from statsmodels.compat.python import lrange import numpy as np import statsmodels.tsa.vector_ar.util as util class MPLConfigurator: def __init__(self): self._inverse_actions = [] def revert(self): for action in self._inverse_actions: action() def set_fontsize(self, size): import matplotlib as mpl old_size = mpl.rcParams['font.size'] mpl.rcParams['font.size'] = size def revert(): mpl.rcParams['font.size'] = old_size self._inverse_actions.append(revert) #------------------------------------------------------------------------------- # Plotting functions def plot_mts(Y, names=None, index=None): """ Plot multiple time series """ import matplotlib.pyplot as plt k = Y.shape[1] rows, cols = k, 1 fig = plt.figure(figsize=(10, 10)) for j in range(k): ts = Y[:, j] ax = fig.add_subplot(rows, cols, j+1) if index is not None: ax.plot(index, ts) else: ax.plot(ts) if names is not None: ax.set_title(names[j]) return fig def plot_var_forc(prior, forc, err_upper, err_lower, index=None, names=None, plot_stderr=True, legend_options=None): import matplotlib.pyplot as plt n, k = prior.shape rows, cols = k, 1 fig = plt.figure(figsize=(10, 10)) prange = np.arange(n) rng_f = np.arange(n - 1, n + len(forc)) rng_err = np.arange(n, n + len(forc)) for j in range(k): ax = plt.subplot(rows, cols, j+1) p1 = ax.plot(prange, prior[:, j], 'k', label='Observed') p2 = ax.plot(rng_f, np.r_[prior[-1:, j], forc[:, j]], 'k--', label='Forecast') if plot_stderr: p3 = ax.plot(rng_err, err_upper[:, j], 'k-.', label='Forc 2 STD err') ax.plot(rng_err, err_lower[:, j], 'k-.') if names is not None: ax.set_title(names[j]) if legend_options is None: legend_options = {"loc": "upper right"} ax.legend(**legend_options) return fig def plot_with_error(y, error, x=None, axes=None, value_fmt='k', error_fmt='k--', alpha=0.05, stderr_type = 'asym'): """ Make plot with optional error bars Parameters ---------- y : error : array or None """ import matplotlib.pyplot as plt if axes is None: axes = plt.gca() x = x if x is not None else lrange(len(y)) plot_action = lambda y, fmt: axes.plot(x, y, fmt) plot_action(y, value_fmt) #changed this if error is not None: if stderr_type == 'asym': q = util.norm_signif_level(alpha) plot_action(y - q * error, error_fmt) plot_action(y + q * error, error_fmt) if stderr_type in ('mc','sz1','sz2','sz3'): plot_action(error[0], error_fmt) plot_action(error[1], error_fmt) def plot_full_acorr(acorr, fontsize=8, linewidth=8, xlabel=None, err_bound=None): """ Parameters ---------- """ import matplotlib.pyplot as plt config = MPLConfigurator() config.set_fontsize(fontsize) k = acorr.shape[1] fig, axes = plt.subplots(k, k, figsize=(10, 10), squeeze=False) for i in range(k): for j in range(k): ax = axes[i][j] acorr_plot(acorr[:, i, j], linewidth=linewidth, xlabel=xlabel, ax=ax) if err_bound is not None: ax.axhline(err_bound, color='k', linestyle='--') ax.axhline(-err_bound, color='k', linestyle='--') adjust_subplots() config.revert() return fig def acorr_plot(acorr, linewidth=8, xlabel=None, ax=None): import matplotlib.pyplot as plt if ax is None: ax = plt.gca() if xlabel is None: xlabel = np.arange(len(acorr)) ax.vlines(xlabel, [0], acorr, lw=linewidth) ax.axhline(0, color='k') ax.set_ylim([-1, 1]) # hack? ax.set_xlim([-1, xlabel[-1] + 1]) def plot_acorr_with_error(): raise NotImplementedError def adjust_subplots(**kwds): import matplotlib.pyplot as plt passed_kwds = dict(bottom=0.05, top=0.925, left=0.05, right=0.95, hspace=0.2) passed_kwds.update(kwds) plt.subplots_adjust(**passed_kwds) #------------------------------------------------------------------------------- # Multiple impulse response (cum_effects, etc.) cplots def irf_grid_plot(values, stderr, impcol, rescol, names, title, signif=0.05, hlines=None, subplot_params=None, plot_params=None, figsize=(10,10), stderr_type='asym'): """ Reusable function to make flexible grid plots of impulse responses and comulative effects values : (T + 1) x k x k stderr : T x k x k hlines : k x k """ import matplotlib.pyplot as plt if subplot_params is None: subplot_params = {} if plot_params is None: plot_params = {} nrows, ncols, to_plot = _get_irf_plot_config(names, impcol, rescol) fig, axes = plt.subplots(nrows=nrows, ncols=ncols, sharex=True, squeeze=False, figsize=figsize) # fill out space adjust_subplots() fig.suptitle(title, fontsize=14) subtitle_temp = r'%s$\rightarrow$%s' k = len(names) rng = lrange(len(values)) for (j, i, ai, aj) in to_plot: ax = axes[ai][aj] # HACK? if stderr is not None: if stderr_type == 'asym': sig = np.sqrt(stderr[:, j * k + i, j * k + i]) plot_with_error(values[:, i, j], sig, x=rng, axes=ax, alpha=signif, value_fmt='b', stderr_type=stderr_type) if stderr_type in ('mc','sz1','sz2','sz3'): errs = stderr[0][:, i, j], stderr[1][:, i, j] plot_with_error(values[:, i, j], errs, x=rng, axes=ax, alpha=signif, value_fmt='b', stderr_type=stderr_type) else: plot_with_error(values[:, i, j], None, x=rng, axes=ax, value_fmt='b') ax.axhline(0, color='k') if hlines is not None: ax.axhline(hlines[i,j], color='k') sz = subplot_params.get('fontsize', 12) ax.set_title(subtitle_temp % (names[j], names[i]), fontsize=sz) return fig def _get_irf_plot_config(names, impcol, rescol): nrows = ncols = k = len(names) if impcol is not None and rescol is not None: # plot one impulse-response pair nrows = ncols = 1 j = util.get_index(names, impcol) i = util.get_index(names, rescol) to_plot = [(j, i, 0, 0)] elif impcol is not None: # plot impacts of impulse in one variable ncols = 1 j = util.get_index(names, impcol) to_plot = [(j, i, i, 0) for i in range(k)] elif rescol is not None: # plot only things having impact on particular variable ncols = 1 i = util.get_index(names, rescol) to_plot = [(j, i, j, 0) for j in range(k)] else: # plot everything to_plot = [(j, i, i, j) for i in range(k) for j in range(k)] return nrows, ncols, to_plot #------------------------------------------------------------------------------- # Forecast error variance decomposition