I am hoping someone can help. I am running a few climate models (NetCDF files) in python using iris. All was working well until I added my last model which is formatted differently. The units they use for the time variable in the new models is day as %Y%m%d.%f
but in the other models it is days since …
. This means that when I try to constrain the time variable I get the following error AttributeError: 'numpy.float64' object has no attribute 'year'
.
I tried adding a year variable using iriscc.add_year(EARTH3, 'time')
but that just brings up the error ‘Unit has undefined calendar’
.
I’m wondering if you know how I might fix this? Do I need to convert the calendar type? Or is there is there a way around that? Not sure how to do that anyway!
Thank you! Erika
EDIT: here is the full code for my file the model CanESM2 is working, but the model EARTH3 is not - it is the one with the funny time units.
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
from cf_units import Unit
import datetime
import numpy as np
def main():
#-------------------------------------------------------------------------
#bring in all the GCM models we need and give them a name
CanESM2= '/exports/csce/datastore/geos/users/s0XXXX/Climate_Modelling/GCM_data/tasmin_Amon_CanESM2_historical_r1i1p1_185001-200512.nc'
EARTH3= '/exports/csce/datastore/geos/users/s0XXXX/Climate_Modelling/GCM_data/tas_Amon_EC-EARTH_historical_r3i1p1_1850-2009.nc'
#Load exactly one cube from given file
CanESM2 = iris.load_cube(CanESM2)
EARTH3 = iris.load_cube(EARTH3)
print"CanESM2 time"
print (CanESM2.coord('time'))
print "EARTH3 time"
print (EARTH3.coord('time'))
#fix EARTH3 time units as they differ from all other models
t_coord=EARTH3.coord('time')
t_unit = t_coord.attributes['invalid_units']
timestep, _, t_fmt_str = t_unit.split(' ')
new_t_unit_str= '{} since 1850-01-01 00:00:00'.format(timestep)
new_t_unit = cf_units.Unit(new_t_unit_str, calendar=cf_units.CALENDAR_STANDARD)
new_datetimes = [datetime.datetime.strptime(str(dt), t_fmt_str) for dt in t_coord.points]
new_dt_points = [new_t_unit.date2num(new_dt) for new_dt in new_datetimes]
new_t_coord = iris.coords.DimCoord(new_dt_points, standard_name='time', units=new_t_unit)
print "EARTH3 new time"
print (EARTH3.coord('time'))
#regrid all models to have same latitude and longitude system, all regridded to model with lowest resolution
CanESM2 = CanESM2.regrid(CanESM2, iris.analysis.Linear())
EARTH3 =EARTH3.regrid(CanESM2, iris.analysis.Linear())
#we are only interested in the latitude and longitude relevant to Malawi (has to be slightly larger than country boundary to take into account resolution of GCMs)
Malawi = iris.Constraint(longitude=lambda v: 32.0 <= v <= 36., latitude=lambda v: -17. <= v <= -8.)
CanESM2 =CanESM2.extract(Malawi)
EARTH3 =EARTH3.extract(Malawi)
#time constraignt to make all series the same, for ERAINT this is 1990-2008 and for RCMs and GCMs this is 1961-2005
iris.FUTURE.cell_datetime_objects = True
t_constraint = iris.Constraint(time=lambda cell: 1961 <= cell.point.year <= 2005)
CanESM2 =CanESM2.extract(t_constraint)
EARTH3 =EARTH3.extract(t_constraint)
#Convert units to match, CORDEX data is in Kelvin but Observed data in Celsius, we would like to show all data in Celsius
CanESM2.convert_units('Celsius')
EARTH3.units = Unit('Celsius') #this fixes EARTH3 which has no units defined
EARTH3=EARTH3-273 #this converts the data manually from Kelvin to Celsius
#add year data to files
iriscc.add_year(CanESM2, 'time')
iriscc.add_year(EARTH3, 'time')
#We are interested in plotting the data by year, so we need to take a mean of all the data by year
CanESM2YR=CanESM2.aggregated_by('year', iris.analysis.MEAN)
EARTH3YR = EARTH3.aggregated_by('year', iris.analysis.MEAN)
#Returns an array of area weights, with the same dimensions as the cube
CanESM2YR_grid_areas = iris.analysis.cartography.area_weights(CanESM2YR)
EARTH3YR_grid_areas = iris.analysis.cartography.area_weights(EARTH3YR)
#We want to plot the mean for the whole region so we need a mean of all the lats and lons
CanESM2YR_mean = CanESM2YR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=CanESM2YR_grid_areas)
EARTH3YR_mean = EARTH3YR.collapsed(['latitude', 'longitude'], iris.analysis.MEAN, weights=EARTH3YR_grid_areas)
#-------------------------------------------------------------------------
#PART 4: PLOT LINE GRAPH
#limit x axis
plt.xlim((1961,2005))
#assign the line colours and set x axis to 'year' rather than 'time'
qplt.plot(CanESM2YR_mean.coord('year'), CanESM2YR_mean, label='CanESM2', lw=1.5, color='blue')
qplt.plot(EARTH3YR_mean.coord('year'), EARTH3YR_mean, label='EC-EARTH (r3i1p1', lw=1.5, color='magenta')
#set a title for the y axis
plt.ylabel('Near-Surface Temperature (degrees Celsius)')
#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('Tas for Malawi 1961-2005', fontsize=11)
#add grid lines
plt.grid()
#show the graph in the console
iplt.show()
if __name__ == '__main__':
main()