def show_simple_position(stations_url, sat_name, times, description):
# TO DO: FIND METHOD FOR HANDLING sexagesimal <=> decimal units conversion
from skyfield.api import Topos, load
# %matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
satellites = load.tle(stations_url, reload=True)
satellite = satellites[sat_name]
t = times
fig = plt.figure(figsize=(8, 8))
m = Basemap(
projection='lcc',
resolution=None,
width=35E6,
height=35E6,
lat_0=1,
lon_0=1,
)
m.etopo(scale=0.5, alpha=0.5)
geocentric = satellite.at(t)
subpoint = geocentric.subpoint()
sign1, d1, m1, s1 = subpoint.latitude.signed_dms()
lat = sign1 * (d1 + m1 / 60 + s1 / 3600)
sign2, d2, m2, s2 = subpoint.longitude.signed_dms()
lon = sign2 * (d2 + m2 / 60 + s2 / 3600)
x, y = m(lon, lat)
plt.plot(x, y, 'ok', markersize=4)
fig.suptitle(description, fontsize=20, fontweight='bold')
return ()
def run(pot, method, m, f, g, h, x0):
print("\n Optimizing using " + str(method) + " Method and " + str(pot) +
" Potential:")
start = time.time()
x, y = m(x0, f, g, h)
end = time.time()
t = end - start
return x, y, t
def run(self):
br = m()
br.set_handle_equiv(True)
br.set_handle_gzip(True)
br.set_handle_redirect(True)
br.set_handle_referer(True)
br.set_handle_robots(False)
br.set_handle_refresh(m._http.HTTPRefreshProcessor(), max_time=1)
_ = br.open('http://reddit.tlys.us')
br.select_form(nr=1)
br.form['user'] = u
br.form['passwd'] = p
r = br.submit()
r.read()
def plot_datacoverage(self,
depth,
name='Megavolume',
filter='rff2',
conversion='EU60',
factor=2.):
fig = plt.figure(figsize=(6, 6))
d = np.argmin(np.abs(self.VOL.grid_depth - depth))
slice = self.VOL.volumeweight[:, :, d].copy()
xx, yy = np.meshgrid(self.VOL.grid_lon, self.VOL.grid_lat)
m = Basemap(projection='merc',
llcrnrlat=np.min(self.VOL.grid_lat),
urcrnrlat=np.max(self.VOL.grid_lat),
llcrnrlon=np.min(self.VOL.grid_lon),
urcrnrlon=np.max(self.VOL.grid_lon),
lat_ts=20,
resolution='i')
m.drawparallels(np.arange(self.VOL.latmin, self.VOL.latmax, 2.),
labels=[1, 0, 0, 1],
linewidth=0.5,
dashes=[4, 2],
labelstyle='+/-',
fontsize=10)
m.drawmeridians(np.arange(self.VOL.lonmin, self.VOL.lonmax, 2.),
labels=[1, 0, 0, 1],
linewidth=0.5,
dashes=[4, 2],
labelstyle='+/-',
fontsize=10)
m.drawcountries()
coasts = m.drawcoastlines(zorder=2, color='k', linewidth=1)
m.drawmapboundary(fill_color=[1.0, 1.0, 1.0])
x, y = m(xx, yy)
contours = [1., 15, 1.e2, 1.e3, 1.e4] #[1.e0,1.e1,1.e2,1.e3,1.e4]
im = plt.contourf(x, y, slice.T, contours, norm=LogNorm(), zorder=1)
fig.subplots_adjust(bottom=.2)
cbar_ax = fig.add_axes([0.2, 0.1, 0.6, 0.05])
cb = fig.colorbar(im, cax=cbar_ax, orientation='horizontal')
cb.set_label('Sum of weights at ' + str(depth) + ' km')
def vizInterp(nLons, nLats, ufunct, vfunct, files):
'''Creates a visualization of the interpolation function
nLons: list of mesh node logitudes
nLats: list of mesh node latitudes
uFunct: interpolation function of interpolated current u component magnitudes at each mesh node
vFunct: interpolation function of interpolated current v component magnitudes at each mesh node
files: list of file input names and directories for data retreival.
'''
iModule = np.sqrt(ufunct**2 + vfunct**2) * 1.94384
m = Basemap(llcrnrlon=-100.,
llcrnrlat=0.,
urcrnrlon=30.,
urcrnrlat=70,
projection='merc',
resolution='l')
x, y = m(np.array(nLons), np.array(nLats))
print x[:10]
print y[:10]
print iModule.squeeze()[:10]
#print len(iModule.squeeze())
#print len(x)
sys.exit()
m.drawcoastlines()
#m.fillcontinents(color='coral',lake_color='white')
m.drawparallels(np.arange(0., 81., 20.),
labels=[True, False, False, False])
m.drawmeridians(np.arange(-180., 181., 20.),
labels=[False, False, False, True])
#m.drawmapboundary(fill_color='aqua')
m.pcolormesh(x, y, iModule.squeeze(), cmap='jet', vmin=0, vmax=2)
#m.quiver(x[::n, ::n], y[::n, ::n], ufunct[::n, ::n], vfunct[::n, ::n], scale=10, pivot='mid')
m.colorbar(ticks=np.arange(0, 2.1, .2))
plt.savefig(files[8], bbox_inches='tight')
plt.show()
def plot_crosssection_any(self,
lon1,
lon2,
lat1,
lat2,
numpoints=200,
amplify=1.,
name='Megavolume',
filter='rff2',
conversion='EU60',
factor=2.,
zoom=False,
mincoverage=10.):
# set volume lats and lons
inv = geo.WGS84.Inverse(lat1, lon1, lat2, lon2)
points = np.linspace(0, inv['s12'], numpoints)
line = geo.WGS84.Line(lat1, lon1, inv['azi1'])
lats = []
lons = []
for i in range(len(points)):
lats.append(line.Position(points[i])['lat2'])
lons.append(line.Position(points[i])['lon2'])
lats = np.array(lats)
lons = np.array(lons)
crossec = []
vol_sig = []
w = []
dist = []
for i in range(len(lats)):
dist.append(
haversine(lats[0], lons[0], [lats[i]], [lons[i]]) / 111194.)
# pixelize lon and lat
row = (lons - np.min(self.VOL.grid_lon)) / (self.VOL.grid_lon[1] -
self.VOL.grid_lon[0])
for i in range(len(row)):
if row[i] < 0:
row[i] = row[i] + len(self.lon)
col = (lats - np.min(self.VOL.grid_lat)) / (self.VOL.grid_lat[1] -
self.VOL.grid_lat[0])
for dp in range(len(self.VOL.grid_depth)):
crossec.append(
scipy.ndimage.map_coordinates(self.VOL.volume[:, :, dp],
np.vstack((row, col))))
vol_sig.append(
scipy.ndimage.map_coordinates(self.VOL.volumesigma[:, :, dp],
np.vstack((row, col))))
w.append(
scipy.ndimage.map_coordinates(self.VOL.volumeweight[:, :, dp],
np.vstack((row, col))))
crossec = np.array(crossec)
vol_sig = np.array(vol_sig)
w = np.array(w)
xaxis = self.VOL.grid_lat
xlabel = 'latitude (dg)'
xends = [lon1, lon2]
yends = [lat1, lat2]
depths = self.VOL.grid_depth
# normalize
for i in range(np.shape(w)[0]):
for j in range(np.shape(w)[1]):
if w[i, j] > mincoverage:
crossec[i, j] = crossec[i, j] / w[i, j]
if crossec[i, j] > 0:
vol_sig[i, j] = crossec[i, j] - 1.96 * np.sqrt(
vol_sig[i, j] / (w[i, j] * w[i, j]))
if vol_sig[i, j] < 0:
vol_sig[i, j] = 0.
if crossec[i, j] < 0:
vol_sig[i, j] = crossec[i, j] + 1.96 * np.sqrt(
vol_sig[i, j] / (w[i, j] * w[i, j]))
if vol_sig[i, j] > 0:
vol_sig[i, j] = 0.
else:
crossec[i, j] = 100.
plt.subplot(2, 2, 2)
m = Basemap(projection='merc',
llcrnrlat=self.VOL.latmin,
urcrnrlat=self.VOL.latmax,
llcrnrlon=self.VOL.lonmin,
urcrnrlon=self.VOL.lonmax,
lat_ts=20,
resolution='i')
m.drawparallels(np.arange(0, 90, 10.),
labels=[1, 0, 0, 1],
labelstyle='+/-',
fontsize=10)
m.drawmeridians(np.arange(-180, 60, 10.),
labels=[1, 0, 0, 1],
labelstyle='+/-',
fontsize=10)
m.drawcoastlines()
m.drawcountries()
m.drawmapboundary(fill_color=[1.0, 1.0, 1.0])
x1, y1 = m(xends[0], yends[0])
x2, y2 = m(xends[1], yends[1])
m.plot([x1, x2], [y1, y2], color='r', linewidth=1, zorder=1)
plt.subplot(2, 2, 1)
xx, yy = np.meshgrid(dist, depths)
cs = plt.pcolor(xx,
yy,
crossec,
vmin=-0.15,
vmax=0.15,
rasterized=True,
cmap=cm.coolwarm)
plt.colorbar()
cs.cmap.set_over([0.8, 0.8, 0.8])
if zoom:
plt.ylim([200, 900])
else:
plt.ylim([min(depths), max(depths)])
plt.gca().invert_yaxis()
# corrected by 3D model
# normalize
norm = 0.2 / amplify #np.max(np.max(np.abs(crossec_3D)))/amplify
ax = plt.subplot(2, 1, 2)
pos1 = ax.get_position() # get the original position
pos2 = [pos1.x0, pos1.y0, pos1.width / 14 * 8, pos1.height]
pos2 = [pos1.x0, pos1.y0, pos1.width, pos1.height]
print(pos1, pos2)
ax.set_position(pos2) # set a new position
print(ax.get_position()) #
print(ax.get_position())
# plot
for t in np.arange(0, len(dist), 1):
lx = [
x for x in range(len(depths))
if (np.abs(w[x, t]) > mincoverage)
] # and np.abs(vol_sig[x,t])>std[x,t]/1.96)]
RF = vol_sig[lx, t] / norm + dist[t]
RFfull = crossec[lx, t] / norm + dist[t]
plt.fill_betweenx(depths[lx],
RFfull,
dist[t],
where=RFfull >= dist[t],
facecolor='k',
rasterized=True)
plt.fill_betweenx(depths[lx],
RFfull,
dist[t],
where=dist[t] >= RFfull,
facecolor='k',
rasterized=True)
plt.fill_betweenx(depths[lx],
RF,
dist[t],
where=RF >= dist[t],
facecolor=[1.0, 0., 0.],
rasterized=True)
plt.fill_betweenx(depths[lx],
RF,
dist[t],
where=dist[t] >= RF,
facecolor=[0.0, 0.0, 1.],
rasterized=True)
RF2 = crossec[lx, t] / norm
l410 = [
x for x in range(len(depths[lx]))
if depths[lx[x]] > 366 and depths[lx[x]] < 454
]
l660 = [
x for x in range(len(depths[lx]))
if depths[lx[x]] > 616 and depths[lx[x]] < 704
]
if len(l410) > 20:
max410 = np.argmax(RF2[l410])
ind = lx[l410[max410]]
plt.plot([dist[t] + 0.1, 0.5 * RF2[l410[max410]] + dist[t]],
[depths[ind], depths[ind]],
'y',
linewidth=2)
if len(l660) > 20:
max660 = np.argmax(RF2[l660])
ind = lx[l660[max660]]
plt.plot([dist[t] + 0.1, 0.5 * RF2[l660[max660]] + dist[t]],
[depths[ind], depths[ind]],
'y',
linewidth=2)
plt.ylabel('Depth (km)', fontsize=28)
plt.xlabel('Angular distance (dg)', fontsize=28)
plt.xlim([min(dist), max(dist)])
plt.plot([-5, 40], [410, 410], '--k', linewidth=2)
plt.plot([-5, 40], [660, 660], '--k', linewidth=2)
if zoom:
plt.ylim([200, 800])
else:
plt.ylim([min(depths), max(depths)])
plt.gca().invert_yaxis()
print(ax.get_position())
def plot_datacoverage(self,
depth,
name='Megavolume',
filter='rff2',
conversion='EU60',
factor=2.):
fig = plt.figure(figsize=(6, 6))
d = np.argmin(np.abs(self.VOL.grid_depth - depth))
slice = self.VOL.volumeweight[:, :, d].copy()
xx, yy = np.meshgrid(self.VOL.grid_lon, self.VOL.grid_lat)
m = Basemap(projection='merc',
llcrnrlat=np.min(self.VOL.grid_lat),
urcrnrlat=np.max(self.VOL.grid_lat),
llcrnrlon=np.min(self.VOL.grid_lon),
urcrnrlon=np.max(self.VOL.grid_lon),
lat_ts=20,
resolution='i')
m.drawparallels(np.arange(0, 80, 10.),
labels=[1, 0, 0, 1],
linewidth=0.5,
dashes=[4, 2],
labelstyle='+/-',
fontsize=22)
m.drawmeridians(np.arange(-160, -130, 20.),
labels=[1, 0, 0, 1],
linewidth=0.5,
dashes=[4, 2],
labelstyle='+/-',
fontsize=22)
m.drawcountries()
coasts = m.drawcoastlines(zorder=2, color='k', linewidth=1)
m.drawmapboundary(fill_color=[1.0, 1.0, 1.0])
x, y = m(xx, yy)
contours = [1., 1.e1, 1.e2, 1.e3, 1.e4] #[1.e0,1.e1,1.e2,1.e3,1.e4]
im = plt.contourf(x, y, slice.T, contours, norm=LogNorm(), zorder=1)
lonmin = -157
lonmax = -143
latmin = 58
latmax = 67
#plot_line (m,lonmin,lonmax,latmin,latmax,col="Black")
lonmin = -159
lonmax = -131
latmin = 59.5
latmax = 66.5
plot_line(m, lonmin, lonmax, latmin, latmax, col="Black") #BB
lonmin = -160
lonmax = -142
latmin = 58
latmax = 63
plot_line(m, lonmin, lonmax, latmin, latmax, col="Black") #CC
lonmin = -157
lonmax = -140
latmin = 57
latmax = 69
plot_line(m, lonmin, lonmax, latmin, latmax, col="Black") #AA
lonmin = -164
lonmax = -154
latmin = 56.5
latmax = 60.5
plot_line(m, lonmin, lonmax, latmin, latmax, col="Black") #DD
lonmin = -155.5
lonmax = -152
latmin = 66
latmax = 68
#plot_rectangle(m, lonmin,lonmax,latmin,latmax, col="Black")
lonmin = -154
lonmax = -148.5
latmin = 58
latmax = 60.5
#plot_rectangle(m, lonmin,lonmax,latmin,latmax, col="Black")
fig.subplots_adjust(bottom=.2)
cbar_ax = fig.add_axes([0.2, 0.1, 0.6, 0.05])
cb = fig.colorbar(im, cax=cbar_ax, orientation='horizontal')
cb.set_label('Sum of weights at ' + str(depth) + ' km', fontsize=36)
def markdown(value):
return m(value)
def plot_crosssection(self,
direction,
lonorlat,
amplify=1.,
name='Megavolume',
filter='rff2',
conversion='EU60',
factor=2.,
zoom=False,
mincoverage=10):
# set volume lats and lons
if direction == 'NS':
lon = lonorlat
n = np.argmin(np.abs(self.VOL.grid_lon - lon))
crossec = self.VOL.volume[n, :, :].T.copy()
vol_sig = self.VOL.volumesigma[n, :, :].T.copy()
w = self.VOL.volumeweight[n, :, :].T
xaxis = self.VOL.grid_lat
xlabel = 'latitude (dg)'
yends = [lon, lon]
xends = [self.VOL.latmin, self.VOL.latmax]
lons = lon * np.ones_like(xaxis)
lats = xaxis
if direction == 'EW':
lat = lonorlat
n = np.argmin(np.abs(self.VOL.grid_lat - lat))
crossec = self.VOL.volume[:, n, :].T.copy()
vol_sig = self.VOL.volumesigma[:, n, :].T.copy()
w = self.VOL.volumeweight[:, n, :].T
xaxis = self.VOL.grid_lon
xlabel = 'longitude (dg)'
xends = [self.VOL.lonmin, self.VOL.lonmax]
yends = [lat, lat]
lons = xaxis
lats = lat * np.ones_like(xaxis)
depths = self.VOL.grid_depth
# normalize
for i in range(np.shape(w)[0]):
for j in range(np.shape(w)[1]):
if w[i, j] > mincoverage:
crossec[i, j] = crossec[i, j] / w[i, j]
if crossec[i, j] > 0:
vol_sig[i, j] = crossec[i, j] - 1.96 * np.sqrt(
vol_sig[i, j] / (w[i, j] * w[i, j]))
if vol_sig[i, j] < 0:
vol_sig[i, j] = 0.
if crossec[i, j] < 0:
vol_sig[i, j] = crossec[i, j] + 1.96 * np.sqrt(
vol_sig[i, j] / (w[i, j] * w[i, j]))
if vol_sig[i, j] > 0:
vol_sig[i, j] = 0.
else:
crossec[i, j] = 1000.
plt.figure(figsize=(14, 8))
plt.subplot(2, 2, 2)
m = Basemap(projection='merc',
llcrnrlat=self.VOL.latmin,
urcrnrlat=self.VOL.latmax,
llcrnrlon=self.VOL.lonmin,
urcrnrlon=self.VOL.lonmax,
lat_ts=20,
resolution='i')
m.drawparallels(np.arange(0, 70, 10.),
labels=[1, 0, 0, 1],
labelstyle='+/-',
fontsize=10)
m.drawmeridians(np.arange(-10, 60, 10.),
labels=[1, 0, 0, 1],
labelstyle='+/-',
fontsize=10)
m.drawcoastlines()
m.drawcountries()
m.drawmapboundary(fill_color=[1.0, 1.0, 1.0])
if direction == 'NS':
x1, y1 = m(yends[0], xends[0])
x2, y2 = m(yends[1], xends[1])
m.plot([x1, x2], [y1, y2], color='r', linewidth=1, zorder=1)
x3, y3 = m(lon * np.ones(len(xaxis), ),
np.round(xaxis / 10.) * 10.)
m.scatter(x3, y3, 80, xaxis, zorder=2)
if direction == 'EW':
x1, y1 = m(xends[0], yends[0])
x2, y2 = m(xends[1], yends[1])
m.plot([x1, x2], [y1, y2], color='r', linewidth=1, zorder=1)
x3, y3 = m(np.round(xaxis / 10.) * 10, lat * np.ones(len(xaxis), ))
m.scatter(x3, y3, 80, xaxis, zorder=2)
norm = 0.2 / amplify
# plot
plt.subplot(2, 2, 1)
xx, yy = np.meshgrid(xaxis, depths)
print(xx)
cs = plt.pcolor(xx,
yy,
crossec,
vmin=-0.3,
vmax=0.3,
rasterized=True,
cmap=cm.coolwarm)
#cs = plt.pcolor(xx, yy, crossec, vmin=-0.15, vmax=0.15, rasterized=True,cmap=cm.coolwarm)
plt.colorbar()
cs.cmap.set_over([0.8, 0.8, 0.8])
if zoom:
plt.ylim([300, 800])
else:
plt.ylim([min(depths), max(depths)])
plt.gca().invert_yaxis()
plt.xlim(xends)
plt.subplot(2, 1, 2)
for t in np.arange(0, len(xaxis), 1):
lx = [
x for x in range(0, len(depths))
if (np.abs(w[x, t]) > mincoverage)
] # and np.abs(vol_sig[x,t])>std[x,t]/1.96)]
RF = vol_sig[lx, t] / norm + xaxis[t]
RFfull = crossec[lx, t] / norm + xaxis[t]
plt.fill_betweenx(depths[lx],
RFfull,
xaxis[t],
where=RFfull >= xaxis[t],
facecolor='k',
rasterized=True)
plt.fill_betweenx(depths[lx],
RFfull,
xaxis[t],
where=xaxis[t] >= RFfull,
facecolor='k',
rasterized=True)
plt.fill_betweenx(depths[lx],
RF,
xaxis[t],
where=RF >= xaxis[t],
facecolor=[1.0, 0., 0.],
rasterized=True)
plt.fill_betweenx(depths[lx],
RF,
xaxis[t],
where=xaxis[t] >= RF,
facecolor=[0.0, 0.0, 1.],
rasterized=True)
plt.scatter(np.round(xaxis / 10.) * 10.,
80. * np.ones(len(xaxis), ),
80,
xaxis,
rasterized=True)
plt.plot([-180, 140], [410, 410], '--k', linewidth=2)
#plt.plot([-180, 140], [520, 520], '--k', linewidth=2)
plt.plot([-180, 140], [660, 660], '--k', linewidth=2)
plt.ylabel('Depth (km)')
plt.xlabel(xlabel, fontsize=12)
plt.xlim([min(xaxis), max(xaxis)])
if zoom:
plt.ylim([300, 800])
else:
plt.ylim([min(depths), max(depths)])
plt.gca().invert_yaxis()
print(min(depths))
import re
from re import compile as rec
from tornado.escape import _unicode, xhtml_escape, url_escape
from misaka import HtmlRenderer, Markdown
import misaka as m
from bnw.web.linkshit import LinkParser, shittypes
from bnw.web.linkshit import _URL_RE, _USER_RE, _MSG_RE
#: Displaying thumbs allowed for the following hostings:
linkhostings = [
(ur'(?i)http://rghost.(net|ru)/([0-9]+)', lambda m,s: 'http%s://rghost.%s/%s/thumb.png' % (s,m(1),m(2))),
(ur'(?i)https?://imgur.com/([A-Za-z0-9]+)', lambda m,s: 'http%s://i.imgur.com/%ss.png' % (s,m(1),)),
(ur'http://(2ch.hk|2ch.pm|2ch.re|2ch.tf|2ch.wf|2ch.yt|2-ch.so)/([a-z]+)/src/([0-9]+).(png|gif|jpg)', lambda m,s: 'http%s://%s/%s/thumb/%ss.gif' % (s,m(1),m(2),m(3))),
(ur'https?://(?:www\.)?youtube.com/watch\?(?:.+&)?v\=([A-Z0-9a-z_-]+)(?:&.+)?', lambda m,s: 'http%s://img.youtube.com/vi/%s/default.jpg' % (s,m(1))),
(ur'(?i)https?://upload.wikimedia.org/wikipedia/commons/([0-9]{1}\/[A-Za-z0-9]+)/([A-Za-z0-9_.]+)', lambda m,s: 'http%s://upload.wikimedia.org/wikipedia/commons/thumb/%s/%s/256px-%s' % (s,m(1),m(2),m(2))),
(ur'(?i)https?://(.+.(?:png|gif|jpg|jpeg))', lambda m,s: 'http%s://bnw-thumb.r.worldssl.net/thumb?img=%s' % (s,url_escape(m(0)),)),
]
linkhostings = [(re.compile('^' + k + '$'), v, k) for (k, v) in linkhostings]
def thumbify(text, format='markdown', secure=False):
"""Format text and generate thumbs using available formatters.
:param format: default: 'markdown'. Specify text formatter.
Variants: 'moinmoin', 'markdown' or None,
which fallbacks to markdown.
Return: (formatted_text, thumbs)
l.append(coords[i])
cluster_points[x[i]] = l
plt.figure(figsize=(12, 12))
#plotting points on map
m = Basemap(projection='merc',
resolution='l',
epsg=4269,
llcrnrlon=-122.567288,
llcrnrlat=37.696627,
urcrnrlon=-122.329308,
urcrnrlat=37.852144)
# plot the aftershock
x, y = m(coords[:, 1], coords[:, 0])
m.scatter(x, y, 5, marker='o', color='b')
m.arcgisimage(service='World_Shaded_Relief', xpixels=5000, verbose=False)
plt.show()
def get_cmap(N):
'''
Returns a function that maps each index in 0, 1, ... N-1 to a distinct
RGB color.
'''
color_norm = colors.Normalize(vmin=0, vmax=N - 1)
scalar_map = cmx.ScalarMappable(norm=color_norm, cmap='nipy_spectral')
def map_index_to_rgb_color(index):
return scalar_map.to_rgba(index)
def plot_topography(self,
mindepth,
maxdepth,
name='Megavolume',
filter='rff2',
conversion='prem',
factor=2.,
mincoverage=15.,
amplitude=False,
color_scheme='spectral',
reverse=False):
# Plots topography of maximum between mindepth and maxdepth, masking if sum of weights is beneath mincoverage.
# If amplitude =True, it will plot the amplitude and not the depth
plt.figure(figsize=(10, 8))
depths = self.VOL.grid_depth
val_list = [
x for x in range(len(depths))
if depths[x] > mindepth and depths[x] < maxdepth
]
thickness = np.empty((len(self.VOL.grid_lon), len(self.VOL.grid_lat)))
dmap = np.empty((len(self.VOL.grid_lon), len(self.VOL.grid_lat)))
coverage = np.empty((len(self.VOL.grid_lon), len(self.VOL.grid_lat)))
for i in range(len(self.VOL.grid_lon)):
for j in range(len(self.VOL.grid_lat)):
RF = self.VOL.volume[i, j, :] / self.VOL.volumeweight[i, j, :]
std = 1.96 * np.sqrt(self.VOL.volumesigma[i, j, :] /
(self.VOL.volumeweight[i, j, :] *
self.VOL.volumeweight[i, j, :]))
maxmap = np.argmax(RF[val_list])
if amplitude == False:
dmap[i, j] = depths[val_list[maxmap]]
else:
dmap[i, j] = RF[val_list[maxmap]]
if self.VOL.volumeweight[i, j, val_list[maxmap]] < mincoverage:
dmap[i, j] = 1000.
# Prepare map
m = Basemap(projection='merc',
llcrnrlat=np.min(self.VOL.grid_lat) - 0,
urcrnrlat=np.max(self.VOL.grid_lat) + 0,
llcrnrlon=np.min(self.VOL.grid_lon) - 0,
urcrnrlon=np.max(self.VOL.grid_lon) + 0,
lat_ts=10,
resolution='i')
m.drawparallels(np.arange(0, 70, 10.),
labels=[1, 0, 0, 1],
linewidth=0.5,
dashes=[4, 2],
labelstyle='+/-',
fontsize=8) #[1,0,0,1])
m.drawmeridians(np.arange(-20, 60, 10.),
labels=[1, 0, 0, 1],
linewidth=0.5,
dashes=[4, 2],
labelstyle='+/-',
fontsize=8) #[1,0,0,1])
m.drawcountries()
m.drawstates()
m.drawcoastlines()
coasts = m.drawcoastlines(zorder=1, color='k', linewidth=0.1)
xx, yy = np.meshgrid(self.VOL.grid_lon, self.VOL.grid_lat)
x, y = m(xx, yy)
#-----------------------------------------------------------------------
if color_scheme == 'rainbow' and reverse == True: cmap = cm.rainbow_r
if color_scheme == 'rainbow' and reverse == False: cmap = cm.rainbow
if color_scheme == 'spectral' and reverse == True: cmap = cm.Spectral_r
if color_scheme == 'spectral' and reverse == False: cmap = cm.Spectral
#------------------------------------------------------------------------
if amplitude is False:
cs = plt.pcolor(x,
y,
dmap.T,
vmin=mindepth,
vmax=maxdepth,
cmap=cmap,
linewidth=0,
rasterized=False)
else:
cs = plt.pcolor(x,
y,
dmap.T,
vmin=0.01,
vmax=0.12,
cmap=cm.cmap,
linewidth=0,
rasterized=False)
cs.cmap.set_under([0.8, 0.8, 0.8])
cs.cmap.set_over([0.8, 0.8, 0.8])
cb = plt.colorbar()
cb.set_label('Maximum map between ' + str(mindepth) + ' and ' +
str(maxdepth) + ' (km)')
# cb.set_ticks([380,400,420,440])
cb.solids.set_rasterized(True)
xt, yt = m(-13.2, 70.6)
m.drawcoastlines(zorder=1, color='k', linewidth=1)
dmapall = np.ravel(dmap)
if amplitude == False:
l = [
l for l in range(len(dmapall))
if dmapall[l] > mindepth + 1. and dmapall[l] < maxdepth - 1.
]
print('median', np.median((dmapall[l])))
print('variance', np.var((dmapall[l])))
def plot_mtzwidth_write(self,
name='Megavolume',
filter='rff2',
conversion='EU60',
factor=2.):
#This routine is also used to make a txt file with the significant depths of the 410 and the 660
#depth410660 = open('/raid3/annemijn/scripts/CCP/mincov40MTZ_'+conversion+'_'+filter+'_'+str(int(factor))+'.txt', 'w')
depth410660 = open(
'/raid1/annemijn/scripts/CCP/MTZsign_mincov40_' + conversion +
'_' + filter + '_' + str(int(factor)) + '.txt', 'w')
plt.figure(figsize=(18, 8))
depths = self.VOL.grid_depth
l410 = [
x for x in range(len(depths))
if depths[x] > 380 and depths[x] < 430
]
l660 = [
x for x in range(len(depths))
if depths[x] > 630 and depths[x] < 700
]
thickness1D = np.empty(
(len(self.VOL.grid_lon), len(self.VOL.grid_lat)))
d4101D = np.empty((len(self.VOL.grid_lon), len(self.VOL.grid_lat)))
d6601D = np.empty((len(self.VOL.grid_lon), len(self.VOL.grid_lat)))
for i in range(len(self.VOL.grid_lon)):
for j in range(len(self.VOL.grid_lat)):
RF = self.VOL.volume[i, j, :] / self.VOL.volumeweight[i, j, :]
std = 1.96 * np.sqrt(self.VOL.volumesigma[i, j, :] /
(self.VOL.volumeweight[i, j, :] *
self.VOL.volumeweight[i, j, :]))
max410 = np.argmax(RF[l410])
max660 = np.argmax(RF[l660])
maxamp410 = np.max(RF[l410])
maxamp660 = np.max(RF[l660])
# If both picks are significant, store thickness
if RF[l410[max410]] > std[l410[max410]] and RF[
l660[max660]] > std[l660[max660]]:
d4101D[i, j] = depths[l410[max410]]
d6601D[i, j] = depths[l660[max660]]
thickness1D[i, j] = (depths[l660[max660]] -
depths[l410[max410]])
if self.VOL.volumeweight[i, j, l410[max410]] >= 40:
#This routine is also used to make a txt file with the depth of the 410 and the 660
depth410660.write(str(self.VOL.grid_lat[j]) + '\t')
depth410660.write(str(self.VOL.grid_lon[i]) + '\t')
depth410660.write(str(depths[l410[max410]]) + '\t')
depth410660.write(str(depths[l660[max660]]) + '\t')
depth410660.write(str(maxamp410) + '\t')
depth410660.write(str(maxamp660) + '\n')
# Prepare map
m = Basemap(projection='merc',
llcrnrlat=np.min(self.VOL.grid_lat),
urcrnrlat=np.max(self.VOL.grid_lat),
llcrnrlon=np.min(self.VOL.grid_lon),
urcrnrlon=np.max(self.VOL.grid_lon),
lat_ts=20,
resolution='i')
m.drawparallels(np.arange(np.min(self.VOL.grid_lat),
np.max(self.VOL.grid_lat), 10.),
labels=[0, 0, 0, 0]) #[1,0,0,1])
m.drawmeridians(np.arange(np.min(self.VOL.grid_lon),
np.max(self.VOL.grid_lon), 10.),
labels=[0, 0, 0, 0]) #[1,0,0,1])
m.drawcoastlines(color='k')
m.drawcountries(color='k')
m.drawmapboundary(fill_color=[1.0, 1.0, 1.0])
xx, yy = np.meshgrid(self.VOL.grid_lon, self.VOL.grid_lat)
x, y = m(xx, yy)
cs = plt.contourf(x,
y,
thickness1D.T,
levels=np.linspace(220., 300., 81.),
cmap=cm.gist_earth_r)
cs.cmap.set_under('w')
cs.cmap.set_over('w')
plt.colorbar()
plt.title('MTZ width')
#This routine is also used to make a txt file with the depth of the 410 and the 660
depth410660.close()
# More map configuration # draw coastlines, country boundaries m.drawcoastlines(linewidth=0.25) m.drawcountries(linewidth=0.25) # draw parallels. parallels = np.arange(-40., 40, 10.) m.drawparallels(parallels, labels=[1, 0, 0, 0], fontsize=10) # draw meridians meridians = np.arange(80., 180., 10.) m.drawmeridians(meridians, labels=[0, 0, 0, 1], fontsize=10) # Make grid for TRMM data ny = trmm_precip.shape[0] nx = trmm_precip.shape[1] lons, lats = m.makegrid(nx, ny) # get lat/lons of ny by nx evenly spaced grid. x, y = m(lons, lats) # compute map proj coordinates. # Make grid for SACA data ny_saca = saca_precip.shape[0] nx_saca = saca_precip.shape[1] lons_saca, lats_saca = m.makegrid(nx_saca, ny_saca) x_saca, y_saca = m(lons_saca, lats_saca) # Make grid for TRMM Land Sea mask (updated) lons_mask, lats_mask = np.meshgrid(lons_ls_mask, lats_ls_mask) x_mask, y_mask = m(lons_mask, lats_mask) print 'lons_mask', lons_mask print 'lats_mask', lats_mask print
# draw coastlines, country boundaries
m.drawcoastlines(linewidth=0.25)
m.drawcountries(linewidth=0.25)
# draw parallels.
parallels = np.arange(-40., 40, 10.)
m.drawparallels(parallels, labels=[1, 0, 0, 0], fontsize=10)
# draw meridians
meridians = np.arange(80., 180., 10.)
m.drawmeridians(meridians, labels=[0, 0, 0, 1], fontsize=10)
# Make grid for TRMM data
ny = trmm_precip.shape[0]
nx = trmm_precip.shape[1]
lons, lats = m.makegrid(nx, ny) # get lat/lons of ny by nx evenly spaced grid.
x, y = m(lons, lats) # compute map proj coordinates.
# Make grid for SACA data
ny_saca = saca_precip.shape[0]
nx_saca = saca_precip.shape[1]
lons_saca, lats_saca = m.makegrid(
nx_saca, ny_saca) # get lat/lons of ny by nx evenly space grid.
x_saca, y_saca = m(lons_saca, lats_saca) # compute map proj coordinates.
# Actual plotting and rendering
# ================================================================
# Plot TRMM
# ===============
# cs = m.contourf(x,y,trmm_precip*trmm_lsmask,clevs,cmap=cm.s3pcpn)
# # Add colorbar
norm = matplotlib.colors.BoundaryNorm(levels, 16)
# draw coastlines, country boundaries, fill continents.
m.drawcoastlines(linewidth=0.25)
m.drawcountries(linewidth=0.25)
# draw parallels.
parallels = np.arange(-40., 40, 10.)
m.drawparallels(parallels, labels=[1, 0, 0, 0], fontsize=10)
# draw meridians
meridians = np.arange(80., 180., 10.)
m.drawmeridians(meridians, labels=[0, 0, 0, 1], fontsize=10)
ny = trmm_precip.shape[0]
nx = trmm_precip.shape[1]
lons, lats = m.makegrid(nx, ny) # get lat/lons of ny by nx evenly space grid.
x, y = m(lons, lats) # compute map proj coordinates.
#print x
# Land sea mask
#topo = maskoceans(lons, lats, topoin)
#cs = m.pcolor(lons,lats,trmm_precip)
#cs = plt.contour(trmm_precip, cmap=cm.jet)
# From my old script
#m.drawlsmask(land_color='white',ocean_color='white')
#trmm_precip.mask
cs = m.contourf(x, y, trmm_precip * trmm_lsmask, clevs,
cmap=cm.s3pcpn) # works
#cs = m.pcolormesh(x,y,trmm_precip) # works but standard matlab look
import re
from re import compile as rec
from tornado.escape import _unicode, xhtml_escape, url_escape
from misaka import HtmlRenderer, Markdown
import misaka as m
from bnw_web.linkshit import LinkParser, shittypes
from bnw_web.linkshit import _URL_RE, _USER_RE, _MSG_RE
#: Displaying thumbs allowed for the following hostings:
linkhostings = [
(ur'(?i)http://rghost.ru/([0-9]+)', lambda m: 'http://rghost.ru/%s/thumb.png' % (m(1),)),
(ur'(?i)http://imgur.com/([A-Za-z0-9]+)', lambda m: 'http://i.imgur.com/%ss.png' % (m(1),)),
(ur'http://ompldr.org/v([A-Za-z0-9]+)(/.+)?', lambda m: 'http://ompldr.org/t%s' % (m(1),)),
(ur'http://2-ch.ru/([a-z]+)/src/([0-9]+).(png|gif|jpg)', lambda m: 'http://2-ch.ru/%s/thumb/%ss.%s' % (m(1),m(2),m(3))),
(ur'(?i)http://(.+.(?:png|gif|jpg|jpeg))', lambda m: 'http://f**k.blasux.ru/thumb?img=%s' % (url_escape(m(0)),)),
]
linkhostings = [(re.compile('^' + k + '$'), v, k) for (k, v) in linkhostings]
def thumbify(text, format='markdown'):
"""Format text and generate thumbs using available formatters.
:param format: default: 'markdown'. Specify text formatter.
Variants: 'moinmoin', 'markdown' or None,
which fallbacks to markdown.
Return: (formatted_text, thumbs)
Thumbs: list of (original_file_url, thumb_url)
def plot_slip(self, slip, filename=None,slipmax=None):
sbfs=self.sbfs
sbf_ind=self.sbf_ind
num_sbr=self.num_sbr
plt.clf()
fig = plt.figure(figsize=(8,8))
ax = fig.add_axes([0.1, 0.15, .8, 0.6])
m=self.drawmap()
cmap=plt.cm.seismic#jet
#if slipmax==None:
slipmax=slip.max()
slipmin=slip.min()
slipmag=slipmax-slipmin
if slipmag==0:
slipmag=1.0
plot_ind=True
for k in range(num_sbr):
sbf=sbfs[k][1]
sfply = []
for xyz in sbf:
sfply.append(m(xyz[0],xyz[1]))
cmval=(slip[k]+slipmax)/float(2*slipmax)
#print k, slip[k], cmval
color = cmap(cmval)
poly = Polygon(sfply,facecolor=rgb2hex(color),edgecolor='blue')
ax.add_patch(poly)
#plt.text(ln,lt,'%s'%int(slip[k]))
if plot_ind:
if k%13==0:
cntx,cnty=sbfs[k][0]
(ln,lt)=m(cntx,cnty)
plt.text(ln,lt,'%d'%(sbf_ind[k]))
# draw trench line
x = []
y = []
for line in open('trench.xy','r'):
if line.startswith('>'):
if len(x) > 0:
(x0,y0) = m(x,y)
plt.plot(x0,y0,'-r',linewidth=2)
x = []
y = []
continue
x.append(float(line.split()[0]))
y.append(float(line.split()[1]))
hlon, hlat=self.hypo
yc,xc=m(hlon, hlat)
plt.plot(yc, xc,'r*',markersize=18)
norm1 = MidpointNormalize(vmin=-slipmax, vmax=slipmax, midpoint=0)
#norm1 = mpl.colors.Normalize(vmin=slipmin, vmax=slipmax)
ax2 = fig.add_axes([0.72, .15, 0.045, 0.6])
cb1 = mpl.colorbar.ColorbarBase(ax2, cmap=cmap,
norm=norm1,
orientation='vertical')
cb1.set_label('amplitude(m)')
extent = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
plt.savefig(filename,bbox_inches='tight',pad_inches = 0.02)
def plot_topography(self,
mindepth,
maxdepth,
name='Megavolume',
filter='rff2',
conversion='prem',
factor=2.,
mincoverage=10.,
amplitude=True,
blobs=True):
# Plots topography of maximum between mindepth and maxdepth, masking if sum of weights is beneath mincoverage.
# If amplitude =True, it will plot the amplitude and not the depth
plt.figure(figsize=(10, 8))
depths = self.VOL.grid_depth
#print('depths are ', depths)
val_list = [
x for x in range(len(depths))
if depths[x] > mindepth and depths[x] < maxdepth
]
thickness = np.empty((len(self.VOL.grid_lon), len(self.VOL.grid_lat)))
dmap = np.empty((len(self.VOL.grid_lon), len(self.VOL.grid_lat)))
coverage = np.empty((len(self.VOL.grid_lon), len(self.VOL.grid_lat)))
dsign = np.empty((len(self.VOL.grid_lon), len(self.VOL.grid_lat)))
amparray = []
for i in range(len(self.VOL.grid_lon)):
for j in range(len(self.VOL.grid_lat)):
RF = self.VOL.volume[i, j, :] / self.VOL.volumeweight[i, j, :]
plt.plot(RF)
std = 1.96 * np.sqrt(self.VOL.volumesigma[i, j, :] /
(self.VOL.volumeweight[i, j, :] *
self.VOL.volumeweight[i, j, :]))
maxmap = np.argmax(RF[val_list])
if amplitude == False:
dmap[i, j] = depths[val_list[maxmap]]
else:
dmap[i, j] = RF[val_list[maxmap]]
#array for significance, 0=significant, 1=not significant
if abs(RF[val_list[maxmap]]) > std[val_list[maxmap]]:
dsign[i, j] = 0.0
else:
dsign[i, j] = 1.
if self.VOL.volumeweight[i, j, val_list[maxmap]] < mincoverage:
dmap[i, j] = 1000.
dsign[i, j] = 0.
#plt.show()
# Prepare map
m = Basemap(projection='merc',
llcrnrlat=np.min(self.VOL.grid_lat),
urcrnrlat=np.max(self.VOL.grid_lat),
llcrnrlon=np.min(self.VOL.grid_lon),
urcrnrlon=np.max(self.VOL.grid_lon),
lat_ts=20,
resolution='i')
m.drawparallels(np.arange(0, 90, 5.),
labels=[1, 0, 0, 1],
linewidth=0.5,
dashes=[4, 2],
labelstyle='+/-',
fontsize=20)
m.drawmeridians(np.arange(-180, -110, 10.),
labels=[1, 0, 0, 1],
linewidth=0.5,
dashes=[4, 2],
labelstyle='+/-',
fontsize=20)
m.drawcountries()
coasts = m.drawcoastlines(zorder=1, color='k', linewidth=1)
xx, yy = np.meshgrid(self.VOL.grid_lon, self.VOL.grid_lat)
x, y = m(xx, yy)
if amplitude is False:
cs = plt.pcolor(x,
y,
dmap.T,
vmin=mindepth,
vmax=maxdepth,
cmap=cm.BrBG,
linewidth=0,
rasterized=True)
#mask area if not significant
dsign = ma.masked_array(dsign, dsign == 0)
sign = plt.contourf(x,
y,
dsign.T,
vmin=0.5,
vmax=1.0,
cmap=cm.Greys,
linewidth=0,
alpha=0.7,
rasterized=False)
else:
cs = plt.pcolor(x,
y,
dmap.T,
vmin=0.02,
vmax=0.12,
cmap=cm.pink_r,
linewidth=0,
rasterized=True)
cs.cmap.set_under([0.8, 0.8, 0.8])
cs.cmap.set_over([0.8, 0.8, 0.8])
#mindepth=np.argmin(dmap.T)
#maxdepth=np.argmax(dmap.T)
cb = plt.colorbar(cs,
ticks=[
mindepth, mindepth + (maxdepth - mindepth) / 6,
mindepth + 2 * (maxdepth - mindepth) / 6,
mindepth + 3 * (maxdepth - mindepth) / 6,
mindepth + 4 * (maxdepth - mindepth) / 6,
mindepth + 5 * (maxdepth - mindepth) / 6,
maxdepth
])
#cb.set_label('Maximum map between ' + str(mindepth)+' and ' + str(maxdepth)+' (km)', size=30)
if mindepth == 380:
cb.set_label('Depth of 410 (km)', size=30)
else:
cb.set_label('Depth of 660 (km)', size=30)
cb.ax.tick_params(labelsize=30)
#cb.set_label('Amplitude')
# cb.set_ticks([380,400,420,440])
cb.solids.set_rasterized(True)
xt, yt = m(-13.2, 70.6)
m.drawcoastlines(zorder=1, color='k', linewidth=1)
dmapall = np.ravel(dmap)
if amplitude == False:
l = [
l for l in range(len(dmapall))
if dmapall[l] > mindepth + 1. and dmapall[l] < maxdepth - 1.
]
print('median', np.median((dmapall[l])))
print('variance', np.var((dmapall[l])))
if blobs == True:
latblob = []
lonblob = []
with open('/raid1/annemijn/scripts/CCP/areared.txt') as blobs:
for line in blobs:
row = line.split()
latblob.append(float(row[0]))
lonblob.append(float(row[1]))
print(latblob)
x, y = m(lonblob, latblob)
m.scatter(x, y, marker='o', color='firebrick')
latblob = []
lonblob = []
with open('/raid1/annemijn/scripts/CCP/areayellow.txt') as blobs:
for line in blobs:
row = line.split()
latblob.append(float(row[0]))
lonblob.append(float(row[1]))
print(latblob)
x, y = m(lonblob, latblob)
m.scatter(x, y, marker='o', color='gold')
latblob = []
lonblob = []
with open('/raid1/annemijn/scripts/CCP/areagreen.txt') as blobs:
for line in blobs:
row = line.split()
latblob.append(float(row[0]))
lonblob.append(float(row[1]))
print(latblob)
x, y = m(lonblob, latblob)
m.scatter(x, y, marker='o', color='forestgreen')
latblob = []
lonblob = []
with open('/raid1/annemijn/scripts/CCP/areablue.txt') as blobs:
for line in blobs:
row = line.split()
latblob.append(float(row[0]))
lonblob.append(float(row[1]))
print(latblob)
x, y = m(lonblob, latblob)
m.scatter(x, y, marker='o', color='dodgerblue')
lonmin = -142.1
lonmax = -128
latmin = 69
latmax = 74
#plot_rectangle(m, lonmin,lonmax,latmin,latmax)
lonmin = -136
lonmax = -128
latmin = 54
latmax = 63
#plot_rectangle(m, lonmin,lonmax,latmin,latmax)
lonmin = -157
lonmax = -150
latmin = 67.5
latmax = 69
#plot_rectangle(m, lonmin,lonmax,latmin,latmax)
lonmin = -154
lonmax = -149
latmin = 66
latmax = 68
#plot_rectangle(m, lonmin,lonmax,latmin,latmax, col="Black")
lonmin = -155
lonmax = -150
latmin = 56.5
latmax = 59.5
#plot_rectangle(m, lonmin,lonmax,latmin,latmax, col="Black")
lonmin = -159
lonmax = -131
latmin = 59.5
latmax = 66.5
#plot_line (m,lonmin,lonmax,latmin,latmax,col="Black")#BB
lonmin = -160
lonmax = -142
latmin = 58
latmax = 63
#plot_line (m,lonmin,lonmax,latmin,latmax,col="Black")#CC
lonmin = -157
lonmax = -140
latmin = 57
latmax = 69
# draw coastlines, country boundaries m.drawcoastlines(linewidth=0.25) m.drawcountries(linewidth=0.25) # draw parallels. parallels = np.arange(-40.,40,10.) m.drawparallels(parallels,labels=[1,0,0,0],fontsize=10) # draw meridians meridians = np.arange(80.,180.,10.) m.drawmeridians(meridians,labels=[0,0,0,1],fontsize=10) # Make grid for TRMM data ny = trmm_precip.shape[0] nx = trmm_precip.shape[1] lons, lats = m.makegrid(nx, ny) # get lat/lons of ny by nx evenly spaced grid. x, y = m(lons, lats) # compute map proj coordinates. # Make grid for SACA data ny_saca = saca_precip.shape[0] nx_saca = saca_precip.shape[1] lons_saca, lats_saca = m.makegrid(nx_saca, ny_saca) x_saca, y_saca = m(lons_saca, lats_saca) # Make grid for TRMM Land Sea mask (updated) lons_mask, lats_mask = np.meshgrid(lons_ls_mask, lats_ls_mask) x_mask, y_mask = m(lons_mask, lats_mask) print 'lons_mask', lons_mask print 'lats_mask', lats_mask print
def plot_mtzwidth(self,
name='Megavolume',
filter='rff2',
conversion='EU60',
factor=2.,
mincoverage=10.):
# Plots topography of maximum between mindepth and maxdepth, masking if sum of weights is beneath mincoverage.
#depth410660 = open('/raid3/annemijn/scripts/CCP/mincov20MTZ_'+conversion+'_'+filter+'_'+str(int(factor))+'.txt', 'w')
plt.figure(figsize=(10, 8))
depths = self.VOL.grid_depth
print(depths)
l410 = [
x for x in range(len(depths))
if depths[x] > 380 and depths[x] < 430
]
l660 = [
x for x in range(len(depths))
if depths[x] > 630 and depths[x] < 700
]
thickness = np.empty((len(self.VOL.grid_lon), len(self.VOL.grid_lat)))
dmap = np.empty((len(self.VOL.grid_lon), len(self.VOL.grid_lat)))
coverage = np.empty((len(self.VOL.grid_lon), len(self.VOL.grid_lat)))
amparray = []
for i in range(len(self.VOL.grid_lon)):
for j in range(len(self.VOL.grid_lat)):
RF = self.VOL.volume[i, j, :] / self.VOL.volumeweight[i, j, :]
plt.plot(RF)
std = 1.96 * np.sqrt(self.VOL.volumesigma[i, j, :] /
(self.VOL.volumeweight[i, j, :] *
self.VOL.volumeweight[i, j, :]))
maxmap410 = np.argmax(RF[l410])
maxmap660 = np.argmax(RF[l660])
if RF[l410[maxmap410]] > std[l410[maxmap410]] and RF[
l660[maxmap660]] > std[l660[maxmap660]]:
dmap[i,
j] = depths[l660[maxmap660]] - depths[l410[maxmap410]]
#depth410660.write(str(self.VOL.grid_lat[j])+'\t')
#depth410660.write(str(self.VOL.grid_lon[i])+'\t')
#depth410660.write(str(depths[l410[maxmap410]])+'\t')
#depth410660.write(str(depths[l660[maxmap660]])+'\t')
if self.VOL.volumeweight[i, j, l410[maxmap410]] < mincoverage:
dmap[i, j] = 1000.
# Prepare map
m = Basemap(projection='merc',
llcrnrlat=np.min(self.VOL.grid_lat),
urcrnrlat=np.max(self.VOL.grid_lat),
llcrnrlon=np.min(self.VOL.grid_lon),
urcrnrlon=np.max(self.VOL.grid_lon),
lat_ts=20,
resolution='i')
m.drawparallels(np.arange(0, 90, 5.),
labels=[1, 0, 0, 1],
linewidth=0.5,
dashes=[4, 2],
labelstyle='+/-',
fontsize=20)
m.drawmeridians(np.arange(-180, -110, 10.),
labels=[1, 0, 0, 1],
linewidth=0.5,
dashes=[4, 2],
labelstyle='+/-',
fontsize=20)
m.drawcountries()
coasts = m.drawcoastlines(zorder=1, color='k', linewidth=1)
xx, yy = np.meshgrid(self.VOL.grid_lon, self.VOL.grid_lat)
x, y = m(xx, yy)
#cs = plt.contourf(x, y, dmap.T, vmin=220.,levels=np.linspace(220., 280., 81.), cmap=cm.RdBu)
cs = plt.pcolor(x,
y,
dmap.T,
vmin=220.,
vmax=280.,
cmap=cm.RdBu,
linewidth=0,
rasterized=True)
cs.cmap.set_under([0.8, 0.8, 0.8])
cs.cmap.set_over([0.8, 0.8, 0.8])
cb = plt.colorbar()
cb.set_label('Transition zone thickness (km)', size=30)
cb.set_ticks([220, 235, 250, 265, 280])
cb.ax.tick_params(labelsize=30)
cb.solids.set_rasterized(True)
xt, yt = m(-13.2, 70.6)
m.drawcoastlines(zorder=1, color='k', linewidth=1)
dmapall = np.ravel(dmap)
lonmin = -158
lonmax = -154
latmin = 57.5
latmax = 60
# More map configuration # draw coastlines, country boundaries m.drawcoastlines(linewidth=0.25) m.drawcountries(linewidth=0.25) # draw parallels. parallels = np.arange(-40., 40, 10.) m.drawparallels(parallels, labels=[1, 0, 0, 0], fontsize=10) # draw meridians meridians = np.arange(80., 180., 10.) m.drawmeridians(meridians, labels=[0, 0, 0, 1], fontsize=10) # Make grid for TRMM data ny = trmm_precip.shape[0] nx = trmm_precip.shape[1] lons, lats = m.makegrid(nx, ny) # get lat/lons of ny by nx evenly spaced grid. x, y = m(lons, lats) # compute map proj coordinates. # Make grid for SACA data ny_saca = saca_precip.shape[0] nx_saca = saca_precip.shape[1] lons_saca, lats_saca = m.makegrid(nx_saca, ny_saca) x_saca, y_saca = m(lons_saca, lats_saca) # Make grid for TRMM Land Sea mask (updated) lons_mask, lats_mask = np.meshgrid(lons_ls_mask, lats_ls_mask) x_mask, y_mask = m(lons_mask, lats_mask) print 'lons_mask', lons_mask print 'lats_mask', lats_mask print
import re
from re import compile as rec
from tornado.escape import _unicode, xhtml_escape, url_escape
from misaka import HtmlRenderer, Markdown
import misaka as m
from bnw_web.linkshit import LinkParser, shittypes
from bnw_web.linkshit import _URL_RE, _USER_RE, _MSG_RE
#: Displaying thumbs allowed for the following hostings:
linkhostings = [
(ur'(?i)http://rghost.ru/([0-9]+)', lambda m: 'http://rghost.ru/%s/thumb.png' % (m(1),)),
(ur'(?i)http://imgur.com/([A-Za-z0-9]+)', lambda m: 'http://i.imgur.com/%ss.png' % (m(1),)),
(ur'http://ompldr.org/v([A-Za-z0-9]+)(/.+)?', lambda m: 'http://ompldr.org/t%s' % (m(1),)),
(ur'http://2-ch.ru/([a-z]+)/src/([0-9]+).(png|gif|jpg)', lambda m: 'http://2-ch.ru/%s/thumb/%ss.%s' % (m(1),m(2),m(3))),
(ur'https?://(?:www\.)?youtube.com/watch\?(?:.+&)?v\=([A-Z0-9a-z_-]+)(?:&.+)?', lambda m: 'http://img.youtube.com/vi/%s/default.jpg' % (m(1),)),
(ur'(?i)http://(.+.(?:png|gif|jpg|jpeg))', lambda m: 'http://f**k.blasux.ru/thumb?img=%s' % (url_escape(m(0)),)),
]
linkhostings = [(re.compile('^' + k + '$'), v, k) for (k, v) in linkhostings]
def thumbify(text, format='markdown'):
"""Format text and generate thumbs using available formatters.
:param format: default: 'markdown'. Specify text formatter.
Variants: 'moinmoin', 'markdown' or None,
which fallbacks to markdown.
Return: (formatted_text, thumbs)
# draw parallels. parallels = np.arange(-40., 40, 10.) m.drawparallels(parallels, labels=[1, 0, 0, 0], fontsize=10) # draw meridians meridians = np.arange(80., 180., 10.) m.drawmeridians(meridians, labels=[0, 0, 0, 1], fontsize=10) # Make grid for TRMM data # ny = trmm_precip.shape[0] # nx = trmm_precip.shape[1] # lons, lats = m.makegrid(nx, ny) # get lat/lons of ny by nx evenly spaced grid. # x, y = m(lons, lats) # compute map proj coordinates. # Alternative grid lonst, latst = np.meshgrid(lons, lats) x, y = m(lonst, latst) # Make grid for SACA data ny_saca = saca_precip.shape[0] nx_saca = saca_precip.shape[1] lons_saca, lats_saca = m.makegrid(nx_saca, ny_saca) x_saca, y_saca = m(lons_saca, lats_saca) # Make grid for TRMM Land Sea mask (updated) lons_mask, lats_mask = np.meshgrid(lons_ls_mask, lats_ls_mask) x_mask, y_mask = m(lons_mask, lats_mask) print 'lons_mask', lons_mask print 'lats_mask', lats_mask print
"#FCBC00", "#FFAF58", "#FFA28D", "#FF97BA")
print 'plt_diff_fc_abs_error'
fig, axarr = plt.subplots(1, 2, figsize=(7.87, 3))
for itim, tim in enumerate(timelist):
print itim, tim
dateobj = datetime.datetime.strptime(tim, '%Y%m%d')
ax = axarr[itim]
plt.sca(ax)
m = Basemap(projection='cyl', llcrnrlon=-40, \
urcrnrlon=30.,llcrnrlat=25,urcrnrlat=80, \
resolution='i', area_thresh=10000.)
m.drawcoastlines(False)
m.drawcountries()
m.drawcoastlines(linewidth=0.5)
x, y = m(lon, lat)
mylevels = np.arange(520, 590, 5)
# cs0 = m.contourf(x,y,((datadict_od_geopot_500[itim][timelist[itim]]/9.80665)/10.)-((datadict_od_geopot_1000[itim][timelist[itim]]/9.80665)/10.),
# shading='flat',latlon=True, levels=mylevels,
# cmap=plt.cm.gist_rainbow_r, extend = 'both',
# alpha=0.8)
# cs1 = m.contour(x,y,(datadict_od_geopot_500[itim][timelist[itim]]/9.80665)/10.,
# latlon = True, levels = np.arange(520,581,5),
# colors='k', linewidths=2,
# zorder=3)
cs1 = m.contourf(
x,
y, (datadict_od_geopot_500[itim][timelist[itim]] / 9.80665) / 10.,
latlon=True,
levels=np.arange(520, 601, 5),
colors=colors,
def plot_mtzwidth(self,
name='Megavolume',
filter='rff2',
conversion='prem',
factor=2.,
Max_Thickness=290,
Min_Thickness=230):
plt.figure(figsize=(18, 8))
depths = self.VOL.grid_depth
l410 = [
x for x in range(len(depths))
if depths[x] > 370 and depths[x] < 460
] # limit between 370 and 460
l660 = [
x for x in range(len(depths))
if depths[x] > 630 and depths[x] < 710
] # limit between 630 and 710
thickness1D = np.empty(
(len(self.VOL.grid_lon), len(self.VOL.grid_lat)
)) # create grids the size of the box to plot the data into
d4101D = np.empty((len(self.VOL.grid_lon), len(self.VOL.grid_lat)))
d6601D = np.empty((len(self.VOL.grid_lon), len(self.VOL.grid_lat)))
with open(
root + 'CCP_Stack/MTZ_' + conversion + '_' + filter + '_' +
str(int(factor)) + '.txt', 'w') as output:
for i in range(len(self.VOL.grid_lon)):
for j in range(len(self.VOL.grid_lat)):
RF = self.VOL.volume[i, j, :] / self.VOL.volumeweight[i,
j, :]
std = 1.96 * np.sqrt(self.VOL.volumesigma[i, j, :] /
(self.VOL.volumeweight[i, j, :] *
self.VOL.volumeweight[i, j, :]))
max410 = np.argmax(RF[l410])
max660 = np.argmax(RF[l660])
# If both picks are significant, store thickness
if RF[l410[max410]] > std[l410[max410]] and RF[
l660[max660]] > std[l660[max660]]:
d4101D[i, j] = depths[l410[max410]]
d6601D[i, j] = depths[l660[max660]]
thickness1D[i, j] = (depths[l660[max660]] -
depths[l410[max410]])
output.write(
str(depths[l410[max410]]) + '\t' +
str(depths[l660[max660]]) + '\n')
output.close()
# Prepare map
m = Basemap(projection='merc',
llcrnrlat=np.min(self.VOL.grid_lat),
urcrnrlat=np.max(self.VOL.grid_lat),
llcrnrlon=np.min(self.VOL.grid_lon),
urcrnrlon=np.max(self.VOL.grid_lon),
lat_ts=20,
resolution='i')
m.drawparallels(np.arange(np.min(self.VOL.grid_lat),
np.max(self.VOL.grid_lat), 5.),
labels=[1, 0, 0, 1])
m.drawmeridians(np.arange(np.min(self.VOL.grid_lon),
np.max(self.VOL.grid_lon), 5.),
labels=[1, 0, 0, 1])
m.drawcoastlines(color='k')
m.drawcountries(color='k')
m.drawstates()
m.drawmapboundary(fill_color=[1.0, 1.0, 1.0])
xx, yy = np.meshgrid(self.VOL.grid_lon, self.VOL.grid_lat)
x, y = m(xx, yy)
cs = plt.contourf(x,
y,
thickness1D.T,
levels=np.linspace(Min_Thickness, Max_Thickness,
81.),
cmap=cm.RdYlBu)
cs.cmap.set_under('w') #Max_Thickness = 290, Min_Thickness=230
cs.cmap.set_over('w')
plt.colorbar()
plt.title('MTZ width')
parallels = np.arange(-40.,40,10.) m.drawparallels(parallels,labels=[1,0,0,0],fontsize=10) # draw meridians meridians = np.arange(80.,180.,10.) m.drawmeridians(meridians,labels=[0,0,0,1],fontsize=10) # Make grid for TRMM data # ny = trmm_precip.shape[0] # nx = trmm_precip.shape[1] # lons, lats = m.makegrid(nx, ny) # get lat/lons of ny by nx evenly spaced grid. # x, y = m(lons, lats) # compute map proj coordinates. # Alternative grid lonst, latst = np.meshgrid(lons, lats) x, y = m(lonst, latst) # Make grid for SACA data ny_saca = saca_precip.shape[0] nx_saca = saca_precip.shape[1] lons_saca, lats_saca = m.makegrid(nx_saca, ny_saca) x_saca, y_saca = m(lons_saca, lats_saca) # Make grid for TRMM Land Sea mask (updated) lons_mask, lats_mask = np.meshgrid(lons_ls_mask, lats_ls_mask) x_mask, y_mask = m(lons_mask, lats_mask) print 'lons_mask', lons_mask print 'lats_mask', lats_mask
print temp
m = Basemap(width=width,height=width,resolution='h',projection='aeqd',
lat_0=lat_0,lon_0=lon_0)
m.bluemarble(ax=None, scale=None)
m.warpimage(image='bluemarble', scale=None)
#m.arcgisimage(server='http://server.arcgisonline.com/ArcGIS', service='ESRI_Imagery_World_2D', xpixels=400, ypixels=None, dpi=96, verbose=False)
# fill background.
m.drawmapboundary(fill_color='aqua')
# draw coasts and fill continents.
m.drawcoastlines(linewidth=0.5)
#m.fillcontinents(color='coral',lake_color='aqua')
# 20 degree graticule.
m.drawparallels(np.arange(-80,81,20))
m.drawmeridians(np.arange(-180,180,20))
# draw a black dot at the center.
xpt, ypt = m(lon_0, lat_0)
m.plot([xpt],[ypt],'ko')
# draw the title.
plt.title('Azimuthal Equidistant Projection')
plt.show()
# for x in range(len(fakeDataLat)):
# plotGPSMap(fakeDataLong[x],fakeDataLat[x],canvas)
#canvas.update()
# draw coastlines, country boundaries m.drawcoastlines(linewidth=0.25) m.drawcountries(linewidth=0.25) # draw parallels. parallels = np.arange(-40.,40,10.) m.drawparallels(parallels,labels=[1,0,0,0],fontsize=10) # draw meridians meridians = np.arange(80.,180.,10.) m.drawmeridians(meridians,labels=[0,0,0,1],fontsize=10) # Make grid for TRMM data ny = trmm_precip.shape[0] nx = trmm_precip.shape[1] lons, lats = m.makegrid(nx, ny) # get lat/lons of ny by nx evenly spaced grid. x, y = m(lons, lats) # compute map proj coordinates. # Make grid for SACA data ny_saca = saca_precip.shape[0] nx_saca = saca_precip.shape[1] lons_saca, lats_saca = m.makegrid(nx_saca, ny_saca) x_saca, y_saca = m(lons_saca, lats_saca) # Make grid for TRMM Land Sea mask (updated) lons_mask, lats_mask = np.meshgrid(lons_ls_mask, lats_ls_mask) x_mask, y_mask = m(lons_mask, lats_mask) print 'lons_mask', lons_mask print 'lats_mask', lats_mask print
# draw coastlines, country boundaries m.drawcoastlines(linewidth=0.25) m.drawcountries(linewidth=0.25) # draw parallels. parallels = np.arange(-40.,40,10.) m.drawparallels(parallels,labels=[1,0,0,0],fontsize=10) # draw meridians meridians = np.arange(80.,180.,10.) m.drawmeridians(meridians,labels=[0,0,0,1],fontsize=10) # Make grid for TRMM data ny = trmm_precip.shape[0]; nx = trmm_precip.shape[1] lons, lats = m.makegrid(nx, ny) # get lat/lons of ny by nx evenly spaced grid. x, y = m(lons, lats) # compute map proj coordinates. # Make grid for SACA data ny_saca = saca_precip.shape[0]; nx_saca = saca_precip.shape[1] lons_saca, lats_saca = m.makegrid(nx_saca, ny_saca) # get lat/lons of ny by nx evenly space grid. x_saca, y_saca = m(lons_saca, lats_saca) # compute map proj coordinates. # Actual plotting and rendering # ================================================================ # Plot TRMM # =============== # cs = m.contourf(x,y,trmm_precip*trmm_lsmask,clevs,cmap=cm.s3pcpn) # # Add colorbar
