def plot(xx, yy, target, label, figfiles, figfile, lon=None, lat=None, show=False): xs, ys, mask = coord2slice(target, lon=lon, lat=lat) P.figure(figsize=(6, 3.5)) P.title('Target=%(label)s / select: lon=%(lon)s, lat=%(lat)s' % locals()) add_grid((xx, yy)) xx = xx.asma() yy = yy.asma() if isinstance(lon, tuple): P.axvline(lon[0], color='m', ls='--', lw=2) P.axvline(lon[1], color='m', ls='--', lw=2) elif isinstance(lon, slice): i, j, k = lon.indices(xx.shape[1]) P.plot(xx[:, i], yy[:, i], 'c--', lw=2) P.plot(xx[:, j - 1], yy[:, j - 1], 'c--', lw=2) if isinstance(lat, tuple): P.axhline(lat[0], color='m', ls='--', lw=2) P.axhline(lat[1], color='m', ls='--', lw=2) elif isinstance(lat, slice): i, j, k = lat.indices(yy.shape[0]) P.plot(xx[i], yy[i], 'c--', lw=2) P.plot(xx[j - 1], yy[j - 1], 'c--', lw=2) P.xticks(N.arange(xx.min() - 1, xx.max() + 1)) P.yticks(N.arange(yy.min() - 1, yy.max() + 1)) xxi, yyi = xx, yy xx = xx[ys, xs] yy = yy[ys, xs] # mask = mask[ys, xs] xxb, yyb = meshbounds(xx, yy) P.pcolormesh(xxb, yyb, mask, shading='faceted') P.scatter(xx.ravel(), yy.ravel(), c=(0, 1, 0)) P.grid(True) P.axis('image') P.tight_layout() i = len(figfiles) savefig = figfile % i if os.path.exists(savefig): os.remove(savefig) P.savefig(savefig) figfiles.append(savefig) if show: P.show() else: P.close()
def plot(xx, yy, target, label, figfiles, figfile, lon=None, lat=None, show=False): xs, ys, mask = coord2slice(target, lon=lon, lat=lat) P.figure(figsize=(6, 3.5)) P.title('Target=%(label)s / select: lon=%(lon)s, lat=%(lat)s'%locals()) add_grid((xx, yy)) xx = xx.asma() yy = yy.asma() if isinstance(lon, tuple): P.axvline(lon[0], color='m', ls='--', lw=2) P.axvline(lon[1], color='m', ls='--', lw=2) elif isinstance(lon, slice): i, j, k = lon.indices(xx.shape[1]) P.plot(xx[:, i], yy[:, i], 'c--', lw=2) P.plot(xx[:, j-1], yy[:, j-1], 'c--', lw=2) if isinstance(lat, tuple): P.axhline(lat[0], color='m', ls='--', lw=2) P.axhline(lat[1], color='m', ls='--', lw=2) elif isinstance(lat, slice): i, j, k = lat.indices(yy.shape[0]) P.plot(xx[i], yy[i], 'c--', lw=2) P.plot(xx[j-1], yy[j-1], 'c--', lw=2) P.xticks(N.arange(xx.min()-1, xx.max()+1)) P.yticks(N.arange(yy.min()-1, yy.max()+1)) xxi, yyi = xx, yy xx = xx[ys, xs] yy = yy[ys, xs] # mask = mask[ys, xs] xxb, yyb = meshbounds(xx, yy) P.pcolor(xxb, yyb, mask, shading='faceted') P.scatter(xx.ravel(), yy.ravel(), c=(0, 1, 0)) P.grid('on') P.axis('image') P.tight_layout() i = len(figfiles) savefig = figfile%i if os.path.exists(savefig): os.remove(savefig) P.savefig(savefig) figfiles.append(savefig) if show: P.show() else: P.close()
'or-', markersize=5, lw=.8, label='Interpolated' if label else None) P.legend(loc='best', framealpha=0.5) P.grid() P.title('Section') P.tight_layout() savefigs(code_file_name(ext='_0.png'), verbose=False, pdf=True) # Maps P.figure(figsize=(8, 4)) levels = auto_scale(vmin=zzi.min(), vmax=zzi.max(), nmax=30) P.subplot(121) P.contourf(xxi, yyi, zzi, levels=levels) P.contour(xxi, yyi, zzi, linewidths=0.1, levels=levels, colors='k') add_grid((xxi, yyi), alpha=.3, centers=True) for iyi in iyis: P.axhline(yi[iyi], linestyle='--', color='k') P.title('Original') P.subplot(122) P.contourf(xxo, yyo, zzo, levels=levels) P.contour(xxo, yyo, zzo, linewidths=0.1, levels=levels, colors='k') add_grid((xxo, yyo), alpha=.2, centers=True) for iyi in iyis: P.axhline(yi[iyi], linestyle='--', color='k') P.title('Interpolated') P.tight_layout() savefigs(code_file_name(ext='_1.png'), verbose=False, pdf=True) P.close()
taxo = create_time(lindates(ct0, ct1, 1, 'hour'), taxi.units) # Lag error # - estimation els = [] lags = N.arange(1, 6) for lag in lags: els.append(N.sqrt(((sp[lag:] - sp[:-lag])**2).mean())) els = N.array(els) a, b, _, _, _ = linregress(lags, els) # - plot P.figure(figsize=(6, 6)) P.subplot(211) P.plot(lags, els, 'o') P.plot([0, lags[-1]], [b, a * lags[-1] + b], 'g') P.axhline(b, color='0.8', ls='--') P.ylim(ymin=0) P.xlabel('Lag [hour]') P.ylabel('Error [m s-1]') add_key(1) P.title('Linear lag error model') # Interpolation sph, speh = regrid1d(sp, taxo, method='cellerr', erri=spe, errl=-a, geterr=True) # Time zoom for plot clarity
taxo = create_time(lindates(ct0, ct1, 1, "hour"), taxi.units) # Lag error # - estimation els = [] lags = N.arange(1, 6) for lag in lags: els.append(N.sqrt(((sp[lag:] - sp[:-lag]) ** 2).mean())) els = N.array(els) a, b, _, _, _ = linregress(lags, els) # - plot P.figure(figsize=(6, 6)) P.subplot(211) P.plot(lags, els, "o") P.plot([0, lags[-1]], [b, a * lags[-1] + b], "g") P.axhline(b, color="0.8", ls="--") P.ylim(ymin=0) P.xlabel("Lag [hour]") P.ylabel("Error [m s-1]") add_key(1) P.title("Linear lag error model") # Interpolation sph, speh = regrid1d(sp, taxo, method="cellerr", erri=spe, errl=-a, geterr=True) # Time zoom for plot clarity tzoom = (ct1.sub(7, cdtime.Hour), ctimesi[-1]) sp = sp(tzoom) spe = spe(tzoom) sph = sph(tzoom) speh = speh(tzoom)
for iyi in iyis: label = iyi==iyis[0] P.plot(xi, zzi[iyi], 'ob-', markersize=8, label='Original' if label else None) P.plot(xo, zzo[iyi*r], 'or-', markersize=5, lw=.8, label='Interpolated' if label else None) P.legend(loc='best', framealpha=0.5) P.grid() P.title('Section') P.tight_layout() savefigs(code_file_name(ext='_0.png'), verbose=False, pdf=True) # Maps P.figure(figsize=(8, 4)) levels = auto_scale(vmin=zzi.min(), vmax=zzi.max(), nmax=30) P.subplot(121) P.contourf(xxi, yyi, zzi, levels=levels) P.contour(xxi, yyi, zzi, linewidths=0.1, levels=levels, colors='k') add_grid((xxi, yyi), alpha=.3, centers=True) for iyi in iyis: P.axhline(yi[iyi], linestyle='--', color='k') P.title('Original') P.subplot(122) P.contourf(xxo, yyo, zzo, levels=levels) P.contour(xxo, yyo, zzo, linewidths=0.1, levels=levels, colors='k') add_grid((xxo, yyo), alpha=.2, centers=True) for iyi in iyis: P.axhline(yi[iyi], linestyle='--', color='k') P.title('Interpolated') P.tight_layout() savefigs(code_file_name(ext='_1.png'), verbose=False, pdf=True) P.close()