def test_no_extent_in_cartopy_projection(self):
'''
make sure ProjInds works with projections requiring no extent
'''
import os
import numpy as np
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import domutils
import domutils.legs as legs
import domutils.geo_tools as geo_tools
import domutils._py_tools as py_tools
regular_lons = [[-100., -100, -100], [-90., -90, -90],
[-80., -80, -80]]
regular_lats = [[30, 40, 50], [30, 40, 50], [30, 40, 50]]
data_vals = [[6.5, 3.5, .5], [7.5, 4.5, 1.5], [8.5, 5.5, 2.5]]
missing = -9999.
image_ratio = .5
rec_w = 6.4 #inches!
rec_h = image_ratio * rec_w #inches!
grid_w_pts = 2000.
image_res = [grid_w_pts, image_ratio * grid_w_pts] #100 x 50
#cartopy projection with no extent
proj_rob = ccrs.Robinson()
#instantiate object to handle geographical projection of data
# onto geoAxes with this specific crs
ProjInds = geo_tools.ProjInds(src_lon=regular_lons,
src_lat=regular_lats,
dest_crs=proj_rob,
image_res=image_res,
extend_x=False,
extend_y=False)
#geographical projection of data into axes space
projected_data = ProjInds.project_data(data_vals)
#plot data to make sure it works
#the image that is generated can be looked at to insure proper functionning of the test
#it is not currently tested
color_map = legs.PalObj(range_arr=[0., 9.],
color_arr=[
'brown', 'blue', 'green', 'orange', 'red',
'pink', 'purple', 'yellow', 'b_w'
],
excep_val=missing,
excep_col='grey_220')
fig_w = 9.
fig_h = image_ratio * fig_w
fig = plt.figure(figsize=(fig_w, fig_h))
pos = [.1, .1, rec_w / fig_w, rec_h / fig_h]
ax = fig.add_axes(pos, projection=proj_rob)
x1, x2, y1, y2 = ax.get_extent()
ax.outline_patch.set_linewidth(.3)
projected_rgb = color_map.to_rgb(projected_data)
ax.imshow(projected_rgb,
interpolation='nearest',
aspect='auto',
extent=[x1, x2, y1, y2],
origin='upper')
ax.coastlines(resolution='110m',
linewidth=0.3,
edgecolor='0.3',
zorder=10)
color_map.plot_palette(data_ax=ax)
domutils_dir = os.path.dirname(domutils.__file__)
package_dir = os.path.dirname(domutils_dir)
test_results_dir = package_dir + '/test_results/'
if not os.path.isdir(test_results_dir):
os.mkdir(test_results_dir)
svg_name = test_results_dir + '/test_no_extent_in_cartopy_projection.svg'
plt.savefig(svg_name, dpi=500)
plt.close(fig)
print('saved: ' + svg_name)
#compare image with saved reference
#copy reference image to testdir
reference_image = package_dir + '/test_data/_static/' + os.path.basename(
svg_name)
images_are_similar = py_tools.render_similarly(svg_name,
reference_image)
#test fails if images are not similar
self.assertEqual(images_are_similar, True)
def test_general_lam_projection(self):
import os, inspect
import pickle
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import domutils
import domutils._py_tools as py_tools
# In your scripts use something like :
import domutils.legs as legs
import domutils.geo_tools as geo_tools
#recover previously prepared data
domutils_dir = os.path.dirname(domutils.__file__)
package_dir = os.path.dirname(domutils_dir) + '/'
source_file = package_dir + '/test_data/pal_demo_data.pickle'
with open(source_file, 'rb') as f:
data_dict = pickle.load(f)
longitudes = data_dict['longitudes'] #2D longitudes [deg]
latitudes = data_dict['latitudes'] #2D latitudes [deg]
ground_mask = data_dict[
'groundMask'] #2D land fraction [0-1]; 1 = all land
terrain_height = data_dict[
'terrainHeight'] #2D terrain height of model [m ASL]
#flag non-terrain (ocean and lakes) as -3333.
inds = np.asarray((ground_mask.ravel() <= .01)).nonzero()
if inds[0].size != 0:
terrain_height.flat[inds] = -3333.
#missing value
missing = -9999.
#pixel density of image to plot
ratio = 0.8
hpix = 600. #number of horizontal pixels
vpix = ratio * hpix #number of vertical pixels
img_res = (int(hpix), int(vpix))
##define Albers projection and extend of map
#Obtained through trial and error for good fit of the mdel grid being plotted
proj_aea = ccrs.AlbersEqualArea(central_longitude=-94.,
central_latitude=35.,
standard_parallels=(30., 40.))
map_extent = [-104.8, -75.2, 27.8, 48.5]
#point density for figure
mpl.rcParams['figure.dpi'] = 400
#larger characters
mpl.rcParams.update({'font.size': 15})
#instantiate figure
fig = plt.figure(figsize=(7.5, 6.))
#instantiate object to handle geographical projection of data
proj_inds = geo_tools.ProjInds(src_lon=longitudes,
src_lat=latitudes,
extent=map_extent,
dest_crs=proj_aea,
image_res=img_res,
missing=missing)
#axes for this plot
ax = fig.add_axes([.01, .1, .8, .8], projection=proj_aea)
ax.set_extent(map_extent)
# Set up colormapping object
#
# Two color segments for this palette
red_green = [
[[227, 209, 130], [20, 89,
69]], # bottom color leg : yellow , dark green
[[227, 209, 130], [140, 10, 10]]
] # top color leg : yellow , dark red
map_terrain = legs.PalObj(
range_arr=[0., 750, 1500.],
color_arr=red_green,
dark_pos=['low', 'high'],
excep_val=[-3333., missing],
excep_col=[[170, 200, 250], [120, 120, 120]], #blue , grey_120
over_high='extend')
#geographical projection of data into axes space
proj_data = proj_inds.project_data(terrain_height)
#plot data & palette
map_terrain.plot_data(ax=ax,
data=proj_data,
zorder=0,
palette='right',
pal_units='[meters]',
pal_format='{:4.0f}') #palette options
#add political boundaries
ax.add_feature(cfeature.STATES.with_scale('50m'),
linewidth=0.5,
edgecolor='0.2',
zorder=1)
#plot border and mask everything outside model domain
proj_inds.plot_border(ax, mask_outside=True, linewidth=.5)
#uncomment to save figure
output_dir = package_dir + 'test_results/'
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
image_name = output_dir + 'test_general_lam_projection.svg'
plt.savefig(image_name)
plt.close(fig)
print('saved: ' + image_name)
#compare image with saved reference
#copy reference image to testdir
reference_image = package_dir + '/test_data/_static/' + os.path.basename(
image_name)
images_are_similar = py_tools.render_similarly(image_name,
reference_image)
#test fails if images are not similar
self.assertEqual(images_are_similar, True)
def test_make_radar_fst(self):
''' test funtion that make a fst file from odim h5 mosaic file
I would need to find a way to test the entire script that parses arguments with argparse
'''
import os
import numpy as np
import datetime
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import domutils
import domutils.legs as legs
import domutils.geo_tools as geo_tools
import domcmc.fst_tools as fst_tools
import domutils.radar_tools as radar_tools
import domutils.radar_tools.make_radar_fst as make_radar_fst
import domutils._py_tools as py_tools
#setting up directories
domutils_dir = os.path.dirname(domutils.__file__)
package_dir = os.path.dirname(domutils_dir)
test_data_dir = package_dir + '/test_data/'
test_results_dir = package_dir + '/test_results/make_radar_fst/'
if not os.path.isdir(test_results_dir):
os.makedirs(test_results_dir)
#a small class that mimics the output of argparse
class FstArgs():
radar_data_dir = package_dir + '/test_data/odimh5_radar_composites/'
output_dir = test_results_dir
fst_file_struc = '%Y%m%d%H%M_mosaic.fst'
h5_file_struc = '%Y/%m/%d/qcomp_%Y%m%d%H%M.h5'
h5_latlon_file = test_data_dir + 'radar_continental_2.5km_2882x2032.pickle'
t0 = datetime.datetime(2019, 10, 31, 16, 0)
tf = datetime.datetime(2019, 10, 31, 16, 0)
fcst_len = None
accum_len = None
output_dt = 10
sample_pr_file = test_data_dir + 'hrdps_5p1_prp0.fst'
ncores = 1
complete_dataset = False
median_filt = None
smooth_radius = None
figure_dir = test_results_dir
figure_format = 'svg'
args = FstArgs()
#file containing the PR variable
fst_template = fst_tools.get_data(args.sample_pr_file,
var_name='PR',
latlon=True)
#this command writes a fst file and makes a figure from it
make_radar_fst.to_fst(args.t0, fst_template, args)
#pre saved figure
new_figure = '/fs/homeu1/eccc/mrd/ords/rpndat/dja001/python/packages/domutils_package/test_results//make_radar_fst/20191031_1600.svg'
#pre saved figure for what the results should be
reference_image = package_dir + '/test_data/_static/' + os.path.basename(
new_figure)
#compare image with saved reference
#copy reference image to testdir
images_are_similar = py_tools.render_similarly(new_figure,
reference_image)
#test fails if images are not similar
self.assertEqual(images_are_similar, True)