Seaborn 问题地图上的灰度前景未正确缩放

Question

嗨，下面的代码我获得了一个不适合地图的灰度前景 enlignetnin 知道如何纠正吗？

import sys
import datetime as dt
from datetime import datetime
from datetime import date
import numpy as np
import pandas as pd
import xarray as xr
import regionmask
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import seaborn as sns
import io
import imageio
from os import listdir
from os.path import isfile, join
from tqdm import tqdm  
import warnings
warnings.filterwarnings("ignore")  

today = date.today()  
print(today)
class compute_maps:
    def __init__(self):
        self.status = None
        self.data = None
  
    def max_normalize(self, x):
        return (x - x.min()) / (x.max() - x.min())

    def progressbar(self, it, prefix="", size=60, file=sys.stdout):
        count = len(it)
        def show(j):
            x = int(size*j/count)
            file.write("%s[%s%s] %i/%i\r" % (prefix, "#"*x, "."*(size-x), j, count))
            file.flush()
        show(0)
        for i, item in enumerate(it):
            yield item
            show(i+1)
        file.write("\n")
        file.flush()
        return None
    
    def findlatestdateofcamsdata(self, mypath):
            dates = []
            onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]
            for filename in onlyfiles:
                dates.append(pd.to_datetime(filename[14:24]))
                print(dates)
            
            if dates != []:
                return (dates, max(dates))
            else:
                return (dates, dt.date.today() - pd.Timedelta("3 Y"))

    def compute_maps(self):
        currentDate = pd.to_datetime(today, dayfirst = False)
        grayDark = '#e1e1e1'
        grayLight = '#404040'

        sns.set(
        context     = 'paper',
        style       = 'dark',
        palette     = 'muted',
        color_codes = True,
            font_scale  = 2.0,
        font        = 'sans-serif',
        rc={
            'axes.edgecolor'    : grayDark
            ,'text.color'       : grayDark
            ,'axes.labelcolor'  : grayDark
            ,'xtick.color'      : grayDark
            ,'ytick.color'      : grayDark
                ,'figure.facecolor' : grayLight
                ,'axes.facecolor'   : grayLight
                ,'savefig.facecolor': grayLight
            }
        )

        print('Data pre-processing:', flush=True)

        countryEU = regionmask.defined_regions.natural_earth_v5_0_0
    
        times = ["0 days","1 days","2 days", "3 days",'4 days']
        counter = 0
        images1 = []
        images2 = []
        images3 = []
        images4 = []
        images5 = []
        images6 = []
        images7 = []

        risk1Maps = []
        risk2Maps = []
        risk3Maps = []
        risk4Maps = []
        risk5Maps = []
        risk6Maps = []
        newhospipredmaps = []
        currentDatestring = currentDate.strftime('%Y-%m-%d')

        # covidExtraToCom[['1MMaxpm25','1MMaxpm10','1MMaxo3','1MMaxno2','1MMaxco','pm107davg','pm257davg','o37davg','no27davg','co7davg','pm101Mavg',\
        #   'pm251Mavg','o31Mavg','no21Mavg','co1Mavg','population','hospi','CovidPosTest' ]] \
        #     = [dfpollution3[dfpollution3['numero'] == depNum].reindex(columns = ['1MMaxpm25','1MMaxpm10','1MMaxo3','1MMaxno2','1MMaxco','pm107davg','pm257davg','o37davg','no27davg','co7davg','pm101Mavg',\
        #   'pm251Mavg','o31Mavg','no21Mavg','co1Mavg','idx','hospi','CovidPosTest' ]).values.squeeze() for depNum in covidExtraToCom['dep']]

        for j in tqdm(times):
            print(j)
            # filename = "/home/ludo915/code/covsco/predictions/fr/" + currentDatestring + "_predictions_for_day_" + str(counter) +".csv"
            # newhospipredictionsdf = pd.read_csv(filename)
            # print(filename + " Read!")

            filePath = '/home/ludo915/eco-tech-h2gam/cams/fr/forecast/'
            latestfiledatestring = self.findlatestdateofcamsdata(filePath)[1].strftime('%Y-%m-%d')
            fileName = "cams-forecast-"+latestfiledatestring +".nc"
            pollutants = xr.open_dataset(filePath + fileName).sel(time = j)

            pm25 = pollutants.pm2p5_conc
            o3 = pollutants.o3_conc
            no2 = pollutants.no2_conc
            co = pollutants.co_conc
            pm10 = pollutants.pm10_conc
            so2 = pollutants.so2_conc

            pm25 = pm25.drop('time').squeeze().drop('level').squeeze()
            o3 = o3.drop('time').squeeze().drop('level').squeeze()
            no2 = no2.drop('time').squeeze().drop('level').squeeze()
            co = co.drop('time').squeeze().drop('level').squeeze()
            pm10 = pm10.drop('time').squeeze().drop('level').squeeze()
            so2 = so2.drop('time').squeeze().drop('level').squeeze()

            pm25.coords['longitude'] = (pm25.coords['longitude'] + 180) % 360 - 180
            o3.coords['longitude'] = (o3.coords['longitude'] + 180) % 360 - 180
            no2.coords['longitude'] = (no2.coords['longitude'] + 180) % 360 - 180
            co.coords['longitude'] = (co.coords['longitude'] + 180) % 360 - 180
            pm10.coords['longitude'] = (pm10.coords['longitude'] + 180) % 360 - 180
            so2.coords['longitude'] = (so2.coords['longitude'] + 180) % 360 -180

            pm25 = pm25.sortby('longitude')
            pm25 = pm25.sel(longitude=slice(-10,10),latitude=slice(55,40))
            o3 = o3.sortby('longitude') 
            o3 = o3.sel(longitude=slice(-10,10),latitude=slice(55,40))
            no2 = no2.sortby('longitude')
            no2 = no2.sel(longitude=slice(-10,10),latitude=slice(55,40))
            co = co.sortby('longitude')
            co = co.sel(longitude=slice(-10,10),latitude=slice(55,40))
            pm10 = pm10.sortby('longitude')
            pm10 = pm10.sel(longitude=slice(-10,10),latitude=slice(55,40))
            so2 = so2.sortby('longitude')
            so2 = so2.sel(longitude=slice(-10,10),latitude=slice(55,40))
            print('OK')

            # =============================================================================
            # Interpolation
            # =============================================================================
            pop = pd.read_csv("/home/ludo915/eco-tech-h2gam/pop.csv", usecols=[0,1,2,3,4,5,6,42])
            pop.columns = ['reg', 'dep', 'com', 'article', 'com_nom', 'lon', 'lat', 'total']
            lons, lats = pop.lon, pop.lat
            xrLons = xr.DataArray(lons, dims='com')
            xrLats = xr.DataArray(lats, dims='com')
            pm25Interpolated = pm25.interp(longitude=xrLons, latitude=xrLats)
            o3Interpolated = o3.interp(longitude=xrLons, latitude=xrLats)
            no2Interpolated = no2.interp(longitude=xrLons, latitude=xrLats)
            coInterpolated = co.interp(longitude=xrLons, latitude=xrLats)
            pm10Interpolated = pm10.interp(longitude=xrLons, latitude=xrLats)
            so2Interpolated = so2.interp(longitude=xrLons, latitude=xrLats)
            
            #endpart

            # =============================================================================
            # Risk Assessment
            # =============================================================================
            #part| #%%
            
            #for lead in progressbar(range(97), 'Compute risk: ', 60):
            print("Computing Atmospheric Pollutants maps ...")
            
            dfpollution2 = pd.read_csv("/home/ludo915/eco-tech-h2gam/Enriched_Covid_history_data.csv")
            dfpollution2= dfpollution2.dropna()
            
            risk1 = pm25Interpolated
            risk2 = coInterpolated
            risk3 = o3Interpolated
            risk4 = no2Interpolated
            risk5 = pm10Interpolated
            risk6 = so2Interpolated
            
            risk1 = np.array(risk1)
            risk1 = np.vstack((lons,lats,risk1))
            risk1 = risk1.T
            risk1 = pd.DataFrame(risk1, columns = ['lon', 'lat', 'idx'])
            risk1max = dfpollution2['pm25'].max()

            risk2 = np.array(risk2)
            risk2 = np.vstack((lons,lats,risk2))
            risk2 = risk2.T
            risk2 = pd.DataFrame(risk2, columns = ['lon', 'lat', 'idx'])
            risk2max = dfpollution2['co'].max()
        

            risk3 = np.array(risk3)
            risk3 = np.vstack((lons,lats,risk3))
            risk3 = risk3.T
            risk3 = pd.DataFrame(risk3, columns = ['lon', 'lat', 'idx'])
            risk3max = dfpollution2['o3'].max()

            risk4 = np.array(risk4)
            risk4 = np.vstack((lons,lats,risk4))
            risk4 = risk4.T
            risk4 = pd.DataFrame(risk4, columns = ['lon', 'lat', 'idx'])
            risk4max = dfpollution2['no2'].max()

            risk5 = np.array(risk5)
            risk5 = np.vstack((lons,lats,risk5))
            risk5 = risk5.T
            risk5 = pd.DataFrame(risk5, columns = ['lon', 'lat', 'idx'])
            risk5max = dfpollution2['pm10'].max()

            risk6 = np.array(risk6)
            risk6 = np.vstack((lons,lats,risk6))
            risk6 = risk6.T
            risk6 = pd.DataFrame(risk6, columns = ['lon', 'lat', 'idx'])
            risk6max = dfpollution2['so2'].max()

            risk1Maps.append(risk1)
            risk2Maps.append(risk2)
            risk3Maps.append(risk3)
            risk4Maps.append(risk4)
            risk5Maps.append(risk5)
            risk6Maps.append(risk6)

            markersize = .1
            

            fig = plt.figure(figsize=(8,8))
            gs = fig.add_gridspec(1, 1)
            ax1 = fig.add_subplot(gs[0, 0], projection=ccrs.PlateCarree())
            axes = [ax1]
            ax1.background_patch.set_fill(False)
            for a in axes:
                a.add_geometries([regionmask.defined_regions.prudence["FR"].polygon,], ccrs.PlateCarree(),
                edgecolor=grayDark, lw=2, facecolor=grayDark, alpha=0.6, zorder=0)
                a.set_extent([-5,10,41,52])
                a.set_aspect('auto')
                a.outline_patch.set_linewidth(0.)
                pass

            cax = ax1.scatter(risk1Maps[counter].lon,risk1Maps[counter].lat,c=risk1Maps[counter].idx,
            cmap='RdYlGn_r', s=markersize*5, vmin=0, vmax=risk1max, zorder=4)
            cbar = fig.colorbar(cax, orientation='horizontal', pad=0, aspect=50,
            fraction=.01, extend='max', drawedges=False, ticks=[0, risk1max])
            cbar.ax.set_xticklabels(['low', 'high'])
            cbar.ax.xaxis.set_ticks_position('top')
            cbar.ax.xaxis.set_label_position('top')

            ax1.text(0,.0,'Data \nCAMS \ndata.gouv.fr', transform=ax1.transAxes,fontdict={'size':12})

            currentDateWD = today.strftime('%a, %d - %m, %Y')
            ax1.set_title('PM2.5 concentrations: \n{:}\n'.format(currentDateWD + " + "+ str (counter) + " days"),
            loc='left', pad=-60)

            fig.subplots_adjust(bottom=.01, left=.01, right=.99, top=.99)
            #plt.show()
            buffer = io.BytesIO()
            plt.savefig(buffer, format='png', dpi=70)
            buffer.seek(0)
            images1.append(imageio.imread(buffer))
            buffer.close()
            plt.close()

            fig = plt.figure(figsize=(8,8))
            gs = fig.add_gridspec(1, 1)
            ax1 = fig.add_subplot(gs[0, 0], projection=ccrs.PlateCarree())
            axes = [ax1]
            ax1.background_patch.set_fill(False)
            for a in axes:
                a.add_geometries([regionmask.defined_regions.prudence["FR"].polygon,], ccrs.PlateCarree(),
                edgecolor=grayDark, lw=2, facecolor=grayDark, alpha=0.6, zorder=0)
                a.set_extent([-5,10,41,52])
                a.set_aspect('auto')
                a.outline_patch.set_linewidth(0.)
                pass

            cax = ax1.scatter(risk2Maps[counter].lon,risk2Maps[counter].lat,c=risk2Maps[counter].idx,
            cmap='RdYlGn_r', s=markersize*5, vmin=0, vmax=risk2max, zorder=4)
            cbar = fig.colorbar(cax, orientation='horizontal', pad=0, aspect=50,
            fraction=.01, extend='max', drawedges=False, ticks=[0, risk2max])
            cbar.ax.set_xticklabels(['low', 'high'])
            cbar.ax.xaxis.set_ticks_position('top')
            cbar.ax.xaxis.set_label_position('top')

            ax1.text(0,.0,'Data \nCAMS \ndata.gouv.fr', transform=ax1.transAxes,fontdict={'size':12})

            #currentDateWD = datetime.strptime(str(dfpollution3["date"].max())).strftime('%a, %d %b %Y')
            currentDateWD = pd.to_datetime(today, dayfirst = False).strftime('%a, %d %b %Y')
            ax1.set_title('CO concentrations: \n{:}\n'.format(currentDateWD + " + "+ str (counter) + " days"),
            loc='left', pad=-60)

            fig.subplots_adjust(bottom=.01, left=.01, right=.99, top=.99)
            #plt.show()
            buffer = io.BytesIO()
            plt.savefig(buffer, format='png', dpi=70)
            buffer.seek(0)
            images2.append(imageio.imread(buffer))
            buffer.close()
            plt.close()

            fig = plt.figure(figsize=(8,8))
            gs = fig.add_gridspec(1, 1)
            ax1 = fig.add_subplot(gs[0, 0], projection=ccrs.PlateCarree())
            axes = [ax1]
            ax1.background_patch.set_fill(False)
            for a in axes:
                a.add_geometries([regionmask.defined_regions.prudence["FR"].polygon,], ccrs.PlateCarree(),
                edgecolor=grayDark, lw=2, facecolor=grayDark, alpha=0.6, zorder=0)
                a.set_extent([-5,10,41,52])
                a.set_aspect('auto')
                a.outline_patch.set_linewidth(0.)
                pass

            cax = ax1.scatter(risk3Maps[counter].lon,risk3Maps[counter].lat,c=risk3Maps[counter].idx,
            cmap='RdYlGn_r', s=markersize*5, vmin=0, vmax=risk3max, zorder=4)
            cbar = fig.colorbar(cax, orientation='horizontal', pad=0, aspect=50,
            fraction=.01, extend='max', drawedges=False, ticks=[0, risk3max])
            cbar.ax.set_xticklabels(['low', 'high'])
            cbar.ax.xaxis.set_ticks_position('top')
            cbar.ax.xaxis.set_label_position('top')

            ax1.text(0,.0,'Data \nCAMS \ndata.gouv.fr', transform=ax1.transAxes,fontdict={'size':12})

            currentDateWD = pd.to_datetime(today, dayfirst = False).strftime('%a, %d %b %Y')
            ax1.set_title('O3 concentrations: \n{:}\n'.format(currentDateWD + " + "+ str (counter) + " days"),
            loc='left', pad=-60)

            fig.subplots_adjust(bottom=.01, left=.01, right=.99, top=.99)
            #plt.show()
            buffer = io.BytesIO()
            plt.savefig(buffer, format='png', dpi=70)
            buffer.seek(0)
            images3.append(imageio.imread(buffer))
            buffer.close()
            plt.close()

            fig = plt.figure(figsize=(8,8))
            gs = fig.add_gridspec(1, 1)
            ax1 = fig.add_subplot(gs[0, 0], projection=ccrs.PlateCarree())
            axes = [ax1]
            ax1.background_patch.set_fill(False)
            for a in axes:
                a.add_geometries([regionmask.defined_regions.prudence["FR"].polygon,], ccrs.PlateCarree(),
                edgecolor=grayDark, lw=2, facecolor=grayDark, alpha=0.6, zorder=0)
                a.set_extent([-5,10,41,52])
                a.set_aspect('auto')
                a.outline_patch.set_linewidth(0.)
                pass

            cax = ax1.scatter(risk4Maps[counter].lon,risk4Maps[counter].lat,c=risk4Maps[counter].idx,
            cmap='RdYlGn_r', s=markersize*5, vmin=0, vmax=risk4max, zorder=4)
            cbar = fig.colorbar(cax, orientation='horizontal', pad=0, aspect=50,
            fraction=.01, extend='max', drawedges=False, ticks=[0, risk4max])
            cbar.ax.set_xticklabels(['low', 'high'])
            cbar.ax.xaxis.set_ticks_position('top')
            cbar.ax.xaxis.set_label_position('top')

            ax1.text(0,.0,'Data \nCAMS \ndata.gouv.fr', transform=ax1.transAxes,fontdict={'size':12})

            currentDateWD = pd.to_datetime(today, dayfirst = False).strftime('%a, %d %b %Y')
            ax1.set_title('NO2 concentrations: \n{:}\n'.format(currentDateWD + " + "+ str (counter) + " days"),
            loc='left', pad=-60)

            fig.subplots_adjust(bottom=.01, left=.01, right=.99, top=.99)
            #plt.show()
            buffer = io.BytesIO()
            plt.savefig(buffer, format='png', dpi=70)
            buffer.seek(0)
            images4.append(imageio.imread(buffer))
            buffer.close()
            plt.close()

            fig = plt.figure(figsize=(8,8))
            gs = fig.add_gridspec(1, 1)
            ax1 = fig.add_subplot(gs[0, 0], projection=ccrs.PlateCarree())
            axes = [ax1]
            ax1.background_patch.set_fill(False)
            for a in axes:
                a.add_geometries([regionmask.defined_regions.prudence["FR"].polygon,], ccrs.PlateCarree(),
                edgecolor=grayDark, lw=2, facecolor=grayDark, alpha=0.6, zorder=0)
                a.set_extent([-5,10,41,52])
                a.set_aspect('auto')
                a.outline_patch.set_linewidth(0.)
                pass

            cax = ax1.scatter(risk5Maps[counter].lon,risk5Maps[counter].lat,c=risk5Maps[counter].idx,
            cmap='RdYlGn_r', s=markersize*5, vmin=0, vmax=risk5max, zorder=4)
            cbar = fig.colorbar(cax, orientation='horizontal', pad=0, aspect=50,
            fraction=.01, extend='max', drawedges=False, ticks=[0, risk5max])
            cbar.ax.set_xticklabels(['low', 'high'])
            cbar.ax.xaxis.set_ticks_position('top')
            cbar.ax.xaxis.set_label_position('top')

            ax1.text(0,.0,'Data \nCAMS \ndata.gouv.fr', transform=ax1.transAxes,fontdict={'size':12})

            currentDateWD = pd.to_datetime(today, dayfirst = False).strftime('%a, %d %b %Y')
            ax1.set_title('PM10 concentrations: \n{:}\n'.format(currentDateWD + " + "+ str (counter) + " days"),
            loc='left', pad=-60)

            fig.subplots_adjust(bottom=.01, left=.01, right=.99, top=.99)
            #plt.show()
            buffer = io.BytesIO()
            plt.savefig(buffer, format='png', dpi=70)
            buffer.seek(0)
            images5.append(imageio.imread(buffer))
            buffer.close()
            plt.close()

            fig = plt.figure(figsize=(8,8))
            gs = fig.add_gridspec(1, 1)
            ax1 = fig.add_subplot(gs[0, 0], projection=ccrs.PlateCarree())
            axes = [ax1]
            ax1.background_patch.set_fill(False)
            for a in axes:
                a.add_geometries([regionmask.defined_regions.prudence["FR"].polygon,], ccrs.PlateCarree(),
                edgecolor=grayDark, lw=2, facecolor=grayDark, alpha=0.6, zorder=0)
                a.set_extent([-5,10,41,52])
                a.set_aspect('auto')
                a.outline_patch.set_linewidth(0.)
                pass

            cax = ax1.scatter(risk6Maps[counter].lon,risk6Maps[counter].lat,c=risk6Maps[counter].idx,
            cmap='RdYlGn_r', s=markersize*5, vmin=0, vmax=risk6max, zorder=4)
            cbar = fig.colorbar(cax, orientation='horizontal', pad=0, aspect=50,
            fraction=.01, extend='max', drawedges=False, ticks=[0, risk6max])
            cbar.ax.set_xticklabels(['low', 'high'])
            cbar.ax.xaxis.set_ticks_position('top')
            cbar.ax.xaxis.set_label_position('top')

            ax1.text(0,.0,'Data \nCAMS \ndata.gouv.fr', transform=ax1.transAxes,fontdict={'size':12})

            currentDateWD = pd.to_datetime(today, dayfirst = False).strftime('%a, %d %b %Y')
            ax1.set_title('SO2 concentrations: \n{:}\n'.format(currentDateWD + " + "+ str (counter) + " days"),
            loc='left', pad=-60)

            fig.subplots_adjust(bottom=.01, left=.01, right=.99, top=.99)
            #plt.show()
            buffer = io.BytesIO()
            plt.savefig(buffer, format='png', dpi=70)
            buffer.seek(0)
            images6.append(imageio.imread(buffer))
            buffer.close()
            plt.close()

            fig.subplots_adjust(bottom=.01, left=.01, right=.99, top=.99)
            #plt.show()
            buffer = io.BytesIO()
            plt.savefig(buffer, format='png', dpi=70)
            buffer.seek(0)
            images7.append(imageio.imread(buffer))
            buffer.close()
            plt.close()
            counter += 1

        print('Create gif ...', flush=True, end='')
        gifPath = '/home/ludo915/eco-tech-h2gam/forecast/fr/'
        gifName = 'PM2.5-concentration-{:}.gif'.format(currentDatestring)
        kargs = { 'duration': 1 }
        imageio.mimwrite(gifPath + gifName, images1, 'GIF', **kargs)

        print('Create gif ...', flush=True, end='')
        gifPath = '/home/ludo915/eco-tech-h2gam/forecast/fr/'
        gifName = 'CO-concentration-{:}.gif'.format(currentDatestring)
        kargs = { 'duration': 1 }
        imageio.mimwrite(gifPath + gifName, images2, 'GIF', **kargs)

        print('Create gif ...', flush=True, end='')
        gifPath = '/home/ludo915/eco-tech-h2gam/forecast/fr/'
        gifName = 'O3-concentration-{:}.gif'.format(currentDatestring)
        kargs = { 'duration': 1 }
        imageio.mimwrite(gifPath + gifName, images3, 'GIF', **kargs)

        print('Create gif ...', flush=True, end='')
        gifPath = '/home/ludo915/eco-tech-h2gam/forecast/fr/'
        gifName = 'NO2-concentration-{:}.gif'.format(currentDatestring)
        kargs = { 'duration': 1 }
        imageio.mimwrite(gifPath + gifName, images4, 'GIF', **kargs)

        print('Create gif ...', flush=True, end='')
        gifPath = '/home/ludo915/eco-tech-h2gam/forecast/fr/'
        gifName = 'PM10-concentration-{:}.gif'.format(currentDatestring)
        kargs = { 'duration': 1 }
        imageio.mimwrite(gifPath + gifName, images5, 'GIF', **kargs)
        
        print('Create gif ...', flush=True, end='')
        gifPath = '/home/ludo915/eco-tech-h2gam/forecast/fr/'
        gifName = 'SO2-concentration-{:}.gif'.format(currentDatestring)
        kargs = { 'duration': 1 }
        imageio.mimwrite(gifPath + gifName, images6, 'GIF', **kargs)

        print('OK')
        print('Finished.')

        return None
 
if __name__ == '__main__':
  Maps = compute_maps()
  Maps.compute_maps()

请在下面找到地图输出，因为您可以看到灰度前景 elightnin 不适合地图：

这个问题很烦人，我尝试过设置grayLight = greyDark，但它并没有改变输出中的nethin。

Answer 1

设置grayLight=grayDark确实解决了这个问题