continue
if contcode != geocountries[iso]['continentcode']:
continue
if contcode not in continents:
continents[contcode] = {'lats': [], 'lons': []}
continents[contcode]['lats'].append(float(row[5]))
continents[contcode]['lons'].append(float(row[6]))
geocont = geocontinents[contcode]
m = Basemap(projection='mill',
lon_0=geocont['lon'],
lat_0=geocont['lat'],
llcrnrlon=geocont['bbox']['w'],
llcrnrlat=geocont['bbox']['s'],
urcrnrlon=geocont['bbox']['e'],
urcrnrlat=geocont['bbox']['n'])
# no visible border around map
m.drawmapboundary(fill_color='#ffffff', linewidth=0.0)
cont = continents[contcode]
x, y = m(cont['lons'], cont['lats'])
m.scatter(x, y, 1, marker='.', color='#325CA9')
fig = plt.gcf()
fig.set_alpha(.7)
fig.set_size_inches(36, 24)
def plot_global_map(self):
"""
Plot global map of event and stations
"""
# import basemap here due to error
from mpl_toolkits.basemap import Basemap
# ax = plt.subplot(211)
plt.title(self.cmtsource.eventname)
m = Basemap(projection='cyl', lon_0=0.0, lat_0=0.0, resolution='c')
m.drawcoastlines()
m.fillcontinents()
m.drawparallels(np.arange(-90., 120., 30.))
m.drawmeridians(np.arange(0., 420., 60.))
m.drawmapboundary()
x, y = m(self.sta_lon, self.sta_lat)
#print(self.sta_lon,self.sta_lat)
#print(self.metas)
#print(len(self.metas),len(self.sta_lon))
#print(w)
plt.scatter(x,
y,
s=30,
c='red',
cmap='afmhot',
marker="^",
edgecolor="k",
linewidth=0.3,
zorder=3)
cmt_lat = self.cmtsource.latitude
cmt_lon = self.cmtsource.longitude
focmecs = get_cmt_par(self.cmtsource)[:6]
ax = plt.gca()
if self.mode == 'regional':
minlon = min(self.sta_lon)
maxlon = max(self.sta_lon)
minlat = min(self.sta_lat)
maxlat = max(self.sta_lat)
padding = 0.2
m.drawparallels(np.arange(-90., 120., padding),
labels=[1, 1, 0, 0],
fmt="%.2f",
dashes=[2, 2])
m.drawmeridians(np.arange(0., 420., padding),
labels=[0, 0, 1, 1],
fmt="%.2f",
dashes=[2, 2])
m.etopo()
ax.set_xlim(minlon - padding, maxlon + padding)
ax.set_ylim(minlat - padding, maxlat + padding)
width_beach = min((maxlon + 2 * padding - minlon) / (40 * padding),
(maxlat + 2 * padding - minlat) / (40 * padding))
else:
width_beach = 20
bb = beach(focmecs,
xy=(cmt_lon, cmt_lat),
width=width_beach,
linewidth=1,
alpha=1.0)
bb.set_zorder(10)
ax.add_collection(bb)
plottimeseries = 0
figdir = "/home/ctroupin/DataOceano/MyOcean/figures/mooring/NRT/"
figdir2 = "/home/ctroupin/Projects/201501_InsTAC/MaterialQ2/"
#mooringdir = "/home/ctroupin/DataOceano/MyOcean/INSITU_MED_TS_REP_OBSERVATIONS_013_041/history/mooring/"
mooringdir = "/home/ctroupin/DataOceano/MyOcean/INSITU_MED_NRT_OBSERVATIONS_013_035/history/mooring/"
mooringlist = sorted(glob.glob(mooringdir + '*.nc'))
coordinates = np.array((-6.75, 37., 29, 46.))
dlon, dlat = 5., 3.
m = Basemap(projection='merc',
llcrnrlon=coordinates[0],
llcrnrlat=coordinates[2],
urcrnrlon=coordinates[1],
urcrnrlat=coordinates[3],
lat_ts=0.5 * (coordinates[2] + coordinates[3]),
resolution='h')
if doplot == 1:
fig = plt.figure()
ax = fig.add_subplot(111)
m.ax = ax
for moorings in mooringlist:
print moorings
with netCDF4.Dataset(moorings) as nc:
lon = nc.variables['LONGITUDE'][:]
lat = nc.variables['LATITUDE'][:]
def run(FILE_NAME):
DATAFIELD_NAME = '89.0V_Res.5B_TB_(not-resampled)'
if USE_NETCDF4:
from netCDF4 import Dataset
nc = Dataset(FILE_NAME)
data = nc.variables[DATAFIELD_NAME][:].astype(np.float64)
latitude = nc.variables['Latitude'][:]
longitude = nc.variables['Longitude'][:]
# Replace the filled value with NaN, replace with a masked array.
# Apply the scaling equation. These attributes are named in a VERY
# non-standard manner.
scale_factor = getattr(nc.variables[DATAFIELD_NAME], 'SCALE FACTOR')
add_offset = nc.variables[DATAFIELD_NAME].OFFSET
else:
from pyhdf.SD import SD, SDC
hdf = SD(FILE_NAME, SDC.READ)
# Read dataset.
data2D = hdf.select(DATAFIELD_NAME)
data = data2D[:,:].astype(np.float64)
# Read geolocation dataset.
# This product has multiple 'Latitude' and 'Longitude' pair under different groups.
lat = hdf.select(hdf.reftoindex(192)) # Use HDFView to get ref number 192.
latitude = lat[:,:]
lon = hdf.select(hdf.reftoindex(194)) # Use HDFView to get ref number 194.
longitude = lon[:,:]
# Retrieve attributes.
attrs = data2D.attributes(full=1)
sfa=attrs["SCALE FACTOR"]
scale_factor = sfa[0]
aoa=attrs["OFFSET"]
add_offset = aoa[0]
data[data == -32768] = np.nan
data = data * scale_factor + add_offset
datam = np.ma.masked_array(data, np.isnan(data))
units = "degrees K"
long_name = DATAFIELD_NAME
# Since the swath starts near the south pole, but also extends over the
# north pole, the equidistant cylindrical becomes a possibly poor choice
# for a projection. We show the full global map plus a limited polar map.
fig = plt.figure(figsize=(15, 6))
ax1 = plt.subplot(1, 2, 1)
m = Basemap(projection='cyl', resolution='l',
llcrnrlat=-90, urcrnrlat=90,
llcrnrlon=-180, urcrnrlon=180)
m.drawcoastlines(linewidth=0.5)
m.drawparallels(np.arange(-90, 91, 30), labels=[1, 0, 0, 0])
m.drawmeridians(np.arange(-180, 181., 45), labels=[0, 0, 0, 1])
m.pcolormesh(longitude, latitude, datam, latlon=True)
ax2 = plt.subplot(1, 2, 2)
m = Basemap(projection='npstere', resolution='l',
boundinglat=65, lon_0=0)
m.drawcoastlines(linewidth=0.5)
m.drawparallels(np.arange(60, 81, 10), labels=[1, 0, 0, 0])
m.drawmeridians(np.arange(-180., 181., 30.), labels=[1, 0, 0, 1])
m.pcolormesh(longitude, latitude, datam, latlon=True)
cax = plt.axes([0.92, 0.1, 0.03, 0.8])
cb = plt.colorbar(cax=cax)
cb.set_label(units)
basename = os.path.basename(FILE_NAME)
fig = plt.gcf()
fig.suptitle('{0}\n{1}'.format(basename, long_name))
# plt.show()
pngfile = "{0}.py.png".format(basename)
fig.savefig(pngfile)
#------------------------------------------------
# time step for ploting is 24 hours (once every day)
Nt = len(pasv_mean[:, 0, 0])
print(Nt)
i = 0
while i < Nt:
plt.figure(figsize=(7, 9))
plt.subplot(2, 1, 2)
#ax = fig.add_axes([0.1,0.1,0.4,0.4])
m = Basemap(projection='cyl',
llcrnrlat=-90,
urcrnrlat=90,
llcrnrlon=-180,
urcrnrlon=180)
m.drawcoastlines()
m.drawparallels(np.arange(-90., 91., 30.))
m.drawmeridians(np.arange(-180., 181., 60.))
m.drawmapboundary(fill_color='white')
parallels = np.arange(-90., 90, 30.)
m.drawparallels(parallels, labels=[1, 0, 0, 0], fontsize=8)
meridians = np.arange(-180., -180., 60.)
m.drawmeridians(meridians, labels=[0, 0, 0, 1], fontsize=8)
#m.drawcountries()
# for geos =====================
ny = pasv_mean.shape[1]
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from matplotlib.path import Path
import matplotlib.patches as patches
fig = plt.figure()
ax1 = plt.subplot2grid((2, 2), (0, 0))
ax2 = plt.subplot2grid((2, 2), (1, 0))
ax3 = plt.subplot2grid((2, 2), (0, 1), rowspan=2)
map1 = Basemap(projection='ortho', lon_0=0, lat_0=40, ax=ax1)
map1.drawmapboundary(fill_color='#9999FF')
map1.fillcontinents(color='#ddaa66', lake_color='#9999FF')
map1.drawcoastlines()
map2 = Basemap(projection='cyl',
llcrnrlon=-15,
llcrnrlat=30,
urcrnrlon=15.,
urcrnrlat=50.,
resolution='i',
ax=ax2)
map2.drawmapboundary(fill_color='#9999FF')
map2.fillcontinents(color='#ddaa66', lake_color='#9999FF')
map2.drawcoastlines()
map3 = Basemap(llcrnrlon=-1.,
llcrnrlat=37.5,
array = gd.ReadAsArray()
# get lat/lon coordinates from DEM file.
coords = gd.GetGeoTransform()
nlons = array.shape[1]
nlats = array.shape[0]
delon = coords[1]
delat = coords[5]
lons = coords[0] + delon * np.arange(nlons)
lats = coords[3] + delat * np.arange(nlats)[::-1] # reverse lats
# setup figure.
fig = plt.figure(figsize=(11, 6))
# setup basemap instance.
m = Basemap(llcrnrlon=-119,
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])
if sys.hexversion > 0x03000000:
return input(prompt)
else:
return raw_input(prompt)
# create Basemap instance for Orthographic (satellite view) projection.
lon_0 = float(get_input('enter reference longitude (lon_0):'))
lat_0 = float(get_input('enter reference latitude (lat_0):'))
# map with land/sea mask plotted
fig = plt.figure()
resolution = 'l'
grid = 5
m = Basemap(projection='ortho',
lon_0=lon_0,
lat_0=lat_0,
resolution=resolution)
# land coral, oceans aqua.
# lakes=True means plot inland lakes with ocean color.
# resolution = 5 (default) means use 5 min dataset (can use 2.5)
m.drawcoastlines()
m.drawlsmask(land_color='coral',ocean_color='aqua', lakes=True,\
resolution=resolution,grid=grid)
# draw parallels and meridians.
m.drawparallels(np.arange(-90., 120., 30.))
m.drawmeridians(np.arange(0., 420., 60.))
m.drawmapboundary()
plt.title('Orthographic Map Centered on Lon=%s, Lat=%s' % (lon_0, lat_0))
# map with continents drawn and filled (continent filling fails for
# lon=-120,lat=60).
def plot_anomaly(z, title, outname, lon, lat, colorbar=True):
fig = plt.figure(figsize=(9, 6.5))
# some plot parameters
llim = -6
ulim = 6
lby = 1
tby = 1
levs = np.arange(llim, ulim + lby, lby)
cticks = np.arange(llim, ulim + tby, tby)
# make cyclic for plotting (add the first longitude + its data to the end)
ibase, ilon = addcyclic(z, lon)
icbase, iclon = addcyclic(z, lon)
# set up grid for plotting
glon, glat = np.meshgrid(ilon, lat)
cmap = cm.bwr
cnorm = colors.Normalize(cmap, clip=False)
cmap.set_under(color=cmap(0.0), alpha=1.0)
cmap.set_over(color=cmap(1.0), alpha=1.0)
# lat/lon boundaries for regions
llat = -90
ulat = 90
llon = -180
ulon = 180
map = Basemap(projection='cyl',
llcrnrlat=llat,
urcrnrlat=ulat,
llcrnrlon=llon,
urcrnrlon=ulon,
fix_aspect=False)
# cylindrical is regular in latitude/longitude so no complicated mapping onto map coordinates
x, y = map(glon, glat)
cols = ccol.custom_colors('grads')
plt_ax = fig.add_axes([0.12, 0.27, 0.83,
0.65]) # left, bottom, width, height
cplot = map.contourf(x,
y,
ibase,
levs,
extend="both",
cmap=cols,
ax=plt_ax)
# plot coastlines, draw label meridians and parallels.
map.drawcoastlines(linewidth=1, color="black")
#map.drawcountries(linewidth=0.5,color="black")
#map.drawstates()
map.drawparallels(np.arange(-90, 100, 30),
labels=[1, 0, 0, 0],
fontsize=26,
linewidth=0)
map.drawmeridians(np.arange(-180, 240, 60),
labels=[0, 0, 0, 1],
fontsize=26,
linewidth=0)
map.drawmapboundary()
plt.title(title, fontsize=30, weight='bold')
#plt.figtext(0.12,0.93,plotlett, size=30, weight='bold')
##print 'ok4'
#if zsig!=0:
# # Shade OUT non-significance
# itvals,itlon = addcyclic(zsig['tvals'],lon)
# for m in range(len(itlon)-1):
# for n in range(len(lat)-1):
# print m, len(itlon)-1
# if(itvals[n,m]<zsig['pval']):
# x1 = itlon[m]
# x2 = itlon[m+1]
# y1 = lat[n]
# y2 = lat[n+1]
# plt.fill([x1,x2,x2,x1],[y1,y1,y2,y2],edgecolor='grey',fill=False,hatch='//',linewidth=0)
# plt.fill([x1,x2,x2,x1],[y1,y1,y2,y2],edgecolor='grey',fill=False,hatch='\\',linewidth=0)
if colorbar:
cbar_ax = fig.add_axes([0.12, 0.13, 0.83, 0.04])
cbar = plt.colorbar(cplot,
cax=cbar_ax,
orientation='horizontal',
ticks=cticks)
cbar.set_label('$^{\circ}$C', size=26) #,labelpad=-0.09)
cbar.ax.tick_params(labelsize=24)
plt.savefig(outdir + outname + '.png')
plt.close()
def _map_init(self, showArea=None, ax=None):
'''
地图底图
'''
if showArea == "China":
self.projection = "aea"
m = Basemap(# width=5500000, height=4900000,
projection=self.projection, \
lat_1=25., lat_2=47, lon_0=105, lat_0=35,
resolution=self.resolution, ax=ax,
llcrnrlon=78, llcrnrlat=13,
urcrnrlon=146, urcrnrlat=51)
elif showArea == "SouthPole":
self.projection = "spaeqd"
m = Basemap(projection=self.projection, boundinglat=-58, lon_0=180,
resolution=self.resolution, ax=ax)
elif showArea == "NorthPole":
self.projection = "npaeqd"
m = Basemap(projection=self.projection, boundinglat=58, lon_0=0,
resolution=self.resolution, ax=ax)
elif showArea == "South":
self.projection = "ortho"
m = Basemap(projection=self.projection, lon_0=-40, lat_0=-65,
resolution=self.resolution, ax=ax)
elif showArea == "North":
self.projection = "ortho"
m = Basemap(projection=self.projection, lon_0=140, lat_0=65,
resolution=self.resolution, ax=ax)
elif len(self.box) == 4:
# 矩形
nlat, slat, wlon, elon = self.box
m = Basemap(llcrnrlon=wlon, llcrnrlat=slat,
urcrnrlon=elon, urcrnrlat=nlat,
projection=self.projection,
lat_1=25., lat_2=47, lon_0=105, lat_0=35,
resolution=self.resolution, ax=ax)
elif len(self.box) == 2:
lat0, lon0 = self.box
self.projection = "ortho"
m = Basemap(projection=self.projection, lon_0=lon0, lat_0=lat0, resolution=self.resolution, ax=ax)
# 背景颜色
if self.show_bg_color:
m.fillcontinents(color=self.color_land, lake_color=self.color_ocean)
m.drawmapboundary(fill_color=self.color_ocean)
# draw parallels
if self.projection in ["ortho"]:
labels = [0, 0, 0, 0]
else:
labels = [1, 0, 0, 1]
fnt = get_DV_Font(self.fontName)
fnt.set_size(self.fontsize_tick)
if self.delat is not None:
parallels = np.arange(0., 91., self.delat).tolist() + \
np.arange(-self.delat, -91., -self.delat).tolist()
m.drawparallels(parallels, linewidth=self.lw_latlon, labels=labels, color=self.color_latlon,
dashes=[100, .0001], fontproperties=fnt, textcolor=self.color_ticker,
latmax=90, zorder=10)
m.drawparallels(parallels, linewidth=self.lw_latlon, labels=labels, color=self.color_latlon,
dashes=[100, .0001], fontproperties=fnt, textcolor=self.color_ticker,
latmax=90, zorder=10)
# draw meridians
if self.delon is not None:
meridians = np.arange(0., 181., self.delon).tolist() + \
np.arange(-self.delon, -180., -self.delon).tolist()
m.drawmeridians(meridians, linewidth=self.lw_latlon, labels=labels, color=self.color_latlon,
dashes=[100, .0001], fontproperties=fnt, textcolor=self.color_ticker,
latmax=90, zorder=10)
m.drawmeridians(meridians, linewidth=self.lw_latlon, labels=labels, color=self.color_latlon,
dashes=[100, .0001], fontproperties=fnt, textcolor=self.color_ticker,
latmax=90, zorder=10)
# 边框粗细
ax_self = plt.gca()
if self.projection == "ortho":
# set earth no edge line
for eachpatch in ax_self.patches:
eachpatch.set_linewidth(EDGE_LW)
eachpatch.set_edgecolor(self.color_ticker)
else:
spines = ax_self.spines
for eachspine in spines:
spines[eachspine].set_linewidth(EDGE_LW)
return m
def draw_boundary(self):
'''
边界线
'''
if self.show_china_boundary :
self.m.readshapefile(os.path.join(selfPath, u'SHP/国界'), 'china',
linewidth=self.lw_boundray,
color=self.color_contry)
if self.show_coastlines:
# 画 海岸线
self.m.drawcoastlines(linewidth=self.lw_boundray, color=self.color_coast)
if self.show_countries:
# 画 国境线
self.m.drawcountries(linewidth=self.lw_boundray, color=self.color_contry)
if self.show_china_province:
# 画省线
# self.m.readshapefile(os.path.join(selfPath, 'SHP/CHN_adm1'), 'province',
# linewidth=0.2, color=color_contry)
self.m.readshapefile(os.path.join(selfPath, u'SHP/省界'), 'province',
linewidth=self.lw_boundray,
color=self.color_contry)
if self.show_china_county:
# 画省线
# self.m.readshapefile(os.path.join(selfPath, 'SHP/CHN_adm1'), 'province',
# linewidth=0.2, color=color_contry)
self.m.readshapefile(os.path.join(selfPath, u'SHP/中国县市'), 'province',
linewidth=self.lw_boundray * 0.8,
color=self.color_contry)
if self.show_north_pole or self.show_south_pole:
self._setLatsLabelWithinFig()
if self.show_china:
# 画南海 十段线
ax = plt.gca()
axins = zoomed_inset_axes(ax, 0.55, loc=4, borderpad=0.4)
axins.set_xlim(105, 125)
axins.set_ylim(5, 25)
plt.xticks(visible=False)
plt.yticks(visible=False)
map2 = Basemap(projection='aea',
lat_1=25., lat_2=47, lon_0=105, lat_0=35,
resolution=self.resolution, ax=axins,
llcrnrlon=106, llcrnrlat=2,
urcrnrlon=124, urcrnrlat=25)
if self.show_coastlines:
map2.drawcoastlines(linewidth=self.lw_boundray, color=self.color_coast)
if self.show_bg_color:
map2.fillcontinents(color=self.color_land, lake_color=self.color_ocean)
map2.drawmapboundary(fill_color=self.color_ocean)
map2.readshapefile(os.path.join(selfPath, u'SHP/国界'), 'china',
linewidth=self.lw_boundray, color=self.color_contry)
self.m2 = map2
self.ax2 = axins
# box line width
spines = axins.spines
for eachspine in spines:
spines[eachspine].set_linewidth(EDGE_LW * 0.9)
def draw_map(map_type, geo_dict, filename, headless):
import matplotlib as mpl
if headless:
mpl.use("Agg")
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
## Map stuff ##
plt.figure(figsize=(16, 9), dpi=100, frameon=False, tight_layout=True)
lon_r = 0
lon_l = 0
if map_type == 'NA':
m = Basemap(llcrnrlon=-119,
llcrnrlat=22,
urcrnrlon=-54,
urcrnrlat=55,
projection='lcc',
resolution='l',
lat_1=32,
lat_2=45,
lon_0=-95)
lon_r = 66.0
lon_l = -124.0
elif map_type == 'EU':
m = Basemap(llcrnrlon=-15.,
llcrnrlat=20,
urcrnrlon=75.,
urcrnrlat=70,
projection='lcc',
resolution='l',
lat_1=30,
lat_2=60,
lon_0=35.)
lon_r = 50.83
lon_l = -69.03
elif map_type == 'World':
m = Basemap(projection='robin',
lat_0=0,
lon_0=-100,
resolution='l',
area_thresh=100000.0)
m.drawmeridians(np.arange(0, 360, 30))
m.drawparallels(np.arange(-90, 90, 30))
lon_r = 180
lon_l = -180.0
# remove line in legend
mpl.rcParams['legend.handlelength'] = 0
m.drawmapboundary(fill_color='#1F1F1F')
m.drawcoastlines()
m.drawstates()
m.drawcountries()
m.fillcontinents(color='#3C3C3C', lake_color='#1F1F1F')
for key, values in geo_dict.items():
# add Accuracy as plot/marker size, change play count to del_s value.
for data in values:
if key == SERVER_FRIENDLY:
color = '#FFAC05'
marker = '*'
markersize = 10
zord = 3
alph = 1
else:
if data['platform'] in PLATFORM_COLORS:
color = PLATFORM_COLORS[data['platform']]
else:
color = DEFAULT_COLOR
print(
'Platform: {} is missing from PLATFORM_COLORS. Using DEFAULT_COLOR.'
.format(data['platform']))
marker = '.'
if data['play_count'] >= 100:
markersize = (data['play_count'] * .1)
elif data['play_count'] <= 2:
markersize = 2
else:
markersize = 2
zord = 2
alph = 0.4
px, py = m(float(data['lon']), float(data['lat']))
x, y = m(
[float(data['lon']), float(SERVER_LON)],
[float(data['lat']), float(SERVER_LAT)])
legend = 'Location: {}, {}, User: {}\nPlatform: {}, IP: {}, Play Count: {}'.format(
data['city'], data['region'], key, data['platform'],
data['ip'], data['play_count'])
# Keeping lines inside the Location. Plots outside Location will still be in legend
if float(data['lon']) != float(SERVER_LON) and float(data['lat']) != float(SERVER_LAT) and \
lon_l < float(data['lon']) < lon_r:
# Drawing lines from Server location to client location
if data['location_count'] > 1:
lines = m.plot(x,
y,
marker=marker,
color=color,
markersize=0,
label=legend,
alpha=.6,
zorder=zord,
linewidth=2)
# Adding dash sequence to 2nd, 3rd, etc lines from same city,state
for line in lines:
line.set_solid_capstyle('round')
dashes = [
x * data['location_count'] for x in [5, 8, 5, 8]
]
line.set_dashes(dashes)
else:
lines = m.plot(x,
y,
marker=marker,
color=color,
markersize=0,
label=legend,
alpha=.4,
zorder=zord,
linewidth=2)
client_plot = m.plot(px,
py,
marker=marker,
color=color,
markersize=markersize,
label=legend,
alpha=alph,
zorder=zord)
handles, labels = plt.gca().get_legend_handles_labels()
idx = labels.index(
'Location: {}, {}, User: {}\nPlatform: {}, IP: {}, Play Count: {}'.
format(SERVER_CITY, SERVER_STATE, SERVER_FRIENDLY, SERVER_PLATFORM,
REPLACEMENT_WAN_IP, 0))
labels = labels[idx:] + labels[:idx]
handles = handles[idx:] + handles[:idx]
by_label = OrderedDict(zip(labels, handles))
leg = plt.legend(by_label.values(),
by_label.keys(),
fancybox=True,
fontsize='x-small',
numpoints=1,
title="Legend",
labelspacing=1.,
borderpad=1.5,
handletextpad=2.)
if leg:
lleng = len(leg.legendHandles)
for i in range(1, lleng):
leg.legendHandles[i]._legmarker.set_markersize(10)
leg.legendHandles[i]._legmarker.set_alpha(1)
leg.get_title().set_color('#7B777C')
leg.draggable()
leg.get_frame().set_facecolor('#2C2C2C')
for text in leg.get_texts():
plt.setp(text, color='#A5A5A7')
plt.title(title_string)
if filename:
plt.savefig('{}.png'.format(filename))
print('Image saved as: {}.png'.format(filename))
if not headless:
mng = plt.get_current_fig_manager()
mng.window.state('zoomed')
plt.show()
###############################################################################
import os
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
para = {
'projection': 'merc',
'lat_0': 0,
'lon_0': 120,
'resolution': 'l',
'area_thresh': 1000.0,
'llcrnrlon': 116,
'llcrnrlat': 36.6,
'urcrnrlon': 124,
'urcrnrlat': 40.2
}
###############################################################################
my_map = Basemap(**para)
my_map.drawcoastlines()
plt.show()
###############################################################################
para['resolution'] = 'h'
my_map = Basemap(**para)
my_map.drawcoastlines()
plt.show()
###############################################################################
para['area_thresh'] = .1
my_map = Basemap(**para)
my_map.drawcoastlines()
plt.show()
# Suppose you'd like to make a map of the world using an orthographic, or
# satellite projection and plot some data on it. Here's how to make the
# map (using matplotlib \>= 0.98.0 and basemap \>= 0.99):
#
# <codecell>
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
# set up orthographic map projection with
# perspective of satellite looking down at 50N, 100W.
# use low resolution coastlines.
# don't plot features that are smaller than 1000 square km.
map = Basemap(projection='ortho',
lat_0=50,
lon_0=-100,
resolution='l',
area_thresh=1000.)
# draw coastlines, country boundaries, fill continents.
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color='coral')
# draw the edge of the map projection region (the projection limb)
map.drawmapboundary()
# draw lat/lon grid lines every 30 degrees.
map.drawmeridians(np.arange(0, 360, 30))
map.drawparallels(np.arange(-90, 90, 30))
plt.show()
# <markdowncell>
# -*- coding: utf-8 -*-
"""
Spyder Editor
This temporary script file is located here:
/home/fabio/.spyder2/.temp.py
"""
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
# make sure the value of resolution is a lowercase L,
# for 'low', not a numeral 1
map = Basemap(projection='ortho',
lat_0=12,
lon_0=41,
resolution='l',
area_thresh=1000.0)
map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color='coral')
plt.show()
from mpl_toolkits.basemap import Basemap
from mpl_toolkits.basemap import maskoceans
from mpl_toolkits.basemap import interp
import matplotlib.pyplot as plt
from osgeo import gdal
import numpy as np
map = Basemap(llcrnrlon=-93.7,
llcrnrlat=28.,
urcrnrlon=-66.1,
urcrnrlat=39.5,
projection='lcc',
lat_1=30.,
lat_2=60.,
lat_0=34.83158,
lon_0=-98.,
resolution="h")
ds = gdal.Open("../sample_files/wrf.tiff")
lons = ds.GetRasterBand(4).ReadAsArray()
lats = ds.GetRasterBand(5).ReadAsArray()
data = ds.GetRasterBand(3).ReadAsArray()
x, y = map(lons, lats)
plt.figure(0)
mdata = maskoceans(lons, lats, data)
map.drawcoastlines(color='0.15')
map.contourf(x, y, mdata)
def mosaic_texture(humfile, sonpath, cs2cs_args = "epsg:26949", res = 99, nn = 5, weight = 1):
'''
Create mosaics of the spatially referenced sidescan echograms
Syntax
----------
[] = PyHum.mosaic_texture(humfile, sonpath, cs2cs_args, res, nn, weight)
Parameters
----------
humfile : str
path to the .DAT file
sonpath : str
path where the *.SON files are
cs2cs_args : int, *optional* [Default="epsg:26949"]
arguments to create coordinates in a projected coordinate system
this argument gets given to pyproj to turn wgs84 (lat/lon) coordinates
into any projection supported by the proj.4 libraries
res : float, *optional* [Default=0]
grid resolution of output gridded texture map
if res=99, res will be determined automatically from the spatial resolution of 1 pixel
nn: int, *optional* [Default=5]
number of nearest neighbours for gridding
weight: int, *optional* [Default=1]
specifies the type of pixel weighting in the gridding process
weight = 1, based on grazing angle and inverse distance weighting
weight = 2, based on grazing angle only
weight = 3, inverse distance weighting only
weight = 4, no weighting
Returns
-------
sonpath+'GroundOverlay.kml': kml file
contains gridded (or point cloud) sidescan intensity map for importing into google earth
of the pth chunk
sonpath+'map.png' :
image overlay associated with the kml file
'''
# prompt user to supply file if no input file given
if not humfile:
print('An input file is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
humfile = askopenfilename(filetypes=[("DAT files","*.DAT")])
# prompt user to supply directory if no input sonpath is given
if not sonpath:
print('A *.SON directory is required!!!!!!')
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
sonpath = askdirectory()
# print given arguments to screen and convert data type where necessary
if humfile:
print('Input file is %s' % (humfile))
if sonpath:
print('Sonar file path is %s' % (sonpath))
if cs2cs_args:
print('cs2cs arguments are %s' % (cs2cs_args))
if res:
res = np.asarray(res,float)
print('Gridding resolution: %s' % (str(res)))
if nn:
nn = int(nn)
print('Number of nearest neighbours for gridding: %s' % (str(nn)))
if weight:
weight = int(weight)
print('Weighting for gridding: %s' % (str(weight)))
##nn = 5 #number of nearest neighbours in gridding
##noisefloor=10 # noise threshold in dB W
# start timer
if os.name=='posix': # true if linux/mac or cygwin on windows
start = time.time()
else: # windows
start = time.clock()
trans = pyproj.Proj(init=cs2cs_args)
# if son path name supplied has no separator at end, put one on
if sonpath[-1]!=os.sep:
sonpath = sonpath + os.sep
base = humfile.split('.DAT') # get base of file name for output
base = base[0].split(os.sep)[-1]
# remove underscores, negatives and spaces from basename
base = humutils.strip_base(base)
meta = loadmat(os.path.normpath(os.path.join(sonpath,base+'meta.mat')))
esi = np.squeeze(meta['e'])
nsi = np.squeeze(meta['n'])
theta = np.squeeze(meta['heading'])/(180/np.pi)
# load memory mapped scans
shape_port = np.squeeze(meta['shape_port'])
if shape_port!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_port_lar.dat'))):
port_fp = io.get_mmap_data(sonpath, base, '_data_port_lar.dat', 'float32', tuple(shape_port))
else:
port_fp = io.get_mmap_data(sonpath, base, '_data_port_la.dat', 'float32', tuple(shape_port))
shape_star = np.squeeze(meta['shape_star'])
if shape_star!='':
if os.path.isfile(os.path.normpath(os.path.join(sonpath,base+'_data_star_lar.dat'))):
star_fp = io.get_mmap_data(sonpath, base, '_data_star_lar.dat', 'float32', tuple(shape_star))
else:
star_fp = io.get_mmap_data(sonpath, base, '_data_star_la.dat', 'float32', tuple(shape_star))
# time varying gain
tvg = ((8.5*10**-5)+(3/76923)+((8.5*10**-5)/4))*meta['c']
# depth correction
dist_tvg = np.squeeze(((np.tan(np.radians(25)))*np.squeeze(meta['dep_m']))-(tvg))
# read in range data
R_fp = io.get_mmap_data(sonpath, base, '_data_range.dat', 'float32', tuple(shape_star))
dx = np.arcsin(meta['c']/(1000*meta['t']*meta['f']))
pix_m = meta['pix_m']
c = meta['c']
if not os.path.isfile( os.path.normpath(os.path.join(sonpath,base+"S.p")) ):
#if 2 > 1:
inputfiles = []
if len(shape_star)>2:
for p in range(len(star_fp)):
e = esi[shape_port[-1]*p:shape_port[-1]*(p+1)]
n = nsi[shape_port[-1]*p:shape_port[-1]*(p+1)]
t = theta[shape_port[-1]*p:shape_port[-1]*(p+1)]
d = dist_tvg[shape_port[-1]*p:shape_port[-1]*(p+1)]
dat_port = port_fp[p]
dat_star = star_fp[p]
data_R = R_fp[p]
print("writing chunk %s " % (str(p)))
write_points(e, n, t, d, dat_port, dat_star, data_R, pix_m, res, cs2cs_args, sonpath, p, c, dx)
inputfiles.append(os.path.normpath(os.path.join(sonpath,'x_y_class'+str(p)+'.asc')))
else:
p=0
print("writing chunk %s " % (str(p)))
write_points(esi, nsi, theta, dist_tvg, port_fp, star_fp, R_fp, meta['pix_m'], res, cs2cs_args, sonpath, 0, c, dx)
inputfiles.append(os.path.normpath(os.path.join(sonpath,'x_y_class'+str(p)+'.asc')))
#trans = pyproj.Proj(init=cs2cs_args)
# D, R, h, t
print("reading points from %s files" % (str(len(inputfiles))))
X,Y,S,D,R,h,t,i = getxys(inputfiles)
print("%s points read from %s files" % (str(len(S)), str(len(inputfiles))))
# remove values where sidescan intensity is zero
ind = np.where(np.logical_not(S==0))[0]
X = X[ind]; Y = Y[ind]
S = S[ind]; D = D[ind]
R = R[ind]; h = h[ind]
t = t[ind]; i = i[ind]
del ind
# save to file for temporary storage
pickle.dump( S, open( os.path.normpath(os.path.join(sonpath,base+"S.p")), "wb" ) ); del S
pickle.dump( D, open( os.path.normpath(os.path.join(sonpath,base+"D.p")), "wb" ) ); del D
pickle.dump( t, open( os.path.normpath(os.path.join(sonpath,base+"t.p")), "wb" ) ); del t
pickle.dump( i, open( os.path.normpath(os.path.join(sonpath,base+"i.p")), "wb" ) ); del i
pickle.dump( X, open( os.path.normpath(os.path.join(sonpath,base+"X.p")), "wb" ) ); del X
pickle.dump( Y, open( os.path.normpath(os.path.join(sonpath,base+"Y.p")), "wb" ) ); del Y
pickle.dump( R, open( os.path.normpath(os.path.join(sonpath,base+"R.p")), "wb" ) );
pickle.dump( h, open( os.path.normpath(os.path.join(sonpath,base+"h.p")), "wb" ) );
#grazing angle
g = np.arctan(R.flatten(),h.flatten())
pickle.dump( g, open( os.path.normpath(os.path.join(sonpath,base+"g.p")), "wb" ) ); del g, R, h
print("creating grids ...")
if res==0:
res=99
if res==99:
#### prepare grids
R = pickle.load( open( os.path.normpath(os.path.join(sonpath,base+"R.p")), "rb" ) )
## actual along-track resolution is this: dx times dy = Af
tmp = R * dx * (c*0.007 / 2)
del R
resg = np.min(tmp[tmp>0])
del tmp
else:
resg = res
X = pickle.load( open( os.path.normpath(os.path.join(sonpath,base+"X.p")), "rb" ) )
Y = pickle.load( open( os.path.normpath(os.path.join(sonpath,base+"Y.p")), "rb" ) )
humlon, humlat = trans(X, Y, inverse=True)
grid_x, grid_y = np.meshgrid( np.arange(np.min(X), np.max(X), resg), np.arange(np.min(Y), np.max(Y), resg) )
shape = np.shape(grid_x)
tree = KDTree(zip(X.flatten(), Y.flatten()))
del X, Y
print("mosaicking ...")
#k nearest neighbour
try:
dist, inds = tree.query(zip(grid_x.flatten(), grid_y.flatten()), k = nn, n_jobs=-1)
except:
#print ".... update your scipy installation to use faster kd-tree"
dist, inds = tree.query(zip(grid_x.flatten(), grid_y.flatten()), k = nn)
#del grid_x, grid_y
if weight==1:
g = pickle.load( open( os.path.normpath(os.path.join(sonpath,base+"g.p")), "rb" ) )
w = g[inds] + 1.0 / dist**2
del g
elif weight==2:
g = pickle.load( open( os.path.normpath(os.path.join(sonpath,base+"g.p")), "rb" ) )
w = g[inds]
del g
elif weight==3:
w = 1.0 / dist**2
elif weight==4:
w = 1.0
#g = pickle.load( open( os.path.normpath(os.path.join(sonpath,base+"g.p")), "rb" ) )
#w = g[inds] + 1.0 / dist**2
#del g
if weight < 4:
w[np.isinf(w)]=1
w[np.isnan(w)]=1
w[w>10000]=10000
w[w<=0]=1
# load in sidescan intensity
S = pickle.load( open( os.path.normpath(os.path.join(sonpath,base+"S.p")), "rb" ) )
# filter out noise pixels
S[S<noisefloor] = np.nan
if nn==1:
Sdat_g = (w * S.flatten()[inds]).reshape(shape)
del w
dist = dist.reshape(shape)
else:
if weight < 4:
Sdat_g = (np.nansum(w * S.flatten()[inds], axis=1) / np.nansum(w, axis=1)).reshape(shape)
else:
Sdat_g = (np.nansum(S.flatten()[inds], axis=1)).reshape(shape)
del w
dist = np.nanmean(dist,axis=1).reshape(shape)
del S
Sdat_g[dist>1] = np.nan
Sdat_g[Sdat_g<noisefloor] = np.nan
dat = Sdat_g.copy()
dat[dist>1] = 0
dat2 = replace_nans.RN(dat.astype('float64'),1000,0.01,2,'localmean').getdata()
dat2[dat==0] = np.nan
del dat
dat2[dat2<noisefloor] = np.nan
Sdat_g = dat2.copy()
del dat2
Sdat_g[Sdat_g==0] = np.nan
Sdat_g[np.isinf(Sdat_g)] = np.nan
Sdat_gm = np.ma.masked_invalid(Sdat_g)
del Sdat_g
glon, glat = trans(grid_x, grid_y, inverse=True)
del grid_x, grid_y
# =========================================================
print("creating kmz file ...")
## new way to create kml file
pixels = 1024 * 10
fig, ax = humutils.gearth_fig(llcrnrlon=glon.min(),
llcrnrlat=glat.min(),
urcrnrlon=glon.max(),
urcrnrlat=glat.max(),
pixels=pixels)
cs = ax.pcolormesh(glon, glat, Sdat_gm)
ax.set_axis_off()
fig.savefig(os.path.normpath(os.path.join(sonpath,'class_overlay1.png')), transparent=True, format='png')
fig = plt.figure(figsize=(1.0, 4.0), facecolor=None, frameon=False)
ax = fig.add_axes([0.0, 0.05, 0.2, 0.9])
cb = fig.colorbar(cs, cax=ax)
cb.set_label('Texture lengthscale [m]', rotation=-90, color='k', labelpad=20)
fig.savefig(os.path.normpath(os.path.join(sonpath,'class_legend.png')), transparent=False, format='png')
humutils.make_kml(llcrnrlon=glon.min(), llcrnrlat=glat.min(),
urcrnrlon=glon.max(), urcrnrlat=glat.max(),
figs=[os.path.normpath(os.path.join(sonpath,'class_overlay1.png'))],
colorbar=os.path.normpath(os.path.join(sonpath,'class_legend.png')),
kmzfile=os.path.normpath(os.path.join(sonpath,'class_GroundOverlay.kmz')),
name='Sidescan Intensity')
# =========================================================
print("drawing and printing map ...")
fig = plt.figure(frameon=False)
map = Basemap(projection='merc', epsg=cs2cs_args.split(':')[1],
resolution = 'i', #h #f
llcrnrlon=np.min(humlon)-0.001, llcrnrlat=np.min(humlat)-0.001,
urcrnrlon=np.max(humlon)+0.001, urcrnrlat=np.max(humlat)+0.001)
gx,gy = map.projtran(glon, glat)
try:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='ESRI_Imagery_World_2D', xpixels=1000, ypixels=None, dpi=300)
except:
map.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='World_Imagery', xpixels=1000, ypixels=None, dpi=300)
#finally:
# print "error: map could not be created..."
ax = plt.Axes(fig, [0., 0., 1., 1.], )
ax.set_axis_off()
fig.add_axes(ax)
if Sdat_gm.size > 25000000:
print("matrix size > 25,000,000 - decimating by factor of 5 for display")
map.pcolormesh(gx[::5,::5], gy[::5,::5], Sdat_gm[::5,::5], vmin=np.nanmin(Sdat_gm), vmax=np.nanmax(Sdat_gm))
else:
map.pcolormesh(gx, gy, Sdat_gm, vmin=np.nanmin(Sdat_gm), vmax=np.nanmax(Sdat_gm))
custom_save2(sonpath,'class_map_imagery')
del fig
if os.name=='posix': # true if linux/mac
elapsed = (time.time() - start)
else: # windows
elapsed = (time.clock() - start)
print("Processing took "+str(elapsed)+"seconds to analyse")
print("Done!")
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import numpy as np
map = Basemap(projection='cea',
llcrnrlat=0,
urcrnrlat=50,
llcrnrlon=-120,
urcrnrlon=-80)
map.drawmapboundary(fill_color="blue")
map.fillcontinents(color="coral", lake_color="yellow")
import re
def latlon(coord):
#22°21′00″N
r = re.search("(\d+).(\d+).(\d+)..(\w)", coord)
g = int(r.group(1))
m = int(r.group(2))
s = int(r.group(3))
return g + m / 60.0 + s / 3600.0
import sci
mSeis = mSeis[sel].T
mWells = np.loadtxt(file_well).T
#--------------------------2---------------------------------------------
# map view, select boundaries of seismicity
#------------------------------------------------------------------------
plt.figure(1)
ax1 = plt.subplot(111)
lon_0, lat_0 = .5 * (dPar['xmin'] + dPar['xmax']), .5 * (dPar['ymin'] +
dPar['ymax'])
m = Basemap(
llcrnrlon=dPar['xmin'],
urcrnrlon=dPar['xmax'],
llcrnrlat=dPar['ymin'],
urcrnrlat=dPar['ymax'],
lon_0=lon_0,
lat_0=lat_0,
resolution='l',
projection=dPar['projection'],
)
m.drawstates()
xpt, ypt = m(mSeis[1], mSeis[2])
xpt2, ypt2 = m(mWells[2], mWells[3])
# plot wells
for k in range(0, len(mWells[1])):
if (mWells[k].all):
ax2.plot(xpt2[k], ypt2[k], 'bo')
# plot seismicity
for j in range(0, len(sel)):
if (sel[j]):
ax2.plot(xpt[j], ypt[j], 'ro')
def event_plot(self,
assoc_id,
west=-104.5,
east=-94,
south=33.5,
north=37.5,
deltalon=1.0,
deltalat=1.0):
""" Plot all the circles, stations, location and residual distribution on one map by calling the event number after the event been associated.
"""
plot_colors = itertools.cycle(['r', 'g', 'b', 'c', 'm', 'y'])
fig = plt.figure(figsize=(15, 8))
ax = fig.add_subplot(111)
# =============================================================
### Map bound
#
# North
#
# West East
#
# South
#
# =============================================================
### Basemap Module
m = Basemap(projection='merc',
llcrnrlat=south,
urcrnrlat=north,
llcrnrlon=west,
urcrnrlon=east,
lat_ts=0,
resolution='c')
m.drawcountries()
m.drawstates()
m.fillcontinents(color='white', lake_color='aqua', zorder=1)
# =============================================================
# draw parallels, meridians and structures.
# m.drawparallels(np.arange(South,North,deltalat),labels=[1,0,0,0],color='gray',dashes=[1,1e-5],labelstyle='+/-',linewidth=0.1)
# m.drawmeridians(np.arange(West,East,deltalon),labels=[0,0,0,1],color='gray',dashes=[1,1e-5],labelstyle='+/-',linewidth=0.1)
m.drawmapboundary(fill_color='blue')
# =============================================================
# plot matches and mismatches circles
matches = self.assoc_db.query(Candidate).filter(
Candidate.assoc_id == assoc_id).filter(
Candidate.locate_flag == True).all()
mismatches = self.assoc_db.query(Candidate).filter(
Candidate.assoc_id == assoc_id).filter(
Candidate.locate_flag == False).all()
lon_eve, lat_eve = self.assoc_db.query(
Associated.longitude,
Associated.latitude).filter(Associated.id == assoc_id).first()
radius_rainbow = []
s_p_rainbow = []
rainbow = []
sta_rainbow = []
for match in matches:
lon, lat = self.tt_stations_db_1D.query(
Station1D.longitude,
Station1D.latitude).filter(Station1D.sta == match.sta).first()
s_p_rainbow.append((match.ts - match.tp).total_seconds())
radius_rainbow.append(match.d_km)
# Color = plot_colors.next()
Color = next(plot_colors)
rainbow.append(Color)
sta_rainbow.append(match.sta)
LocPair, = equi(m, lon, lat, match.d_km, lw=2., color=Color)
legend_list = {"Pair for locating": LocPair}
for mismatch in mismatches:
lon, lat = self.tt_stations_db_1D.query(
Station1D.longitude, Station1D.latitude).filter(
Station1D.sta == mismatch.sta).first()
s_p_rainbow.append((mismatch.ts - mismatch.tp).total_seconds())
radius_rainbow.append(mismatch.d_km)
# Color = plot_colors.next()
Color = next(plot_colors)
rainbow.append(Color)
sta_rainbow.append(mismatch.sta)
UnlocPair, = equi(m,
lon,
lat,
mismatch.d_km,
ls='--',
lw=1.,
color=Color)
try:
mismatch
except NameError:
pass
else:
legend_list["Pair not for locating"] = UnlocPair
# =============================================================
# plot stations
# stations = self.assoc_db.query(Pick.sta).filter(Pick.assoc_id==assoc_id).distinct().all()
stations = sta_rainbow
lon_STA = []
lat_STA = []
sta_STA = []
for index, sta in enumerate(stations): # the comma is needed
lon, lat = self.tt_stations_db_1D.query(
Station1D.longitude,
Station1D.latitude).filter(Station1D.sta == sta).first()
lon_STA.append(lon)
lat_STA.append(lat)
sta_STA.append(sta)
x, y = m(lon_STA, lat_STA)
m.scatter(x, y, marker='^', s=100, c=rainbow, zorder=3)
for xi, yi, stai in zip(x, y, sta_STA):
plt.text(xi,
yi + 1,
stai,
fontweight='bold',
ha='center',
color='k')
# =============================================================
# plot event location and uncertainty
event = self.assoc_db.query(Associated).filter(
Associated.id == assoc_id).first()
t_create = event.t_create
t_update = event.t_update
ot = event.ot
uncert = event.loc_uncert * 6371 * np.pi / 180.
lon = event.longitude
lat = event.latitude
location = (lon, lat)
x, y = m(lon, lat)
m.scatter(x, y, marker='*', s=100, c='k', zorder=3)
equi(m, lon, lat, uncert, color='k', lw=2.)
# plt.title('Event %d at Origin Time: %s'%(event.id,ot), fontsize=18)
plt.title('Event at Origin Time: %s' % (ot), fontsize=18)
# add in the create and update time text
# x_text,y_text=m(West+(East-West)*0.2,South+(North-South)*0.05)
# plt.text(x_text,y_text,'Create Time: %s\nUpdate Time:%s'%(t_create,t_update),horizontalalignment='center',fontsize=12,fontweight='bold')
# =============================================================
# plot single phases circles
single_phases = self.assoc_db.query(PickModified).filter(
PickModified.assoc_id == assoc_id).filter(
PickModified.locate_flag == None).all()
for single_phase in single_phases:
lon, lat = self.tt_stations_db_1D.query(
Station1D.longitude, Station1D.latitude).filter(
Station1D.sta == single_phase.sta).first()
x, y = m(lon, lat)
pick_time = single_phase.time
travel_time = (pick_time - ot).total_seconds()
if single_phase.phase == 'P':
tt, tt_uncert = self.distance_singlephase(
single_phase.phase, travel_time)
d_km = tt.d_km
UnlocPhase, = equi(m,
lon,
lat,
d_km,
ls=':',
lw=2.,
c='gray',
zorder=4)
m.scatter(x, y, marker='^', s=100, c='gray', zorder=5)
plt.text(x,
y + 1,
single_phase.sta,
fontweight='bold',
ha='center')
if single_phase.phase == 'S':
tt, tt_uncert = self.distance_singlephase(
single_phase.phase, travel_time)
d_km = tt.d_km
UnlocPhase, = equi(m,
lon,
lat,
d_km,
ls=':',
lw=2.,
c='gray',
zorder=4)
m.scatter(x, y, marker='^', s=100, c='gray', zorder=5)
plt.text(x,
y + 1,
single_phase.sta,
fontweight='bold',
ha='center')
try:
single_phase
except NameError:
pass
else:
legend_list["Single Phase"] = UnlocPhase
# add on the legend
# legend = plt.legend(legend_list.values(),legend_list.keys(),'upper left')
# =============================================================
# plot residual distribution
subpos = [0.05, 0.28, 0.35, 0.6]
subax = add_subplot_axes(ax, subpos)
Dist = self.tt_stations_db_1D.query(TTtable1D.d_km).all()
SP_intv = self.tt_stations_db_1D.query(TTtable1D.s_p).all()
S_P = []
D_S_P = []
for dist, in Dist:
D_S_P.append(dist)
for s_p, in SP_intv:
S_P.append(s_p)
subax.plot(D_S_P, S_P, 'k-', linewidth=2, zorder=1)
for i in range(len(radius_rainbow)):
subax.scatter(radius_rainbow[i],
s_p_rainbow[i],
s=50,
color=rainbow[i],
zorder=2)
plt.xlim([0, 350])
plt.ylim([0, 40])
plt.xlabel('Distance (km)')
plt.ylabel('S-P (s)')
plt.show()
tcwvg, tcwvc = grid_field(eralat.ravel(),
eralon.ravel(),
tcwv.ravel(),
gsize=2.5,
startlat=65.0)
#%%
lats = np.arange(-65, 65, 2.5)
lons = np.arange(-180, 180, 2.5)
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=[25, 10])
plt.tight_layout()
m = Basemap(projection="cyl",
llcrnrlon=-180,
llcrnrlat=-65,
urcrnrlon=177.5,
urcrnrlat=65,
ax=ax1)
m.drawcoastlines()
m.pcolormesh(lons, lats, y_t0, vmin=0, vmax=25, cmap=cm.rainbow)
ax1.set_title("retrieved WVP")
parallels = np.arange(-80., 80, 20.)
# labels = [left,right,top,bottom]
m.drawparallels(parallels, labels=[True, False, True, False])
meridians = np.arange(0., 360., 40.)
m.drawmeridians(meridians, labels=[True, False, False, True])
latmask = np.abs(lats) <= 45
tcwvg[latmask] = np.nan
m = Basemap(projection="cyl",
###########################################################################
### Plot absolute differences
limit = np.arange(-1, 1.1, 0.1)
barlim = np.arange(-1, 2, 1)
fig = plt.figure()
for i in xrange(years.shape[0]):
diffsit = sitc[i] - sitp[i]
diffsit[np.isnan(diffsit)] = 0.0
ax = plt.subplot(2, 4, i + 1)
m = Basemap(projection='npstere',
boundinglat=59,
lon_0=270,
resolution='l',
round=True,
area_thresh=1000.)
m.drawmapboundary(fill_color='white', color='dimgray', linewidth=0.7)
m.drawcoastlines(color='k', linewidth=0.2)
m.drawlsmask(land_color='dimgrey', ocean_color='mintcream')
x, y = m(lonvals[i], latvals[i])
cs = m.hexbin(x, y, C=diffsit, vmin=-1, vmax=1)
cmap = ncm.cmap('NCV_blu_red')
cs.set_cmap(cmap)
ax.annotate(r'\textbf{%s}' % years[i],
xy=(0, 0),
xytext=(0.7, 0.97),
def netcdf2png(url,
colormapPath,
colormapName,
dirDest,
lat_name,
lon_name,
data_name,
geos=False):
# Dataset is the class behavior to open the file
# and create an instance of the ncCDF4 class
nc_fid = netCDF4.Dataset(url, 'r')
if data_name == 'Band1':
name = basename(url) # obtengo el nombre base del archivo
else:
t_coverage = repr(nc_fid.getncattr('time_coverage_end'))
# print t_coverage
ds_name = repr(nc_fid.getncattr('dataset_name'))
# print ds_name
date = re.search('\'(.*?)\'', t_coverage).group(1)
print date
channel = re.search('-M\d(.*?)_', ds_name).group(1)
print channel
channelInfo(channel)
yyyy = date[0:4]
mm = date[5:7]
dd = date[8:10]
hhmm = date[11:16]
name = channel + " " + dd + "-" + mm + "-" + yyyy + " " + hhmm + " UTC"
print "name: " + name
# if name
# extract/copy the data
lats = nc_fid.variables[lat_name][:]
lons = nc_fid.variables[lon_name][:]
data = nc_fid.variables[data_name][:]
if data_name == 'CMI' or data_name == 'Rad':
# Satellite height
sat_h = nc_fid.variables[
'goes_imager_projection'].perspective_point_height
sat_h -= 10000
# Satellite longitude
sat_lon = nc_fid.variables[
'goes_imager_projection'].longitude_of_projection_origin
# Satellite sweep
sat_sweep = nc_fid.variables['goes_imager_projection'].sweep_angle_axis
X = nc_fid.variables[lon_name][:] * sat_h # longitud, eje X
Y = nc_fid.variables[lat_name][:] * sat_h # latitud, eje Y
# si el canal es el 2 divido las dimensiones de los elementos
if not geos and channel == 'C02':
X = X[7000:9500]
Y = Y[7000:9500]
data = data[7000:9500, 7000:9500]
elif not geos:
X = X[3500:4800]
Y = Y[3500:4800]
data = data[3500:4800, 3500:4800]
elif geos:
X = X[::4]
Y = Y[::4]
data = data[::4, ::4]
print "sat_h: " + str(sat_h)
print "Sat_lon: " + str(sat_lon)
# end if data_name == 'CMI'
nc_fid.close()
min_lon = numpy.amin(lons)
max_lon = numpy.amax(lons)
min_lat = numpy.amin(lats)
max_lat = numpy.amax(lats)
print "min_lat: " + str(min_lon)
print "max_lat: " + str(max_lon)
print "min_lon: " + str(min_lat)
print "max_lon: " + str(max_lat)
# for i in data:
# print i
print "Data min: " + str(numpy.amin(data))
print "Data max: " + str(numpy.amax(data))
# seteo los minimos y maximos de la imagen en funcion de los min y max de lat y long
# axes = plt.gca()
# axes.set_xlim([min_lon, max_lon])
# axes.set_ylim([min_lat, max_lat])
zona = 'plata'
if data_name == 'Band1': # para archivos nc ya proyectados a mercator
X = map(lambda x: x + 0.11, lons) # incremento X
Y = map(lambda y: y + 0.11, lats) # incremento Y
ax = Basemap(projection='merc',\
llcrnrlat=-42.94,urcrnrlat=-22.0,\
llcrnrlon=-67.0,urcrnrlon=-45.04,\
resolution='f')
# resolution: c, l, i, h, f
lons2d, lats2d = numpy.meshgrid(X, Y)
# dadas las lat y lon del archivo, obtengo las coordenadas x y para
# la ventana seleccionada como proyeccion
x, y = ax(lons2d, lats2d)
elif geos: # proyecto toda la foto completa de geo estacionario
print "Ventana Globo geoestacionario"
min_Y = numpy.amin(Y)
max_Y = numpy.amax(Y)
min_X = numpy.amin(X)
max_X = numpy.amax(X)
XX = map(lambda x: x + max_X, X) # incremento X
YY = map(lambda y: y + max_Y, Y) # incremento Y
print "min_Y: " + str(min_Y)
print "max_Y: " + str(max_Y)
print "min_X: " + str(min_X)
print "max_X: " + str(max_X)
print numpy.amin(XX)
print numpy.amin(YY)
x, y = numpy.meshgrid(XX, YY)
ax = Basemap(projection='geos', lon_0=sat_lon, satellite_height=sat_h,\
llcrnrx=-x.max()/2,llcrnry=-y.max()/2,\
urcrnrx=x.max()/2,urcrnry=y.max()/2,\
resolution='l')
elif zona == 'plata': # proyecto con mercator en la región del río de la plata
print "Ventana Ŕío de la Plata"
# https://github.com/blaylockbk/pyBKB_v2/blob/master/BB_goes16/mapping_GOES16_data.ipynb
min_Y = numpy.amin(Y)
max_Y = numpy.amax(Y)
min_X = numpy.amin(X)
max_X = numpy.amax(X)
# parche para alinear la fotografía con las coordenadas geográficas
# supongo que una vez esté calibrado el satélite hay que eliminar estas líneas
X = map(lambda x: x + 10000, X) # incremento X
Y = map(lambda y: y + 10000, Y) # incremento Y
print "min_Y: " + str(min_Y)
print "max_Y: " + str(max_Y)
print "min_X: " + str(min_X)
print "max_X: " + str(max_X)
print numpy.amin(X)
print numpy.amin(Y)
# Región
ax = Basemap(projection='merc',\
llcrnrlat=-42.94,urcrnrlat=-22.0,\
llcrnrlon=-67.0,urcrnrlon=-45.04,\
resolution='f')
projection = Proj(proj='geos', h=sat_h, lon_0=sat_lon, sweep=sat_sweep)
x_mesh, y_mesh = numpy.meshgrid(X, Y)
lons, lats = projection(x_mesh, y_mesh, inverse=True)
x, y = ax(lons, lats)
elif zona == 'sur': # proyecto con mercator en la región del río de la plata
print "Ventana Sur"
# https://github.com/blaylockbk/pyBKB_v2/blob/master/BB_goes16/mapping_GOES16_data.ipynb
min_Y = numpy.amin(Y)
max_Y = numpy.amax(Y)
min_X = numpy.amin(X)
max_X = numpy.amax(X)
# parche para alinear la fotografía con las coordenadas geográficas
# supongo que una vez esté calibrado el satélite hay que eliminar estas líneas
X = map(lambda x: x + 10000, X) # incremento X
Y = map(lambda y: y + 3000, Y) # incremento Y
# X = map(lambda x: x+10000, X) # incremento X
# Y = map(lambda y: y+10000, Y) # incremento Y
print "min_Y: " + str(min_Y)
print "max_Y: " + str(max_Y)
print "min_X: " + str(min_X)
print "max_X: " + str(max_X)
print numpy.amin(X)
print numpy.amin(Y)
# Región
ax = Basemap(projection='merc',\
llcrnrlat=-49.4947,urcrnrlat=-13.6169,\
llcrnrlon=-73.4699,urcrnrlon=-39.2205,\
resolution='f')
projection = Proj(proj='geos', h=sat_h, lon_0=sat_lon, sweep=sat_sweep)
x_mesh, y_mesh = numpy.meshgrid(X, Y)
lons, lats = projection(x_mesh, y_mesh, inverse=True)
x, y = ax(lons, lats)
elif zona == 'uy': # proyecto con mercator en la región del río de la plata
print "Ventana Uruguay"
# https://github.com/blaylockbk/pyBKB_v2/blob/master/BB_goes16/mapping_GOES16_data.ipynb
min_Y = numpy.amin(Y)
max_Y = numpy.amax(Y)
min_X = numpy.amin(X)
max_X = numpy.amax(X)
# parche para alinear la fotografía con las coordenadas geográficas
# supongo que una vez esté calibrado el satélite hay que eliminar estas líneas
X = map(lambda x: x + 10000, X) # incremento X
Y = map(lambda y: y + 10000, Y) # incremento Y
print "min_Y: " + str(min_Y)
print "max_Y: " + str(max_Y)
print "min_X: " + str(min_X)
print "max_X: " + str(max_X)
print numpy.amin(X)
print numpy.amin(Y)
# Región
ax = Basemap(projection='merc',\
llcrnrlat=-35.2138,urcrnrlat=-29.7466,\
llcrnrlon=-58.9073,urcrnrlon=-52.7591,\
# llcrnrx=-x.max()/2,llcrnry=-y.max()/2,\
# urcrnrx=x.max()/2,urcrnry=y.max()/2,\
resolution='f')
projection = Proj(proj='geos', h=sat_h, lon_0=sat_lon, sweep=sat_sweep)
x_mesh, y_mesh = numpy.meshgrid(X, Y)
lons, lats = projection(x_mesh, y_mesh, inverse=True)
x, y = ax(lons, lats)
# end if
# agrego los vectores de las costas, departamentos/estados/provincias y paises
ax.drawcoastlines(linewidth=0.25)
ax.drawcountries(linewidth=0.50)
ax.drawstates(linewidth=0.25)
if not geos:
# dibujo los valores de latitudes y longitudes al margen de la imagen
par = ax.drawparallels(numpy.arange(-45, -20, 5),
labels=[1, 0, 0, 0],
linewidth=0.0,
fontsize=10,
color='white')
mer = ax.drawmeridians(numpy.arange(-70, -45, 5),
labels=[0, 0, 1, 0],
linewidth=0.0,
fontsize=10,
color='white')
setcolor(par, 'white')
setcolor(mer, 'white')
# llamo al garbage collector para que borre los elementos que ya no se van a usar
gc.collect()
if channel == 'C02':
data *= 100
else:
# Los datos de estan en kelvin, asi que los paso a Celsius
data -= 273.15
# defino el min y max en funcion de la banda
if data_name == 'Band1':
vmin = numpy.amin(data)
vmax = numpy.amax(data)
else:
vmin, vmax = rangoColorbar(channel)
print numpy.amin(data)
print numpy.amax(data)
data = numpy.ma.masked_where(numpy.isnan(data), data)
# dibujo img en las coordenadas x e y calculadas
# cmap = gmtColormap(colormapName, colormapPath, 2048)
# defino el colormap y la disposicion de los ticks segun la banda
if channel == 'C02':
ticks = [0, 20, 40, 60, 80, 100]
ticksLabels = ticks
elif channel == 'C08' or channel == 'C09':
ticks = [-60, -50, -40, -30, -20, -10, 0]
ticksLabels = ticks
else:
ticks = [
-80, -75.2, -70.2, -65.2, -60.2, -55.2, -50.2, -45.2, -40.2, -35.2,
-30.2, -20, -10, 0, 10, 20, 30, 40, 50, 60, 70
]
# defino las etiquetas del colorbar
ticksLabels = [
-80, -75, -70, -65, -60, -55, -50, -45, -40, -35, -30, -20, -10, 0,
10, 20, 30, 40, 50, 60, 70
]
# if FR o RP
cmap = gmtColormap(colormapName, colormapPath, 2048)
cs = ax.pcolormesh(x, y, data, vmin=vmin, vmax=vmax, cmap=cmap)
# seteo los limites del colorbar
plt.clim(vmin, vmax)
# agrego el colorbar
cbar = ax.colorbar(cs, location='bottom', pad='3%', ticks=ticks)
if channel == 'C02':
cbar.ax.set_xticklabels(ticksLabels, fontsize=7, color='white')
else:
cbar.ax.set_xticklabels(ticksLabels,
rotation=45,
fontsize=7,
color='white')
cbar.ax.set_xlabel("Temperatura de brillo ($^\circ$C)",
fontsize=7,
color='white')
if channel != 'C02':
cbar.ax.xaxis.labelpad = 0
# agrego el logo en el documento
logo = plt.imread('./logo_300_bw.png')
plt.figimage(logo, 5, 5)
# si estoy dibujando toda la proyección geos adjunto el string al nombre del archivo
if geos:
destFile = dirDest + name + '_geos.png' # determino el nombre del archivo a escribir
else:
destFile = dirDest + name + '.png' # determino el nombre del archivo a escribir
# llamo al garbage collector para que borre los elementos que ya no se van a usar
gc.collect()
print destFile
# genero el pie de la imagen, con el logo y la info del archivo
plt.annotate(name, (0, 0), (106, -60),
xycoords='axes fraction',
textcoords='offset points',
va='top',
fontsize=14,
family='monospace',
color='white')
plt.savefig(destFile, bbox_inches='tight', dpi=300,
transparent=True) # , facecolor='#4F7293'
plt.close()
#---------------------------------------------------------
# Visualize stations on map
#---------------------------------------------------------
#plotstations = 'yes'
plotstations = 'no'
if plotstations == 'yes':
# create figure
plt.close('all')
fig = plt.figure(figsize=(22, 30))
# define map projection and add basic geographical features
#map = Basemap(width=0.4e6,height=0.3e6,projection='stere',lat_ts=46.8,lat_0=46.8,lon_0=8.21,resolution='h')
map = Basemap(projection='merc',llcrnrlat=45.7,urcrnrlat=47.95,\
llcrnrlon=5.7,urcrnrlon=10.7,lat_ts=46.8,resolution='h',epsg=3395)
map.drawcountries(linewidth=1.5)
map.drawcoastlines()
#map.drawrivers(color='blue')
map.arcgisimage(service='World_Shaded_Relief', xpixels=2000)
# plot meteoswiss automatic measurement network
x, y = map(stationcoords_lon, stationcoords_lat)
map.plot(x, y, 'vk', markersize=10)
# plot TreeNet station network
i, j = map(treenetcoords_lon, treenetcoords_lat)
map.plot(i, j, 'ro', markersize=10)
# export figure
saveas = '\stationmap.png'
'lat': 37.7267,
'lon': -84.3001,
'color': 'r',
'tz': 'US/Eastern'
}
cloud_temp = -25.0
local = pytz.timezone('US/Central')
utc = pytz.timezone('UTC')
plt.figure(num=None, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')
# setup Lambert Conformal basemap.
m = Basemap(llcrnrlon=-95.,
llcrnrlat=30.,
urcrnrlon=-75.,
urcrnrlat=45.,
projection='mill',
resolution='i')
# projection='merc', area_thresh=1000,
# resolution='i',lat_1=45.,lat_2=55,lat_0=40,lon_0=-85.)
# draw coastlines and fill the continents (alpha value is set to partial transparancy because for some
# reason the fill is over top the quivers used to denote the wind vectors
m.drawcoastlines()
m.drawstates()
m.fillcontinents(color='0.8')
m.drawparallels(np.arange(30, 45, 5), labels=[1, 1, 0, 0])
m.drawmeridians(np.arange(-95, -75, 5), labels=[0, 0, 0, 1])
# draw coastlines and fill the continents (alpha value is set to partial transparancy because for some
# reason the fill is over top the quivers used to denote the wind vectors
import os
import numpy as np
from netCDF4 import Dataset
import matplotlib.pyplot as pl
from mpl_toolkits.basemap import Basemap
from shapely.geometry import Polygon, Point, LineString
from geopy.distance import vincenty
import functions as funct
m = Basemap(projection='spstere', boundinglat=-60, lon_0=180, resolution='l')
length = 200
dot_array = np.full((6, 12, length, 11), fill_value=np.NaN)
dist_2 = np.full((6, 12, length, 11), fill_value=np.NaN)
coast_distance_array = np.full((6, 12, length), fill_value=np.NaN)
for year in ['2011', '2012', '2013', '2014', '2015', '2016']:
for month in [
'01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11',
'12'
]:
file = '/Users/jmh2g09/Documents/PhD/Data/Gridded/' + year + month + '_grid.nc'
nc = Dataset(file, 'r')
latitude = nc.variables['lat'][:]
longitude = nc.variables['lon'][:]
dot = nc.variables[
'filtered_dynamic_ocean_topography_seasonal_offset'][:]
nc.close()
nc_grid = Dataset(
'/Users/jmh2g09/Documents/PhD/Data/CoastalCurrent/grid.nc', 'w')
def plot_global_map_weights(self):
"""
Plot global map of event and stations
"""
# import basemap here due to error
from mpl_toolkits.basemap import Basemap
# ax = plt.subplot(211)
plt.title(self.cmtsource.eventname + "_Weights")
m = Basemap(projection='cyl', lon_0=0.0, lat_0=0.0, resolution='c')
m.drawcoastlines()
m.fillcontinents()
m.drawparallels(np.arange(-90., 120., 30.))
m.drawmeridians(np.arange(0., 420., 60.))
m.drawmapboundary()
#x, y = m(self.sta_lon, self.sta_lat)
x, y = self.sta_lon, self.sta_lat
w = [meta.weights for meta in self.metas]
print(w)
w /= max(w)
w = map(float, w)
#w = map(str, w)
w = list(w)
s = [np.sqrt(x) * 100 for x in w]
w = [x**0.5 for x in w]
#print(self.sta_lon,self.sta_lat)
#print(self.metas)
#print(len(self.metas),len(self.sta_lon))
#print(w)
m.scatter(x, y, s=s, c=w, marker="^", cmap='hsv', zorder=3)
cmt_lat = self.cmtsource.latitude
cmt_lon = self.cmtsource.longitude
focmecs = get_cmt_par(self.cmtsource)[:6]
ax = plt.gca()
if self.mode == 'regional':
minlon = min(self.sta_lon)
maxlon = max(self.sta_lon)
minlat = min(self.sta_lat)
maxlat = max(self.sta_lat)
padding = 0.2
m.drawparallels(np.arange(-90., 120., padding),
labels=[1, 1, 0, 0],
fmt="%.2f",
dashes=[2, 2])
m.drawmeridians(np.arange(0., 420., padding),
labels=[0, 0, 1, 1],
fmt="%.2f",
dashes=[2, 2])
m.etopo()
ax.set_xlim(minlon - padding, maxlon + padding)
ax.set_ylim(minlat - padding, maxlat + padding)
width_beach = min((maxlon + 2 * padding - minlon) / (40 * padding),
(maxlat + 2 * padding - minlat) / (40 * padding))
bb = beach(focmecs,
xy=(cmt_lon, cmt_lat),
width=width_beach,
linewidth=1,
alpha=1.0)
bb.set_zorder(10)
ax.add_collection(bb)
def plotConc(concData, lonData, latData, saveLoc, modelRun, prtype, ip1, spc, cmapType, bins, minVal, maxVal, extension, name, removed,buff):
""" Plots and saves concentration data.
Will plot difference data if difference data was passed.
"""
# useful things for determining projection details
lowLon = np.amin(lonData)
lowLat = np.amin(latData)
highLon = np.amax(lonData)
highLat = np.amax(latData)
maxDim = maxDimensions(lowLon,highLon,lowLat,highLat,buff)
midLon = (highLon+lowLon)/2
midLat = (highLat + lowLat)/2
# Uncomment the following lines for an alternative midLatLon calculation
#mids = midLatLon(lonData,latData)
#midLon = mids['midLon']
#midLat = mids['midLat']
max_width = abs(maxDim['xDist'])
max_height = abs(maxDim['yDist'])
# Initialize the figure
fig = plt.figure(figsize=(8,8))
# Create the map based on projection type
if (prtype=='ortho') or (prtype == 'nsper') or (prtype == 'laea') or (prtype == 'aeqd') or (prtype == 'gnom') or (prtype == 'lcc'):
concMap = Basemap(projection=prtype, resolution = 'c', lon_0=midLon, lat_0=midLat, width=max_width, height=max_height)
elif prtype == 'stere':
concMap = Basemap(projection=prtype, lon_0=midLon,lat_0=midLat,width=max_width,height=max_height)
elif (prtype == 'cyl') or (prtype == 'merc'):
concMap = Basemap(projection=prtype, resolution='c', llcrnrlat=lowLat, urcrnrlat=highLat, llcrnrlon=lowLon, urcrnrlon=highLon)
elif (prtype == 'aea') or (prtype == 'eqdc'):
concMap = Basemap(projection = prtype, lon_0=midLon, lat_0=midLat, llcrnrlat=lowLat, urcrnrlat=highLat, llcrnrlon=lowLon, urcrnrlon=highLon)
elif(prtype == 'global'):
concMap = Basemap(projection = 'cyl', resolution='c', llcrnrlat=lowLat, urcrnrlat=highLat, llcrnrlon=lowLon, urcrnrlon=highLon)
mapColor = cmapType
mapBins = bins
midPoint = (len(lonData))/2
xFirst, yFirst = concMap(lonData[:midPoint], latData[:midPoint])
xLast, yLast = concMap(lonData[midPoint:], latData[midPoint:])
concFirst = concData[:midPoint]
concLast = concData[midPoint:]
concMap.pcolormesh(xFirst, yFirst, concFirst, cmap=plt.cm.get_cmap(mapColor, mapBins))
concMap.pcolormesh(xLast, yLast, concLast, cmap=plt.cm.get_cmap(mapColor, mapBins))
concMap.drawcoastlines(color='lightgray')
concMap.drawcountries(color='gray')
concMap.drawstates(color='gray')
concMap.drawmeridians(np.arange(-180, 180,10), labels=[0,0,0,1], fontsize=6)
concMap.drawparallels(np.arange(-90,90,10), labels=[1,0,0,0],fontsize=6)
cbar = plt.colorbar(extend = extension, shrink=0.5)
cbar.set_label=('Concentration: '+spc)
plt.clim(minVal, maxVal)
hy = ((os.popen('r.ip1 {}'.format(ip1))).read()).lstrip()
plt.title('{}, {} \n hy: {}, Spc: {}'.format(name,modelRun, hy,spc))
fig.savefig(os.path.join(saveLoc, modelRun) + '_' + spc + '_' + name + '.png', dpi=300, bbox_inches='tight', pad_inches=0.3)
plt.close('all')
else:
print('Error: Could not generate map. Try a different projection.')
# Can check the available basemap types and add to existing if statements
# Add in other map details
mapColor = cmapType
mapBins = bins
# if stripping the borders of the data....
if removed != 0:
ni = len(lonData)
nj = len(lonData[0])
n_pil = removed
x, y = concMap(lonData[n_pil:ni-n_pil,n_pil:nj-n_pil], latData[n_pil:ni-n_pil,n_pil:nj-n_pil])
concMap.pcolormesh(x,y,concData[n_pil:ni-n_pil,n_pil:nj-n_pil],cmap=plt.cm.get_cmap(mapColor,mapBins))
else:
x, y = concMap(lonData, latData)
concMap.pcolormesh(x, y, concData, cmap=plt.cm.get_cmap(mapColor, mapBins))
concMap.drawcoastlines(color='lightgray')
concMap.drawcountries(color='gray')
concMap.drawstates(color='gray')
# Comment out the following to remove lonlat lines
concMap.drawmeridians(np.arange(-180, 180,10), labels=[0,0,0,1], fontsize=6)
concMap.drawparallels(np.arange(-90,90,10), labels=[1,0,0,0],fontsize=6)
# Add colorbar and details
#TODO: Fix the set label option for the colorbar, right now the colorbar doesn't have a title
cbar = plt.colorbar(extend = extension, shrink=0.5)
cbar.set_label=('Concentration: '+spc)
plt.clim(minVal, maxVal)
# Name and save the figure
hy = ((os.popen('r.ip1 {}'.format(ip1))).read()).lstrip()
plt.title('{}, {} \n hy: {}, Spc: {}'.format(name,modelRun, hy,spc))
fig.savefig(os.path.join(saveLoc, modelRun) + '_' + spc + '_' + name + '.png', dpi=300, bbox_inches='tight', pad_inches=0.3)
plt.close('all')
lats_sl = lats[620:720]
rain_sl_raw = precip_raw[620:720,785:830].T #30 x 35
Total_rain.append(rain_sl_raw)
rain_sl_sw_raw = precip_raw[649:691,780:806]
Total_rain_sw.append(rain_sl_sw_raw)
rain_sl=sum(Total_rain)
rain_sl_sw=sum(Total_rain_sw)
#SW SL
lons_sl_sw=lons[780:806] #77.9E to 80.4E
lats_sl_sw=lats[649:691]#5N to 9N
cmap=cm.rainbow
#cmap.set_under("w",alpha=0)
lon_0 = lons_sl.mean()
lat_0 = lats_sl.mean()
levels=[0, 1, 2, 4, 8, 16, 32, 64, 128, 256]
m = Basemap(llcrnrlon=77, llcrnrlat=5, urcrnrlon=82,urcrnrlat=10, projection='lcc',lat_0=lat_0,lon_0=lon_0, resolution='f')
#lats_sl=lats_sl[::-1]
#rain_sl=rain_sl[::-1]
#precip_T=np.flipud(precip_T)
lon, lat = np.meshgrid(lons_sl, lats_sl)
xi, yi = m(lon, lat)
fig = plt.figure(figsize=(15.,15.))
precip_T=rain_sl
# Plot Data
cs = m.contourf(xi,yi,precip_T,cmap=plt.cm.rainbow, levels=levels)
# Add Grid Lines
m.drawparallels(np.arange(-80., 81., 1.), labels=[1,0,0,0], fontsize=1)
m.drawmeridians(np.arange(-180., 181., 1.), labels=[0,0,0,1], fontsize=1)
# Add Coastlines, States, and Country Boundaries
m.drawcoastlines()
m.drawstates()
from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
# llcrnrlat,llcrnrlon,urcrnrlat,urcrnrlon
# are the lat/lon values of the lower left and upper right corners
# of the map.
# resolution = 'c' means use crude resolution coastlines.
m = Basemap(projection='cyl',llcrnrlat=-90,urcrnrlat=90,\
llcrnrlon=-180,urcrnrlon=180,resolution='c')
m.drawcoastlines()
m.fillcontinents(color='coral', lake_color='aqua')
# draw parallels and meridians.
m.drawparallels(np.arange(-90., 91., 30.))
m.drawmeridians(np.arange(-180., 181., 60.))
m.drawmapboundary(fill_color='aqua')
plt.title("Equidistant Cylindrical Projection")
plt.show()