Move plotting into a separate file (Py)

python/fbprophet/forecaster.py

@@ -14,24 +14,28 @@ from collections import defaultdict
 from copy import deepcopy
 from datetime import timedelta
 import logging
-
-logging.basicConfig()
-logger = logging.getLogger(__name__)
-
-try:
-    from matplotlib import pyplot as plt
-    from matplotlib.dates import MonthLocator, num2date
-    from matplotlib.ticker import FuncFormatter
-except ImportError:
-    logger.exception('Importing matplotlib failed. Plotting will not work.')
+import warnings
 
 import numpy as np
 import pandas as pd
 
 # fb-block 1 start
 from fbprophet.models import prophet_stan_models
+from fbprophet.plot import (
+    plot,
+    plot_components,
+    plot_forecast_component,
+    seasonality_plot_df,
+    plot_weekly,
+    plot_yearly,
+    plot_seasonality,
+)
 # fb-block 1 end
 
+logging.basicConfig()
+logger = logging.getLogger(__name__)
+warnings.filterwarnings("default", category=DeprecationWarning)
+
 try:
     import pystan  # noqa F401
 except ImportError:
@@ -1251,26 +1255,10 @@ class Prophet(object):
         -------
         A matplotlib figure.
         """
-        if ax is None:
-            fig = plt.figure(facecolor='w', figsize=(10, 6))
-            ax = fig.add_subplot(111)
-        else:
-            fig = ax.get_figure()
-        fcst_t = fcst['ds'].dt.to_pydatetime()
-        ax.plot(self.history['ds'].dt.to_pydatetime(), self.history['y'], 'k.')
-        ax.plot(fcst_t, fcst['yhat'], ls='-', c='#0072B2')
-        if 'cap' in fcst and plot_cap:
-            ax.plot(fcst_t, fcst['cap'], ls='--', c='k')
-        if self.logistic_floor and 'floor' in fcst and plot_cap:
-            ax.plot(fcst_t, fcst['floor'], ls='--', c='k')
-        if uncertainty:
-            ax.fill_between(fcst_t, fcst['yhat_lower'], fcst['yhat_upper'],
-                            color='#0072B2', alpha=0.2)
-        ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
-        ax.set_xlabel(xlabel)
-        ax.set_ylabel(ylabel)
-        fig.tight_layout()
-        return fig
+        return plot(
+            m=self, fcst=fcst, ax=ax, uncertainty=uncertainty,
+            plot_cap=plot_cap, xlabel=xlabel, ylabel=ylabel,
+        )
 
     def plot_components(self, fcst, uncertainty=True, plot_cap=True,
                         weekly_start=0, yearly_start=0):
@@ -1296,225 +1284,60 @@ class Prophet(object):
         -------
         A matplotlib figure.
         """
-        # Identify components to be plotted
-        components = ['trend']
-        if self.holidays is not None and 'holidays' in fcst:
-            components.append('holidays')
-        components.extend([name for name in self.seasonalities
-                           if name in fcst])
-        if len(self.extra_regressors) > 0 and 'extra_regressors' in fcst:
-            components.append('extra_regressors')
-        npanel = len(components)
-
-        fig, axes = plt.subplots(npanel, 1, facecolor='w',
-                                 figsize=(9, 3 * npanel))
-
-        if npanel == 1:
-            axes = [axes]
-
-        for ax, plot in zip(axes, components):
-            if plot == 'trend':
-                self.plot_forecast_component(
-                    fcst, 'trend', ax, uncertainty, plot_cap)
-            elif plot == 'holidays':
-                self.plot_forecast_component(
-                    fcst, 'holidays', ax, uncertainty, False)
-            elif plot == 'weekly':
-                self.plot_weekly(
-                    ax=ax, uncertainty=uncertainty, weekly_start=weekly_start)
-            elif plot == 'yearly':
-                self.plot_yearly(
-                    ax=ax, uncertainty=uncertainty, yearly_start=yearly_start)
-            elif plot == 'extra_regressors':
-                self.plot_forecast_component(
-                    fcst, 'extra_regressors', ax, uncertainty, False)
-            else:
-                self.plot_seasonality(
-                    name=plot, ax=ax, uncertainty=uncertainty)
-
-        fig.tight_layout()
-        return fig
+        return plot_components(
+            m=self, fcst=fcst, uncertainty=uncertainty, plot_cap=plot_cap,
+            weekly_start=weekly_start, yearly_start=yearly_start,
+        )
 
     def plot_forecast_component(
             self, fcst, name, ax=None, uncertainty=True, plot_cap=False):
-        """Plot a particular component of the forecast.
-
-        Parameters
-        ----------
-        fcst: pd.DataFrame output of self.predict.
-        name: Name of the component to plot.
-        ax: Optional matplotlib Axes to plot on.
-        uncertainty: Optional boolean to plot uncertainty intervals.
-        plot_cap: Optional boolean indicating if the capacity should be shown
-            in the figure, if available.
-
-        Returns
-        -------
-        a list of matplotlib artists
-        """
-        artists = []
-        if not ax:
-            fig = plt.figure(facecolor='w', figsize=(10, 6))
-            ax = fig.add_subplot(111)
-        fcst_t = fcst['ds'].dt.to_pydatetime()
-        artists += ax.plot(fcst_t, fcst[name], ls='-', c='#0072B2')
-        if 'cap' in fcst and plot_cap:
-            artists += ax.plot(fcst_t, fcst['cap'], ls='--', c='k')
-        if self.logistic_floor and 'floor' in fcst and plot_cap:
-            ax.plot(fcst_t, fcst['floor'], ls='--', c='k')
-        if uncertainty:
-            artists += [ax.fill_between(
-                fcst_t, fcst[name + '_lower'], fcst[name + '_upper'],
-                color='#0072B2', alpha=0.2)]
-        ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
-        ax.set_xlabel('ds')
-        ax.set_ylabel(name)
-        return artists
+        warnings.warn(
+            'This method will be removed in the next version. '
+            'Please use fbprophet.plot.plot_forecast_component. ',
+            DeprecationWarning,
+        )
+        return plot_forecast_component(
+            self, fcst=fcst, name=name, ax=ax, uncertainty=uncertainty,
+            plot_cap=plot_cap,
+        )
 
     def seasonality_plot_df(self, ds):
-        """Prepare dataframe for plotting seasonal components.
-
-        Parameters
-        ----------
-        ds: List of dates for column ds.
-
-        Returns
-        -------
-        A dataframe with seasonal components on ds.
-        """
-        df_dict = {'ds': ds, 'cap': 1., 'floor': 0.}
-        for name in self.extra_regressors:
-            df_dict[name] = 0.
-        df = pd.DataFrame(df_dict)
-        df = self.setup_dataframe(df)
-        return df
+        warnings.warn(
+            'This method will be removed in the next version. '
+            'Please use fbprophet.plot.seasonality_plot_df. ',
+            DeprecationWarning,
+        )
+        return seasonality_plot_df(self, ds=ds)
 
     def plot_weekly(self, ax=None, uncertainty=True, weekly_start=0):
-        """Plot the weekly component of the forecast.
-
-        Parameters
-        ----------
-        ax: Optional matplotlib Axes to plot on. One will be created if this
-            is not provided.
-        uncertainty: Optional boolean to plot uncertainty intervals.
-        weekly_start: Optional int specifying the start day of the weekly
-            seasonality plot. 0 (default) starts the week on Sunday. 1 shifts
-            by 1 day to Monday, and so on.
-
-        Returns
-        -------
-        a list of matplotlib artists
-        """
-        artists = []
-        if not ax:
-            fig = plt.figure(facecolor='w', figsize=(10, 6))
-            ax = fig.add_subplot(111)
-        # Compute weekly seasonality for a Sun-Sat sequence of dates.
-        days = (pd.date_range(start='2017-01-01', periods=7) +
-                pd.Timedelta(days=weekly_start))
-        df_w = self.seasonality_plot_df(days)
-        seas = self.predict_seasonal_components(df_w)
-        days = days.weekday_name
-        artists += ax.plot(range(len(days)), seas['weekly'], ls='-',
-                           c='#0072B2')
-        if uncertainty:
-            artists += [ax.fill_between(range(len(days)),
-                                        seas['weekly_lower'], seas['weekly_upper'],
-                                        color='#0072B2', alpha=0.2)]
-        ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
-        ax.set_xticks(range(len(days)))
-        ax.set_xticklabels(days)
-        ax.set_xlabel('Day of week')
-        ax.set_ylabel('weekly')
-        return artists
+        warnings.warn(
+            'This method will be removed in the next version. '
+            'Please use fbprophet.plot.plot_weekly. ',
+            DeprecationWarning,
+        )
+        return plot_weekly(
+            self, ax=ax, uncertainty=uncertainty, weekly_start=weekly_start,
+        )
 
     def plot_yearly(self, ax=None, uncertainty=True, yearly_start=0):
-        """Plot the yearly component of the forecast.
-
-        Parameters
-        ----------
-        ax: Optional matplotlib Axes to plot on. One will be created if
-            this is not provided.
-        uncertainty: Optional boolean to plot uncertainty intervals.
-        yearly_start: Optional int specifying the start day of the yearly
-            seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts
-            by 1 day to Jan 2, and so on.
-
-        Returns
-        -------
-        a list of matplotlib artists
-        """
-        artists = []
-        if not ax:
-            fig = plt.figure(facecolor='w', figsize=(10, 6))
-            ax = fig.add_subplot(111)
-        # Compute yearly seasonality for a Jan 1 - Dec 31 sequence of dates.
-        days = (pd.date_range(start='2017-01-01', periods=365) +
-                pd.Timedelta(days=yearly_start))
-        df_y = self.seasonality_plot_df(days)
-        seas = self.predict_seasonal_components(df_y)
-        artists += ax.plot(
-            df_y['ds'].dt.to_pydatetime(), seas['yearly'], ls='-', c='#0072B2')
-        if uncertainty:
-            artists += [ax.fill_between(
-                df_y['ds'].dt.to_pydatetime(), seas['yearly_lower'],
-                seas['yearly_upper'], color='#0072B2', alpha=0.2)]
-        ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
-        months = MonthLocator(range(1, 13), bymonthday=1, interval=2)
-        ax.xaxis.set_major_formatter(FuncFormatter(
-            lambda x, pos=None: '{dt:%B} {dt.day}'.format(dt=num2date(x))))
-        ax.xaxis.set_major_locator(months)
-        ax.set_xlabel('Day of year')
-        ax.set_ylabel('yearly')
-        return artists
+        warnings.warn(
+            'This method will be removed in the next version. '
+            'Please use fbprophet.plot.plot_yearly. ',
+            DeprecationWarning,
+        )
+        return plot_yearly(
+            self, ax=ax, uncertainty=uncertainty, yearly_start=yearly_start,
+        )
 
     def plot_seasonality(self, name, ax=None, uncertainty=True):
-        """Plot a custom seasonal component.
-
-        Parameters
-        ----------
-        name: Seasonality name, like 'daily', 'weekly'.
-        ax: Optional matplotlib Axes to plot on. One will be created if
-            this is not provided.
-        uncertainty: Optional boolean to plot uncertainty intervals.
-
-        Returns
-        -------
-        a list of matplotlib artists
-        """
-        artists = []
-        if not ax:
-            fig = plt.figure(facecolor='w', figsize=(10, 6))
-            ax = fig.add_subplot(111)
-        # Compute seasonality from Jan 1 through a single period.
-        start = pd.to_datetime('2017-01-01 0000')
-        period = self.seasonalities[name]['period']
-        end = start + pd.Timedelta(days=period)
-        plot_points = 200
-        days = pd.to_datetime(np.linspace(start.value, end.value, plot_points))
-        df_y = self.seasonality_plot_df(days)
-        seas = self.predict_seasonal_components(df_y)
-        artists += ax.plot(df_y['ds'].dt.to_pydatetime(), seas[name], ls='-',
-                            c='#0072B2')
-        if uncertainty:
-            artists += [ax.fill_between(
-                df_y['ds'].dt.to_pydatetime(), seas[name + '_lower'],
-                seas[name + '_upper'], color='#0072B2', alpha=0.2)]
-        ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
-        xticks = pd.to_datetime(np.linspace(start.value, end.value, 7)
-            ).to_pydatetime()
-        ax.set_xticks(xticks)
-        if period <= 2:
-            fmt_str = '{dt:%T}'
-        elif period < 14:
-            fmt_str = '{dt:%m}/{dt:%d} {dt:%R}'
-        else:
-            fmt_str = '{dt:%m}/{dt:%d}'
-        ax.xaxis.set_major_formatter(FuncFormatter(
-            lambda x, pos=None: fmt_str.format(dt=num2date(x))))
-        ax.set_xlabel('ds')
-        ax.set_ylabel(name)
-        return artists
+        warnings.warn(
+            'This method will be removed in the next version. '
+            'Please use fbprophet.plot.plot_seasonality. ',
+            DeprecationWarning,
+        )
+        return plot_seasonality(
+            self, name=name, ax=ax, uncertainty=uncertainty,
+        )
 
     def copy(self, cutoff=None):
         """Copy Prophet object

python/fbprophet/plot.py

@@ -0,0 +1,335 @@
+# Copyright (c) 2017-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree. An additional grant
+# of patent rights can be found in the PATENTS file in the same directory.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import logging
+
+import numpy as np
+import pandas as pd
+
+logging.basicConfig()
+logger = logging.getLogger(__name__)
+
+try:
+    from matplotlib import pyplot as plt
+    from matplotlib.dates import MonthLocator, num2date
+    from matplotlib.ticker import FuncFormatter
+except ImportError:
+    logger.error('Importing matplotlib failed. Plotting will not work.')
+
+
+def plot(
+    m, fcst, ax=None, uncertainty=True, plot_cap=True, xlabel='ds', ylabel='y',
+):
+    """Plot the Prophet forecast.
+
+    Parameters
+    ----------
+    m: Prophet model.
+    fcst: pd.DataFrame output of m.predict.
+    ax: Optional matplotlib axes on which to plot.
+    uncertainty: Optional boolean to plot uncertainty intervals.
+    plot_cap: Optional boolean indicating if the capacity should be shown
+        in the figure, if available.
+    xlabel: Optional label name on X-axis
+    ylabel: Optional label name on Y-axis
+
+    Returns
+    -------
+    A matplotlib figure.
+    """
+    if ax is None:
+        fig = plt.figure(facecolor='w', figsize=(10, 6))
+        ax = fig.add_subplot(111)
+    else:
+        fig = ax.get_figure()
+    fcst_t = fcst['ds'].dt.to_pydatetime()
+    ax.plot(m.history['ds'].dt.to_pydatetime(), m.history['y'], 'k.')
+    ax.plot(fcst_t, fcst['yhat'], ls='-', c='#0072B2')
+    if 'cap' in fcst and plot_cap:
+        ax.plot(fcst_t, fcst['cap'], ls='--', c='k')
+    if m.logistic_floor and 'floor' in fcst and plot_cap:
+        ax.plot(fcst_t, fcst['floor'], ls='--', c='k')
+    if uncertainty:
+        ax.fill_between(fcst_t, fcst['yhat_lower'], fcst['yhat_upper'],
+                        color='#0072B2', alpha=0.2)
+    ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
+    ax.set_xlabel(xlabel)
+    ax.set_ylabel(ylabel)
+    fig.tight_layout()
+    return fig
+
+
+def plot_components(
+    m, fcst, uncertainty=True, plot_cap=True, weekly_start=0, yearly_start=0,
+):
+    """Plot the Prophet forecast components.
+
+    Will plot whichever are available of: trend, holidays, weekly
+    seasonality, and yearly seasonality.
+
+    Parameters
+    ----------
+    m: Prophet model.
+    fcst: pd.DataFrame output of m.predict.
+    uncertainty: Optional boolean to plot uncertainty intervals.
+    plot_cap: Optional boolean indicating if the capacity should be shown
+        in the figure, if available.
+    weekly_start: Optional int specifying the start day of the weekly
+        seasonality plot. 0 (default) starts the week on Sunday. 1 shifts
+        by 1 day to Monday, and so on.
+    yearly_start: Optional int specifying the start day of the yearly
+        seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts
+        by 1 day to Jan 2, and so on.
+
+    Returns
+    -------
+    A matplotlib figure.
+    """
+    # Identify components to be plotted
+    components = ['trend']
+    if m.holidays is not None and 'holidays' in fcst:
+        components.append('holidays')
+    components.extend([name for name in m.seasonalities
+                    if name in fcst])
+    if len(m.extra_regressors) > 0 and 'extra_regressors' in fcst:
+        components.append('extra_regressors')
+    npanel = len(components)
+
+    fig, axes = plt.subplots(npanel, 1, facecolor='w',
+                            figsize=(9, 3 * npanel))
+
+    if npanel == 1:
+        axes = [axes]
+
+    for ax, plot_name in zip(axes, components):
+        if plot_name == 'trend':
+            plot_forecast_component(
+                m=m, fcst=fcst, name='trend', ax=ax, uncertainty=uncertainty,
+                plot_cap=plot_cap,
+            )
+        elif plot_name == 'holidays':
+            plot_forecast_component(
+                m=m, fcst=fcst, name='holidays', ax=ax,
+                uncertainty=uncertainty, plot_cap=False,
+            )
+        elif plot_name == 'weekly':
+            plot_weekly(
+                m=m, ax=ax, uncertainty=uncertainty, weekly_start=weekly_start,
+            )
+        elif plot_name == 'yearly':
+            plot_yearly(
+                m=m, ax=ax, uncertainty=uncertainty, yearly_start=yearly_start,
+            )
+        elif plot_name == 'extra_regressors':
+            plot_forecast_component(
+                m=m, fcst=fcst, name='extra_regressors', ax=ax,
+                uncertainty=uncertainty, plot_cap=False,
+            )
+        else:
+            plot_seasonality(
+                m=m, name=plot_name, ax=ax, uncertainty=uncertainty,
+            )
+
+    fig.tight_layout()
+    return fig
+
+
+def plot_forecast_component(
+    m, fcst, name, ax=None, uncertainty=True, plot_cap=False,
+):
+    """Plot a particular component of the forecast.
+
+    Parameters
+    ----------
+    m: Prophet model.
+    fcst: pd.DataFrame output of m.predict.
+    name: Name of the component to plot.
+    ax: Optional matplotlib Axes to plot on.
+    uncertainty: Optional boolean to plot uncertainty intervals.
+    plot_cap: Optional boolean indicating if the capacity should be shown
+        in the figure, if available.
+
+    Returns
+    -------
+    a list of matplotlib artists
+    """
+    artists = []
+    if not ax:
+        fig = plt.figure(facecolor='w', figsize=(10, 6))
+        ax = fig.add_subplot(111)
+    fcst_t = fcst['ds'].dt.to_pydatetime()
+    artists += ax.plot(fcst_t, fcst[name], ls='-', c='#0072B2')
+    if 'cap' in fcst and plot_cap:
+        artists += ax.plot(fcst_t, fcst['cap'], ls='--', c='k')
+    if m.logistic_floor and 'floor' in fcst and plot_cap:
+        ax.plot(fcst_t, fcst['floor'], ls='--', c='k')
+    if uncertainty:
+        artists += [ax.fill_between(
+            fcst_t, fcst[name + '_lower'], fcst[name + '_upper'],
+            color='#0072B2', alpha=0.2)]
+    ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
+    ax.set_xlabel('ds')
+    ax.set_ylabel(name)
+    return artists
+
+
+def seasonality_plot_df(m, ds):
+    """Prepare dataframe for plotting seasonal components.
+
+    Parameters
+    ----------
+    m: Prophet model.
+    ds: List of dates for column ds.
+
+    Returns
+    -------
+    A dataframe with seasonal components on ds.
+    """
+    df_dict = {'ds': ds, 'cap': 1., 'floor': 0.}
+    for name in m.extra_regressors:
+        df_dict[name] = 0.
+    df = pd.DataFrame(df_dict)
+    df = m.setup_dataframe(df)
+    return df
+
+
+def plot_weekly(m, ax=None, uncertainty=True, weekly_start=0):
+    """Plot the weekly component of the forecast.
+
+    Parameters
+    ----------
+    m: Prophet model.
+    ax: Optional matplotlib Axes to plot on. One will be created if this
+        is not provided.
+    uncertainty: Optional boolean to plot uncertainty intervals.
+    weekly_start: Optional int specifying the start day of the weekly
+        seasonality plot. 0 (default) starts the week on Sunday. 1 shifts
+        by 1 day to Monday, and so on.
+
+    Returns
+    -------
+    a list of matplotlib artists
+    """
+    artists = []
+    if not ax:
+        fig = plt.figure(facecolor='w', figsize=(10, 6))
+        ax = fig.add_subplot(111)
+    # Compute weekly seasonality for a Sun-Sat sequence of dates.
+    days = (pd.date_range(start='2017-01-01', periods=7) +
+            pd.Timedelta(days=weekly_start))
+    df_w = seasonality_plot_df(m, days)
+    seas = m.predict_seasonal_components(df_w)
+    days = days.weekday_name
+    artists += ax.plot(range(len(days)), seas['weekly'], ls='-',
+                    c='#0072B2')
+    if uncertainty:
+        artists += [ax.fill_between(range(len(days)),
+                                    seas['weekly_lower'], seas['weekly_upper'],
+                                    color='#0072B2', alpha=0.2)]
+    ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
+    ax.set_xticks(range(len(days)))
+    ax.set_xticklabels(days)
+    ax.set_xlabel('Day of week')
+    ax.set_ylabel('weekly')
+    return artists
+
+
+def plot_yearly(m, ax=None, uncertainty=True, yearly_start=0):
+    """Plot the yearly component of the forecast.
+
+    Parameters
+    ----------
+    m: Prophet model.
+    ax: Optional matplotlib Axes to plot on. One will be created if
+        this is not provided.
+    uncertainty: Optional boolean to plot uncertainty intervals.
+    yearly_start: Optional int specifying the start day of the yearly
+        seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts
+        by 1 day to Jan 2, and so on.
+
+    Returns
+    -------
+    a list of matplotlib artists
+    """
+    artists = []
+    if not ax:
+        fig = plt.figure(facecolor='w', figsize=(10, 6))
+        ax = fig.add_subplot(111)
+    # Compute yearly seasonality for a Jan 1 - Dec 31 sequence of dates.
+    days = (pd.date_range(start='2017-01-01', periods=365) +
+            pd.Timedelta(days=yearly_start))
+    df_y = seasonality_plot_df(m, days)
+    seas = m.predict_seasonal_components(df_y)
+    artists += ax.plot(
+        df_y['ds'].dt.to_pydatetime(), seas['yearly'], ls='-', c='#0072B2')
+    if uncertainty:
+        artists += [ax.fill_between(
+            df_y['ds'].dt.to_pydatetime(), seas['yearly_lower'],
+            seas['yearly_upper'], color='#0072B2', alpha=0.2)]
+    ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
+    months = MonthLocator(range(1, 13), bymonthday=1, interval=2)
+    ax.xaxis.set_major_formatter(FuncFormatter(
+        lambda x, pos=None: '{dt:%B} {dt.day}'.format(dt=num2date(x))))
+    ax.xaxis.set_major_locator(months)
+    ax.set_xlabel('Day of year')
+    ax.set_ylabel('yearly')
+    return artists
+
+
+def plot_seasonality(m, name, ax=None, uncertainty=True):
+    """Plot a custom seasonal component.
+
+    Parameters
+    ----------
+    m: Prophet model.
+    name: Seasonality name, like 'daily', 'weekly'.
+    ax: Optional matplotlib Axes to plot on. One will be created if
+        this is not provided.
+    uncertainty: Optional boolean to plot uncertainty intervals.
+
+    Returns
+    -------
+    a list of matplotlib artists
+    """
+    artists = []
+    if not ax:
+        fig = plt.figure(facecolor='w', figsize=(10, 6))
+        ax = fig.add_subplot(111)
+    # Compute seasonality from Jan 1 through a single period.
+    start = pd.to_datetime('2017-01-01 0000')
+    period = m.seasonalities[name]['period']
+    end = start + pd.Timedelta(days=period)
+    plot_points = 200
+    days = pd.to_datetime(np.linspace(start.value, end.value, plot_points))
+    df_y = seasonality_plot_df(m, days)
+    seas = m.predict_seasonal_components(df_y)
+    artists += ax.plot(df_y['ds'].dt.to_pydatetime(), seas[name], ls='-',
+                        c='#0072B2')
+    if uncertainty:
+        artists += [ax.fill_between(
+            df_y['ds'].dt.to_pydatetime(), seas[name + '_lower'],
+            seas[name + '_upper'], color='#0072B2', alpha=0.2)]
+    ax.grid(True, which='major', c='gray', ls='-', lw=1, alpha=0.2)
+    xticks = pd.to_datetime(np.linspace(start.value, end.value, 7)
+        ).to_pydatetime()
+    ax.set_xticks(xticks)
+    if period <= 2:
+        fmt_str = '{dt:%T}'
+    elif period < 14:
+        fmt_str = '{dt:%m}/{dt:%d} {dt:%R}'
+    else:
+        fmt_str = '{dt:%m}/{dt:%d}'
+    ax.xaxis.set_major_formatter(FuncFormatter(
+        lambda x, pos=None: fmt_str.format(dt=num2date(x))))
+    ax.set_xlabel('ds')
+    ax.set_ylabel(name)
+    return artists