def grid_and_plot(h5_filenames,
base_sort_dir,
dx=1.0e3,
dy=1.0e3,
dz=1.0e3,
frame_interval=60.0,
x_bnd=(-200.0e3, 200.0e3),
y_bnd=(-200.0e3, 200.0e3),
z_bnd=(0.0e3, 20.0e3),
ctr_lat=33.5,
ctr_lon=-101.5,
center_ID='WTLMA',
n_cols=2,
base_date=None):
""" Given a list of HDF5 filenames (sorted by time order) in h5_filenames,
create 2D and 3D NetCDF grids with spacing dx, dy, dz in meters,
frame_interval in seconds, and tuples of grid edges
x_bnd, y_bnd, and z_bnd in meters
The actual grids are in regular lat,lon coordinates, with spacing at the
grid center matched to the dx, dy values given.
n_cols controls how many columns are plotted on each page.
Grids and plots are written to base_sort_dir/grid_files/ and base_sort_dir/plots/
base_date is used to optionally set a common reference time for each of the NetCDF grids.
"""
# not really in km, just a different name to distinguish from similar variables below.
dx_km = dx
dy_km = dy
x_bnd_km = x_bnd
y_bnd_km = y_bnd
z_bnd_km = z_bnd
grid_dir = os.path.join(base_sort_dir, 'grid_files')
plot_dir = os.path.join(base_sort_dir, 'plots')
# There are similar functions in lmatools to grid on a regular x,y grid in some map projection.
dx, dy, x_bnd, y_bnd = dlonlat_at_grid_center(ctr_lat,
ctr_lon,
dx=dx_km,
dy=dy_km,
x_bnd=x_bnd_km,
y_bnd=y_bnd_km)
# print("dx, dy = {0}, {1} deg".format(dx,dy))
# print("lon_range = {0} deg".format(x_bnd))
# print("lat_range = {0} deg".format(y_bnd))
for f in h5_filenames:
y, m, d, H, M, S = tfromfile(f)
# print y,m,d,H,M,S
start_time = datetime(y, m, d, H, M, S)
end_time = start_time + timedelta(0, 600)
date = start_time
# print start_time, end_time
outpath = grid_dir + '/20%s' % (date.strftime('%y/%b/%d'))
if os.path.exists(outpath) == False:
os.makedirs(outpath)
subprocess.call([
'chmod', 'a+w', outpath,
grid_dir + '/20%s' % (date.strftime('%y/%b')),
grid_dir + '/20%s' % (date.strftime('%y'))
])
if True:
grid_h5flashfiles(h5_filenames,
start_time,
end_time,
frame_interval=frame_interval,
proj_name='latlong',
base_date=base_date,
energy_grids=True,
dx=dx,
dy=dy,
x_bnd=x_bnd,
y_bnd=y_bnd,
z_bnd=z_bnd_km,
ctr_lon=ctr_lon,
ctr_lat=ctr_lat,
outpath=outpath,
output_writer=write_cf_netcdf_latlon,
output_writer_3d=write_cf_netcdf_3d_latlon,
output_filename_prefix=center_ID,
spatial_scale_factor=1.0)
# Create plots
mapping = {
'source': 'lma_source',
'flash_extent': 'flash_extent',
'flash_init': 'flash_initiation',
'footprint': 'flash_footprint',
'specific_energy': 'specific_energy',
'flashsize_std': 'flashsize_std',
'total_energy': 'total_energy'
}
nc_names = glob.glob(grid_dir + '/20%s/*.nc' % (date.strftime('%y/%b/%d')))
nc_names_3d = glob.glob(grid_dir + '/20%s/*_3d.nc' %
(date.strftime('%y/%b/%d')))
nc_names_2d = list(set(nc_names) - set(nc_names_3d))
nc_names_2d.sort()
nc_names_3d.sort()
outpath = plot_dir + '/20%s' % (date.strftime('%y/%b/%d'))
if os.path.exists(outpath) == False:
os.makedirs(outpath)
subprocess.call([
'chmod', 'a+w', outpath,
plot_dir + '/20%s' % (date.strftime('%y/%b')),
plot_dir + '/20%s' % (date.strftime('%y'))
])
for f in nc_names_2d:
gridtype = f.split('dx_')[-1].replace('.nc', '')
var = mapping[gridtype]
make_plot(f,
var,
n_cols=n_cols,
x_name='longitude',
y_name='latitude',
outpath=outpath)
for f in nc_names_3d:
gridtype = f.split('dx_')[-1].replace('.nc', '').replace('_3d', '')
var = mapping[gridtype]
# grid_range = range_mapping[gridtype]
###Read grid files, then plot in either 2d or 3d space###
grid, grid_name, x, y, z, t, grid_t_idx, grid_x_idx, grid_z_idx = read_file_3d(
f, var, x_name='longitude', y_name='latitude', z_name='altitude')
make_plot_3d(grid,
grid_name,
x,
y,
z,
t,
grid_t_idx,
grid_x_idx,
grid_z_idx,
n_cols=n_cols,
outpath=outpath)
#, grid_range=grid_range)
return nc_names_2d, nc_names_3d
def test_sort_and_grid_and_plot(outpath):
""" Given an output path, run sample data included in lmatools through flash sorting and gridding"""
base_sort_dir = outpath
logger_setup(outpath)
files = get_sample_data_list()
center_ID = 'WTLMA'
ctr_lat, ctr_lon = 33.5, -101.5
params = {
'stations': (6, 13),
'chi2': (0, 1.0),
'ctr_lat': ctr_lat,
'ctr_lon': ctr_lon,
'distance': 3000.0,
'thresh_critical_time': 0.15,
'thresh_duration': 3.0,
'mask_length': 6,
}
h5_dir = os.path.join(base_sort_dir, 'h5_files')
grid_dir = os.path.join(base_sort_dir, 'grid_files')
plot_dir = os.path.join(base_sort_dir, 'plots')
y, m, d, H, M, S = tfromfile(files[0])
date = datetime(y, m, d, H, M, S)
# Create HDF5 flash files
base_out_dir = (h5_dir + "/20%s" % (date.strftime('%y/%b/%d')))
if os.path.exists(base_out_dir) == False:
os.makedirs(base_out_dir)
subprocess.call([
'chmod', 'a+w', base_out_dir,
h5_dir + '/20%s' % (date.strftime('%y/%b')),
h5_dir + '/20%s' % (date.strftime('%y'))
])
tag = ''
outdir = os.path.join(base_out_dir, tag)
info = open(os.path.join(outdir, 'input_params.py'), 'w')
info.write(str(params))
info.close()
run_files_with_params(files,
outdir,
params,
cluster,
retain_ascii_output=False,
cleanup_tmp=True)
# Figure out which HDF5 files were created
h5_filenames = glob.glob(h5_dir + '/20%s/LYLOUT*.dat.flash.h5' %
(date.strftime('%y/%b/%d')))
h5_filenames.sort()
# Create NetCDF gridded data
frame_interval = 60.0 * 2 # seconds
dx_km = 3.0e3 # meters
dy_km = 3.0e3
x_bnd_km = (-200e3, 200e3)
y_bnd_km = (-200e3, 200e3)
# There are similar functions in lmatools to grid on a regular x,y grid in some map projection.
dx, dy, x_bnd, y_bnd = dlonlat_at_grid_center(ctr_lat,
ctr_lon,
dx=dx_km,
dy=dy_km,
x_bnd=x_bnd_km,
y_bnd=y_bnd_km)
# print("dx, dy = {0}, {1} deg".format(dx,dy))
# print("lon_range = {0} deg".format(x_bnd))
# print("lat_range = {0} deg".format(y_bnd))
for f in h5_filenames:
y, m, d, H, M, S = tfromfile(f)
# print y,m,d,H,M,S
start_time = datetime(y, m, d, H, M, S)
end_time = start_time + timedelta(0, 600)
# print start_time, end_time
outpath = grid_dir + '/20%s' % (date.strftime('%y/%b/%d'))
if os.path.exists(outpath) == False:
os.makedirs(outpath)
subprocess.call([
'chmod', 'a+w', outpath,
grid_dir + '/20%s' % (date.strftime('%y/%b')),
grid_dir + '/20%s' % (date.strftime('%y'))
])
grid_h5flashfiles(h5_filenames,
start_time,
end_time,
frame_interval=frame_interval,
proj_name='latlong',
dx=dx,
dy=dy,
x_bnd=x_bnd,
y_bnd=y_bnd,
ctr_lon=ctr_lon,
ctr_lat=ctr_lat,
outpath=outpath,
output_writer=write_cf_netcdf_latlon,
output_filename_prefix=center_ID,
spatial_scale_factor=1.0,
energy_grids=True)
# Create plots
n_cols = 2
mapping = {
'source': 'lma_source',
'flash_extent': 'flash_extent',
'flash_init': 'flash_initiation',
'footprint': 'flash_footprint'
}
#nc_names = glob.glob('/home/ebruning/Mar18-19/grids/*.nc')
nc_names = glob.glob(grid_dir + '/20%s/*.nc' % (date.strftime('%y/%b/%d')))
nc_names.sort()
outpath = plot_dir + '/20%s' % (date.strftime('%y/%b/%d'))
if os.path.exists(outpath) == False:
os.makedirs(outpath)
subprocess.call([
'chmod', 'a+w', outpath,
plot_dir + '/20%s' % (date.strftime('%y/%b')),
plot_dir + '/20%s' % (date.strftime('%y'))
])
for f in nc_names:
gridtype = f.split('dx_')[-1].replace('.nc', '')
var = mapping[gridtype]
# print f
make_plot(f,
var,
n_cols=n_cols,
x_name='longitude',
y_name='latitude',
outpath=outpath)
def test_sort_and_grid_and_plot(outpath):
""" Given an output path, run sample data included in lmatools through flash sorting and gridding"""
base_sort_dir = outpath
logger_setup(outpath)
files = get_sample_data_list()
center_ID = 'WTLMA'
ctr_lat, ctr_lon = 33.5, -101.5
params = {'stations':(6,13),
'chi2':(0,1.0),
'ctr_lat':ctr_lat, 'ctr_lon':ctr_lon,
'distance':3000.0, 'thresh_critical_time':0.15,
'thresh_duration':3.0,
'mask_length':6,
}
h5_dir = os.path.join(base_sort_dir, 'h5_files')
grid_dir = os.path.join(base_sort_dir, 'grid_files')
plot_dir = os.path.join(base_sort_dir, 'plots')
y,m,d,H,M,S = tfromfile(files[0])
date = datetime(y,m,d, H,M,S)
# Create HDF5 flash files
base_out_dir = (h5_dir+"/20%s" %(date.strftime('%y/%b/%d')))
if os.path.exists(base_out_dir) == False:
os.makedirs(base_out_dir)
subprocess.call(['chmod', 'a+w', base_out_dir, h5_dir+'/20%s' %(date.strftime('%y/%b')), h5_dir+'/20%s' %(date.strftime('%y'))])
tag = ''
outdir = os.path.join(base_out_dir, tag)
info = open(os.path.join(outdir, 'input_params.py'), 'w')
info.write(str(params))
info.close()
if True:
cluster = DBSCANFlashSorter(params).cluster
sort_files(files, outdir, cluster)
# Figure out which HDF5 files were created
h5_filenames = glob.glob(h5_dir+'/20%s/LYLOUT*.dat.flash.h5' %(date.strftime('%y/%b/%d')))
h5_filenames.sort()
# Create NetCDF gridded data
frame_interval=60.0*2 # seconds
dx_km=3.0e3 # meters
dy_km=3.0e3
x_bnd_km = (-200e3, 200e3)
y_bnd_km = (-200e3, 200e3)
z_bnd_km = (0.0e3, 15.0e3)
# There are similar functions in lmatools to grid on a regular x,y grid in some map projection.
# dx, dy, x_bnd, y_bnd = dlonlat_at_grid_center(ctr_lat, ctr_lon,
# dx=dx_km, dy=dy_km,
# x_bnd = x_bnd_km, y_bnd = y_bnd_km )
dx, dy, x_bnd, y_bnd = dx_km, dy_km, x_bnd_km, y_bnd_km
# print("dx, dy = {0}, {1} deg".format(dx,dy))
# print("lon_range = {0} deg".format(x_bnd))
# print("lat_range = {0} deg".format(y_bnd))
for f in h5_filenames:
y,m,d,H,M,S = tfromfile(f)
# print y,m,d,H,M,S
start_time = datetime(y,m,d, H,M,S)
end_time = start_time + timedelta(0,600)
# print start_time, end_time
outpath = grid_dir+'/20%s' %(date.strftime('%y/%b/%d'))
if os.path.exists(outpath) == False:
os.makedirs(outpath)
subprocess.call(['chmod', 'a+w', outpath, grid_dir+'/20%s' %(date.strftime('%y/%b')), grid_dir+'/20%s' %(date.strftime('%y'))])
if True:
grid_h5flashfiles(h5_filenames, start_time, end_time, frame_interval=frame_interval,
dx=dx, dy=dy, x_bnd=x_bnd, y_bnd=y_bnd, z_bnd=z_bnd_km,
ctr_lon=ctr_lon, ctr_lat=ctr_lat, outpath = outpath,
proj_name='aeqd',
output_writer = write_cf_netcdf,
output_writer_3d = write_cf_netcdf_3d,
output_filename_prefix=center_ID, spatial_scale_factor=1.0e-3,
# proj_name='latlong',
# output_writer = write_cf_netcdf_latlon,
# output_writer_3d = write_cf_netcdf_3d_latlon,
# output_filename_prefix=center_ID, spatial_scale_factor=1.0,
energy_grids = True
)
# Create plots
n_cols=2
mapping = { 'source':'lma_source',
'flash_extent':'flash_extent',
'flash_init':'flash_initiation',
'footprint':'flash_footprint',
'specific_energy':'specific_energy',
'flashsize_std':'flashsize_std',
'total_energy': 'total_energy'
}
nc_names = glob.glob(grid_dir+'/20%s/*.nc' %(date.strftime('%y/%b/%d')))
nc_names_3d = glob.glob(grid_dir+'/20%s/*_3d.nc' %(date.strftime('%y/%b/%d')))
nc_names_2d = list(set(nc_names) - set(nc_names_3d))
nc_names_2d.sort()
nc_names_3d.sort()
outpath = plot_dir+'/20%s' %(date.strftime('%y/%b/%d'))
if os.path.exists(outpath) == False:
os.makedirs(outpath)
subprocess.call(['chmod', 'a+w', outpath, plot_dir+'/20%s' %(date.strftime('%y/%b')), plot_dir+'/20%s' %(date.strftime('%y'))])
for f in nc_names_2d:
gridtype = f.split('dx_')[-1].replace('.nc', '')
var = mapping[gridtype]
# make_plot(f, var, n_cols=n_cols, x_name='longitude', y_name='latitude', outpath = outpath)
make_plot(f, var, n_cols=n_cols, x_name='x', y_name='y', outpath = outpath)
for f in nc_names_3d:
gridtype = f.split('dx_')[-1].replace('.nc', '').replace('_3d', '')
var = mapping[gridtype]
# grid_range = range_mapping[gridtype]
###Read grid files, then plot in either 2d or 3d space###
# grid, grid_name, x, y, z, t, grid_t_idx, grid_x_idx, grid_z_idx = read_file_3d(f, var, x_name='longitude', y_name='latitude', z_name='altitude')
grid, grid_name, x, y, z, t, grid_t_idx, grid_x_idx, grid_z_idx = read_file_3d(f, var, x_name='x', y_name='y', z_name='z')
make_plot_3d(grid, grid_name, x, y, z, t,
grid_t_idx, grid_x_idx, grid_z_idx,
n_cols = n_cols, outpath = outpath)
This is useful if one wishes to add additional data to the plot, e.g., an GPS track.
Be careful to use the correct map projection information from the NetCDF file when
plotting new data.
It also demonstrates how to change the backend from the default (non-windowed) backend so
that the figure can be worked with interactively.
It is necessary to edit the "backend" import line to use a backend that can
run on your computer.
"""
from __future__ import absolute_import
from lmatools.vis.multiples_nc import make_plot
f,p,start,fname=make_plot('LMA_20040622_052000_600_10src_flash_extent.nc', 'flash_extent', n_cols=1, do_save=False)
# Plot some other stuff here, using
# ax = p.multiples.flat[frame_sequence_id]
# ax.plot(...)
# etc.
# ---EDIT ME --- Import a backend that can run on your computer
from matplotlib.backends.backend_macosx import FigureManagerMac, FigureCanvasMac
# Replace the existing canvas with one that matches the backend
canvas = FigureCanvasMac(f)
f.set_canvas(canvas)
# Tell the OS to make a window
fm = FigureManagerMac(canvas, 1)
def test_sort_and_grid_and_plot(outpath):
""" Given an output path, run sample data included in lmatools through flash sorting and gridding"""
base_sort_dir = outpath
logger_setup(outpath)
files = get_sample_data_list()
center_ID = 'WTLMA'
ctr_lat, ctr_lon = 33.5, -101.5
params = {'stations':(6,13),
'chi2':(0,1.0),
'ctr_lat':ctr_lat, 'ctr_lon':ctr_lon,
'distance':3000.0, 'thresh_critical_time':0.15,
'thresh_duration':3.0,
'mask_length':6,
}
h5_dir = os.path.join(base_sort_dir, 'h5_files')
grid_dir = os.path.join(base_sort_dir, 'grid_files')
plot_dir = os.path.join(base_sort_dir, 'plots')
y,m,d,H,M,S = tfromfile(files[0])
date = datetime(y,m,d, H,M,S)
# Create HDF5 flash files
base_out_dir = (h5_dir+"/20%s" %(date.strftime('%y/%b/%d')))
if os.path.exists(base_out_dir) == False:
os.makedirs(base_out_dir)
subprocess.call(['chmod', 'a+w', base_out_dir, h5_dir+'/20%s' %(date.strftime('%y/%b')), h5_dir+'/20%s' %(date.strftime('%y'))])
tag = ''
outdir = os.path.join(base_out_dir, tag)
info = open(os.path.join(outdir, 'input_params.py'), 'w')
info.write(str(params))
info.close()
if True:
cluster = DBSCANFlashSorter(params).cluster
sort_files(files, outdir, cluster)
# Figure out which HDF5 files were created
h5_filenames = glob.glob(h5_dir+'/20%s/LYLOUT*.dat.flash.h5' %(date.strftime('%y/%b/%d')))
h5_filenames.sort()
# Create NetCDF gridded data
frame_interval=60.0*2 # seconds
dx_km=3.0e3 # meters
dy_km=3.0e3
x_bnd_km = (-200e3, 200e3)
y_bnd_km = (-200e3, 200e3)
z_bnd_km = (0.0e3, 15.0e3)
# There are similar functions in lmatools to grid on a regular x,y grid in some map projection.
dx, dy, x_bnd, y_bnd = dlonlat_at_grid_center(ctr_lat, ctr_lon,
dx=dx_km, dy=dy_km,
x_bnd = x_bnd_km, y_bnd = y_bnd_km )
# print("dx, dy = {0}, {1} deg".format(dx,dy))
# print("lon_range = {0} deg".format(x_bnd))
# print("lat_range = {0} deg".format(y_bnd))
for f in h5_filenames:
y,m,d,H,M,S = tfromfile(f)
# print y,m,d,H,M,S
start_time = datetime(y,m,d, H,M,S)
end_time = start_time + timedelta(0,600)
# print start_time, end_time
outpath = grid_dir+'/20%s' %(date.strftime('%y/%b/%d'))
if os.path.exists(outpath) == False:
os.makedirs(outpath)
subprocess.call(['chmod', 'a+w', outpath, grid_dir+'/20%s' %(date.strftime('%y/%b')), grid_dir+'/20%s' %(date.strftime('%y'))])
if True:
grid_h5flashfiles(h5_filenames, start_time, end_time, frame_interval=frame_interval, proj_name='latlong',
dx=dx, dy=dy, x_bnd=x_bnd, y_bnd=y_bnd, z_bnd=z_bnd_km,
ctr_lon=ctr_lon, ctr_lat=ctr_lat, outpath = outpath,
output_writer = write_cf_netcdf_latlon, output_writer_3d = write_cf_netcdf_3d_latlon,
output_filename_prefix=center_ID, spatial_scale_factor=1.0
)
# Create plots
n_cols=2
mapping = { 'source':'lma_source',
'flash_extent':'flash_extent',
'flash_init':'flash_initiation',
'footprint':'flash_footprint'}
nc_names = glob.glob(grid_dir+'/20%s/*.nc' %(date.strftime('%y/%b/%d')))
nc_names_3d = glob.glob(grid_dir+'/20%s/*_3d.nc' %(date.strftime('%y/%b/%d')))
nc_names_2d = list(set(nc_names) - set(nc_names_3d))
nc_names_2d.sort()
nc_names_3d.sort()
outpath = plot_dir+'/20%s' %(date.strftime('%y/%b/%d'))
if os.path.exists(outpath) == False:
os.makedirs(outpath)
subprocess.call(['chmod', 'a+w', outpath, plot_dir+'/20%s' %(date.strftime('%y/%b')), plot_dir+'/20%s' %(date.strftime('%y'))])
for f in nc_names_2d:
gridtype = f.split('dx_')[-1].replace('.nc', '')
var = mapping[gridtype]
make_plot(f, var, n_cols=n_cols, x_name='longitude', y_name='latitude', outpath = outpath)
for f in nc_names_3d:
gridtype = f.split('dx_')[-1].replace('.nc', '').replace('_3d', '')
var = mapping[gridtype]
# grid_range = range_mapping[gridtype]
###Read grid files, then plot in either 2d or 3d space###
grid, grid_name, x, y, z, t, grid_t_idx, grid_x_idx, grid_z_idx = read_file_3d(f, var, x_name='longitude', y_name='latitude', z_name='altitude')
make_plot_3d(grid, grid_name, x, y, z, t,
grid_t_idx, grid_x_idx, grid_z_idx,
n_cols = n_cols, outpath = outpath)
def grid_and_plot(h5_filenames, base_sort_dir, dx=1.0e3, dy=1.0e3, dz=1.0e3, frame_interval=60.0,
x_bnd=(-200.0e3, 200.0e3), y_bnd=(-200.0e3, 200.0e3), z_bnd=(0.0e3, 20.0e3),
ctr_lat=33.5, ctr_lon=-101.5, center_ID='WTLMA',
n_cols=2, base_date=None
):
""" Given a list of HDF5 filenames (sorted by time order) in h5_filenames,
create 2D and 3D NetCDF grids with spacing dx, dy, dz in meters,
frame_interval in seconds, and tuples of grid edges
x_bnd, y_bnd, and z_bnd in meters
The actual grids are in regular lat,lon coordinates, with spacing at the
grid center matched to the dx, dy values given.
n_cols controls how many columns are plotted on each page.
Grids and plots are written to base_sort_dir/grid_files/ and base_sort_dir/plots/
base_date is used to optionally set a common reference time for each of the NetCDF grids.
"""
# not really in km, just a different name to distinguish from similar variables below.
dx_km=dx
dy_km=dy
x_bnd_km = x_bnd
y_bnd_km = y_bnd
z_bnd_km = z_bnd
grid_dir = os.path.join(base_sort_dir, 'grid_files')
plot_dir = os.path.join(base_sort_dir, 'plots')
# There are similar functions in lmatools to grid on a regular x,y grid in some map projection.
dx, dy, x_bnd, y_bnd = dlonlat_at_grid_center(ctr_lat, ctr_lon,
dx=dx_km, dy=dy_km,
x_bnd = x_bnd_km, y_bnd = y_bnd_km )
# print("dx, dy = {0}, {1} deg".format(dx,dy))
# print("lon_range = {0} deg".format(x_bnd))
# print("lat_range = {0} deg".format(y_bnd))
for f in h5_filenames:
y,m,d,H,M,S = tfromfile(f)
# print y,m,d,H,M,S
start_time = datetime(y,m,d, H,M,S)
end_time = start_time + timedelta(0,600)
date = start_time
# print start_time, end_time
outpath = grid_dir+'/20%s' %(date.strftime('%y/%b/%d'))
if os.path.exists(outpath) == False:
os.makedirs(outpath)
subprocess.call(['chmod', 'a+w', outpath, grid_dir+'/20%s' %(date.strftime('%y/%b')), grid_dir+'/20%s' %(date.strftime('%y'))])
if True:
grid_h5flashfiles(h5_filenames, start_time, end_time, frame_interval=frame_interval, proj_name='latlong',
base_date = base_date, energy_grids=True,
dx=dx, dy=dy, x_bnd=x_bnd, y_bnd=y_bnd, z_bnd=z_bnd_km,
ctr_lon=ctr_lon, ctr_lat=ctr_lat, outpath = outpath,
output_writer = write_cf_netcdf_latlon, output_writer_3d = write_cf_netcdf_3d_latlon,
output_filename_prefix=center_ID, spatial_scale_factor=1.0
)
# Create plots
mapping = { 'source':'lma_source',
'flash_extent':'flash_extent',
'flash_init':'flash_initiation',
'footprint':'flash_footprint',
'specific_energy':'specific_energy',
'flashsize_std':'flashsize_std',
'total_energy': 'total_energy'
}
nc_names = glob.glob(grid_dir+'/20%s/*.nc' %(date.strftime('%y/%b/%d')))
nc_names_3d = glob.glob(grid_dir+'/20%s/*_3d.nc' %(date.strftime('%y/%b/%d')))
nc_names_2d = list(set(nc_names) - set(nc_names_3d))
nc_names_2d.sort()
nc_names_3d.sort()
outpath = plot_dir+'/20%s' %(date.strftime('%y/%b/%d'))
if os.path.exists(outpath) == False:
os.makedirs(outpath)
subprocess.call(['chmod', 'a+w', outpath, plot_dir+'/20%s' %(date.strftime('%y/%b')), plot_dir+'/20%s' %(date.strftime('%y'))])
for f in nc_names_2d:
gridtype = f.split('dx_')[-1].replace('.nc', '')
var = mapping[gridtype]
make_plot(f, var, n_cols=n_cols, x_name='longitude', y_name='latitude', outpath = outpath)
for f in nc_names_3d:
gridtype = f.split('dx_')[-1].replace('.nc', '').replace('_3d', '')
var = mapping[gridtype]
# grid_range = range_mapping[gridtype]
###Read grid files, then plot in either 2d or 3d space###
grid, grid_name, x, y, z, t, grid_t_idx, grid_x_idx, grid_z_idx = read_file_3d(f, var, x_name='longitude', y_name='latitude', z_name='altitude')
make_plot_3d(grid, grid_name, x, y, z, t,
grid_t_idx, grid_x_idx, grid_z_idx,
n_cols = n_cols, outpath = outpath)
#, grid_range=grid_range)
return nc_names_2d, nc_names_3d
n_cols = 2
mapping = {
'source': 'lma_source',
'flash_extent': 'flash_extent',
'flash_init': 'flash_initiation',
'footprint': 'flash_footprint'
}
#nc_names = glob.glob('/home/ebruning/Mar18-19/grids/*.nc')
nc_names = glob.glob(grid_dir + '/20%s/*.nc' % (date.strftime('%y/%b/%d')))
nc_names.sort()
outpath = plot_dir + '/20%s' % (date.strftime('%y/%b/%d'))
if os.path.exists(outpath) == False:
os.makedirs(outpath)
subprocess.call([
'chmod', 'a+w', outpath,
plot_dir + '/20%s' % (date.strftime('%y/%b')),
plot_dir + '/20%s' % (date.strftime('%y'))
])
for f in nc_names:
gridtype = f.split('dx_')[-1].replace('.nc', '')
var = mapping[gridtype]
print(f)
make_plot(f, var, n_cols=n_cols, outpath=outpath)
# make_plot('/data/rtlma/flash_sort/LMA_20120319_010000_600_10src_4000.0m-dx_flash_extent.nc', 'flash_extent', n_cols=n_cols)
# make_plot('/data/rtlma/flash_sort/LMA_20120319_010000_600_10src_4000.0m-dx_flash_init.nc', 'flash_initiation', n_cols=n_cols)
# make_plot('/data/rtlma/flash_sort/LMA_20120319_010000_600_10src_4000.0m-dx_source.nc', 'lma_source', n_cols=n_cols)
# make_plot('/data/rtlma/flash_sort/LMA_20120319_010000_600_10src_4000.0m-dx_footprint.nc', 'flash_footprint', n_cols=n_cols)
dx=dx, dy=dy, x_bnd=x_bnd, y_bnd=y_bnd, ctr_lon=ctr_lon, ctr_lat=ctr_lat, outpath = outpath,
output_writer = write_cf_netcdf_latlon, output_filename_prefix=center_ID, spatial_scale_factor=1.0
)
# Make plots
if False:
n_cols=2
mapping = { 'source':'lma_source',
'flash_extent':'flash_extent',
'flash_init':'flash_initiation',
'footprint':'flash_footprint'}
#nc_names = glob.glob('/home/ebruning/Mar18-19/grids/*.nc')
nc_names = glob.glob(grid_dir+'/20%s/*.nc' %(date.strftime('%y/%b/%d')))
nc_names.sort()
outpath = plot_dir+'/20%s' %(date.strftime('%y/%b/%d'))
if os.path.exists(outpath) == False:
os.makedirs(outpath)
subprocess.call(['chmod', 'a+w', outpath, plot_dir+'/20%s' %(date.strftime('%y/%b')), plot_dir+'/20%s' %(date.strftime('%y'))])
for f in nc_names:
gridtype = f.split('dx_')[-1].replace('.nc', '')
var = mapping[gridtype]
print(f)
make_plot(f, var, n_cols=n_cols, outpath = outpath)
# make_plot('/data/rtlma/flash_sort/LMA_20120319_010000_600_10src_4000.0m-dx_flash_extent.nc', 'flash_extent', n_cols=n_cols)
# make_plot('/data/rtlma/flash_sort/LMA_20120319_010000_600_10src_4000.0m-dx_flash_init.nc', 'flash_initiation', n_cols=n_cols)
# make_plot('/data/rtlma/flash_sort/LMA_20120319_010000_600_10src_4000.0m-dx_source.nc', 'lma_source', n_cols=n_cols)
# make_plot('/data/rtlma/flash_sort/LMA_20120319_010000_600_10src_4000.0m-dx_footprint.nc', 'flash_footprint', n_cols=n_cols)