def plot_files(fname, **args):
# Converts the CPT file to be used in Python
cpt = loadCPT('IR4AVHRR6.cpt')
# Makes a linear interpolation with the CPT file
cpt_convert = LinearSegmentedColormap('cpt', cpt)
# Search for the Scan Start in the file name
time = (fname[fname.find("MG_")+3:fname.find(".nc")])
# Format the "Observation Start" string
date = datetime.strptime(time,'%Y%m%d%H%M%S')
# Check if we already created the image
image_string = folder+'images/%s_%s.png' % (channel, datetime.strftime(date,'%Y%m%d%H%M%S'))
if os.path.isfile(image_string):
print('Skipping '+fname)
return None
# print('Using '+fname)
# Open the file using the NetCDF4 library
nc = Dataset(fname)
# Extract the Brightness Temperature values from the NetCDF
temp_b = brigthness_temp(nc.variables[channel])
hrv = nc.variables[channel_hrv][:]
if (hrv.std() > 0.9):
args['bmap'].pcolormesh(args['x_hrv'], args['y_hrv'], hrv, vmin=0, vmax=20, cmap='gray')
args['bmap'].pcolormesh(args['x'], args['y'], np.ma.masked_array(temp_b, temp_b > -38),
vmin=-80, vmax=50, cmap=cpt_convert,
alpha=0.3, linewidth=0, antialiased=True)
else:
args['bmap'].pcolormesh(args['x'], args['y'], temp_b, vmin=-80, vmax=50, cmap=cpt_convert,
antialiased=True)
args['bmap'].drawcoastlines(linewidth=0.5, linestyle='solid', color='white')
args['bmap'].drawcountries(linewidth=0.5, linestyle='solid', color='white')
args['bmap'].drawparallels(np.arange(-90.0, 90.0, 5.), linewidth=0.2,
color='white', labels=[True, False, False, True], fontsize=7)
args['bmap'].drawmeridians(np.arange(0.0, 360.0, 5.), linewidth=0.2,
color='white', labels=[True, False, False, True], fontsize=7)
# Insert the legend
args['bmap'].colorbar(location='right', label='Brightness Temperature [C] / HRV radiance',
size='2%', pad='1%')
date_formatted = datetime.strftime(date,'%a %d %b %Y, %H:%MZ')
annotation(plt.gca(), date_formatted,
loc='upper center',fontsize=9)
annotation(plt.gca(), "SEVIRI %s,%s High Rate data" %(channel, channel_hrv) + u"\N{COPYRIGHT SIGN}"+'EUMETSAT - prepared by Guido Cioni (www.guidocioni.it)' ,
loc='lower left', fontsize=7)
#print('Saving file %s' % image_string)
plt.savefig(image_string, bbox_inches='tight', dpi=200)
plt.clf()
dashes=[4, 4],
color='white',
labels=[True, False, False, True],
fmt='%g',
labelstyle="+/-",
size=8)
dataAboveLimit = masked_array(dataArray,
dataArray < -20) #TIRA OS MENORES QUE -20
dataBelowLimit = masked_array(dataArray,
dataArray >= -20) #TIRA OS MAIORES QUE -20
if int(Band) <= 7 or int(Band) == 15:
# Converts a CPT file to be used in Python
cpt = loadCPT(
'/home/cendas/GOES16_WS_Rodrigo/CloudTopTemperature/Colortables/Square Root Visible Enhancement.cpt'
)
# Makes a linear interpolation
cpt_convert = LinearSegmentedColormap('cpt', cpt)
# Plot the GOES-16 channel with the converted CPT colors (you may alter the min and max to match your preference)
bmap.imshow(dataArray, origin='upper', cmap=cpt_convert, vmin=0, vmax=1)
elif int(Band) > 7 and int(Band) <= 10:
# Converts a CPT file to be used in Python
cptHot = loadCPT(
'/home/cendas/GOES16_WS_Rodrigo/CloudTopTemperature/Colortables/GMT_hot.cpt'
)
cptGray = loadCPT(
'/home/cendas/GOES16_WS_Rodrigo/CloudTopTemperature/Colortables/Square Root Visible Enhancement.cpt'
)
# Makes a linear interpolation
def plot(sistemas_convectivos_nc, glm_nc, local, extent, resolution=2):
if (local == 'Brasil'):
degrees = 10
label_fontsize = 50
elif (local == 'Sudeste'):
degrees = 5
label_fontsize = 8
else:
degrees = 2
resolution = 0.8
label_fontsize = 8
g16glm = Dataset(glm_nc, 'r')
nc = Dataset(sistemas_convectivos_nc)
# Get the latitude and longitude image bounds
geo_extent = nc.variables['geospatial_lat_lon_extent']
min_lon = float(geo_extent.geospatial_westbound_longitude)
max_lon = float(geo_extent.geospatial_eastbound_longitude)
min_lat = float(geo_extent.geospatial_southbound_latitude)
max_lat = float(geo_extent.geospatial_northbound_latitude)
# Choose the visualization extent (min lon, min lat, max lon, max lat)
#extent = [-45.0, -24.5, -39.0, -20.7]
# Choose the image resolution (the higher the number the faster the processing is)
#resolution = 0.8 #2
# Calculate the image extent required for the reprojection
H = nc.variables['goes_imager_projection'].perspective_point_height
x1 = nc.variables['x_image_bounds'][0] * H
x2 = nc.variables['x_image_bounds'][1] * H
y1 = nc.variables['y_image_bounds'][1] * H
y2 = nc.variables['y_image_bounds'][0] * H
# Call the reprojection funcion
grid = remap(sistemas_convectivos_nc, extent, resolution, x1, y1, x2, y2)
tipos = ["todos", "event", "group", "flash"]
for formato in range(4):
glm_variables = [False, False, False, False]
glm_variables[formato] = True
#print(glm_variables)
# Read the data returned by the function ==============================================================
# If it is an IR channel subtract 273.15 to convert to ° Celsius
data = grid.ReadAsArray() - 273.15
# Make pixels outside the footprint invisible
data[data <= -180] = np.nan
# Define the size of the saved picture =================================================================
DPI = 150
fig = plt.figure(figsize=(data.shape[1] / float(DPI),
data.shape[0] / float(DPI)),
frameon=False,
dpi=DPI)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
ax = plt.axis('off')
# Plot the Data =======================================================================================
# Create the basemap reference for the Rectangular Projection
bmap = Basemap(llcrnrlon=extent[0],
llcrnrlat=extent[1],
urcrnrlon=extent[2],
urcrnrlat=extent[3],
epsg=4326)
# Draw the countries and Brazilian states shapefiles
bmap.readshapefile('/home/cendas/GOES16_WS_Rodrigo/GLM/src/BRA_adm1',
'BRA_adm1',
linewidth=0.50,
color='#000000')
if (local == 'Brasil'):
bmap.readshapefile(
'/home/cendas/GOES16_WS_Rodrigo/GLM/src/ne_10m_coastline',
'ne_10m_coastline',
linewidth=0.50,
color='#000000')
#ne_10m_coastline
# Draw parallels and meridians
if (not (local == 'Brasil')):
bmap.drawparallels(np.arange(-90.0, 90.0, degrees),
linewidth=0.3,
dashes=[4, 4],
color='white',
labels=[True, False, False, True],
fmt='%g',
labelstyle="+/-",
size=8)
bmap.drawmeridians(np.arange(0.0, 360.0, degrees),
linewidth=0.3,
dashes=[4, 4],
color='white',
labels=[True, False, False, True],
fmt='%g',
labelstyle="+/-",
size=8)
else:
bmap.drawparallels(np.arange(-90.0, 90.0, degrees),
linewidth=0.3,
dashes=[4, 4],
color='white',
labels=[True, False, False, True],
fmt='%g',
labelstyle="+/-",
size=30)
bmap.drawmeridians(np.arange(0.0, 360.0, degrees),
linewidth=0.3,
dashes=[4, 4],
color='white',
labels=[True, False, False, True],
fmt='%g',
labelstyle="+/-",
size=30)
#Split the dataset with temperatures above and below -20°C
temp = -80
tempAbove = masked_array(data, data < temp)
tempBelow = masked_array(data, data >= temp)
# Converts a CPT file to be used in Python
cptSquareRoot = loadCPT(
'/home/cendas/GOES16_WS_Rodrigo/GLM/src/Square Root Visible Enhancement.cpt'
)
# Makes a linear interpolation
cpt_convert_SquareRoot = LinearSegmentedColormap('cpt', cptSquareRoot)
# Plot the GOES-16 channel with the converted CPT colors (you may alter the min and max to match your preference)
plot_SquareRoot = bmap.imshow(tempAbove,
origin='upper',
cmap=cpt_convert_SquareRoot,
vmin=-80,
vmax=100)
# ===================== LEGENDA ==========================
# Get the unit based on the channel. If channels 1 trough 6 is Albedo. If channels 7 to 16 is BT.
Unit = "Cloud Top Temperature [°C]"
# Choose a title for the plot
Title = " Geostationary Lightning Mapper (GLM) - GOES Satellite"
Latitude = "Latitude"
Longitude = "Longitude"
# Add a black rectangle in the bottom to insert the image description
lon_difference = (extent[2] - extent[0]) # Max Lon - Min Lon
# Add the image description inside the black rectangle
lat_difference = (extent[3] - extent[1]) # Max lat - Min lat
if (not (local == 'Brasil')):
#Labels and its positions #
plt.text(extent[0] + lon_difference * 0.5,
extent[3] + lat_difference * 0.035,
Title,
horizontalalignment='center',
color='black',
size=9)
plt.text(extent[0] + lon_difference * 0.5,
extent[3] + lat_difference * 0.065,
" ",
horizontalalignment='center',
color='black',
size=10)
plt.text(extent[0] + lon_difference * 0.5,
extent[1] - lat_difference * 0.11,
Longitude,
horizontalalignment='center',
color='black',
size=10)
plt.text(extent[0] + lon_difference * 0.5,
extent[1] - lat_difference * 0.15,
" ",
horizontalalignment='center',
color='black',
size=18)
plt.text(extent[0] - lon_difference * 0.15,
extent[1] + lat_difference * 0.5,
Latitude,
verticalalignment='center',
rotation="vertical",
color='black',
size=10)
else:
plt.text(extent[0] + lon_difference * 0.5,
extent[3] + lat_difference * 0.035,
Title,
horizontalalignment='center',
color='black',
size=40)
plt.text(extent[0] + lon_difference * 0.5,
extent[3] + lat_difference * 0.065,
" ",
horizontalalignment='center',
color='black',
size=10)
plt.text(extent[0] + lon_difference * 0.5,
extent[1] - lat_difference * 0.11,
Longitude,
horizontalalignment='center',
color='black',
size=40)
plt.text(extent[0] + lon_difference * 0.5,
extent[1] - lat_difference * 0.15,
" ",
horizontalalignment='center',
color='black',
size=18)
plt.text(extent[0] - lon_difference * 0.15,
extent[1] + lat_difference * 0.5,
Latitude,
verticalalignment='center',
rotation="vertical",
color='black',
size=40)
# ========================================
if (glm_variables[0]): #Todos
# Get Events, Group and flash from Glm file
event_lat = g16glm.variables['event_lat'][:]
event_lon = g16glm.variables['event_lon'][:]
group_lat = g16glm.variables['group_lat'][:]
group_lon = g16glm.variables['group_lon'][:]
flash_lat = g16glm.variables['flash_lat'][:]
flash_lon = g16glm.variables['flash_lon'][:]
# Plot events as large blue dots
event_x, event_y = bmap(event_lon, event_lat)
bmap.plot(event_x, event_y, 'bo', markersize=7, label='Events')
# Plot groups as medium green dots
group_x, group_y = bmap(group_lon, group_lat)
bmap.plot(group_x, group_y, 'go', markersize=3, label='Group')
# Plot flashes as small red dots
flash_x, flash_y = bmap(flash_lon, flash_lat)
bmap.plot(flash_x, flash_y, 'ro', markersize=1, label='Flash')
plt.legend(fontsize=label_fontsize, loc=4)
else:
if (glm_variables[1]):
# Get Events from Glm file
event_lat = g16glm.variables['event_lat'][:]
event_lon = g16glm.variables['event_lon'][:]
# Plot events as large blue dots
event_x, event_y = bmap(event_lon, event_lat)
bmap.plot(event_x, event_y, 'bo', markersize=7, label='Events')
if (glm_variables[2]):
# Get Group from Glm file
group_lat = g16glm.variables['group_lat'][:]
group_lon = g16glm.variables['group_lon'][:]
# Plot groups as medium green dots
group_x, group_y = bmap(group_lon, group_lat)
bmap.plot(group_x, group_y, 'go', markersize=3, label='Group')
if (glm_variables[3]):
# Get Flash from Glm file
flash_lat = g16glm.variables['flash_lat'][:]
flash_lon = g16glm.variables['flash_lon'][:]
# Plot flashes as small red dots
flash_x, flash_y = bmap(flash_lon, flash_lat)
bmap.plot(flash_x, flash_y, 'ro', markersize=1, label='Flash')
plt.legend(fontsize=label_fontsize, loc=4)
#plt.show()
Start = glm_nc[glm_nc.find('_s') + 1:glm_nc.find("_e") - 3]
year = int(Start[1:5])
dayjulian = int(
Start[5:8]) - 1 # Subtract 1 because the year starts at "0"
dayconventional = datetime.datetime(year, 1, 1) + datetime.timedelta(
dayjulian) # Convert from julian to conventional
month = dayconventional.strftime('%m')
day = dayconventional.strftime('%d')
hour = int(Start[8:12])
plt.savefig('/home/cendas/GOES16_WS_Rodrigo/GLM/Output/' + local +
'/glm_' + str(year) + str(month) + str(day) + '_' +
str(hour) + '_' + tipos[formato] + '.png',
dpi=DPI,
pad_inches=0,
transparent=True,
bbox_inches='tight')
plt.close()
# Required libraries
from mpl_toolkits.basemap import Basemap # Import the Basemap toolkit
import matplotlib.pyplot as plt
from netCDF4 import Dataset
import numpy as np # Import the Numpy package
from cpt_convert import loadCPT # Import the CPT convert function
from matplotlib.colors import LinearSegmentedColormap # Linear interpolation for color maps
from datetime import datetime
from glob import glob
import os
from utils import brigthness_temp
# Converts the CPT file to be used in Python
cpt = loadCPT('IR4AVHRR6.cpt')
# Makes a linear interpolation with the CPT file
cpt_convert = LinearSegmentedColormap('cpt', cpt)
path = '/work/mh0731/m300382/sat/ilona/'
fnames = sorted(glob(path + "*.nc"))
first = True
coords = []
dates = []
# for fname in fnames[35:340]:
for fname in fnames[35:340]:
# Search for the Scan Start in the file name
time = (fname[fname.find("MG_") + 3:fname.find(".nc")])
# Format the "Observation Start" string
date = datetime.strptime(time, '%Y%m%d%H%M%S')
print ('\n4. Ploteando datos')
# Obtiene las coordenadas extremas de la ventana
coorven = coordenadasVentana(x,y,offset)
# Plotea los datos
plotDatos(x,y,offset,var,path,coorven,dataVen,dataMin,dataMax,dataMean,varBus,varName,stdName,unidades,varCal,paleta,logoPath,muestraDatos)
# Muestra la información del proceso
print ('Region Mapeada en:', round(t.time() - start,4), 'segundos')
print ('Latitud :',y)
print ('Longitud :',x)
print (var+' :',varBus)
# TEST DE LA FUNCIÓN
cpt = loadCPT('temperature.cpt')
cpt_convert = LinearSegmentedColormap('cpt', cpt)
# Parametros requeridos
path = 'OR_ABI-L2-SSTF-M3_G16_s20180661300403_e20180661356170_c20180661400158.nc'
var = 'SST'
x = -112
y = 22
offset = 15
paleta = 'Spectral'
logoPath = 'LANOT.png'
muestraDatos = True
# Función plot buscador Goes16
plotBuscador(path,var,x,y,offset,paleta,logoPath,muestraDatos)
def dataproc(ruta, path, paleta):
# path="OR_ABI-L2-CMIPM1-M3C14_G16_s20190280235234_e20190280235291_c20190280235337.nc"
# Open NC file
nc = Dataset(path)
# Load data
data_subset = nc.variables['CMI'][:][:, :]
# Create the projection variables
ori_proj = nc.variables['goes_imager_projection']
sat_h = ori_proj.perspective_point_height
sat_lon = ori_proj.longitude_of_projection_origin
sat_sweep = ori_proj.sweep_angle_axis
# The projection x and y coordinates equals
# the scanning angle (in radians) multiplied by the satellite height (http://proj4.org/projections/geos.html)
X = nc.variables['x'][:] * sat_h
Y = nc.variables['y'][:] * sat_h
p = Proj(proj='geos', h=sat_h, lon_0=sat_lon, sweep=sat_sweep)
# Convert map points to latitude and longitude with the magic provided by Pyproj
XX, YY = np.meshgrid(X, Y)
lons, lats = p(XX, YY, inverse=True)
ii = []
jj = []
for i in range(lons.shape[1]):
for j in range(lats.shape[0]):
if lons[-1, i] >= -83.2 and lats[j, -1] >= 22.3 and lons[
-1, i] <= -81.4 and lats[j, -1] <= 23.7:
ii.append(i)
jj.append(j)
jmin, jmax = (int(np.min(ii)), int(np.max(ii)))
imin, imax = (int(np.min(jj)), int(np.max(jj)))
# Create map
DPI = 150
ax = plt.figure(figsize=(2000 / float(DPI), 2000 / float(DPI)),
frameon=True,
dpi=DPI)
bmap = Basemap(projection='cyl',
llcrnrlon=lons[-1, jmin],
llcrnrlat=lats[imax, -1],
urcrnrlon=lons[-1, jmax],
urcrnrlat=lats[imin, -1],
resolution='h')
bmap.drawparallels(np.arange(-90.0, 90.0, 10.0),
linewidth=0.1,
color='black',
labels=[True, False, False, True])
bmap.drawmeridians(np.arange(0.0, 360.0, 10.0),
linewidth=0.1,
color='black',
labels=[True, False, False, True])
# Load Shapefile
# bmap.readshapefile('Nueva_DPA','Nueva_DPA',linewidth=0.90,color='darkslategray')
bmap.drawstates(color='gray', linewidth=0.25)
bmap.drawcoastlines(color='k', linewidth=0.9)
bmap.drawcountries(color='k', linewidth=0.9)
# Converts a CPT file to be used in Python
cpt = loadCPT(paleta)
# Makes a linear interpolation
cpt_convert = LinearSegmentedColormap('cpt', cpt)
# Plot the GOES-16 channel with the converted CPT colors
#bmap.pcolormesh(lons[imin:imax,jmin:jmax],lats[imin:imax,jmin:jmax],data_subset[imin:imax,jmin:jmax], cmap=cpt_convert, vmin=170, vmax=378)
bmap.pcolormesh(lons[imin:imax, jmin:jmax],
lats[imin:imax, jmin:jmax],
data_subset[imin:imax, jmin:jmax] - 273.15,
cmap=cpt_convert,
vmin=-103,
vmax=104)
# Search for the GOES-16 channel in the file name
Band = (path[path.find("M3C") + 3:path.find("_G16")])
# Search for the Scan start in the file name
Start = (path[path.find("_s") + 2:path.find("_e")])
Start_Formatted = Start[0:4] + " Day " + Start[4:7] + " - " + Start[
7:9] + ":" + Start[9:11] + ":" + Start[11:13] + "." + Start[
13:14] + " UTC"
# Search for the Scan end in the file name
End = (path[path.find("_e") + 2:path.find("_c")])
End_Formatted = End[0:4] + " Day " + End[4:7] + " - " + End[
7:9] + ":" + End[9:11] + ":" + End[11:13] + "." + End[13:14] + " UTC"
# Add a title to the plot
plt.title("GOES-16 ABI Band " + Band + "\n Scan from " + Start_Formatted +
" to " + End_Formatted,
fontsize=10)
#bmap.colorbar(location='right', label='Brightness Temperature [K]')
bmap.colorbar(location='right', label='Brightness Temperature [C]')
# Save the result
# Converting from julian day to dd-mm-yyyy
year = int(Start[0:4])
dayjulian = int(
Start[4:7]) - 1 # Subtract 1 because the year starts at "0"
dayconventional = datetime.datetime(year, 1, 1) + datetime.timedelta(
dayjulian) # Convert from julian to conventional
date = dayconventional.strftime(
'%d-%b-%Y') # Format the date according to the strftime directives
date_save = dayconventional.strftime('%d%m%Y')
time_save = Start[7:9] + Start[9:11] + Start[11:13]
plt.savefig('G16_C' + str(Band) + '_' + date_save + '_' + time_save +
'_meso.png',
dpi=DPI,
bbox_inches='tight',
pad_inches=0)
plt.clf()
plt.cla()
bmap.drawmeridians(np.arange(0.0, 360.0, 5),
linewidth=0.3,
dashes=[4, 4],
color='white',
labels=[True, False, False, True],
fmt='%g',
labelstyle="+/-",
size=10)
#Split the dataset with temperatures above and below -20°C
temp = -20
tempAbove = masked_array(data, data < temp)
tempBelow = masked_array(data, data >= temp)
# Converts a CPT file to be used in Python
cptRainbow = loadCPT('..\\colortable\\Rainbow.cpt')
cptSquareRoot = loadCPT('..\\colortable\\Square Root Visible Enhancement.cpt')
# Makes a linear interpolation
cpt_convert_SquareRoot = LinearSegmentedColormap('cpt', cptSquareRoot)
cpt_convert_Rainbow = LinearSegmentedColormap('cpt', cptRainbow)
# Plot the GOES-16 channel with the converted CPT colors (you may alter the min and max to match your preference)
plot_SquareRoot = bmap.imshow(tempAbove,
origin='upper',
cmap=cpt_convert_SquareRoot,
vmin=-20,
vmax=100)
plot_Rainbow = bmap.imshow(tempBelow,
origin='upper',
cmap=cpt_convert_Rainbow,
def extract_band_info(Band):
'''
Create strings with:
- Central wavelength (Center_WL)
- Variable units (Unit)
- Conversion (K to Celsius) (Conversion)
- Colortable (CPT)
- Min and max values in imshow (Min, Max)
according to the selected band.
'''
if Band == '01':
Center_WL = '(0.47 µm)'
elif Band == '02':
Center_WL = '(0.64 µm)'
elif Band == '03':
Center_WL = '(0.865 µm)'
elif Band == '04':
Center_WL = '(1.378 µm)'
elif Band == '05':
Center_WL = '(1.61 µm)'
elif Band == '06':
Center_WL = '(2.25 µm)'
elif Band == '07':
Center_WL = '(3.90 µm)'
elif Band == '08':
Center_WL = '(6.19 µm)'
elif Band == '09':
Center_WL = '(6.95 µm)'
elif Band == '10':
Center_WL = '(7.34 µm)'
elif Band == '11':
Center_WL = '(8.50 µm)'
elif Band == '12':
Center_WL = '(9.61 µm)'
elif Band == '13':
Center_WL = '(10.35 µm)'
elif Band == '14':
Center_WL = '(11.20 µm)'
elif Band == '15':
Center_WL = '(12.30 µm)'
elif Band == '16':
Center_WL = '(13.30 µm)'
if int(Band) <= 6:
# Unit = "Reflectance"
Unit = "Refletância" # pt-br
Conversion = 0.
else:
# Unit = "Brightness Temperature (°C)"
Unit = "Temperatura de Brilho (°C)" # pt-br
Conversion = -273.15
if int(Band) <= 6:
CPT = loadCPT(
'../Data/GENERAL/colortables/Square Root Visible Enhancement.cpt')
Min, Max = 0, 1
elif int(Band) == 7:
CPT = loadCPT('../Data/GENERAL/colortables/SVGAIR2_TEMP.cpt')
Min, Max = -112.15, 56.85
elif int(Band) > 7 and int(Band) < 11:
CPT = loadCPT('../Data/GENERAL/colortables/SVGAWVX_TEMP.cpt')
Min, Max = -112.15, 56.85
elif int(Band) > 10:
CPT = loadCPT('../Data/GENERAL/colortables/IR4AVHRR6.cpt')
Min, Max = -103, 84
return Center_WL, Unit, Conversion, CPT, Min, Max
fig.add_axes(ax)
ax = plt.axis('off')
#=====================================================================================================
# Plot the Data =======================================================================================
# Create the basemap reference for the Rectangular Projection
bmap = Basemap(llcrnrlon=extent[0], llcrnrlat=extent[1], urcrnrlon=extent[2], urcrnrlat=extent[3], epsg=4326)
# Draw the countries
bmap.readshapefile('/home/usuario/Escritorio/BELEN/geonet/Countries_Shape/ne_10m_admin_0_countries','ne_10m_admin_0_countries',linewidth=0.70,color='black')
# Draw parallels and meridians
bmap.drawparallels(np.arange(-90.0, 90.0, 5.0), linewidth=0.2, dashes=[4, 4], color='white', labels=[False,False,False,False], fmt='%g', labelstyle="+/-", xoffset=-0.80, yoffset=-1.00, size=7)
bmap.drawmeridians(np.arange(0.0, 360.0, 5.0), linewidth=0.2, dashes=[4, 4], color='white', labels=[False,False,False,False], fmt='%g', labelstyle="+/-", xoffset=-0.80, yoffset=-1.00, size=7)
if Band <= 6:
cpt = loadCPT('/home/usuario/Escritorio/BELEN/geonet/Colortables/Square_Root_Visible_Enhancement.cpt')
cpt_convert = LinearSegmentedColormap('cpt', cpt)
bmap.imshow(data, origin='upper', cmap=cpt_convert, vmin=0, vmax=1)
#cb = bmap.colorbar(location='bottom', size = '1%', pad = '-4%', ticks=[0.2, 0.4, 0.6, 0.8])
#cb.ax.set_xticklabels(['20', '40', '60', '80'])
elif Band == 7:
cpt = loadCPT('/home/usuario/Escritorio/BELEN/geonet/Colortables/SVGAIR2_TEMP.cpt')
cpt_convert = LinearSegmentedColormap('cpt', cpt)
bmap.imshow(data, origin='upper', cmap=cpt_convert, vmin=-112.15, vmax=56.85)
#cb = bmap.colorbar(location='bottom', size = '1%', pad = '-4%')
elif Band > 7 and Band < 11:
cpt = loadCPT('/home/usuario/Escritorio/BELEN/geonet/Colortables/SVGAWVX_TEMP.cpt')
cpt_convert = LinearSegmentedColormap('cpt', cpt)
bmap.imshow(data, origin='upper', cmap=cpt_convert, vmin=-112.15, vmax=56.85)
#cb = bmap.colorbar(location='bottom', size = '1%', pad = '-4%')
elif Band > 10:
def ploteador(nombre):
# archi=dire+nombre+'.nc'
# archiM=dire+nombre+'.txt'
archi = dataPATH + nombre # archi=dire+nombre+'.nc'
archiM = dataPATH + nombre[0:73] + '.txt'
if (nombre[8] == '1'):
VAR = nombre[11:14]
elif (archi[8] == '2'):
VAR = nombre[10:13]
VAR = 'CMI'
start = t.time()
# %% Lectura de Metadatos
arch = open(archiM, 'r')
lines = arch.readlines()
#icanal = int(lines[3].split('#')[0])
icanal = 10
cols = int(lines[4].split('#')[0])
rows = int(lines[5].split('#')[0])
t_0 = float(lines[10].split('#')[0])
t_start = lines[11][14:33] # Fecha correspondiente a 0s
# Parametross de calibracion
offset = 0 # float(lines[25].split('#')[0]) # DN->L
scale = 1 # float(lines[26].split('#')[0])
#esun = float(lines[28].split('#')[0])
#kapa0 = float(lines[30].split('#')[0]) # L->reflectancia
#ukapa0 = lines[31].split('#')[0] # unidades
#fk1 = float(lines[34].split('#')[0]) # DN->K
#fk2 = float(lines[36].split('#')[0])
#bc1 = float(lines[38].split('#')[0])
#bc2 = float(lines[40].split('#')[0])
# Parametros de proyeccion
#proj = lines[14].split('#')[0]
lat_0 = lines[13].split('#')[0]
lon_0 = lines[14].split('#')[0]
h = lines[15].split('#')[0]
a = lines[16].split('#')[0]
b = lines[17].split('#')[0]
f = 1 / float(lines[18].split('#')[0])
arch.close
# %%Configuraciones espec'ificas para cada banda
canal = ('%02d' % icanal)
if icanal > 11:
cptfile = 'IR4AVHRR6.cpt'
elif icanal > 7:
cptfile = 'SVGAWVX_TEMP.cpt'
else:
cptfile = 'SVGAIR2_TEMP.cpt'
print(cptfile)
# %% lectura y extraccion de informacion de la pasada
connectionInfo = 'HDF5:\"' + archi + '\"://' + VAR
raw = gdal.Open(connectionInfo, gdal.GA_ReadOnly)
driver = raw.GetDriver().LongName
band = raw.GetRasterBand(1)
bandtype = gdal.GetDataTypeName(band.DataType)
print(bandtype)
# data = band.ReadAsArray(0, 0, cols, rows)
data = band.ReadAsArray()
# %% Proyecciones
# GOES-16 Spatial Reference System
sourcePrj = osr.SpatialReference()
# sourcePrj.ImportFromProj4('+proj='+proj+' +h='+h+' +a='+a+' +b='+b+' +
# f='+str(f)#no es valida proj+'geostationary'
# +'lat_0='+lat_0+' +lon_0='+lon_0+' +sweep=x +no_defs')
sourcePrj.ImportFromProj4('+proj=geos +h=' + h + ' +a=' + a + ' +b=' + b +
' +f=' + str(f) + 'lat_0=' + lat_0 +
' +lon_0=' + lon_0 + ' +sweep=x +no_defs')
# Lat/lon WSG84 Spatial Reference System
targetPrj = osr.SpatialReference()
targetPrj.ImportFromProj4('+proj=longlat +ellps=WGS84 \
+datum=WGS84 + no_defs')
# Setup projection and geo-transformation
raw.SetProjection(sourcePrj.ExportToWkt())
raw.SetGeoTransform(
getGeoT(GOES16_EXTENT, raw.RasterYSize, raw.RasterXSize))
# Compute grid dimension
sizex = int(((extent[2] - extent[0]) * KM_PER_DEGREE) / resolution)
sizey = int(((extent[3] - extent[1]) * KM_PER_DEGREE) / resolution)
# Get memory driver
memDriver = gdal.GetDriverByName('MEM')
# Create grid
grid = memDriver.Create('grid', sizex, sizey, 1, gdal.GDT_Float32)
# Setup projection and geo-transformation
grid.SetProjection(targetPrj.ExportToWkt())
grid.SetGeoTransform(getGeoT(extent, grid.RasterYSize, grid.RasterXSize))
# Perform the projection/resampling
gdal.ReprojectImage(raw,
grid,
sourcePrj.ExportToWkt(),
targetPrj.ExportToWkt(),
gdal.GRA_NearestNeighbour,
options=['NUM_THREADS=ALL_CPUS'])
# Read grid data
array1 = grid.ReadAsArray()
# Mask fill values (i.e. invalid values)
np.ma.masked_where(array1, array1 == -1, False)
# %% Calibracion
array = array1 * scale + offset # DN ->mW m-2 sr-1 mum-1
# if icanal>=7:#DN ->C
# Temp=(fk2 / (np.log((fk1 / array) + 1)) - bc1 ) /
# bc2-273.15#mW m-2 sr-1 mum-1 ->C
# else:
# Temp=kapa0*array#REVISAR!!!
Temp = array - 273.15
# Temp=array
grid.GetRasterBand(1).SetNoDataValue(-1)
grid.GetRasterBand(1).WriteArray(array)
# %% Plot the Data ========================================================
# Create the basemap reference for the Rectangular Projection
t_ini = t_0 + float(
calendar.timegm(t.strptime(t_start, '%Y-%m-%d %H:%M:%S')))
fechaT, fechaN = fecha(t_ini) # Fechas para titulos y nombre de archivo
fig = plt.figure(num=1, clear='True')
# frameon es para NO desplegar la figura
fig = plt.figure(num=1, figsize=[20, 16], dpi=300, frameon='False')
# 4326 es WGS84 (LatLOn)
bmap = Basemap(resolution='h',
llcrnrlon=extent[0],
llcrnrlat=extent[1],
urcrnrlon=extent[2],
urcrnrlat=extent[3],
epsg=4326)
# Draw the shapefiles
bmap.readshapefile(dires + '008_limites_provinciales_LL',
'008_limites_provinciales_LL',
linewidth=0.3,
color='w')
bmap.readshapefile(dires + 'DEPARTAMENTOS_linea',
'DEPARTAMENTOS_linea',
linewidth=0.1,
color='gray')
# bmap.readshapefile(dires3+'Límites_internacionales','Límites_internacionales',linewidth=0.3,color='w')
# bmap.readshapefile(dires+'Ejidos_urbanos_linea','Ejidos_urbanos_linea',linewidth=0.1,color='yellow')#bmap.readshapefile(dires3+'Límites_internacionales','Límites_internacionales',linewidth=0.3,color='w')
# bmap.readshapefile(dires+'Lago_San_Roque','Lago_San_Roque',linewidth=0.5,color='blue')#bmap.readshapefile(dires3+'Límites_internacionales','Límites_internacionales',linewidth=0.3,color='w')
# bmap.readshapefile(dires+'Ejidos_afectados_linea','Ejidos_afectados_linea',linewidth=0.7,color='w')#bmap.readshapefile(dires3+'Límites_internacionales','Límites_internacionales',linewidth=0.3,color='w')
# bmap.readshapefile(dires+'Zona_Afectada_EPSG4326_linea','Zona_Afectada_EPSG4326_linea',linewidth=0.7,color='w')#bmap.readshapefile(dires3+'Límites_internacionales','Límites_internacionales',linewidth=0.3,color='w')
# bmap.readshapefile(dires+'ne_10m_admin_0_map_units/ne_10m_admin_0_map_units','ne_10m_admin_0_map_units',linewidth=0.3,color='w')
# bmap.readshapefile(dires+'limite_interprovincial/Límites_interprovinciales','Límites_interprovinciales',linewidth=0.3,color='w')
# bmap.readshapefile(dires3+'Límites_internacionales','Límites_internacionales',linewidth=0.3,color='w')
# Draw parallels and meridians
bmap.drawparallels(np.arange(-90.0, 90.0, 3.),
linewidth=0.3,
dashes=[4, 4],
color='white',
labels=[True, True, False, False],
fmt='%g',
labelstyle="+/-",
xoffset=0.10,
yoffset=-1.00,
size=5)
bmap.drawmeridians(np.arange(0.0, 360.0, 3.),
linewidth=0.3,
dashes=[4, 4],
color='white',
labels=[False, False, True, False],
fmt='%g',
labelstyle="+/-",
xoffset=-0.80,
yoffset=0.20,
size=5)
# Converts a CPT file to be used in Python
# cptfile = "crisp_ice.cpt"
cpt = loadCPT(direcpt + cptfile)
# Makes a linear interpolation
cpt_convert = LinearSegmentedColormap('cpt', cpt)
# Plot the GOES-16 channel with the converted CPT colors (you may alter
# the min and max to match your preference)
if icanal >= 7:
img_plot = bmap.imshow(Temp,
origin='upper',
cmap=cpt_convert,
vmin=-90,
vmax=50)
else:
img_plot = bmap.imshow(Temp,
origin='upper',
cmap='Greys',
vmin=0.001,
vmax=0.1) # 'Greys'
# Add a black rectangle in the bottom to insert the image description
lon_difference = (extent[2] - extent[0]) # Max Lon - Min Lon
currentAxis = plt.gca()
currentAxis.add_patch(
Rectangle((extent[0], extent[1]),
lon_difference, (lon_difference) * 0.040,
alpha=1,
zorder=3,
facecolor='black'))
if icanal >= 7:
Unit = "Temperatura de Brillo [°C]"
else:
Unit = "Reflectancia"
t_ini = raw.GetMetadata()['date_created']
date = str(t_ini[0:10])
time = str(nombre[34:36]) + ":" + str(nombre[36:38])
Title = " GOES-16 ABI Canal " + canal + " " + date + " " + time + " UTC"
# Title = " GOES-16 ABI Canal "+ canal +" "+ fechaT
Institution = "CONAE-Argentina"
# Add the image description inside the black rectangle
lat_difference = (extent[3] - extent[1]) # Max lat - Min lat
plt.text(extent[0],
extent[1] + lat_difference * 0.005,
Title,
horizontalalignment='left',
color='white',
size=5)
plt.text(extent[2],
extent[1] + lat_difference * 0.005,
Institution,
horizontalalignment='right',
color='white',
size=5)
# Insert the colorbar at the right
cb = bmap.colorbar(location='bottom', size='2%', pad='1%')
cb.outline.set_visible(True) # Remove the colorbar outline
cb.ax.tick_params(width=0) # Remove the colorbar ticks
cb.ax.xaxis.set_tick_params(pad=-2) # Put the colobar labels inside
# the colorbar
cb.ax.tick_params(axis='x', colors='black', labelsize=6) # Change the
# color and size of the colorbar labels
cb.set_label(Unit)
# Export the result to GeoTIFF
# driver = gdal.GetDriverByName('GTiff')
# driver.CreateCopy(dire+'Channel_'+ canal+'_'+
# Region+fechaN+'_WGS84.tif', grid, 0)
# grabar a png
# Volver este
# plt.savefig(dire_out+'Channel_'+ canal+'_'+Region+'_'+
# date+'_'+time+'_WGS84.png',
# dpi=300,bbox_inches='tight', pad_inches=0)
# dire_out2='/media/andres/Elements/GOES16/BTCH13/ANTARTIDA/'
plt.savefig(dire_out + 'Channel_' + canal + '_' + Region + '_' + date +
'_' + time + '_WGS84.png',
dpi=300,
bbox_inches='tight',
pad_inches=0)
# plt.savefig(dire+'Channel_'+ canal+'_'+Region+'_'+date+'_'+time+
# '_WGS84.png', dpi=500,figsize=[20,16])
# Close file
raw = None
print('- finished! Time:', t.time() - start, 'seconds')
# ##############################################################GEOTIFF
grid.GetRasterBand(1).WriteArray(array)
driver = gdal.GetDriverByName('GTiff')
driver.CreateCopy(
dire_out + 'Channel_' + canal + '_' + Region + '_' + date + '_' +
time + '_WGS84.tif', grid, 0)
def _generate_cmap(self):
self._cmap_convert_cpt = loadCPT(self._cmap_to_goes_plot)
self._cmap = LinearSegmentedColormap('cpt', self._cmap_convert_cpt)
def plotRGB(data, extension, imagen, fechaInicio, fechaFinal, fecha, anio,
region, numEvento, nomEvento, tipoCiclon, productoNom,
productoMapa, pathOutputPNG, pathOutputMap, pathOutputTrack):
import os
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
#import matplotlib.image as image
from cpt_convert import loadCPT # Import the CPT convert function
from matplotlib.colors import LinearSegmentedColormap # Linear interpolation for color maps
#import matplotlib.ticker as mticker
import cartopy.crs as crrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import geopandas as gpd
from glob import glob
from PIL import Image
#import cartopy.feature as cfeature
#from matplotlib.lines import Line2D
#from mpl_toolkits.axes_grid1 import make_axes_locatable
# Es X
dimMapY = (extension[2] - extension[0]) / 445000
#print("AQUIIIIIX",dimMapY)
#dimMapX = (extension[1]-extension[3])/445000
#print("AQUIIIIIX",dimMapY)
#print("AQUII2",dimMapX)
#print('EXTENCION',dimMapX,dimMapY)
fechaNom = fecha.split(' ')[0].replace(
'/', '') + '_' + fecha.split(' ')[1].replace(':', '')
if 'EDL_CT_' + anio + '_' + numEvento + '_' + nomEvento + '_' + productoNom + '_' + fecha + ".png" in os.listdir(
pathOutputPNG + region + '/EDL_CT_' + anio + '_' + numEvento +
'_' + nomEvento + '_' + productoNom + '/'):
print('Archivo EDL_CT_' + anio + '_' + numEvento + '_' + nomEvento +
'_' + productoNom + '_' + fecha + ".png" + ' ya esta creado')
else:
cpt = loadCPT('./paletas/IR4AVHRR6.cpt')
cpt_convert = LinearSegmentedColormap('cpt', cpt)
fig = plt.figure(figsize=((extension[2] - extension[0]) / 445000,
(extension[1] - extension[3]) / 445000))
ax = plt.axes([0, 0, 1, 1], projection=crrs.Mercator(), frameon=False)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.autoscale(tight=True)
plt.gca().outline_patch.set_visible(False)
ax.set_extent([extension[0], extension[2], extension[3], extension[1]],
crs=crrs.Mercator())
plt.imshow(
data,
extent=[extension[0], extension[2], extension[3], extension[1]],
vmin=-103.15,
vmax=104.85,
cmap=cpt_convert)
plt.savefig(pathOutputPNG + region + '/EDL_CT_' + anio + '_' +
numEvento + '_' + nomEvento + '_' + productoNom +
'/EDL_CT_' + anio + '_' + numEvento + '_' + nomEvento +
'_' + productoNom + '_' + fechaNom + ".png",
dpi=300,
bbox_inches='tight',
pad_inches=0)
# =========================================================
# Ploteo de mapa de trayectoria
#plt.suptitle(tipoCiclon+' '+nomEvento, y=dimMapY/5)
plt.title(
fechaInicio.strftime(tipoCiclon.upper() + ' ' + nomEvento.upper() +
'\n' + '%Y/%m/%dT%H:%M:%SZ') + ' - ' +
fechaFinal.strftime('%Y/%m/%dT%H:%M:%SZ') + '\n' + productoMapa,
fontsize=dimMapY * 1.4,
color="black")
#grids
gl = ax.gridlines(crs=crrs.PlateCarree(),
draw_labels=True,
linewidth=dimMapY / 10,
color='black',
alpha=0.5,
linestyle='--')
gl.xlabels_top = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.xlabel_style = {'size': dimMapY, 'color': 'black'}
gl.ylabel_style = {'size': dimMapY, 'color': 'black'}
#elemnts
file = glob(pathOutputTrack + region + '/' + anio + '_' + numEvento +
'_' + nomEvento + '/*_lin.shp')
shp_track = file[0].split('/')[-1]
df_track = gpd.read_file(pathOutputTrack + region + '/' + anio + '_' +
numEvento + '_' + nomEvento + '/' + shp_track)
df_track = df_track.to_crs({'init': 'epsg:3857'})
df_track.plot(ax=ax,
column='STORMTYPE',
linewidth=dimMapY / 5,
cmap='tab20',
legend=True,
legend_kwds={
'loc': 'lower right',
'fontsize': dimMapY,
'title': 'Estado',
'title_fontsize': dimMapY,
'borderpad': 0.1,
'handletextpad': 0.1,
'markerscale': dimMapY / 10,
'shadow': False,
'edgecolor': 'none'
})
#for i in range(len(df_track)):
#if df_track.iloc(i)['STORMTYPE'] == 'Baja Presión':
#df_track.plot(ax=ax,column='STORMTYPE', linewidth = dimMapY/5, color=['#FF0000', '#FF6C00', '#FFEC00', '#B6FF00', '#64FF00', '#14BC00', '#4AFFB2', '#00FFE4', '#00AEFF', '#00AEFF','#0065EE','#5A49FF','#AF49FF','#FF00E0'],legend = True,legend_kwds={'loc': 'lower right','fontsize':dimMapY,'title':'Estado','title_fontsize':dimMapY,'borderpad':0.1,'handletextpad':0.1,'markerscale':dimMapY/10,'shadow':False,'edgecolor':'none'})
file = glob(pathOutputTrack + region + '/' + anio + '_' + numEvento +
'_' + nomEvento + '/*_pts.shp')
shp_pts = file[0].split('/')[-1]
df_pts = gpd.read_file(pathOutputTrack + region + '/' + anio + '_' +
numEvento + '_' + nomEvento + '/' + shp_pts)
df_pts = df_pts.to_crs({'init': 'epsg:3857'})
df_pts.plot(ax=ax, color='black', markersize=dimMapY / 11, zorder=2)
props = dict(boxstyle='round',
facecolor='white',
edgecolor='none',
alpha=0.7)
for x, y, label, label1 in zip(df_pts.geometry.x, df_pts.geometry.y,
df_pts.HHMM, df_pts.DAY):
if label == '1200':
ax.annotate(label,
xy=(x, y),
xytext=(dimMapY * 0.6, dimMapY * 0.6),
textcoords="offset points",
fontsize=dimMapY / 1.5,
bbox=props)
if label == '0000':
ax.annotate(int(label1),
xy=(x, y),
xytext=(dimMapY * 0.6, dimMapY * 1.4),
textcoords="offset points",
fontsize=dimMapY * 0.9,
color='r',
bbox=props)
#ax.add_feature(cfeature.LAND)
#ax.coastlines(linewidth=dimMapY/16,resolution='10m')
df_enti = gpd.read_file(
'./shapefiles/coastlines_mexicoMer/coastlines_mexicoMer.shp')
#df_enti = df_enti.to_crs({'init':'epsg:3857'})
#plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
df_enti.plot(ax=ax, color='black', linewidth=0.3)
#props = dict(boxstyle='round', facecolor='white',edgecolor='none', alpha=0.7)
plt.text(extension[0] + dimMapY * 5500,
extension[3] + dimMapY * 8000,
fecha,
color='black',
fontsize=dimMapY * 1.6,
bbox=props)
#ax.imshow(logo,extent=[extension[0],extension[2],extension[3],extension[1]])
plt.imshow(
data,
extent=[extension[0], extension[2], extension[3], extension[1]],
vmin=-103.15,
vmax=104.85,
cmap=cpt_convert)
#cax = fig.add_axes([0.001,0.99, 0.998, 0.01])
#if dimMapY > dimMapX:
# dimcb = dimMapX
#else:
# dimcb = dimMapY
# dimcb*0.0019097747932542944
cax = fig.add_axes([0.001, 0.99, 0.998, 0.01])
cbar = plt.colorbar(cax=cax, orientation='horizontal')
cbar.ax.tick_params(labelsize=dimMapY, grid_alpha=0.5)
cbar.outline.set_linewidth(dimMapY / 15)
#cbar.outline.set_linewidth(0)
plt.savefig(pathOutputMap + region + '/EDL_CT_' + anio + '_' +
numEvento + '_' + nomEvento + '_' + productoNom +
'/EDL_CT_' + anio + '_' + numEvento + '_' + nomEvento +
'_' + productoNom + '_' + fechaNom + ".png",
dpi=300,
bbox_inches='tight',
pad_inches=0)
logo = Image.open('./logos/lanot_logo.png')
logo = logo.resize((int(dimMapY * 37), int(dimMapY * 11)),
Image.ANTIALIAS)
mapa = Image.open(pathOutputMap + region + '/EDL_CT_' + anio + '_' +
numEvento + '_' + nomEvento + '_' + productoNom +
'/EDL_CT_' + anio + '_' + numEvento + '_' +
nomEvento + '_' + productoNom + '_' + fechaNom +
".png")
mapa.paste(logo, (int(mapa.width) - int(
(dimMapY * 40)), int(dimMapY * 3)), logo)
mapa.save(pathOutputMap + region + '/EDL_CT_' + anio + '_' +
numEvento + '_' + nomEvento + '_' + productoNom +
'/EDL_CT_' + anio + '_' + numEvento + '_' + nomEvento + '_' +
productoNom + '_' + fechaNom + ".png")
fig.clear()
ax.clear()
plt.close(fig)
#Sahpes - Furnas:
bmap.readshapefile('/dados/backup_homefazzt/download/KML_to_shape/138kV_2D','138kV',linewidth=1.0,color='#FF0000')
bmap.readshapefile('/dados/backup_homefazzt/download/KML_to_shape/230kV_2D','230kV',linewidth=1.0,color='#00FF09')
bmap.readshapefile('/dados/backup_homefazzt/download/KML_to_shape/345kV_2D','345kV',linewidth=1.0,color='#FFC400')
bmap.readshapefile('/dados/backup_homefazzt/download/KML_to_shape/500kV_2D','500kV',linewidth=1.0,color='#001AFF')
bmap.readshapefile('/dados/backup_homefazzt/download/KML_to_shape/600kV_2D','600kV',linewidth=1.0,color='#FF00EF')
bmap.readshapefile('/dados/backup_homefazzt/download/KML_to_shape/750kV_2D','750kV',linewidth=1.0,color='#00FFFF')
# Draw parallels and meridians
#bmap.drawparallels(np.arange(-90.0, 90.0, 5.0), linewidth=0.2, dashes=[4, 4], color='white', labels=[False,False,False,False], fmt='%g', labelstyle="+/-", xoffset=-0.80, yoffset=-1.00, size=7)
#bmap.drawmeridians(np.arange(0.0, 360.0, 5.0), linewidth=0.2, dashes=[4, 4], color='white', labels=[False,False,False,False], fmt='%g', labelstyle="+/-", xoffset=-0.80, yoffset=-1.00, size=7)
#if Band = 7 and Band = 10:
# Converts a CPT file to be used in Python
if Band == 2:
cpt = loadCPT('/dados/backup_homefazzt/download/GMT_grey.cpt')
cpt_convert = LinearSegmentedColormap('cpt', cpt)
bmap.imshow(data, origin='upper', cmap=cpt_convert, vmin=0, vmax=1)
elif Band==8 or Band==9: #WV
cpt= loadCPT('/dados/backup_homefazzt/download/WVCOLOR35.cpt')
cpt_convert = LinearSegmentedColormap('cpt', cpt)
bmap.imshow(data, origin='upper', cmap=cpt_convert, vmin=-105,vmax=105)
else:
cpt = loadCPT('/dados/backup_homefazzt/download/IR4AVHRR6.cpt')
cpt_convert = LinearSegmentedColormap('cpt', cpt)
bmap.imshow(data, origin='upper', cmap=cpt_convert, vmin=-103, vmax=84)
# Makes a linear interpolation
#cpt_convert = LinearSegmentedColormap('cpt', cpt)
# Plot the GOES-16 channel with the converted CPT colors (you may alter the min and max to match your preference)
#bmap.imshow(data, origin='upper', cmap=cpt_convert, vmin=-103, vmax=84)
# Insert the colorbar at the bottom
def plot_field_panel(
grid,
field,
level,
fmin,
fmax,
lat_index=None,
lon_index=None,
date="",
name_multi="",
shp_name="",
hailpad_pos=None,
zero_height=3.0,
minusforty_height=10.0,
grid_spc=0.25,
cmap=None,
reverse_cmap=False,
norm=None,
xlim=(-48, -46),
ylim=(-24, -22),
save_path="./",
):
"""
Using gridded multidoppler processed data, plot horizontal and vertical
views:
- In a specific height (defined by index)
- In a specific cross-section (defined by lat_index and lon_index)
Parameters
----------
grid: gridded multidoppler processed data
field: field to be plotted
level: level of horizontal plot
fmin, fmax: field min and max values
lat_index: tuple of latitude indexes for cross section
(end, start) in degrees
lon_index: tuple of longitude indexes for cross section
(end, start) in degrees
date: date to be shown on main title
name_multi: acronym with all radar names
shp_name: path of shapefiles
hailpad_pos: tuple of hailpad position
(lon, lat)
zero_height: 0 degrees height
grid_spc: grid spacing for horizontal plot
cmap: define colorbar. None will use Py-ART defauts
reverse_cmap: If cmap is defined and this is True, the colormap will be
reversed
norm: normalization of the colormap
xlim, ylim: plot limits in lon, lat for horizontal view
(min, max) in degrees
save_path: path to save the figures
"""
# Getting lat-lon-z points
lons, lats = grid.get_point_longitude_latitude(level)
xz, z = np.meshgrid(grid.get_point_longitude_latitude()[0], grid.z["data"])
# Opening colortables
if field != "FH":
if cmap:
cpt = loadCPT(cmap)
if reverse_cmap:
cmap = LinearSegmentedColormap("cpt_r", revcmap(cpt))
else:
cmap = LinearSegmentedColormap("cpt", cpt)
# Main figure
display = pyart.graph.GridMapDisplay(grid)
fig = plt.figure(figsize=(10, 3.25), constrained_layout=True)
if field == "FH":
gs = GridSpec(nrows=1, ncols=8, figure=fig)
else:
gs = GridSpec(nrows=1, ncols=7, figure=fig)
# - Horizontal view
print("-- Plotting horizontal view --")
ax1 = fig.add_subplot(gs[0, :3])
display.plot_basemap(
min_lon=xlim[0],
max_lon=xlim[1],
min_lat=ylim[0],
max_lat=ylim[1],
lon_lines=np.arange(xlim[0], xlim[1], grid_spc),
lat_lines=np.arange(ylim[0], ylim[1], grid_spc),
auto_range=False,
)
display.basemap.readshapefile(shp_name, "sao_paulo", color="gray")
# -- Reflectivity (shaded)
display.plot_grid(
field,
level,
vmin=fmin,
vmax=fmax,
cmap=cmap,
colorbar_flag=False,
norm=norm,
)
# -- Hailpad position
display.basemap.plot(
hailpad_pos[0],
hailpad_pos[1],
"kX",
markersize=15,
markerfacecolor="None",
latlon=True,
)
# -- Cross section position
display.basemap.plot(lon_index, lat_index, "k--", latlon=True)
# - Vertical view
print("-- Plotting vertical view --")
ax2 = fig.add_subplot(gs[0, 3:])
# -- Reflectivity (shaded)
display.plot_latlon_slice(
field,
vmin=fmin,
vmax=fmax,
coord1=(lon_index[0], lat_index[0]),
coord2=(lon_index[1], lat_index[1]),
zerodeg_height=zero_height,
minusfortydeg_height=minusforty_height,
zdh_col="k",
cmap=cmap,
dot_pos=hailpad_pos,
colorbar_flag=False,
norm=norm,
)
cb = display.plot_colorbar(
orientation="vertical", label=grid.fields[field]["units"]
)
if field == "FH":
cb = adjust_fhc_colorbar_for_pyart(cb)
# - General aspects
plt.suptitle(
name_multi + " " + date,
weight="bold",
stretch="condensed",
size="x-large",
)
ax1.set_title(
str(level + 1) + " km " + grid.fields[field]["standard_name"].title()
)
ax2.set_title(
"Cross Section " + grid.fields[field]["standard_name"].title()
)
ax2.set_xlabel("")
ax2.set_ylabel("Distance above Ground (km)")
ax2.grid(linestyle="-", linewidth=0.25)
plt.savefig(
save_path
+ name_multi
+ " "
+ grid.fields[field]["standard_name"].title()
+ " "
+ date
+ ".png",
dpi=300,
bbox_inches="tight",
transparent=True,
)
#ax.add_feature(ccrs.cartopy.feature.STATES)
#plt.title('GOES-16 Day Snow-Fog', loc='left', fontweight='semibold', fontsize=15)
#plt.title('%s' % scan_start.strftime('%d %B %Y %H:%M UTC '), loc='right');
globe = ccrs.Globe(semimajor_axis=semi_major, semiminor_axis=semi_minor)
geos = ccrs.Geostationary(central_longitude=sat_lon,
satellite_height=sat_h, globe=globe)
dtext = ('%s' % scan_start.strftime('%d %B %Y %H:%M UTC '))
fig = plt.figure(1, figsize=(15, 12), dpi=300)
ax1 = fig.add_subplot(1, 1, 1, projection=geos)
cpt = loadCPT('SVGAWVX_TEMP.cpt')
# Makes a linear interpolation
cpt_convert = LinearSegmentedColormap('cpt', cpt)
# Plot the GOES-16 channel with the converted CPT colors (you may alter the min and max to match your preference)
# ax1.imshow(data, origin='upper', cmap=cpt_convert, vmin=-112.15, vmax=56.85)
print(RGB-273.15)
cf=ax1.imshow(RGB-273.15, origin='upper', cmap=cpt_convert,
extent=(x.min(), x.max(), y.min(), y.max()),
transform=geos,
interpolation='nearest', vmin=-112.15, vmax=56.85)
cb = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50, extendrect=True)
cb.set_label('Abs. Vorticity ($s^{-1}$)')
def plot_files(fnames):
# Converts the CPT file to be used in Python
cpt = loadCPT('IR4AVHRR6.cpt')
# Makes a linear interpolation with the CPT file
cpt_convert = LinearSegmentedColormap('cpt', cpt)
first = True
for fname in fnames:
# Search for the Scan Start in the file name
time = (fname[fname.find("MG_") + 3:fname.find(".nc")])
# Format the "Observation Start" string
date = datetime.strptime(time, '%Y%m%d%H%M%S')
# Check if we already created the image
image_string = folder + 'images/%s_%s.png' % (
channel, datetime.strftime(date, '%Y%m%d%H%M%S'))
if os.path.isfile(image_string):
#print('Skipping '+fname)
continue
print('Using ' + fname)
# Open the file using the NetCDF4 library
nc = Dataset(fname)
# Extract the Brightness Temperature values from the NetCDF
temp_b = brigthness_temp(nc.variables[channel])
hrv = nc.variables[channel_hrv][:]
fig = plt.figure(figsize=(15, 15))
if first:
lons = np.ma.masked_less(np.array(nc.variables['lon']), -180)
lats = np.ma.masked_less(np.array(nc.variables['lat']), -90)
lats_hrv, lons_hrv = create_coord_hrv(lats, lons)
bmap = Basemap(projection='cyl',
llcrnrlon=4,
llcrnrlat=31,
urcrnrlon=26,
urcrnrlat=45,
resolution='i')
x, y = bmap(lons, lats)
x_hrv, y_hrv = bmap(lons_hrv, lats_hrv)
first = False
if (hrv.std() > 0.9):
bmap.pcolormesh(x_hrv, y_hrv, hrv, vmin=0, vmax=20, cmap='gray')
bmap.pcolormesh(x,
y,
np.ma.masked_array(temp_b, temp_b > -38),
vmin=-80,
vmax=50,
cmap=cpt_convert,
alpha=0.3,
linewidth=0,
antialiased=True)
else:
bmap.pcolormesh(x,
y,
temp_b,
vmin=-80,
vmax=50,
cmap=cpt_convert,
antialiased=True)
bmap.drawcoastlines(linewidth=0.5, linestyle='solid', color='white')
bmap.drawcountries(linewidth=0.5, linestyle='solid', color='white')
bmap.drawparallels(np.arange(-90.0, 90.0, 5.),
linewidth=0.2,
color='white',
labels=[True, False, False, True],
fontsize=7)
bmap.drawmeridians(np.arange(0.0, 360.0, 5.),
linewidth=0.2,
color='white',
labels=[True, False, False, True],
fontsize=7)
# Insert the legend
bmap.colorbar(location='right',
label='Brightness Temperature [C] / HRV radiance',
size='2%',
pad='1%')
date_formatted = datetime.strftime(date, '%a %d %b %Y, %H:%MZ')
annotation(plt.gca(), date_formatted, loc='upper center', fontsize=9)
annotation(plt.gca(),
"SEVIRI %s,%s High Rate data" % (channel, channel_hrv) +
u"\N{COPYRIGHT SIGN}" +
'EUMETSAT - prepared by Guido Cioni (www.guidocioni.it)',
loc='lower left',
fontsize=7)
#print('Saving file %s' % image_string)
plt.savefig(image_string, bbox_inches='tight', dpi=200)
plt.clf()
plt.close('all')
