attempt to demonstrate usage of plotly as a wrapper, issue #861

docs/examples/plot_using_plotly_wrapper.py

@@ -0,0 +1,114 @@
+"""
+Plotly Wrapper for pvlib
+====================
+
+Example of preparing plots using clear sky model as an example
+"""
+
+import plotly.graph_objects as go
+import plotly.io as pio
+from plotly.subplots import make_subplots
+import pandas as pd
+from pvlib.location import Location
+
+
+def make_plotly_figure(template='gridon', rows=1, columns=1, column_widths=None, title='plotly_figure',
+                       x_axis_title='x-axis-title', y_axis_title='y-axis-title'):
+    """
+    This function is a helper that builds a template figure using the plotly library that can be further
+    customized as needed by the user.
+
+    :param str template: currently supports plotly default templates. acceptable arguments are:
+    ['ggplot2', 'seaborn', 'simple_white', 'plotly', 'plotly_white', 'plotly_dark', 'presentation', 'xgridoff',
+    'ygridoff', 'gridon', 'none']. Defaults to 'gridon'
+    :param int rows: indicates how many vertical subplots are desired. Defaults to 1
+    :param int columns: indicates how many horizontal subplots are desired. Defaults to 1
+    :param list column_widths: helps set the width of the subplots when there are multiple. Defaults to None
+    :param str title: name of the figure. Defaults to 'plotly_figure'
+    :param x_axis_title: name of x-axis title. Defaults to 'x-axis'
+    :param y_axis_title: name of y-axis title. Defaults to 'y-axis'
+    :return: plotly figure object
+    :rtype: plotly.graph_object
+    """
+
+    # Set template for plotly figure
+    pio.templates.default = template
+
+    # Create figure object
+    # Test whether subplots are desired or single plot is desired
+    if rows > 1 or columns > 1:
+        # Test whether size of each subplot has been specified or if defaults can be used
+        if column_widths is None:
+            figure = make_subplots(rows=rows, cols=columns, column_widths=[0.7, 0.3])
+        else:
+            if max([rows, columns]) == len(column_widths):
+                figure = make_subplots(rows=rows, cols=columns, column_widths=column_widths)
+            else:
+                raise ValueError(
+                    '\n In order to customize subplots, require same # of widths as the # of unique subplots ')
+    else:
+        figure = go.Figure()
+
+    # Update figure with title, x-axis and y-axis titles
+    figure.update_layout(
+        title={'text': title, 'y': 0.9, 'x': 0.5, 'xanchor': 'center', 'yanchor': 'top'},
+        xaxis_title=x_axis_title,
+        yaxis_title=y_axis_title
+    )
+
+    # Return figure object
+    return figure
+
+
+def plot_time_series(input_data, y_axis, x_axis, title='', x_axis_title='', y_axis_title='', mode='lines'):
+    """
+    This function is used to build simple time-series plots in plotly
+
+    :param pd.DataFrame input_data: data set which contains columns to plot
+    :param list y_axis: column name of the data frame to plot on y-axis
+    :param str x_axis: column name of the data frame to plot on x-axis
+    :param str title: title for the plot, defaults to ''
+    :param str x_axis_title: x-axis title in the plot, defaults to ''
+    :param str y_axis_title: y-axis title in the plot, defaults to ''
+    :param str mode: which mode to be used in preparation of the plot, defaults to 'lines'. Other common arguments are
+    'lines+markers' and 'markers'
+    :return: plotly figure object
+    :rtype: plotly.graph_object
+    """
+
+    # Prepare a figure object
+    figure = make_plotly_figure(title=title, x_axis_title=x_axis_title, y_axis_title=y_axis_title)
+
+    # Add traces
+    for column in y_axis:
+        if x_axis is 'index':
+            figure.add_trace(go.Scatter(x=input_data.index.values, y=input_data[column], name=column))
+        else:
+            figure.add_trace(go.Scatter(x=input_data[x_axis], y=input_data[column], name=column))
+
+    # Update legend to show legend entries
+    figure.update_layout(showlegend=True)
+
+    # Return figure
+    return figure
+
+
+tus = Location(32.2, -111, 'US/Arizona', 700, 'Tucson')
+times = pd.date_range(start='2016-07-01', end='2016-07-04', freq='1min', tz=tus.tz)
+cs = tus.get_clearsky(times)  # ineichen with climatology table by default
+# Format the index as pd.DatetimeIndex
+cs.index = pd.DatetimeIndex(cs.index, freq=None)
+
+# Prepare plot
+# Get figure object
+title = 'Ineichen, climatological turbidity'
+x_axis_title = 'DateTime'
+y_axis_title = 'Irradiance W/m^2'
+
+test_figure = plot_time_series(input_data=cs, y_axis=['ghi', 'dhi', 'dni'], x_axis='index', title=title,
+                               x_axis_title=x_axis_title, y_axis_title=y_axis_title)
+test_figure.show()
+
+
+
+

setup.py

@@ -47,7 +47,7 @@
     'optional': ['ephem', 'cython', 'netcdf4', 'nrel-pysam', 'numba',
                  'pvfactors', 'scipy', 'siphon', 'tables'],
     'doc': ['ipython', 'matplotlib', 'sphinx == 1.8.5', 'sphinx_rtd_theme',
-            'sphinx-gallery'],
+            'sphinx-gallery', 'plotly'],
     'test': TESTS_REQUIRE
 }
 EXTRAS_REQUIRE['all'] = sorted(set(sum(EXTRAS_REQUIRE.values(), [])))