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#!/usr/bin/env python3 """ ======== Overview ======== Python3 library of plot functions for LiCSBAS. ========= Changelog ========= 20230827 Qi Ou, UoL - plot_strong_weak_cuts_network - plot_coloured_network - plot_corrected_network - plot_network with user-defined labels for the blue ifgs 20230623 Milan Lazecky, UoL - updated matplotlib grid v1.3.1 20200909 Yu Morishita, GSI - fix loc = upper right to avoid UserWarning in plot_hgt_corr v1.3 20200902 Yu Morishita, GSI - Always use nearest interpolation to avoid expanded nan v1.2 20200828 Yu Morioshita, GSI - Bug fix in plot_network; use datetime instead of ordinal - Update for matplotlib >= 3.3 - Use nearest interpolation for insar cmap to avoid aliasing v1.1 20200228 Yu Morioshita, Uni of Leeds and GSI - Remove pdf option in plot_network - Add plot_hgt_corr - Add plot_gacos_info v1.0 20190729 Yu Morioshita, Uni of Leeds and GSI - Original implementation """ import os os.environ['QT_QPA_PLATFORM']='offscreen' import numpy as np import datetime as dt import warnings import matplotlib as mpl with warnings.catch_warnings(): ## To silence user warning warnings.simplefilter('ignore', UserWarning) mpl.use('Agg') from matplotlib import pyplot as plt from matplotlib import dates as mdates from matplotlib import colors import LiCSBAS_tools_lib as tools_lib import LiCSBAS_inv_lib as inv_lib #%% def make_im_png(data, pngfile, cmap, title, vmin=None, vmax=None, cbar=True, ref_window=[None, None, None, None], logscale=False): """ Make png image. cmap can be 'insar'. To wrap data, np.angle(np.exp(1j*x/cycle)*cycle) """ if cmap=='insar': cdict = tools_lib.cmap_insar() plt.register_cmap(cmap=mpl.colors.LinearSegmentedColormap('insar', cdict)) interp = 'nearest' else: interp = 'nearest' #'antialiased' length, width = data.shape figsizex = 8 xmergin = 2 if cbar else 0 figsizey = int((figsizex-xmergin)*(length/width))+1 ### Plot fig, ax = plt.subplots(1, 1, figsize=(figsizex, figsizey)) plt.tight_layout() if logscale: im = ax.imshow(data, cmap=cmap, interpolation=interp, norm=colors.LogNorm(vmin=vmin, vmax=vmax)) else: im = ax.imshow(data, vmin=vmin, vmax=vmax, cmap=cmap, interpolation=interp) if ref_window[0] is not None: x1 = ref_window[0] x2 = ref_window[1] y1 = ref_window[2] y2 = ref_window[3] ax.plot([x1, x2, x2, x1, x1], [y1, y1, y2, y2, y1], c='grey') ax.set_xticklabels([]) ax.set_yticklabels([]) ax.set_title(title) if cbar: cbar = fig.colorbar(im) if logscale and vmax == 1: cbar.set_ticks([0.05, 0.1, 0.2, 0.5, 1]) cbar.ax.set_yticklabels([0.05, 0.1, 0.2, 0.5, 1]) plt.savefig(pngfile) plt.close() return def da_to_xyz(da, out_path, varname=None): # Standardize coord names if "x" in da.dims: da = da.rename({"x": "lon"}) if "y" in da.dims: da = da.rename({"y": "lat"}) if "longitude" in da.dims: da = da.rename({"longitude": "lon"}) if "latitude" in da.dims: da = da.rename({"latitude": "lat"}) name = varname or (da.name if da.name else "z") da = da.rename(name) # Flatten -> DataFrame (lon, lat, value) df = ( da.stack(pt=("lat", "lon")) .to_series() .dropna() .reset_index() .rename(columns={name: "value"}) [ ["lon", "lat", "value"] ] ) # Create parent dir only if one was provided parent = os.path.dirname(out_path) if parent: os.makedirs(parent, exist_ok=True) # Write XYZ (space-separated, no header) df.to_csv(out_path, sep=" ", header=False, index=False) #%% def make_3im_png(data3, pngfile, cmap, title3, vmin=None, vmax=None, cbar=True): """ Make png with 3 images for comparison. data3 and title3 must be list with 3 elements. cmap can be 'insar'. To wrap data, np.angle(np.exp(1j*x/cycle)*cycle) """ ### Plot setting if cmap=='insar': cdict = tools_lib.cmap_insar() plt.register_cmap(cmap=mpl.colors.LinearSegmentedColormap('insar', cdict)) interp = 'nearest' else: interp = 'nearest' #'antialiased' length, width = data3[0].shape figsizex = 12 xmergin = 4 if cbar else 0 figsizey = int((figsizex-xmergin)/3*length/width)+2 fig = plt.figure(figsize = (figsizex, figsizey)) for i in range(3): ax = fig.add_subplot(1, 3, i+1) #index start from 1 im = ax.imshow(data3[i], vmin=vmin, vmax=vmax, cmap=cmap, interpolation=interp) ax.set_title(title3[i]) ax.set_xticklabels([]) ax.set_yticklabels([]) if cbar: fig.colorbar(im, ax=ax) plt.tight_layout() plt.savefig(pngfile) plt.close() return #%% def plot_gacos_info(gacos_infofile, pngfile): figsize = (7, 3) #3x7 sizec, colorc, markerc, alphac = 2, 'k', 'o', 0.8 ### Figure fig = plt.figure(figsize=figsize) ax1 = fig.add_subplot(1, 2, 1) #index start from 1 ax2 = fig.add_subplot(1, 2, 2) #index start from 1 ### Read data with open(gacos_infofile, "r") as f: info = f.readlines()[1:] std_bf, std_af, rate = [], [], [] for line in info: date, std_bf1, std_af1, rate1 = line.split() if std_bf1=='0.0' or std_bf1=='nan' or std_af1=='0.0' or std_af1=='nan': continue std_bf.append(float(std_bf1)) std_af.append(float(std_af1)) rate.append(float(rate1[:-1])) std_bf = np.array(std_bf) std_af = np.array(std_af) rate = np.array(rate) rate[rate>99] = 99 rate[rate< -99] = -99 ### Plot xylim1 = np.max(np.concatenate((std_bf, std_af)))+1 ax1.scatter(std_bf, std_af, s=sizec, c=colorc, marker=markerc, alpha=alphac, zorder=4) ax1.set_xlim(0, xylim1) ax1.set_ylim(0, xylim1) ax1.plot([0, xylim1], [0, xylim1], linewidth=2, color='grey', alpha=0.5, zorder=2) ax1.grid(zorder=0) ax1.set_xlabel('STD before GACOS (rad)') ax1.set_ylabel('STD after GACOS (rad)') ### Plot ax2.scatter(std_bf, rate, s=sizec, c=colorc, marker=markerc, alpha=alphac, zorder=4) ax2.plot([0, xylim1], [0, 0], linewidth=2, color='grey', alpha=0.5, zorder=2) ax2.grid(zorder=0) ax2.set_xlim(0, xylim1) ax2.set_xlabel('STD before GACOS (rad)') ax2.set_ylabel('STD reduction rate (%)') fig.tight_layout() fig.savefig(pngfile) #%% def plot_hgt_corr(data_bf, fit_hgt, hgt, title, pngfile): """ """ bool_nan = np.isnan(data_bf) data_af = data_bf - fit_hgt ### Correction ix_hgt0 = np.nanargmin(hgt[~bool_nan]) ix_hgt1 = np.nanargmax(hgt[~bool_nan]) hgt0 = hgt[~bool_nan][ix_hgt0] hgt1 = hgt[~bool_nan][ix_hgt1] fit_hgt0 = fit_hgt[~bool_nan][ix_hgt0] fit_hgt1 = fit_hgt[~bool_nan][ix_hgt1] ### Downsample data to plot large number of scatters fast hgt_data_bf = np.stack((np.round(hgt[~bool_nan]), np.round(data_bf[~bool_nan], 1))).T ## Round values hgt_data_bf = np.unique(hgt_data_bf, axis = 0) ## Keep only uniques hgt_data_af = np.stack((np.round(hgt[~bool_nan]), np.round(data_af[~bool_nan], 1))).T ## Round values hgt_data_af = np.unique(hgt_data_af, axis = 0) ## Keep only uniques ### Plot figsize = (5, 4) sbf, cbf, mbf, zbf, lbf = 0.2, '0.5', 'p', 4, 'Before' saf, caf, maf, zaf, laf = 0.2, 'c', 'p', 6, 'After' fig, ax = plt.subplots(nrows=1, ncols=1, figsize=figsize) ax.scatter(hgt_data_bf[:, 0], hgt_data_bf[:, 1], s=sbf, c=cbf, marker=mbf, zorder=zbf, label=lbf) ax.scatter(hgt_data_af[:, 0], hgt_data_af[:, 1], s=saf, c=caf, marker=maf, zorder=zaf, label=laf) ax.plot([hgt0, hgt1], [fit_hgt0, fit_hgt1], linewidth=2, color='k', alpha=0.8, zorder=8, label='Correction') ax.grid(zorder=0) ax.set_title(title, fontsize=10) ax.set_xlabel('Height (m)') ax.set_ylabel('Displacement (mm)') ax.legend(loc='upper right') fig.tight_layout() fig.savefig(pngfile) plt.close() return #%% def plot_network(ifgdates, bperp, rm_ifgdates, pngfile, plot_bad=True, label_name='Removed IFG'): """ Plot network of interferometric pairs. bperp can be dummy (-1~1). Suffix of pngfile can be png, ps, pdf, or svg. plot_bad True : Plot bad ifgs by red lines False : Do not plot bad ifgs """ if label_name is None: label_name = 'Removed IFG' imdates_all = tools_lib.ifgdates2imdates(ifgdates) n_im_all = len(imdates_all) imdates_dt_all = np.array(([dt.datetime.strptime(imd, '%Y%m%d') for imd in imdates_all])) ##datetime ifgdates = list(set(ifgdates)-set(rm_ifgdates)) ifgdates.sort() imdates = tools_lib.ifgdates2imdates(ifgdates) n_im = len(imdates) imdates_dt = np.array(([dt.datetime.strptime(imd, '%Y%m%d') for imd in imdates])) ##datetime ### Identify gaps G = inv_lib.make_sb_matrix(ifgdates) ixs_inc_gap = np.where(G.sum(axis=0)==0)[0] ### Plot fig figsize_x = np.round(((imdates_dt_all[-1]-imdates_dt_all[0]).days)/80)+2 fig = plt.figure(figsize=(figsize_x, 6)) ax = fig.add_axes([0.06, 0.12, 0.92,0.85]) ### IFG blue lines for i, ifgd in enumerate(ifgdates): ix_m = imdates_all.index(ifgd[:8]) ix_s = imdates_all.index(ifgd[-8:]) label = 'IFG' if i==0 else '' #label only first plt.plot([imdates_dt_all[ix_m], imdates_dt_all[ix_s]], [bperp[ix_m], bperp[ix_s]], color='b', alpha=0.6, zorder=2, label=label) ### IFG bad red lines if plot_bad: for i, ifgd in enumerate(rm_ifgdates): ix_m = imdates_all.index(ifgd[:8]) ix_s = imdates_all.index(ifgd[-8:]) label = label_name if i==0 else '' #label only first plt.plot([imdates_dt_all[ix_m], imdates_dt_all[ix_s]], [bperp[ix_m], bperp[ix_s]], color='r', alpha=0.6, zorder=6, label=label) ### Image points and dates ax.scatter(imdates_dt_all, bperp, alpha=0.6, zorder=4) for i in range(n_im_all): if bperp[i] > np.median(bperp): va='bottom' else: va = 'top' ax.annotate(imdates_all[i][4:6]+'/'+imdates_all[i][6:], (imdates_dt_all[i], bperp[i]), ha='center', va=va, zorder=8) ### gaps if len(ixs_inc_gap)!=0: gap_dates_dt = [] for ix_gap in ixs_inc_gap: ddays_td = imdates_dt[ix_gap+1]-imdates_dt[ix_gap] gap_dates_dt.append(imdates_dt[ix_gap]+ddays_td/2) plt.vlines(gap_dates_dt, 0, 1, transform=ax.get_xaxis_transform(), zorder=1, label='Gap', alpha=0.6, colors='k', linewidth=3) ### Locater # loc = ax.xaxis.set_major_locator(mdates.AutoDateLocator()) locator = mdates.AutoDateLocator() ax.xaxis.set_major_locator(locator) try: # Only support from Matplotlib 3.1 # locator.axis = ax.xaxis # not sure if this needed? ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(locator)) except: ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d')) for label in ax.get_xticklabels(): label.set_rotation(20) label.set_horizontalalignment('right') ax.grid(which='major') ### Add bold line every 1yr ax.xaxis.set_minor_locator(mdates.YearLocator()) ax.grid(which='minor', linewidth=2) ax.set_xlim((imdates_dt_all[0]-dt.timedelta(days=10), imdates_dt_all[-1]+dt.timedelta(days=10))) ### Labels and legend plt.xlabel('Time') if np.all(np.abs(np.array(bperp))<=1): ## dummy plt.ylabel('dummy') else: plt.ylabel('Bperp [m]') plt.legend() ### Save try: plt.savefig(pngfile) #, bbox_inches='tight') except: print('WARNING, generating network plot failed - new matplotlib changes?') plt.close() return len(ixs_inc_gap) # %% def plot_corrected_network(ifgdates, bperp, corrected_ifgdates, pngfile, plot_corrected=True, label_name='Corrected IFG'): """ Plot network of interferometric pairs. bperp can be dummy (-1~1). Suffix of pngfile can be png, ps, pdf, or svg. plot_bad True : Plot corrected ifgs by red lines False : Do not plot corrected ifgs """ imdates_all = tools_lib.ifgdates2imdates(ifgdates) n_im_all = len(imdates_all) imdates_dt_all = np.array(([dt.datetime.strptime(imd, '%Y%m%d') for imd in imdates_all])) ##datetime good_ifgdates = list(set(ifgdates) - set(corrected_ifgdates)) good_ifgdates.sort() ### Plot fig figsize_x = np.round(((imdates_dt_all[-1] - imdates_dt_all[0]).days) / 80) + 2 fig = plt.figure(figsize=(figsize_x, 6)) ax = fig.add_axes([0.06, 0.12, 0.92, 0.85]) ### IFG good blue lines for i, ifgd in enumerate(good_ifgdates): ix_m = imdates_all.index(ifgd[:8]) ix_s = imdates_all.index(ifgd[-8:]) label = 'IFG' if i == 0 else '' # label only first plt.plot([imdates_dt_all[ix_m], imdates_dt_all[ix_s]], [bperp[ix_m], bperp[ix_s]], color='b', alpha=0.6, zorder=2, label=label) ### IFG corrected red lines if plot_corrected: for i, ifgd in enumerate(corrected_ifgdates): ix_m = imdates_all.index(ifgd[:8]) ix_s = imdates_all.index(ifgd[-8:]) label = label_name if i == 0 else '' # label only first plt.plot([imdates_dt_all[ix_m], imdates_dt_all[ix_s]], [bperp[ix_m], bperp[ix_s]], color='r', alpha=0.6, zorder=6, label=label) ### Image points and dates ax.scatter(imdates_dt_all, bperp, alpha=0.6, zorder=4) for i in range(n_im_all): if bperp[i] > np.median(bperp): va = 'bottom' else: va = 'top' ax.annotate(imdates_all[i][4:6] + '/' + imdates_all[i][6:], (imdates_dt_all[i], bperp[i]), ha='center', va=va, zorder=8) ### Identify gaps if plot_corrected: G = inv_lib.make_sb_matrix(ifgdates) ixs_inc_gap = np.where(G.sum(axis=0) == 0)[0] imdates = tools_lib.ifgdates2imdates(ifgdates) imdates_dt = np.array(([dt.datetime.strptime(imd, '%Y%m%d') for imd in imdates])) ##datetime else: G = inv_lib.make_sb_matrix(good_ifgdates) ixs_inc_gap = np.where(G.sum(axis=0) == 0)[0] imdates = tools_lib.ifgdates2imdates(good_ifgdates) imdates_dt = np.array(([dt.datetime.strptime(imd, '%Y%m%d') for imd in imdates])) ##datetime ### plot gaps if len(ixs_inc_gap) != 0: gap_dates_dt = [] for ix_gap in ixs_inc_gap: ddays_td = imdates_dt[ix_gap + 1] - imdates_dt[ix_gap] gap_dates_dt.append(imdates_dt[ix_gap] + ddays_td / 2) plt.vlines(gap_dates_dt, 0, 1, transform=ax.get_xaxis_transform(), zorder=1, label='Gap', alpha=0.6, colors='k', linewidth=3) ### Locater # loc = ax.xaxis.set_major_locator(mdates.AutoDateLocator()) locator = mdates.AutoDateLocator() ax.xaxis.set_major_locator(locator) try: # Only support from Matplotlib 3.1 ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(locator)) except: ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d')) for label in ax.get_xticklabels(): label.set_rotation(20) label.set_horizontalalignment('right') ax.grid(which='major') ### Add bold line every 1yr ax.xaxis.set_minor_locator(mdates.YearLocator()) ax.grid(which='minor', linewidth=2) ax.set_xlim((imdates_dt_all[0] - dt.timedelta(days=10), imdates_dt_all[-1] + dt.timedelta(days=10))) ### Labels and legend plt.xlabel('Time') if np.all(np.abs(np.array(bperp)) <= 1): ## dummy plt.ylabel('dummy') else: plt.ylabel('Bperp [m]') plt.legend() ### Save plt.savefig(pngfile) #, bbox_inches='tight') plt.close() return len(ixs_inc_gap) def plot_coloured_network(ifgdates, bperp, perc_list, pngfile): """Plot network with link colour controlled by perc_list.""" imdates_all = tools_lib.ifgdates2imdates(ifgdates) n_im_all = len(imdates_all) imdates_dt_all = np.array(([dt.datetime.strptime(imd, '%Y%m%d') for imd in imdates_all])) ##datetime ### Plot fig figsize_x = np.round(((imdates_dt_all[-1] - imdates_dt_all[0]).days) / 80) + 2 fig = plt.figure(figsize=(figsize_x, 6)) ax = fig.add_axes([0.06, 0.12, 0.92, 0.85]) # colorbar to change line colour according to unw pixel percentage cmap = plt.cm.ScalarMappable(cmap='hot_r', norm=plt.Normalize(vmin=0, vmax=100)) cmap.set_array([]) ### IFG good blue lines for i, ifgd in enumerate(ifgdates): ix_m = imdates_all.index(ifgd[:8]) ix_s = imdates_all.index(ifgd[-8:]) label = 'IFG' if i == 0 else '' # label only first plt.plot([imdates_dt_all[ix_m], imdates_dt_all[ix_s]], [bperp[ix_m], bperp[ix_s]], color=cmap.to_rgba(perc_list[i]), alpha=0.6, zorder=2, label=label) plt.colorbar(cmap, label='Pixel Percentage in Masked Unw') ### Image points and dates ax.scatter(imdates_dt_all, bperp, alpha=0.6, zorder=4) for i in range(n_im_all): if bperp[i] > np.median(bperp): va = 'bottom' else: va = 'top' ax.annotate(imdates_all[i][4:6] + '/' + imdates_all[i][6:], (imdates_dt_all[i], bperp[i]), ha='center', va=va, zorder=8) ### Identify gaps G = inv_lib.make_sb_matrix(ifgdates) ixs_inc_gap = np.where(G.sum(axis=0) == 0)[0] imdates = tools_lib.ifgdates2imdates(ifgdates) imdates_dt = np.array(([dt.datetime.strptime(imd, '%Y%m%d') for imd in imdates])) ##datetime ### plot gaps if len(ixs_inc_gap) != 0: gap_dates_dt = [] for ix_gap in ixs_inc_gap: ddays_td = imdates_dt[ix_gap + 1] - imdates_dt[ix_gap] gap_dates_dt.append(imdates_dt[ix_gap] + ddays_td / 2) plt.vlines(gap_dates_dt, 0, 1, transform=ax.get_xaxis_transform(), zorder=1, label='Gap', alpha=0.6, colors='k', linewidth=3) ### Locater #loc = ax.xaxis.set_major_locator(mdates.AutoDateLocator()) locator = mdates.AutoDateLocator() ax.xaxis.set_major_locator(locator) try: # Only support from Matplotlib 3.1 ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(locator)) except: ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d')) for label in ax.get_xticklabels(): label.set_rotation(20) label.set_horizontalalignment('right') ax.grid(which='major') ### Add bold line every 1yr ax.xaxis.set_minor_locator(mdates.YearLocator()) ax.grid(which='minor', linewidth=2) ax.set_xlim((imdates_dt_all[0] - dt.timedelta(days=10), imdates_dt_all[-1] + dt.timedelta(days=10))) ### Labels and legend plt.xlabel('Time') if np.all(np.abs(np.array(bperp)) <= 1): ## dummy plt.ylabel('dummy') else: plt.ylabel('Bperp [m]') plt.legend() ### Save plt.savefig(pngfile) #, bbox_inches='tight') plt.close() def plot_strong_weak_cuts_network(ifgdates, bperp, weak_links, edge_cuts, node_cuts, pngfile, plot_weak=True): """ Plot network of interferometric pairs. bperp can be dummy (-1~1). Suffix of pngfile can be png, ps, pdf, or svg. plot_bad True : Plot corrected ifgs by grey lines False : Do not plot corrected ifgs plot strong_links as blue lines plot weak_links as grey lines plot edge_cutes as red lines plot node_cuts as red dots """ imdates_all = tools_lib.ifgdates2imdates(ifgdates) n_im_all = len(imdates_all) imdates_dt_all = np.array(([dt.datetime.strptime(imd, '%Y%m%d') for imd in imdates_all])) ##datetime good_ifgdates = list(set(ifgdates) - set(weak_links)) good_ifgdates.sort() ### Plot fig figsize_x = np.round(((imdates_dt_all[-1] - imdates_dt_all[0]).days) / 80) + 2 fig = plt.figure(figsize=(figsize_x, 6)) ax = fig.add_axes([0.06, 0.12, 0.92, 0.85]) ### IFG good blue lines for i, ifgd in enumerate(good_ifgdates): ix_m = imdates_all.index(ifgd[:8]) ix_s = imdates_all.index(ifgd[-8:]) label = 'IFG' if i == 0 else '' # label only first plt.plot([imdates_dt_all[ix_m], imdates_dt_all[ix_s]], [bperp[ix_m], bperp[ix_s]], color='b', alpha=0.6, zorder=2, label=label) ### IFG corrected red lines if plot_weak: for i, ifgd in enumerate(weak_links): ix_m = imdates_all.index(ifgd[:8]) ix_s = imdates_all.index(ifgd[-8:]) label = 'Weak_links' if i == 0 else '' # label only first plt.plot([imdates_dt_all[ix_m], imdates_dt_all[ix_s]], [bperp[ix_m], bperp[ix_s]], color='grey', alpha=0.6, zorder=6, label=label) for i, ifgd in enumerate(edge_cuts): ix_m = imdates_all.index(ifgd[:8]) ix_s = imdates_all.index(ifgd[-8:]) label = 'Edge_cuts' if i == 0 else '' # label only first plt.plot([imdates_dt_all[ix_m], imdates_dt_all[ix_s]], [bperp[ix_m], bperp[ix_s]], color='r', alpha=0.6, zorder=6, label=label) ### Image points and dates ax.scatter(imdates_dt_all, bperp, alpha=0.6, zorder=4) for i, node in enumerate(node_cuts): ix_n = imdates_all.index(node) label = 'Node_cuts' if i == 0 else '' # label only first plt.scatter(imdates_dt_all[ix_n], bperp[ix_n], color='r', alpha=0.6, zorder=6, label=label) for i in range(n_im_all): if bperp[i] > np.median(bperp): va = 'bottom' else: va = 'top' ax.annotate(imdates_all[i][4:6] + '/' + imdates_all[i][6:], (imdates_dt_all[i], bperp[i]), ha='center', va=va, zorder=8) ### Locater # loc = ax.xaxis.set_major_locator(mdates.AutoDateLocator()) locator = mdates.AutoDateLocator() ax.xaxis.set_major_locator(locator) try: # Only support from Matplotlib 3.1 ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(locator)) except: ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d')) for label in ax.get_xticklabels(): label.set_rotation(20) label.set_horizontalalignment('right') ax.grid(which='major') ### Add bold line every 1yr ax.xaxis.set_minor_locator(mdates.YearLocator()) ax.grid(which='minor', linewidth=2) ax.set_xlim((imdates_dt_all[0] - dt.timedelta(days=10), imdates_dt_all[-1] + dt.timedelta(days=10))) ### Labels and legend plt.xlabel('Time') if np.all(np.abs(np.array(bperp)) <= 1): ## dummy plt.ylabel('dummy') else: plt.ylabel('Bperp [m]') plt.legend() ### Save plt.savefig(pngfile) #, bbox_inches='tight') plt.close() return