Source code for pytesmo.time_series.plotting

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'''
Created on Mar 7, 2014

Plot anomalies around climatology using colors

@author: Christoph Paulik christoph.paulik@geo.tuwien.ac.at
'''

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import pandas as pd

import pytesmo.time_series.anomaly as anom


[docs]def plot_clim_anom(df, clim=None, axes=None, markersize=0.75, mfc='0.3', mec='0.3', clim_color='0.0', clim_linewidth=0.5, clim_linestyle='-', pos_anom_color='#799ADA', neg_anom_color='#FD8086', anom_linewidth=0.2, add_titles=True): """ Takes a pandas DataFrame and calculates the climatology and anomaly and plots them in a nice way for each column Parameters ---------- df : pandas.DataFrame clim : pandas.DataFrame, optional if given these climatologies will be used if not given then climatologies will be calculated this DataFrame must have the same number of columns as df and also the column names. each climatology must have doy as index. axes : list of matplotlib.Axes, optional list of axes on which each column should be plotted if not given a standard layout is generated markersize : float, optional size of the markers for the datapoints mfc : matplotlib color, optional markerfacecolor, color of the marker face mec : matplotlib color, optional markeredgecolor clim_color : matplotlib color, optional color of the climatology clim_linewidth : float, optional linewidth of the climatology clim_linestyle : string, optional linestyle of the climatology pos_anom_color : matplotlib color, optional color of the positive anomaly neg_anom_color : matplotlib color, optional color of the negative anomaly anom_linewidth : float, optional linewidth of the anomaly lines add_titles : boolean, optional if set each subplot will have it's column name as title Default : True Returns ------- Figure : matplotlib.Figure if no axes were given axes : list of matploblib.Axes if no axes were given """ if type(df) == pd.Series: df = pd.DataFrame(df) nr_columns = len(df.columns) # make own axis if necessary if axes is None: own_axis = True gs = gridspec.GridSpec(nr_columns, 1, right=0.8) fig = plt.figure(num=None, figsize=(6, 2 * nr_columns), dpi=150, facecolor='w', edgecolor='k') last_axis = fig.add_subplot(gs[nr_columns - 1]) axes = [] for i, grid in enumerate(gs): if i < nr_columns - 1: ax = fig.add_subplot(grid, sharex=last_axis) axes.append(ax) ax.xaxis.set_visible(False) axes.append(last_axis) else: own_axis = False for i, column in enumerate(df): Ser = df[column] ax = axes[i] if clim is None: clima = anom.calc_climatology(Ser) else: clima = pd.Series(clim[column]) anomaly = anom.calc_anomaly(Ser, climatology=clima, return_clim=True) anomaly[Ser.name] = Ser anomaly = anomaly.dropna() pos_anom = anomaly[Ser.name].values > anomaly['climatology'].values neg_anom = anomaly[Ser.name].values < anomaly['climatology'].values ax.plot(anomaly.index, anomaly[Ser.name].values, 'o', markersize=markersize, mfc=mfc, mec=mec) ax.plot(anomaly.index, anomaly['climatology'].values, linestyle=clim_linestyle, color=clim_color, linewidth=clim_linewidth) ax.fill_between(anomaly.index, anomaly[Ser.name].values, anomaly['climatology'].values, interpolate=True, where=pos_anom, color=pos_anom_color, linewidth=anom_linewidth) ax.fill_between(anomaly.index, anomaly[Ser.name].values, anomaly['climatology'].values, interpolate=True, where=neg_anom, color=neg_anom_color, linewidth=anom_linewidth) if add_titles: ax.set_title(column) if own_axis: return fig, axes else: return None, None