def filterMetric(reader, timeFrame=None):
"""
@param reader is a MetricReader
@param timeFrame is a tuple of start and end timestamps
it can also be None (no time filter) or have one of
the components (or both) as None (no filter on that
respective side of time.
@return two masked array"""
import matplotlib.numerix.ma as MArray
if timeFrame == None:
timeFrame = [None, None]
if timeFrame[0] == None:
timeFrame[0] = 0
if timeFrame[1] == None:
timeFrame[1] = sys.maxint
timeVector = []
valueVector = []
for m in reader:
if m.timestamp != None:
if m.timestamp >= timeFrame[0] and m.timestamp <= timeFrame[1]:
timeVector.append(m.timestamp)
valueVector.append(m.value)
timeVector = MArray.array(timeVector)
valueVector = MArray.array(valueVector)
valueVector_masked = MArray.masked_where(valueVector == None, valueVector)
return [timeVector, valueVector_masked]
def reconstruction_plot_w(mapobj, lats, lons, data_cube, lvl_num, parm, u, v,w, angs, mask, **kwargs):
um=M.masked_where(M.array(mask) < 0.5, M.array(u))
vm=M.masked_where(M.array(mask) < 0.5, M.array(v))
mag=M.array(sqrt(u**2+v**2))
magm=M.masked_where(M.array(mask)<0.5, mag)
fig_name=kwargs.get('fig_name', 'recon_'+parm+'_.png')
longr, latgr=meshgrid(lons,lats)
xx, yy = mapobj(longr, latgr)
mapobj.drawmapboundary()
mapobj.readshapefile(getenv('HOME')+'/bom_mds/shapes/cstntcd_r','coast',drawbounds=True, linewidth=0.5,color='k',antialiased=1,ax=None)
mapobj.drawmeridians(array([130,130.5, 131, 131.5, 132]), labels=[1,0,0,1])
mapobj.drawparallels(array([-13,-12.5,-12,-11.5,-11]), labels=[1,0,0,1])
data=data_cube[parm][:,:,lvl_num]
if parm in ['diff']:
data=M.masked_where(M.array(mask) < 0.5, M.array(data))
comp_levs=linspace(-1.,1., 30)
levs_dict={'VR':linspace(-15,15,31), 'CZ': linspace(-8,64,10), 'TEST':linspace(((abs(data)+data)/2.0).min(), data.max(), 200), 'i_comp':comp_levs,'j_comp':comp_levs,'k_comp':comp_levs,'diff':linspace(-15,15,31)}
titles_dict={'VR': 'Radial velocity m/s', 'CZ':'Corrected Reflectivity dBz', 'TEST':'TEST', 'i_comp':'i component','j_comp':'j component','k_comp':'k component', 'diff':'diff'}
mapobj.contourf(xx,yy,data, levels=levs_dict[parm])
colorbar()
#mapobj.quiver(xx,yy,u/sqrt(u**2+v**2),v/sqrt(u**2+v**2), scale=50)
qq=mapobj.quiver(xx,yy,um,vm, scale=kwargs.get('qscale', 200))
quiverkey(qq, 0.1, 0.8, 10, '10 m/s', coordinates='figure')
quiverkey(qq, 0.1, 0.75, 5, '5 m/s', coordinates='figure')
quiverkey(qq, 0.1, 0.7, 2, '2 m/s', coordinates='figure')
cobject=mapobj.contour(xx,yy,w, colors=['k'], levels=linspace(0,20,11))
fon = { 'fontname':'Tahoma', 'fontsize':10 }
clabel(cobject, fmt="%d", **fon)
mapobj.contour(xx,yy,angs, levels=[30.0, 150.0],colors=['r'])
def plot_slice_lon(lat_sl, data_cube, lats, lons, levs, u,w, mask, par='CZ', w_mag=2.0, **kwargs):
ksp=kwargs.get('ksp', 0.05)
bquiver=kwargs.get('bquiver', [0.1, 0.2])
my_lat=argsort(abs(lats-lat_sl))[0]
print lats[my_lat]
data=M.masked_where(M.array(mask[my_lat,:,:])<0.5, M.array(data_cube[par][my_lat,:,:]))
wm=M.masked_where(M.array(mask[my_lat,:,:]) < 0.5, M.array(w[my_lat,:,:]))
um=M.masked_where(M.array(mask[my_lat,:,:]) < 0.5, M.array(u[my_lat,:,:]))
if par in ['diff']:
data=M.masked_where(M.array(mask) < 0.5, M.array(data))
comp_levs=linspace(-1.,1., 30)
levs_dict={'VR':linspace(-25,25,51), 'CZ': linspace(-8,64,10), 'TEST':linspace(((abs(data)+data)/2.0).min(), data.max(), 200), 'i_comp':comp_levs,'j_comp':comp_levs,'k_comp':comp_levs,'diff':linspace(-15,15,31), 'VE':linspace(-25,25,51)}
titles_dict={'VR': 'Radial velocity m/s', 'CZ':'Corrected Reflectivity dBz', 'TEST':'TEST', 'i_comp':'i component','j_comp':'j component','k_comp':'k component', 'diff':'diff', 'VE':'Edited velocity'}
xx,yy=meshgrid(lons,levs)
#gca().xaxis.major.formatter.set_powerlimits((-10.,10.))
#contourf(xx, yy, transpose(data), levs_dict[par])
pcolor(xx, yy, transpose(data), vmin=levs_dict[par].min(), vmax=levs_dict[par].max())
#gca().xaxis.major.formatter.set_scientific(False)
#print myaxis.formatter.limits
colorbar()
print "scale ", kwargs.get('qscale', 200)
qq=quiver(xx, yy, transpose(um), transpose(wm*w_mag), scale=kwargs.get('qscale', 200))
quiverkey(qq, bquiver[0], bquiver[1]+2.*ksp, 10, '10 m/s', coordinates='figure',fontproperties={'size':rcParams['xtick.labelsize']})
print bquiver[0], bquiver[1]+2.*ksp
quiverkey(qq, bquiver[0], bquiver[1]+ksp, 5, '5 m/s', coordinates='figure',fontproperties={'size':rcParams['xtick.labelsize']})
quiverkey(qq, bquiver[0], bquiver[1], 2, '2 m/s', coordinates='figure',fontproperties={'size':rcParams['xtick.labelsize']})
xlabel('Longitude (degrees)')
ylabel('Height (Metres)')
if 'box' in kwargs.keys():
box=kwargs['box']
ax=gca()
cr=ax.axis()
ax.axis([box[0], box[1], cr[2], cr[3]])
return lats[my_lat]
def plot_slice_lat(lon_sl, data_cube, lats, lons, levs, v,w, mask, par='CZ', w_mag=2.0, **kwargs):
ksp=kwargs.get('ksp', 0.05)
bquiver=kwargs.get('bquiver', [0.95, 0.75])
my_lon=argsort(abs(lons-lon_sl))[0]
print lons[my_lon]
data=M.masked_where(M.array(mask[:,my_lon,:])<0.5, M.array(data_cube[par][:,my_lon,:]))
wm=M.masked_where(M.array(mask[:,my_lon,:]) < 0.5, M.array(w[:,my_lon,:]))
vm=M.masked_where(M.array(mask[:,my_lon,:]) < 0.5, M.array(v[:,my_lon,:]))
if par in ['diff']:
data=M.masked_where(M.array(mask) < 0.5, M.array(data))
comp_levs=linspace(-1.,1., 30)
levs_dict={'VR':linspace(-25,25,51), 'CZ': linspace(-8,64,10), 'TEST':linspace(((abs(data)+data)/2.0).min(), data.max(), 200), 'i_comp':comp_levs,'j_comp':comp_levs,'k_comp':comp_levs,'diff':linspace(-15,15,31), 'VE':linspace(-25,25,51)}
titles_dict={'VR': 'Radial velocity m/s', 'CZ':'Corrected Reflectivity dBz', 'TEST':'TEST', 'i_comp':'i component','j_comp':'j component','k_comp':'k component', 'diff':'diff', 'VE':'Edited velocity'}
print data.shape
print wm.shape
print vm.shape
print lats.shape
print levs.shape
xx,yy=meshgrid(lats,levs)
#contourf(xx, yy, transpose(data), levs_dict[par])
pcolor(xx, yy, transpose(data), vmin=levs_dict[par].min(), vmax=levs_dict[par].max())
colorbar()
qq=quiver(xx, yy, transpose(vm), transpose(wm*w_mag), scale=kwargs.get('qscale', 200))
quiverkey(qq, bquiver[0], bquiver[1]+2.*ksp, 10, '10 m/s', coordinates='figure',fontproperties={'size':rcParams['xtick.labelsize']})
print bquiver[0], bquiver[1]+2.*ksp
quiverkey(qq, bquiver[0], bquiver[1]+ksp, 5, '5 m/s', coordinates='figure',fontproperties={'size':rcParams['xtick.labelsize']})
quiverkey(qq, bquiver[0], bquiver[1], 2, '2 m/s', coordinates='figure',fontproperties={'size':rcParams['xtick.labelsize']})
xlabel('Latitude (degrees)')
ylabel('Height (Metres)')
if 'box' in kwargs.keys():
box=kwargs['box']
ax=gca()
cr=ax.axis()
ax.axis([box[2], box[3], cr[2], cr[3]])
return lons[my_lon]
def plot_cappi(data,parm,lnum, **kwargs):
x=data['xar']
y=data['yar']
radar_loc=data['radar_loc']
radar_name=kwargs.get('radar_name', data['radar_name'])
fig_name=kwargs.get('fig_name', 'cappi_'+parm+'_.png')
f=figure()
Re=6371.0*1000.0
rad_at_radar=Re*sin(pi/2.0 -abs(radar_loc[0]*pi/180.0))#ax_radius(float(lat_cpol), units='degrees')
lons=radar_loc[1]+360.0*(x-data['displacement'][0])/(rad_at_radar*2.0*pi)
lats=radar_loc[0] + 360.0*(y-data['displacement'][1])/(Re*2.0*pi)
def_loc_dict={'lat_0':lats.mean(), 'lon_0':lons.mean(),'llcrnrlat':lats.min(), 'llcrnrlon':lons.min(), 'urcrnrlat':lats.max() , 'urcrnrlon':lons.max(), 'lat_ts':lats.mean()}
loc_dict=kwargs.get('loc_dict', def_loc_dict)
#print 'Here'
map= Basemap(projection='merc', resolution='l',area_thresh=1., **loc_dict)
longr, latgr=meshgrid(lons,lats)
xx, yy = map(longr, latgr)
#print 'there'
map.drawmapboundary()
darwin_loc=[-12.5, 130.85]
disp_from_darwin=mathematics.corner_to_point(darwin_loc, radar_loc)
dist_from_darwin=sqrt(disp_from_darwin[0]**2+disp_from_darwin[1]**2)
if dist_from_darwin < 500.*1000.:
map.readshapefile(getenv('HOME')+'/bom_mds/shapes/cstntcd_r','coast',drawbounds=True, linewidth=0.5,color='k',antialiased=1,ax=None)
else:
map.drawcoastlines()
map.drawmeridians(array([129,130,131,132,133]), labels=[1,0,0,1])
map.drawparallels(array([-14,-13,-12,-11,-10]), labels=[1,0,0,1])
comp_levs=linspace(-1.,1., 30)
#print 'everywhere'
levs_dict={'VR':linspace(-15,15,31), 'CZ': linspace(-8,64,10), 'PH': linspace(0,185,255), "RH": linspace(0,1.5,16), "SW":linspace(0, 5, 11), "ZD":linspace(-10,10,21), 'VE':linspace(-30,30,31), 'TI':linspace(-30,30,31)}
#print 'or here?'
titles_dict={'VR': 'Velocity m/s', 'CZ':'Corrected Reflectivity dBz', 'PH': 'Differential Prop Phase (degrees)', 'RH':'Correlation Co-ef', 'SW':'Spectral Width (m/s)', 'ZD':'Differentail Reflectivity dBz', 'VE':'Edited Velocity (m/s)', 'TI':'Simualated winds,(m/s)'}
#print parm
if 'mask' in kwargs.keys():
mask=kwargs['mask'][:,:,lnum]
mdata=M.masked_where(M.array(mask) < 0.5, M.array(data[parm][:,:,lnum]))
map.contourf(xx,yy,mdata, levels=levs_dict[parm])
else:
map.contourf(xx,yy,data[parm][:,:,lnum], levels=levs_dict[parm])
colorbar()
if 'angs' in kwargs.keys():
map.contour(xx,yy,kwargs['angs'], levels=[30.0, 150.0],colors=['r'])
p=data['date']
dtstr='%(#1)02d-%(#2)02d-%(#3)04d %(#4)02d%(#5)02dZ ' %{"#1":p.day, "#2":p.month, "#3":p.year, "#4":p.hour, '#5':p.minute}
title(radar_name+' '+dtstr+titles_dict[parm])
savefig(getenv('HOME')+'/bom_mds/output/'+fig_name)
close(f)
def reconstruction_plot_pcolor(lats, lons, data_cube, lvl_num, parm, u, v,w, angs, mask, **kwargs):
ber_loc=[-12.4, 130.85] #location of Berrimah radar
gp_loc=[-12.2492, 131.0444]#location of CPOL at Gunn Point
box=kwargs.get('box',[130.0, 132.0, -13.0, -11.5])#box for the plot
bquiver=kwargs.get('bquiver', [0.1, 0.75])
ksp=kwargs.get('ksp', 0.05)
#Set up the map and projection
mapobj= Basemap(projection='merc',lat_0=(box[2]+box[3])/2.0, lon_0=(box[0]+box[1])/2.0,llcrnrlat=box[2], llcrnrlon=box[0], urcrnrlat=box[3] , urcrnrlon=box[1], resolution='l',area_thresh=1., lat_ts=(box[2]+box[3])/2.0)
#map.drawmapboundary()
um=M.masked_where(M.array(mask) < 0.5, M.array(u))
vm=M.masked_where(M.array(mask) < 0.5, M.array(v))
mag=M.array(sqrt(u**2+v**2))
magm=M.masked_where(M.array(mask)<0.5, mag)
fig_name=kwargs.get('fig_name', 'recon_'+parm+'_.png')
longr, latgr=meshgrid(lons,lats)
xx, yy = mapobj(longr, latgr)
mapobj.drawmapboundary()
mapobj.readshapefile(getenv('HOME')+'/bom_mds/shapes/cstntcd_r','coast',drawbounds=True, linewidth=0.5,color='k',antialiased=1,ax=None)
mapobj.readshapefile(getenv('HOME')+'/bom_mds/shapes/cstqldmd_r', 'qcoast',drawbounds=True, linewidth=0.5,color='k',antialiased=1,ax=None)
#mapobj.drawmeridians(array([130,130.5, 131, 131.5, 132]), labels=[1,0,0,1])
#mapobj.drawparallels(array([-13,-12.5,-12,-11.5,-11]), labels=[1,0,0,1])
data=M.masked_where(M.array(mask) < 0.5, M.array(data_cube[parm][:,:,lvl_num]))
if parm in ['diff']:
data=M.masked_where(M.array(mask) < 0.5, M.array(data))
comp_levs=linspace(-1.,1., 30)
levs_dict={'VR':linspace(-25,25,51), 'CZ': linspace(-8,64,10), 'i_comp':comp_levs,'j_comp':comp_levs,'k_comp':comp_levs,'diff':linspace(-15,15,31), 'VE':linspace(-25,25,51)}
titles_dict={'VR': 'Radial velocity m/s', 'CZ':'Corrected Reflectivity dBz', 'TEST':'TEST', 'i_comp':'i component','j_comp':'j component','k_comp':'k component', 'diff':'diff', 'VE':'Edited velocity'}
mapobj.pcolor(xx,yy,data, vmin=levs_dict[parm].min(), vmax=levs_dict[parm].max())
colorbar()
#mapobj.quiver(xx,yy,u/sqrt(u**2+v**2),v/sqrt(u**2+v**2), scale=50)
qq=mapobj.quiver(xx,yy,um,vm, scale=kwargs.get('qscale', 200))
quiverkey(qq, bquiver[0], bquiver[1]+2.*ksp, 10, '10 m/s', coordinates='figure',fontproperties={'size':rcParams['xtick.labelsize']})
print bquiver[0], bquiver[1]+2.*ksp
quiverkey(qq, bquiver[0], bquiver[1]+ksp, 5, '5 m/s', coordinates='figure',fontproperties={'size':rcParams['xtick.labelsize']})
quiverkey(qq, bquiver[0], bquiver[1], 2, '2 m/s', coordinates='figure',fontproperties={'size':rcParams['xtick.labelsize']})
cobject=mapobj.contour(xx,yy,w, colors=['k'], levels=linspace(-10,10,6))
fon = { 'fontname':'Tahoma', 'fontsize':5 }
clabel(cobject, fmt="%1.1f", **fon)
mapobj.contour(xx,yy,angs, levels=[30.0, 150.0],colors=['r'])
mapobj.readshapefile(getenv('HOME')+'/bom_mds/shapes/nt_coast','coast',drawbounds=True, linewidth=0.5,color='k',antialiased=1,ax=None)
mapobj.drawmeridians(linspace(0,4,41)+130., labels=[1,0,0,1], fontsize=rcParams['xtick.labelsize'])
mapobj.drawparallels(linspace(0,4,41)-15.0, labels=[1,0,0,1], fontsize=rcParams['ytick.labelsize'])
return mapobj
def plot_slice_lon_hydro_sym_refl(lat_sl, data_cube,hydro_cube, hydro_bins, u,w, mask, par='CZ', w_mag=2.0, **kwargs):
lats=hydro_cube['lats']
lons=hydro_cube['lons']
levs=hydro_cube['zar']
ksp=kwargs.get('ksp', 0.05)
bquiver=kwargs.get('bquiver', [0.1, 0.2])
my_lat=argsort(abs(lats-lat_sl))[0]
print lats[my_lat]
data=M.masked_where(M.array(hydro_cube['CZ'][my_lat,:,:])<0.0, M.array(hydro_cube['CZ'][my_lat,:,:]))
wm=M.masked_where(M.array(mask[my_lat,:,:]) < 0.5, M.array(w[my_lat,:,:]))
um=M.masked_where(M.array(mask[my_lat,:,:]) < 0.5, M.array(u[my_lat,:,:]))
if par in ['diff']:
data=M.masked_where(M.array(mask) < 0.5, M.array(data))
comp_levs=linspace(-1.,1., 30)
levs_dict={'VR':linspace(-15,15,31), 'CZ': linspace(-8,64,10), 'TEST':linspace(((abs(data)+data)/2.0).min(), data.max(), 200), 'i_comp':comp_levs,'j_comp':comp_levs,'k_comp':comp_levs,'diff':linspace(-15,15,31)}
titles_dict={'VR': 'Radial velocity m/s', 'CZ':'Corrected Reflectivity dBz', 'TEST':'TEST', 'i_comp':'i component','j_comp':'j component','k_comp':'k component', 'diff':'diff'}
xx,yy=meshgrid(lons,levs)
plotting_syms={"unclassified":'k.', "Drizzle":'co', "Rain":'bo', "Dry LD snow":'cx', "Dry HD snow":'bx', "Melting Snow":'r+', "Dry Graupel":'bs', "Wet Graupel": 'rs', "Small Hail": 'cd', "Large Hail":'bD', "Rain + Hail":'g>'}
for item in set(hydro_bins['bins'])-set(['unclassified']):
plot(hydro_bins[item+"_lons"], hydro_bins[item+"_z"], plotting_syms[item])
legend(set(hydro_bins['bins'])-set(['unclassified']))
#gca().xaxis.major.formatter.set_powerlimits((-10.,10.))
#gca().xaxis.major.formatter.set_scientific(False)
#print myaxis.formatter.limits
#pcolor(xx_hydro, yy_hydro, transpose(hydro_data), vmin=-0.5, vmax=10.5, cmap=cmap_discretize(cm.jet, 11))
#colorbar()
contour(xx, yy, transpose(data), levs_dict[par])
print "QSCALE ", kwargs.get('qscale', 200)
qq=quiver(xx, yy, transpose(um), transpose(wm*w_mag), scale=kwargs.get('qscale', 200))
quiverkey(qq, bquiver[0], bquiver[1]+2.*ksp, 10, '10 m/s', coordinates='figure',fontproperties={'size':rcParams['xtick.labelsize']})
print bquiver[0], bquiver[1]+2.*ksp
quiverkey(qq, bquiver[0], bquiver[1]+ksp, 5, '5 m/s', coordinates='figure',fontproperties={'size':rcParams['xtick.labelsize']})
quiverkey(qq, bquiver[0], bquiver[1], 2, '2 m/s', coordinates='figure',fontproperties={'size':rcParams['xtick.labelsize']})
xlabel('Longitude (degrees)')
ylabel('Height (Metres)')
#item="Rain"
if 'box' in kwargs.keys():
box=kwargs['box']
ax=gca()
cr=ax.axis()
ax.axis([box[0], box[1], cr[2], cr[3]])
return lats[my_lat]
def plot_slice_lon_hydro(lat_sl, data_cube, lats, lons, levs, hydro_cube, u,w, mask, par='CZ', w_mag=2.0, **kwargs):
ksp=kwargs.get('ksp', 0.05)
bquiver=kwargs.get('bquiver', [0.1, 0.2])
my_lat=argsort(abs(lats-lat_sl))[0]
my_lat_hydro=argsort(abs(hydro_cube['lats']-lat_sl))[0]
print lats[my_lat]
data=M.masked_where(M.array(mask[my_lat,:,:])<0.5, M.array(data_cube[par][my_lat,:,:]))
hydro_data=M.masked_where(classify[my_lat_hydro,:,:]==-9.0, classify[my_lat_hydro,:,:])
wm=M.masked_where(M.array(mask[my_lat,:,:]) < 0.5, M.array(w[my_lat,:,:]))
um=M.masked_where(M.array(mask[my_lat,:,:]) < 0.5, M.array(u[my_lat,:,:]))
if par in ['diff']:
data=M.masked_where(M.array(mask) < 0.5, M.array(data))
comp_levs=linspace(-1.,1., 30)
levs_dict={'VR':linspace(-15,15,31), 'CZ': linspace(-8,64,10), 'TEST':linspace(((abs(data)+data)/2.0).min(), data.max(), 200), 'i_comp':comp_levs,'j_comp':comp_levs,'k_comp':comp_levs,'diff':linspace(-15,15,31)}
titles_dict={'VR': 'Radial velocity m/s', 'CZ':'Corrected Reflectivity dBz', 'TEST':'TEST', 'i_comp':'i component','j_comp':'j component','k_comp':'k component', 'diff':'diff'}
xx,yy=meshgrid(lons,levs)
xx_hydro, yy_hydro=meshgrid(hydro_cube['lons'], hydro_cube['zar'])
#gca().xaxis.major.formatter.set_powerlimits((-10.,10.))
#gca().xaxis.major.formatter.set_scientific(False)
#print myaxis.formatter.limits
print "my_lat_hydro:", my_lat_hydro
print hydro_cube['lats'][my_lat_hydro]
pcolor(xx_hydro, yy_hydro, transpose(hydro_data), vmin=-0.5, vmax=10.5, cmap=cmap_discretize(cm.jet, 11))
myc=colorbar()
myc.ax.text(0.0, 3.0, "HD snow")
#yticks((0,1,2,3),("Uncl", "Dz", "Ra", "HD Sn"))
contour(xx, yy, transpose(data), levs_dict[par])
qq=quiver(xx, yy, transpose(um), transpose(wm*w_mag), scale=kwargs.get('qscale', 200))
quiverkey(qq, bquiver[0], bquiver[1]+2.*ksp, 10, '10 m/s', coordinates='figure',fontproperties={'size':rcParams['xtick.labelsize']})
print bquiver[0], bquiver[1]+2.*ksp
quiverkey(qq, bquiver[0], bquiver[1]+ksp, 5, '5 m/s', coordinates='figure',fontproperties={'size':rcParams['xtick.labelsize']})
quiverkey(qq, bquiver[0], bquiver[1], 2, '2 m/s', coordinates='figure',fontproperties={'size':rcParams['xtick.labelsize']})
xlabel('Longitude (degrees)')
ylabel('Height (Metres)')
if 'box' in kwargs.keys():
box=kwargs['box']
ax=gca()
cr=ax.axis()
ax.axis([box[0], box[1], cr[2], cr[3]])
return lats[my_lat]
X, Y = meshgrid(x, y)
Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = Z1 - Z2
# interior badmask doesn't work yet for filled contours
if test_masking:
badmask = zeros(shape(Z))
badmask[5, 5] = 1
badmask[5, 6] = 1
Z[5, 5] = 0
Z[5, 6] = 0
badmask[0, 0] = 1
Z[0, 0] = 0
Z = ma.array(Z, mask=badmask)
levels, colls = contourf(
X,
Y,
Z,
10, # [-1, -0.1, 0, 0.1],
#alpha=0.5,
cmap=cm.bone,
origin=origin)
levs2, colls2 = contour(X, Y, Z, levels, colors='r', origin=origin, hold='on')
savefig('contourf_demo')
show()
b = mandg_means[breed][parity]['b']
c = mandg_means[breed][parity]['c']
d = mandg_means[breed][parity]['d']
y = a - (b * day10) + ((c * day10**2) / 2) + (d / day10)
return y
if __name__ == '__main__':
for day in xrange(1, dimin + 1):
dim.append(day)
point = compute_curve_point(breed, trait, parity, day)
std.append(point)
stdsum = stdsum + point
plotdim = M.array(dim)
plotstd = M.array(std)
maxdim = plotdim.max()
if kgtolb == 1 and trait < 4:
plotstd = plotstd * 2.2
stdsum = stdsum * 2.2
# Plot standard curves
pylab.rcParams.update(params)
fig = pylab.figure()
plot = fig.add_subplot(111)
plot_title = 'Standard curve for %s parity %s %s (%s d)' % ( number2parity[parity], \
number2breed[breed], number2name[trait], dimin )
pylab.title(plot_title)
pylab.xlabel('DIM')
if cowid != lastcowid or trait != lasttrait:
if int(lasttrait) == int(plottrait) or int(plottrait) == 5:
if cowid != lastcowid:
#print 'Cowid changed from %s to %s' % ( lastcowid, cowid )
_cowid = lastcowid
_lacnum = lastlacnum
else:
_cowid = cowid
_lacnum = lacnum
if trait != lasttrait:
#print 'Trait changed from %s to %s' % ( lasttrait, trait )
_trait = lasttrait
else:
_trait = trait
# We're processing a new animal
plotdim = M.array(dim)
plottd_ = M.array(tdy)
plottdy = M.masked_where(plottd_ == threshold, plottd_)
plotla_ = M.array(lac)
plotlac = M.masked_where(plotla_ == -2.0, plotla_)
plotst_ = M.array(std)
plotstd = M.masked_where(plotst_ == threshold, plotst_)
plotdb_ = M.array(dbp)
plotdbp = M.masked_where(plotdb_ == threshold, plotdb_)
plotde_ = M.array(dev)
plotdev = M.masked_where(plotde_ == threshold, plotde_)
maxdim = plotdim.max()
if lbtokg == 1 and int(_trait) < 4:
plottdy = plottdy / 2.2
plotlac = plotlac / 2.2
Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = 10 * (Z1 - Z2)
# interior badmask doesn't work yet for filled contours
if test_masking:
badmask = zeros(shape(Z))
badmask[5,5] = 1
badmask[5,6] = 1
Z[5,5] = 0
Z[5,6] = 0
badmask[0,0] = 1
Z[0,0] = 0
Z = ma.array(Z, mask=badmask)
# We are using automatic selection of contour levels;
# this is usually not such a good idea, because they don't
# occur on nice boundaries, but we do it here for purposes
# of illustration.
CS = contourf(X, Y, Z, 10, # [-1, -0.1, 0, 0.1],
#alpha=0.5,
cmap=cm.bone,
origin=origin)
# Note that in the following, we explicitly pass in
# the contour levels used for the filled contours, so that
# the regions have outlines. We could also pass in additional
# levels to provide extra resolution.
if cowid != lastcowid or trait != lasttrait:
if int(lasttrait) == int(plottrait) or int(plottrait) == 5:
if cowid != lastcowid:
#print 'Cowid changed from %s to %s' % ( lastcowid, cowid )
_cowid = lastcowid
_lacnum = lastlacnum
else:
_cowid = cowid
_lacnum = lacnum
if trait != lasttrait:
#print 'Trait changed from %s to %s' % ( lasttrait, trait )
_trait = lasttrait
else:
_trait = trait
# We're processing a new animal
plotdim = M.array(dim)
plottd_ = M.array(tdy)
plottdy = M.masked_where(plottd_ == threshold, plottd_)
plotla_ = M.array(lac)
plotlac = M.masked_where(plotla_ == -2.0, plotla_)
plotst_ = M.array(std)
plotstd = M.masked_where(plotst_ == threshold, plotst_)
plotdb_ = M.array(dbp)
plotdbp = M.masked_where(plotdb_ == threshold, plotdb_)
plotde_ = M.array(dev)
plotdev = M.masked_where(plotde_ == threshold, plotde_)
maxdim = plotdim.max()
if lbtokg == 1 and int(_trait) < 4:
plottdy = plottdy / 2.2
plotlac = plotlac / 2.2
def analyze(filter):
sink = filter()
#from mpl_toolkits.mplot3d import Axes3D
import matplotlib.axes
import matplotlib.pyplot as plt
import numpy as np
import random
import matplotlib.numerix.ma as M
fig = plt.figure()
ax=fig.add_subplot(111)
X = []
Y = []
Z = []
for k in sink:
X += [k[0][0]]
Y += [k[0][1]]
Z += [k[1]]
threshold = 1
#prepare for masking arrays - 'conventional' arrays won't do it
Y = M.array(Y)
#mask values below a certain threshold
Y = M.masked_where(Y < threshold , Y)
X_min = min(X)
X_range = max(X)-min(X)
X_scale = 20.0/X_range
Y_min = min(Y)
Y_range = max(Y)-min(Y)
Y_scale = 20.0/Y_range
Z_min = min(Z)
Z_range = max(Z)-min(Z)
Z_scale = 1.0/Z_range
colors = []
for i in range(len(sink)):
X[i] = (X[i]-X_min)*110.83575 #* X_scale
Y[i] = (Y[i]-Y_min)*97.43888 #* Y_scale
colors.append(tuple([0.2,0.1]+[1-Z[i]/max(Z)]*1+[1]))
z = zip(*filter.coords)
coords = zip( (np.array(z[0])-X_min)*110.83575, (np.array(z[1])-Y_min)*97.43888)
X = np.array(X)
Y = np.array(Y)
Z = np.array(Z)
ax.scatter(X, Y, marker = 's', color=colors, s = ((Z-Z_min+Z_min/Z_range)*Z_scale)**2*100)# color = [['r','b','y'][random.randint(0,2)] for i in range(len(sink))])
ax.scatter(*zip(*coords), marker = '+')
for i in range(len(filter.intersections)):
ax.text(coords[i][0], coords[i][1], filter.intersections[i].replace(", Houston TX", "").replace("and", "\n"), fontsize=8, alpha = 0.5)
#print ((Z-Z_min)*Z_scale)**4*15
ax.set_xlabel('Latitudinal Offset')
ax.set_ylabel('Longitudinal Offset')
return plt
a = mandg_means[breed][parity]['a']
b = mandg_means[breed][parity]['b']
c = mandg_means[breed][parity]['c']
d = mandg_means[breed][parity]['d']
y = a - ( b*day10 ) + (( c*day10**2 ) / 2) + ( d/day10 )
return y
if __name__ == '__main__':
for day in xrange(1,dimin+1):
dim.append(day)
point = compute_curve_point(breed,trait,parity,day)
std.append(point)
stdsum = stdsum + point
plotdim = M.array(dim)
plotstd = M.array(std)
maxdim = plotdim.max()
if kgtolb == 1 and trait < 4:
plotstd = plotstd * 2.2
stdsum = stdsum * 2.2
# Plot standard curves
pylab.rcParams.update(params)
fig = pylab.figure()
plot = fig.add_subplot(111)
plot_title = 'Standard curve for %s parity %s %s (%s d)' % ( number2parity[parity], \
number2breed[breed], number2name[trait], dimin )
pylab.title(plot_title)
pylab.xlabel('DIM')
