Merge pull request #68 from pastas/dev

Minor release for bug fix

pastastore/datasets.py

@@ -6,6 +6,7 @@
 import pastastore as pst
 from pastastore.base import BaseConnector
 
+
 def example_pastastore(conn="DictConnector"):
     """Example dataset loaded into PastaStore.
 

pastastore/plotting.py

@@ -882,8 +882,9 @@ def stresslinks(self, kinds=None, model_names=None, color_lines=False,
 
                     stused = np.append(stused, s)
 
-        if labels:                
-            self.add_labels(oseries.loc[struct['oseries'].unique()], ax, adjust=adjust)
+        if labels:
+            self.add_labels(
+                oseries.loc[struct['oseries'].unique()], ax, adjust=adjust)
             self.add_labels(stresses.loc[np.unique(stused)], ax, adjust=adjust)
 
         ax.scatter([x[1][0] for x in segments],

pastastore/store.py

@@ -173,12 +173,14 @@ def get_nearest_oseries(self, names: Optional[Union[list, str]] = None,
 
         data = pd.DataFrame(columns=np.arange(n))
 
-        for series in distances.index:
-            others = distances.loc[series].dropna(
+        for series_name in distances.index:
+            others = distances.loc[series_name].dropna(
             ).sort_values().index.tolist()
             # remove self
-            others.remove(series)
-            series = pd.Series(others[:n], name=series)
+            others.remove(series_name)
+            series = pd.DataFrame(index=[series_name],
+                                  columns=data.columns,
+                                  data=[others[:n]])
             data = pd.concat([data, series], axis=0)
         return data
 

pastastore/version.py

@@ -1 +1 @@
-__version__ = "0.8.0"
+__version__ = "0.8.1"

readme.md

@@ -10,60 +10,59 @@ This module contains a tool to manage
 [Pastas](https://pastas.readthedocs.io/en/latest/) timeseries and models in a
 database.
 
-Storing timeseries and models in a database gives the user 
-a simple way to manage Pastas projects with the added bonus of allowing the user 
-to pick upwhere they left off, without having to (re)load everything into memory.
+Storing timeseries and models in a database gives the user a simple way to
+manage Pastas projects with the added bonus of allowing the user to pick
+upwhere they left off, without having to (re)load everything into memory.
 
 The connection to database/disk/memory is managed by a connector object.
-Currently, four connectors are included. The first implementation is an 
-in-memory connector. The other three store data on disk or in a database. 
-The PasConnector implementation writes human-readable JSON files to disk. 
-The ArcticConnector and PystoreConnector implementations are designed to have 
-fast read/write operations, while also compressing the stored data.
+Currently, four connectors are included. The first implementation is an
+in-memory connector. The other three store data on disk or in a database. The
+PasConnector implementation writes human-readable JSON files to disk. The
+ArcticConnector and PystoreConnector implementations are designed to have fast
+read/write operations, while also compressing the stored data.
 
 -   In-memory: uses dictionaries to hold timeseries and pastas Models in-memory.
-      Does not require any additional packages to use. 
+    Does not require any additional packages to use.
 
 -   Pastas: uses Pastas write and read methods to store data as JSON files on
-      disk. Does not require any additional packages to use.
+    disk. Does not require any additional packages to use.
 
 -   [Arctic](https://arctic.readthedocs.io/en/latest/) is a timeseries/dataframe
-      database that sits atop [MongoDB](https://www.mongodb.com). Arctic supports
-      pandas.DataFrames.
+    database that sits atop [MongoDB](https://www.mongodb.com). Arctic supports
+    pandas.DataFrames.
 
--   [PyStore](https://github.com/ranaroussi/pystore) is a datastore (inspired
-      by Arctic) created for storing pandas dataframes (especially timeseries) on
-      disk. Data is stored using fastparquet and compressed with Snappy.
+-   [PyStore](https://github.com/ranaroussi/pystore) is a datastore (inspired by
+    Arctic) created for storing pandas dataframes (especially timeseries) on disk.
+    Data is stored using fastparquet and compressed with Snappy.
 
 ## Installation
 
 Install the module by typing `pip install pastastore`.
 
-For installing in development mode, clone the repository and install by
-typing `pip install -e .` from the module root directory.
+For installing in development mode, clone the repository and install by typing
+`pip install -e .` from the module root directory.
 
-For plotting backgroundmaps, the `contextily` and `pyproj` packages are required. 
-For a full install, including an optional dependency for plotting and labeling data 
-on maps, use: `pip install pastastore[full]` or `pip install .[full]` when on MacOS
-or Linux. Windows users are asked to install `rasterio` themselves since it often 
-cannot be installed using `pip`. `rasterio` is a dependency of `contextily`. 
-Windows can install `pastastore` with the optional labeling package adjustText 
-using `pip install pastastore[adjusttext]` or `.[adjusttext]`
-
-There are external dependencies when using the `pystore` or `arctic` connectors. 
-To install these dependencies read (see [Connector Dependencies section](#dependencies))! 
-since these are _not_ automatically installed.
+For plotting backgroundmaps, the `contextily` and `pyproj` packages are
+required. For a full install, including an optional dependency for plotting and
+labeling data on maps, use: `pip install pastastore[full]` or `pip install
+.[full]` when on MacOS or Linux. Windows users are asked to install `rasterio`
+themselves since it often cannot be installed using `pip`. `rasterio` is a
+dependency of `contextily`. Windows can install `pastastore` with the optional
+labeling package adjustText using `pip install pastastore[adjusttext]` or
+`.[adjusttext]`
 
+There are external dependencies when using the `pystore` or `arctic`
+connectors. To install these dependencies read (see [Connector Dependencies
+section](#dependencies))! since these are _not_ automatically installed.
 
 ## Usage
 
 The following snippets show typical usage. The general idea is to first define
-the connector object. The next step is to pass that connector to
-`PastaStore`.
+the connector object. The next step is to pass that connector to `PastaStore`.
 
 ### Using in-memory dictionaries
 
-This works out of the box after installing with `pip` without installing any 
+This works out of the box after installing with `pip` without installing any
 additional Python dependencies or external software.
 
 ```python
@@ -78,8 +77,8 @@ store = pst.PastaStore("my_project", conn)
 
 ### Using Pastas read/load methods
 
-Store data on disk as JSON files (with .pas extension) using Pastas read and 
-load methods. This works out of the box after installing with `pip` without 
+Store data on disk as JSON files (with .pas extension) using Pastas read and
+load methods. This works out of the box after installing with `pip` without
 installing any additional Python dependencies or external software.
 
 ```python
@@ -111,7 +110,7 @@ store = pst.PastaStore("my_project", conn)
 
 ### Using Pystore
 
-Store data on disk as parquet files using compression. Only works if 
+Store data on disk as parquet files using compression. Only works if
 `python-snappy` and `pystore` are installed.
 
 ```python
@@ -125,9 +124,9 @@ conn = pst.PystoreConnector("my_connector", path)
 store = pst.PastaStore("my_project", conn)
 ```
 
-The database read/write/delete methods can be accessed through the reference
-to the connector object. For easy access, the
-most common methods are registered to the `store` object. E.g.
+The database read/write/delete methods can be accessed through the reference to
+the connector object. For easy access, the most common methods are registered
+to the `store` object. E.g.
 
 ```python
 series = store.conn.get_oseries("my_oseries")
@@ -153,12 +152,12 @@ If using `ArcticConnector`:
     ([Windows](https://fastdl.mongodb.org/win32/mongodb-win32-x86_64-2012plus-4.2.1-signed.msi),
     [MacOS](https://fastdl.mongodb.org/osx/mongodb-macos-x86_64-4.2.1.tgz)).
 
--   OR, if you wish to use Docker for running MongoDB see the installation instructions [here](https://github.com/pastas/pastastore/tree/master/dockerfiles#running-mongodb-from-docker).
+-   OR, if you wish to use Docker for running MongoDB see the installation
+    instructions [here](https://github.com/pastas/pastastore/tree/master/dockerfiles#running-mongodb-from-docker).
 
 If using `PystoreConnector`:
 
--   PyStore uses [Snappy](http://google.github.io/snappy/), a fast and
-    efficient compression/decompression library from Google. You'll need to
-    install Snappy on your system before installing PyStore. See links for
-    installation instructions here:
-    <https://github.com/ranaroussi/pystore#dependencies>
+-   PyStore uses [Snappy](http://google.github.io/snappy/), a fast and efficient
+    compression/decompression library from Google. You'll need to install Snappy on
+    your system before installing PyStore. See links for installation instructions
+    here: <https://github.com/ranaroussi/pystore#dependencies>

tests/test_003_pastastore.py

@@ -272,3 +272,9 @@ def test_to_from_zip(pstore):
     finally:
         os.remove(zipname)
     return store
+
+
+def test_example_pastastore():
+    from pastastore.datasets import example_pastastore
+    pstore = example_pastastore()
+    return

tests/test_005_maps_plots.py

@@ -1,5 +1,3 @@
-import warnings
-
 import matplotlib.pyplot as plt
 import pytest
 from pytest_dependency import depends