def avg_time_series(nc_in, param_in, region, box_in, model_in, print_info):
'''
avg_time_series ....
Parameters
----------
nc_in : string
path to nc file to be analyzed
param : string
the parameter to be read from the nc files
region : string
Just the name of a region
box_in : numerical arrays
array of 4 numbers that indicates the long1, long2, lat1, lat2 of the
region
model_in : string
Just the name of the model
Returns
-------
'''
print(nc_in)
print(param_in)
print(region)
print(box_in)
print(model_in)
nc_in_filename = os.path.basename(nc_in) # ###
nc_fldmean = os.path.splitext(nc_in_filename)[0]+'_'+region+"_fldmean_85.nc"
nc_ymean = os.path.splitext(nc_in_filename)[0]+'_'+region+"_ymean_85.nc"
png_fldmean = os.path.splitext(nc_in_filename)[0]+'_'+region+"_fldmean_85.png"
png_ymean = os.path.splitext(nc_in_filename)[0]+'_'+region+"_ymean_85.png"
out_dir = nc_in.replace(nc_in_filename, 'avg_85')
check_and_create(out_dir)
nc_fldmean = nc_in.replace(nc_in_filename, 'avg_85/'+nc_fldmean)
nc_ymean = nc_in.replace(nc_in_filename, 'avg_85/'+nc_ymean)
png_fldmean = nc_in.replace(nc_in_filename, 'avg_85/'+png_fldmean)
png_ymean = nc_in.replace(nc_in_filename, 'avg_85/'+png_ymean)
print(nc_fldmean)
print(nc_ymean)
print("-"*80)
# Initialize CDO
cdo = Cdo()
cdo.degub = True
data_in = Dataset(nc_in, mode='r') # file handler
if print_info:
ncdump(data_in, True)
print("-"*80)
data_in.close()
box = "-sellonlatbox,%d,%d,%d,%d" % (box_in[0], box_in[1], box_in[2], box_in[3])
# Create nc files for field mean and year mean.
if os.path.exists(nc_fldmean):
print("%s already exists", nc_fldmean)
else:
print("%s Create", nc_fldmean)
cdo.fldmean(input=box+" "+nc_in, output=nc_fldmean, options='-f nc', returnCdf=True)
if os.path.exists(nc_ymean):
print("%s already exists", nc_ymean)
else:
print("%s Create", nc_ymean)
cdo.yearmean(input=nc_fldmean, output=nc_ymean, options='-f nc', returnCdf=True)
# Create file handlers for field mean and year mean
data_fldmean = Dataset(nc_fldmean, mode='r')
data_ymean = Dataset(nc_ymean, mode='r')
# Check field mean is ok
if print_info:
ncdump(data_fldmean, True)
# Check year mean is ok
if print_info:
ncdump(data_ymean, True)
param_year = data_ymean.variables[param_in][:]
print("number of data points: %d" % len(param_year))
print("-"*80)
# plot_time_series (data_fldmeanmean, param_in, region, title_fldmean)
plot_time_series(data_ymean, param_in, region)
# plt.savefig(png_ymean, dpi=300)
data_fldmean.close()
data_ymean.close()
def plot_basempap_regions(nc_in, param_in, region_in, box_in, model_in):
'''
'''
nc_in_filename = os.path.basename(nc_in)
nc_box = os.path.splitext(nc_in_filename)[0]+'_'+region_in+"_box.nc"
png_box = os.path.splitext(nc_in_filename)[0]+'_'+region_in+"_box.png"
out_dir = nc_in.replace(nc_in_filename, 'box/')
check_and_create(out_dir)
nc_box = nc_in.replace(nc_in_filename, 'box/'+nc_box)
png_box = nc_in.replace(nc_in_filename, 'box/'+png_box)
# Initialize CDO
cdo = Cdo()
cdo.degub = True
box = "%d,%d,%d,%d" % (box_in[0], box_in[1], box_in[2], box_in[3]) # box of Cdo
print(box)
print(nc_in)
print(nc_box)
print(png_box)
print(param_in)
cdo.sellonlatbox(box, input=nc_in, output=nc_box, options='-f nc',
returnCdf=True)
fh = Dataset(nc_box, 'r')
lons = fh.variables['lon'][:]
lats = fh.variables['lat'][:]
param = fh.variables[param_in][:, :, :]
# last month of last year
param = param[-1, :, :]
param_units = fh.variables[param_in].units
param_name = fh.variables[param_in].long_name
# close file
fh.close()
# Get some parameters for the Stereographic Projection
# lon_0 = lons.mean()
# lat_0 = lats.mean()
# m = Basemap(projection='moll',lon_0=0,resolution='l')
# m = Basemap(width=50000, height=10000,
# resolution='l', projection='moll',\
# lat_ts=40, lat_0=lat_0, lon_0=lon_0) # stere=stereographic projection
#
# m = Basemap(projection='ortho', lat_0=5, lon_0=-60, resolution='l')
m = Basemap(projection='cass', llcrnrlat=box_in[2]-5, urcrnrlat=box_in[3]+5,\
llcrnrlon=box_in[0]-5, urcrnrlon=box_in[1]+5, resolution='h' ,\
lon_0=box_in[0]+3, lat_0=box_in[2]+3)
lons_dim = len(lons.shape)
if 2 == lons_dim:
lon = lons
lat = lats
elif 1 == lons_dim:
lon, lat = np.meshgrid(lons, lats)
else:
print("Error in lon lat array dimension: %d" % lons_dim)
xi, yi = m(lon, lat)
# Plot Data
cs = m.pcolor(xi, yi, np.squeeze(param), alpha=0.7)
# Add Grid Lines
m.drawparallels(np.arange(-80., 81., 10.), labels=[1, 0, 0, 0], fontsize=10)
m.drawmeridians(np.arange(-180., 181., 10.), labels=[0, 0, 0, 1], fontsize=10)
# Add Coastlines, States, and Country Boundaries
m.drawcoastlines()
# m.drawstates()
m.drawcountries()
m.shadedrelief()
# Add Colorbar
cbar = m.colorbar(cs, location='bottom', pad="10%")
cbar.set_label("%s (%s)" % (param_name, param_units))
draw_screen_poly(box_in, m)
# Add Title
title_region = ('Model ' + model_in + ' for region ' + region_in + ' box '+ box)
plt.title(title_region)
plt.savefig(png_box, dpi=200)
# plt.show()
plt.close()
i_models = 0
# model_array = ['EC-EARTH']
# loop of all models inside the cmip5 project dir
for model, model_path in get_subdirs(project_dir):
# if model not in model_array:
# continue
print(model)
i_models = i_models + 1
if(i_models > max_models):
break
model_path_converted = model_path.replace(project_dir, project_dir_converted)
check_and_create(model_path_converted)
# loop of all parameters inside each model
for param, param_path in get_subdirs(model_path): # me da todo lo que hay dentro de la carpeta modelo (osea parametros)
param_path_converted = param_path.replace(project_dir, project_dir_converted)
check_and_create(param_path_converted)
for region, region_path in get_subdirs(param_path):
region_path_converted = region_path.replace(project_dir, project_dir_converted)
check_and_create(region_path_converted)
# loop all files inside the param path
for file, file_path in get_subfiles(region_path):
if file.startswith("._"):
pass # does nothing
def plot_basempap_regions(nc_in, param_in, region_in, box_in, model_in):
'''
'''
nc_in_filename = os.path.basename(nc_in)
nc_box = os.path.splitext(nc_in_filename)[0] + '_' + region_in + "_box.nc"
png_box = os.path.splitext(
nc_in_filename)[0] + '_' + region_in + "_box.png"
out_dir = nc_in.replace(nc_in_filename, 'box/')
check_and_create(out_dir)
nc_box = nc_in.replace(nc_in_filename, 'box/' + nc_box)
png_box = nc_in.replace(nc_in_filename, 'box/' + png_box)
# Initialize CDO
cdo = Cdo()
cdo.degub = True
box = "%d,%d,%d,%d" % (box_in[0] - 7, box_in[1] + 8, box_in[2] - 10,
box_in[3] + 7) # box of Cdo
print(box)
print(nc_in)
print(nc_box)
print(png_box)
print(param_in)
cdo.sellonlatbox(box,
input=nc_in,
output=nc_box,
options='-f nc',
returnCdf=True)
fh = Dataset(nc_box, 'r')
lons = fh.variables['lon'][:]
lats = fh.variables['lat'][:]
param = fh.variables[param_in][:, :]
# last month of last year
#param = param[-1, :, :]
param_units = fh.variables[param_in].units
param_name = fh.variables[param_in].long_name
# close file
fh.close()
# Get some parameters for the Stereographic Projection
# lon_0 = lons.mean()
# lat_0 = lats.mean()
# m = Basemap(projection='moll',lon_0=0,resolution='l')
# m = Basemap(width=50000, height=10000,
# resolution='l', projection='moll',\
# lat_ts=40, lat_0=lat_0, lon_0=lon_0) # stere=stereographic projection
#
# m = Basemap(projection='ortho', lat_0=5, lon_0=-60, resolution='l')
m = Basemap(projection='cass', llcrnrlat=box_in[2]-6, urcrnrlat=box_in[3]+4,\
llcrnrlon=box_in[0]-4, urcrnrlon=box_in[1]+6, resolution='h' ,\
lon_0=box_in[0]+3, lat_0=box_in[2]+3)
lons_dim = len(lons.shape)
if 2 == lons_dim:
lon = lons
lat = lats
elif 1 == lons_dim:
lon, lat = np.meshgrid(lons, lats)
else:
print("Error in lon lat array dimension: %d" % lons_dim)
xi, yi = m(lon, lat)
# Plot Data
# cmap = plt.get_cmap('terrain')''
cmap = mpl.colors.ListedColormap([
'royalblue', 'darkgreen', 'green', 'forestgreen', 'yellowgreen',
'khaki', 'wheat', 'burlywood', 'tan', 'goldenrod', 'darkgoldenrod',
'sienna', 'saddlebrown', 'whitesmoke'
])
cmap.set_over('white')
cmap.set_under('blue')
bounds = [0, 50, 200, 400, 600, 800, 1000, 1200, 1400, 1600,
1800] # 11 colors
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
# cb3 = mpl.colorbar.ColorbarBase(ax, cmap=cmap,
# norm=norm,
# boundaries=[-10] + bounds + [10],
# extend='both',
# extendfrac='auto',
# ticks=bounds,
# spacing='uniform',
# orientation='horizontal')
cs = m.pcolor(xi, yi, np.squeeze(param), alpha=0.7, cmap=cmap, norm=norm)
# Add Grid Lines
m.drawparallels(np.arange(-80., 81., 10.),
labels=[1, 0, 0, 0],
fontsize=10)
m.drawmeridians(np.arange(-180., 181., 10.),
labels=[0, 0, 0, 1],
fontsize=10)
# Add Coastlines, States, and Country Boundaries
m.drawcoastlines()
# m.drawstates()
m.drawcountries()
m.shadedrelief()
# Add Colorbar
cbar = m.colorbar(cs, location='bottom', pad="10%")
cbar.set_label("%s (%s)" % (param_name, param_units))
draw_screen_poly(box_in, m)
# Add Title
title_region = ('Model ' + model_in + ' for region ' + region_in +
' box ' + box)
plt.title(title_region)
plt.savefig(png_box, dpi=200)
# plt.show()
plt.close()
def pr_time_series(nc_in, param_in, model_in, print_info=False):
'''
avg_time_series ....
Parameters
----------
nc_in : string
path to nc file to be analyzed
param : string
the parameter to be read from the nc files
region : string
Just the name of a region
box_in : numerical arrays
array of 4 numbers that indicates the long1, long2, lat1, lat2 of the
region
model_in : string
Just the name of the model
Returns
-------
'''
print(nc_in)
print(param_in)
print(model_in)
nc_in_filename = os.path.basename(nc_in) # ###
nc_pmax = os.path.splitext(nc_in_filename)[0]+"_pmax.nc"
nc_pmax_fldmax = os.path.splitext(nc_in_filename)[0]+"_pmax_fldmax.nc"
png_pmax = os.path.splitext(nc_in_filename)[0]+"_pmax.png"
png_pmax_fldmax = os.path.splitext(nc_in_filename)[0]+"_pmax_fldmax.png"
out_dir = nc_in.replace(nc_in_filename, 'pmax')
check_and_create(out_dir)
nc_pmax = nc_in.replace(nc_in_filename, 'pmax/'+nc_pmax)
nc_pmax_fldmax = nc_in.replace(nc_in_filename, 'pmax/'+nc_pmax_fldmax)
png_pmax = nc_in.replace(nc_in_filename, 'pmax/'+png_pmax)
png_pmax_fldmax = nc_in.replace(nc_in_filename, 'pmax/'+png_pmax_fldmax)
print(nc_pmax_fldmax)
print("-"*80)
# Initialize CDO
cdo = Cdo()
cdo.degub = True
data_in = Dataset(nc_in, mode='r') # file handler
if print_info:
ncdump(data_in, True)
print("-"*80)
data_in.close()
# box = "-sellonlatbox,%d,%d,%d,%d" % (box_in[0], box_in[1], box_in[2], box_in[3])
# Create nc files for precipitation max for year.
if os.path.exists(nc_pmax):
print("%s already exists", nc_pmax)
else:
print("%s Create", nc_pmax)
# cdo.yearmax(input=box+" "+nc_in, output=nc_pmax, options='-f nc', returnCdf=True)
cdo.yearmax(input=nc_in, output=nc_pmax, options='-f nc', returnCdf=True)
if os.path.exists(nc_pmax_fldmax):
print("%s already exists", nc_pmax_fldmax)
else:
print("%s Create", nc_pmax_fldmax)
cdo.fldmax(input=nc_pmax, output=nc_pmax_fldmax, options='-f nc', returnCdf=True)
# Create file handlers for field mean and year mean
data_pmax = Dataset(nc_pmax, mode='r')
data_fld_max = Dataset(nc_pmax_fldmax, mode='r')
# Check pmax and fldmax are ok
if print_info:
ncdump(data_pmax, True)
if print_info:
ncdump(data_fld_max, True)
# Check pmax temporal is ok
if print_info:
ncdump(data_pmax, True)
param_year = data_pmax.variables[param_in][:]
print("number of data points: %d" % len(param_year))
print("-"*80)
title_pmax = ('Precipitation max for ' + nc_in_filename)
plot_time_series(data_fld_max, param_in, title_pmax)
# plt.savefig(png_pmax_fldmax)
plt.savefig('../'+region+'_'+param+'.png', dpi=300) # ../ queda guardado en el directorio anterior en donde esta
data_pmax.close()
data_fld_max.close()
plt.show()