Module gdess.graphics.single_source_plots

Functions

def plot_annual_series(df_anomaly_yearly: pandas.core.frame.DataFrame, df_anomaly_cycle: pandas.core.frame.DataFrame, titlestr: str) -> (, , )

Make timeseries plot with annual anomalies of co2 concentration.

Parameters

df_anomaly_yearly : pd.DataFrame,

 

df_anomaly_cycle : pd.DataFrame,

 

titlestr : str

 

Returns

matplotlib figure

 

matplotlib axis

 

tuple

Extra matplotlib artists used for the bounding box (bbox) when saving a figure

Expand source code

def plot_annual_series(df_anomaly_yearly: pd.DataFrame,
                       df_anomaly_cycle: pd.DataFrame,
                       titlestr: str
                       ) -> (plt.Figure, plt.Axes, tuple):
    """Make timeseries plot with annual anomalies of co2 concentration.

    Parameters
    ----------
    df_anomaly_yearly : pd.DataFrame,
    df_anomaly_cycle : pd.DataFrame,
    titlestr : str

    Returns
    -------
    matplotlib figure
    matplotlib axis
    tuple
        Extra matplotlib artists used for the bounding box (bbox) when saving a figure
    """
    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(7, 5))

    # ---- Plot Observations ----
    ax.plot(df_anomaly_yearly, label='annual cycle',
            color='#C0C0C0', linestyle='-', alpha=0.3, marker='.', zorder=-32)
    ax.plot(df_anomaly_cycle['moy'], df_anomaly_cycle['monthly_anomaly_from_year'],
            label='mean annual cycle', marker='o', zorder=10,
            color=(18 / 255, 140 / 255, 126 / 255))  # (255/255, 127/255, 14/255))
    #
    ax.set_ylim((-13, 7))
    #
    ax.set_ylabel('$CO_2$ (ppm)')
    ax.set_xlabel('month')
    ax.set_title(titlestr, fontsize=12)
    #
    #         ax.text(0.02, 0.92, f"{sc.upper()}, {station_dict[sc]['lat']:.1f}, {station_dict[sc]['lon']:.1f}",
    #                     horizontalalignment='left', verticalalignment='center', transform = ax.transAxes)
    ax.text(0.02, 0.06, f"Global, surface level mean",
            horizontalalignment='left', verticalalignment='center', transform=ax.transAxes)
    #
    # Define the legend
    handles, labels = ax.get_legend_handles_labels()
    display = (0, len(handles) - 1)
    leg = ax.legend([handle for i, handle in enumerate(handles) if i in display],
                    [label for i, label in enumerate(labels) if i in display],
                    loc='best', fontsize=12)
    for lh in leg.legendHandles:
        lh.set_alpha(1)
    #
    #         ax.grid(linestyle='--', color='lightgray')
    #         for k in ax.spines.keys():
    #             ax.spines[k].set_alpha(0.5)
    bbox_artists = (leg,)

    return fig, ax, bbox_artists

def plot_filter_components(filter_object, original_x, original_y, figure_title='', savepath=None) -> NoneType

Parameters

filter_object

 

original_x

 

original_y

 

figure_title : str

 

savepath : str

 

Expand source code

def plot_filter_components(filter_object, original_x, original_y,
                           figure_title='', savepath=None) -> None:
    """

    Parameters
    ----------
    filter_object
    original_x
    original_y
    figure_title : str
    savepath : str
    """
    # --- Make the figure ---
    fig, ax = plt.subplots(1, 1, figsize=(10, 7))
    ax.plot(filter_object.xinterp, filter_object.getFunctionValue(filter_object.xinterp), label='Function values',
            color=[x / 255 for x in [255, 127, 14]], alpha=1, linewidth=2.5, )
    ax.plot(filter_object.xinterp, filter_object.getPolyValue(filter_object.xinterp), label='Poly values',
            color=[x / 255 for x in [31, 119, 180]], alpha=1, linewidth=2.5, )
    ax.plot(filter_object.xinterp, filter_object.getTrendValue(filter_object.xinterp), label='Trend values',
            color=[x / 255 for x in [44, 160, 44]], alpha=1, linewidth=2.5, )
    # ax.plot(x0, y3, label='Smooth values',
    #        alpha=1, linewidth=2.5, )
    ax.plot(original_x, original_y, label='original',
            marker='.', linestyle='none', color='gray', zorder=-10, alpha=0.2)
    ax.set_ylabel("$CO_2$ (ppm)")
    ax.set_xlabel("year")
    #
    aesthetic_grid_no_spines(ax)
    #
    plt.title(figure_title)
    #
    lgd = plt.legend()
    #
    plt.tight_layout()
    #
    if savepath:
        mysavefig(fig=fig, plot_save_name=append_before_extension(savepath, 'filter_components'),
                  bbox_inches='tight', bbox_extra_artists=(lgd, ))

def plot_zonal_mean(darray: xarray.core.dataarray.DataArray, titlestr: str) -> (, , )

Make zonal mean plot of co2 concentrations.

Parameters

darray : xr.DataArray,

 

titlestr : str

 

Returns

matplotlib figure

 

matplotlib axis

 

tuple

Extra matplotlib artists used for the bounding box (bbox) when saving a figure

Expand source code

def plot_zonal_mean(darray: xr.DataArray,
                    titlestr: str
                    ) -> (plt.Figure, plt.Axes, tuple):
    """Make zonal mean plot of co2 concentrations.

    Parameters
    ----------
    darray : xr.DataArray,
    titlestr : str

    Returns
    -------
    matplotlib figure
    matplotlib axis
    tuple
        Extra matplotlib artists used for the bounding box (bbox) when saving a figure
    """
    # --- Make Figure ---
    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 5))

    darray.plot.contourf(ax=ax, x='lat', y='plev', levels=40,
                         cbar_kwargs={'label': '$CO_2$ (ppm)', 'spacing': 'proportional'})
    #
    ax.invert_yaxis()
    ax.set_title(titlestr, fontsize=12)
    #
    ax.grid(linestyle='--', color='lightgray', alpha=0.3)
    for k in ax.spines.keys():
        ax.spines[k].set_alpha(0.5)
    bbox_artists = ()

    return fig, ax, bbox_artists