Add bbox and station name filter functions for stations

Add units for distance filter

.github/release/full/Dockerfile

@@ -13,8 +13,12 @@ FROM python:3.8.8-slim as build-step
 ENV DEBIAN_FRONTEND noninteractive
 ENV TERM linux
 
-# Install GDAL.
 RUN apt-get update
+
+# Install gcc compiler for python-levenstein
+RUN apt-get install gcc --yes
+
+# Install GDAL.
 RUN apt-get --yes install build-essential libgdal-dev libmariadbclient-dev
 
 # Make sure you have numpy installed before you attempt to install the GDAL Python bindings.

.github/release/standard/Dockerfile

@@ -3,6 +3,11 @@ FROM python:3.8.8-slim
 ENV DEBIAN_FRONTEND noninteractive
 ENV TERM linux
 
+RUN apt-get update
+
+# Install gcc compiler for python-levenstein
+RUN apt-get install gcc --yes
+
 # Use "poetry build --format=wheel" to build wheel packages.
 COPY dist/wetterdienst-*.whl /tmp/
 

CHANGELOG.rst

@@ -7,6 +7,10 @@ Development
 - Add capability to export data to Zarr format
 - Add Wetterdienst UI. Thanks, @meteoDaniel!
 - Add MAC ARM64 supoort with dependency restrictions
+- Add support for stations filtering via bbox and name
+- Add support for units in distance filtering
+- Rename station_name to name
+- Rename filter methods to .filter_by_station_id and .filter_by_name
 
 0.16.1 (31.03.2021)
 *******************

README.rst

@@ -32,7 +32,7 @@ Overview
 .. image:: https://img.shields.io/pypi/status/wetterdienst.svg
    :target: https://pypi.python.org/pypi/wetterdienst/
 .. image:: https://pepy.tech/badge/wetterdienst/month
-   :target: https://pepy.tech/project/wetterdienst/month
+   :target: https://pepy.tech/project/wetterdienst
 .. image:: https://img.shields.io/github/license/earthobservations/wetterdienst
    :target: https://github.com/earthobservations/wetterdienst/blob/main/LICENSE
 .. image:: https://zenodo.org/badge/160953150.svg
@@ -224,7 +224,7 @@ Acquisition of historical data for specific stations using ``wetterdienst`` as l
     ...    end_date="2020-01-01",  # Timezone: UTC
     ...    tidy=True,  # default, tidy data
     ...    humanize=True,  # default, humanized parameters
-    ... ).filter(station_id=(1048, 4411))
+    ... ).filter_by_station_id(station_id=(1048, 4411))
     >>> stations = request.df  # station list
     >>> values = request.values.all().df  # values
 

docs/img/readme_img.py

@@ -21,8 +21,8 @@ def create_temperature_ts_plot():
 
     df = stations.all()
 
-    station_id, _, _, height, lat, lon, station_name, state = df.sort_values("FROM_DATE").iloc[0].values
-    station_name = station_name.replace(u"ß", "ss")
+    station_id, _, _, height, lat, lon, name, state = df.sort_values("FROM_DATE").iloc[0].values
+    name = name.replace(u"ß", "ss")
 
     data = DwdObservationValues(
         [station_id],
@@ -57,7 +57,7 @@ def create_temperature_ts_plot():
 
     ax.set_xlabel("Date")
 
-    title = f"temperature (°C) at {station_name} (GER)\n" \
+    title = f"temperature (°C) at {name} (GER)\n" \
             f"ID {station_id}\n" \
             f"{lat}N {lon}E {height}m"
     ax.set_title(title)

docs/library/core.rst

@@ -34,7 +34,7 @@ Download
 Enumerations
 ============
 
-.. automodule:: wetterdienst.provider.dwd.metadata.column_names
+.. automodule:: wetterdienst.metadata.columns
     :members:
 
 .. automodule:: wetterdienst.provider.dwd.metadata.datetime

docs/usage/api.rst

@@ -143,6 +143,38 @@ Get station information for a given *parameter/parameter_set*, *resolution* and
 
 The function returns a Pandas DataFrame with information about the available stations.
 
+Filter for specific station ids:
+
+.. ipython:: python
+
+    from wetterdienst.provider.dwd.observation import DwdObservationRequest, DwdObservationDataset, DwdObservationPeriod, DwdObservationResolution
+
+    stations = DwdObservationRequest(
+        parameter=DwdObservationDataset.PRECIPITATION_MORE,
+        resolution=DwdObservationResolution.DAILY,
+        period=DwdObservationPeriod.HISTORICAL
+    ).filter_by_station_id(station_id=("01048", ))
+
+    df = stations.df
+
+    print(df.head())
+
+Filter for specific station name:
+
+.. ipython:: python
+
+    from wetterdienst.provider.dwd.observation import DwdObservationRequest, DwdObservationDataset, DwdObservationPeriod, DwdObservationResolution
+
+    stations = DwdObservationRequest(
+        parameter=DwdObservationDataset.PRECIPITATION_MORE,
+        resolution=DwdObservationResolution.DAILY,
+        period=DwdObservationPeriod.HISTORICAL
+    ).filter_by_name(name="Dresden-Klotzsche")
+
+    df = stations.df
+
+    print(df.head())
+
 Values
 ------
 
@@ -159,7 +191,7 @@ Use the ``DwdObservationRequest`` class in order to get hold of stations.
         end_date="2020-01-01",
         tidy=True,
         humanize=True,
-    ).filter(station_id=[3, 1048])
+    ).filter_by_station_id(station_id=[3, 1048])
 
 From here you can query data by station:
 
@@ -187,7 +219,12 @@ Geospatial support
 Inquire the list of stations by geographic coordinates.
 
 - Calculate weather stations close to the given coordinates and set of parameters.
-- Either select by rank (n stations) or by distance in km.
+- Select stations by
+    - rank (n stations)
+    - distance (km, mi,...)
+    - bbox
+
+Distance with default (kilometers)
 
 .. ipython:: python
 
@@ -202,14 +239,53 @@ Inquire the list of stations by geographic coordinates.
         end_date=datetime(2020, 1, 20)
     )
 
-    df = stations.nearby_radius(
+    df = stations.nearby_distance(
         latitude=50.0,
         longitude=8.9,
-        max_distance_in_km=30
+        distance=30
     ).df
 
     print(df.head())
 
+Distance with miles
+
+.. ipython:: python
+
+    from datetime import datetime
+    from wetterdienst.provider.dwd.observation import DwdObservationRequest, DwdObservationDataset, DwdObservationPeriod, DwdObservationResolution
+
+    stations = DwdObservationRequest(
+        parameter=DwdObservationDataset.TEMPERATURE_AIR,
+        resolution=DwdObservationResolution.HOURLY,
+        period=DwdObservationPeriod.RECENT,
+        start_date=datetime(2020, 1, 1),
+        end_date=datetime(2020, 1, 20)
+    )
+
+    df = stations.nearby_distance(
+        latitude=50.0,
+        longitude=8.9,
+        distance=30,
+        unit="mi"
+    ).df
+
+    print(df.head())
+
+Rank selection
+
+.. ipython:: python
+
+    from datetime import datetime
+    from wetterdienst.provider.dwd.observation import DwdObservationRequest, DwdObservationDataset, DwdObservationPeriod, DwdObservationResolution
+
+    stations = DwdObservationRequest(
+        parameter=DwdObservationDataset.TEMPERATURE_AIR,
+        resolution=DwdObservationResolution.HOURLY,
+        period=DwdObservationPeriod.RECENT,
+        start_date=datetime(2020, 1, 1),
+        end_date=datetime(2020, 1, 20)
+    )
+
     df = stations.nearby_number(
         latitude=50.0,
         longitude=8.9,
@@ -218,6 +294,30 @@ Inquire the list of stations by geographic coordinates.
 
     print(df.head())
 
+Bbox selection
+
+.. ipython:: python
+
+    from datetime import datetime
+    from wetterdienst.provider.dwd.observation import DwdObservationRequest, DwdObservationDataset, DwdObservationPeriod, DwdObservationResolution
+
+    stations = DwdObservationRequest(
+        parameter=DwdObservationDataset.TEMPERATURE_AIR,
+        resolution=DwdObservationResolution.HOURLY,
+        period=DwdObservationPeriod.RECENT,
+        start_date=datetime(2020, 1, 1),
+        end_date=datetime(2020, 1, 20)
+    )
+
+    df = stations.bbox(
+        bottom=50.0,
+        left=8.9,
+        top=50.01,
+        right=8.91,
+    ).df
+
+    print(df.head())
+
 
 The function returns a StationsResult with the list of stations being filtered for
 distances [in km] to the given coordinates.
@@ -232,10 +332,10 @@ Again from here we can jump to the corresponding data:
         period=DwdObservationPeriod.RECENT,
         start_date=datetime(2020, 1, 1),
         end_date=datetime(2020, 1, 20)
-    ).nearby_radius(
+    ).nearby_distance(
         latitude=50.0,
         longitude=8.9,
-        max_distance_in_km=30
+        distance=30
     )
 
     for result in stations.values.query():