Python 虹膜设置 X 轴限制和刻度
Python Iris Set X Axis limit and ticks
我正在创建以年或月为 x 轴的折线图。
这里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的名称？我还希望图表在 12 月完成（之后没有白色 space）。
同样，对于年折线图，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()
这会产生这张图：
如您所见，我限制了从 1989 年到 2008 年的数据，但轴从 1985 年到 2010 年，我怎样才能使它更紧凑？
谢谢！
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
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编号而不是月份名称）。您需要 import calendar 才能使上述工作正常进行。
对于您的年度图表，您应该能够使用
设置 x 轴限制
plt.xlim((xmin, xmax))
其中 xmin 可能是 1989，xmax 是 2008。
我正在创建以年或月为 x 轴的折线图。
这里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的名称？我还希望图表在 12 月完成（之后没有白色 space）。
同样，对于年折线图，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()
这会产生这张图：
如您所见，我限制了从 1989 年到 2008 年的数据，但轴从 1985 年到 2010 年，我怎样才能使它更紧凑？
谢谢！
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
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编号而不是月份名称）。您需要 import calendar 才能使上述工作正常进行。
对于您的年度图表，您应该能够使用
设置 x 轴限制plt.xlim((xmin, xmax))
其中 xmin 可能是 1989，xmax 是 2008。