def _plot_colorbar(lats, lons, data, units='', cb_tick_fmt="%.0f", cb_labels=None, extend='both', proj=self._DEFAULT_PROJ, **kwargs): # Draw colorbar fig = plt.figure(figsize=(1.5,2)) ax1 = fig.add_axes([0.05, 0.05, 0.125, 0.85]) cb = mpl.colorbar.ColorbarBase( ax1, cmap=d_cmap, norm=norm, orientation='vertical', drawedges='True', extend=extend, ticks=tick_pos) if cb_labels is None: cb.set_ticklabels([cb_tick_fmt % k for k in cm_edge_values]) else: cb.set_ticklabels(cb_labels) cb.set_label(units, rotation='horizontal', fontsize=6, fontweight='bold') cb.set_ticks(tick_pos) for tick in cb.ax.get_yticklabels(): tick.set_fontsize(6) tick.set_fontweight('bold') plt.savefig(colorbar_filename, dpi=120, transparent=True) plt.close() pngcrush(colorbar_filename)
def plotTidalGauge(outputFilename, saveData=True, **args): """ Plot tidal gauge data """ tidalGaugeId = args['tidalGaugeId'] tidalGaugeName = args['tidalGaugeName'] data = TideGauge(tidalGaugeId) figure = plt.figure() plt.rc('font', size=8) plt.title("Monthly sea levels for " + tidalGaugeName) plt.ylabel("Sea Level Height (metres)") plt.xlabel("Year") ax = figure.gca() ax.grid(True) maxPlot, = ax.plot(data.date, data.maximum, 'r-') meanPlot, = ax.plot(data.date, data.mean_, 'k-') minPlot, = ax.plot(data.date, data.minimum, 'b-') # add legend ax.legend([maxPlot, meanPlot, minPlot], ['Max', 'Mean', 'Min'], ncol=3, loc='lower right') plt.axhline(y=0, color='k') plt.figtext(0.02, 0.02, getCopyright(), fontsize=6) plt.figtext(0.90, 0.05, "0.0 = Tidal Gauge Zero", fontsize=8, horizontalalignment='right') plt.savefig(outputFilename + '.png', dpi=150, bbox_inches='tight', pad_inches=.1) plt.close() pngcrush(outputFilename + '.png') with open(outputFilename + '.csv', 'w') as f: writer = csv.writer(f) writer.writerow(["# Monthly sea levels for " + tidalGaugeName]) writer.writerow(["# Data from tidalGaugeId"]) writer.writerow( ["# Sea Level Height (metres) relative to Tidal Gauge Zero"]) writer.writerow(data.headers[1:]) for row in data: writer.writerow(list(row)[1:])
def _plot_basemap(region, lats, lons, data, units='', cb_tick_fmt="%.0f", cb_labels=None, proj=self._DEFAULT_PROJ, **kwargs): m = Basemap(projection=proj, llcrnrlat=region['lat_min'], llcrnrlon=region['lon_min'], urcrnrlat=region['lat_max'], urcrnrlon=region['lon_max'], resolution='c') m.drawmapboundary(linewidth=0.0, fill_color='0.02') # Convert centre lat/lons to corner values required for pcolormesh lons2 = get_grid_edges(lons) lats2 = get_grid_edges(lats) # Plot data x2, y2 = m(*np.meshgrid(lons2, lats2)) #img = m.pcolormesh(x2, y2, data, shading='flat', cmap=d_cmap, norm=norm) img = m.pcolormesh(x2, y2, data, shading='flat', cmap=basemap_cmap, norm=basemap_norm) img.set_clim(cm_edge_values.min(), cm_edge_values.max()) plt.savefig(region['output_filename'], dpi=120, bbox_inches='tight', pad_inches=0.0) # generate shape file gdal_process(region['output_filename'], region['lon_min'], region['lat_max'], region['lon_max'], region['lat_min']) pngcrush(region['output_filename']) #plot front baseName = os.path.splitext(region['output_filename'])[0] # plt.clf() m.drawmapboundary(linewidth=0.0) if shapefile is not None: if os.path.exists(shapefile + ".shp"): shutil.copyfile(shapefile + '.shp', baseName + '_front.shp') shutil.copyfile(shapefile + '.shx', baseName + '_front.shx') shutil.copyfile(shapefile + '.dbf', baseName + '_front.dbf') front_info = m.readshapefile(shapefile, 'front') marker = '_' if hasattr(m, 'front'): for front in m.front: m.plot(front[0] if front[0] > 0 else front[0] + 360, front[1], color='k', marker=marker, markersize=1, antialiased=False) frontFile = baseName + '_front.png' plt.savefig(frontFile, dpi=120, bbox_inches='tight', pad_inches=0.0, transparent=True) # generate shape file # gdal_process(frontFile, region['lon_min'], # region['lat_max'], # region['lon_max'], # region['lat_min']) # pngcrush(frontFile) # m.drawmapboundary(linewidth=0.0, fill_color=fill_color) # m.fillcontinents(color='#F1EBB7', zorder=7) # Save figure # plt.savefig(region['output_filename'], dpi=150, # bbox_inches='tight', pad_inches=0.0) plt.close()
def _plot_surface_data(lats, lons, data, lat_min, lat_max, lon_min, lon_max, output_filename='noname.png', title='', units='', cm_edge_values=None, cb_tick_fmt="%.0f", cb_labels=None, cb_label_pos=None, colormap_strategy='nonlinear', cmp_name='jet', colors=None, extend='both', fill_color='1.0', plotStyle='contourf', contourLines=True, contourLabels=True, smoothFactor=1, proj=self._DEFAULT_PROJ, product_label_str=None, vlat=None, vlon=None, u=None, v=None, draw_every=1, arrow_scale=10, resolution=None, area=None, boundaryInUse='True', shapefile=None): ''' TODO color map needs to be consilidated into one method. The existing discrete_colormap method is less flexible. Actually, the overall plot method needs to be more flexible. 1. Introduce the colormap strategy discrete: discrete_cmap levels: from_levels_and_colors 2. Depending on the strategy, various combination of the arguments should be passed in. discrete: cmp_name extend cm_edge_values levels: color_array extend cm_edge_values ''' if resolution is None and area is not None: # try and get a resolution from the area default resolution = regions[area][3].get('resolution', None) if resolution is None: # still no resolution? try and guess resolution = guess_resolution(lat_min, lat_max, lon_min, lon_max) m = Basemap(projection=proj, llcrnrlat=lat_min, llcrnrlon=lon_min, urcrnrlat=lat_max, urcrnrlon=lon_max, resolution=resolution) #GAS this was removed because different colour ranges makes comparison difficult #if cm_edge_values is None: # cm_edge_values = get_tick_values(data.min(), data.max(), 10)[0] n_colours = cm_edge_values.size - 1 if colormap_strategy == 'discrete': d_cmap = discrete_cmap(cmp_name, n_colours, extend=extend) norm = None elif colormap_strategy == 'levels': d_cmap, norm = from_levels_and_colors(cm_edge_values, np.array(colors) / 255.0, None, extend=extend) elif colormap_strategy == 'nonlinear': d_cmap, norm = from_levels_and_colors(cm_edge_values, None, cmp_name, extend=extend) #GAS Smoothing section based on smoothFactor if smoothFactor > 1: #if smoothFactor > 1 and (lat_extent>20 or lon_extent>20): size = int(smoothFactor) x, y = np.mgrid[-size:size + 1, -size:size + 1] g = np.exp(-(x**2 / float(size) + y**2 / float(size))) g = g / g.sum() #data=np.ma.masked_less(data,-998)a data[data < -9.9] = 0 data[data > 1000] = 5 data = convolve2d(data, g, mode='same', boundary='symm') #a=ma.masked_less(data,-998) #np.savetxt('/data/comp/raster/filename.txt',data,delimiter=",")a # Plot data x, y = None, None if plotStyle == 'contourf': x, y = m(*np.meshgrid(lons, lats)) img = plt.contourf(x, y, data, levels=cm_edge_values, norm=norm, shading='flat', cmap=d_cmap, extend=extend) elif plotStyle == 'pcolormesh': # Convert centre lat/lons to corner values required for # pcolormesh lons2 = get_grid_edges(lons) lats2 = get_grid_edges(lats) x2, y2 = m(*np.meshgrid(lons2, lats2)) img = m.pcolormesh(x2, y2, data, shading='flat', cmap=d_cmap, norm=norm) # Draw contours if contourLines: if x is None: x, y = m(*np.meshgrid(lons, lats)) #GAS negative contour not to be dashed plt.rcParams['contour.negative_linestyle'] = 'solid' cnt = plt.contour(x, y, data, levels=cm_edge_values, norm=norm, colors='k', linewidths=0.4, hold='on') if contourLabels: plt.clabel(cnt, inline=True, fmt=cb_tick_fmt, fontsize=8) img.set_clim(cm_edge_values.min(), cm_edge_values.max()) # Plot vector data if provided if (u is not None) and (v is not None) and \ (vlat is not None) and (vlon is not None): # Draw vectors if draw_every is not None: draw_vector_plot(m, vlon, vlat, u, v, draw_every=draw_every, arrow_scale=arrow_scale) if shapefile is not None: if os.path.exists(shapefile + ".shp"): front_info = m.readshapefile(shapefile, 'front') marker = '_' if hasattr(m, 'front'): for front in m.front: m.plot(front[0] if front[0] > 0 else front[0] + 360, front[1], color='k', marker=marker, markersize=1, markeredgewidth=1) # Draw land, coastlines, parallels, meridians and add title m.drawmapboundary(linewidth=1.0, fill_color=fill_color) m.drawcoastlines(linewidth=0.5, color='#505050', zorder=8) # m.fillcontinents(color='#F1EBB7', zorder=7) m.fillcontinents(color='0.58', zorder=7) parallels, p_dec_places = get_tick_values(lat_min, lat_max) meridians, m_dec_places = get_tick_values(lon_min, lon_max) m.drawparallels(parallels, labels=[True, False, False, False], fmt='%.' + str(p_dec_places) + 'f', fontsize=6, dashes=[3, 3], color='gray') m.drawmeridians(meridians, labels=[False, False, False, True], fmt='%.' + str(m_dec_places) + 'f', fontsize=6, dashes=[3, 3], color='gray') plt.title(title, fontsize=9) # Draw colorbar ax = plt.gca() divider = make_axes_locatable(ax) cax = divider.append_axes("right", size=0.2, pad=0.3) if cb_label_pos is None: tick_pos = cm_edge_values else: tick_pos = cb_label_pos if boundaryInUse == 'True': cb = plt.colorbar(img, cax=cax, spacing='uniform', drawedges='False', norm=norm, orientation='vertical', extend=extend, ticks=tick_pos) #boundaries=cm_edge_values) else: cb = plt.colorbar(img, cax=cax, spacing='uniform', drawedges='False', orientation='vertical', extend=extend, ticks=tick_pos) if cb_labels is None: cb.set_ticklabels([cb_tick_fmt % k for k in cm_edge_values]) else: cb.set_ticklabels(cb_labels) for tick in cb.ax.get_yticklabels(): tick.set_fontsize(7) cb.set_label(units, fontsize=8) # Patch for graphics bug that affects label positions for # long/narrow plots lat_extent = np.float(lat_max) - np.float(lat_min) lon_extent = np.float(lon_max) - np.float(lon_min) aspect_ratio = abs(lon_extent / lat_extent) if aspect_ratio > 1.7: copyright_label_yadj = -0.25 else: copyright_label_yadj = -0.15 if aspect_ratio < 0.7: copyright_label_xadj = -0.2 product_label_xadj = 1.4 else: copyright_label_xadj = -0.1 product_label_xadj = 1.04 # Draw copyright and product labels box = TextArea(getCopyright(), textprops=dict(color='k', fontsize=6)) copyrightBox = AnchoredOffsetbox( loc=3, child=box, borderpad=0.1, bbox_to_anchor=(copyright_label_xadj, copyright_label_yadj), frameon=False, bbox_transform=ax.transAxes) ax.add_artist(copyrightBox) if product_label_str is not None: box = TextArea(product_label_str, textprops=dict(color='k', fontsize=6)) copyrightBox = AnchoredOffsetbox( loc=4, child=box, borderpad=0.1, bbox_to_anchor=(product_label_xadj, copyright_label_yadj), frameon=False, bbox_transform=ax.transAxes) ax.add_artist(copyrightBox) # Save figure plt.savefig(output_filename, dpi=150, bbox_inches='tight', pad_inches=0.6) plt.close() pngcrush(output_filename)
def _plot_surface_data(lats, lons, data, lat_min, lat_max, lon_min, lon_max, output_filename='noname.png', title='', units='', cm_edge_values=None, cb_tick_fmt="%.0f", cb_labels=None, cb_label_pos=None, colormap_strategy='nonlinear', cmp_name='jet', colors=None, extend='both', fill_color='1.0', plotStyle='contourf', contourLines=True, contourLabels=True, smoothFactor=1, proj=self._DEFAULT_PROJ, product_label_str=None, vlat=None, vlon=None, u=None, v=None, draw_every=1, arrow_scale=10, vector=False, overlay_grid=None, resolution=None, area=None, boundaryInUse='True'): cmArray = customColorMap[cmp_name] if colormap_strategy == 'discrete': n_colours = cm_edge_values.size - 1 d_cmap = discrete_cmap(cmp_name, n_colours, extend) norm = None elif colormap_strategy == 'levels': d_cmap, norm = from_levels_and_colors( customTicks[cm_edge_values], np.array(cmArray) / 255.0) elif colormap_strategy == 'nonlinear': d_cmap, norm = from_levels_and_colors( customTicks[cm_edge_values], None, cmp_name, extend) cm_edge_values = np.array(customTicks[cm_edge_values]) m = Basemap(projection=proj, llcrnrlat=lat_min, llcrnrlon=lon_min, urcrnrlat=lat_max, urcrnrlon=lon_max, resolution='i') # Plot data x2, y2 = m(*np.meshgrid(lons, lats)) img = m.contourf(x2, y2, data, levels=cm_edge_values, cmap=d_cmap, norm=norm, antialiased=True, zorder=0) img.set_clim(cm_edge_values.min(), cm_edge_values.max()) #plot contouring labels if contourLabels: labels = plt.clabel(img, cm_edge_values[::4], inline=True, fmt='%.0f', colors='k', fontsize=5, zorder=2) bbox_props = dict(boxstyle="round", fc="w", ec="w", alpha=0.9) for text in labels: text.set_linespacing(1) text.set_bbox(bbox_props) #plot vectors if vector: every = 10 lons = lons[::every] lats = lats[::every] x2, y2 = m(*np.meshgrid(lons, lats)) if overlay_grid is not None: radians_array = np.radians(overlay_grid) radians_array = np.pi + radians_array radians_array = radians_array[::every, ::every] m.quiver(x2, y2, np.sin(radians_array), np.cos(radians_array), scale=60, zorder=3) # Draw land, coastlines, parallels, meridians and add title m.drawmapboundary(linewidth=1.0, fill_color=fill_color) m.drawcoastlines(linewidth=0.5, color='#505050', zorder=8) m.fillcontinents(color='0.58', zorder=7) parallels, p_dec_places = get_tick_values(lat_min, lat_max) meridians, m_dec_places = get_tick_values(lon_min, lon_max) m.drawparallels(parallels, labels=[True, False, False, False], fmt='%.' + str(p_dec_places) + 'f', fontsize=6, dashes=[3, 3], color='gray') m.drawmeridians(meridians, labels=[False, False, False, True], fmt='%.' + str(m_dec_places) + 'f', fontsize=6, dashes=[3, 3], color='gray') plt.title(title, fontsize=9) # Draw colorbar ax = plt.gca() divider = make_axes_locatable(ax) cax = divider.append_axes("right", size=0.2, pad=0.3) if cb_label_pos is None: tick_pos = cm_edge_values else: tick_pos = cb_label_pos if boundaryInUse == 'True': cb = plt.colorbar( img, cax=cax, # spacing='proportional', spacing='uniform', drawedges='False', orientation='vertical', extend=extend, ticks=tick_pos, boundaries=cm_edge_values) else: cb = plt.colorbar(img, cax=cax, spacing='uniform', drawedges='False', orientation='vertical', extend=extend, ticks=tick_pos) if cb_labels is None: cb.set_ticklabels([cb_tick_fmt % k for k in cm_edge_values]) else: cb.set_ticklabels(cb_labels) for tick in cb.ax.get_yticklabels(): tick.set_fontsize(7) cb.set_label(units, fontsize=8) # Patch for graphics bug that affects label positions for # long/narrow plots lat_extent = np.float(lat_max) - np.float(lat_min) lon_extent = np.float(lon_max) - np.float(lon_min) aspect_ratio = abs(lon_extent / lat_extent) if aspect_ratio > 1.7: copyright_label_yadj = -0.25 else: copyright_label_yadj = -0.10 if aspect_ratio < 0.7: copyright_label_xadj = -0.2 product_label_xadj = 1.4 else: copyright_label_xadj = -0.1 product_label_xadj = 1.04 # Draw copyright and product labels box = TextArea(getCopyright(), textprops=dict(color='k', fontsize=6)) copyrightBox = AnchoredOffsetbox( loc=3, child=box, borderpad=0.1, bbox_to_anchor=(copyright_label_xadj, copyright_label_yadj), frameon=False, bbox_transform=ax.transAxes) ax.add_artist(copyrightBox) if product_label_str is not None: box = TextArea(product_label_str, textprops=dict(color='k', fontsize=6)) copyrightBox = AnchoredOffsetbox( loc=4, child=box, borderpad=0.1, bbox_to_anchor=(product_label_xadj, copyright_label_yadj), frameon=False, bbox_transform=ax.transAxes) ax.add_artist(copyrightBox) # Save figure plt.savefig(output_filename, dpi=150, bbox_inches='tight', pad_inches=0.6) plt.close() pngcrush(output_filename)
def _plot_basemap(region, lats, lons, data, units='', cb_tick_fmt="%.0f", cb_labels=None, proj=self._DEFAULT_PROJ, **kwargs): m = Basemap(projection='cyl', llcrnrlat=region['lat_min'], llcrnrlon=region['lon_min'], urcrnrlat=region['lat_max'], urcrnrlon=region['lon_max'], resolution='i') m.drawmapboundary(linewidth=0.0, fill_color='0.02') # Plot data x2, y2 = m(*np.meshgrid(lons, lats)) # img = m.pcolormesh(x2, y2, data, shading='flat', cmap=d_cmap, norm=norm) #img = m.contourf(x2, y2, data, levels=cm_edge_values, cmap=basemap_cmap, norm=norm, antialiased=True, zorder=0) img = m.contourf(x2, y2, data, levels=cm_edge_values, cmap=basemap_cmap, norm=basemap_norm, antialiased=False, zorder=0) img.set_clim(cm_edge_values.min(), cm_edge_values.max()) #bug798 comment out #img = plt.contour(x2, y2, data, levels=cm_edge_values, colors='w', norm=norm, linewidths=0.5, zorder=1) plt.savefig(region['output_filename'], dpi=120, bbox_inches='tight', pad_inches=0.0) # generate shape file gdal_process(region['output_filename'], region['lon_min'], region['lat_max'], region['lon_max'], region['lat_min']) pngcrush(region['output_filename']) baseName = os.path.splitext(region['output_filename'])[0] #plot contouring labels if clabel: labels = plt.clabel(img, cm_edge_values[::4], inline=True, fmt='%.0f', colors='k', fontsize=5, zorder=2) bbox_props = dict(boxstyle="round", fc="w", ec="w", alpha=0.9) for text in labels: text.set_linespacing(1) text.set_bbox(bbox_props) #extract the overlay grid if vector: plt.clf() m.drawmapboundary(linewidth=0.0) # overlay_grid = kwargs.get('overlay_grid', None) every = 10 lons = lons[::every] lats = lats[::every] x2, y2 = m(*np.meshgrid(lons, lats)) if overlay_grid is not None: radians_array = np.radians(overlay_grid) radians_array = np.pi + radians_array radians_array = radians_array[::every, ::every] m.quiver(x2, y2, np.sin(radians_array), np.cos(radians_array), scale=60, zorder=3) arrowFile = baseName + '_arrow.png' plt.savefig(arrowFile, dpi=120, bbox_inches='tight', pad_inches=0.0, transparent=True) # generate shape file gdal_process(arrowFile, region['lon_min'], region['lat_max'], region['lon_max'], region['lat_min']) pngcrush(arrowFile) # m.drawmapboundary(linewidth=0.0) # m.drawcoastlines(linewidth=0.5, color='#505050', zorder=8) # m.fillcontinents(color='#F1EBB7', zorder=7) # m.fillcontinents(color='0.58', zorder=7) # Save figure # plt.savefig(region['output_filename'], dpi=120, # bbox_inches='tight', pad_inches=0.0) plt.close()
def plotTimeseries(outputFilename, saveData=True, **args): """ Plot altimetry/reconstruction timeseries """ tidalGaugeName = args["tidalGaugeName"] variable = args['variable'] lat = args['lat'] lon = args['lon'] titlePrefix = { 'alt': "Altimetry", 'rec': "Reconstruction", }[variable] grid = SeaLevelSeries(variable, latrange=(lat, lat), lonrange=(lon, lon)) if saveData: with open(outputFilename + ".txt", 'w') as file: writer = csv.writer(file, delimiter='\t') writer.writerow( ('# Sea Level %s for %s' % (titlePrefix, tidalGaugeName), )) if variable == 'alt': writer.writerow([ '# Corrections: IB not removed; seasonal not removed; global trend not removed; GIA removed' ]) writer.writerow(['# Data from CSIRO CMAR']) else: writer.writerow([ '# Corrections: IB removed; seasonal cycle removed; global trend not removed; GIA removed' ]) writer.writerow( ['# Data from CSIRO CMAR, Church and White 2009']) writer.writerow(['Date (YYYY-MM)', '%s (mm)' % titlePrefix]) for date, height in zip(grid.time, grid.data): writer.writerow([date.strftime('%Y-%m'), height]) figure = plt.figure() plt.rc('font', size=8) plt.title("Sea Level %s for %s" % (titlePrefix, tidalGaugeName)) plt.ylabel('Sea-Surface Height (mm)') plt.xlabel('Year') ax = figure.gca() ax.grid(True) ax.set_ylim(-350, 350) ax.plot(grid.time, grid.data, 'b-') plt.axhline(y=0, color='k') if variable == 'alt': PRODUCT_NAME = """ Data from CSIRO CMAR IB not removed; seasonal not removed; global trend not removed; GIA removed """ else: PRODUCT_NAME = """ Data from CSIRO CMAR IB removed; seasonal cycle removed; global trend not removed; GIA removed """ plt.figtext(0.02, 0.02, getCopyright(), fontsize=6) plt.figtext(0.90, 0.02, PRODUCT_NAME.strip(), fontsize=6, horizontalalignment='right') plt.savefig(outputFilename + ".png", dpi=150, bbox_inches='tight', pad_inches=.1) plt.close() pngcrush(outputFilename + ".png") return 0
def plot_BRAN_depth_slice(depths, lats, lons, zonal_data, meridional_data, lats_all, lons_all, data_sf, lat_cnt, lon_cnt, output_filename='noname.png', title='', units='m/s', cb_ticks=None, cb_tick_fmt="%.0f", cmp_name='jet', proj='cyl', product_label_str=None): fg = plt.figure() gs = mpl.gridspec.GridSpec(2,5) ax1=plt.subplot(gs[1,0:2]) n_colours = cb_ticks.size - 1 d_cmap = discrete_cmap(cmp_name, n_colours) # Draw contour x, y = np.meshgrid(lons, depths) ctr = plt.contour(x, y, zonal_data, levels=cb_ticks, colors='k', linewidths=0.4) plt.clabel(ctr, inline=True, fmt=cb_tick_fmt, fontsize=8) lons2 = get_grid_edges(lons) depths2 = get_grid_edges(depths) # Plot data x2, y2 = np.meshgrid(lons2, depths2) img = plt.pcolormesh(x2, y2, zonal_data, shading='flat', cmap=d_cmap, vmin=cb_ticks.min(), vmax=cb_ticks.max()) ax = plt.gca() ax.set_ylim(0,300) ax.set_ylim(ax.get_ylim()[::-1]) ax.xaxis.set_major_formatter(mpl.ticker.FuncFormatter(format_longitude)) ax.tick_params(axis='x', labelsize=8) ax.tick_params(axis='y', labelsize=9) plt.ylabel("Depth (m)", fontsize=10) plt.xlabel("Longitude", fontsize=10) plt.subplots_adjust(right=1.0) ax2=plt.subplot(gs[1,2:4]) # Draw contour x, y = np.meshgrid(lats, depths) ctr = plt.contour(x, y, meridional_data, levels=cb_ticks, colors='k', linewidths=0.4) plt.clabel(ctr, inline=True, fmt=cb_tick_fmt, fontsize=8) lats2 = get_grid_edges(lats) depths2 = get_grid_edges(depths) # Plot data x2, y2 = np.meshgrid(lats2, depths2) img = plt.pcolormesh(x2, y2, meridional_data, shading='flat', cmap=d_cmap, vmin=cb_ticks.min(), vmax=cb_ticks.max()) ax = plt.gca() ax.set_ylim(0,300) ax.set_ylim(ax.get_ylim()[::-1]) ax.set_yticklabels(['']) ax.xaxis.set_major_formatter(mpl.ticker.FuncFormatter(format_latitude)) ax.tick_params(axis='x', labelsize=8) plt.xlabel("Latitude", fontsize=10) ax3=plt.subplot(gs[0,:-1]) lat_min = lats[0] - 5.0 lat_max = lats[-1] + 5.0 lon_min = lons[0] - 20.0 lon_max = lons[-1] + 20.0 m = Basemap(projection=proj, llcrnrlat=lat_min, llcrnrlon=lon_min, \ urcrnrlat=lat_max, urcrnrlon=lon_max, resolution='h') m.drawcoastlines(linewidth=0.5, zorder=8, color='#505050') m.fillcontinents(color='#F1EBB7', zorder=7) plt.hold(True) plt.plot([lon_cnt,lon_cnt],[lats[0],lats[-1]], color='k', linestyle='--', linewidth=2, zorder=8) plt.plot([lons[0],lons[-1]],[lat_cnt,lat_cnt], color='k', linestyle='--', linewidth=2, zorder=8) # Convert centre lat/lons to corner values required for pcolormesh lons2 = get_grid_edges(lons_all) lats2 = get_grid_edges(lats_all) # Plot data x2, y2 = m(*np.meshgrid(lons2, lats2)) img = m.pcolormesh(x2, y2, data_sf, shading='flat', cmap=d_cmap, vmin=cb_ticks.min(), vmax=cb_ticks.max()) plt.title(title, fontsize=12) parallels, p_dec_places = get_tick_values(lat_min, lat_max, 3) meridians, m_dec_places = get_tick_values(lon_min, lon_max) m.drawparallels(parallels, labels=[True, False, False, False], fmt='%.' + str(p_dec_places) + 'f', fontsize=8, dashes=[3, 3], color='gray') m.drawmeridians(meridians, labels=[False, False, False, True], fmt='%.' + str(m_dec_places) + 'f', fontsize=8, dashes=[3, 3], color='gray') cbaxes = fg.add_axes([0.85, 0.15, 0.03, 0.7]) # setup colorbar axes. cb = fg.colorbar(img, spacing='proportional', drawedges='True', cax=cbaxes,orientation='vertical', extend='both', ticks=cb_ticks) cb.set_ticklabels([cb_tick_fmt % k for k in cb_ticks]) cb.set_label(units) box = TextArea(getCopyright(), textprops=dict(color='k', fontsize=6)) copyrightBox = AnchoredOffsetbox(loc=3, child=box, bbox_to_anchor=(-1.3, -0.45), frameon=False, bbox_transform=ax.transAxes) ax.add_artist(copyrightBox) box = TextArea(product_label_str, textprops=dict(color='k', fontsize=8)) copyrightBox = AnchoredOffsetbox(loc=4, child=box, bbox_to_anchor=(1.4, -0.45), frameon=False, bbox_transform=ax.transAxes) ax.add_artist(copyrightBox) plt.savefig(output_filename, dpi=150, bbox_inches='tight', pad_inches=1.) plt.close() pngcrush(output_filename) return
def _plot_basemap(region, lats, lons, data, units='', cb_tick_fmt="%.0f", cb_labels=None, contourLines=True, extend='both', contourLabels=False, proj=self._DEFAULT_PROJ, **kwargs): m = Basemap(projection=proj, llcrnrlat=region['lat_min'], llcrnrlon=region['lon_min'], urcrnrlat=region['lat_max'], urcrnrlon=region['lon_max'], resolution='c') # Convert centre lat/lons to corner values required for pcolormesh lons2 = get_grid_edges(lons) lats2 = get_grid_edges(lats) # Plot data m.drawmapboundary(linewidth=0.0, fill_color='0.02') x2, y2 = m(*np.meshgrid(lons2, lats2)) img = m.pcolormesh(x2, y2, data, shading='flat', cmap=basemap_cmap, norm=basemap_norm) img.set_clim(cm_edge_values.min(), cm_edge_values.max()) plt.savefig(region['output_filename'], dpi=120, bbox_inches='tight', pad_inches=0.0) # generate shape file gdal_process(region['output_filename'], region['lon_min'], region['lat_max'], region['lon_max'], region['lat_min']) pngcrush(region['output_filename']) baseName = os.path.splitext(region['output_filename'])[0] # Draw contours if contourLines: plt.clf() m.drawmapboundary(linewidth=0.0) x, y = m(*np.meshgrid(lons, lats)) #GAS negative contour not to be dashed plt.rcParams['contour.negative_linestyle'] = 'solid' #cnt = plt.contour(x, y, data,levels = [29], colors=('purple',),linestyles=('-',),linewidths=(1,)) cnt = plt.contour(x, y, data, levels=[29], colors=('k', ), linestyles=('-', ), linewidths=(1, ), antialiased=False) if contourLabels: plt.clabel(cnt, inline=True, fmt=cb_tick_fmt, fontsize=8, colors='purple') contourFile = baseName + '_contour.png' plt.savefig(contourFile, dpi=120, bbox_inches='tight', pad_inches=0.0, transparent=True) # generate shape file gdal_process(contourFile, region['lon_min'], region['lat_max'], region['lon_max'], region['lat_min']) pngcrush(contourFile) #extract the overlay grid if overlay_grid is not None: plt.clf() m.drawmapboundary(linewidth=0.0) x, y = m(*np.meshgrid(overlay_grid.lons, overlay_grid.lats)) #cnt = plt.contour(x, y, overlay_grid.data,levels = [29], colors=('g',),linestyles=('-',),linewidths=(1,)) cnt = plt.contour(x, y, overlay_grid.data, levels=[29], colors=('k', ), linestyles=('-', ), linewidths=(1, ), antialiased=False) if contourLabels: plt.clabel(cnt, inline=True, fmt=cb_tick_fmt, fontsize=8, colors='g', zorder=3) normalFile = baseName + '_normal.png' plt.savefig(normalFile, dpi=120, bbox_inches='tight', pad_inches=0.0, transparent=True) # generate shape file gdal_process(normalFile, region['lon_min'], region['lat_max'], region['lon_max'], region['lat_min']) pngcrush(normalFile) # m.drawmapboundary(linewidth=0.0, fill_color=fill_color) # m.fillcontinents(color='0.58', zorder=7) # Save figure # plt.savefig(region['output_filename'], dpi=150, # bbox_inches='tight', pad_inches=0.0) plt.close()
def main(): queryMap = urlparse.parse_qs(os.environ["QUERY_STRING"]) # update the query string if 'map' in queryMap: map = queryMap['map'][0] queryMap['map'] = util.get_resource('maps', map + '.map') # if map == 'raster': # queryMap['eastFileName'] = os.path.join(config['outputDir'], queryMap['eastmap'][0]) # queryMap['westFileName'] = os.path.join(config['outputDir'], queryMap['westmap'][0]) # rasters = queryMap['raster'][0].split(' ') # # MAP_EAST_PATTERN = re.compile('east.png$') # MAP_WEST_PATTERN = re.compile('west.png$') # # for file in rasters: # filename = os.path.basename(file) # # if MAP_EAST_PATTERN.search(filename): # queryMap['eastFileName'] = os.path.join(config['outputDir'], # filename) # elif MAP_WEST_PATTERN.search(filename): # queryMap['westFileName'] = os.path.join(config['outputDir'], # filename) if map in ['mean', 'mean_sub', 'anom', 'dec', 'trend', 'hs', 'chlo', 'coral_daily', 'coral_outlook',\ 'wav', 'wnd', 'grey', 'poamasla', 'current', 'mur', 'contour', 'normal', 'salt', 'uv', 'front',\ 'height']: queryMap['base'] = config['outputDir'] + os.path.basename( os.path.splitext(queryMap['mapimg'][0])[0]) queryString = urllib.urlencode(queryMap, True) m = hashlib.sha256() m.update(queryString) hash = m.hexdigest() etag = '"%s"' % hash filename = os.path.join(config['outputDir'], 'maptiles', '%s.png' % hash) # determine whether or not we need to call into mapserver try: mapimg = os.path.join(config['outputDir'] + os.path.basename(queryMap['mapimg'][0])) cache_mtime = os.path.getmtime(filename) call_mapserver = cache_mtime < os.path.getmtime(mapimg) # cache_mtime < os.path.getmtime(queryMap['mapimg'][0]) except os.error: call_mapserver = True except KeyError: call_mapserver = not os.path.exists(filename) # call mapserver if required if call_mapserver: with os.tmpfile() as tmpfile: os.environ['QUERY_STRING'] = queryString subprocess.call(config['mapservPath'], stdout=tmpfile) # remove the first two lines from the tmpfile to make it a png tmpfile.seek(0) content_type = tmpfile.readline().strip().split(' ', 2)[1] # check to see if mapserver generated an error if content_type != 'image/png': print 'Status: 500 Internal Server Error' print 'Content-Type: %s' % content_type print 'X-Portal-Version: %s' % util.__version__ print print tmpfile.read() return tmpfile.readline() with open(filename, 'wb') as cachefile: # copy it to the output file shutil.copyfileobj(tmpfile, cachefile) pngcrush(filename) else: try: if os.environ['HTTP_IF_NONE_MATCH'] == etag: print 'Status: 304 Not Modified' print 'X-Portal-Version: %s' % util.__version__ print return except KeyError: pass # play the cached file to the web server with open(filename, 'rb') as cachefile: print 'Status: 200 Ok' print 'Content-Type: image/png' print 'ETag: %s' % etag print 'Cache-Control: max-age=3600' # FIXME: value? print 'X-Tile-Cached:', 'no' if call_mapserver else 'yes' print 'X-Portal-Version: %s' % util.__version__ print shutil.copyfileobj(cachefile, sys.stdout)
def _plot_surface_data(lats, lons, data, time, lat_min, lat_max, lon_min, lon_max, output_filename='noname.png', title='', units='', cm_edge_values=None, cb_tick_fmt="%.0f", cb_labels=None, cb_label_pos=None, colormap_strategy='nonlinear', cmp_name='jet', colors=None, extend='both', fill_color='1.0', plotStyle='contourf', contourLines=True, contourLabels=True, smoothFactor=1, proj=self._DEFAULT_PROJ, product_label_str=None, vlat=None, vlon=None, u=None, v=None, draw_every=1, arrow_scale=10, resolution=None, area=None, boundaryInUse='True'): d_cmap, norm = from_levels_and_colors(cm_edge_values, None, cmp_name, extend=extend) m = Basemap(projection=proj, llcrnrlat=lat_min, llcrnrlon=lon_min, urcrnrlat=lat_max, urcrnrlon=lon_max, resolution='i') # Plot data x2, y2 = m(*np.meshgrid(lons, lats)) u = data[0][time] v = data[1][time] mag = np.sqrt(u**2 + v**2) img = m.pcolormesh(x2, y2, mag, shading='flat', cmap=d_cmap, norm=norm) img.set_clim(cm_edge_values.min(), cm_edge_values.max()) #plot vectors if area == 'pac': every = 50 scale = 10 else: every = 10 scale = 10 lons = lons[::every] lats = lats[::every] x2, y2 = m(*np.meshgrid(lons, lats)) u2 = u[::every, ::every] v2 = v[::every, ::every] rad = np.arctan2(v2, u2) m.quiver(x2, y2, np.cos(rad), np.sin(rad), scale=scale, zorder=3, scale_units='inches', pivot='middle') # Draw land, coastlines, parallels, meridians and add title m.drawmapboundary(linewidth=1.0, fill_color=fill_color) m.drawcoastlines(linewidth=0.5, color='#505050', zorder=8) m.fillcontinents(color='0.58', zorder=7) parallels, p_dec_places = get_tick_values(lat_min, lat_max) meridians, m_dec_places = get_tick_values(lon_min, lon_max) m.drawparallels(parallels, labels=[True, False, False, False], fmt='%.' + str(p_dec_places) + 'f', fontsize=6, dashes=[3, 3], color='gray') m.drawmeridians(meridians, labels=[False, False, False, True], fmt='%.' + str(m_dec_places) + 'f', fontsize=6, dashes=[3, 3], color='gray') plt.title(title, fontsize=9) # Draw colorbar ax = plt.gca() divider = make_axes_locatable(ax) cax = divider.append_axes("right", size=0.2, pad=0.3) if cb_label_pos is None: tick_pos = cm_edge_values else: tick_pos = cb_label_pos if boundaryInUse == 'True': cb = plt.colorbar( img, cax=cax, # spacing='proportional', spacing='uniform', drawedges='False', orientation='vertical', extend=extend, ticks=tick_pos, boundaries=cm_edge_values) else: cb = plt.colorbar(img, cax=cax, spacing='uniform', drawedges='False', orientation='vertical', extend=extend, ticks=tick_pos) if cb_labels is None: cb.set_ticklabels([cb_tick_fmt % k for k in cm_edge_values]) else: cb.set_ticklabels(cb_labels) for tick in cb.ax.get_yticklabels(): tick.set_fontsize(7) cb.set_label(units, fontsize=8) # Patch for graphics bug that affects label positions for # long/narrow plots lat_extent = np.float(lat_max) - np.float(lat_min) lon_extent = np.float(lon_max) - np.float(lon_min) aspect_ratio = abs(lon_extent / lat_extent) if aspect_ratio > 1.7: copyright_label_yadj = -0.25 else: copyright_label_yadj = -0.10 if aspect_ratio < 0.7: copyright_label_xadj = -0.2 product_label_xadj = 1.4 else: copyright_label_xadj = -0.1 product_label_xadj = 1.04 # Draw copyright and product labels box = TextArea(getCopyright(), textprops=dict(color='k', fontsize=6)) copyrightBox = AnchoredOffsetbox( loc=3, child=box, borderpad=0.1, bbox_to_anchor=(copyright_label_xadj, copyright_label_yadj), frameon=False, bbox_transform=ax.transAxes) ax.add_artist(copyrightBox) if product_label_str is not None: box = TextArea(product_label_str, textprops=dict(color='k', fontsize=6)) copyrightBox = AnchoredOffsetbox( loc=4, child=box, borderpad=0.1, bbox_to_anchor=(product_label_xadj, copyright_label_yadj), frameon=False, bbox_transform=ax.transAxes) ax.add_artist(copyrightBox) # Save figure plt.savefig(output_filename, dpi=150, bbox_inches='tight', pad_inches=0.6) plt.close() pngcrush(output_filename)
def _plot_basemap(region, lats, lons, data, time, units='', cb_tick_fmt="%.0f", cb_labels=None, proj=self._DEFAULT_PROJ, **kwargs): m = Basemap(projection=proj, llcrnrlat=region['lat_min'], llcrnrlon=region['lon_min'], urcrnrlat=region['lat_max'], urcrnrlon=region['lon_max'], resolution='i') # Plot data m.drawmapboundary(linewidth=1.0, fill_color='0.02') x2, y2 = m(*np.meshgrid(lons, lats)) u = data[0][time] v = data[1][time] mag = np.sqrt(u**2 + v**2) #img = m.pcolormesh(x2, y2, mag, shading='flat', cmap=d_cmap, norm=norm) img = m.pcolormesh(x2, y2, mag, shading='flat', cmap=basemap_cmap, norm=basemap_norm) img.set_clim(cm_edge_values.min(), cm_edge_values.max()) plt.savefig(region['output_filename'], dpi=120, bbox_inches='tight', pad_inches=0.0) # generate shape file gdal_process(region['output_filename'], region['lon_min'], region['lat_max'], region['lon_max'], region['lat_min']) pngcrush(region['output_filename']) #plot contouring labels if clabel: labels = plt.clabel(img, cm_edge_values[::4], inline=True, fmt='%.0f', colors='k', fontsize=5, zorder=2) bbox_props = dict(boxstyle="round", fc="w", ec="w", alpha=0.9) for text in labels: text.set_linespacing(1) text.set_bbox(bbox_props) #plot vectors baseName = os.path.splitext(region['output_filename'])[0] plt.clf() m.drawmapboundary(linewidth=0.0) if regionName == 'pac': every = 50 scale = 10 else: every = 10 scale = 10 lons = lons[::every] lats = lats[::every] x2, y2 = m(*np.meshgrid(lons, lats)) u2 = u[::every, ::every] v2 = v[::every, ::every] # u2 = u[::every, ::every] # v2 = v[::every, ::every] rad = np.arctan2(v2, u2) m.quiver(x2, y2, np.cos(rad), np.sin(rad), scale=scale, zorder=3, scale_units='inches', pivot='middle') arrowFile = baseName + '_arrow.png' plt.savefig(arrowFile, dpi=120, bbox_inches='tight', pad_inches=0.0, transparent=True) # generate shape file gdal_process(arrowFile, region['lon_min'], region['lat_max'], region['lon_max'], region['lat_min']) pngcrush(arrowFile) # m.drawmapboundary(linewidth=0.0) # m.drawcoastlines(linewidth=0.5, color='#505050', zorder=8) # m.fillcontinents(color='#F1EBB7', zorder=7) # m.fillcontinents(color='0.58', zorder=7) # Save figure # plt.savefig(region['output_filename'], dpi=120, # bbox_inches='tight', pad_inches=0.0) plt.close()
def RosePlot(opath, wdir, units, lat, lon, ilat, ilon, xstr, title, var, binwd): '''Plots a windrose of angular values in wdir, with annotations Arguments: wdir -- an array of angles in the range (0,360). (NumPy Array) units -- measurements units for wdir variable. (float) lat -- latitude of point. (float) lon -- longitude of point. (float) xstr -- x-axis label. (string) title -- plot title. (string) var -- the variable that is measured by wdir. (string) binwd -- width of histogram bins. (float) Returns: rosefig -- An annotated windrose of wdir values. ''' #set number of bins and max bin value. N,wdnbin,wdmax = 8,8,2*np.pi degree = ur'\u00b0' if wdir[0] == -999.0: raise LandError() #flip directions as WWIII direction are given in meteorological convention. if var == "Dm": wdir = conv.dirflip(wdir) wdir = conv.dirshift(wdir) else: wdir = conv.dirshift(wdir) #make a basic histogram of wave directions over the range (-22.5,337.5). try: whist = np.histogram(wdir, wdnbin, (-22.5,337.5), density=False) except TypeError: # support older numpy whist = np.histogram(wdir, wdnbin, (-22.5,337.5), normed=False) #calculate mean bearing meanb = meanbearing(wdir) #force square figure and square axes looks better for polar rosefig = figure(figsize=(10,7.5)) ax = rosefig.add_axes([0.075, 0.1, 0.6, 0.725], polar=True) #plot radial gridlines at seperations of 45 degrees plt.thetagrids((0,45,90,135,180,225,270,315),('N', '45' + degree, 'E', '135' + degree, 'S', '215' + degree, 'W', '315' + degree),frac = 1.1) #rotate axis to zero at North and set direction of increasing angle clockwise. plt.rgrids((0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9), angle=0) plt.ylim(0.0,1.0) try: ax.set_theta_direction(-1) ax.set_theta_zero_location('N') except: # hack around older matplotlib pass #lines, labels = plt.rgrids() #set Nmax Nmax = 1 #initialize prob and perc arrays prob = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] perc = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] #variable to normalize bins so that they sum to 1 (probability) normalizer = 1/float(len(wdir)) #normalize histogram to calculate probabilities and percentages for i in range(0,N): prob[i] = float(whist[0][i])*normalizer perc[i] = round(100*prob[i],2) units = '%' theta = np.linspace(-2*np.pi/(2*N), 2*np.pi-2*np.pi/(2*N), N, endpoint=False) #some basic settings for graphics width = 2*np.pi/N length = np.size(wdir) #rounded mean bearing for labelling purposes meanbr = round((meanb*180)/(np.pi),1) #set up bar chart properties bars = ax.bar(theta, prob, width, bottom=0.0) my_cmap = hc.decile_rose() #plot a line to indicate mean bearing ax.arrow(0, 0, meanb, 1.0, edgecolor = 'r', facecolor = 'r', lw='3') #plot bar chart of histogram in theta space for r,bar in zip(prob, bars): bar.set_facecolor(my_cmap(r)) bar.set_alpha(0.5) lpack.rosepack(title) formilat,formilon = NESWformat(ilat,ilon) formlat,formlon = NESWformat(lat,lon) #various annotations to plot plt.figtext(0.76, 0.82,'Probabilities:', fontsize = 10, weight = 550) plt.figtext(0.76, 0.575,'Location & Plot Data:', fontsize=10, weight = 550) plt.figtext(0.76, 0.51, 'Input Lat/Lon:\n %s %s' % (formilat,formilon), fontsize=10) plt.figtext(0.76, 0.46, 'Grid Lat/Lon:\n %s %s' % (formlat, formlon), fontsize=10) plt.figtext(0.76, 0.425, 'Data points: %s' % length, fontsize=10) plt.figtext(0.76, 0.4, 'Bins: %s' % int(N), fontsize=10) plt.figtext(0.76, 0.375, 'Bin Width: %s %s' % (binwd, degree), fontsize=10) plt.figtext(0.76, 0.275, 'Mean Bearing: %s%s %s' % (meanbr, degree,'T'), color='r', fontsize=10) #display wave direction percentages for 8 primary compass points in the windrose plt.figtext(0.76, 0.325, 'Directional Statistics:', fontsize=10, weight=550) plt.figtext(0.76, 0.25, 'North: %s %s' % (perc[0], units), fontsize=10) plt.figtext(0.76, 0.225, 'North East: %s %s' % (perc[1], units), fontsize=10) plt.figtext(0.76, 0.2, 'East: %s %s' % (perc[2], units), fontsize=10) plt.figtext(0.76, 0.175, 'South East: %s %s' % (perc[3], units), fontsize=10) plt.figtext(0.76, 0.15, 'South: %s %s' % (perc[4], units), fontsize=10) plt.figtext(0.76, 0.125, 'South West: %s %s' % (perc[5], units), fontsize=10) plt.figtext(0.76, 0.1, 'West: %s %s' % (perc[6], units), fontsize=10) plt.figtext(0.76, 0.075, 'North West: %s %s' % (perc[7], units), fontsize=10) #Bureau of Meteorology Copyright plt.figtext(0.58, 0.03, u'WAVEWATCH III$^{\u24C7}$', fontsize=10) plt.figtext(0.02, 0.02, getCopyright(), fontsize=8) #define image name imgname = opath + '.png' #write image file plt.savefig(imgname) plt.close() pngcrush(imgname) return
def HistPlot(opath, wheight, units, lat, lon, ilat, ilon, xstr, title, var, binwd): '''Returns a normalized, annotated histogram for values of wheight Arguments: wheight -- an array of positive doubles. (NumPy Array) units -- units associated with wheight. (string) lat -- latitude of the point. (float) lon -- longitude of the point. (float) xstr -- x-axis label. (string) title -- title of the plot. (string) var -- variable associated with the values in wheight. (string) binwd -- width of histogram bins. (float) Returns: histfig -- An annotated histogram of wheight values. ''' #calculate the values of some pertinent statistical quantities wavg = np.average(wheight) wavgr = round(wavg,2) maxwave = round(np.max(wheight),2) minwave = round(np.min(wheight),2) imaxwave = int(maxwave)+ 1 iminwave = int(minwave)- 1 Nmax = imaxwave Nmin = iminwave length = np.size(wheight) debug = False #calculate range of data #binthresh = maxwave - minwave #Error message if selected coordinates are out of range or on land if maxwave == -999.0: raise LandError() #alter bin width depending on range of data #if binthresh < 10: # binwd = 0.1 #elif binthresh > 10: # binwd = 0.2 #else: # print "Error" #bins per unit and total number of bins binperunit = float(1/binwd) binnum = (Nmax - Nmin)*binperunit initial = round(0.001,3) final = round(Nmax+0.001,3) #histogram of selected variable try: whist,wbins = np.histogram(wheight, Nmax * binperunit, (initial, final), density=True) except TypeError: # support older numpy whist,wbins = np.histogram(wheight, Nmax * binperunit, (initial, final), normed=True) #print wbins #calculate max and mins of histogram maxy = np.max(whist*binwd) maxx = np.max(wheight) minx = np.min(wheight) #do some basic statistics on histogram, std. deviation and quartiles. q1 = round(sci.stats.scoreatpercentile(wheight,25),1) q3 = round(sci.stats.scoreatpercentile(wheight,75),1) x=linspace(0,maxx+0.5,Nmax*binperunit) #set up figure histfig=plt.figure(figsize = (10,7.5)) histax=histfig.add_axes([0.1,0.1,0.675,0.75]) plt.xlim(0,maxx+0.5) plt.ylim(0,maxy+0.01) whistnorm = whist*binwd #np.hist(wheight,100,(0,Nmax),color='b',normed=True, histtype = 'stepfille) #set up bar chart properties bars=plt.bar(wbins[:-1],whistnorm,binwd,0) if debug == True: FILE = open('/home/jsmith/Test/verification/WW3' + '_' + str(lat) + '_' + str(lon) + '_' + var + '_array.txt', "w") FILE.writelines(list( "%s \n" % i for i in whist)) FILE.close() my_cmap = hc.quartile_colors(wheight,wavg,Nmax,binwd) #bar chart of histogram for r,bar in zip(wbins[:-1], bars): bar.set_facecolor(my_cmap(r/Nmax)) bar.set_alpha(0.5) #specify plot properties and plot guassian kde and average line plt.xlim(minx-0.5,maxx+0.5) plt.ylim(0,maxy+0.01) #histax.plot(x, approximate_pdf(x), color = 'black', linewidth=3, alpha=1) histax.axvline(wavg, color='r', lw='3') #choose which legend to display based on variable if var == 'Hs': lpack.heightpack(title,wavg) #plt.figtext(0.79,0.175, 'Rogue Wave Height: %s %s' % (2*wavgr,units), fontsize = 10, color = 'm') elif var == 'Tm': lpack.timepack(title) formilat, formilon = NESWformat(ilat,ilon) formlat, formlon = NESWformat(lat,lon) #specify graph grid properties xticks = [10,20,30,40,50,60,70,80,90,100] plt.xticks = xticks, [('%s' % str(x/10))] plt.grid(True) #x,y axis labels plt.xlabel('%s (%s)' % (xstr,units), fontsize=12) plt.ylabel('Frequency', fontsize=12) #various annotations for graphics plt.figtext(0.79, 0.775, 'Distribution:', fontsize=10, weight=550) plt.figtext(0.79, 0.615, 'Location & Plot Data:',fontsize = 10, weight = 550) plt.figtext(0.79, 0.55, 'Input Lat/Lon:\n %s %s' % (formilat,formilon), fontsize=10) plt.figtext(0.79, 0.50, 'Grid Lat/Lon:\n %s %s' % (formlat,formlon), fontsize=10) plt.figtext(0.79, 0.465, 'Data points: %s' % length ,fontsize=10) plt.figtext(0.79, 0.44, 'Bins: %s' % int(binnum), fontsize=10) plt.figtext(0.79, 0.415, 'Bin Width: %s %s' % (binwd,units), fontsize=10) plt.figtext(0.79, 0.365, 'Statistical Information:', fontsize=10, weight=550) plt.figtext(0.79, 0.265, 'Mean: %s %s' % (round(wavgr,1), units), color='r', fontsize=10) plt.figtext(0.79, 0.315, 'Max: %s %s' % (round(maxwave,1), units), fontsize=10) plt.figtext(0.79, 0.215, 'Min: %s %s' % (round(minwave,1), units), fontsize=10) plt.figtext(0.79, 0.24,'25th Percentile: %s %s' % (round(q1,1),units), fontsize=10) plt.figtext(0.79, 0.29,'75th Percentile: %s %s' % (round(q3,1),units), fontsize=10) #Bureau of Meteorology Copyright plt.figtext(0.68, 0.03, u'WAVEWATCH III$^{\u24C7}$', fontsize=10) plt.figtext(0.02, 0.02, getCopyright(), fontsize=8) #define image name imgname = opath + '.png' #write image file plt.savefig(imgname) plt.close() pngcrush(imgname) return