import Ngl
default_type = "numpy"
import numpy
from utils import *
plat = 0.0
plon = -2.0
lat = [0.0, 1.0, 0.0, -1.0, 0.0]
lon = [-3.0, -2.0, -1.0, -2.0, -3.0]
inout1 = Ngl.gc_inout(0.0, -2.0, lat, lon)
inout2 = Ngl.gc_inout(0.0, 0.0, lat, lon)
test_value(
"gc_inout",
inout1,
True,
)
test_value("gc_inout", inout2, False)
lat = [[[ 0.0, 0.0, 2.0], [ 0.0, 0.0, 1.0] ], \
[[ 0.0, 0.0, 2.0], [ 0.0, 0.0, 1.0] ], \
[[ 89.0, 89.0, 89.0], [ 0.0, 0.0, 80.0] ]]
lon = [[[ 0.0, 2.0, 1.0], [ -1.0, 1.0, -1.0] ], \
[[ 0.0, 2.0, 1.0], [ -1.0, 1.0, -1.0] ], \
[[ 0.0, 120.0, 240.0], [ 0.0, 90.0, 45.0] ]]
p0_lat = numpy.array([[ 1.0, 0.0], \
[ 1.0, -0.1], \
[90.0, 45.0]])
p0_lon = numpy.array([[ 1.0, 0.0], \
min_mrb_lon = min(mrb_lon)
max_mrb_lon = max(mrb_lon)
ilt_min = numpy.greater_equal(lat1d,min_mrb_lat).tolist().index(True)
ilt_max = numpy.less_equal(lat1d,max_mrb_lat).tolist().index(False)-1
iln_min = numpy.greater_equal(lon1d,min_mrb_lon).tolist().index(True)
iln_max = numpy.less_equal(lon1d,max_mrb_lon).tolist().index(False)-1
#
# Loop through area of interest, and create a mask for values
# inside the MRB area.
#
mask = numpy.ones(data.shape)
for ilt in range(ilt_min,ilt_max+1):
for iln in range(iln_min,iln_max+1):
if(Ngl.gc_inout(lat1d[ilt],lon1d[iln],mrb_lat,mrb_lon) != 0):
mask[ilt][iln] = 0
#---Mask original data array against mask array
data_mask = ma.array(data,mask=mask)
#---Change the main title
res.tiMainString = "Contours masked by Mississippi River Basin"
#---Create contours of masked array and add MRB outline
map = Ngl.contour_map(wks,data_mask,res)
lines = add_mrb_lines(wks,map,mrb_lat,mrb_lon)
#---Draw masked contours and advance frame
Ngl.draw(map)
Ngl.frame(wks)
radius=radius_in_meters,
n_samples=n_points,
endpoint=False)
circle_array = np.asarray(circle_points)
#create circle and extract circle latitude and longitudes into separate arrays
circle_lats = circle_array[:, 1]
circle_lons = circle_array[:, 0]
#create empty arrays for holding storms indices within circle
storm_in_range = []
times_in_range = []
for i in range(nstorms): #loop through storms
tmp = []
for j in range(ntimes): #loop through times
if Ngl.gc_inout(clats[i, j], clons[i, j], circle_lats,
circle_lons) == 1: #if track crosses inside circle
storm_in_range.append(i) #append the storm index to list
tmp.append(
j
) #append the time indices when the storm is within the circle
times_in_range.append(tmp)
storms = np.unique(storm_in_range) #get rid of repeating storms
print(len(storms))
out_precip = []
out_wind = []
out_gusts = []
out_distance = []
out_category = []
out_years = []
