update docs

docs/index.rst

@@ -2,15 +2,15 @@
     :maxdepth: 3
     :numbered:
 
-*******************************************
-Grids: Temporal Informatics of Gridded Data
-*******************************************
+*********************
+The Grids Python Tool
+*********************
 
 .. image:: https://img.shields.io/pypi/v/grids
     :target: https://pypi.org/project/grids
     :alt: PYPI Version
 .. image:: https://readthedocs.org/projects/tsgrids/badge/?version=latest
-    :target: https://tsgrids.readthedocs.io/en/latest/?badge=latest
+    :target: https://grids.rileyhales.com/en/latest/?badge=latest
     :alt: Documentation Status
 
 Tools for extracting time series subsets from n-dimensional arrays in NetCDF, GRIB, HDF, and GeoTIFF formats. Time series
@@ -20,7 +20,14 @@ can be extracted for:
 #. Ranges or Bounding Boxes - by specifying the minimum and maximum coordinates for each dimension
 #. Spatial data - if the rasters are spatial data and the appropriate dimensions are specified
 #. Masks - any irregularly shaped subset of the array which you can create a binary mask array for
-#. Statistical summaries - of the entire array
+
+Citations
+*********
+
+If you use Grids, please cite
+
+- Our journal article at MDPI Water. doi: `10.3390/w13152066 <https://doi.org/10.3390/w13152066>`_
+- The source code through Zenodo. doi: `10.5281/zenodo.5225437 <https://doi.org/10.5281/zenodo.5225437>`_
 
 Installation
 ************

publication_tests/nwm/national_water_model_tests.py

@@ -2,16 +2,16 @@
 Copyright Riley Chad Hales 2021
 All Rights Reserved
 """
-import datetime
-import os
-import shutil
+# import datetime
+# import os
+# import shutil
 
 import pandas as pd
-import requests
-import glob
-import grids
+# import requests
+# import glob
+# import grids
 
-import plotly.express as px
+import plotly.graph_objs as go
 
 
 def calc_most_recent_forecast_datetime(fcrange: str, previous: bool = False, steps_back: int = 1):
@@ -57,7 +57,7 @@ def get_forecast_data(fcrange: str,
         ens_prefix = '_'
     else:  # elif fcrange == 'long':
         # ens_members = (1, 2, 3, 4)
-        ens_members = (4, )
+        ens_members = (4,)
         timesteps = [f'{(i + 1) * 6:03}' for i in range(120)]
         dir = f'{fcrange}_range_mem'
         ens_prefix = '_'
@@ -100,7 +100,8 @@ def get_forecast_data(fcrange: str,
 
 
 # set path to download the nwm data
-path_to_nwm_directory = "/Users/rchales/nwm_data"
+# path_to_nwm_directory = "/Users/rchales/nwm_data"
+
 # if os.path.exists(path_to_nwm_directory):
 #     shutil.rmtree(path_to_nwm_directory)
 # os.mkdir(path_to_nwm_directory)
@@ -126,9 +127,27 @@ def get_forecast_data(fcrange: str,
 master_df = pd.read_csv('national_water_model_extracted_timeseries.csv', index_col=0)
 master_df.index = pd.to_datetime(master_df.index)
 
-plot = px.line(master_df,
-               title='National Water Model Time Series Using Grids')
-plot.update_xaxes(title='Date / Time (UTC)')
-plot.update_yaxes(title='ft^3/sec',)
-plot.write_image('national_water_model_extracted_timeseries.svg')
+plot_lines = []
+for i in (1, 2, 3, 4):
+    plot_lines.append(
+        go.Scatter(
+            x=master_df.index,
+            y=master_df[f'member_{i}'],
+            line=dict(color='blue', width=5),
+            showlegend=False
+        )
+    )
+for i in (1, 2, 3, 4):
+    plot_lines.append(
+        go.Scatter(
+            x=master_df.index,
+            y=master_df[f'member_{i}'],
+            line=dict(color='red', dash='dot'),
+            showlegend=False
+        )
+    )
+plot = go.Figure(plot_lines)
+plot.update_xaxes(title='Datetime (UTC)')
+plot.update_yaxes(title='Forecasted Discharge (ft^3/sec)')
+plot.write_image('new_timeseries_plot.png')
 plot.show()