkhhP/R6/V07NgRo9Ho7aGIFiYBYDNyBH9t27YlJCQEnU6CHAerzY4lpBiATvHh8toIIUQLs9vtHD9+nOzsbDp06CDvw35GAsBmYrPZtOAvNjbW28NpdSqsNnSGCvQ6HcHBwd4ejhBC+KW4uDiOHz+O1WolMDDQ28MRLUgm/puJY81fSEiIl0fSOtns6ne9/I9TCCG8xjH1a7PZvDwS0dIkAGxmklJ3z64oAATIb6AQQniNfEb5L/n4FV5hqwwAJQMohBBCtDwJAIVX2O2VAaBeAsCWlJqayptvvqld1ul0fPvtt14bj7+bO3cuUVFRjbpP9X/D1mT06NE8/PDD3h6GEKIBJAAUGp1OV+fXc88957HH0qaAvZwBbM0fpi0hOzubcePGeXsY4hzxzTff8MILL3h7GEKIBpAqYKHJzs7Wfp4/fz7PPPMM+/bt064LCwvz2GM1ZxGIoijYbDYMhpb79a6oqPDJfbQSEhK8PQTRCnjq9zcmJsYDoxFCtATJAApNQkKC9hUZGYlOp3O5bt68eaSlpREUFESPHj145513tPsePnwYnU7Hl19+yQUXXEBwcDBDhgwhPT2dDRs2MHjwYMLCwhg3bhy5ublaBvCxP93DxIkTmTFjBnFxcURERHDfffdRUVGhndtutzNz5kw6duxIcHAw/fr146uvvtJuX7ZsGTqdjkWLFjFo0CBMJhOrVq0iIyODCRMmEB8fT1hYGEOGDGHJkiXa/UaPHs2RI0d45JFHtCwnwHPPPUf//v1dXps333yT1NRU7fLUqVOZOHEiL730EklJSXTv3h2ATz/9lMGDBxMeHk5CQgK33HILOTk5db7uqampvPjii0yZMoWwsDBSUlL4/vvvyc3NZcKECYSFhdG3b182btzocr9Vq1Zpr3VycjIPPvggJSUl2u05OTmMHz+e4OBgOnbsyGeffVbjsatPAT/++ON069aNkJAQOnXqxNNPP+2RLjapqam8/PLL3HHHHYSHh9OhQwfef/99l2OysrK48cYbiYqKIiYmhgkTJnD48GHt9mXLlnHeeecRGhpKVFQUI0eO5MiRIwBs27aNiy66iPDwcCIiIhg0aJD2ejXm3/O1114jMTGR2NhYHnjgAZfn3pR/W7PZzPTp02nXrh2hoaEMHTqUZcuW1Xp8fb+zDkVFRdx8882EhobSrl07Zs2a5XJ7Zmam9rsTERHBjTfeyMmTJ7XbHa/Jhx9+SMeOHQkKCgLgp59+4vzzzycqKorY2FiuuuoqMjIyXO7nbnZg7ty5QM0p4DNnzjBlyhSio6MJCQlh3Lhx7N+/X7vdMQX+888/k5aWRlhYGGPHjnX5zyjAhx9+WOt7jxCiaSQAbEGKolBaYW3xL6Uy2Dobn332Gc888wwvvfQSe/bs4eWXX+bpp5/m448/djnu2Wef5amnnmLz5s0YDAZuueUW/vKXv/DWW2+xcuVKDhw4wDPPPKMFgHp0LF26lD179rBs2TK++OILvvnmG2bMmKGdc+bMmXzyySe8++677Nq1i0ceeYRbb72V5cuXuzz2X//6V1555RX27NlD3759KS4u5oorrmDp0qVs2bKFsWPHMn78eDIzMwF1uqp9+/Y8//zzZGdn1/jQqc/SpUvZt28fixcv5ocffgDU7X9eeOEFtm3bxrfffsvhw4eZOnVqved64403GDlyJFu2bOHKK6/ktttuY8qUKdx6661s3ryZzp07M2XKFO3fMiMjg7Fjx3Ldddexfft25s+fz6pVq5g2bZp2zqlTp5KVlcVvv/3GV199xTvvvFNvwBIeHs7cuXPZvXs3b731Fh988AFvvPFGo16X2vzzn/9k8ODBbNmyhfvvv58//vGPWobZYrEwZswYwsPDWblyJatXr9aCgYqKCqxWKxMnTmTUqFFs376dtWvXcs8992hB++TJk2nfvj0bNmxg06ZN/PWvf230nma//fYbGRkZ/Pbbb3z88cfMnTtXC2wcY2zsv+20adNYu3Yt8+bNY/v27dxwww2MHTvWJQhyVt/vrMOrr75Kv3792LJlC3/961956KGHWLx4MaD+h2nChAmcPn2a5cuXs3jxYg4ePMikSZNcznHgwAG+/vprvvnmG7Zu3QpASUkJjz76KBs3bmTp0qXo9XquueYa7HY1ZT99+nTtbyU7O5vXXnuNkJAQBg8e7Pb5TJ06lY0bN/L999+zdu1aFEXhiiuucAmsS0tLee211/j0009ZsWIFmZmZTJ8+Xbu9oe89QohGUkSTFRQUKIBSUFBQ47aysjJl9+7dSllZmXZdidmipDz+Q4t/lZgtjX5uc+bMUSIjI7XLnTt3Vj7//HOXY1544QVl+PDhiqIoyqFDhxRA+fDDD7Xbv/jiCwVQli5dql03c+ZMpXv37krW6RJlW9YZ5cZbblViYmKUkpIS7ZjZs2crYWFhis1mU8rLy5WQkBBlzZo1Lo995513KjfffLOiKIry22+/KYDy7bff1vu8evXqpfzrX//SLqekpChvvPGGyzHPPvus0q9fP5fr3njjDSUlJUW7fPvttyvx8fGK2Wyu8/E2bNigAEpRUVGtx6SkpCi33nqrdjk7O1sBlKefflq7bu3atQqgZGdnK4qiPv977rnH5TwrV65U9Hq9UlZWpuzbt08BlPXr12u379mzRwFcni+gLFiwoNaxvfrqq8qgQYPqfI4NUf052u12pW3btsrs2bMVRVGUTz/9VOnevbtit9u1Y8xmsxIcHKz8/PPPSl5engIoy5Ytc3v+8PBwZe7cuW5va+i/Z0pKimK1WrXrbrjhBmXSpEm1Pqf6/m2PHDmiBAQEKMeOHXO5/pJLLlGeeOIJRVFq/p254+53duzYsS7HTJo0SRk3bpyiKIryyy+/KAEBAUpmZqZ2+65du1x+H5599lklMDBQycnJqfOxc3NzFUDZsWNHjdvWrl2rBAUFKfPnz9euGzVqlPLQQw8piqIo6enpCqCsXr1au/3UqVNKcHCw8uWXX2rPH1AOHDigHTNr1iwlPj5eu1zfe484O+4+q/xBXZ/f/kIygKJeJSUlZGRkcOeddxIWFqZ9vfjiiy7TQwB9+/bVfo6PjwegT58+Ltfl5ORQWQSMDujXr5/LhtnDhw+nuLiYrKwsDhw4QGlpKZdddpnLY3/yySc1Hrt6FqK4uJjp06eTlpZGVFQUYWFh7Nmzp0Y2pan69OlTY93Upk2bGD9+PB06dCA8PJxRo0YB1PuYDXndAC2Dt23bNubOnevymowZMwa73c6hQ4fYs2cPBoOBQYMGaefo0aNHvRWn8+fPZ+TIkSQkJBAWFsZTTz1V59jHjRunPX6vXr0a/Bwdywucn8+BAwcIDw/XzhcTE0N5eTkZGRnExMQwdepUxowZw/jx43nrrbdcMraPPvood911F5deeimvvPJKjd+NhujVqxcBAQHa5cTERJeMaWP/bXfs2IHNZqNbt24u/07Lly+vdXwN/Z0dPnx4jct79uwBYM+ePSQnJ5OcnKzd3rNnT6KiorRjAFJSUoiLi3M5z/79+7n55pvp1KkTERER2jR59cfPzMxk4sSJTJ8+nRtvvNHtc3H8Dg4dOlS7LjY2lu7du7uMIyQkhM6dO2uXnV/3xrz3CCEaR4pAWlBwYAC7nx/jlcc9G8XFas/eDz74wOXNHHD5wARcpt0c03PVr7Pb7do2MPXVgDgee+HChbRr187lNpPJ5HI5NDTU5fL06dNZvHgxr732Gl26dCE4OJjrr7/eZX2hO3q9vsa0ubt1cNUfr6SkhDFjxjBmzBg+++wz4uLiyMzMZMyYMfU+ZkNeN0CbiisuLubee+/lwQcfrHGuDh06kJ6eXufjubN27VomT57MjBkzGDNmDJGRkcybN49//vOftd7nww8/pKysrMZ43al+u+N3AdTnM2jQILfrFB1Bypw5c3jwwQf56aefmD9/Pk899RSLFy9m2LBhPPfcc9xyyy0sXLiQRYsW8eyzzzJv3jyuueaaBv971jW+pvzbFhcXExAQwKZNm2r8ndRWUNXU39mmqP77CzB+/HhSUlL44IMPSEpKwm6307t3b5fHLykp4eqrr2b48OE8//zzZz0Od6+749+rMe89QojGkQCwBel0OkKMvveSx8fHk5SUxMGDB5k8ebJHzunYCFqHjm3btlFWVqb1BF63bh1hYWEkJycTExODyWQiMzNTy7g01OrVq5k6dSrXXHMNoH6YOBcVgNoGqXoLpLi4OE6cOIGiKFrg5VgjVZe9e/eSl5fHK6+8omVfqhdueMrAgQPZvXs3Xbp0cXt7jx49sFqtbNq0iSFDhgCwb98+8vPzaz3nmjVrSElJ4cknn9SucxRZ1KZ6UN5UAwcOZP78+bRt25aIiIhajxswYAADBgzgiSeeYPjw4Xz++ecMGzYMgG7dutGtWzceeeQRbr75ZubMmcM111zT5H9PZ035tx0wYAA2m42cnBwuuOCCBj1OQ35nQf0bqX45LS0NgLS0NLKyssjKytLGunv3bvLz8+nZs2etj52Xl8e+ffv44IMPtPGuWrXK5RhFUbj11lux2+18+umndXaRSEtLw2q18vvvvzNixAiXx6hrHM6a471HCKGSKWDRIDNmzGDmzJm8/fbbpKens2PHDubMmcPrr7/epPM5ZwArKiq488472b17Nz/++CPPPvss06ZNQ6/XEx4ezvTp03nkkUf4+OOPycjIYPPmzfzrX/+qdxF4165dtQXu27Zt45ZbbtEyOg6pqamsWLGCY8eOcerUKUCtZMzNzeUf//gHGRkZzJo1i0WLFtX7nDp06IDRaORf//oXBw8e5Pvvv2+2PdEef/xx1qxZw7Rp09i6dSv79+/nu+++04pAunfvztixY7n33nv5/fff2bRpE3fddZcWZLvTtWtXMjMzmTdvHhkZGbz99tssWLCgWcZf3eTJk2nTpg0TJkxg5cqVHDp0iGXLlvHggw9y9OhRDh06xBNPPMHatWs5cuQIv/zyC/v37yctLY2ysjKmTZvGsmXLOHLkCKtXr2bDhg1aQNTUf09nTfm37datG5MnT2bKlCl88803HDp0iPXr1zNz5kwWLlzo9j4N+Z0FNVD8xz/+QXp6OrNmzeK///0vDz30EACXXnopffr0YfLkyWzevJn169czZcoURo0aVWuxBkB0dDSxsbG8//77HDhwgF9//ZVHH33U5ZjnnnuOJUuW8N5771FcXMyJEyc4ceKElgWu/lwmTJjA3XffzapVq9i2bRu33nor7dq1Y8KECXW+ds48/d4jhFBJACga5K677uLDDz9kzpw59OnTh1GjRjF37lw6duzYpPM5rwG85JJL6Nq1KxdeeCGTJk3i6quvdtl0+oUXXuDpp59m5syZpKWlMXbsWBYuXFjvY7/++utER0czYsQIxo8fz5gxYxg4cKDLMc8//zyHDx+mc+fO2lRjWloa77zzDrNmzaJfv36sX7/epSqxNnFxccydO5f//ve/9OzZk1deeYXXXnutUa9LQ/Xt25fly5eTnp7OBRdcwIABA3jmmWdISkrSjpkzZw5JSUmMGjWKa6+9lnvuuYe2bdvWes6rr76aRx55hGnTptG/f3/WrFnD008/3Szjry4kJIQVK1bQoUMHrr32WtLS0rjzzjspLy8nIiKCkJAQ9u7dy3XXXUe3bt245557eOCBB7j33nsJCAggLy+PKVOm0K1bN2688UbGjRunVZI39d/TWVP/befMmcOUKVN47LHH6N69OxMnTmTDhg106NDB7fEN+Z0FeOyxx9i4cSMDBgzgxRdf5PXXX2fMGHV5iU6n47vvviM6OpoLL7yQSy+9lE6dOjF//vw6x6rX65k3bx6bNm2id+/ePPLII7z66qsuxyxfvpzi4mJGjBhBYmKi9lXbuefMmcOgQYO46qqrGD58OIqi8OOPPzaqQtvT7z1CCJVOqb44RjRYYWEhkZGRFBQU1Ji2Ki8v59ChQy57bIkqu7MLsdrsvPq3hygqLJB2ZEII4QX++llV1+e3v5AMoPAKxxSwEEIIIVqeBICixSmKom0E7eVWwEIIIYRf8r2SVOHz7E6rDubOndss/YCFEEIIUTvJAIoWZ6ssatTpdBL8CSGEEF4gAaBocVofYIn9hBBCCK+QAFC0OEcBSIBk/4QQQgivkABQtDhHFxC9pACFEEIIr5AAULQ4xw4wsv5PCCGE8A4JAEWL06aAJQMohBBCeIUEgKLF2aQIxGtSU1N58803tcs6ne6c68LiK8/x8OHD6HQ6tm7dWusxy5YtQ6fTkZ+fD6jbJkVFRbXI+MS5oSG/Z8I/SQAoNDqdrs4v5/68Z8ORAWwNU8DVgwV/k52dzbhx47w9jGbly89xxIgRZGdnExkZ6e2hNMro0aN5+OGHPXY+b/6dlpeXM3XqVPr06YPBYGDixIk1jsnOzuaWW26hW7du6PV6jz73s5WcnEx2dja9e/f29lBEKyMbQQtNdna29vP8+fN55pln2Ldvn3ZdWFiYRx7HsQ1Mc00BK4qCzWbDYGi5X++KigqMRmOLPZ6nJCQkeHsIzc6Xn6PRaPTp8dfFG3+nTWGz2QgODubBBx/k66+/dnuM2WwmLi6Op556ijfeeKOFR1i3gICAc/Z3SJwdv88Azpo1i9TUVIKCghg6dCjr16/39pC8JiEhQfuKjIxEp9O5XDdv3jzS0tIICgqiR48evPPOO9p9HdMMX375JRdccAHBwcEMGTKE9PR0NmzYwODBgwkLC2PcuHHk5OYCagZw6tSpTJw4kRkzZhAXF0dERAT33XcfFRUV2rntdjszZ86kY8eOBAcH069fP7766ivtdsc02aJFixg0aBAmk4lVq1aRkZHBhAkTiI+PJywsjCFDhrBkyRLtfqNHj+bIkSM88sgjWpYT4LnnnqN///4ur82bb75Jamqqdtkx7pdeeomkpCS6d+8OwKeffsrgwYMJDw8nISGBW265hZycnDpf99TUVF588UWmTJlCWFgYKSkpfP/99+Tm5jJhwgTCwsLo27cvGzdudLnfqlWrtNc6OTmZBx98kJKSEu32nJwcxo8fT3BwMB07duSzzz6r8djVp0cff/xxunXrRkhICJ06deLpp5/GYrHUOf6G8OXn2Jjf7dzK321Qf2+ff/552rdvj8lkon///vz00081zr93715GjBhBUFAQvXv3Zvny5dpt1aeA3fnuu+8YOHAgQUFBdOrUiRkzZmC1Wms9HuCjjz6iV69emEwmEhMTmTZtmnZbfn4+d911l/b3ePHFF7Nt2zbtdsffx6effkpqaiqRkZHcdNNNFBUVAerfxvLly3nrrbe0v6vDhw97/O80Ly+Pm2++mXbt2hESEkKfPn344osv6nzeTblPaGgos2fP5u677641kEpNTeWtt95iypQpDc7W2mw27rzzTu19rXv37rz11lsuxzjeZ1577TUSExOJjY3lgQcecPl9TU1N5eWXX+aOO+4gPDycDh068P7772u3yxSwqJXix+bNm6cYjUblo48+Unbt2qXcfffdSlRUlHLy5MkG3b+goEABlIKCghq3lZWVKbt371bKysqqrrTbFcVc3PJfdnujX5s5c+YokZGR2uX//Oc/SmJiovL1118rBw8eVL7++mslJiZGmTt3rqIoinLo0CEFUHr06KH89NNPyu7du5Vhw4YpgwYNUkaPHq2sWrVK2bx5s9KlSxfl1ql3KduyzignC8uU22+/XQkLC1MmTZqk7Ny5U/nhhx+UuLg45W9/+5v22C+++KJ23oyMDGXOnDmKyWRSli1bpiiKovz2228KoPTt21f55ZdflAMHDih5eXnK1q1blXfffVfZsWOHkp6erjz11FNKUFCQcuTIEUVRFCUvL09p37698vzzzyvZ2dlKdna2oiiK8uyzzyr9+vVzeT3eeOMNJSUlRbvsGPdtt92m7Ny5U9m5c6eiKIryf//3f8qPP/6oZGRkKGvXrlWGDx+ujBs3rs7XOiUlRYmJiVHeffddJT09XfnjH/+oREREKGPHjlW+/PJLZd++fcrEiROVtLQ0xV75b3ngwAElNDRUeeONN5T09HRl9erVyoABA5SpU6dq5x03bpzSr18/Ze3atcrGjRuVESNGKMHBwcobb7yhHQMoCxYs0C6/8MILyurVq5VDhw4p33//vRIfH6/8/e9/r3P8DeHLz7Exv9v33Xefdr/XX39diYiIUL744gtl7969yl/+8hclMDBQSU9Pdzlv+/btla+++krZvXu3ctdddynh4eHKqVOnFEWp+t0+c+aMoig1/y5XrFihREREKHPnzlUyMjKUX375RUlNTVWee+65Wp/PO++8owQFBSlvvvmmsm/fPmX9+vUur9ell16qjB8/XtmwYYOSnp6uPPbYY0psbKySl5enKIr69xEWFqZce+21yo4dO5QVK1YoCQkJ2t9sfn6+Mnz4cOXuu+/W/q6sVqvH/06PHj2qvPrqq8qWLVuUjIwM5e2331YCAgKU33//vdbn3pT7OLv99tuVCRMm1HnMqFGjlIceeqjec1VUVCjPPPOMsmHDBuXgwYPKf/7zHyUkJESZP3++y+NFREQo9913n7Jnzx7lf//7nxISEqK8//772jGOv61Zs2Yp+/fvV2bOnKno9Xpl7969iqJU/Z5t2bLF7Tjcflb5gbo+v/2FXweA5513nvLAAw9ol202m5KUlKTMnDmzQfdvdABoLlaUZyNa/stc3OjXpvoHTefOnZXPP//c5ZgXXnhBGT58uKIoVW8yH374oXb7F198oQDK0qVLtetmzpypdOrSVdmWdUY5VVSu3H777UpMTIxSUlKiHTN79mwlLCxMsdlsSnl5uRISEqKsWbPG5bHvvPNO5eabb1YUpepD8ttvv633efXq1Uv517/+pV1OSUlx+fBTlIYHgPHx8YrZbK7z8TZs2KAASlFRUa3HpKSkKLfeeqt2OTs7WwGUp59+Wrtu7dq1CqB9+N15553KPffc43KelStXKnq9XikrK1P27dunAMr69eu12/fs2aMAdQZH1b366qvKoEGD6nyODeHLz7Exv9vdu3fXLiclJSkvvfSSy7mGDBmi3H///S7nfeWVV7TbLRaL0r59ey0grS8AvOSSS5SXX37Z5TE+/fRTJTExsdbnk5SUpDz55JNub1u5cqUSERGhlJeXu1zfuXNn5b333lMURf37CAkJUQoLC7Xb//znPytDhw7VLrsLgjz9d+rOlVdeqTz22GP1HtfU+3gyAHTngQceUK677jqXx0tJSVGsVqt23Q033KBMmjRJu1z9b8tutytt27ZVZs+erSiKBIC1kQBQUVr34otmVFFRwaZNm3jiiSe06/R6PZdeeilr1651ex+z2YzZbNYuFxYWNvs4W4OSkhIyMjK48847ufvuu7XrrVZrjemOvn37aj/Hx8cD0KdPH5fr8k6dAqo2gu7Xrx8hISHaMcOHD6e4uJisrCyKi4spLS3lsssuc3mciooKBgwY4HLd4MGDXS4XFxfz3HPPsXDhQrKzs7FarZSVlZGZmdno18CdPn361Fj3t2nTJp577jm2bdvGmTNnsNvVxseZmZn07Nmz1nM15HUDdcozISGBbdu2sX37dpcpT0VRsNvtHDp0iPT0dAwGA4MGDdJu79GjR70VpPPnz+ftt98mIyOD4uJirFYrERERtR4/btw4Vq5cCUBKSgq7du06555jY8bvmO4vLCzk+PHjjBw50uUcI0eOdJlOBfX33cFgMDB48GD27NlT73gAtm3bxurVq3nppZe062w2G+Xl5ZSWlrr8XYH62h4/fpxLLrmk1vMVFxcTGxvrcn1ZWRkZGRna5dTUVMLDw7XLiYmJ9S51cPDU36nNZuPll1/myy+/5NixY1RUVGA2m2s857O9T3OaNWsWH330EZmZmZSVlVFRUVFj+UmvXr0ICAjQLicmJrJjxw6XY5x/Nx1Ldxr67yH8l98GgKdOncJms2lv5A7x8fHs3bvX7X1mzpzJjBkzmv6ggSHwt+NNv//ZPO5ZKC4uBuCDDz5g6NChLrc5vzEBBAYGaj871upUv84RFDWkFZzjsRcuXEi7du1cbjOZTC6XQ0NDXS5Pnz6dxYsX89prr9GlSxeCg4O5/vrrXdYXuqPX61EqC1Uc3K0Rq/54JSUljBkzhjFjxvDZZ58RFxdHZmYmY8aMqfcxG/K6AdprV1xczL333suDDz5Y41wdOnQgPT29zsdzZ+3atUyePJkZM2YwZswYIiMjmTdvHv/85z9rvc+HH35IWVlZjfG646vPsTHjd4y9pRQXFzNjxgyuvfbaGrcFBQXVuC44OLje8yUmJrJs2bIatzkH1tX/rRvz3D31d/rqq6/y1ltv8eabb9KnTx9CQ0N5+OGH67xfU+7TXObNm8f06dP55z//yfDhwwkPD+fVV1/l999/dzmuIa/12fx7CP/ltwFgUzzxxBM8+uij2uXCwkKSk5MbfgKdDoyh9R/XysTHx5OUlMTBgweZPHmyx87rKALetm0bZWVl2ofTunXrCAsLIzk5mZiYGEwmE5mZmYwaNapR51+9ejVTp07lmmuuAdQPt8OHD7scYzQasdlsLtfFxcVx4sQJFEXRPugbsoB679695OXl8corr2i/F9WLGjxl4MCB7N69my5duri9vUePHlitVjZt2sSQIUMA2LdvX53FBGvWrCElJYUnn3xSu+7IkSN1jqN6UO5JreU5NkVERARJSUmsXr3a5fd29erVnHfeeS7Hrlu3jgsvvBBAez7ORRl1GThwIPv27av1NaouPDyc1NRUli5dykUXXeT2fCdOnMBgMLgUPTWWu7+r2jT173T16tVMmDCBW2+9FVD/45Cenl5npr0p92kuq1evZsSIEdx///3adc5ZViGam98GgG3atCEgIICTJ0+6XH/y5MlaK71MJlONrJO/mDFjBg8++CCRkZGMHTsWs9nMxo0bOXPmjEtQ3BiOKeCKigruvPNOnnrqKQ4fPsyzzz7LtGnT0Ov1hIeHM336dB555BHsdjvnn38+BQUFrF69moiICG6//fZaz9+1a1e++eYbxo8fj06n4+mnn67xv+LU1FRWrFjBTTfdhMlkok2bNowePZrc3Fz+8Y9/cP311/PTTz+xaNGieqcJO3TogNFo5F//+hf33XcfO3fu5IUXXmjSa1Ofxx9/nGHDhjFt2jTuuusuQkND2b17N4sXL+bf//433bt3Z+zYsdx7773Mnj0bg8HAww8/XGcGqGvXrmRmZjJv3jyGDBnCwoULWbBgQbOMvyF8/Tn++c9/5tlnn6Vz587079+fOXPmsHXr1hqVyrNmzaJr166kpaXxxhtvcObMGe64444GPcYzzzzDVVddRYcOHbj++uvR6/Vs27aNnTt38uKLL7q9z3PPPcd9991H27ZtGTduHEVFRaxevZo//elPXHrppQwfPpyJEyfyj3/8g27dunH8+HEWLlzINddcU2P6tjapqan8/vvvHD58mLCwMGJiYmo9tql/p127duWrr75izZo1REdH8/rrr3Py5Mk6g7mm3Adg9+7dVFRUcPr0aYqKirT/EDpP1zquKy4uJjc3l61bt2I0Gms9d9euXfnkk0/4+eef6dixI59++ikbNmygY8eOdY5FCE/x221gjEYjgwYNYunSpdp1drudpUuXuqzJEaq77rqLDz/8kDlz5tCnTx9GjRrF3Llzm/hm5boR9CWXXELXrl258MILmTRpEldffbXLptMvvPACTz/9NDNnziQtLY2xY8eycOHCeh/79ddfJzo6mhEjRjB+/HjGjBnDwIEDXY55/vnnOXz4MJ07dyYuLg6AtLQ03nnnHWbNmkW/fv1Yv34906dPr/dZxcXFMXfuXP773//Ss2dPXnnlFV577bXGvDAN1rdvX5YvX056ejoXXHABAwYM4JlnniEpKUk7Zs6cOSQlJTFq1CiuvfZa7rnnHtq2bVvrOa+++moeeeQRpk2bRv/+/VmzZg1PP/10s4y/IXz9OT744IM8+uijPPbYY/Tp04effvqJ77//nq5du7oc98orr/DKK6/Qr18/Vq1axffff0+bNm0a9Bhjxozhhx9+4JdffmHIkCEMGzaMN954g5SUlFrvc/vtt/Pmm2/yzjvv0KtXL6666ir2798PqFOHP/74IxdeeCF/+MMf6NatGzfddBNHjhypsVymLtOnTycgIICePXtqSyFq09S/06eeeoqBAwcyZswYRo8eTUJCgttNmp015T4AV1xxBQMGDOB///sfy5YtY8CAATXWIDuu27RpE59//jkDBgzgiiuuqPWc9957L9deey2TJk1i6NCh5OXluWQDhWhuOqX6Yic/Mn/+fG6//Xbee+89zjvvPN58802+/PJL9u7d26A3u8LCQiIjIykoKKiRHSovL+fQoUN07NjR7Vocf6UoCjuPFaAAaYkR3H3nHeTn57fKVl1CCHGu89fPqro+v/2F304BA0yaNInc3FyeeeYZTpw4oW3U2pj/6YrGURRH/q91tIITQggh/JFfB4AA06ZNa/CCa3H2bE4J52bqBCeEEEKIevh9AChalt1e2Qe4sqXT3LlzvTsgIYQQwg/5bRGI8A57ZQZQL+k/IYQQwmskABQtylY5Ayzr/4QQQgjvkQBQtChtClh+84QQQgivkY9h0aK0KWDJAAohhBBeIwGgaFE2uwSAQgghhLdJAChalCMDGCBFIEIIIYTXSAAoWpQUgXhXamoqb775pnZZp9NJFxYhhPBDEgAKja5yb77avpz78zaVowhE30p+86oHRP4mOzubcePGNfvjjB49mocffrjW20+fPs3DDz9MSkoKRqORpKQk7rjjjho9ZKdOner2d3Ps2LHaMdu2bePqq6+mbdu2BAUFkZqayqRJk8jJydGOWbBgAcOGDSMyMpLw8HB69epV5/iEEOJcIxtBC012drb28/z583nmmWfYt2+fdl1YWNhZP4bzRtDNRVEUbDYbBkPL/XpXVFRgNBpb7PE8JSEhwdtD4PTp0wwbNgyj0ci7775Lr169OHz4ME899RRDhgxh7dq1dOrUSTt+7NixzJkzx+UcJpMJgNzcXC655BKuuuoqfv75Z6Kiojh8+DDff/89JSUlACxdupRJkybx0ksvcfXVV6PT6di9ezeLFy9uuScthBBe1kryMKI1SEhI0L4iIyPR6XQu182bN4+0tDSCgoLo0aMH77zzjnbfw4cPo9Pp+PLLL7ngggsIDg5myJAhpKens2HDBgYPHkxYWBi33jiR03mntI2gp06dysSJE5kxYwZxcXFERERw3333UVFRoZ3bbrczc+ZMOnbsSHBwMP369eOrr77Sbl+2bBk6nY5FixYxaNAgTCYTq1atIiMjgwkTJhAfH09YWBhDhgxhyZIl2v1Gjx7NkSNHeOSRR7RMEsBzzz1H//79XV6bN998k9TUVO2yY9wvvfQSSUlJdO/eHYBPP/2UwYMHEx4eTkJCArfccotL5smd1NRUXnzxRaZMmUJYWBgpKSl8//335ObmMmHCBMLCwujbty8bN250ud+qVau01zo5OZkHH3xQC3IAcnJyGD9+PMHBwXTs2JHPPvusxmNXnwJ+/PHH6datGyEhIXTq1Imnn34ai8VS5/jP1pNPPsnx48dZsmQJ48aNo0OHDlx44YX8/PPPBAYG8sADD7gcbzKZXH4vExISiI6OBmD16tUUFBTw4YcfMmDAADp27MhFF13EG2+8QceOHQH43//+x8iRI/nzn/9M9+7d6datGxMnTmTWrFnN+jyFEKI1kQCwBSmKQqmltMW/FKf+u0312Wef8cwzz/DSSy+xZ88eXn75ZZ5++mk+/vhjl+OeffZZnnrqKTZv3ozBYOCWW27hL3/5C2+99RYrV67k8MGDvPPayy5rAJcuXcqePXtYtmwZX3zxBd988w0zZszQbp85cyaffPIJ7777Lrt27eKRRx7h1ltvZfny5S6P/de//pVXXnmFPXv20LdvX4qLi7niiitYunQpW7ZsYezYsYwfP16bVvzmm29o3749zz//PNnZ2S4Z0IZYunQp+/btY/Hixfzwww8AWCwWXnjhBbZt28a3337L4cOHmTp1ar3neuONNxg5ciRbtmzhyiuv5LbbbmPKlCnceuutbN68mc6dOzNlyhTt3zIjI4OxY8dy3XXXsX37dubPn8+qVatc+lpPnTqVrKwsfvvtN7766iveeeedeoPR8PBw5s6dy+7du3nrrbf44IMPeOONNxr1ujSG3W5n3rx5TJ48uUY2Mjg4mPvvv5+ff/6Z06dPN+h8CQkJWK1WFixYUOvvfUJCArt27WLnzp1nPX4hhPBVMgXcgsqsZQz9fGiLP+7vt/xOSGDIWZ3j2Wef5Z///CfXXnstAB07dmT37t2899573H777dpx06dPZ8yYMQA89NBD3HzzzSxdupSRI0cCcMMtt/HVvM8IcJoBNhqNfPTRR4SEhNCrVy+ef/55/vznP/PCCy9gsVh4+eWXWbJkCcOHDwegU6dOrFq1ivfee49Ro0Zp53n++ee57LLLtMsxMTH069dPu/zCCy+wYMECvv/+e6ZNm0ZMTAwBAQFatq6xQkND+fDDD12mfu+44w7t506dOvH2228zZMgQiouL65xCv+KKK7j33nsBeOaZZ5g9ezZDhgzhhhtuANTM3PDhwzl58iQJCQnMnDmTyZMna+vWunbtyttvv82oUaOYPXs2mZmZLFq0iPXr1zNkyBAA/u///o+0tLQ6n9NTTz2l/Zyamsr06dOZN28ef/nLXxr34jRQbm4u+fn5tY4rLS0NRVE4cOAA5513HgA//PBDjdfyb3/7G3/7298YNmwYf/vb37jlllu47777OO+887j44ouZMmUK8fHxAPzpT39i5cqV9OnTh5SUFIYNG8bll1/O5MmTtalkIYQ410kAKOpVUlJCRkYGd955J3fffbd2vdVqJTIy0uXYvn37aj87PnD79OmjXRfTpi2nT+W69ALu168fISFVAerw4cMpLi4mKyuL4uJiSktLXQI7UNfcDRgwwOW6wYMHu1wuLi7mueeeY+HChWRnZ2O1WikrK6tRWNBUffr0qbHub9OmTTz33HNs27aNM2fOYLfbAcjMzKRnz561nqu+181xXU5ODgkJCWzbto3t27e7TOsqioLdbufQoUOkp6djMBgYNGiQdnuPHj2Iioqq8znNnz+ft99+m4yMDIqLi7FarURERNR6/Lhx41i5ciUAKSkp7Nq1q87z16YxWeqLLrqI2bNnu1wXExOj/fzSSy/x6KOP8uuvv/L777/z7rvv8vLLL7NixQr69OlDaGgoCxcuJCMjg99++41169bx2GOP8dZbb7F27VqX30UhhDhXSQDYgoINwfx+y+//397dR0VV538Af8/wMM6gw+MAEg8WmGI+lyfwIXXRBmvN1cryqJutHrXkeHqw7XjUZdOzai61W6adUhdOuyrqpnY2lZMppikaukBOIJpKtAXqggO4IKDz+f3hb+7xAsaIw+N9v87hnObeL9/7/b6NuZ9z597vtMtx78W1a9cAABs2bMCjj6qvYHp4eKhee3l5Kf/tvKfu9m3QASIOl5eBcR57z549uO+++1T7Gl6t8fHxUb1etGgR9u/fj5SUFMTExMBoNOKZZ55R3V/YFL1e36ggaeo+uIbH+9///ger1Qqr1YrNmzfDYrGguLgYVqu12WM2l5tzm7OgvHbtGubNm4eFCxc26isyMhJnz579xeM1JSsrC9OnT8dbb70Fq9UKX19fpKen45133rnj72zcuBE1NTWNxusqi8UCPz8/FBQUNLm/oKAAOp0OMTExyjYfHx/V66YEBgbi2WefxbPPPouVK1diyJAhSElJUd2yEB0djejoaMyZMwdLlizBgw8+iG3btuHFF1+863kQEXU2LADbkE6nu+ePYttDSEgIwsLCcOHCBUyfPv2e+vr/+kW1EHReXh5qampgNN4qVI8fP47u3bsjIiICAQEBMBgMKC4uVn3c64qjR49i1qxZmDx5MoBbRVNRUZGqjbe3N27evKnaZrFYUFpaChFRCq/c3Nxmj3fmzBmUlZVh9erViIiIAIBGD264y9ChQ5Gfn3/HQqhv3764ceMGTp06pXwEXFhYCLvdfsc+jx07hqioKCxZskTZ9sMPP/ziOBoW5XdLr9dj6tSp2Lx5M5YvX676KL6mpgbr16+H1WpVXeG7W97e3oiOjlY9INNQr169YDKZfrENEVFXwgKQXPLWW29h4cKF8PX1RWJiImpra3Hy5ElcvXoVr732msv9CBp/FVxdXR1mz56NpUuXoqioCMnJyUhKSoJer0ePHj2waNEivPrqq3A4HBg5ciQqKipw9OhRmM1m1f2HDfXu3Rs7d+7ExIkTodPpsGzZMuUKmlOvXr1w+PBhPP/88zAYDAgKCsKYMWNw5coVrFmzBs888wwyMjKwb9++X/woFLh15c3b2xtr167F/PnzYbPZsGLFCpezuRtvvvkm4uLikJSUhDlz5sDHx0dZyuSDDz5Anz59kJiYiHnz5uHDDz+Ep6cnXnnlFaXIbkrv3r1RXFyM9PR0DBs2DHv27MGuXbvcNuYrV640KqR79uyJlStX4sCBAxg/fjzWrFmD/v374+LFi1i6dCnq6+sbPZ1bW1uL0tJS1TZPT08EBQXh888/R3p6Op5//nk8+OCDEBH861//wt69e5WlY/74xz+iuroaTzzxBKKiomC32/H++++jvr6+0a0GRERdFZ8CJpfMmTMHGzduRGpqKgYMGIDRo0cjLS1NWVrDFSICP6MXdDodPG+7ApiQkIDevXvjsccew3PPPYennnpKtej0ihUrsGzZMqxatQqxsbFITEzEnj17mj32u+++C39/fwwfPhwTJ06E1WrF0KFDVW2WL1+OoqIiREdHw2KxALj14MH69euxbt06DBo0CN988w0WLVrU7PwsFgvS0tKwY8cO9OvXD6tXr0ZKSorL+dyNgQMH4quvvsLZs2cxatQoDBkyBH/4wx8QFhamtElNTUVYWBhGjx6NKVOmYO7cuQgODr5jn0899RReffVVJCUlYfDgwTh27BiWLVvmtjFv2bIFQ4YMUf1s2LABgYGBOH78OMaOHYt58+YhOjoaU6dORXR0NLKzs1VrAAJARkYGevbsqfoZOXIkAKBfv34wmUx4/fXXMXjwYMTFxWH79u3YuHEjZs6cCQAYPXo0Lly4gN/+9rfo27cvJkyYgNLSUnzxxRfKcj5ERF2dTtyxRohGVVZWwtfXFxUVFY2uDl2/fh0XL17E/fffj27durXTCDu+WbNmwW638+vIiIjagVbPVb90/tYKXgEkIiIi0hgWgEREREQaw4dAqF2lpaW19xCIiIg0h1cAiYiIiDSGBWAr4zM2RETUUfEcpV0sAFuJ81sRqqur23kkRERETXN+S1HDb3Wiro/3ALYSDw8P+Pn54fLlywAAk8mkfKsEERFRe3M4HLhy5QpMJhM8PVkOaA3/xVuR82utnEUgERFRR6LX6xEZGckLFBrEArAV6XQ69OzZE8HBwaivr2/v4RAREal4e3tDr+fdYFrEArANeHh48P4KIiIi6jBY9hMRERFpDAtAIiIiIo1hAUhERESkMbwH8B44F9CsrKxs55EQERGRq5znbS0vhM0C8B5UVVUBACIiItp5JERERHS3qqqq4Ovr297DaBc60XL5e48cDgd+/vln9OjRo8OsoVRZWYmIiAj8+OOPMJvN7T2cDokZNY8ZuYY5NY8ZNY8ZucadOYkIqqqqEBYWptllcHgF8B7o9XqEh4e39zCaZDab+UbSDGbUPGbkGubUPGbUPGbkGnflpNUrf07aLHuJiIiINIwFIBEREZHGsADsYgwGA5KTk2EwGNp7KB0WM2oeM3INc2oeM2oeM3INc3IvPgRCREREpDG8AkhERESkMSwAiYiIiDSGBSARERGRxrAAJCIiItIYFoAd0OHDhzFx4kSEhYVBp9Nh9+7dqv2XLl3CrFmzEBYWBpPJhMTERJw7d07VprS0FDNnzkRoaCh8fHwwdOhQfPrpp6o25eXlmD59OsxmM/z8/DB79mxcu3attafnFqtWrcKwYcPQo0cPBAcH4ze/+Q0KCwtVba5fv44FCxYgMDAQ3bt3x9NPP41Lly6p2hQXF+PJJ5+EyWRCcHAw3njjDdy4cUPV5tChQxg6dCgMBgNiYmKQlpbW2tNzC3dklJeXh2nTpiEiIgJGoxGxsbF47733Gh1LyxndrqysDOHh4dDpdLDb7ap9zAhIS0vDwIED0a1bNwQHB2PBggWq/d9++y1GjRqFbt26ISIiAmvWrGnVubmTu3LKzs5GQkIC/Pz84O/vD6vViry8PFWbzpqTKxl9/PHHGDNmDMxmc5N/R4Br567OmlGbEupw9u7dK0uWLJGdO3cKANm1a5eyz+FwSFxcnIwaNUq++eYbOXPmjMydO1ciIyPl2rVrSrvx48fLsGHD5MSJE3L+/HlZsWKF6PV6+fe//620SUxMlEGDBsnx48flyJEjEhMTI9OmTWvLqbaY1WqV1NRUsdlskpubK0888USjDObPny8RERFy4MABOXnypMTFxcnw4cOV/Tdu3JD+/fvLuHHjJCcnR/bu3StBQUGyePFipc2FCxfEZDLJa6+9Jvn5+bJ27Vrx8PCQjIyMNp1vS7gjo02bNsnChQvl0KFDcv78efn73/8uRqNR1q5dq7TReka3mzRpkkyYMEEAyNWrV5XtzEjknXfekbCwMNm8ebN8//33kpeXJ5999pmyv6KiQkJCQmT69Olis9lk69atYjQa5aOPPmqzud4Ld+RUVVUlAQEBMmvWLDlz5ozYbDZ5+umnJSQkROrq6kSkc+fkSkZ/+ctfZNWqVbJq1apGf0dOzZ27OnNGbYkFYAfXsAAsLCwUAGKz2ZRtN2/eFIvFIhs2bFC2+fj4yCeffKLqKyAgQGmTn58vACQ7O1vZv2/fPtHpdPLTTz+10mxaz+XLlwWAfPXVVyIiYrfbxcvLS3bs2KG0KSgoEACSlZUlIrcKbb1eL6WlpUqbDz/8UMxms9TW1oqIyO9//3t56KGHVMd67rnnxGq1tvaU3K4lGTXl5ZdflrFjxyqvmdEt69evl9GjR8uBAwcanbi0nlF5ebkYjUb58ssv79jv+vXrxd/fX/nbExF58803pU+fPq00k9bVkpyys7MFgBQXFyttvv32WwEg586dE5GulVPDjG6XmZnZZAHoyrmrK2XUmvgRcCdTW1sLAOjWrZuyTa/Xw2Aw4Ouvv1a2DR8+HNu2bUN5eTkcDgfS09Nx/fp1jBkzBgCQlZUFPz8/PPLII8rvjBs3Dnq9HidOnGibybhRRUUFACAgIAAAcOrUKdTX12PcuHFKm759+yIyMhJZWVkAbmUwYMAAhISEKG2sVisqKyvx3XffKW1u78PZxtlHZ9KSjO7Uj7MPgBkBQH5+PpYvX45PPvmkyS+W13pG+/fvh8PhwE8//YTY2FiEh4dj6tSp+PHHH5XfycrKwmOPPQZvb29lm9VqRWFhIa5evdoWU3OrluTUp08fBAYGYtOmTairq0NNTQ02bdqE2NhY9OrVC0DXyqlhRq5w5dzVlTJqTSwAOxnnG8bixYt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\n", - " \n", + " \n", "
\n", " " ], @@ -750,7 +4272,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.10.14" } }, "nbformat": 4, diff --git a/tests/test_webapp_v2_routers_coverages.py b/tests/test_webapp_v2_routers_coverages.py index 346c5c90..eab00946 100644 --- a/tests/test_webapp_v2_routers_coverages.py +++ b/tests/test_webapp_v2_routers_coverages.py @@ -1,6 +1,13 @@ +import random +import re + import httpx +import pytest_httpx from arpav_ppcv.schemas import coverages +from arpav_ppcv import database + +random.seed(0) def test_coverage_configurations_list( @@ -24,3 +31,41 @@ def test_coverage_identifiers_list( headers={"accept": "application/json"}, ) assert list_response.status_code == 200 + + +def test_get_time_series( + httpx_mock: pytest_httpx.HTTPXMock, + test_client_v2_app: httpx.Client, + arpav_db_session, + sample_tas_csv_data: str, +): + db_cov_conf = coverages.CoverageConfiguration( + name="fake_tas", + netcdf_main_dataset_name="tas", + thredds_url_pattern="fake", + palette="fake", + ) + arpav_db_session.add(db_cov_conf) + arpav_db_session.commit() + arpav_db_session.refresh(db_cov_conf) + + httpx_mock.add_response( + url=re.compile(r".*ncss/grid.*"), + method="get", + text=sample_tas_csv_data, + ) + identifiers = database.generate_coverage_identifiers(db_cov_conf) + cov_id = random.choice(identifiers) + series_response = test_client_v2_app.get( + test_client_v2_app.app.url_path_for( + "get_time_series", coverage_identifier=cov_id + ), + params={ + "coords": "POINT(11.5469 44.9524)", + "include_observation_data": False, + "coverage_data_smoothing": ["NO_SMOOTHING"], + }, + headers={"accept": "application/json"}, + ) + print(series_response.content) + assert series_response.status_code == 200