def show_ws(groups, size):
plt.close('all')
fig = plt.figure(figsize = (size, size))
ax = fig.add_subplot(111)
m = Basemap(projection = 'cyl', resolution = 'l', area_thresh = 10000, llcrnrlat = lat0, urcrnrlat = lat1, llcrnrlon = lon0, urcrnrlon = lon1)
m.drawcoastlines()
m.drawcountries()
m.drawparallels(np.arange(lat0, lat1, 10), labels=[1,1,0,0])
m.drawmeridians(np.arange(lon0, lon1, 10), labels=[0,0,0,1])
this_mask = np.zeros((int((lat1 - lat0) / acc_res), int((lon1 - lon0) / acc_res)), dtype = 'uint8')
for this_group in groups:
this_rand = int(np.random.uniform(1, 255))
for this_odr in this_group:
mask = df_ws.loc[this_odr, 'mask']
latlon = df_ws.loc[this_odr, 'latlon']
y0 = int(round((lat1 - latlon[0]) / acc_res))
y1 = y0 + mask.shape[0]
x0 = int(round((latlon[1] - lon0) / acc_res))
x1 = x0 + mask.shape[1]
this_mask[y0:y1, x0:x1] = this_mask[y0:y1, x0:x1] + mask * this_rand
this_mask = np.where(this_mask == 0, np.nan, this_mask)
m.imshow(this_mask, origin = 'upper', interpolation = 'nearest', zorder = 10, alpha = 1)
plt.show()
def plot_data(data,lon_data, lat_data, periodname, AODcatname,maptype,cmapname,minv=0,maxv=0,folder=""):
fig = plt.figure()
#ax = fig.add_axes([0.1,0.1,0.8,0.8])
m = Basemap(llcrnrlon=19,llcrnrlat=34,urcrnrlon=29,urcrnrlat=42,
resolution='h',projection='cass',lon_0=24,lat_0=38)
nx = int((m.xmax-m.xmin)/1000.)+1
ny = int((m.ymax-m.ymin)/1000.)+1
topodat = m.transform_scalar(data,lon_data,lat_data,nx,ny)
if minv<>0 or maxv<>0 :
im = m.imshow(topodat,cmap=plt.get_cmap(cmapname),vmin=minv,vmax=maxv)
else:
im = m.imshow(topodat,cmap=plt.get_cmap(cmapname))
m.drawcoastlines()
m.drawmapboundary()
m.drawcountries()
m.drawparallels(np.arange(35,42.,1.), labels=[1,0,0,1])
m.drawmeridians(np.arange(-20.,29.,1.), labels=[1,0,0,1])
cb = m.colorbar(im,"right", size="5%", pad='2%')
title=maptype+" AOD "+AODcatname+" "+periodname+" 2007-2014"
plt.title(title)
pylab.savefig(folder+maptype+"AOD"+AODcatname+"_"+periodname + ".png")
def plot_2Ddata_with_underlay(data_array, lons=None, lats=None):
""" Plot of a 2D array on top of coast lines and country lines.
"""
figure()
# Create the basemap instance and draw the continents
# Basemap kw args: llcrnrlon = lower left corner longitude, and so on
m = Basemap(llcrnrlon=-180, llcrnrlat=-90, urcrnrlon=180, urcrnrlat=90, projection="mill")
m.drawcoastlines(linewidth=1.25)
m.drawcountries(linewidth=0.5)
# m.fillcontinents(color='0.8')
m.drawparallels(np.arange(-90, 91, 20), labels=[1, 1, 0, 0])
m.drawmeridians(np.arange(-180, 180, 60), labels=[0, 0, 0, 1])
# Shift the data by 180 in longitude to center the map on longitude=0
data_array, lon_out = shiftgrid(180, data_array, lons)
# Add the data, and place origin in upper left since the origin of the numpy
# array corresponds tolat = 90 and long = 0
m.imshow(data_array, origin="upper")
# More complex version that involves interpolation on a grid of points in
# map coordinates.
# nx = int((m.xmax-m.xmin)/500.)+1; ny = int((m.ymax-m.ymin)/500.)+1
# dat_array = m.transform_scalar(data_array, lons,lats,nx,ny)
# m.imshow(dat_array, origin = 'upper')
# figure()
# m = Basemap(projection='ortho',lon_0=-105,lat_0=40)
# m.drawcoastlines(linewidth=1.25)
# m.drawcountries(linewidth=0.5)
# dat_transformed = m.transform_scalar(data_array,
return
def __init__(self, subPlot = None, gpxData = None):
GeoSectionViewerGpxData.__init__(self, subPlot, gpxData)
latitudes, longitudes, elevations, timestamps = self.gpxData.getLatLongElevTs()
latitudeMax = max(latitudes)
latitudeMin = min(latitudes)
longitudeMax = max(longitudes)
longitudeMin = min(longitudes)
elevationMax = max(elevations)
elevationMin = min(elevations)
scalledMax = 30
scalledMin = 1
scalledElevations = [((((point.elevation - elevationMin) * (scalledMax - scalledMin)) / (elevationMax - elevationMin)) + scalledMin) for i, point in enumerate(self.gpxData)]
bbox = longitudeMin, latitudeMin, longitudeMax, latitudeMax
mm = geotiler.Map(extent=bbox, zoom=14)
img = geotiler.render_map(mm)
img.save("geotiler.png")
map = Basemap(
llcrnrlon=bbox[0], llcrnrlat=bbox[1],
urcrnrlon=bbox[2], urcrnrlat=bbox[3],
projection='merc',
resolution='i',
ax=self.subPlot)
map.imshow(img, aspect='auto', origin='upper')
map.scatter(longitudes, latitudes, c='red', s=scalledElevations, marker='o', cmap=cm.hot_r, alpha=0.4, latlon=True)
map.plot(longitudes, latitudes, 'k', c='red', latlon=True)
def mapper(image):
fig = plt.figure()
cmap = mpc.ListedColormap(palettable.colorbrewer.diverging.PiYG_5.mpl_colors)
# cmap.set_bad('grey',1.)
# ax = fig.add_subplot(1)
plt.plot()
plt.title("a. ((maxNDVI/mm AcuPrecip)/year)", loc= 'left')
#set the spatial cordinates of the grid, mask the ocean and draw coastlines
map = Basemap(llcrnrlon=112.0,llcrnrlat=-44.5,urcrnrlon=156.25,urcrnrlat=-10, resolution = 'h', epsg=4326)
map.drawmapboundary(fill_color='grey')
map.fillcontinents(color= 'none', lake_color='white')
map.drawlsmask(land_color='none', )
map.drawcoastlines()
map.imshow(np.flipud(image), cmap=cmap, vmin=-0.006, vmax=0.009,)
# cb = plt.colorbar()
#Tweaking the colorbar so the the ticks allign with the grid.
cb = map.colorbar()#ticks= [-0.01, -0.0075, -0.0050, -0.00250, 0.0000, 0.0025, 0.0050, 0.0075, 0.010, 0.0125])
tick_locator = ticker.MaxNLocator(nbins=5)
cb.locator = tick_locator
cb.update_ticks()
#add in the grid
map.drawparallels(np.arange(-50, -10, 10),labels=[1,0,0,0], dashes=[1,2])#color= 'none')
map.drawmeridians(np.arange(110, 156.25, 10),labels=[0,0,0,1], dashes=[1,2])
plt.show()
def background_map(self, ax):
llcrnrlat, urcrnrlat, llcrnrlon, urcrnrlon, lat_ts = (31, 44, -126, -113, 37.5)
m = Basemap(projection='merc', llcrnrlat=llcrnrlat,
urcrnrlat=urcrnrlat, llcrnrlon=llcrnrlon, urcrnrlon=urcrnrlon,
lat_ts=lat_ts, resolution='i', ax=ax)
m.drawmapboundary(fill_color='lightblue', zorder=0)
m.fillcontinents(zorder=0)
etopofn = '/home/behry/uni/data/etopo1_central_europe_gmt.grd'
etopodata = Dataset(etopofn, 'r')
z = etopodata.variables['z'][:]
x_range = etopodata.variables['x_range'][:]
y_range = etopodata.variables['y_range'][:]
spc = etopodata.variables['spacing'][:]
lats = np.arange(y_range[0], y_range[1], spc[1])
lons = np.arange(x_range[0], x_range[1], spc[0])
topoin = z.reshape(lats.size, lons.size, order='C')
# transform to nx x ny regularly spaced 5km native projection grid
nx = int((m.xmax - m.xmin) / 5000.) + 1; ny = int((m.ymax - m.ymin) / 5000.) + 1
topodat, x, y = m.transform_scalar(np.flipud(topoin), lons, lats, nx, ny, returnxy=True)
ls = LightSource(azdeg=300, altdeg=15, hsv_min_sat=0.2, hsv_max_sat=0.3,
hsv_min_val=0.2, hsv_max_val=0.3)
# shade data, creating an rgb array.
rgb = ls.shade(np.ma.masked_less(topodat / 1000.0, 0.0), cm.gist_gray_r)
m.imshow(rgb)
m.drawmeridians(np.arange(6, 12, 2), labels=[0, 0, 0, 1], color='white',
linewidth=0.5, zorder=0)
m.drawparallels(np.arange(44, 50, 2), labels=[1, 0, 0, 0], color='white',
linewidth=0.5, zorder=0)
m.drawcoastlines(zorder=1)
m.drawcountries(linewidth=1.5, zorder=1)
m.drawstates()
m.drawrivers(color='lightblue', zorder=1)
return m
def plot_world_sst():
# Read some NetCDF data
import netCDF4 as nc
ostia = nc.Dataset('ostia.nc')
tmp = ostia.variables['analysed_sst'][0]
ice = ostia.variables['sea_ice_fraction'][0]
lon = ostia.variables['lon'][:]
lat = ostia.variables['lat'][:]
from mpl_toolkits.basemap import Basemap
# Set up a map
map = Basemap(projection='cyl')
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color='lightgreen', lake_color='lightblue');
map.drawmapboundary(fill_color='lightblue')
# Re-project the data onto the map
image = map.transform_scalar(tmp,lon,lat,200,200)
# Plot the data
map.imshow(image);
plt.show()
def mk_regmap_contour(a2in, bnd, scm, stitle, soname, cbarname, miss_out):
#------------------------
# Basemap
#------------------------
print "Basemap"
figmap = plt.figure()
axmap = figmap.add_axes([0.1, 0.0, 0.8, 1.0])
M = Basemap( resolution="l", llcrnrlat=lllat, llcrnrlon=lllon, urcrnrlat=urlat, urcrnrlon=urlon, ax=axmap)
#-- transform -----------
print "transform"
a2value_trans = M.transform_scalar( a2in, a1lon, a1lat, nnx, nny)
a2value_trans = a2value_trans.filled(miss_out)
#-- to contour ----------
a2value_trans = dtanl_fsub.mk_a2contour_regional(a2value_trans.T, 0.0, 0.0, -9999.0).T
#-- boundaries ----------
bnd_cbar = [-1.0e+40] + bnd + [1.0e+40]
#-- color ---------------
cminst = matplotlib.cm.get_cmap(scm, len(bnd))
acm = cminst( arange( len(bnd) ) )
lcm = [[1,1,1,1]]+ acm.tolist()
mycm = matplotlib.colors.ListedColormap( lcm )
#-- imshow -----------
im = M.imshow(a2value_trans, origin="lower", norm=BoundaryNormSymm(bnd), cmap=mycm, interpolation="nearest")
#-- shade -----------
a2shade_trans = ma.masked_not_equal(a2value_trans, miss_out)
cmshade = matplotlib.colors.ListedColormap([(0.8,0.8,0.8), (0.8,0.8,0.8)])
im = M.imshow(a2shade_trans, origin="lower", cmap=cmshade)
#-- coastline ---------------
print "coastlines"
M.drawcoastlines()
#-- meridians and parallels
M.drawmeridians(arange(0.0,360.0, meridians), labels=[0, 0, 0, 1])
M.drawparallels(arange(-90.0,90.0, parallels), labels=[1, 0, 0, 0])
#-- title -------------------
axmap.set_title("%s"%(stitle))
#-- save --------------------
plt.savefig(soname)
print soname
# for colorbar ---
if cbarflag == "True":
figmap = plt.figure()
axmap = figmap.add_axes([0.1, 0.0, 0.8, 1.0])
M = Basemap( resolution="l", llcrnrlat=lllat, llcrnrlon=lllon, urcrnrlat=urlat, urcrnrlon=urlon, ax=axmap)
a2v_trans = M.transform_scalar( a2in, a1lon, a1lat, nnx, nny)
im = M.imshow(a2value_trans, origin="lower", norm=BoundaryNormSymm(bnd), cmap=mycm)
figcbar = plt.figure(figsize=(5, 0.6))
axcbar = figcbar.add_axes([0, 0.4, 1.0, 0.6])
bnd_cbar = [-1.0e+40] + bnd + [1.0e+40]
plt.colorbar(im, boundaries= bnd_cbar, extend="both", cax=axcbar, orientation="horizontal")
figcbar.savefig(cbarname)
def main():
ncols = 841
nrows = 681
ndays = 366
f = open("modis_climatology_splined.bin", "r")
data = np.fromfile(f).reshape((ndays,nrows,ncols))
f.close()
ncolours = 7
vmin = 0
vmax = 6
cmap = sns.blend_palette(["white", "#1b7837"], ncolours, as_cmap=True)
sns.set(style="white")
fig = plt.figure(figsize=(10, 6))
grid = AxesGrid(fig, [0.05,0.05,0.9,0.9], nrows_ncols=(1,1), axes_pad=0.1,
cbar_mode='single', cbar_pad=0.4, cbar_size="7%",
cbar_location='bottom', share_all=True)
# 111.975 + (841. * 0.05)
# -44.025 + (681. * 0.05)
m = Basemap(projection='cyl', llcrnrlon=111.975, llcrnrlat=-44.025, \
urcrnrlon=154.025, urcrnrlat=-9.974999999999994, resolution='h')
ax = grid[0]
m.ax = ax
shp_info = m.readshapefile('/Users/mdekauwe/research/Drought_linkage/'
'Bios2_SWC_1979_2013/'
'AUS_shape/STE11aAust',
'STE11aAust', drawbounds=True)
#m.drawrivers(linewidth=0.5, color='k')
ax.set_xlim(140.5, 154)
ax.set_ylim(-38, -28)
#cmap = cmap_discretize(plt.cm.YlGnBu, ncolours)
m.imshow(data[20,:,:], cmap,
colors.Normalize(vmin=vmin, vmax=vmax, clip=True),
origin='upper', interpolation='nearest')
cbar = colorbar_index(cax=grid.cbar_axes[0], ncolours=ncolours, cmap=cmap,
orientation='horizontal', vmin=vmin, vmax=vmax)
fig.savefig("/Users/mdekauwe/Desktop/LAI_NSW.png", bbox_inches='tight',
pad_inches=0.1, dpi=300)
plt.show()
def radagast_test():
lower_left, upper_right = corner_coords()
m = Basemap(projection='geos', lon_0=0, resolution='i',
llcrnrlon=lower_left[0], llcrnrlat=lower_left[1],
urcrnrlon=upper_right[0], urcrnrlat=upper_right[1])
m.drawcoastlines()
im = Image.open('TEST-radagast.jpg')
m.imshow(im)
plt.show()
class CoordinateAnimation:
def __init__(self, corners, zoom, data_pipe, figsize=(16, 9), scatter_params={}):
self.corners = corners
self.zoom = zoom
self.scatter_params = scatter_params
self.data_pipe = data_pipe
self.plot_data = np.zeros(shape=(1, 2))
self.fig, self.ax = plt.figure(figsize=figsize), plt.subplot(111)
self._init_basemap()
self.anim_scatter = animation.FuncAnimation(self.fig, self._update_scatter,
init_func=self._scatter_init)
self.plot_scat = None
self.plot_plot = None
def _init_basemap(self):
self.m = geotiler.Map(extent=self.corners, zoom=self.zoom)
self.img = geotiler.render_map(self.m)
self.bmap = Basemap(
llcrnrlon=self.corners[0], llcrnrlat=self.corners[1],
urcrnrlon=self.corners[2], urcrnrlat=self.corners[3],
projection="merc", ax=self.ax
)
self.bmap.imshow(self.img, interpolation='lanczos', origin='upper')
def _scatter_init(self):
self.plot_scat = plt.scatter(self.plot_data[:, 0], self.plot_data[:, 1],
**self.scatter_params)
self.plot_plot = plt.plot(self.plot_data[:, 0], self.plot_data[:, 1])
def _update_scatter(self, i):
try:
if not self.data_pipe.poll(3):
return self.plot_scat,
except KeyboardInterrupt:
return self.plot_scat,
try:
gps_json = self.data_pipe.recv()
except KeyboardInterrupt:
return self.plot_scat,
gga = gps_json['gga']
try:
parsed = pynmea2.parse(gga)
except pynmea2.nmea.ChecksumError:
return self.plot_scat,
array = np.array(self.bmap(parsed.longitude, parsed.latitude))
self.plot_data = np.vstack( (self.plot_data, array) )
self.plot_scat.set_offsets(array)
plt.plot(self.plot_data[1:, 0], self.plot_data[1:, 1])
return self.plot_scat,
def plot(self):
plt.show()
def make_map(llcrnrlon, urcrnrlon, llcrnrlat, urcrnrlat, image):
fig, ax = plt.subplots()
m = Basemap(llcrnrlon=llcrnrlon, urcrnrlon=urcrnrlon,
llcrnrlat=llcrnrlat, urcrnrlat=urcrnrlat,
resolution='c', projection='merc',
lon_0=(urcrnrlon + llcrnrlon) / 2,
lat_0=(urcrnrlat + llcrnrlat) / 2,
lat_ts=-23.5)
m.ax = ax
m.imshow(plt.imread(image), origin='upper', zorder=1)
return fig, ax, m
def makeFigs():
m = Basemap(llcrnrlon=-9.382918,llcrnrlat=39.119533,urcrnrlon= -9.376521,urcrnrlat= 39.127501,
resolution='l',projection='merc')
x1,y1=m(x,y)
xarray.append(x1)
yarray.append(y1)
m.drawcoastlines()
im = plt.imread("map.png")
m.imshow(im, origin='upper')
m.scatter(xarray,yarray,c='b',marker=".",alpha=1.0)
def PlotTomoMap(fname, dlon=0.5, dlat=0.5, title='', datatype='ph', outfname='', browseflag=False, saveflag=True):
"""
Plot Tomography Map
longitude latidute ZValue
"""
if title=='':
title=fname;
if outfname=='':
outfname=fname;
Inarray=np.loadtxt(fname)
LonLst=Inarray[:,0]
LatLst=Inarray[:,1]
ZValue=Inarray[:,2]
llcrnrlon=LonLst.min()
llcrnrlat=LatLst.min()
urcrnrlon=LonLst.max()
urcrnrlat=LatLst.max()
Nlon=int((urcrnrlon-llcrnrlon)/dlon)+1
Nlat=int((urcrnrlat-llcrnrlat)/dlat)+1
fig=plt.figure(num=None, figsize=(8, 12), dpi=80, facecolor='w', edgecolor='k')
m = Basemap(llcrnrlon=llcrnrlon, llcrnrlat=llcrnrlat, urcrnrlon=urcrnrlon, urcrnrlat=urcrnrlat, \
rsphere=(6378137.00,6356752.3142), resolution='l', projection='merc')
lon = LonLst
lat = LatLst
x,y = m(lon, lat)
xi = np.linspace(x.min(), x.max(), Nlon)
yi = np.linspace(y.min(), y.max(), Nlat)
xi, yi = np.meshgrid(xi, yi)
#-- Interpolating at the points in xi, yi
zi = griddata(x, y, ZValue, xi, yi)
# m.pcolormesh(xi, yi, zi, cmap='seismic_r', shading='gouraud')
cmap=matplotlib.cm.seismic_r
cmap.set_bad('w',1.)
m.imshow(zi, cmap=cmap)
m.drawcoastlines()
m.colorbar(location='bottom',size='2%')
# m.fillcontinents()
# draw parallels
m.drawparallels(np.arange(-90,90,10),labels=[1,1,0,1])
# draw meridians
m.drawmeridians(np.arange(-180,180,10),labels=[1,1,1,0])
plt.suptitle(title,y=0.9, fontsize=22);
if browseflag==True:
plt.draw()
plt.pause(1) # <-------
raw_input("<Hit Enter To Close>")
plt.close('all')
if saveflag==True:
fig.savefig(outfname+'.ps', format='ps')
return
def plot_file(filename):
# Open the file using the NetCDF4 library
nc = Dataset(filename)
# analyze_dataset(nc)
latlon = nc.variables['geospatial_lat_lon_extent']
# print('geospatial_lat_lon_extent:', latlon)
# Original example uses Brightness Temperature values, 'CMI'
# But that's not in our dataset.
whichvar = 'Rad'
data = nc.variables[whichvar][:]
prettyname = nc.variables[whichvar].long_name
# Show it without basemap:
# plt.imshow(data, cmap='Greys')
# plt.show()
# Create the basemap reference for the Satellite Projection
bmap = Basemap(projection='geos',
# Extents
llcrnrlon=latlon.geospatial_westbound_longitude,
llcrnrlat=latlon.geospatial_southbound_latitude,
urcrnrlon=latlon.geospatial_eastbound_longitude,
urcrnrlat=latlon.geospatial_northbound_latitude,
# Should these be nadir, or center?
lon_0=latlon.geospatial_lon_nadir,
lat_0=latlon.geospatial_lat_nadir,
satellite_height=35786023.0, ellps='GRS80')
# Plot GOES-16 Channel using 170 and 378 as the temperature thresholds
bmap.imshow(data, origin='upper', cmap='Greys')
# Draw the coastlines, countries, parallels and meridians
bmap.drawcoastlines(linewidth=0.3, linestyle='solid', color='blue')
bmap.drawcountries(linewidth=0.3, linestyle='solid', color='blue')
# These don't seem to do anything
bmap.drawparallels(np.arange(-90.0, 90.0, 10.0),
linewidth=0.1, color='yellow')
bmap.drawmeridians(np.arange(0.0, 360.0, 10.0),
linewidth=0.1, color='yellow')
# Insert the legend
bmap.colorbar(location='bottom', label=prettyname)
# Show the plot
plt.show()
def __init__(self, subPlot = None, gpxData = None, geoMarkers = None):
GeoSectionViewerGpxData.__init__(self, subPlot, gpxData)
latitudes, longitudes, elevations, timestamps = self.gpxData.getLatLongElevTs()
latitudeMax = max(latitudes)
latitudeMin = min(latitudes)
longitudeMax = max(longitudes)
longitudeMin = min(longitudes)
elevationMax = max(elevations)
elevationMin = min(elevations)
scalledMax = 30
scalledMin = 1
scalledElevations = [((((point.elevation - elevationMin) * (scalledMax - scalledMin)) / (elevationMax - elevationMin)) + scalledMin) for i, point in enumerate(self.gpxData)]
bbox = longitudeMin, latitudeMin, longitudeMax, latitudeMax
mm = geotiler.Map(extent=bbox, zoom=14)
self.rawMapImage = geotiler.render_map(mm)
map = Basemap(
llcrnrlon=bbox[0], llcrnrlat=bbox[1],
urcrnrlon=bbox[2], urcrnrlat=bbox[3],
projection='merc',
resolution='i',
ax=self.subPlot)
map.imshow(self.rawMapImage, aspect='auto', origin='upper')
#--draw path
map.scatter(longitudes, latitudes, c='red', s=scalledElevations, marker='o', cmap=cm.hot_r, alpha=0.4, latlon=True)
map.plot(longitudes, latitudes, 'k', c='red', latlon=True)
#--draw labels
if(geoMarkers is not None):
for geoMarker in geoMarkers:
xy = map(geoMarker["longitude"], geoMarker["latitude"])
#
# subPlot.annotate(
# geoMarker["name"],
# xy, xytext = (-20, 20),
# textcoords = 'offset points', ha = 'right', va = 'bottom',
# # bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),
# bbox = dict(fc = 'yellow', alpha = 0.5),
# arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))
#
subPlot.text(xy[0], xy[1], "{0}-{1} {2}".format(geoMarker["name"], geoMarker["elevation"], geoMarker["time"]),fontsize=9,
ha='center',va='top',color='r',
# bbox = dict(ec='None',fc=(1,1,1,0.5)))
bbox = dict(fc = 'yellow', alpha = 0.5))
def plot_mappa(paese):
def GetExtent(gt,cols,rows):
ext=[]
xarr=[0, cols]
yarr=[0, rows]
for px in xarr:
for py in yarr:
x=gt[0]+(px*gt[1])+(py*gt[2])
y=gt[3]+(px*gt[4])+(py*gt[5])
ext.append([x,y])
#print x,y
yarr.reverse()
return ext
pathToRaster = "input_data/geocoded/risk_map/" + paese + ".tif"
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
from osgeo import gdal
raster = gdal.Open(pathToRaster, gdal.GA_ReadOnly)
array = raster.GetRasterBand(1).ReadAsArray()
msk_array = np.ma.masked_equal(array, value=65535)
# print 'Raster Projection:\n', raster.GetProjection()
geotransform = raster.GetGeoTransform()
cols = raster.RasterXSize
rows = raster.RasterYSize
ext = GetExtent(geotransform, cols, rows)
#print ext[1][0], ext[1][1]
#print ext[3][0], ext[3][1]
#map = Basemap(projection='merc',llcrnrlat=-80, urcrnrlat=80, llcrnrlon=-180,urcrnrlon=180,lat_ts=20,resolution='c')
map = Basemap(projection='merc', llcrnrlat=ext[1][1], urcrnrlat=ext[3][1], llcrnrlon=ext[1][0], urcrnrlon=ext[3][0],lat_ts=20, resolution='c')
# Add some additional info to the map
map.drawcoastlines(linewidth=1.3, color='white')
#map.drawrivers(linewidth=.4, color='white')
map.drawcountries(linewidth=.75, color='white')
#datain = np.flipud(msk_array)
datain = np.flipud(msk_array)
map.imshow(datain)#,origin='lower',extent=[ext[1][0], ext[3][0],ext[1][1],ext[3][1]])
plt.show()
#plot_mappa("India")
def plotField(fileName, vmin, vmax, colormap='goddard', output_folder='/', lonlatbox='-180,180,-90,90'):
'''
Plot any field variable
:param fileName: filepath
:param vmin: min value for colorbar
:param vmax: max value for colorbar
'''
fig1 = plt.figure(figsize=(12,7),dpi=500)
if(colormap == 'goddard'):
my_cmap = mpl.colors.LinearSegmentedColormap('my_colormap',Plotter.colorDict2,256)
else:
my_cmap = plt.cm.RdBu_r
file_values = FileHandler.openNetCDFFile(fileName)
mVar = file_values['variable']
lon = file_values['lon']
lat = file_values['lat']
if lonlatbox is None:
if lon[0] < 0:
lonlatbox = '-180,180,-90,90'
else:
lonlatbox = '0,360,-90,90'
lonlatbox = map(int,lonlatbox.split(','))
m = Basemap(llcrnrlon=lonlatbox[0],llcrnrlat=lonlatbox[2],urcrnrlon=lonlatbox[1],urcrnrlat=lonlatbox[3])
def divi(x):
return float(x)/10
colorSteps = map(divi,range(int(vmin*10),int((vmax*10)+1),1))#(vmax-vmin)/2))
if vmax == 0.5:
colorSteps = [-0.5, -0.45, -0.4, -0.35, -0.3, -0.25, -0.2, -0.15, -0.1, -0.05, 0,
0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5]
if vmax == 0. and vmin == -0.5:
colorSteps = [-0.5, -0.475, -0.45, -0.425, -0.4, -0.375, -0.35, -0.325, -0.3, -0.275, -0.25, -0.225,
-0.2, -0.175, -0.15, -0.125, -0.1, -0.075, -0.05, -0.025, 0]
if vmax == 2. and vmin == 0:
colorSteps = [0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9,0.95,
1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0]
colorTicks = colorSteps[0::2]
my_cmap.set_bad("grey") #set missing value color
#maskedArray = N.ma.masked_greater(mVar, 0.8e20) #mask missing Values
maskedArray = N.ma.masked_outside(mVar, -0.8e20, 0.8e20)
#discrete colormap
norm = mpl.colors.BoundaryNorm(colorSteps, my_cmap.N)
cs = m.imshow(maskedArray, interpolation="nearest", cmap=my_cmap, norm=norm)
cb = m.colorbar(cs,"right", size="5%", pad='5%' , ticks=colorTicks)
m.drawcoastlines()
m.drawparallels(N.arange(-90.,120.,30.),labels=[1,0,0,0]) # draw parallels
m.drawmeridians(N.arange(0.,420.,60.),labels=[0,0,0,1]) # draw meridians
plt.title(Plotter.__getTitle(fileName))
plt.text(lonlatbox[0]+(lon[1]-lon[0])/2, lonlatbox[2]+(lat[1]-lat[0])/2, 'MurCSS')
#print lonlatbox[0]+lon[1]-lon[0]
return m
def stere(data,latitude,longitude,filename):
"""
:param data: CO浓度数据矩阵
:param latitude: 纬度
:param longitude: 经度
:param filename:
:return:
"""
ax = plt.gca()
fig = plt.figure()
m = Basemap(width=10000000,height=7500000,
resolution='l',projection='stere',\
lat_ts=35,lat_0=35,lon_0=107.)
nx = int((m.xmax-m.xmin)/5000.)+1
ny = int((m.ymax-m.ymin)/5000.)+1
topodat = m.transform_scalar(data,longitude,latitude,nx,ny)
im = m.imshow(topodat,cm.GMT_haxby,vmin=0,vmax=4e18)
m.drawcoastlines()
m.drawcountries()
m.drawparallels(np.arange(-80.,81.,20.),labels=[1,0,0,0])
# 画平行的纬度,前一个参数是表示起点，终点，间距的序列，后一个参数是指在哪一个方向显示纬度、经度值
m.drawmeridians(np.arange(-180.,181.,20.),labels=[0,0,0,1])
# 画平行的经度
m.colorbar(im)
plt.title("CO"+filename[12:14])
outname = filename+'.png'
fig.savefig(outname, dpi=fig.dpi)
def do_plot(self, dataset):
#for dataset in r533:
points, plot_type, lons, lats, num_pts_lon, num_pts_lat, params = dataset
plot_dt, plot_title, freq, idx = params
#plt.figure(figsize=(12,6))
m = Basemap(projection='cyl', resolution='l')
m.drawcoastlines(color='black', linewidth=0.75)
m.drawcountries(color='grey')
m.drawmapboundary(color='black', linewidth=1.0)
m.drawmeridians(np.arange(0,360,30))
m.drawparallels(np.arange(-90,90,30))
X,Y = np.meshgrid(lons, lats)
#todo remove hard-coded values for vmin and vmax
#im = m.pcolormesh(X, Y, points, shading='gouraud', cmap=plt.cm.jet, latlon=True, vmin=-20, vmax=40)
#im = m.pcolormesh(X, Y, points, shading='gouraud', cmap=plt.cm.jet, latlon=True, vmin=-20, vmax=40)
im = m.imshow(points, interpolation='bilinear', vmin=0, vmax=100)
if self.plot_terminator:
m.nightshade(plot_dt)
cb = m.colorbar(im,"bottom", size="5%", pad="2%")
plt.title(plot_title)
plot_fn = "area_{:s}_{:s}_{:s}.png".format(plot_type, plot_dt.strftime("%H%M_%b_%Y"), "d".join(str(freq).split('.')))
print ("Saving file ", plot_fn)
plt.savefig(plot_fn, dpi=float(self.dpi), bbox_inches='tight')
def plot_ndvi(hdf_fname, fig_name,
min_lat, min_lon, max_lat, max_lon, title='NDVI plot'):
"""Produce a plot of the NDVI data in file `hdf_fname`."""
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.basemap import Basemap
ndvi = read_ndvi(hdf_fname, min_lat, min_lon, max_lat, max_lon)
print 'Data obtained'
print type(ndvi)
fig = plt.figure()
ax = fig.add_subplot(111)
curr_map = Basemap(projection='cyl', llcrnrlat=min_lat, urcrnrlat=max_lat,
llcrnrlon=min_lon, urcrnrlon=max_lon, resolution='l')
curr_map.drawcoastlines(linewidth=1.5)
curr_map.drawcountries(linewidth=1.5)
curr_map.drawparallels(np.arange(-90.,91.,2.), labels=[1, 0, 0, 0])
curr_map.drawmeridians(np.arange(-180.,181.,2.), labels=[1, 0, 0, 1])
im = curr_map.imshow(ndvi, cmap=cm.BrBG,
origin='upper', interpolation='nearest')
cbar = fig.colorbar(im, ticks=[0, 0.4, 0.8])
cbar.set_label('NDVI')
cbar.ax.set_yticklabels(['< 0', '0.4', '> 0.8'])
ax.set_title(title, fontsize=16, fontweight='bold')
fig.savefig(fig_name)
def plot_hmap_ortho(h, cmap='jet', mode='log', mx=None, drng=None,
res=0.25, verbose=False):
m = Basemap(projection='ortho',lat_0=90,lon_0=180,rsphere=1.)
if verbose:
print 'SCHEME:', h.scheme()
print 'NSIDE:', h.nside()
lons,lats,x,y = m.makegrid(360/res,180/res, returnxy=True)
lons = 360 - lons
lats *= a.img.deg2rad; lons *= a.img.deg2rad
y,x,z = a.coord.radec2eq(n.array([lons.flatten(), lats.flatten()]))
ax,ay,az = a.coord.latlong2xyz(n.array([0,0]))
data = h[x,y,z]
data.shape = lats.shape
data /= h[0,0,1]
#data = data**2 # only if a voltage beam
data = data_mode(data, mode)
m.drawmapboundary()
m.drawmeridians(n.arange(0, 360, 30))
m.drawparallels(n.arange(0, 90, 10))
if mx is None: mx = data.max()
if drng is None:
mn = data.min()
# if min < (max - 10): min = max-10
else: mn = mx - drng
step = (mx - mn) / 10
levels = n.arange(mn-step, mx+step, step)
return m.imshow(data, vmax=mx, vmin=mn, cmap=cmap)
def map_vis(data,title=[],vmin=None,vmax=None,barlabel=None,cmap=None,outputdir=None):
# print("visualizing : "+title)
if cmap==None:
cmap=cm.jet
if len(data.shape)>2:
plotdata=data[0,:,:]
else:
plotdata=data
plt.clf()
# plt.figure(figsize=(3,3),dpi=200)
ny,nx=plotdata.shape
geo=[35,43,-113,-101]
if experiment=="conus":geo=[25,52.7,-124.7,-67]
m = Basemap(projection='cyl',llcrnrlat=geo[0],urcrnrlat=geo[1],\
llcrnrlon=geo[2],urcrnrlon=geo[3],resolution="i")
mapimg=m.imshow(plotdata,vmin=vmin,vmax=vmax,cmap=cmap)
if experiment=="conus":
m.drawparallels(np.arange(25,55,5.),labels=[1,0,0,0],dashes=[1,4])
m.drawmeridians(np.arange(-120,-65,10.),labels=[0,0,0,1],dashes=[1,4])
m.drawstates(linewidth=0.5)
m.drawcountries(linewidth=0.5)
m.drawcoastlines(linewidth=0.5)
else:
m.drawparallels(np.arange(36,43,2.),labels=[1,0,0,0],dashes=[1,4])
m.drawmeridians(np.arange(-112,-103,4.),labels=[0,0,0,1],dashes=[1,4])
m.drawstates(linewidth=1.5)
cbar=m.colorbar()
if barlabel:
cbar.set_label(barlabel)
plt.title(" ".join(title))
if outputdir:
plt.savefig(outputdir+"_".join(title)+'_map.png')
else:
plt.savefig("_".join(title)+'_map.png')
def imshow(self, data, low=0, high=255, **kwargs):
''' Make RGB plot over the map
data : numpy array
RGB or RGBA input data
**kwargs:
Parameters for Basemap.imshow
Modifies
---------
self.mpl : list
append AxesImage object with imshow
'''
# Create X/Y axes
self._create_xy_grids()
# add random colormap
if 'cmap' in kwargs and kwargs['cmap'] == 'random':
values = np.unique(data[np.isfinite(data)])
cmap, norm = self._create_random_colormap(values,
low=low, high=high)
kwargs['cmap'] = cmap
kwargs['norm'] = norm
# Plot data using imshow
self.mpl.append(Basemap.imshow(self, data,
extent=[self.x.min(), self.x.max(),
self.y.min(), self.y.max()],
origin='upper', **kwargs))
self.colorbar = len(self.mpl) - 1
def maping(rawimage, rawname, image, name):
import palettable
import matplotlib.colors as mpc
fig = plt.figure()
cmap = mpc.ListedColormap(palettable.colorbrewer.diverging.PiYG_9.mpl_colors)
# cmap.set_bad('grey',1.)
ax = fig.add_subplot(121)
ax.set_title("a. (maxNDVI/year)", loc= 'left')
#set the spatial cordinates of the grid, mask the ocean and draw coastlines
map = Basemap(llcrnrlon=112.0,llcrnrlat=-44.5,urcrnrlon=156.25,urcrnrlat=-10, resolution = 'h', epsg=4326)
map.drawmapboundary(fill_color='grey')
map.fillcontinents(color= 'none', lake_color='white')
map.drawlsmask(land_color='none', )
map.drawcoastlines()
map.imshow(np.flipud(rawimage), cmap=cmap, vmin=-0.01, vmax=0.0125,)
map.colorbar()#ticks= [-0.01, -0.0075, -0.0050, -0.00250, 0.0000, 0.0025, 0.0050, 0.0075, 0.010, 0.0125])
#add in the grid
map.drawparallels(np.arange(-50, -10, 10),labels=[1,0,0,0], dashes=[1,2])#color= 'none')
map.drawmeridians(np.arange(110, 156.25, 10),labels=[0,0,0,1], dashes=[1,2])
# from palettable.colorbrewer.qualitative import Dark2_7
ax = fig.add_subplot(122)
ax.set_title("b. ((maxNDVI/mmrainfall)/year)", loc= 'left')
#set the spatial cordinates of the grid, mask the ocean and draw coastlines
map = Basemap(llcrnrlon=112.0,llcrnrlat=-44.5,urcrnrlon=156.25,urcrnrlat=-10, resolution = 'h', epsg=4326)
map.drawmapboundary(fill_color='grey')
map.fillcontinents(color= 'none', lake_color='white')
map.drawlsmask(land_color='none', )
map.drawcoastlines()
#load the data into a figure
map.imshow(np.flipud(image), cmap=cmap, vmin=-0.01, vmax=0.0125,)
map.colorbar()#ticks= [-0.01, -0.0075, -0.0050, -0.00250, 0.0000, 0.0025, 0.0050, 0.0075, 0.010, 0.0125])
#add in the grid
map.drawparallels(np.arange(-50, -10, 10),labels=[1,0,0,0], dashes=[1,2])#color= 'none')
map.drawmeridians(np.arange(110, 156.25, 10),labels=[0,0,0,1], dashes=[1,2])
plt.show()
def cat_mapper(image, cmap, title, vmin, vmax, vmin2=False, vmax2=False, cmap2=False, norm=False, save=False):
"""To map things by a given number of catogeries"""
fig = plt.figure()
# cmap.set_bad('grey',1.)
# ax = fig.add_subplot(1)
plt.plot()
if title is True:
plt.title(title, loc= 'left')
else:
pass
#set the spatial cordinates of the grid, mask the ocean and draw coastlines
map = Basemap(llcrnrlon=112.0,llcrnrlat=-44.5,urcrnrlon=156.25,urcrnrlat=-10, resolution = 'h', epsg=4326)
map.drawmapboundary(fill_color='grey')
map.fillcontinents(color= 'none', lake_color='white')
map.drawlsmask(land_color='none', )
map.drawcoastlines()
map.drawstates()
if norm == False:
map.imshow(np.flipud(image), cmap=cmap, vmin=vmin, vmax=vmax)
else:
map.imshow(np.flipud(image), cmap=cmap, vmin=vmin, vmax=vmax, norm=norm)
# cb = plt.colorbar()
#Tweaking the colorbar so the the ticks allign with the grid.
cb = map.colorbar()#ticks= [-0.01, -0.0075, -0.0050, -0.00250, 0.0000, 0.0025, 0.0050, 0.0075, 0.010, 0.0125])
if vmax-vmin <= 1:
tick_locator = ticker.MaxNLocator(nbins=20)
else:
tick_locator = ticker.MaxNLocator(nbins=vmax-vmin)
cb.locator = tick_locator
cb.update_ticks()
#add in the grid
map.drawparallels(np.arange(-50, -10, 10),labels=[1,0,0,0], dashes=[1,2])#color= 'none')
map.drawmeridians(np.arange(110, 156.25, 10),labels=[0,0,0,1], dashes=[1,2])
if save is not False:
plt.savefig("%s/CTSR/%d/R4_paperfigures/%s.png" % (path, crn, save))
plt.show()
def satellite_plot():
imgsrc = 'http://lance-modis.eosdis.nasa.gov/imagery/subsets/?subset=ARM_Azores.' \
+ iter_date.strftime('%Y%j') \
+ '.aqua.2km.jpg'
#ENA - Graciosa Island, Azores
#39° 5' 29.68" N, 28° 1' 32.34" W
#Altitude: 30.48 meters
#from shapelib import ShapeFile
file = cStringIO.StringIO(urllib.urlopen(imgsrc).read())
try:
img = Image.open(file)
except IOError:
img = None
#print(imgsrc)
fig, ax = plt.subplots(figsize=(12,12))
ax.set_title(iter_date.strftime('%A %B %d %Y'), y=1.05)
m = Basemap(llcrnrlat=28.9951, llcrnrlon=-38.0039,
urcrnrlat=48.9990, urcrnrlon=-18.000,
resolution='c', ax=ax, area_thresh=0.1)
#m.drawcoastlines()
m.drawparallels(np.arange(-90, 90, 5), labels=[1,1,1,1])
m.drawmeridians(np.arange(-180, 180, 5), labels=[1,1,1,1])
if img is not None:
m.imshow(img, origin='upper')
m.plot(-28.02565, 39.091578, 'r*', markersize=20, latlon=True)
m.readshapefile(os.path.join(support_directory, 'PRT_adm/PRT_adm0'),
'prt', drawbounds=True)
if img is not None:
img.close()
figout = os.path.join(plot_directory, 'satellite/')
if not os.path.isdir(figout):
os.makedirs(figout)
fig.savefig(os.path.join(figout, iter_date.strftime('%Y-%m-%d')+'.png'),
transparent=False)
def map_test():
# image edges for west africa seviri
# (measured from the fennec browser hr panner)
ll_lat = 2.60
ll_lon = -30.64
ur_lat = 46.31
ur_lon = 44.66
# ul_lat = 47.28
# ul_lon = -54.00
# lr_lat = 2.58
# lr_lon = 27.08
m = Basemap(projection='geos', llcrnrlon=ll_lon, llcrnrlat=ll_lat,
urcrnrlon=ur_lon, urcrnrlat=ur_lat, resolution='h')
image = Image.open('TEST.png')
m.imshow(image)
m.drawcountries()
return m
def plot_hpx(hpx, mode='log', pol='x', mx=None, drng=2, interpolation=False,
colorbar=False, nogrid=False):
im = a.img.Img(size=400, res=.4)
x,y,z = im.get_top(center=n.array(im.shape)/2)
assert(pol in 'xy')
hpx.set_interpol(interpolation)
if pol == 'x': d = hpx[x,y,z]
else: d = hpx[-y,x,z]
d.shape = x.shape
d = n.where(x.mask, 0, d)
if mode.startswith('phs'): d = n.angle(d)
elif mode.startswith('lin'): d = n.absolute(d)
elif mode.startswith('real'): d = d.real
elif mode.startswith('imag'): d = d.imag
elif mode.startswith('log'):
d = n.absolute(d)
d = n.ma.masked_less_equal(d, 0)
d = n.ma.log10(d)
else: raise ValueError('Unrecognized plot mode.')
if mx is None: mx = d.max()
if drng is None: drng = mx - d.min()
mn = mx - drng
if not nogrid:
from mpl_toolkits.basemap import Basemap
map = Basemap(projection='ortho', lon_0=180, lat_0=90,
rsphere=1, llcrnrx=-1.25, llcrnry=-1.25, urcrnrx=1.25,urcrnry=1.25)
map.drawmeridians(n.arange(-180,180,30))
map.drawparallels(n.arange(0,90,15))
map.drawmapboundary()
map.imshow(d, vmin=mn, vmax=mx, interpolation='nearest')
else: p.imshow(d, vmin=mn, vmax=mx, origin='lower', interpolation='nearest')
#data.shape = az.shape
#m.drawmapboundary()
#m.drawmeridians(n.arange(0,360,30))
#m.drawparallels(n.arange(0,90,10))
#step = (max - min) / 35.
#levels = n.arange(min-step, max+step, step)
#m.contourf(cx, cy, data, levels)
if colorbar: p.colorbar(shrink=.5)
def map_plot(self,ax):
xmin = self.geodict['xmin']
xmax = self.geodict['xmax']
ymin = self.geodict['ymin']
ymax = self.geodict['ymax']
clat = ymin + (ymax-ymin)/2.0
bmap = Basemap(llcrnrlon=xmin,llcrnrlat=ymin,
urcrnrlon=xmax,urcrnrlat=ymax,
projection='merc',lat_ts=clat,
resolution='h',ax=ax)
img = bmap.imshow(flipud(self.griddata))
bmap.drawcoastlines(color='w')
return img
lats = np.arange(-60., 60., 0.05)[::-1]
lons1, lats1 = np.meshgrid(lons, lats)
fig = plt.figure()
ax1 = plt.subplot2grid((7, 6), (0, 0), colspan=6, rowspan=3)
m1 = Basemap(llcrnrlon=-180.,llcrnrlat=-60.,urcrnrlon=180.,urcrnrlat=60.,\
projection='mill',lon_0=0)
m1.drawcountries(linewidth=0.2)
m1.drawcoastlines(linewidth=0.3)
m1.drawparallels(np.arange(-60., 60., 30.), linewidth=0.05)
m1.drawmeridians(np.arange(-180., 180., 60.), linewidth=0.05)
cmap = LinearSegmentedColormap.from_list('mycmap', [(0 / 2., 'gray'),
(1 / 2., 'black'),
(2 / 2., 'white')])
#im1 = m1.pcolormesh(lons1, lats1, merged_map[0,:,:],shading='flat', cmap=cmap, latlon=True)
im1 = m1.imshow(np.flipud(merged_map[0, :, :]), cmap=cmap)
ax1.set_ylabel('Merged MODIS image', fontsize=10)
cb1 = m1.colorbar(im1,
'right',
pad='1%',
ticks=[0, 1, 2],
boundaries=[0, 0.1, 1.9, 2])
cb1.ax.set_yticklabels(['Ground', 'Cloud', 'Snow'], size='x-small')
title1 = ax1.set_title('Day: ' + t0.strftime('%Y-%m-%d'))
ax2 = plt.subplot2grid((7, 6), (3, 0), colspan=6, rowspan=3)
m2 = Basemap(llcrnrlon=-180.,llcrnrlat=-60.,urcrnrlon=180.,urcrnrlat=60.,\
projection='mill',lon_0=0)
m2.drawcountries(linewidth=0.2)
m2.drawcoastlines(linewidth=0.3)
m2.drawparallels(np.arange(-60., 60., 30.), linewidth=0.05)
m2.drawmeridians(np.arange(-180., 180., 60.), linewidth=0.05)
m = Basemap(projection='cyl',
llcrnrlat=rlat_0,
urcrnrlat=rlat_1,
llcrnrlon=rlon_0,
urcrnrlon=rlon_1,
resolution='c')
m.drawcoastlines()
m.drawparallels(
np.arange(-10, 60, 20), labels=[1, 0, 0, 0],
fontsize=9) #labels=[left,right,top,bottom] #horizontal line
m.drawmeridians(
np.arange(80, 180, 20), labels=[0, 0, 0, 1],
fontsize=9) #labels=[left,right,top,bottom] #vertical line
m.imshow(array, cmap=cmap, vmin=0, vmax=100) #draw figure
plt.title(fn_name, fontsize=9)
cbar = m.colorbar(
size="5%", pad=0.05,
extend='max') #pad=distance, extend='colorbar shape,,'
cbar.ax.tick_params(labelsize=8) #labelsize=colorbar font size
cbar.set_label('Rainfall intensity(mm/hr)', fontsize=8,
ha='center') #ha='label text position'
#------------------
'''
n=n+1
#---PE FILE SUBPLOT---
plt.subplot(6,2,n) #subplot(y,x,position)
m = Basemap(projection='ortho', lat_0=19, lon_0=-166, resolution='i')
#m = Basemap(llcrnrlon=lonmin,llcrnrlat=latmin,urcrnrlon=lonmax,urcrnrlat=latmax#,
# resolution='i',projection='merc',lon_0=27.,lat_0=46.7)
clip_path = m.drawmapboundary()
#m.contour(x,y,layer,cmap=RGB_light,linewidth=0,rasterized=True)
tmp = np.loadtxt(
'/raid1/sc845/Tomographic_models/SEMUCB_WM1/UCB_a3d_dist.SEMUCB-WM1.r20151019/SEMUCB_WM1_2800km.dat'
)
lon = tmp[:, 1].reshape((181, 361))
lat = tmp[:, 2].reshape((181, 361))
dvs = tmp[:, 3].reshape((181, 361))
s = m.transform_scalar(dvs, lon[0, :], lat[:, 0], 1000, 500)
im = m.imshow(s, cmap=plt.cm.seismic_r, clip_path=clip_path, vmin=-3, vmax=0)
cb = plt.colorbar()
cb.set_label('dlnVs (%)')
cb.set_clim(-15, 15)
cb.remove()
x, y = m(lon, lat)
lon, lat, layer = LMC.get_slice(piercedepth, whattoplot='votesslow')
x, y = m(lon.ravel(), lat.ravel())
x = x.reshape(np.shape(layer))
y = y.reshape(np.shape(layer))
minval = np.min(np.min(layer))
maxval = np.max(np.max(layer)) + .1
#m.shadedrelief()
m.drawcoastlines()
def plot_MWAconstellations(outfile=None,
obsinfo=None,
viewgps=None,
observing=True,
showbeam=True,
constellations=True,
gleamsources=False,
notext=False,
inverse=False,
skydata=None,
background=None,
hidenulls=False,
channel=None, # Frequency channel to use for the beam map, defaults to mean of all channels in obs.
xmas=XMAS,
plotscale=SCALE, # A scale of 1.0 gives a 1200x1200 pixel plot
logger=DEFAULTLOGGER):
if obsinfo is None:
logger.error('Unable to find observation info')
return None
if skydata is None:
skydata = SkyData()
if not skydata.valid:
logger.error('Unable to load star/planet data, aborting.')
return None
if background is None:
background = 'transparent'
if channel is None:
if 0 in obsinfo['rfstreams']:
channel = obsinfo['rfstreams'][0]['frequencies'][12]
elif '0' in obsinfo['rfstreams']:
channel = obsinfo['rfstreams']['0']['frequencies'][12]
s_obstime = su.time2tai(obsinfo['starttime'])
a_obstime = Time(obsinfo['starttime'], format='gps', scale='utc')
if viewgps is None:
s_viewtime = s_obstime
a_viewtime = a_obstime
else:
s_viewtime = su.time2tai(viewgps)
a_viewtime = Time(viewgps, format='gps', scale='utc')
a_viewtime.delta_ut1_utc = 0 # We don't care about IERS tables and high precision answers
LST_hours = a_viewtime.sidereal_time(kind='apparent', longitude=config.MWAPOS.lon)
observer = su.S_MWAPOS.at(s_viewtime)
mapzenith = SkyCoord(ra=skydata.skymapRA,
dec=skydata.skymapDec,
equinox='J2000',
unit=(astropy.units.deg, astropy.units.deg))
mapzenith.location = config.MWAPOS
mapzenith.obstime = a_viewtime
altaz = mapzenith.transform_to('altaz')
Az, Alt = altaz.az.deg, altaz.alt.deg
fig = plt.figure(figsize=(FIGSIZE * plotscale, FIGSIZE * plotscale), dpi=DPI)
ax1 = fig.add_subplot(1, 1, 1)
bmap = Basemap(projection='ortho', lat_0=su.MWA_TOPO.latitude.degrees, lon_0=LST_hours.hour * 15 - 360, ax=ax1)
nx = len(skydata.skymapra)
ny = len(skydata.skymapdec)
ax1.cla()
# show the Haslam map
tform_skymap = bmap.transform_scalar(skydata.basemap[0].data[0][:, ::-1],
skydata.skymapra[::-1] * 15,
skydata.skymapdec,
nx, ny,
masked=True)
if inverse:
cmap = CMI
else:
cmap = CM
bmap.imshow(numpy.ma.log10(tform_skymap[:, ::-1]), cmap=cmap, vmin=math.log10(LOW), vmax=math.log10(HIGH))
delays = []
if showbeam:
if not hidenulls:
contours = [0.001, 0.1, 0.5, 0.90]
if observing:
beamcolor = ((0.0, 0.0, 0.0), (0.0, 0.5, 0.0), (0.0, 0.75, 0.0), (0.0, 1.0, 0.0))
else:
beamcolor = ((0.0, 0.0, 0.0), (0.5, 0.5, 0.5), (0.75, 0.75, 0.75), (1.0, 1.0, 1.0))
else:
contours = [0.1, 0.5, 0.90]
if observing:
beamcolor = ((0.0, 0.5, 0.0), (0.0, 0.75, 0.0), (0.0, 1.0, 0.0))
else:
beamcolor = ((0.5, 0.5, 0.5), (0.75, 0.75, 0.75), (1.0, 1.0, 1.0))
# If the observation is in the future, calculate what delays will be used, instead of using the recorded actual delays
if su.tai2gps(s_obstime) > su.tai2gps(su.time2tai()) + 10:
if 0 in obsinfo['rfstreams']:
delays = calc_delays(az=obsinfo['rfstreams'][0]['azimuth'], el=obsinfo['rfstreams'][0]['elevation'])
elif '0' in obsinfo['rfstreams']:
delays = calc_delays(az=obsinfo['rfstreams']['0']['azimuth'], el=obsinfo['rfstreams']['0']['elevation'])
else:
delays = [33] * 16
logger.debug("Calculated future delays: %s" % delays)
else:
if 0 in obsinfo['rfstreams']:
delays = obsinfo['rfstreams'][0]['xdelays']
elif '0' in obsinfo['rfstreams']:
delays = obsinfo['rfstreams']['0']['xdelays']
logger.debug("Used actual delays: %s" % delays)
# get the primary beam
R = primarybeammap.return_beam(Alt,
Az,
delays,
channel * 1.28)
# show the beam
X, Y = bmap(skydata.skymapRA, skydata.skymapDec)
CS = bmap.contour(bmap.xmax - X, Y, R, contours, linewidths=plotscale, colors=beamcolor)
ax1.clabel(CS, inline=1, fontsize=10 * plotscale)
# Find the constellation that the beam is in
if obsinfo['ra_phase_center'] is not None:
ra = obsinfo['ra_phase_center']
dec = obsinfo['dec_phase_center']
else:
ra = obsinfo['metadata']['ra_pointing']
dec = obsinfo['metadata']['dec_pointing']
if (ra is not None) and (dec is not None):
constellation = ephem.constellation((ra * math.pi / 180.0, dec * math.pi / 180.0))
else:
constellation = ["N/A", "N/A"]
X0, Y0 = bmap(LST_hours.hour * 15 - 360, su.MWA_TOPO.latitude.degrees)
if constellations:
# plot the constellations
ConstellationStars = []
for c in skydata.constellations.keys():
for i in range(0, len(skydata.constellations[c][1]), 2):
i1 = numpy.where(skydata.hip['HIP'] == skydata.constellations[c][1][i])[0][0]
i2 = numpy.where(skydata.hip['HIP'] == skydata.constellations[c][1][i + 1])[0][0]
star1 = skydata.hip[i1]
star2 = skydata.hip[i2]
if i1 not in ConstellationStars:
ConstellationStars.append(i1)
if i2 not in ConstellationStars:
ConstellationStars.append(i2)
ra1, dec1 = map(numpy.degrees, (star1['RArad'], star1['DErad']))
ra2, dec2 = map(numpy.degrees, (star2['RArad'], star2['DErad']))
ra = numpy.array([ra1, ra2])
dec = numpy.array([dec1, dec2])
newx, newy = bmap(ra, dec)
testx, testy = bmap(newx, newy, inverse=True)
if testx.max() < 1e30 and testy.max() < 1e30:
bmap.plot(2 * X0 - newx, newy, 'r-', linewidth=plotscale, latlon=False) # This bit generates an error
# figure out the coordinates
# and plot the stars
ra = numpy.degrees(skydata.hip[ConstellationStars]['RArad'])
dec = numpy.degrees(skydata.hip[ConstellationStars]['DErad'])
m = numpy.degrees(skydata.hip[ConstellationStars]['Hpmag'])
newx, newy = bmap(ra, dec)
# testx, testy = bmap(newx, newy, inverse=True)
good = (newx > bmap.xmin) & (newx < bmap.xmax) & (newy > bmap.ymin) & (newy < bmap.ymax)
size = 60 - 15 * m
size[size <= 15] = 15
size[size >= 60] = 60
bmap.scatter(bmap.xmax - newx[good], newy[good],
size[good] * plotscale,
'r',
edgecolor='none',
alpha=0.7)
if gleamsources:
ra = numpy.array([x[1] for x in skydata.gleamcat])
dec = numpy.array([x[2] for x in skydata.gleamcat])
flux = numpy.array([x[3] for x in skydata.gleamcat])
newx, newy = bmap(ra, dec)
# testx, testy = bmap(newx, newy, inverse=True)
good = (newx > bmap.xmin) & (newx < bmap.xmax) & (newy > bmap.ymin) & (newy < bmap.ymax)
size = flux / 1.0
size[size <= 7] = 7
size[size >= 60] = 60
bmap.scatter(bmap.xmax - newx[good], newy[good],
size[good] * plotscale,
'b',
edgecolor='none',
alpha=0.7)
# plot the bodies
for b in skydata.bodies.keys():
name = skydata.bodies[b][2]
color = skydata.bodies[b][1]
if inverse:
if name == 'Moon':
color = 'darkgoldenrod'
elif name == 'Jupiter':
color = 'sienna'
elif name == 'Saturn':
color = 'purple'
size = skydata.bodies[b][0]
body_app = observer.observe(b).apparent()
body_ra_a, body_dec_a, _ = body_app.radec()
ra, dec = body_ra_a._degrees, body_dec_a.degrees
newx, newy = bmap(ra, dec)
testx, testy = bmap(newx, newy, inverse=True)
if testx < 1e30 and testy < 1e30:
bmap.scatter(2 * X0 - newx, newy, s=size * plotscale, c=color, alpha=1.0, latlon=False, edgecolor='none')
ax1.text(bmap.xmax - newx + 2e5, newy,
name,
horizontalalignment='left',
fontsize=12 * plotscale,
color=color,
verticalalignment='center')
# and label some sources
for source in primarybeammap.sources.keys():
if source == 'EOR0b':
continue
if source == 'CenA':
primarybeammap.sources[source][0] = 'Cen A'
if source == 'ForA':
primarybeammap.sources[source][0] = 'For A'
r = Longitude(angle=primarybeammap.sources[source][1], unit=astropy.units.hour).hour
d = Latitude(angle=primarybeammap.sources[source][2], unit=astropy.units.deg).deg
horizontalalignment = 'left'
x = r
if (len(primarybeammap.sources[source]) >= 6 and primarybeammap.sources[source][5] == 'c'):
horizontalalignment = 'center'
x = r
if (len(primarybeammap.sources[source]) >= 6 and primarybeammap.sources[source][5] == 'r'):
horizontalalignment = 'right'
x = r
fontsize = primarybeammap.defaultsize
if (len(primarybeammap.sources[source]) >= 5):
fontsize = primarybeammap.sources[source][4]
color = primarybeammap.defaultcolor
if (len(primarybeammap.sources[source]) >= 4):
color = primarybeammap.sources[source][3]
if color == 'k':
color = 'w'
if inverse:
if color == 'w':
color = 'black'
xx, yy = bmap(x * 15 - 360, d)
try:
if xx < 1e30 and yy < 1e30:
ax1.text(bmap.xmax - xx + 2e5, yy,
primarybeammap.sources[source][0],
horizontalalignment=horizontalalignment,
fontsize=fontsize * plotscale,
color=color,
verticalalignment='center')
except:
pass
if not notext:
if background == 'black':
fontcolor = 'white'
else:
fontcolor = 'black'
if showbeam:
ax1.text(0, bmap.ymax - 2e5,
'Obs ID %d with delays %s\n at %s:\n%s at %d MHz\n in the constellation %s' % (obsinfo['starttime'],
delays,
a_obstime.datetime.strftime('%Y-%m-%d %H:%M UT'),
obsinfo['obsname'],
channel * 1.28,
constellation[1]),
fontsize=10 * plotscale,
color=fontcolor)
else:
ax1.text(0, bmap.ymax - 2e5,
'%s:\nNo recent observation' % (a_obstime.datetime.strftime('%Y-%m-%d %H:%M UT')),
fontsize=10 * plotscale,
color=fontcolor)
ax1.text(bmap.xmax, Y0, 'W', fontsize=12 * plotscale, horizontalalignment='left', verticalalignment='center')
ax1.text(bmap.xmin, Y0, 'E', fontsize=12 * plotscale, horizontalalignment='right', verticalalignment='center')
ax1.text(X0, bmap.ymax, 'N', fontsize=12 * plotscale, horizontalalignment='center', verticalalignment='bottom')
ax1.text(X0, bmap.ymin, 'S', fontsize=12 * plotscale, horizontalalignment='center', verticalalignment='top')
try:
if type(outfile) == str:
if not xmas:
if background.lower() == 'transparent':
fig.savefig(outfile, transparent=True, facecolor='none', dpi=DPI)
else:
fig.savefig(outfile, transparent=False, facecolor=background, dpi=DPI)
return ''
else:
buf = io.BytesIO()
if background.lower() == 'transparent':
fig.savefig(buf, format='png', transparent=True, facecolor='none', dpi=DPI)
else:
fig.savefig(buf, format='png', transparent=False, facecolor=background, dpi=DPI)
buf.seek(0)
im = Image.open(buf)
im.load()
buf.close()
r = Image.open('Treindeers.png')
im.paste(r, box=(250, 300), mask=r)
buf2 = io.BytesIO()
im.save(buf2, format=outfile[outfile.find('.') + 1:].upper())
buf2.seek(0)
outf = open(outfile, 'wb')
outf.write(buf2.read())
return ''
else:
buf = io.BytesIO()
if background.lower() == 'transparent':
fig.savefig(buf, format='png', transparent=True, facecolor='none', dpi=DPI)
else:
fig.savefig(buf, format='png', transparent=False, facecolor=background, dpi=DPI)
buf.seek(0)
if not xmas:
return buf.read()
else:
im = Image.open(buf)
im.load()
buf.close()
r = Image.open('Treindeers.png')
im.paste(r, box=(250, 100), mask=r)
buf2 = io.BytesIO()
im.save(buf2, format='PNG')
buf2.seek(0)
return buf2.read()
except AssertionError:
logger.error('Cannot save output: %s', outfile)
return None
finally:
plt.close(fig)
del ax1
del fig
#with open(filename) as jfile:
# rjsJ = json.load(jfile)
#======================================================================
df = pd.DataFrame(rjsJ, dtype=np.float32)
vplt = plt.figure(figsize=(10, 8), edgecolor=None)
#ax1 = vplt.add_axes([0.05, 0.80, 0.9, 0.15])
m = Basemap(llcrnrlon=-125.671,
llcrnrlat=24.4769,
urcrnrlon=-66.5202,
urcrnrlat=49.4391,
epsg=4326)
#http://server.arcgisonline.com/arcgis/rest/services
#try local copy first
try:
from PIL import Image
m.imshow(Image.open('USAStreetMap.png'), origin='upper', alpha=0.6)
except FileNotFoundError as err:
#arcgisimage occasionally fails to download properly try,try again
while True:
try:
m.arcgisimage(service='ESRI_StreetMap_World_2D',
xpixels=3500,
alpha=0.6)
except RuntimeError as err:
pass
else:
break
#---------------------------------------------------------------------
vvmin = -10 #min temp for scale
vvmax = 50 #max temp for scale
vstep = 10 #step between ticks
# north polar projection.
m = Basemap(lon_0=-135,boundinglat=25,round=True,
resolution='c',area_thresh=10000.,projection='npstere')
# transform from spherical to map projection coordinates (rotation
# and interpolation).
nxv = 41; nyv = 41
nxp = 101; nyp = 101
spd = np.sqrt(u**2+v**2)
udat, vdat, xv, yv = m.transform_vector(u,v,lons1,lats1,nxv,nyv,returnxy=True)
pdat, xp, yp = m.transform_scalar(p,lons1,lats1,nxp,nyp,returnxy=True)
# create a figure, add an axes.
fig=plt.figure(figsize=(8,8))
ax = fig.add_axes([0.1,0.1,0.7,0.7])
# plot image over map
im = m.imshow(pdat,plt.cm.jet)
# plot wind vectors over map.
Q = m.quiver(xv,yv,udat,vdat) #or specify, e.g., width=0.003, scale=400)
qk = plt.quiverkey(Q, 0.95, 1.05, 25, '25 m/s', labelpos='W')
m.colorbar(pad='12%') # draw colorbar
m.drawcoastlines()
m.drawcountries()
# draw parallels
delat = 20.
circles = np.arange(0.,90.+delat,delat).tolist()+\
np.arange(-delat,-90.-delat,-delat).tolist()
m.drawparallels(circles,labels=[1,1,1,1])
# draw meridians
delon = 45.
meridians = np.arange(-180,180,delon)
m.drawmeridians(meridians,labels=[1,1,1,1])
else: min = d.min()
#p.subplot(m2, m1, cnt+1)
if not opts.nogrid:
from mpl_toolkits.basemap import Basemap
xpx,ypx = d.shape
dx1 = -(xpx/2 + .5) * kwds['d_ra'] * a.img.deg2rad
dx2 = (xpx/2 - .5) * kwds['d_ra'] * a.img.deg2rad
dy1 = -(ypx/2 + .5) * kwds['d_dec'] * a.img.deg2rad
dy2 = (ypx/2 - .5) * kwds['d_dec'] * a.img.deg2rad
map = Basemap(projection='ortho', lon_0=180, lat_0=kwds['dec'],
rsphere=1, llcrnrx=dx1, llcrnry=dy1, urcrnrx=dx2,urcrnry=dy2)
map.drawmeridians(n.arange(kwds['ra']-180,kwds['ra']+180,30))
map.drawparallels(n.arange(-90,120,30))
map.drawmapboundary()
map.imshow(d, vmin=min, vmax=max, cmap=cmaps[cnt%len(cmaps)], interpolation='nearest', alpha=1-.25*cnt)
else: p.imshow(d, vmin=min, vmax=max, origin='lower', cmap=cmap, interpolation='nearest')
p.colorbar(shrink=.5, fraction=.05)
p.title(filename)
if opts.batch:
print 'Saving to', outfile
p.savefig(outfile)
p.clf()
# Add right-click functionality for finding locations/strengths in map.
cnt = 1
def click(event):
global cnt
if not event.button in [2,3]: return
#plot the beam contours
if not opts.beam is None:
bm_levels = [0.0001, 0.001, 0.01, 0.1, 0.5,
0.9] #beam levels in beam units
print "plotting beam contours: %s" % ([db(bm) for bm in bm_levels])
lons, lats, x_bm, y_bm = map.makegrid(360 / opts.res,
180 / opts.res,
returnxy=True)
map.contour(x_bm, y_bm, bm_response**2, bm_levels, colors='r', lw=10)
if opts.skip_im: pass
if opts.osys == 'ga':
print data.shape
data = n.fliplr(data)
print data.shape
map.imshow(data, vmax=max, vmin=min, cmap=cmap, interpolation=opts.interp)
ax.format_coord = format_coord
# Plot src labels and markers on top of map image
if not opts.src is None:
print "trying to plot sources"
sx, sy = map(slons, slats)
for name, xpt, ypt, flx in zip(snams, sx, sy, sflxs):
if xpt >= 1e30 or ypt >= 1e30: continue
#if opts.src_mark != '':
#map.plot(sx, sy, opts.src_color+opts.src_mark,markerfacecolor=None)
#map.scatter(sx,sy,'o',color=[(181,237,255)]*len(sx),s=n.log10(flx)*10)
if opts.lockview and flx < 40: continue
if flx < 60:
s = 10
topodat = m.transform_scalar(data, lons, lats, nx, ny) print 'Getting colormap...' # get colormap #cptfile = '//Users//tallen//Documents//DATA//GMT//cpt//mby_topo-bath_mod.cpt' cptfile = '//Users//tallen//Documents//DATA//GMT//cpt//mby_topo-bath.cpt' #cptfile = '//Users//tallen//Documents//DATA//GMT//cpt//gray.cpt' cmap, zvals = cpt2colormap(cptfile, 256) cmap = remove_last_cmap_colour(cmap) # make shading print 'Making map...' ls = LightSource(azdeg=180, altdeg=45) norm = mpl.colors.Normalize(vmin=-8000 / zscale, vmax=5000 / zscale) #myb rgb = ls.shade(topodat, cmap=cmap, norm=norm) im = m.imshow(rgb) ########################################################################################## # plt boundaries ########################################################################################## x, y = m(walon, walat) plt.plot(x, y, '-', c='r', lw=2.5) labelCentroid(plt, m, 'WA', 16, walon, walat, 0) x, y = m(ealon, ealat) plt.plot(x, y, '-', c='r', lw=2.5) labelCentroid(plt, m, 'EA', 16, ealon, ealat, 0) x, y = m(salon, salat) plt.plot(x, y, '-', c='r', lw=2.5)
def run_main():
""" Main """
print "Crawling last near real-time true-color image from MODIS... ",
download(URL_RAPIDFIRE_SUBSET, "data.zip")
try:
zipf = zipfile.ZipFile('data.zip')
except zipfile.BadZipfile:
print "\n\n\tError: BadZipfile, maybe the data is not yet ready on MODIS site !\n"
sys.exit(-1)
tempdir = tempfile.mkdtemp()
for name in zipf.namelist():
data = zipf.read(name)
outfile = os.path.join(tempdir, name)
f = open(outfile, 'wb')
f.write(data)
f.close()
zipf.close()
image_path = os.path.join(tempdir, IMAGE_FILE)
image_modis = Image.open(image_path)
print "done !"
print "Downloading MODIS image metadata... ",
metadata = downloadString(URL_METADATA)
ll_lon = parseTerm(metadata, "LL lon")
ll_lat = parseTerm(metadata, "LL lat")
ur_lon = parseTerm(metadata, "UR lon")
ur_lat = parseTerm(metadata, "UR lat")
print "done !"
print "Downloading shape files from MODIS rapid fire... ",
download(URL_FIRE_SHAPES, "shapes.zip")
zipf = zipfile.ZipFile('shapes.zip')
for name in zipf.namelist():
data = zipf.read(name)
outfile = os.path.join(tempdir, name)
f = open(outfile, 'wb')
f.write(data)
f.close()
zipf.close()
print "done !"
print "Parsing shapefile... ",
shape_path = os.path.join(tempdir, 'Global_%s' % FIRE_LASTS)
r = shapefile.Reader(shape_path)
# print r.fields
sr = r.shapeRecords()
total = len(sr)
xlist = []
ylist = []
confidence = []
for i in xrange(total):
sr_test = sr[i]
xlist.append(sr_test.shape.points[0][0]) #longitude
ylist.append(sr_test.shape.points[0][1]) #latitude
confidence.append(sr_test.record[8])
print "done !"
print "Rendering... ",
m = Basemap(projection='cyl', llcrnrlat=ur_lat, urcrnrlat=ll_lat,\
llcrnrlon=ll_lon, urcrnrlon=ur_lon, resolution='f')
m.drawcoastlines()
m.drawmapboundary(fill_color='aqua')
m.scatter(xlist,
ylist,
20,
c=confidence,
cmap=p.cm.YlOrRd,
marker='o',
edgecolors='none',
zorder=10)
m.imshow(image_modis)
p.title(
"The recent fire hotspots for last %s \n Pixel size: %s | Current Date: %s | Area Mapped: %s"
% (FIRE_LASTS, RAPIDFIRE_RES, time.strftime("%d/%m/%Y"), SUBSET_NAME))
p.show()
os.remove("data.zip")
def initialize(self):
font = {'size': 12}
matplotlib.rc('font', **font)
matplotlib.style.use("dark_background")
pixels = [1920, 1080]
fig = Figure(figsize=(pixels[0] / 96.0, pixels[1] / 96.0),
edgecolor='none',
facecolor='k')
i0 = 1
i = 14
j = 36
s = 3
# ----------------------------------------------------------------------
# Eixos para os graficos
# ----------------------------------------------------------------------
gs1, gs2 = gridspec.GridSpec(i, j), gridspec.GridSpec(i, j)
# ----------------------------------------------------------------------
# Grafico de temperatura xaxis compartilhado com os demais graficos
graf_temp = fig.add_subplot(gs1[i0 + 3 * s:i0 + 4 * s:11, 0:15])
# Grafico de pressao
graf_press = fig.add_subplot(gs1[i0 + 2 * s:i0 + 3 * s:11, 0:15],
sharex=graf_temp)
# Grafico umidade e precipitacao
graf_umid = fig.add_subplot(gs1[i0 + s:i0 + 2 * s, 0:15],
sharex=graf_temp)
graf_rain = graf_umid.twinx()
# Grafico de vento e direcao do vento
graf_vento = fig.add_subplot(gs1[i0:i0 + s, 0:15], sharex=graf_temp)
# ----------------------------------------------------------------------
# Icone da pressao
icon_pres = fig.add_subplot(gs1[i0 + 2 * s:i0 + 3 * s, 16:21],
frameon=False)
# Icone do vento
icon_vent = fig.add_subplot(gs1[i0:i0 + s, 16:18], frameon=False)
# Icone de umidade
icon_umid = fig.add_subplot(gs1[i0 + s:i0 + 2 * s, 16:18],
frameon=False)
# Icone de temperatura
icon_temp = fig.add_subplot(gs1[i0 + 3 * s:i0 + 4 * s, 16:18],
frameon=False)
# Icone direcao do vento agora
icon_vento_dir = fig.add_subplot(gs2[i0:i0 + s, 19:22],
frameon=False,
projection='polar')
# Icone de precipitacao
icon_rain = fig.add_subplot(gs2[i0 + s:i0 + 2 * s, 18:21],
frameon=False)
# Icone de sensação térmica
self.icon_feel = fig.add_subplot(gs2[i0 + 3 * s:i0 + 4 * s, 18:21],
frameon=False)
# Icone de rajada
icon_gust = fig.add_subplot(gs1[i0:4, 23:27], frameon=False)
# Icone de windrose
icon_rose = fig.add_subplot(gs1[i0:4, 28:32],
frameon=False,
projection='polar')
# ----------------------------------------------------------------------
# Titulo Gráficos
self.title_1 = fig.add_subplot(gs1[0, 0:12], frameon=False)
# Titulo Icones
self.title_2 = fig.add_subplot(gs1[0, 16:21], frameon=False)
# Titulo Logos
self.title_3 = fig.add_subplot(gs1[-2, 23::], frameon=False)
map = fig.add_subplot(gs1[5:-2, 23::])
gs1.update(wspace=0.01, hspace=0.3, left=0.04, top=1, bottom=0.000001)
gs2.update(wspace=0.03, hspace=0.75, top=1, bottom=0.000001)
# ----------------------------------------------------------------------
# Plota os icones
# ----------------------------------------------------------------------
dir_icon = '.\images'
zoom_f = 1.6
kw = dict(frameon=False,
xycoords="data",
boxcoords="data",
xy=(0, 0),
box_alignment=(0.0, 0.0))
# Desenha os icones de vento
img_today = read_png(os.path.join(dir_icon, 'wind.png'))
imagebox = OffsetImage(img_today, zoom=0.07 * zoom_f)
ab = AnnotationBbox(imagebox, xybox=(0.1, 0.1), **kw)
icon_vent.add_artist(ab)
# ----------------------------------------------------------------------
# Gerando eixo polar para as setas para o vento
directions = ['N', 'NW', 'W', 'SW', 'S', 'SE', 'E', 'NE']
icon_vento_dir.set_theta_zero_location('N')
icon_vento_dir.set_theta_direction(1)
icon_vento_dir.set_xticklabels(directions)
icon_vento_dir.tick_params(axis='x', colors='w')
icon_vento_dir.tick_params(axis='y', colors='w')
icon_vento_dir.grid(color='w')
# Desenha o icone de rajada
img_today = read_png(os.path.join(dir_icon, 'rajada.png'))
imagebox = OffsetImage(img_today, zoom=0.15 * zoom_f)
ab = AnnotationBbox(imagebox, xybox=(0.0, 0.1), **kw)
icon_gust.add_artist(ab)
icon_gust.set_title(u'Rajada 2 minutos',
fontsize=11,
x=0.4,
y=1,
fontweight='bold')
# ----------------------------------------------------------------------
# Gerando eixo polar para plot
icon_rose.set_theta_zero_location('N')
icon_rose.set_theta_direction(1)
icon_rose.set_xticklabels(directions)
icon_rose.tick_params(axis='x', colors='w')
icon_rose.tick_params(axis='y', colors='w')
icon_rose.grid(color='w')
# Desenha os icones de umidade
img_today = read_png(os.path.join(dir_icon, 'humidity.png'))
imagebox = OffsetImage(img_today, zoom=0.075 * zoom_f)
ab = AnnotationBbox(imagebox, xybox=(0.325, 0.10), **kw)
icon_umid.add_artist(ab)
# Desenha os icones de precipitacao
img_today = read_png(os.path.join(dir_icon, 'rain.png'))
imagebox = OffsetImage(img_today, zoom=0.09 * zoom_f)
ab = AnnotationBbox(imagebox, xybox=(0.275, 0.0), **kw)
icon_rain.add_artist(ab)
# Desenha os icones de pressao
img_today = read_png(os.path.join(dir_icon, 'pressure.png'))
imagebox = OffsetImage(img_today, zoom=0.125 * zoom_f)
ab = AnnotationBbox(imagebox, xybox=(0.35, 0.0), **kw)
icon_pres.add_artist(ab)
# Desenha os icones de temperatura
img_today = read_png(os.path.join(dir_icon, 'temp.png'))
imagebox = OffsetImage(img_today, zoom=0.11 * zoom_f)
ab = AnnotationBbox(imagebox, xybox=(0.22, 0.0), **kw)
icon_temp.add_artist(ab)
# Desenha o icone de sensacao termica atual
img_today = read_png(os.path.join(dir_icon, 'realfeel.png'))
imagebox = OffsetImage(img_today, zoom=0.10 * zoom_f)
self.ab = AnnotationBbox(imagebox, xybox=(0.3, 0.1), **kw)
self.icon_feel.add_artist(self.ab)
self.icon_feel.set_title(u'Sensação\nTérmica',
fontsize=11,
x=0.55,
y=-0.2)
self.img = itertools.cycle(
['realfeel_cky.png', 'realfeel_fms.png', 'realfeel_fna.png'])
# logo Petrobras
img_today = read_png(os.path.join(dir_icon, 'Principal_h_cor_RGB.png'))
imagebox = OffsetImage(img_today, zoom=0.2)
# opcao 1: logo ao lado do credito
ab = AnnotationBbox(imagebox, xybox=(0.0, 0.05), **kw)
self.title_3.add_artist(ab)
# logo Oceanop
img_today = read_png(os.path.join(dir_icon, 'oceanop_logo.png'))
imagebox = OffsetImage(img_today, zoom=0.4)
# opcao 1: logo ao lado do credito
ab = AnnotationBbox(imagebox, xybox=(0.8, 0.1), **kw)
self.title_3.add_artist(ab)
# ----------------------------------------------------------------------
# Cria mapa base
# ----------------------------------------------------------------------
# Coordenadas da estacao meteorologica # 22 24 4.02**41 48 29.30
Xest, Yest = -41.808138888889, -22.401116666667
# Coordenadas da janela da imagem 2223.936_4148.723_2224.175_4148.264
Xmin, Xmax = -41.81205, -41.8044
Ymin, Ymax = -22.402916666667, -22.398933333333
# Mapa base com a localizacao da estacao
m = Basemap(ax=map,
epsg=4326,
projection='merc',
llcrnrlat=Ymin,
urcrnrlat=Ymax,
llcrnrlon=Xmin,
urcrnrlon=Xmax,
resolution='c')
imgfile = os.path.join(dir_icon,
'2223.936_4148.723_2224.175_4148.264.jpg')
img = plt.imread(imgfile)
m.imshow(img, origin='upper')
# Plota pontos de interesse
bprops = dict(boxstyle='round', facecolor='w', alpha=0.35)
aprops2 = dict(arrowstyle='simple',
connectionstyle='arc3, rad=-0.2',
relpos=(0., 1.),
facecolor='k',
alpha=0.6)
map.text(-41.8066588889,
-22.40111,
'EDINC',
horizontalalignment='center',
verticalalignment='center',
fontweight='bold',
fontsize=16,
color='k',
bbox=bprops)
pest, = map.plot(Xest, Yest, 'ro', markersize=4)
map.annotate(u"Estação\nMeteorológica",
xy=(Xest, Yest),
xycoords='data',
xytext=(-41.809, -22.403),
size=12,
va='top',
ha='left',
arrowprops=aprops2,
bbox=bprops,
fontweight='bold')
# Insere seta apontando para o norte no canto inferior direito
imgfile = read_png(os.path.join(dir_icon, 'north_arrow.png'))
imagebox = OffsetImage(imgfile, zoom=0.11)
lonmin, lonmax = m.boundarylonmin, m.boundarylonmax
latmin, latmax = min(m.boundarylats), max(m.boundarylats)
xy = (lonmax - 0.08 * (lonmax - lonmin),
latmax - 0.9 * (latmax - latmin))
ab = AnnotationBbox(imagebox,
xy,
frameon=False,
xycoords="data",
boxcoords="data",
box_alignment=(0.0, 0.0))
map.add_artist(ab)
fontdict = {
'color': 'w',
'va': 'center',
'ha': 'center',
'fontsize': 13,
'fontweight': 'bold'
}
self.i_vent = icon_vent.text(0.5, 0.8, 'NA', fontdict=fontdict)
self.i_gust = icon_gust.text(0.5, 0.8, 'NA', fontdict=fontdict)
self.i_umid = icon_umid.text(0.5, 0.75, 'NA', fontdict=fontdict)
self.i_rain = icon_rain.text(0.5, 0.8, 'NA', fontdict=fontdict)
self.i_pres = icon_pres.text(0.2, 0.7, 'NA', fontdict=fontdict)
self.i_temp = icon_temp.text(0.25, 0.75, 'NA', fontdict=fontdict)
self.i_feel = self.icon_feel.text(0.275, 0.75, 'NA', fontdict=fontdict)
# Set icones e titulos
axis_icon_names = [
icon_pres, icon_vent, icon_umid, icon_rain, icon_gust, icon_pres,
icon_temp, self.icon_feel, self.title_1, self.title_2, self.title_3
]
for i in axis_icon_names:
i.set_axis_off()
# Set titulos nos graficos
graf_vento.set_title(u'Vento [km/h]',
x=0.12,
y=0.75,
fontweight='bold',
fontsize=11)
graf_press.set_title(u'Pressão Atmosférica [hPa]',
x=0.23,
y=0.75,
fontweight='bold',
fontsize=11)
graf_umid.set_title(u'Umidade Relativa [%]',
x=0.19,
y=0.75,
fontweight='bold',
fontsize=11)
graf_temp.set_title(u'Temperatura do Ar [\N{DEGREE SIGN}C]',
x=0.205,
y=0.75,
fontweight='bold',
fontsize=11)
map.set_title(u'Localização da Estação Meteorólogica',
fontsize=15,
fontweight='bold',
loc='left',
y=1.05)
self.canvasFig = fig
# [0]wind, [1]humi, [2]pres, [3]temp
self.cor = ['#4CB050', '#2196F5', '#FF9702', '#F44236']
x, y = [], []
self.line0, = graf_vento.plot(x, y, self.cor[0])
self.line1, = graf_umid.plot(x, y, self.cor[1])
self.line2, = graf_rain.plot(x, y, self.cor[1], linestyle='--')
self.line3, = graf_press.plot(x, y, self.cor[2])
self.line4, = graf_temp.plot(x, y, self.cor[3])
icon_vento_dir.axes.yaxis.set_ticklabels([])
icon_rose.axes.yaxis.set_ticklabels([])
self.canvas = FigureCanvasTkAgg(fig, master=self)
self.canvas.show()
self.canvas.get_tk_widget().pack(side=Tkinter.TOP,
fill=Tkinter.BOTH,
expand=1)
self.canvas._tkcanvas.pack(side=Tkinter.TOP,
fill=Tkinter.BOTH,
expand=1)
self.after(500, self.icon_refresh, icon_vento_dir, icon_rose)
self.update()
def visualize_ensemble_raster(crop, osc, raster_data, raster_mask, savepath,
save, cmap, colorbar_name, data_type):
# Initialize the map figure using Basemap
m = Basemap(
projection='cyl',
resolution='c',
llcrnrlat=-60,
urcrnrlat=90,
llcrnrlon=-180,
urcrnrlon=180,
)
fig = plt.figure()
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
# Create the colorscale, using RGB information and the function LinearSegmentedColormap
if cmap == 'redblue':
cdict = {
'blue': ((0.0, 0.0, 0.0), (0.25, 0.0, 0.0), (0.49, 0.8, 0.9),
(0.51, 0.9, 1.0), (0.75, 1.0, 1.0), (1.0, 0.4, 1.0)),
'green': ((0.0, 0.0, 0.0), (0.25, 0.0, 0.0), (0.49, 0.9, 0.9),
(0.51, 0.9, 0.9), (0.75, 0.0, 0.0), (1.0, 0.0, 0.0)),
'red': ((0.0, 0.0, 0.4), (0.25, 1.0, 1.0), (0.49, 1.0, 0.9),
(0.51, 0.9, 0.8), (0.75, 0.0, 0.0), (1.0, 0.0, 0.0))
}
clim = (-5, 5)
cbar_label = 'Crop yield deviation (%) per unit index change'
cmap = LinearSegmentedColormap('cmap', cdict)
elif cmap == 'whiteblue':
cdict = {
'blue': ((0.0, 0.8, 0.9), (0.01, 0.9, 1.0), (0.5, 1.0, 1.0),
(1.0, 0.4, 1.0)),
'green': ((0.0, 0.9, 0.9), (0.01, 0.9, 0.9), (0.5, 0.0, 0.0),
(1.0, 0.0, 0.0)),
'red': ((0.0, 1.0, 0.9), (0.01, 0.9, 0.8), (0.5, 0.0, 0.0),
(1.0, 0.0, 0.0))
}
clim = (0, 100)
cbar_label = 'Proportion of individual models that agree with the ensemble sensitivity sign'
cmap = LinearSegmentedColormap('cmap', cdict)
# Modify settings for the raster visualization.
raster_data = np.flipud(raster_data)
raster_mask = np.flipud(raster_mask)
mask_index = raster_mask * np.isnan(raster_data)
raster_data[mask_index] = 0
cs = m.imshow(raster_data[60:, :] * 100, clim=clim, cmap=cmap)
m.drawcoastlines(linewidth=0.25)
# m.drawcountries(linewidth=0.25)
m.fillcontinents(color='white', lake_color='white', zorder=0)
m.drawmapboundary(fill_color='White')
# Save the figure.
if save == 1:
os.chdir(savepath)
plt.savefig(crop + '_' + osc + '_' + data_type + '.png',
dpi=300,
bbox_inches='tight')
plt.show(m)
# If colorbar for sensitivity doesn't exist in folder, create it.
if colorbar_name not in os.listdir(savepath):
# add colorbar
plt.figure()
cs = plt.imshow(raster_data[60:, :] * 100, clim=clim, cmap=cmap)
plt.gca().set_visible(False)
cbar = plt.colorbar(cs, extend='both', orientation='horizontal')
cbar.set_label(cbar_label, fontsize=12)
plt.savefig(colorbar_name, dpi=300, bbox_inches='tight')
zl = zl[::d][:,::d]
return xl, yl, zl
lont, latt, zt = get_xyz()
zt = zt.T
lont, latt = np.meshgrid(lont, latt)
# n = 1000
# nx = 1 + int( (map.xmax-map.xmin)/n )
# ny = 1 + int( (map.ymax-map.ymin)/n )
nx = 2000; ny = 3000
topodat = map.transform_scalar(zt, lont[0,:], latt[:,0], nx, ny)
# im = map.imshow(topodat * 0 + 1, cmap=plt.get_cmap('Greys'), alpha = 1,interpolation = 'bilinear', rasterized=True)
im = map.imshow(topodat, cmap=plt.get_cmap('terrain'), alpha = 1,interpolation = 'bilinear', rasterized=True)
xt, yt = map(lont, latt)
ls = LightSource(azdeg=315, altdeg=45)
hs = ls.hillshade(zt,
vert_exag = 2, dx=xt[0,1] - xt[0,0] , dy=yt[0,0] - yt[1,0])
hs = map.transform_scalar(hs, lont[0,:], latt[:,0], nx, ny)
map.imshow(hs, cmap = 'gray', alpha = .5,interpolation = 'bilinear', rasterized=True)
#%%
# plt.xlim([0, width]); plt.ylim([0, height])
# map.drawmapboundary(fill_color=None)
# map.fillcontinents(color='lightgrey',lake_color=None)
# map.shadedrelief()
# map.etopo(scale = 1)
#y = y - ay
#z = z - az
try:
data, indices = h[x, y, z]
except (ValueError):
data = h[x, y, z]
data.shape = lats.shape
map.drawmapboundary()
map.drawmeridians(n.arange(0, 360, 30))
map.drawparallels(n.arange(0, 90, 10))
if opts.max is None: max = data.max()
else: max = opts.max
if opts.drng is None:
min = data.min()
# if min < (max - 10): min = max-10
else:
min = max - opts.drng
step = (max - min) / 10
levels = n.arange(min - step, max + step, step)
print min, max
#data = data.clip(min, max)
#data = n.ma.array(data, mask=mask)
#min=0
map.imshow(data, vmax=max, vmin=min, cmap=cmap)
#map.contourf(cx,cy,data,levels,linewidth=0,cmap=cmap)
p.colorbar()
p.show()
masked_chan31 = np.ma.masked_invalid(chan31)
fig, ax = plt.subplots(1, 1, figsize=(14, 14))
basemap_args = result_dict['basemap_args']
basemap_args['ax'] = ax
basemap_args['resolution'] = 'c'
bmap = Basemap(**basemap_args)
lat_sep, lon_sep = 5, 5
parallels = np.arange(30, 60, lat_sep)
meridians = np.arange(-135, -100, lon_sep)
bmap.drawparallels(parallels, labels=[1, 0, 0, 0], fontsize=10, latmax=90)
bmap.drawmeridians(meridians, labels=[0, 0, 0, 1], fontsize=10, latmax=90)
bmap.drawcoastlines(linewidth=1.5, linestyle='solid', color='k')
with warnings.catch_warnings():
warnings.simplefilter("ignore")
col = bmap.imshow(masked_chan31, origin='upper', norm=the_norm, cmap=cmap)
cax, kw = matplotlib.colorbar.make_axes(ax,
location='bottom',
pad=0.05,
shrink=0.7)
out = fig.colorbar(col, cax=cax, extend='both', **kw)
out.set_label('Chan 31 radiance (W/m^2/micron/sr)', size=20)
ax.set_title('Chan 31 radiance vancouver', size=25)
print(kw)
# In[8]:
wavel = 11.e-6 #chan 31 central wavelength, meters
chan31_mks = result_dict['channels'][:, :, 0] * 1.e6 #W/m^2/m/sr
Tbright = planckInvert(wavel, chan31_mks)
Tbright = Tbright - 273.15 #convert to Centigrade
##############################################################################
print('Reading netCDF file...')
nc = NetCDFFile(ncf)
data = nc.variables['z'][:]
lons = nc.variables['x'][:]
lats = nc.variables['y'][:]
# transform to metres
nx = int((m.xmax - m.xmin) / 500.) + 1
ny = int((m.ymax - m.ymin) / 500.) + 1
hazdat = m.transform_scalar(data, lons, lats, nx, ny)
im = m.imshow(hazdat, cmap=cmap, norm=norm, alpha=1.0)
##########################################################################################
# get land & lake polygons for masking
##########################################################################################
# mask non-AU polygons
nonmask = [0, 1, 2, 3, 4, 6, 7, 11, 13, 16, 17] # polygon number
nonmask = [0, 9, 14, 17, 18, 19] # polygon number
landpolys = []
for pidx, polygon in enumerate(m.landpolygons):
maskPoly = True
for nmidx in nonmask:
if pidx == nmidx:
maskPoly = False
if maskPoly == True:
poly = polygon.get_coords()
def main():
global inputArgs, grib, dir_path #Make our global vars: grib is the object that will hold our Grib Class.
dir_path = os.path.dirname(os.path.realpath(__file__))
comparison_days = [0, -7]
inputArgs = handle_args(
sys.argv) #All input arguments if run on the command line.
for deltaDay in comparison_days:
# MAKE SURE TO UNCOMMENT #inputArgs.date2 when putting back into production
if deltaDay == 0:
date2 = None
else:
date2 = ((datetime.datetime.now(pytz.timezone('US/Pacific'))) +
datetime.timedelta(days=deltaDay)).strftime("%Y%m%d")
##############
# Debugging
inputArgs.date = (datetime.datetime.today() -
datetime.timedelta(days=1)).strftime("%Y%m%d")
#inputArgs.date = time.strftime("%Y%m%d")
#inputArgs.date = str(dayNum)
inputArgs.date2 = date2 #Comment this out for just one date
inputArgs.map = True # Make the map and save png to folder.
inputArgs.plot = False
findValueAtPoint = False # Find all the values at specific lat/lng points within an excel file.
#################
grib = Grib() #Assign variable to the Grib Class.
grib.model = inputArgs.model #Our model will always be "snodas" for this program
grib.displayunits = inputArgs.displayunits
grib.basin = inputArgs.basin # Basin can be "French_Meadows", "Hell_Hole", or "MFP", this gets shapefile
# Bounding box will clip the raster to focus in on a region of interest (e.g. CA) This makes the raster MUCH smaller
# and easier to work with. See gdal.Open -> gdal.Translate below for where this is acutally used.
grib.bbox = [
-125.0, 50.0, -115.0, 30.0
] #[upper left lon, upper left lat, lower left lon, lower left lat]
grib = get_snowdas(
grib, inputArgs.date
) #Get the snodas file and save data into the object variable grib
#pngFile = makePNG()
#Any reprojections of grib.gribAll have already been done in get_snowdas.
#The original projection of snodas is EPSG:4326 (lat/lng), so it has been changed to EPSG:3875 (x/y) in get_snodas
projInfo = grib.gribAll.GetProjection()
geoinformation = grib.gribAll.GetGeoTransform(
) #Get the geoinformation from the grib file.
xres = geoinformation[1]
yres = geoinformation[5]
xmin = geoinformation[0]
xmax = geoinformation[0] + (xres * grib.gribAll.RasterXSize)
ymin = geoinformation[3] + (yres * grib.gribAll.RasterYSize)
ymax = geoinformation[3]
spatialRef = osr.SpatialReference()
spatialRef.ImportFromWkt(projInfo)
spatialRefProj = spatialRef.ExportToProj4()
# create a grid of xy (or lat/lng) coordinates in the original projection
xy_source = np.mgrid[xmin:xmax:xres, ymax:ymin:yres]
xx, yy = xy_source
# A numpy grid of all the x/y into lat/lng
# This will convert your projection to lat/lng (it's this simple).
lons, lats = Proj(spatialRefProj)(xx, yy, inverse=True)
# Find the center point of each grid box.
# This says move over 1/2 a grid box in the x direction and move down (since yres is -) in the
# y direction. Also, the +yres (remember, yres is -) is saying the starting point of this array will
# trim off the y direction by one row (since it's shifted off the grid)
xy_source_centerPt = np.mgrid[xmin + (xres / 2):xmax:xres,
ymax + (yres / 2):ymin:yres]
xxC, yyC = xy_source_centerPt
lons_centerPt, lats_centerPt = Proj(spatialRefProj)(xxC,
yyC,
inverse=True)
if grib.basin != "Hell_Hole":
mask = createMask(xxC, yyC, spatialRefProj)
grib.basinTotal, grib.basinSWE = calculateBasin(
mask, grib, xres, yres)
# The shape file for the Hell Hole basin includes the SMUD domain. Therefore, if we want to extract the SMUD
# domain, then we will create another mask on top of the Hell Hole mask. This means that any grid point
# outside of both domains will still = 0. The SMUD domain will contain it's mask AND the Hell Hole mask.
if grib.basin == 'Hell_Hole':
grib.basin = 'Hell_Hole_SMUD' #This is just to get the correct directory structure
submask = createMask(
xxC, yyC, spatialRefProj
) # All areas in array = to 1 will be in SMUD's basin
smud_BasinArea = grib.basinArea # Used to remove SMUD's basin area (in m^2) from Hell_Hole's basin.
# Get SMUD's information for the SMUD submask
grib.SMUDbasinTotal, grib.SMUDbasinSWE = calculateBasin(
submask, grib, xres, yres)
grib.basin = 'Hell_Hole' # reset back
mask = createMask(
xxC, yyC, spatialRefProj
) #This will be the entire Hell Hole basin, which includes SMUD
hhMask = mask + submask # Hell hole basin is now anywhere where hhMask = 1 and SMUD is anywhere it = 2
hhMask[hhMask !=
1] = 0 # Set anything outside of Hell Hole's mask = 0
grib.basinArea = grib.basinArea - smud_BasinArea # grib.basinArea currently includes smuds basin, so remove it.
# Get HellHoles's information from the hell hole submask
grib.basinTotal, grib.basinSWE = calculateBasin(
hhMask, grib, xres, yres)
print("Current Basin Total: " + str(grib.basinTotal) +
" SMUD Total: " + str(grib.SMUDbasinTotal))
#grib.basinTotal = grib.basinTotal - (0.92 * smudBasinTotal[0])
mask = hhMask # Need to do this so we can use the correct mask in compareDates and in calculateByElevation
# Calculate the difference between two rasters
if inputArgs.date2 != None:
grib.basinTotal, grib.basinSWE = compareDates(
mask, grib, xres, yres)
#Need to do this after Hell_Hole's data has been manipulated (to account for SMUD)
elevation_bins = calculateByElevation(mask, grib, xres, yres)
#Send data for writing to Excel File
if deltaDay == 0:
excel_output(elevation_bins)
if inputArgs.plot == True:
makePlot(elevation_bins, deltaDay)
print(elevation_bins)
print(inputArgs.date, " Basin Total: ", grib.basinTotal)
#findValue will return a dataframe with SWE values at various lat/lng points.
df_ptVal = None
if findValueAtPoint == True:
df_ptVal = findPointValue(spatialRefProj, xy_source)
if inputArgs.map == True:
fig = plt.figure()
ax = fig.add_subplot(111)
m = Basemap(llcrnrlon=-122.8,
llcrnrlat=37.3,
urcrnrlon=-119.0,
urcrnrlat=40.3,
ax=ax)
#m.arcgisimage(service='World_Imagery', xpixels=2000, verbose=True)
im = Image.open(
urlopen(
"http://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/export?bbox=-122.8,37.3,-119.0,40.3&bboxSR=4326&imageSR=4326&size=2000,1578&dpi=96&format=png32&transparent=true&f=image"
))
m.imshow(im, origin='upper')
#For inset
# loc =>'upper right': 1,
# 'upper left': 2,
# 'lower left': 3,
# 'lower right': 4,
# 'right': 5,
# 'center left': 6,
# 'center right': 7,
# 'lower center': 8,
# 'upper center': 9,
# 'center': 10
axin = inset_axes(m.ax, width="40%", height="40%", loc=8)
m2 = Basemap(llcrnrlon=-120.7,
llcrnrlat=38.7,
urcrnrlon=-120.1,
urcrnrlat=39.3,
ax=axin)
im2 = Image.open(
urlopen(
"http://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/export?bbox=-120.7,38.7,-120.09999999999998,39.3&bboxSR=4326&imageSR=4326&size=2000,1999&dpi=96&format=png32&transparent=true&f=image"
))
m2.imshow(im2, origin='upper')
#m2.arcgisimage(service='World_Imagery', xpixels=2000, verbose=True)
mark_inset(ax, axin, loc1=2, loc2=4, fc="none", ec="0.5")
###################################DEBUGGING AREA###############################################################
# Debugging: Test to prove a given lat/lng pair is accessing the correct grid box:
#*********TEST 1: Test for center points
#grib.data[0,0] = 15 #increase the variable by some arbitrary amount so it stands out.
#xpts, ypts = m(lons_centerPt[0,0],lats_centerPt[0,0]) #This should be in the dead center of grid[0,0]
#m.plot(xpts,ypts, 'ro')
#*********TEST 2: Test for first grid box
# Test to see a if the point at [x,y] is in the upper right corner of the cell (it better be!)
#xpts, ypts = m(lons[0, 0], lats[0, 0]) # should be in upper right corner of cell
#m.plot(xpts, ypts, 'bo')
# *********TEST 3: Test for first grid box
# Test to see the location of center points of each grid in polygon
# To make this work, uncomment the variables in def create_mask
#debug_Xpoly_center_pts, debug_Ypoly_center_pts = m(debugCenterX, debugCenterY)
#m.plot(debug_Xpoly_center_pts, debug_Ypoly_center_pts, 'bo')
# *********TEST 4: Test grid box size (In lat lng coords)
# This is for use in a Basemap projection with lat/lon (e.g. EPSG:4326)
#testX = np.array([[-120.1, -120.1], [-120.10833, -120.10833]])
#testY = np.array([[39.0, 39.00833], [39.0, 39.00833]])
# testVal = np.array([[4,4],[4,4]])
# For use in basemap projection with x/y (e.g. espg:3857. In m=basemap just include the argument projection='merc')
# testX = np.array([[500975, 500975], [(500975 + 1172), (500975 + 1172)]])
# testY = np.array([[502363, (502363 + 1172)], [502363, (502363 + 1172)]])
#testVal = np.array([[18, 18], [18, 18]])
#im1 = m.pcolormesh(testX, testY, testVal, cmap=plt.cm.jet, vmin=0.1, vmax=10, latlon=False, alpha=0.5)
# Test to see all points
# xtest, ytest = m(lons,lats)
# m.plot(xtest,ytest, 'bo')
################################################################################################################
hr = 0
makeMap(lons, lats, hr, m, m2, df_ptVal, deltaDay)
return
llcrnrlat=22,
urcrnrlon=-64,
urcrnrlat=49,
projection='lcc',
lat_1=33,
lat_2=45,
lon_0=-95)
# create masked array, reversing data in latitude direction
# (so that data is oriented in increasing latitude, as transform_scalar requires).
topoin = ma.masked_values(array[::-1, :], -999.)
# transform DEM data to a 4 km native projection grid
nx = int((m.xmax - m.xmin) / 4000.) + 1
ny = int((m.ymax - m.ymin) / 4000.) + 1
topodat = m.transform_scalar(topoin, lons, lats, nx, ny, masked=True)
# plot DEM image on map.
im = m.imshow(topodat, cmap=cm.GMT_haxby_r)
# draw meridians and parallels.
m.drawparallels(np.arange(20, 71, 10), labels=[1, 0, 1, 0])
m.drawmeridians(np.arange(-120, -40, 10), labels=[0, 1, 0, 1])
# plot state boundaries from shapefile using ogr.
g = ogr.Open("st99_d00.shp")
L = g.GetLayer(0) # data is in 1st layer.
for feat in L: # iterate over features in layer
geo = feat.GetGeometryRef()
# iterate over geometries.
for count in range(geo.GetGeometryCount()):
geom = geo.GetGeometryRef(count)
if not geom.GetGeometryCount(): # just one geometry.
# get lon,lat points
lons = [geom.GetX(i) for i in range(geom.GetPointCount())]
lats = [geom.GetY(i) for i in range(geom.GetPointCount())]
topodat = m.transform_scalar(data, lons, lats, nx, ny)
print('Getting colormap...')
# get colormap
cptfile = '//Users//trev//Documents//DATA//GMT//cpt//mby_topo-bath.cpt'
#cptfile = '//Users//tallen//Documents//DATA//GMT//cpt//gray.cpt'
cmap, zvals = cpt2colormap(cptfile, 256)
cmap = remove_last_cmap_colour(cmap)
# make shading
print('Making map...')
ls = LightSource(azdeg=180, altdeg=45)
norm = mpl.colors.Normalize(vmin=-8000 / zscale, vmax=5000 / zscale) #myb
rgb = ls.shade(topodat, cmap=cmap, norm=norm)
im = m.imshow(rgb, alpha=0.6)
'''
##########################################################################################
# add epicentres
##########################################################################################
ncols = 18
# get year range
minyear = 10*floor(cat.data['year'][0]/10.)
maxyear = cat.data['year'][-1]
yearrng = float(round(maxyear - minyear))
#cmap = plt.cm.get_cmap('Spectral', ncols)
cptfile = '/Users/trev/Documents/DATA/GMT/cpt/temperature.cpt'
#cptfile = '/Users/trev/Documents/DATA/GMT/cpt/grayscale08.cpt'
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 13 15:54:40 2019
@author: Administrator
"""
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
from osgeo import gdal
map = Basemap(projection='cyl',
lat_0=0,
lon_0=0,
llcrnrlon=85,
llcrnrlat=25,
urcrnrlon=90,
urcrnrlat=30)
#读取tiff
ds = gdal.Open("sample_files/dem/srtm_54_07.img")
data = ds.ReadAsArray()
map.imshow(data, cmap=plt.get_cmap('terrain'), alpha=0.5)
plt.show()
class CoordinateHelper(object):
def __init__(self, x=None,y=None, zone=None, zone_number=None, zone_letter=None, mm=None, bbox_expand=0.05, projection='merc', **kwargs):
self.expand = bbox_expand
if zone and not (zone_number and zone_letter):
self._zone_number = int(zone[0:2])
self._zone_letter = zone[2:3]
else:
self._zone_number=zone_number
self._zone_letter = zone_letter
self.zone_numbers = None
self.zone_letters = None
self.set_xy(x,y)
if not x is None:
if not mm:
self.mm = self.get_basemap(projection=projection, **kwargs)
elif isinstance(mm, Basemap):
self.mm = mm
else:
raise ValueError('bmap must be a mpl_toolkits.basemap.Basemap but is a {type(mm)}')
def set_xy(self,x,y, ctype=None):
self.x = np.array(x)
self.y = np.array(y)
if self.x.shape != self.y.shape:
raise ValueError(f'x and y must have same shape! ({self.x.shape} != {self.y.shape})')
if ctype in ['latlon', 'img', 'utm']:
self.type = ctype
else:
self._detect_type()
# try:
if not self._zone_number is None:
self.zone_numbers = np.tile(self._zone_number, self.x.shape)
if not self._zone_letter is None:
self.zone_letters = np.tile(self._zone_letter, self.x.shape)
return
self.zone_numbers, self.zone_letters = self._get_zones()
# self.zone_numbers = zone_numbers
# print(zone_numbers, zone_letters )
# self.zone_letter = zone_letter
# and not self.zone_letter is None:
# if (self.zone_number, self.zone_letter) != (zone_number, zone_letter):
# Warning('The zone for the new data ((zone_number, zone_letter)} does not correspond to the zone already initialized ({self.zone_number, self.zone_letter}).')
# except Exception as ex:
# print(ex)
def _detect_type(self):
# print(self.y, type(self.y))
if all([np.all((-180<=c) & (c<=180)) for c in (self.x,self.y)]):
self.type = 'latlon'
y = self.y
self.y = self.x
self.x = y
elif all([np.all((0<c) & (c<1e5)) for c in (self.x,self.y)]):
self.type = 'img'
elif all([np.all((0<c) & (c<1e7)) for c in (self.x,self.y)]):
self.type = 'utm'
else:
raise ValueError('Could not detect coord type (latlon/utm/img) based on data ranges')
print(f'{self.type} detected')
def get_bbox_latlon(self, format='latlon'):
lat,lon = self.to_latlon()
lonm = lon.min()
lonM = lon.max()
latm = lat.min()
latM = lat.max()
lonex = (lonM-lonm)*self.expand/2
latex = (latM-latm)*self.expand/2
if format=='basemap':
return {
'llcrnrlon':lonm - lonex,
'llcrnrlat':latm - latex,
'urcrnrlon':lonM + lonex,
'urcrnrlat':latM + latex
}
elif format[0:6]=='lonlat':
bbox = (
lonm - lonex,
latm - latex,
lonM + lonex,
latM + latex
)
else:
bbox = (
latm - latex,
lonm - lonex,
latM + latex,
lonM + lonex
)
if format[-1]==',':
return (bbox[0:2], bbox[2:4])
else:
return bbox
def get_bbox_utm(self):
bbox = self.get_bbox_latlon(format='latlon')
return tuple([utm.from_latlon(lat, lon)[0:2] for lat,lon in zip(bbox[::2], bbox[1::2])])
def get_basemap(self, **kwargs):
self.mm = Basemap(**self.get_bbox_latlon(format='basemap'), **kwargs)
return self.mm
def get_osm_zoom_level(self, bbox):
m0 = 156412
bbox = self.get_bbox_latlon(format='latlon,')
dlat = bbox[1][0]-bbox[0][0]
dlon = bbox[1][1] - bbox[0][1]
level_diff =np.abs(np.array([360/2**x for x in range(20)])-max(dlat, dlon))
level = level_diff.argmin()+2
print(f'OSM level {level} selected to display minimum {max(dlat, dlon)} degrees')
return level
def get_osm(self, *args, provider='osm', **kwargs):
#geotiler cannot handle the bbox to be of type numpy, so convert to native floats:
bbox = [x.item() for x in self.get_bbox_latlon(format='lonlat')]
gm = geotiler.Map(extent=bbox, zoom=self.get_osm_zoom_level(bbox), provider=provider)
self.img = geotiler.render_map(gm)
return self.mm.imshow(self.img, *args, interpolation='lanczos', origin='upper', **kwargs)
def to_img(self):
if self.type=='img':
return (self.x, self.y)
else:
if isinstance(self.mm, Basemap):
if self.type=='latlon':
return self.mm(self.x,self.y, latlon=True)
elif self.type=='utm':
f = np.vectorize(utm.to_latlon)
return self.mm(f(self.x,self.y, zone_number=self.zone_numbers, zone_letter=self.zone_letters), latlon=True)
else:
raise AttributeError('Cannot convert image coordinates to latlon as no basemap is defined!')
def to_utm(self):
f = np.vectorize(utm.from_latlon)
if self.type=='latlon':
return f(self.x,self.y)[0:2]
elif self.type=='utm':
return (self.x, self.y)
elif self.type=='img':
if isinstance(self.mm, Basemap):
return f(self.mm(self.x, self.y))[0:2]
else:
raise AttributeError('Cannot convert image coordinates to UTM as no basemap is defined!')
def to_lonlat(self):
lat, lon = self.to_latlon()
return (lon, lat)
def to_latlon(self):
if self.type=='latlon':
return (self.x, self.y)
elif self.type=='utm':
f = np.vectorize(utm.to_latlon)
return f(self.x,self.y, zone_number=self.zone_numbers, zone_letter=self.zone_letters)
elif self.type=='img':
if isinstance(self.mm, Basemap):
return self.mm(self.x, self.y)
else:
raise AttributeError('Cannot convert image coordinates to latlon as no basemap is defined!')
def _get_zones(self):
f_letter = np.vectorize(utm.latitude_to_zone_letter)
f_number = np.vectorize(utm.latlon_to_zone_number)
lat, lon = self.to_latlon()
zone_numbers = f_number(lat,lon)
zone_letters = f_letter(lat)
if len(set(zip(zone_numbers.ravel(), zone_letters.ravel()))) !=1:
print(f'Coordinates are spread across several UTM zones {[str(n) + l for n,l in set(zip(zone_numbers.ravel(), zone_letters.ravel()))]}')
return zone_numbers, zone_letters
except:
# Draw some map elements on the map
m.drawcoastlines()
m.drawstates()
m.drawcountries()
m.drawrivers(color='blue')
print ("Normal map")
#m.drawcoastlines( linewidth=0.1, color='k' )
# m.fillcontinents(color=land,lake_color=water,zorder=99)
# ax.set_extent([lllon,urlon,lllat,urlat],crs=ccrs.PlateCarree())
# ax.add_feature(cfeature.NaturalEarthFeature('physical', 'land', '10m', edgecolor='face', facecolor=land),zorder=100)
# #
m.drawparallels([lllat,urlat], labels = [1,0,0,0], fontsize=10,linewidth=0,zorder=102)
m.drawmeridians([lllon,urlon], labels = [0,0,0,1], fontsize=10,linewidth=0,zorder=102)
data = ma.masked_less_equal(visual[npix:spix,wpix:epix],2)
im=m.imshow(data,interpolation="nearest",origin="upper",cmap=cmapL,norm=norml,zorder=110) # interpolation="nearest",origin="upper",
# print (lon,lat)
# m.scatter(lon,lat,s=0.5,marker="o",zorder=110,edgecolors="g", facecolors="g")#,transform=ccrs.PlateCarree()) #,
ax0.plot(lon ,lat ,color="g",marker="o",label="intitial",markersize=7,zorder=111) #fillstyle="none",
#================
kx1= kx1lt[point]
ky1= ky1lt[point]
lat1 = south + 10.0 - res/2.0 - ky1*res
lon1 = west + res/2.0 + kx1*res
ax0.plot(lon1 ,lat1 ,color="r",marker="o",label="best-loc",markersize=7,zorder=112) #fillstyle="none",
#================
kx2= kx2lt[point]
ky2= ky2lt[point]
if kx2 != -9999 and ky2 != -9999:
lat2 = south + 10.0 - res/2.0 - ky2*res
lon2 = west + res/2.0 + kx2*res
bwr_cmap = plt.get_cmap('bwr')
cdict_new = bwr_cmap._segmentdata.copy()
cdict_new['alpha'] = ((0.0, 1.0, 1.0),
# (0.25,1.0, 1.0),
(0.6, 0.4, 0.0),
# (0.75,1.0, 1.0),
(1.0, 1.0, 1.0))
blue_red1 = LinearSegmentedColormap('BlueRed1', cdict_new)
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Instantiate the Basemap - all subsequent images will be applied directly to the map.
m = Basemap(llcrnrlon=lon0, llcrnrlat=lat0, urcrnrlon=lon1, urcrnrlat=lat1, resolution='l', lat_0=lat0, lon_0=lon0)
# grab the openstreetmaps image
osm_image = misc.imread('map.png')
#plot it onto the map
m.imshow(osm_image, extent=[lon0, lat0, lon1, lat1], origin='upper', aspect='auto')
##
## READ THE DATA:
# read in the undocking data
undocking_dataframe = pd.read_csv('2013-07-CitiBikeTripData.csv', usecols=[1,5,6], index_col=0, parse_dates=True)
#read in the docking datax
docking_dataframe_unsorted = pd.read_csv('2013-07-CitiBikeTripData.csv', usecols=[2,9,10], index_col=0, parse_dates=True)
docking_dataframe = docking_dataframe_unsorted.sort_index()
# find the first timestamp
initial_start_date_undocking = undocking_dataframe.index[0]
# ims will hold the series of images that make up the animation
ims = []
# determine the number of frames in the animation
z_02 = griddata(xy1_igrid, z_igrid_02, (xi, yi), method='cubic')
z_03 = griddata(xy1_igrid, z_igrid_03, (xi, yi), method='cubic')
rgb_projected = np.zeros((1000, 1000, 3))
for i in np.arange(1000):
for j in np.arange(1000):
rgb_projected[i, j, 0] = z_01[i, j]
rgb_projected[i, j, 1] = z_02[i, j]
rgb_projected[i, j, 2] = z_03[i, j]
#rgb_projected[ z > 1 ] = 1.0
#rgb_projected[ z < 0 ] = 0.0
whereAreNaNs = np.isnan(rgb_projected)
rgb_projected[whereAreNaNs] = 0.75
img = m.imshow(np.rot90(np.fliplr(rgb_projected)), origin='lower')
m.drawcoastlines()
m.drawparallels(np.arange(-90., 120., 5.),
color='k',
labels=[True, False, False, False])
m.drawmeridians(np.arange(0., 420., 5.),
color='k',
labels=[False, False, False, True])
ax.set_xlabel("", fontsize=10)
ax.set_ylabel("", fontsize=10)
#boundingbox = [60.0, 29.0, 52.5, 45.0]
lats = [52.5, 52.5, 60., 60.]
basemap_kws = dict(resolution='c', ax=the_ax)
basemap_kws.update(project_channels['basemap_args'])
bmap = Basemap(**basemap_kws)
if index == 3:
channel = project_channels['channels'][:, :, 0] - project_channels[
'channels'][:, :, 1]
chan_list.append('1-4')
cmap = cmap_div
cbar_label = 'reflectivity difference'
elif index == 2:
channel = project_channels['channels'][:, :, index]
cbar_label = 'radiance W/m^2/sr/um'
else:
cbar_label = 'reflectance'
channel = project_channels['channels'][:, :, index]
col = bmap.imshow(channel, origin='upper', cmap=cmap, **limits[index])
bmap.drawparallels(parallels,
labels=[1, 0, 0, 0],
fontsize=10,
latmax=90)
bmap.drawmeridians(meridians,
labels=[0, 0, 0, 1],
fontsize=10,
latmax=90)
bmap.drawcoastlines()
_ = the_ax.set(title='vancouver chan {}'.format(chan_list[index]))
colorbar = fig.colorbar(col,
shrink=0.5,
pad=0.05,
extend='both',
ax=the_ax)
chl_mat.mask = chl_mat == -999
m = Basemap(llcrnrlat=min(lat0),urcrnrlat=max(lat0),\
llcrnrlon=min(lon0),urcrnrlon=max(lon0), resolution='l')
x, y = np.meshgrid(lon0, lat0)
m.drawparallels(parallel_steps,
labels=[1, 0, 0, 1],
color='grey',
linewidth=0.1)
m.drawmeridians(meridian_steps,
labels=[1, 0, 0, 1],
color='grey',
linewidth=0.1)
m.drawcoastlines(linewidth=0.1)
m.imshow(chl_mat,origin='upper', extent=[min(lon0), max(lon0), min(lat0), max(lat0)],\
norm=LogNorm(),cmap='rainbow',interpolation='nearest')
clb = plt.colorbar(fraction=0.046, pad=0.05, orientation='horizontal')
# clb.ax.set_xlabel('Absolute Density (counts)')
# wtm
ax = plt.subplot(2, 2, 2)
ax.set_title('wtm(0 =< p1/(p1+p2) =< 1')
current_cmap = plt.cm.get_cmap()
current_cmap.set_bad(color='white')
class_mask.mask = class_mask == -999
m = Basemap(llcrnrlat=min(lat0),urcrnrlat=max(lat0),\
llcrnrlon=min(lon0),urcrnrlon=max(lon0), resolution='l')
x, y = np.meshgrid(lon0, lat0)
def map_haz(fig, plt, haz_map_file, sitelon, sitelat, **kwargs):
'''
kwargs:
shpfile: path to area source - is a list of files
resolution: c (crude), l (low), i (intermediate), h (high), f (full)
mbuffer: map buffer in degrees
'''
from openquake.nrmllib.hazard.parsers import GMFScenarioParser
from mpl_toolkits.basemap import Basemap
from numpy import arange, array, log10, mean, mgrid, percentile
from matplotlib.mlab import griddata
from matplotlib import colors, colorbar, cm
from os import path
from mapping_tools import drawshapepoly, labelpolygon
import shapefile
# set kwargs
drawshape = False
res = 'c'
mbuff = -0.3
keys = ['shapefile', 'resolution', 'mbuffer']
for key in keys:
if key in kwargs:
if key == 'shapefile':
shpfile = kwargs[key]
drawshape = True
if key == 'resolution':
res = kwargs[key]
if key == 'mbuffer':
mbuff = kwargs[key]
gmfsp = GMFScenarioParser(haz_map_file).parse()
metadata, values = parse_nrml_hazard_map(haz_map_file)
hazvals = []
latlist = []
lonlist = []
for val in values:
lonlist.append(val[0])
latlist.append(val[1])
hazvals.append(val[2])
# get map bounds
llcrnrlat = min(latlist) - mbuff / 2.
urcrnrlat = max(latlist) + mbuff / 2.
llcrnrlon = min(lonlist) - mbuff
urcrnrlon = max(lonlist) + mbuff
lon_0 = mean([llcrnrlon, urcrnrlon])
lat_1 = percentile([llcrnrlat, urcrnrlat], 25)
lat_2 = percentile([llcrnrlat, urcrnrlat], 75)
m = Basemap(llcrnrlon=llcrnrlon,llcrnrlat=llcrnrlat, \
urcrnrlon=urcrnrlon,urcrnrlat=urcrnrlat,
projection='lcc',lat_1=lat_1,lat_2=lat_2,lon_0=lon_0,
resolution=res,area_thresh=1000.)
m.drawcoastlines(linewidth=0.5, color='k')
m.drawcountries()
# draw parallels and meridians.
m.drawparallels(arange(-90., 90., 1.),
labels=[1, 0, 0, 0],
fontsize=10,
dashes=[2, 2],
color='0.5',
linewidth=0.5)
m.drawmeridians(arange(0., 360., 1.),
labels=[0, 0, 0, 1],
fontsize=10,
dashes=[2, 2],
color='0.5',
linewidth=0.5)
# make regular grid
N = 150j
extent = (min(lonlist), max(lonlist), min(latlist), max(latlist))
xs, ys = mgrid[extent[0]:extent[1]:N, extent[2]:extent[3]:N]
resampled = griddata(array(lonlist), array(latlist), log10(array(hazvals)),
xs, ys)
#############################################################################
# transform grid to basemap
# get 1D lats and lons for map transform
lons = ogrid[extent[0]:extent[1]:N]
lats = ogrid[extent[2]:extent[3]:N]
# transform to map projection
if max(lonlist) - min(lonlist) < 1:
transspace = 500
elif max(lonlist) - min(lonlist) < 5:
transspace = 1000
elif max(lonlist) - min(lonlist) < 10:
transspace = 2000
else:
transspace = 5000
nx = int((m.xmax - m.xmin) / transspace) + 1
ny = int((m.ymax - m.ymin) / transspace) + 1
transhaz = m.transform_scalar(resampled.T, lons, lats, nx, ny)
m.imshow(transhaz,
cmap='Spectral_r',
extent=extent,
vmin=-2,
vmax=log10(2.),
zorder=0)
# plot site
xx, yy = m(sitelon, sitelat)
plt.plot(xx, yy, '*', ms=20, mec='k', mew=2.0, mfc="None")
# superimpose area source shapefile
if drawshape == True:
for shp in shpfile:
sf = shapefile.Reader(shp)
drawshapepoly(m, plt, sf, col='k', lw=1.5, polyline=True)
labelpolygon(m, plt, sf, 'CODE', fsize=14)
# set colourbar
# set cb for final fig
plt.gcf().subplots_adjust(bottom=0.1)
cax = fig.add_axes([0.6, 0.05, 0.25, 0.02]) # setup colorbar axes.
norm = colors.Normalize(vmin=-2, vmax=log10(2.))
cb = colorbar.ColorbarBase(cax,
cmap=cm.Spectral_r,
norm=norm,
orientation='horizontal')
# set cb labels
#linticks = array([0.01, 0.03, 0.1, 0.3 ])
logticks = arange(-2, log10(2.), 0.25)
cb.set_ticks(logticks)
labels = [str('%0.2f' % 10**x) for x in logticks]
cb.set_ticklabels(labels)
# get map probabiltiy from filename
mprob = path.split(haz_map_file)[-1].split('_')[-1].split('-')[0]
probstr = str(int(float(mprob) * 100))
# set colourbar title
if metadata['statistics'] == 'mean':
titlestr = ''.join((metadata['imt'], ' ', metadata['sa_period'], ' s ', \
probstr,'% in ',metadata['investigation_time'][0:-2], \
' Year Mean Hazard (g)'))
#cb.set_ticklabels(labels)
cb.set_label(titlestr, fontsize=12)
return plt
import util
import sys, os
import d4PDF
srcdir = '/home/utsumi/mnt/lab_tank/utsumi/bams2020/XX-HPB_NAT-100/6hr/pgrad/2010/01'
idtime = datetime(2010,1,1,0)
edtime = datetime(2010,1,31,18)
ldtime = util.ret_lDTime(idtime, edtime, timedelta(hours=6))
ny,nx = 320,640
a1lat = d4PDF.Lat()
a1lon = d4PDF.Lon()
[[lllat,lllon],[urlat,urlon]] = [[-90,0],[90,360]]
a2out = np.zeros([ny,nx], 'int32')
a2zero= np.zeros([ny,nx], 'int32')
for dtime in ldtime:
year,mon,day,hour = dtime.timetuple()[:4]
srcpath = srcdir + '/pgrad.2010012806.320x640'
srcpath = srcdir + '/pgrad.%04d%02d%02d%02d.320x640'%(year,mon,day,hour)
a2in = np.fromfile(srcpath,'float32').reshape(ny,nx)
a2out = a2out + ma.masked_where(a2in>0, a2zero).filled(1).astype('int32')
figmap = plt.figure()
axmap = figmap.add_axes([0.1, 0.1, 0.8, 0.8])
M = Basemap( resolution="l", llcrnrlat=lllat, llcrnrlon=lllon, urcrnrlat=urlat, urcrnrlon=urlon, ax=axmap)
im = M.imshow(ma.masked_equal(a2out,0), origin='lower',vmax=5)
plt.colorbar(im)
M.drawcoastlines()
plt.savefig('/home/utsumi/temp/bams2020/temp.centers.png')
fi.write(str(anchor_positions_procrustes.tolist()) + "\n")
fi.write(str(anchor_gt_positions.tolist()) + "\n")
fi.write(str(pos_selfloc.tolist()) + "\n")
fi.write(str(pos_selfloc_procrustes.tolist()) + "\n")
# fi.write(str(tform.keys()) + "\n")
# tform["rotation"] = tform["rotation"].tolist()
# tform["translation"] = tform["translation"].tolist()
# fi.write(str(tform.values()) + "\n")
fi.write(str(stress) + "\n")
fi.close()
print "selfloc test results written to: \n" + results_file_selfloc
if options.map:
figure_map.canvas.set_window_title(
args[0].split("/")[-1].split(".")[0] + "_map")
basemap.imshow(img, interpolation='lanczos', origin='upper')
i = 1
if options.crop_ict:
ax.axis([85, 230, 48, 140])
basemap.drawmapscale(lon=6.06201,
lat=50.77870,
lon0=6.06201,
lat0=50.77870,
length=20,
units='m',
barstyle='fancy',
fontsize=24,
yoffset=1.2)
else:
basemap.drawmapscale(lon=bbox[0] - 0.1 * (bbox[0] - bbox[2]),
lat=bbox[1] - 0.07 * (bbox[1] - bbox[3]),
def processing(path):
"""
Generates images from NetCDF files
pathDir: root direcotry with "Data", "Output" and "Scripts" folders
pathFile: path to NetCDF file to be manipulated
outFolder: path to directory where the generated images should be saved
Output: .png image file
"""
# Getting information from the file name ============================================================
# Search for the GOES-16 channel in the file name
INPE_Band_ID = (path[path.find("S10635") + 6:path.find("_")])
# print(INPE_Band_ID)
# Get the band number subtracting the value by 332
Band = int(INPE_Band_ID) - 332
# Create a GOES-16 Bands string array
Wavelenghts = [
'[]', '[0.47 μm]', '[0.64 μm]', '[0.865 μm]', '[1.378 μm]',
'[1.61 μm]', '[2.25 μm]', '[3.90 μm]', '[6.19 μm]', '[6.95 μm]',
'[7.34 μm]', '[8.50 μm]', '[9.61 μm]', '[10.35 μm]', '[11.20 μm]',
'[12.30 μm]', '[13.30 μm]'
]
Band_Wavelenght = Wavelenghts[int(Band)]
# Search for the Scan start in the file name
Start = (path[path.find(INPE_Band_ID + "_") + 4:path.find(".nc")])
# Getting the date from the file name
year = Start[0:4]
month = Start[4:6]
day = Start[6:8]
date = day + "-" + month + "-" + year
time = Start[8:10] + ":" + Start[
10:12] + " UTC" # Time of the Start of the Scan
# Get the unit based on the channel. If channels 1 trough 6 is Albedo. If channels 7 to 16 is BT.
Unit = "Albedo (%)"
# Choose a title for the plot
Title = " GOES-16 ABI CMI Band " + str(
Band) + " " + Band_Wavelenght + " " + Unit + " " + date + " " + time
# Insert the institution name
Institution = "CEPAGRI - UNICAMP"
# Required libraries ================================================================================
# Open the file using the NetCDF4 library
nc = Dataset(path)
# Choose the visualization extent (min lon, min lat, max lon, max lat)
extent = [-115.98, -55.98, -25.01, 34.98]
min_lon = extent[0]
max_lon = extent[2]
min_lat = extent[1]
max_lat = extent[3]
# Get the latitudes
lats = nc.variables['lat'][:]
# Get the longitudes
lons = nc.variables['lon'][:]
# print (lats)
# print (lons)
# latitude lower and upper index
latli = np.argmin(np.abs(lats - extent[1]))
latui = np.argmin(np.abs(lats - extent[3]))
# longitude lower and upper index
lonli = np.argmin(np.abs(lons - extent[0]))
lonui = np.argmin(np.abs(lons - extent[2]))
# Extract the Brightness Temperature / Reflectance values from the NetCDF
data = nc.variables['Band1'][latli:latui, lonli:lonui]
# Flip the y axis, divede by 100
data = (np.flipud(data) / 100)
# Define the size of the saved picture ==============================================================
DPI = 150
ax = plt.figure(figsize=(2000 / float(DPI), 2000 / float(DPI)),
frameon=True,
dpi=DPI)
#====================================================================================================
# 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('/Scripts/GEONETCast/Shapefiles/BRA_adm1',
'BRA_adm1',
linewidth=0.50,
color='cyan')
bmap.readshapefile(
'/Scripts/GEONETCast/Shapefiles/ne_10m_admin_0_countries',
'ne_10m_admin_0_countries',
linewidth=0.50,
color='cyan')
# Draw parallels and meridians
bmap.drawparallels(np.arange(-90.0, 90.0, 5.0),
linewidth=0.3,
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=0.3,
dashes=[4, 4],
color='white',
labels=[False, False, False, False],
fmt='%g',
labelstyle="+/-",
xoffset=-0.80,
yoffset=-1.00,
size=7)
# Converts a CPT file to be used in Python
cpt = loadCPT(
'/Scripts/GEONETCast/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(data, origin='upper', cmap=cpt_convert, vmin=0, vmax=100)
# Insert the colorbar at the bottom
cb = bmap.colorbar(location='bottom',
size='2%',
pad='-3.5%',
ticks=[20, 40, 60, 80])
cb.ax.set_xticklabels(['20', '40', '60', '80'])
cb.outline.set_visible(False) # Remove the colorbar outline
cb.ax.tick_params(width=0) # Remove the colorbar ticks
cb.ax.xaxis.set_tick_params(
pad=-14.5) # Put the colobar labels inside the colorbar
cb.ax.tick_params(
axis='x', colors='black',
labelsize=8) # Change the color and size of the colorbar labels
# 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 * 0.015,
alpha=1,
zorder=3,
facecolor='black'))
# 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 * 0.003,
Title,
horizontalalignment='left',
color='white',
size=10)
plt.text(extent[2],
extent[1] + lat_difference * 0.003,
Institution,
horizontalalignment='right',
color='yellow',
size=10)
# Add logos / images to the plot
logo_GNC = plt.imread('/Scripts/GEONETCast/Logos/GNC_Logo.png')
logo_INPE = plt.imread('/Scripts/GEONETCast/Logos/INPE_Logo.png')
logo_NOAA = plt.imread('/Scripts/GEONETCast/Logos/NOAA_Logo.png')
logo_GOES = plt.imread('/Scripts/GEONETCast/Logos/GOES_Logo.png')
logo_cepagri = plt.imread('/Scripts/GEONETCast/Logos/Logo_CEPAGRI.png')
ax.figimage(logo_GNC, 20, 70, zorder=3, alpha=1, origin='upper')
ax.figimage(logo_INPE, 90, 70, zorder=3, alpha=1, origin='upper')
ax.figimage(logo_NOAA, 165, 70, zorder=3, alpha=1, origin='upper')
ax.figimage(logo_GOES, 228, 70, zorder=3, alpha=1, origin='upper')
ax.figimage(logo_cepagri, 326, 70, zorder=3, alpha=1, origin='upper')
# Save the result
# print('/Scripts/GEONETCast/Output/' + path[12:18] + '/INPE_G16_CH' + str(Band) + '_' + Start + '.png')
plt.savefig('/Scripts/GEONETCast/Output/' + path[12:18] + '/INPE_G16_CH' +
str(Band) + '_' + Start + '.png',
dpi=DPI,
bbox_inches='tight',
pad_inches=0)