fixed remaining typos in standard_name

93c4c3a9 · Carsten Hinz · 9f4274fd · 93c4c3a9 · 93c4c3a9 · 93c4c3a9
Commit 93c4c3a9 authored 9 months ago by Carsten Hinz
--- a/tests/produce_data_withOptional.ipynb
+++ b/tests/produce_data_withOptional.ipynb
@@ -43,7 +43,7 @@
    "grid = RegularGrid( lat_resolution=1.9, lon_resolution=2.5, )\n",
    "\n",
    "configs = dict()\n",
-    "for year in range(0,19):\n",
+    "for year in range(18,19):\n",
    "    valid_data = Config(\n",
    "        grid,\n",
    "        TimeSample( start=dt(2000+year,1,1), end=dt(2000+year,12,31), sampling=\"daily\"),#possibly adopt range:-)\n",

 %% Cell type:code id: tags:

 ``` python
 from datetime import datetime as dt
 from collections import namedtuple
 from pathlib import Path

 from toargridding.toar_rest_client import AnalysisServiceDownload, Connection
 from toargridding.grids import RegularGrid
 from toargridding.gridding import get_gridded_toar_data
 from toargridding.metadata import TimeSample
 ```

 %% Cell type:code id: tags:

 ``` python
 #creation of request.

 Config = namedtuple("Config", ["grid", "time", "variables", "stats","moreOptions"])

 #moreOptions is implemented as a dict to add additional arguments to the query to the REST API
 #For example the field toar1_category with its possible values Urban, RuralLowElevation, RuralHighElevation and Unclassified can be added.
 #see page 18 in https://toar-data.fz-juelich.de/sphinx/TOAR_UG_Vol03_Database/build/latex/toardatabase--userguide.pdf
 #or type_of_area with urban, suburban and rural on page 20 can be used

 details4Query ={
    #"toar1_category" : "Urban" #uncomment if wished:-)
    #"toar1_category" : "RuralLowElevation" #uncomment if wished:-)
    #"toar1_category" : "RuralHighElevation" #uncomment if wished:-)
    #"type_of_area" : "Urban" #also test Rural, Suburban,
    "type_of_area" : "Rural" #also test Rural, Suburban,
    #"type_of_area" : "Suburban" #also test Rural, Suburban,
 }

 grid = RegularGrid( lat_resolution=1.9, lon_resolution=2.5, )

 configs = dict()
-for year in range(0,19):
+for year in range(18,19):
    valid_data = Config(
        grid,
        TimeSample( start=dt(2000+year,1,1), end=dt(2000+year,12,31), sampling="daily"),#possibly adopt range:-)
        ["mole_fraction_of_ozone_in_air"],#variable name
        #[ "mean", "dma8epax"],# will start one request after another other...
        [ "dma8epa_strict" ],
        details4Query
    )

    configs[f"test_ta{year}"] = valid_data
 ```

 %% Cell type:code id: tags:

 ``` python
 #CAVE: this cell runs about 45minutes per requested year. therefore we increase the waiting duration to 1h per request.
 #the processing is done on the server of the TOAR database.
 #a restart of the cell continues the request to the REST API if the requested data are ready for download
 # The download can also take a few minutes

 stats_endpoint = "https://toar-data.fz-juelich.de/api/v2/analysis/statistics/"
 cache_basepath = Path("cache")
 result_basepath = Path("results")
 cache_basepath.mkdir(exist_ok=True)
 result_basepath.mkdir(exist_ok=True)
 analysis_service = AnalysisServiceDownload(stats_endpoint=stats_endpoint, cache_dir=cache_basepath, sample_dir=result_basepath, use_downloaded=True)

 Connection.DEBUG=True

 # maybe adopt the interval for requesting the results and the total duration, before the client pauses the requests.
 # as the requests take about 45min, it is more suitable to wait 60min before timing out the requests than the original 30min.
 analysis_service.connection.setRequestTimes(interval_min=5, maxWait_min=60)

 for person, config in configs.items():
    print(f"\nProcessing {person}:")
    print(f"--------------------")
    datasets, metadatas = get_gridded_toar_data(
        analysis_service=analysis_service,
        grid=config.grid,
        time=config.time,
        variables=config.variables,
        stats=config.stats,
        **config.moreOptions
    )

    for dataset, metadata in zip(datasets, metadatas):
        dataset.to_netcdf(result_basepath / f"{metadata.get_id()}_{config.grid.get_id()}.nc")
        print(metadata.get_id())
 ```

--- a/toargridding/gridding.py
+++ b/toargridding/gridding.py
@@ -45,7 +45,7 @@ def get_gridded_toar_data(
    ]

    datasets = []
-    for metadata in metadatas:  # standart_name ?
+    for metadata in metadatas:  # standard_name ?
        data = analysis_service.get_data(metadata)
        ds = grid.as_xarray(data)
        datasets.append(ds)

--- a/toargridding/metadata.py
+++ b/toargridding/metadata.py
@@ -118,7 +118,7 @@ class Metadata:
        
        Parameters:
        ----------
-        standart_name:
+        standard_name:
            standard name according to CF
        stat:
            statistical analysis to be done by the TOAR database
@@ -157,14 +157,14 @@ class Metadata:
    def get_title(self) -> str:
        """creation of a title for metadata of a xarray according to the CF convention
        """
-        return f"{self.time.sampling} {self.statistic} statistic for {self.variable.standart_name} from {self.time.start} to {self.time.end} aggregated on global grid"
+        return f"{self.time.sampling} {self.statistic} statistic for {self.variable.standard_name} from {self.time.start} to {self.time.end} aggregated on global grid"

    def get_summary(self) -> str:
        """creation of a descriptive string
         
        At the moment same as title.
        """
-        return f"{self.time.sampling} {self.statistic} statistic for {self.variable.standart_name} from {self.time.start} to {self.time.end} aggregated on global grid"
+        return f"{self.time.sampling} {self.statistic} statistic for {self.variable.standard_name} from {self.time.start} to {self.time.end} aggregated on global grid"


 @dataclass
@@ -262,7 +262,7 @@ def get_cf_metadata(variable: Variables, metadata: Metadata | None)-> Dict:
                "attributes": {
                    "units": metadata.variable.units,
                    "long_name": metadata.variable.long_name,
-                    "standard_name": metadata.variable.standart_name,
+                    "standard_name": metadata.variable.standard_name,
                    "cell_methods": "time: point, latitude: mean, longitude: mean",
                    "coverage_content_type": "physicalMeasurement",
                },
@@ -274,7 +274,7 @@ def get_cf_metadata(variable: Variables, metadata: Metadata | None)-> Dict:
                    "units": metadata.variable.units,
                    "long_name": metadata.variable.long_name,
                    # TODO standart name extension vs cell_methods
-                    "standard_name": f"{metadata.variable.standart_name} standard_error",
+                    "standard_name": f"{metadata.variable.standard_name} standard_error",
                    "cell_methods": "time: point, latitude: standard_deviation, longitude: standard_deviation",
                    "coverage_content_type": "physicalMeasurement",
                },