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make_combined_csvs.py
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make_combined_csvs.py
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"""
For now : produce full-area for 5 hours from a particular day.
Note that the data arrangements in the original data is peculiar...
the forecast periods (and times) are not regular, even for the same (phenomenon) time next day
-- and this is in the filenames, so those aren't regular either.
-- you must use glob, and then try to pick matching forecast periods ??
"""
import csv
import math
import datetime
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import iris
import iris.quickplot as qplt
#details of test-data set
testcode_basepath = '~/git/grib_to_csv/testdata/'
testdata_filenames = ["201103200900_u1096_ng_ek07_wind0360_speed10m.grib", "201103200900_u1096_ng_ek07_wind0360_dir10m.grib"]
# set maximum clip region to avoid some missing values in the test data
valid_lon_lims = [-10.4, 4.0]
valid_lat_lims = [48.3, 60.25]
def produce_csv_files(
region=None,
in_filenames=None, out_filepath=None,
show_plot=False, use_subplot=None
):
"""
Produce output CSV files for some data of windspeed and direction.
region - required output (lon-min, lat-min, lon-max, lat-max) [default: roughly 'SW-England']
in_filenames - filenames of (speed,angle) input files [default: testdata examples]
out_filepath - pathname of output file [default: "./windspeed_direction.csv"]
show_plot - pop up window with windspeed data [default: False]
"""
# fixup path defaults
if region is None:
region = [(-4.5, 50.0), (-2.0, 52.5)]
region = np.array(region).flat
if in_filenames is None:
in_filenames = testdata_filenames
in_filenames = [testdata_basepath+f for f in in_filenames]
if out_filepath is None:
out_filepath = "./windspeed_direction.csv"
# load the input data
speed_path, angle_path = in_filenames
speeds_full = iris.load_cube(speed_path)
angles_full = iris.load_cube(angle_path)
# set required region
reqd_lon_lims = [region[0], region[2]] #[-4.5,-2.0]
reqd_lat_lims = [region[1], region[3]] #[50.0, 52.5]
# cutout required data block
region_lon_lims = [max(reqd_lon_lims[0], valid_lon_lims[0]), min(reqd_lon_lims[1], valid_lon_lims[1])]
region_lat_lims = [max(reqd_lat_lims[0], valid_lat_lims[0]), min(reqd_lat_lims[1], valid_lat_lims[1])]
region_ll = iris.Constraint(
longitude=lambda x: region_lon_lims[0] <= x <= region_lon_lims[1],
latitude=lambda y: region_lat_lims[0] <= y <= region_lat_lims[1]
)
speeds_region = speeds_full.extract(region_ll)
angles_region = angles_full.extract(region_ll)
# calculate
lats = speeds_region.coord('latitude').points
lons = speeds_region.coord('longitude').points
n_lons = len(lons)
lon0, lon1 = lons[[0,-1]]
d_lons = lons[1]-lons[0]
n_lats = len(lats)
lat0, lat1 = lats[[0,-1]]
d_lats = lats[1]-lats[0]
# show_plot = True
if show_plot:
# if use_subplot is not None:
# plt.subplot(use_subplot)
qplt.contourf(speeds_region, np.arange(0,25,2))
# plt.gca().coastlines(resolution='50m')
plt.gca().coastlines()
# add wind arrows
n_subsam = 25
u_spd = speeds_region.units
q_lons = lons[::n_subsam]
q_lats = lats[::n_subsam]
q_spds = u_spd.convert(speeds_region.data[::n_subsam,::n_subsam], 'knot')
print 'max m/s = ',np.max(speeds_region.data)
print 'max kts = ',np.max(q_spds)
q_angs_c = angles_region.data[::n_subsam,::n_subsam] * math.pi/180.0
arrow_speed_scaling = 0.1
q_dxs = arrow_speed_scaling * q_spds * np.cos(q_angs_c)
q_dys = arrow_speed_scaling * q_spds * np.sin(q_angs_c)
plt.quiver(q_lons,q_lats,q_dxs,q_dys)
# plt.show()
#fix basic comment header
header_lines = \
"""
# header: latlon data grid of values
#
# content: "windspeed", "direction"
# units: "kt", "deg"
# file format:
# n-comment-lines: (n)
# comment lines [I.E. _these_ lines] ...
# header lines:
# latitude grid info: n-lats, lat-min, lat-max, lat-step
# longitude grid info: n-lons, lon-min, lon-max, lon-step
# data lines:
# n-lats * (line: n-lons * (speed, direction))
"""
# convert header : split at newlines, skip blank lines, save each as a 1-element list for an output 'row'
# split comment into lines
header_lines = header_lines.split('\n')
# strip out spaces
header_lines = [l.strip() for l in header_lines]
# each nonblank becomes a single output item (one "column" in the file)
header_lines = [[l] for l in header_lines if len(l)]
array_speeds_knots = speeds_region.units.convert(speeds_region.data, 'knots')
array_angles_degrees = angles_region.data #angles_region.units.convert(angles_region.data, 'degrees')
reshaped_data = (array_speeds_knots, array_angles_degrees)
reshaped_data = [array.reshape([n_lats, n_lons, 1]) for array in reshaped_data]
reshaped_data = np.concatenate(reshaped_data, 2).reshape((n_lats, 2*n_lons))
with open(out_filepath, 'wb') as f:
wr =csv.writer(f, quoting=csv.QUOTE_NONNUMERIC)
wr.writerow([len(header_lines)])
wr.writerows(header_lines)
wr.writerow([n_lats, lat0, lat1, d_lats])
wr.writerow([n_lons, lon0, lon1, d_lons])
wr.writerows(reshaped_data)
print 'output file produced : ', out_filepath
print 'Done.'
# own backup stored (for one day)
#default_data_basepath = '/data/local/itpp/Remedy/_i_csv_winds_WO0000000037553/captured_testdata/day_0801/'
#default_data_basepath = '/data/local/itpp/Remedy/_i_csv_winds_WO0000000037553/captured_testdata/day_0924/'
# public all-data store (bit massive!)
#default_data_basepath = '/data/nwp1/cfst/MOGUK/grib/'
default_data_basepath = '~/git/grib_to_csv/testdata/'
dayhours_outpath = '~/git/grib_to_csv/testdata/output'
def pairof_MOGUK_search_filepaths(date_string, n_hour, data_basepath=default_data_basepath):
""" Construct a pair of search strings for speed,direction files. """
hr_string = "%02d" % n_hour
glob_spec_strs = [data_basepath + date_string + hr_string + t_s for t_s in ('*speed10mmean.grib','*dir10mmean.grib')]
return glob_spec_strs
import glob
def do_day_hours(day_date_string, hour_numbers, basepath=None, show_plots=False):
n_plots = len(hour_numbers)
n_plotrows = 2 if n_plots>3 else 1 # for now
if show_plots:
# plt.interactive(True)
plt.figure()
for (i_plot, hr) in enumerate(hour_numbers):
# get 2 filespecs to search for datafiles
spec_strings = pairof_MOGUK_search_filepaths(day_date_string, hr)
# for now, take **alphabetical last** of files matching target time (==latest forecast date)
print "spec_strings", spec_strings
filepair = [sorted(glob.glob(spec))[-1] for spec in spec_strings]
base_outname = "alluk_%6s_%02d_" % (day_date_string, hr)
outdir_path=dayhours_outpath+('day_%s/' % day_date_string[-4:])
out_filepath=outdir_path+base_outname+'spd_and_dir.csv'
if show_plots:
n_subplot = 100*n_plotrows + 10*((n_plots+n_plotrows-1) // n_plotrows) + i_plot+1
print 'subplot : ', n_subplot
plt.subplot(n_subplot)
produce_csv_files(
region=[-1000,-1000,1000,1000],
in_filenames=filepair,
out_filepath=out_filepath,
show_plot=show_plots
)
if show_plots:
plt.show()
if __name__ == '__main__':
# script code
# stored example day
t = 1
# do_day_hours('20120801', [12,13,14,15,16,17])
do_day_hours('20120924', [7,9,11,13,15,17], show_plots=False)
# do_day_hours('20120925', [5,9,13,17,21], show_plots=True)
# do_day_hours('20120801', [9,15,21], show_plots=True)