Merge pull request #48 from ArtesiaWater/dev

Release 0.3.1

docs/getting_started.rst

@@ -11,8 +11,8 @@ installed on your computer. We recommend using the
 `Anaconda Distribution <https://www.continuum.io/downloads>`_
 of Python.
 
-Installing `hydropandas`
-------------------------
+Installing hydropandas
+----------------------
 
 Install the module by typing:: 
 
@@ -22,7 +22,8 @@ Please note that some of the dependencies cannot be installed automatically
 on Windows. If you do not have these packages already you can install them 
 manually using the following instructions:
 
-Download these packages from `Christoph Gohlke's website<https://www.lfd.uci.edu/~gohlke/pythonlibs>`
+Download these packages from `Christoph Gohlke's website <https://www.lfd.uci.edu/~gohlke/pythonlibs>`_ :
+
 - GDAL
 - Fiona
 - Shapely
@@ -35,19 +36,22 @@ version. Also the architecture should match (i.e. 64bits vs 32bits). For example
 
 - GDAL-3.1.4-cp38-cp38-win_amd64.whl
 
-This is the GDAL version for Python 3.8 (as can be seen from the cp38 in the name), 
-for 64-bits Python (as derived from the amd64 in the name).
+This is the GDAL version for Python `3.8` (as can be seen from the `cp38` in the name), 
+for `64-bits` Python (as derived from the `amd64` in the name).
 
 Once you have downloaded the correct files, open Anaconda Prompt, and navigate to 
-the directory in which you saved your downloads. Now type the following commands 
-(the order is important):
-1. `pip install GDAL-3.1.4-cp38-cp38-win_amd64.whl`
-2. `pip install Fiona-1.8.17-cp38-cp38-win_amd64.whl`
-3. `pip install Shapely-1.7.1-cp38-cp38-win_amd64.whl`
-4. `pip install python_snappy-0.5.4-cp38-cp38-win_amd64.whl`
-5. `pip install fastparquet-0.4.1-cp38-cp38-win_amd64.whl`
-
-After you've done this you can install hydropandas using `pip install hydropandas`
+the directory in which you saved your downloads. Now use the following commands 
+to install the packages (the order is important)::
+
+	pip install GDAL-3.1.4-cp38-cp38-win_amd64.whl
+	pip install Fiona-1.8.17-cp38-cp38-win_amd64.whl
+	pip install Shapely-1.7.1-cp38-cp38-win_amd64.whl
+	pip install python_snappy-0.5.4-cp38-cp38-win_amd64.whl
+	pip install fastparquet-0.4.1-cp38-cp38-win_amd64.whl
+
+After you've done this you can install hydropandas using::
+
+	pip install hydropandas
 
 For installing in development mode, clone the repository and install by
 typing the following from the module root directory::

examples/01_groundwater_observations.ipynb

@@ -56,7 +56,7 @@
    ],
    "source": [
     "# reading a dino csv file\n",
-    "fname = r'../tests/data/2019-Dino-test/Grondwaterstanden_Put/B33F0080001_1.csv'\n",
+    "fname = '../tests/data/2019-Dino-test/Grondwaterstanden_Put/B33F0080001_1.csv'\n",
     "gw = hpd.GroundwaterObs.from_dino(fname=fname, verbose=True)"
    ]
   },
@@ -717,8 +717,8 @@
     }
    ],
    "source": [
-    "fname1 = r'../tests/data/2019-Dino-test/Grondwaterstanden_Put/B33F0080001_1.csv'\n",
-    "fname2 = r'../tests/data/2019-Dino-test/Grondwaterstanden_Put/B33F0133001_1.csv'\n",
+    "fname1 = '../tests/data/2019-Dino-test/Grondwaterstanden_Put/B33F0080001_1.csv'\n",
+    "fname2 = '../tests/data/2019-Dino-test/Grondwaterstanden_Put/B33F0133001_1.csv'\n",
     "gw1 = hpd.GroundwaterObs.from_dino(fname=fname1, verbose=False)\n",
     "gw2 = hpd.GroundwaterObs.from_dino(fname=fname2, verbose=False)\n",
     "\n",
@@ -1774,7 +1774,7 @@
    ],
    "source": [
     "# read using a .zip file with data\n",
-    "dinozip = r'../tests/data/2019-Dino-test/dino.zip'\n",
+    "dinozip = '../tests/data/2019-Dino-test/dino.zip'\n",
     "dino_gw = hpd.ObsCollection.from_dino(dirname=dinozip, keep_all_obs=False, \n",
     "                                      verbose=False)\n",
     "dino_gw"
@@ -1802,4 +1802,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

examples/02_knmi_observations.ipynb

@@ -146,11 +146,12 @@
     "**attributes**\n",
     "\n",
     "A `KnmiObs` object has the following attributes:\n",
-    "- `name`: station name and variable\n",
-    "- `x`: x-coördinate in m RD\n",
-    "- `y`: y-coördinate in m RD\n",
-    "- `station`: station number\n",
-    "- `meta`: dictionary with other metadata"
+    "\n",
+    "* `name`: station name and variable\n",
+    "* `x`: x-coördinate in m RD\n",
+    "* `y`: y-coördinate in m RD\n",
+    "* `station`: station number\n",
+    "* `meta`: dictionary with other metadata"
    ]
   },
   {
@@ -195,6 +196,7 @@
     "## Get KNMI data<a id=reading></a>\n",
     "\n",
     "There are 2 main functions to obtain KNMI data:\n",
+    "\n",
     "1. `KnmiObs.from_knmi`\n",
     "2. `KnmiObs.from_nearest_xy`\n",
     "\n",
@@ -230,6 +232,7 @@
     "**read options**\n",
     "\n",
     "The `KnmiObs.from_knmi` method contains the following keyword arguments:\n",
+    "\n",
     "- `stn`: station number.\n",
     "- `variable`: the type of measurement you want to get. For a list of all variable names check: http://projects.knmi.nl/klimatologie/daggegevens/selectie.cgi\n",
     "- `startdate`: the start date of the time series you want, default is 1st of January 2019.\n",
@@ -239,6 +242,8 @@
     "- `raise_exception`: option to raise an error when the requested time series is empty.\n",
     "- `verbose`: option to print additional information to the screen.\n",
     "\n",
+    "***\n",
+    "\n",
     "The 3 examples below give a brief summary of these options"
    ]
   },
@@ -902,9 +907,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.7.7"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

examples/03_pastastore_from_observations.ipynb

@@ -662,4 +662,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

hydropandas/__init__.py

@@ -1,6 +1,6 @@
 from .obs_collection import ObsCollection
-from .observation import (GroundwaterObs, GroundwaterQualityObs, KnmiObs,
-                          ModelObs, WaterlvlObs)
+from .observation import (Obs, GroundwaterObs, GroundwaterQualityObs,
+                          KnmiObs, ModelObs, WaterlvlObs)
 from .extensions import geo as _geo
 from .extensions import gwobs as _gwobs
 from .extensions import plots as _plots

hydropandas/extensions/geo.py

@@ -173,7 +173,7 @@ def distance_nearest_point(point_gdf1, pts=pts_gdf2):
             lambda row: distance_nearest_point(row.geometry), axis=1)
 
         return gdf1[['nearest point', 'distance nearest point']]
-    
+
     def get_nearest_polygon(self, gdf=None,
                             xcol_obs='x', ycol_obs='y',
                             verbose=False):
@@ -197,12 +197,13 @@ def get_nearest_polygon(self, gdf=None,
         """
 
         gdf_obs = self._obj.to_gdf(xcol=xcol_obs, ycol=ycol_obs)
-        
+
         for i, point in gdf_obs.geometry.items():
             distances = [point.distance(pol) for pol in gdf.geometry.values]
-            if (np.array(distances)==np.min(distances)).sum()>1:
+            if (np.array(distances) == np.min(distances)).sum() > 1:
                 raise ValueError('multiple polygons are nearest')
-            gdf_obs.loc[i, 'nearest polygon'] = gdf.iloc[np.argmin(distances)].name
+            gdf_obs.loc[i, 'nearest polygon'] = gdf.iloc[np.argmin(
+                distances)].name
             gdf_obs.loc[i, 'distance nearest polygon'] = np.min(distances)
 
         return gdf_obs[['nearest polygon', 'distance nearest polygon']]

hydropandas/extensions/gwobs.py

@@ -1,7 +1,5 @@
 import numpy as np
 import pandas as pd
-import xarray as xr
-import scipy.interpolate as intp
 
 from . import accessor
 
@@ -204,31 +202,27 @@ def get_modellayer_from_filter(ftop, fbot, zvec, left=-999, right=999,
             print(f"- piezometer filter spans {lay_fbot - lay_ftop +1} layers."
                   " checking length per layer")
             print("  - length per layer:")
-            
-        # check which layer has the biggest length of the filter        
-        length_layers = np.zeros(lay_fbot-lay_ftop+1)
+
+        # check which layer has the biggest length of the filter
+        length_layers = np.zeros(lay_fbot - lay_ftop + 1)
         for i in range(len(length_layers)):
-            if i==0:
-                length_layers[i] = ftop - zvec[lay_ftop+1]
-            elif (i+1)==len(length_layers):
-                length_layers[i] = zvec[lay_ftop+i] - fbot
+            if i == 0:
+                length_layers[i] = ftop - zvec[lay_ftop + 1]
+            elif (i + 1) == len(length_layers):
+                length_layers[i] = zvec[lay_ftop + i] - fbot
             else:
-                length_layers[i] = zvec[lay_ftop+i] - zvec[lay_ftop+1+i]
-                
+                length_layers[i] = zvec[lay_ftop + i] - zvec[lay_ftop + 1 + i]
+
             if verbose:
                 print(f"    - lay {lay_ftop+i}: {length_layers[i]:.2f}")
-                
+
         # choose layer with biggest length
         rel_layer = np.argmax(length_layers)
         lay_out = lay_ftop + rel_layer
         if verbose:
             print(f"  - selected layer: {lay_out}")
         return lay_out
 
-    raise ValueError(
-        'Something is wrong with the input. Please submit an issue if you'
-        ' think this is a bug.')
-
     return np.nan
 
 
@@ -470,7 +464,7 @@ def get_modellayers(self, gwf, verbose=False):
                                 name='modellayer')
 
         return modellayers
-    
+
     def get_regis_layers(self, verbose=False):
         """Get the regis layer per observation.
 
@@ -483,10 +477,8 @@ def get_regis_layers(self, verbose=False):
         -------
         pd.Series with the names of the regis layer of each observation
         """
-
 
         return self._obj.obs.apply(lambda o: o.gwobs.get_regis_layer(verbose))
-
 
 
 @accessor.register_obs_accessor("gwobs")
@@ -521,52 +513,51 @@ def get_modellayer_modflow(self, gwf, left=-999, right=999, verbose=False):
                                                     left=left, right=right,
                                                     verbose=verbose)
             return modellayer
-    
+
     def get_regis_layer(self, verbose=False):
         """find the name of the REGIS layer based on the filter depth.
-        
+
         Parameters
         ----------
         verbose : boolean, optional
             Print additional information to the screen (default is False).
-        
+
         Returns
         -------
         str
             name of REGIS layer
-        
-        
         """
+        import xarray as xr
 
         if np.isnan(self._obj.bovenkant_filter) or np.isnan(self._obj.onderkant_filter):
             return 'nan'
-        
-        #connect to regis netcdf
+
+        # connect to regis netcdf
         regis_url = r'http://www.dinodata.nl:80/opendap/REGIS/REGIS.nc'
         regis_ds = xr.open_dataset(regis_url, decode_times=False)
-        
-        #rename layer in regis netcdf
+
+        # rename layer in regis netcdf
         regis_ds = regis_ds.rename({'layer': 'layer_old'})
         regis_ds.coords['layer'] = regis_ds.layer_old.astype(str)
         regis_ds = regis_ds.swap_dims({'layer_old': 'layer'})
-        
-        #get zvec regis netcdf
-        z = regis_ds['bottom'].sel(x=self._obj.x, y=self._obj.y, 
+
+        # get zvec regis netcdf
+        z = regis_ds['bottom'].sel(x=self._obj.x, y=self._obj.y,
                                    method='nearest').dropna(dim='layer')
-        ztop = regis_ds['top'].sel(x=self._obj.x, y=self._obj.y, 
+        ztop = regis_ds['top'].sel(x=self._obj.x, y=self._obj.y,
                                    method='nearest').max()
-        zvec = np.concatenate([[ztop.values],z.values])
-        
-        #get index of regis model layer
+        zvec = np.concatenate([[ztop.values], z.values])
+
+        # get index of regis model layer
         layer_i = get_modellayer_from_filter(self._obj.bovenkant_filter,
                                              self._obj.onderkant_filter,
-                                             zvec, 
+                                             zvec,
                                              left=-999, right=999,
                                              verbose=verbose)
-        
-        if layer_i==999:
+
+        if layer_i == 999:
             return 'above'
-        elif layer_i==-999:
+        elif layer_i == -999:
             return 'below'
-        
+
         return str(z.layer[layer_i].values)