Question

我正在创建沿x轴的年份或月份的折线图。

以下是月线图的简化代码：

import matplotlib.pyplot as plt
import iris
import iris.coord_categorisation as iriscc
import iris.plot as iplt
import iris.quickplot as qplt
import iris.analysis.cartography
import cf_units

#this file is split into parts as follows:
    #PART 1: load and format CORDEX models
    #PART 2: load and format observed data
    #PART 3: format data
    #PART 4: plot data

def main():
    #PART 1: CORDEX MODELS
    #bring in all the models we need and give them a name
    CCCma = '/exports/csce/datastore/geos/users/s0XXXX/Climate_Modelling/AFR_44_tas/ERAINT/1979-2012/tas_AFR-44_ECMWF-ERAINT_evaluation_r1i1p1_CCCma-CanRCM4_r2_mon_198901-200912.nc'

    #Load exactly one cube from given file
    CCCma = iris.load_cube(CCCma)


    #remove flat latitude and longitude and only use grid latitude and grid longitude to make consistent with the observed data, also make sure all of the longitudes are monotonic 
    lats = iris.coords.DimCoord(CCCma.coord('latitude').points[:,0], \
                                standard_name='latitude', units='degrees')
    lons = CCCma.coord('longitude').points[0]
    for i in range(len(lons)):
        if lons[i]>100.:
            lons[i] = lons[i]-360.
    lons = iris.coords.DimCoord(lons, \
                                standard_name='longitude', units='degrees')

    CCCma.remove_coord('latitude')
    CCCma.remove_coord('longitude')
    CCCma.remove_coord('grid_latitude')
    CCCma.remove_coord('grid_longitude')
    CCCma.add_dim_coord(lats, 1)
    CCCma.add_dim_coord(lons, 2)

    #we are only interested in the latitude and longitude relevant to Malawi

    Malawi = iris.Constraint(longitude=lambda v: 32.5 <= v <= 36., \
                         latitude=lambda v: -17. <= v <= -9.) 

    CCCma = CCCma.extract(Malawi)

    #time constraignt to make all series the same
    iris.FUTURE.cell_datetime_objects = True
    t_constraint = iris.Constraint(time=lambda cell: 1989 <= cell.point.year <= 2008)
    CCCma = CCCma.extract(t_constraint)



    #PART 2: OBSERVED DATA
    #bring in all the files we need and give them a name
    CRU= '/exports/csce/datastore/geos/users/s0XXXX/Climate_Modelling/Actual_Data/cru_ts4.00.1901.2015.tmp.dat.nc'

    #Load exactly one cube from given file
    CRU = iris.load_cube(CRU, 'near-surface temperature')

    #define the latitude and longitude
    lats = iris.coords.DimCoord(CRU.coord('latitude').points, \
                                standard_name='latitude', units='degrees')
    lons = CRU.coord('longitude').points

    #we are only interested in the latitude and longitude relevant to Malawi 
    Malawi = iris.Constraint(longitude=lambda v: 32.5 <= v <= 36., \
                         latitude=lambda v: -17. <= v <= -9.) 
    CRU = CRU.extract(Malawi)

    #time constraignt to make all series the same
    iris.FUTURE.cell_datetime_objects = True
    t_constraint = iris.Constraint(time=lambda cell: 1989 <= cell.point.year <= 2008)
    CRU = CRU.extract(t_constraint)



    #PART 3: FORMAT DATA  
    #data is in Kelvin, but we would like to show it in Celcius
    CCCma.convert_units('Celsius')                                                    

    #bring time data into allignment
    new_unit = cf_units.Unit('days since 1900-01-01', calendar = '365_day')
    CCCma.coord('time').convert_units(new_unit)                                                  

    #add years and months to data
    iriscc.add_year(CCCma, 'time')
    iriscc.add_year(CRU, 'time')
    iriscc.add_month(CCCma, 'time')
    iriscc.add_month(CRU, 'time')

    #We are interested in plotting the data by month, so we need to take a mean of all the data by month
    CCCmaYR = CCCma.aggregated_by('month', iris.analysis.MEAN)
    CRUYR = CRU.aggregated_by('month', iris.analysis.MEAN) 

    #regridding scheme requires spatial areas, therefore the longitude and latitude coordinates must be bounded. If the longitude and latitude bounds are not defined in the cube we can guess the bounds based on the coordinates
    CCCmaYR.coord('latitude').guess_bounds()
    CCCmaYR.coord('longitude').guess_bounds()
    CRUYR.coord('latitude').guess_bounds()
    CRUYR.coord('longitude').guess_bounds()

    #Returns an array of area weights, with the same dimensions as the cube
    CCCmaYR_grid_areas = iris.analysis.cartography.area_weights(CCCmaYR)
    CRUYR_grid_areas = iris.analysis.cartography.area_weights(CRUYR)

    #We want to plot the mean for the whole region so we need a mean of all the lats and lons
    CCCmaYR_mean = CCCmaYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CCCmaYR_grid_areas) 
    CRUYR_mean = CRUYR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CRUYR_grid_areas)


    #PART 4: PLOT LINE GRAPH                                                                                               
    #assign the line colours and set x axis to months
    qplt.plot(CCCmaYR_mean.coord('month'),CCCmaYR_mean, label='CanRCM4_ERAINT', lw=1.5, color='blue')
    qplt.plot(CRUYR_mean.coord('month'), CRUYR_mean, label='Observed', lw=2, color='black')

    #create a legend and set its location to under the graph
    plt.legend(loc="upper center", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)

    #create a title
    plt.title('Mean Near Surface Temperature for Malawi by month 1989-2008', fontsize=11)   

    #add grid lines
    plt.grid()

    #save the image of the graph and include full legend
    #plt.savefig('ERAINT_Temperature_LineGraph_Annual', bbox_inches='tight')

    #show the graph in the console
    iplt.show()                                               

if __name__ == '__main__':
    main()

这会生成如下图：

如何更改刻度线以显示所有月份名称？我还希望图表在12月完成（之后没有空格）。

同样，对于年度线图，这里是简化代码：

import matplotlib.pyplot as plt
import iris
import iris.coord_categorisation as iriscc
import iris.plot as iplt
import iris.quickplot as qplt
import iris.analysis.cartography

#this file is split into parts as follows:
    #PART 1: load and format CORDEX models
    #PART 2: load and format observed data
    #PART 3: format data
    #PART 4: plot data

def main():
    #PART 1: CORDEX MODELS
    #bring in all the models we need and give them a name
    CCCma = '/exports/csce/datastore/geos/users/s0XXXX/Climate_Modelling/AFR_44_tas/ERAINT/1979-2012/tas_AFR-44_ECMWF-ERAINT_evaluation_r1i1p1_CCCma-CanRCM4_r2_mon_198901-200912.nc'  

    #Load exactly one cube from given file
    CCCma = iris.load_cube(CCCma) 

    #remove flat latitude and longitude and only use grid latitude and grid longitude to make consistent with the observed data, also make sure all of the longitudes are monotonic 
    lats = iris.coords.DimCoord(CCCma.coord('latitude').points[:,0], \
                                standard_name='latitude', units='degrees')
    lons = CCCma.coord('longitude').points[0]
    for i in range(len(lons)):
        if lons[i]>100.:
            lons[i] = lons[i]-360.
    lons = iris.coords.DimCoord(lons, \
                                standard_name='longitude', units='degrees')

    CCCma.remove_coord('latitude')
    CCCma.remove_coord('longitude')
    CCCma.remove_coord('grid_latitude')
    CCCma.remove_coord('grid_longitude')
    CCCma.add_dim_coord(lats, 1)
    CCCma.add_dim_coord(lons, 2)

    #we are only interested in the latitude and longitude relevant to Malawi      
    Malawi = iris.Constraint(longitude=lambda v: 32.5 <= v <= 36., \
                         latitude=lambda v: -17. <= v <= -9.) 

    CCCma = CCCma.extract(Malawi)

    #time constraignt to make all series the same
    iris.FUTURE.cell_datetime_objects = True
    t_constraint = iris.Constraint(time=lambda cell: 1989 <= cell.point.year <= 2008)

    CCCma = CCCma.extract(t_constraint)                                               


    #PART 2: OBSERVED DATA

    #bring in all the files we need and give them a name
    CRU= '/exports/csce/datastore/geos/users/s0XXXX/Climate_Modelling/Actual_Data/cru_ts4.00.1901.2015.tmp.dat.nc'

    #Load exactly one cube from given file
    CRU = iris.load_cube(CRU, 'near-surface temperature')

    #define the latitude and longitude
    lats = iris.coords.DimCoord(CRU.coord('latitude').points, \
                                standard_name='latitude', units='degrees')
    lons = CRU.coord('longitude').points

    #we are only interested in the latitude and longitude relevant to Malawi 
    Malawi = iris.Constraint(longitude=lambda v: 32.5 <= v <= 36., \
                         latitude=lambda v: -17. <= v <= -9.) 
    CRU = CRU.extract(Malawi)

    #time constraignt to make all series the same
    iris.FUTURE.cell_datetime_objects = True
    t_constraint = iris.Constraint(time=lambda cell: 1989 <= cell.point.year <= 2008)
    CRU = CRU.extract(t_constraint)

    #PART 3: FORMAT DATA       
    #data is in Kelvin, but we would like to show it in Celcius
    CCCma.convert_units('Celsius') 

    #add years to data
    iriscc.add_year(CCCma, 'time')
    iriscc.add_year(CRU, 'time')

    #We are interested in plotting the data by month, so we need to take a mean of all the data by month
    CCCma = CCCma.aggregated_by('year', iris.analysis.MEAN)
    CRU = CRU.aggregated_by('year', iris.analysis.MEAN)    

    #regridding scheme requires spatial areas, therefore the longitude and latitude coordinates must be bounded. If the longitude and latitude bounds are not defined in the cube we can guess the bounds based on the coordinates
    CCCma.coord('latitude').guess_bounds()
    CCCma.coord('longitude').guess_bounds()
    CRU.coord('latitude').guess_bounds()
    CRU.coord('longitude').guess_bounds()

    #Returns an array of area weights, with the same dimensions as the cube
    CCCma_grid_areas = iris.analysis.cartography.area_weights(CCCma)
    CRU_grid_areas = iris.analysis.cartography.area_weights(CRU)

    #We want to plot the mean for the whole region so we need a mean of all the lats and lons
    CCCma_mean = CCCma.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CCCma_grid_areas)
    CRU_mean = CRU.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CRU_grid_areas)                                              



    #PART 4: PLOT LINE GRAPH  
    #assign the line colours
    qplt.plot(CCCma_mean.coord('year'), CCCma_mean, label='CanRCM4_ERAINT', lw=1.5, color='blue')
    qplt.plot(CRU_mean.coord('year'), CRU_mean, label='Observed', lw=2, color='black')

    #create a legend and set its location to under the graph
    plt.legend(loc="upper center", bbox_to_anchor=(0.5,-0.05), fancybox=True, shadow=True, ncol=2)

    #create a title
    plt.title('Mean Near Surface Temperature for Malawi 1989-2008', fontsize=11)   

    #add grid lines
    plt.grid()

    #save the image of the graph and include full legend
    #plt.savefig('ERAINT_Temperature_LineGraph_Annual', bbox_inches='tight')

    #show the graph in the console
    iplt.show()




if __name__ == '__main__':
    main()

这会产生这个图：

正如你所看到的那样，从1989年到2008年我的数据有限，但轴从1985年到2010年，我怎么能让这个更紧张呢？

谢谢！

Answer 1

对于您的月度图表，您可以通过设置xticks来更改它 - 这必须是数字，但您也可以设置要使用的标签而不是数字。像

这样的东西

plt.xticks(range(12), calendar.month_abbr[1:13])

可能有效（取决于您的数据格式，您可能需要绘制月份数而不是月份名称）。您需要import calendar才能完成上述工作。

对于年度图表，您应该只能使用

设置x轴限制

plt.xlim((xmin, xmax))

其中xmin可能是1989年，xmax是2008年。

Python Iris设置X轴限制和刻度

1 个答案: