Python Iris Set X Axis limit and ticks
I am creating line graphs with either the year or month along the x axis.
Here is the simplified code for the monthly line graph:
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()
This produces a plot which looks like this: 
How can I change the tick marks to show me all month names? I would also like the graph to finish at December (no white space after).
Similarly, for the yearly line graph, here is the simplified code:
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()
and this produces this graph:
As you can see I have limited my data from 1989 to 2008, but the axis goes from 1985 to 2010, how can I make this tighter?
Thank you!
Solution
For your monthly graph you may be able to change it by setting the xticks - this has to be numeric but you can also set labels to use instead of the numbers. Something like
plt.xticks(range(12), calendar.month_abbr[1:13])
may work (depends on the format of your data, you may need to plot month number rather than month name). You will need to import calendar to get the above working.
For your yearly graph you should just be able to set the x-axis limits using
plt.xlim((xmin, xmax))
where xmin is probably 1989 and xmax is 2008.
- WSGIServerException: [Errno 8] nodename nor servname provided, or not known
- Python/Flask Login form throws 500 error on IIS
- Matplotlib plt.show() isn't showing graph
- How to avoid duplicate processing in python API server?
- Extract points/coordinates from a polygon in Shapely
- How do I detect collision in pygame?
- Typehint a method that returns new instance using a superclass classmethod
- How to process requests from multiiple users using ML model and FastAPI?
- How can I clear the interpreter console?
- What are the reasons for the differences in output results when using the groupby function in the Python pandas package?
- How to antialias picture to get rid of the jerkiness in the image
- Is there any difference between list(array) and array.tolist() in Python?
- How to move x-axis on top of the plot in plotnine?
- Pulling Zillow Rent Data from Zillow API
- Safest way to incrementally append to a file?
- Unable to install mysqlclient package on EC2 instance
- FastAPI - How to get app instance inside a router?
- SQLAlchemy: engine, connection and session difference
- Generating binary arrays with alternating values based on change indices in NumPy
- How can i solve this error: ValueError: The feature names should match those that were passed
- 'super' object has no attribute '__sklearn_tags__'
- Python: How to copy all attibutes from base class to derived one
- mitm proxy is closed, but the port is still occupied
- How to find pg_config path
- Why does "pip install" inside Python raise a SyntaxError?
- Plotting a matplotlib pie chart using 2 columns
- ImportError: No module named langchain.llms
- How do I disable the security certificate check in Python requests
- How can I import Psycopg2 into a Lambda Function on AWS?
- Django built in Logout view `Method Not Allowed (GET): /users/logout/`