def drawall(self, **kwargs):
if not self.n == self.drawax.shape:
self.drawax = np.ones(self.n, dtype='bool')
if not self.n[1] == self.hrel.shape[0]:
self.hrel = np.ones(self.n[1], dtype='float32')
if not self.n[0] == self.vrel.shape[0]:
self.vrel = np.ones(self.n[0], dtype='float32')
if kwargs.has_key('lw'):
kwargs['linewidth'] = kwargs.pop('lw')
if not kwargs.has_key('linewidth'):
kwargs['linewidth'] = self.linewidth
else:
self.linewidth = kwargs['linewidth']
forcesharex = False
forcesharey = False
if kwargs.has_key('sharex'):
forcesharex = True
if kwargs.has_key('sharey'):
forcesharey = True
inter = pylab.isinteractive()
pylab.interactive(
False) # wait to draw the axes, until they've all been created.
axg = self.axgrid()
for iv in range(self.n[0]):
for ih in range(self.n[1]):
if forcesharex: # I should put this functionality into a func.
pass
elif self.sharex[iv, ih] and self._sharex_ax[self.sharex[iv,
ih]]:
kwargs['sharex'] = self._sharex_ax[self.sharex[iv, ih]]
elif kwargs.has_key('sharex'):
kwargs.pop('sharex')
if forcesharey:
pass
elif self.sharey[iv, ih] and self._sharey_ax[self.sharey[iv,
ih]]:
kwargs['sharey'] = self._sharey_ax[self.sharey[iv, ih]]
elif kwargs.has_key('sharey'):
kwargs.pop('sharey')
if self.drawax[iv, ih]:
#self.ax[iv,ih]=myaxes(axg[iv,ih,:],**kwargs)
self.ax[iv, ih] = axes(axg[iv, ih, :], **kwargs)
self.ax[iv, ih].hold(True)
if self.sharex[
iv,
ih] and not self._sharex_ax[self.sharex[iv, ih]]:
self._sharex_ax[self.sharex[iv, ih]] = self.ax[iv, ih]
if self.sharey[
iv,
ih] and not self._sharey_ax[self.sharey[iv, ih]]:
self._sharey_ax[self.sharey[iv, ih]] = self.ax[iv, ih]
flag = True
self._xlabel_ax = self.ax[-1, 0]
self._ylabel_ax = self._xlabel_ax
pylab.interactive(inter)
pylab.draw_if_interactive()
return self.ax
def __call__(self, **params):
p = ParamOverrides(self, params)
fig = plt.figure(figsize=(5, 5))
# This one-liner works in Octave, but in matplotlib it
# results in lines that are all connected across rows and columns,
# so here we plot each line separately:
# plt.plot(x,y,"k-",transpose(x),transpose(y),"k-")
# Here, the "k-" means plot in black using solid lines;
# see matplotlib for more info.
isint = plt.isinteractive() # Temporarily make non-interactive for
# plotting
plt.ioff()
for r, c in zip(p.y[::p.skip], p.x[::p.skip]):
plt.plot(c, r, "k-")
for r, c in zip(np.transpose(p.y)[::p.skip],np.transpose(p.x)[::p.skip]):
plt.plot(c, r, "k-")
# Force last line avoid leaving cells open
if p.skip != 1:
plt.plot(p.x[-1], p.y[-1], "k-")
plt.plot(np.transpose(p.x)[-1], np.transpose(p.y)[-1], "k-")
plt.xlabel('x')
plt.ylabel('y')
# Currently sets the input range arbitrarily; should presumably figure out
# what the actual possible range is for this simulation (which would presumably
# be the maximum size of any GeneratorSheet?).
plt.axis(p.axis)
if isint: plt.ion()
self._generate_figure(p)
return fig
def __call__(self, t, text=''):
"""
Updates the hill_plot at time t. You can provide a title for the figure.
If bool(title)==False, t is shown.
"""
layer_f = self.layers.get_fluxes3d(t)
surf_f = self.surfacewater.get_fluxes3d(t)
self.q_sub.set_UVC(numpy.asarray(layer_f.X), numpy.asarray(layer_f.Z))
self.q_surf.set_UVC(numpy.asarray(surf_f.X), numpy.asarray(surf_f.Z))
for l in self.layers:
self.polys[l.node_id].set_fc(self.cmap(self.evalfunction(l)))
x, z = self.__get_layer_shape(l, *self.__cells_of_layer[l.node_id])
self.polys[l.node_id].set_xy(numpy.column_stack((x, z)))
self.topline.set_ydata(self.__get_snow_height())
if not text:
text = str(t)
self.title.set_text(text)
if pylab.isinteractive():
pylab.draw()
# Collect all matplotlib artist to use the HillPlot in animations
artists = [self.title]
artists += list(self.polys.values())
artists.append(self.topline)
artists.append(self.q_sub)
artists.append(self.q_surf)
return artists
def drawall(self, **kwargs):
if not self.n == self.drawax.shape:
self.drawax = np.ones(self.n, dtype='bool')
if 'lw' in kwargs.keys():
kwargs['linewidth'] = kwargs.pop('lw', self.linewidth)
if 'linewidth' not in kwargs.keys():
kwargs['linewidth'] = self.linewidth
else:
self.linewidth = kwargs['linewidth']
inter = pylab.isinteractive()
pylab.interactive(False)
# wait to draw the axes, until they've all been
# created.
for iv, ih in self._iter_axinds():
if self.drawax[iv, ih]:
self.ax[iv, ih] = axes(self.axPlacer(iv, ih),
sharex=self.sharex(iv, ih),
sharey=self.sharey(iv, ih),
**kwargs)
self._xlabel_ax = self.ax[-1, 0]
self._ylabel_ax = self._xlabel_ax
pylab.interactive(inter)
pylab.draw_if_interactive()
return self.ax
def __call__(self, **params):
p = ParamOverrides(self, params)
fig = plt.figure(figsize=(5, 5))
# This one-liner works in Octave, but in matplotlib it
# results in lines that are all connected across rows and columns,
# so here we plot each line separately:
# plt.plot(x,y,"k-",transpose(x),transpose(y),"k-")
# Here, the "k-" means plot in black using solid lines;
# see matplotlib for more info.
isint = plt.isinteractive() # Temporarily make non-interactive for
# plotting
plt.ioff()
for r, c in zip(p.y[::p.skip], p.x[::p.skip]):
plt.plot(c, r, "k-")
for r, c in zip(np.transpose(p.y)[::p.skip],np.transpose(p.x)[::p.skip]):
plt.plot(c, r, "k-")
# Force last line avoid leaving cells open
if p.skip != 1:
plt.plot(p.x[-1], p.y[-1], "k-")
plt.plot(np.transpose(p.x)[-1], np.transpose(p.y)[-1], "k-")
plt.xlabel('x')
plt.ylabel('y')
# Currently sets the input range arbitrarily; should presumably figure out
# what the actual possible range is for this simulation (which would presumably
# be the maximum size of any GeneratorSheet?).
plt.axis(p.axis)
if isint: plt.ion()
self._generate_figure(p)
return fig
def matrix_plot(self, matrix, figure_name='matrix_plot.pdf'):
import numpy
from matplotlib import pylab
def _blob(x,y,area,colour):
hs = numpy.sqrt(area) / 2
xcorners = numpy.array([x - hs, x + hs, x + hs, x - hs])
ycorners = numpy.array([y - hs, y - hs, y + hs, y + hs])
pylab.fill(xcorners, ycorners, colour, edgecolor=colour)
reenable = False
if pylab.isinteractive():
pylab.ioff()
pylab.clf()
maxWeight = 2**numpy.ceil(numpy.log(numpy.max(numpy.abs(matrix)))/numpy.log(2))
height, width = matrix.shape
pylab.fill(numpy.array([0,width,width,0]),numpy.array([0,0,height,height]),'white')
pylab.axis('off')
pylab.axis('equal')
for x in xrange(width):
for y in xrange(height):
_x = x+1
_y = y+1
w = matrix[y,x]
if w > 0:
_blob(_x - 0.5, height - _y + 0.5, 0.2,'#0099CC')
elif w < 0:
_blob(_x - 0.5, height - _y + 0.5, 0.2,'#660000')
if reenable:
pylab.ion()
pylab.savefig(figure_name)
def matrix_plot(self, matrix, figure_name='matrix_plot.pdf'):
import numpy
from matplotlib import pylab
def _blob(x, y, area, colour):
hs = numpy.sqrt(area) / 2
xcorners = numpy.array([x - hs, x + hs, x + hs, x - hs])
ycorners = numpy.array([y - hs, y - hs, y + hs, y + hs])
pylab.fill(xcorners, ycorners, colour, edgecolor=colour)
reenable = False
if pylab.isinteractive():
pylab.ioff()
pylab.clf()
maxWeight = 2**numpy.ceil(
numpy.log(numpy.max(numpy.abs(matrix))) / numpy.log(2))
height, width = matrix.shape
pylab.fill(numpy.array([0, width, width, 0]),
numpy.array([0, 0, height, height]), 'white')
pylab.axis('off')
pylab.axis('equal')
for x in xrange(width):
for y in xrange(height):
_x = x + 1
_y = y + 1
w = matrix[y, x]
if w > 0:
_blob(_x - 0.5, height - _y + 0.5, 0.2, '#0099CC')
elif w < 0:
_blob(_x - 0.5, height - _y + 0.5, 0.2, '#660000')
if reenable:
pylab.ion()
pylab.savefig(figure_name)
def drawall(self, **kwargs):
if not self.n == self.drawax.shape:
self.drawax = np.ones(self.n, dtype='bool')
if 'lw' in kwargs.keys():
kwargs['linewidth'] = kwargs.pop('lw', self.linewidth)
if 'linewidth' not in kwargs.keys():
kwargs['linewidth'] = self.linewidth
else:
self.linewidth = kwargs['linewidth']
inter = pylab.isinteractive()
pylab.interactive(False)
# wait to draw the axes, until they've all been
# created.
for iv, ih in self._iter_axinds():
if self.drawax[iv, ih]:
self.ax[iv, ih] = axes(self.axPlacer(iv, ih),
sharex=self.sharex(iv, ih),
sharey=self.sharey(iv, ih),
**kwargs)
self._xlabel_ax = self.ax[-1, 0]
self._ylabel_ax = self._xlabel_ax
pylab.interactive(inter)
pylab.draw_if_interactive()
return self.ax
def drawall(self, **kwargs):
if not self.n == self.drawax.shape:
self.drawax = np.ones(self.n, dtype='bool')
if not self.n[1] == self.hrel.shape[0]:
self.hrel = np.ones(self.n[1], dtype='float32')
if not self.n[0] == self.vrel.shape[0]:
self.vrel = np.ones(self.n[0], dtype='float32')
if 'lw' in kwargs.keys():
kwargs['linewidth'] = kwargs.pop('lw', self.linewidth)
if 'linewidth' not in kwargs.keys():
kwargs['linewidth'] = self.linewidth
else:
self.linewidth = kwargs['linewidth']
forcesharex = False
forcesharey = False
if 'sharex' in kwargs.keys():
forcesharex = True
if 'sharey' in kwargs.keys():
forcesharey = True
inter = pylab.isinteractive()
pylab.interactive(False)
# wait to draw the axes, until they've all been
# created.
axg = self.axgrid()
for iv in range(self.n[0]):
for ih in range(self.n[1]):
if forcesharex: # I should put this functionality into a func.
pass
elif (self.sharex[iv, ih] and
self._sharex_ax[self.sharex[iv, ih]]):
kwargs['sharex'] = self._sharex_ax[self.sharex[iv, ih]]
elif 'sharex' in kwargs.keys():
kwargs.pop('sharex')
if forcesharey:
pass
elif (self.sharey[iv, ih] and
self._sharey_ax[self.sharey[iv, ih]]):
kwargs['sharey'] = self._sharey_ax[self.sharey[iv, ih]]
elif 'sharey' in kwargs.keys():
kwargs.pop('sharey')
if self.drawax[iv, ih]:
# self.ax[iv,ih]=myaxes(axg[iv,ih,:],**kwargs)
self.ax[iv, ih] = axes(axg[iv, ih,:], **kwargs)
self.ax[iv, ih].hold(True)
if self.sharex[iv, ih] and not\
self._sharex_ax[self.sharex[iv, ih]]:
self._sharex_ax[self.sharex[iv, ih]] = self.ax[iv, ih]
if self.sharey[iv, ih] and not\
self._sharey_ax[self.sharey[iv, ih]]:
self._sharey_ax[self.sharey[iv, ih]] = self.ax[iv, ih]
self._xlabel_ax = self.ax[-1, 0]
self._ylabel_ax = self._xlabel_ax
pylab.interactive(inter)
pylab.draw_if_interactive()
return self.ax
def __call__(self, t=None):
a = numpy.array
if t:
self.t = t
f = cmf.cell_flux_directions(self.cells, self.t)
self.quiver.set_UVC(a(f.X), a(f.Y))
if plt.isinteractive():
plt.draw()
def __init__(self, cells, value_function, cmap=default_colormap,
hold=True, vmin=None, vmax=None, **kwargs):
"""
Creates a new map from cells
:param cells:
:param value_function:
:param cmap:
:param hold:
:param vmin:
:param vmax:
:param kwargs:
"""
if not hasattr(cmf.Cell, 'geometry'):
raise NotImplementedError('The geometry of the cells can not be used, shapely is not installed')
self.cmap = cmap
self.cells = [c for c in cells if c.geometry]
self.__f = value_function
was_interactive = plt.isinteractive()
if was_interactive:
plt.ioff()
self.polygons = {}
def flatten_multipolygons(shape):
if hasattr(shape, "geoms"):
return shape.geoms
else:
return [shape]
geos = [flatten_multipolygons(c.geometry) for c in self.cells]
values = [self.f(c) for c in self.cells]
self.maxvalue = vmax or max(values)
self.minvalue = vmin or min(values)
if self.minvalue >= self.maxvalue:
self.minvalue = self.maxvalue - 1
if not hold:
plt.cla()
for cell, shapes, v in zip(self.cells, geos, values):
c = self.cmap(float(v - self.minvalue) / float(self.maxvalue - self.minvalue))
self.polygons[cell] = [self.draw_shapes(s, c, **kwargs) for s in shapes]
plt.axis('equal')
norm = Normalize(self.minvalue, self.maxvalue)
plt.matplotlib.cm.ScalarMappable.__init__(self, norm, cmap)
if was_interactive:
plt.draw()
plt.ion()
def __call__(self, recalc_range=False):
if recalc_range:
self.maxvalue = max((self.f(c) for c in self.cells))
self.minvalue = min((self.f(c) for c in self.cells))
for cell, polys in self.polygons.items():
v = self.f(cell)
c = self.cmap((v - self.minvalue) / (self.maxvalue - self.minvalue))
for poly in polys:
poly.set_fc(c)
if plt.isinteractive():
plt.draw()
def __call__(self, **params):
p = ParamOverrides(self, params)
name = p.plot_template.keys().pop(0)
plot = make_template_plot(p.plot_template,
p.sheet.views.Maps,
p.sheet.xdensity,
p.sheet.bounds,
p.normalize,
name=p.plot_template[name])
fig = plt.figure(figsize=(5, 5))
if plot:
bitmap = plot.bitmap
isint = plt.isinteractive(
) # Temporarily make non-interactive for plotting
plt.ioff() # Turn interactive mode off
plt.imshow(bitmap.image, origin='lower', interpolation='nearest')
plt.axis('off')
for (t, pref, sel, c) in p.overlay:
v = plt.flipud(p.sheet.views.Maps[pref].view()[0])
if (t == 'contours'):
plt.contour(v, [sel, sel], colors=c, linewidths=2)
if (t == 'arrows'):
s = plt.flipud(p.sheet.views.Maps[sel].view()[0])
scale = int(np.ceil(np.log10(len(v))))
X = np.array([x for x in xrange(len(v) / scale)])
v_sc = np.zeros((len(v) / scale, len(v) / scale))
s_sc = np.zeros((len(v) / scale, len(v) / scale))
for i in X:
for j in X:
v_sc[i][j] = v[scale * i][scale * j]
s_sc[i][j] = s[scale * i][scale * j]
plt.quiver(scale * X,
scale * X,
-np.cos(2 * np.pi * v_sc) * s_sc,
-np.sin(2 * np.pi * v_sc) * s_sc,
color=c,
edgecolors=c,
minshaft=3,
linewidths=1)
p.title = '%s overlaid with %s at time %s' % (plot.name, pref,
topo.sim.timestr())
if isint: plt.ion()
p.filename_suffix = "_" + p.sheet.name
self._generate_figure(p)
return fig
def __call__(self, **params):
p=ParamOverrides(self,params)
name=p.plot_template.keys().pop(0)
plot=make_template_plot(p.plot_template,
p.sheet.views.Maps, p.sheet.xdensity,p.sheet.bounds,
p.normalize,name=p.plot_template[name])
fig = plt.figure(figsize=(5,5))
if plot:
bitmap=plot.bitmap
isint=plt.isinteractive() # Temporarily make non-interactive for plotting
plt.ioff() # Turn interactive mode off
plt.imshow(bitmap.image,origin='lower',interpolation='nearest')
plt.axis('off')
for (t,pref,sel,c) in p.overlay:
v = plt.flipud(p.sheet.views.Maps[pref].view()[0])
if (t=='contours'):
plt.contour(v,[sel,sel],colors=c,linewidths=2)
if (t=='arrows'):
s = plt.flipud(p.sheet.views.Maps[sel].view()[0])
scale = int(np.ceil(np.log10(len(v))))
X = np.array([x for x in xrange(len(v)/scale)])
v_sc = np.zeros((len(v)/scale,len(v)/scale))
s_sc = np.zeros((len(v)/scale,len(v)/scale))
for i in X:
for j in X:
v_sc[i][j] = v[scale*i][scale*j]
s_sc[i][j] = s[scale*i][scale*j]
plt.quiver(scale*X, scale*X, -np.cos(2*np.pi*v_sc)*s_sc,
-np.sin(2*np.pi*v_sc)*s_sc, color=c,
edgecolors=c, minshaft=3, linewidths=1)
p.title='%s overlaid with %s at time %s' %(plot.name,pref,topo.sim.timestr())
if isint: plt.ion()
p.filename_suffix="_"+p.sheet.name
self._generate_figure(p)
return fig
def hinton(W, maxWeight=None):
reenable = False
if P.isinteractive():
P.ioff()
P.clf()
height, width = W.shape
if not maxWeight:
maxWeight = 2**numpy.ceil(numpy.log(numpy.max(numpy.abs(W)))/numpy.log(2))
P.fill(numpy.array([0,width,width,0]),numpy.array([0,0,height,height]),'gray')
P.axis('off')
P.axis('equal')
for x in xrange(width):
for y in xrange(height):
_x = x+1
_y = y+1
w = W[y,x]
if w > maxWeight/2:
_blob(_x - 0.5, height - _y + 0.5, min(1,w/maxWeight),'white')
elif w <= maxWeight/2:
_blob(_x - 0.5, height - _y + 0.5, min(1,w/maxWeight),'black')
if reenable:
P.ion()
P.show()
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage as nd
from scipy.ndimage.filters import median_filter as mf
import matplotlib.pylab as pd
d1=sys.argv[1];d2=sys.argv[2]
path=os.path.join(d1,d2)
img_ml=nd.imread(path)
pd.ion()
print pd.isinteractive()
fig1, ax1 = plt.subplots(num=1,figsize=(6,1.0*img_ml.shape[0]/img_ml.shape[1]*6))
fig1.subplots_adjust(0,0,1,1);ax1.grid('off')
ax1.axis('off')
fig1.canvas.set_window_title(d2)
im1 = ax1.imshow(img_ml)
fig1.canvas.draw()
fig2, ax2 = plt.subplots(3,1,num=2,figsize=[14,10])
ax2[0].hist(img_ml[:,:,0].reshape(img_ml.shape[0]*img_ml.shape[1]),bins=256,color='r')
ax2[0].set_xlim([0,256])
ax2[1].hist(img_ml[:,:,1].reshape(img_ml.shape[0]*img_ml.shape[1]),bins=256,color='g')
ax2[1].set_xlim([0,256])
ax2[2].hist(img_ml[:,:,2].reshape(img_ml.shape[0]*img_ml.shape[1]),bins=256,color='b')
ax2[2].set_xlim([0,256])
plt.show(block=False)
print 'yes Im running'
def particle_size_distribution(struct,t_index,z_index):
"""plot particle size distribution"""
ptv,particle_parameters = get_data_at_index(struct,t_index,z_index)
alpha_water = particle_parameters['alpha_water']
alpha_ice = particle_parameters['alpha_ice']
g_ice = particle_parameters['g_ice']
g_water = particle_parameters['g_water']
Dr = particle_parameters['Dr']
print 'Default processing'
print 'effective_diameter_prime = %4i (microns)' %(ptv.eff_diameter_prime[0,0]*1e6)
print 'effective_diameter = %4i (microns)' %(ptv.effective_diameter[0,0]*1e6)
print 'mode diameter = %4i (microns)' %(ptv.dmode[0,0]*1e6)
print 'mean diameter = %4i (microns)' %(ptv.mean_diameter[0,0]*1e6)
print 'Doppler velocity = %4.2f (m/s)' %ptv.MeanDopplerVelocity[0,0]
print 'rw_fall_vel = %4.2f (m/s)' %ptv.rw_fall_velocity[0,0]
print 'mw_fall_vel = %4.2f (m/s)' %ptv.mw_fall_velocity[0,0]
print 'model_spectral_width = %4.2f (m/s)' %ptv.model_spectral_width[0,0]
print 'radar_spectral_width = %4.2f (m/s)' %ptv.radar_spectral_width[0,0]
print 'alpha_water = ' ,alpha_water
print 'alpha_ice = ',alpha_ice
print 'gamma_water = ',g_water
print 'gamma_ice = ',g_ice
print 'zeta = ',ptv.zeta[0,0]
rho_ice = 0.91 *1000.0 # kg/m^3
gas_constant = 287 #J/(kg K)
#vector of diameters in m from 1 micron to 1 cm
D = np.logspace(0.0,4.0,200)/1e6
dD = np.zeros_like(D)
dD[1:] = D[1:]-D[:-1]
if ptv.phase[0,0]:
alpha = alpha_ice
gam = g_ice
else:
alpha = alpha_water
gam = g_water
colors = ['b','g','r','c']
lwidth = 1.0
while 1:
#recalculate size distribution with new alpha?
alpha_gamma_str = raw_input('new params: alpha,gamma,zeta = ? or map ')
#strip all blanks
#alpha_gamma_str.lstrip()
alpha_gamma_str.replace(" ","")
n_alpha = 1
n_gam = 1
print alpha_gamma_str
if not alpha_gamma_str == 'map':
n_times=[2,2]
if len(alpha_gamma_str) == 0:
break
elif alpha_gamma_str.find(',')>= 0:
index = alpha_gamma_str.find(',')
index2 = alpha_gamma_str[(index+1):].find(',')
index2 = index +index2+1
alpha = np.float(alpha_gamma_str[:index])
gam = np.float(alpha_gamma_str[index +1:index2])
ptv.zeta[0,0] = np.float(alpha_gamma_str[index2+1:len(alpha_gamma_str)])
else:
n_alpha = 20
n_gam = 40
n_times = [n_alpha,n_gam]
cost_func = np.zeros((n_alpha,n_gam))
print n_times, n_times[0]
print n_alpha,n_gam,ptv.zeta[0,0]
if alpha_gamma_str == 'map':
for i in range(1,n_times[0]):
for k in range(1,n_times[1]):
alpha = np.float(i)/2.0
gam = .05 * np.float(k)
print
print 'alpha = ',alpha,' gamma= ',gam
particle_parameters['alpha_water'] = alpha
particle_parameters['alpha_ice'] = alpha
particle_parameters['g_water'] = gam
particle_parameters['g_ice'] = gam
beta_ext_radar = np.NaN * np.zeros((1,1))
beta_ext_lidar = np.NaN * np.zeros((1,1))
zeta =np.NaN * np.zeros((1,1))
beta_ext_lidar = rs_inv['beta_a_backscat'][t_index,z_index]/0.05
beta_ext_radar = rs_mmcr['Backscatter'][t_index,z_index] * 8 * np.pi/3.0
zeta = rs_spheroid_particle['zeta'][t_index,z_index]
print beta_ext_lidar.shape
dmode = spu.dmode_from_lidar_radar(beta_ext_lidar,beta_ext_radar,particle_parameters
,ptv.zeta,ptv.phase,8.6e-3)
print dmode
mode_dia,effective_dia,size_dist,mw_size_dist,aw_size_dist,rw_size_dist\
,mw_fall_vel,rw_fall_vel,LWC_ext,LWC_p180 = precip_computation(\
alpha
,gam
,D
,extinction
,beta_a_backscat
,eff_dia_prime
,phase
,temperature
,pressure)
else: #single value not mapping cost function
particle_parameters['alpha_water'] = alpha
particle_parameters['alpha_ice'] = alpha
particle_parameters['g_water'] = gam
particle_parameters['g_ice'] = gam
#eff dia prime from radar and lidar
#ptv.eff_diameter_prime[0,0] = edp.lidar_radar_eff_diameter_prime(
# ptv.beta_ext_lidar
# ,ptv.beta_ext_radar
# ,ptv.phase
# ,8.6e-3)
print 'source',particle_parameters['ext_source']
print ptv.beta_a_backscat
print particle_parameters['p180_ice']
print ptv.beta_ext_radar
print ptv.zeta
print ptv.phase
if ptv.phase[0,0] == 1:
p180 = particle_parameters['p180_ice']
else:
p180 = 0.05
print 'beta_ext' , ptv.beta_a_backscat/p180
if particle_parameters['ext_source'] == 'bs/p180':
ptv.dmode = spu.dmode_from_lidar_radar(
ptv.beta_a_backscat/p180
,ptv.beta_ext_radar
,particle_parameters
,ptv.zeta
,ptv.phase
,8.6e-3)
else:
ptv.dmode = spu.dmode_from_lidar_radar(
ptv.beta_ext_lidar
,ptv.beta_ext_radar
,particle_parameters
,ptv.zeta
,ptv.phase
,8.6e-3)
#compute effective diameter from mode_diameter
ptv.effective_diameter[0,0],ptv.mean_diameter[0,0],ptv.mean_mass_diameter[0,0]\
= spu.effective_diameter(
ptv.dmode
,particle_parameters
,ptv.zeta
,ptv.phase)
#compute liquid water content (kg/m^3)
LWC_ext = spu.liquid_water_content(
ptv.effective_diameter
,ptv.beta_ext_lidar,ptv.phase)
if ptv.phase[0,0] == 1:
ptv.LWC_p180 = spu.liquid_water_content(
ptv.effective_diameter
,ptv.beta_a_backscat/particle_parameters['p180_ice']
,ptv.phase)
elif ptv.phase[0,0] == 0 :
ptv.LWC_p180 = spu.liquid_water_content(
ptv.effective_diameter
,ptv.beta_a_backscat/0.05
,ptv.phase)
else:
ptv.LWC_p180 = np.NaN
#compute precip rate (m/s)
#precip_rate = 1/density (m^3/kg) * LWC (kg/m^3) * fall_velocity (m/s)
ptv.hsrl_radar_precip_rate[0,0] = 0.001 * ptv.LWC_p180[0,0] * ptv.MeanDopplerVelocity[0,0]
#LR_precip_rate[0,0]= 0.001 * LWC_p180[0,0] * MeanDopplerVelocity[0,0
ptv.rw_fall_velocity[0,0],ptv.mw_fall_velocity[0,0],ptv.model_spectral_width[0,0]\
= spu.weighted_fall_velocity(
ptv.dmode
,particle_parameters
,ptv.zeta
,ptv.temps
,ptv.pressures
,ptv.phase)
size_dist = D**alpha * np.exp(-(alpha/gam) * (D/ptv.dmode[0,0])**gam)
norm = np.sum(size_dist * dD)
size_dist /=norm
print 'norm',norm
#area weighted distribution
aw_size_dist = D**(alpha+2.0) * np.exp(-(alpha/gam) * (D/ptv.dmode[0,0])**gam)
norm = np.sum(aw_size_dist * dD)
aw_size_dist /= norm
print norm
#mass weighted distribution
mw_size_dist = D**(alpha+ ptv.zeta + 2.0) * np.exp(-(alpha/gam) * (D/ptv.dmode[0,0])**gam)
norm = np.sum(mw_size_dist * dD)
mw_size_dist /= norm
print norm
#radar weighted distribution
rw_size_dist = D**(alpha+ 2.0 * ptv.zeta + 4.0) * np.exp(-(alpha/gam) * (D/ptv.dmode[0,0])**gam)
norm = np.sum(rw_size_dist * dD)
rw_size_dist /= norm
#compute model fall velocity at this point
#constants, program works in mks
rho_ice=0.91 * 1000.0 #kg/m^3
rho_water = 1000.0 #kg/m^3
gas_constant = 287 #J/(kg K)
rho_air = 100.0 *ptv.pressures/(gas_constant * ptv.temps) #in kg/m^3
eta = spu.dynamic_viscosity(ptv.temps)
#X = spu.best_number(D,Dr,ptv.zeta,rho_ice,rho_air,eta)
X_const = spu.best_constant(rho_air,eta)
if ptv.phase == 1:
X = spu.best_number(D,Dr,X_const,ptv.zeta,rho_ice)
else:
X = spu.best_number(D,Dr,X_const,1.0,rho_water)
Vf = spu.spheroid_fall_velocity(D,X,ptv.zeta,rho_air,eta,ptv.phase)
#cost_func[i,k] = (doppler_vel-rw_fall_vel)**2 + (spectral_width-model_spectral_width)**2
#print 'cost function = ',cost_func[i,k]
if alpha_gamma_str == 'map':
plt.figure(999)
imgplot = plt.imshow(np.log(cost_func))
plt.figure(1000)
#plt.ioff()
lines=plt.plot(D*1e6 ,np.transpose(size_dist), colors[0]
,D*1e6,np.transpose(aw_size_dist),colors[1]
,D*1e6,np.transpose(mw_size_dist),colors[2]
,D*1e6,np.transpose(rw_size_dist),colors[3])
plt.grid(True)
ax=plt.gca()
plt.setp(lines,linewidth=lwidth)
plt.legend(['number','area','mass','radar'])
ax.set_xlim(0,4500)
plt.ylabel('weighted size distributions')
plt.xlabel('Diameter (microns)')
#plt.ion()
plt.figure(1001)
#plt.ioff()
lines1=plt.plot(D*1e6 ,np.transpose(size_dist), colors[0]
,D*1e6,np.transpose(aw_size_dist),colors[1]
,D*1e6,np.transpose(mw_size_dist),colors[2]
,D*1e6,np.transpose(rw_size_dist),colors[3])
plt.grid(True)
ax=plt.gca()
plt.setp(lines1,linewidth=lwidth)
plt.legend(['number','area','mass','radar'])
plt.xlabel('Diameter (microns)')
ax.set_xscale('log')
#plt.show(False)
plt.figure(1002)
#plt.ioff()
lines2=plt.plot(D*1e6,np.transpose(Vf),'r')
plt.grid(True)
ax=plt.gca()
plt.setp(lines2,linewidth=lwidth)
plt.xlabel('diameter (microns)')
plt.ylabel('fall velocity (m/s)')
ax.set_xscale('log')
print
print 'effective_diameter_prime = %4.0f (microns)' %(1e6 * ptv.eff_diameter_prime)
print 'effective diameter = %4.0f (micons)' %(ptv.effective_diameter * 1e6)
print 'mode diameter = %4.0f (microns)' %(ptv.dmode * 1e6)
print 'mean diameter = %4.0f (microns)' %(ptv.mean_diameter *1e6)
print 'LWC , extinction = %6.4f (gr/m^3)' %(ptv.LWC_ext * 1e3)
print 'LWC ,p(180)/4pi = %4.2f = %6.4f (gr/m^3)' \
%(particle_parameters['p180_ice'],ptv.LWC_p180 * 1e3)
if ptv.phase[0,0] == 1:
print 'phase = ice'
elif ptv.phase[0,0] == 0:
print 'phase = water'
else:
print 'phase = NaN'
print 'Doppler velocity = %4.2f (m/s)' %ptv.MeanDopplerVelocity
print 'rw_fall_vel = %4.2f (m/s)' %ptv.rw_fall_velocity
print 'mw_fall_vel = %4.2f (m/s)' %ptv.mw_fall_velocity
print 'model_spectral_width = %4.2f (m/s)' %ptv.model_spectral_width
print 'radar_spectral_width = %4.2f (m/s)' %ptv.radar_spectral_width
print 'alpha = ' ,alpha
print 'gamma = ',gam
print 'zeta = ',ptv.zeta[0,0]
lwidth = lwidth + 1.0
if not plt.isinteractive():
plt.show(False)
#plt.ion()
return
def _start_report(self):
self.was_interactive = plt.isinteractive()
plt.ioff()
self.f = plt.figure(figsize=(self.figwidth, self.figheight))
self.pageno = 1
def __init__(self, cells, t, solute=None, cmap=default_color_map):
"""
Creates a new HillPlot on the active figure, showing the state of each layer
- cells: The a sequence of cmf cells to use in this hill_plot. You can
use the whole project if you like
- t: Current time step. Needed to retrieve the fluxes
- solute:The solute concentration to show. If None, the wetness of the
layer will be shown
- cmap: a matplotlib colormap (see module cm) for coloring
"""
was_interactive = pylab.isinteractive()
if was_interactive:
pylab.ioff()
self.cells = cells
self.layers = cmf.node_list(chain(*[c.layers for c in cells]))
self.surfacewater = cmf.node_list(c.surfacewater for c in cells)
x_pos = [self.__x(c.x, c.y) for c in cells]
self.topline = pylab.plot(x_pos, self.__get_snow_height(), 'k-',
lw=2)[0]
self.__cells_of_layer = {}
self.polys = {}
# Get standard evaluation functions
if isinstance(solute, cmf.solute):
self.evalfunction = lambda layer: layer.conc(solute)
else:
self.evalfunction = lambda layer: layer.wetness
self.cmap = cmap
self.figure = pylab.gcf()
for i, c in enumerate(cells):
c_left = cells[i - 1] if i else c
c_right = cells[i + 1] if i < len(cells) - 1 else c
for l in c.layers:
x, z = self.__get_layer_shape(l, c, c_left, c_right)
self.polys[l.node_id], = pylab.fill(x,
z,
fc=self.cmap(
self.evalfunction(l)),
ec='none',
zorder=0)
self.__cells_of_layer[l.node_id] = (c, c_left, c_right)
layer_pos = self.layers.get_positions()
surf_pos = self.surfacewater.get_positions()
layer_f = self.layers.get_fluxes3d(t)
surf_f = self.surfacewater.get_fluxes3d(t)
scale = max(numpy.linalg.norm(surf_f.X), numpy.linalg.norm(
layer_f.X)) * 10
layer_x = self.__x(numpy.asarray(layer_pos.X),
numpy.asarray(layer_pos.Y))
surf_x = self.__x(numpy.asarray(surf_pos.X), numpy.asarray(surf_pos.Y))
self.q_sub = pylab.quiver(layer_x,
layer_pos.Z,
layer_f.X + layer_f.Y,
layer_f.Z,
scale=scale,
minlength=0.1,
pivot='middle',
zorder=1)
self.q_surf = pylab.quiver(surf_x,
surf_pos.Z,
surf_f.X + surf_f.Y,
surf_f.Z,
color='b',
scale=scale,
minlength=0.1,
pivot='middle',
zorder=1)
self.title = pylab.title(t)
if was_interactive:
pylab.draw()
pylab.ion()
model.compile(loss="binary_crossentropy",
optimizer="adam",
metrics=['accuracy'])
# call the function to fit to the data (training the network)
history = model.fit(x_train,
y_train,
epochs=1000,
batch_size=20,
validation_data=(x_test, y_test))
scores = model.evaluate(X, Y)
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
plt.figure()
plt.isinteractive()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='best')
plt.figure()
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('acc')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='best')
def f(self, funct):
self.__f = funct
if plt.isinteractive():
self(True)
