def run(p1_name, heuristic1, p2_name, heuristic2, times):
rounds = times
#black
p1_color = "B"
p1_win = 0
#white
p2_color = "W"
p2_win = 0
ties = 0
for i in range(rounds):
print 'Started game %s' % i
game = Game(p1_color="B", p1_mode="C", p2_mode="C")
game.start()
winner = play_game(game, heuristic1, heuristic2)
if winner == p1_color:
p1_win += 1
print '>>>>>>>>>>>>>>>>>>> %s win!' % (p1_name)
elif winner == p2_color:
p2_win += 1
print '>>>>>>>>>>>>>>>>>>> %s win!'% (p2_name)
else:
print 'Tie.'
ties += 1
print 'finished game %s' % i
print 'After %s rounds:\n' % rounds
print '%s: %s ' % (p1_name, p1_win)
print '%s: %s' % (p2_name, p2_win)
print 'ties: %s' % ties
print 'Probabilities:'
print '%s: %s' % (p1_name, str(p1_win/float(rounds)))
print '%s: %s' % (p2_name, str(p2_win/float(rounds)))
print 'ties: %s' % str(ties/float(rounds))
graphics.plot(p1_name, p1_win, p2_name, p2_win, ties)
from erode import *
from config import *
from time import time
import matplotlib.pyplot as plt
from map import Map, set_up_landscape
from graphics import plot, plot_gradient, plot3d
scatter = False
size = 128
runs = 1000
debug = Map.of(size)
set_up_landscape(debug, 1, skip_levels)
debug.blur(2)
plot(debug.E, file="debug-before.png")
trs = []
start = time()
for count in range(runs):
if count % 4 == 0:
print("%5.2f%%" % (count / runs * 100))
trs += erode(debug, 100, 100)
end = time()
print("Took %6.2fs" % (end - start))
plot3d(debug.E, file="debug-carved.png")
dpi=300,
bbox_inches='tight')
# If desired, display the plot to the screen
if display:
matplotlib.pyplot.show()
if closeFigure:
matplotlib.pyplot.close('all')
if __name__ == '__main__':
import graphics
import numpy
import os
x = []
y = []
label = []
for i in range(6):
x += [numpy.arange(11)]
y += [(i + 1) * x[i]]
label += ['$y = %ix$' % i]
saveFilename = 'plot.pdf'
graphics.plot(x[0], y[0])
graphics.plot(x, y)
graphics.plot(x, y, label=label, saveFilename=saveFilename)
def go(self):
var = self.metadata['ceof']['var']
if ('Lat' not in self.keys()) or ('Lon' not in self.keys()):
self.data['Lon'], self.data['Lat'] = numpy.meshgrid(
self.data['lon'], self.data['lat'])
if 'prefilter' in self.metadata:
print "Filtering in time"
if self.metadata['prefilter']['type'] == 'bandpass':
self.filter(
var,
l=self.metadata['prefilter']['l'],
type=self.metadata['prefilter']['type'],
l2=self.metadata['prefilter']['l2'],
)
else:
self.filter(var,
l=self.metadata['prefilter']['l'],
type=self.metadata['prefilter']['type'])
# ---- Normalize -----------------------------------------------------
#self.data['ssh']=self.data['ssh']-self.data['ssh'].mean()
# --------------------------------------------------------------------
ind = ma.getmaskarray(self.data[var]).any(axis=0) == False
I, J, K = self.data[var].shape
if 'ceof_coord' in self.metadata:
coord = self.metadata['ceof_coord']
assert type(coord) == list
from shapely.geometry import Point, Polygon
polygon = Polygon(coord)
for j in range(J):
for k in range(K):
ind[j, k] = ind[j, k] & polygon.intersects(
Point(self.data['Lon'][j, k], self.data['Lat'][j, k]))
#ind = ind&tmp
N = int((numpy.ones(ind.shape)[ind]).sum())
grid_index = ma.masked_all((N, 2), dtype='i')
n = -1
for j in range(J):
for k in range(K):
if ind[j, k]:
n += 1
grid_index[n, :] = [j, k]
self.grid_index = grid_index
data2D = numpy.zeros((I, N), dtype=self.data[var].dtype)
for n, ind in enumerate(self.grid_index):
data2D[:, n] = self.data[var][:, ind[0], ind[1]]
print "Running CEOF_2D()"
output = CEOF_2D(data2D, cfg=self.metadata['ceof'])
nmodes = len(output['lambdas'])
for k in [k for k in output.keys() if k is not 'ceof']:
self.data[k] = output[k]
self.data['eofs'] = ma.masked_all((J, K, nmodes), dtype='complex128')
for n, ind in enumerate(self.grid_index):
self.data['eofs'][ind[0], ind[1], :] = output['eofs'][n, :]
# ----
self.set_wavelenght()
#for k in self.data.keys():
# print k, self.data[k].shape, type(self.data[k])
if 'figs' in self.metadata:
print "Creating figures for %s modes" % nmodes
#for nmode in range(5):
#for n in range(10):
for n in range(nmodes):
#eof2D=ceof['eofs'][:,nmode]
#pc2D=ceof['pcs'][:,nmode]
##
##eof=ma.masked_all(self.data['ssh'].shape[1:],dtype=eofs.dtype)
#eof=ma.masked_all(self.data['ssh'].shape[1:],dtype='complex128')
##pc=ma.masked_all(self.data['ssh'].shape[1:],dtype=pcs.dtype)
#pc=ma.masked_all(self.data['ssh'].shape[1:],dtype='complex128')
##
##for n,i in enumerate(ind):
##
## ---- 2D back to grid ----
#for n,ind in enumerate(self.data2D['grid_index']):
# #print n,ind
# eof[ind[0],ind[1]] = eof2D[n]
# #pc[ind[0],ind[1]] = pc2D[n]
#pc = pc2D
#print pc2D.shape
#varfrac = round(getvariancefraction(lambdas)[nmode]*1e2)
#varfrac = ceof['variancefraction'][nmode]
#fig = self.plot(eof_amp,eof_phase,pc_amp,pc_phase,nmode,varfrac)
if 'suffix' in self.metadata['figs']:
filename = "../figs/CEOF_%s_mode%s.eps" % (
self.metadata['figs']['suffix'], (n + 1))
else:
filename = "../figs/CEOF_mode%s.eps" % (n + 1)
limits = {
'LatIni': -5,
'LatFin': 15,
'LonIni': -60,
'LonFin': -25
}
#self.plot(self['eofs'][:,:,n],self['pcs'][:,n],(n+1),self['variancefraction'][n],filename=filename,limits=limits,cumvarfrac=self['variancefraction'][:(n+1)].sum())
import graphics
graphics.plot(self['eofs'][:, :, n],
self['pcs'][:, n], (n + 1),
self['variancefraction'][n],
filename=filename,
data=self.data,
limits=limits,
cumvarfrac=self['variancefraction'][:(n +
1)].sum())
def plotboth(file):
plot(demo.E, file=file)
plot3d(demo.E, file=file[:-len(".png")] + "_3d.png")
raise ValueError(msg)
@property
def wavelengths(self):
return self._wavelengths
@property
def wavelength_minimum(self):
return self._wavelengthMinimum
@property
def wavelength_maximum(self):
return self._wavelengthMaximum
@property
def powers(self):
return self._powers
if __name__ == '__main__':
import graphics
import color
a = color.CIEReferenceIlluminant('a')
d65 = color.CIEReferenceIlluminant()
graphics.plot([a.wavelengths, d65.wavelengths], [a.powers, d65.powers],
title='CIE Reference Illuminants',
xlabel='Wavelength [nm]',
ylabel='Power',
label=['A', 'D65'])
from erode import *
from config import *
from time import time
import matplotlib.pyplot as plt
from map import Map, set_up_landscape
from graphics import plot, plot_gradient, plot3d
scatter = False
size = 128
runs = 1000
debug = Map.of(size)
set_up_landscape(debug, 1, skip_levels)
debug.blur(2)
plot(debug.E, file="debug-before.png")
trs = []
start = time()
for count in range(runs):
if count % 4 == 0:
print("%5.2f%%" % (count / runs * 100))
trs += erode(debug, 100, 100)
end = time()
print("Took %6.2fs" % (end - start))
plot3d(debug.E, file="debug-carved.png")
def plotboth(file):
plot(demo.E, file=file)
plot3d(demo.E, file=file[:-len(".png")] + "_3d.png")
dt = \
datetime.datetime.strptime(data[idx][0][0:10] + ' ' + data[idx][0][11:-1],
'%Y-%m-%d %H:%M:%S.%f')
day = \
(dt.toordinal() - datetime.date(dt.year, 1, 1).toordinal() + 1) + \
dt.hour/24 + \
dt.minute/(24*60) + \
dt.second/(24*60*60)
days = numpy.append(days, day)
# Parse dependent data from the selected column of the logged data
dependent = numpy.empty(0, dtype=numpy.float64)
for idx in range(start_idx, end_idx):
dependent = numpy.append(dependent, float(data[idx][column_selected]))
# Plot the specified dependent data as a function of time
if minimum_dependent is None or maximum_dependent is None:
y_limits = None
else:
y_limits = [minimum_dependent, maximum_dependent]
graphics.plot(days,
dependent,
xlabel='Day Number [UTC]',
ylabel=headers[column_selected],
ylim=y_limits,
xgrid=True,
ygrid=True,
display=display,
saveFilename=plot_filename)
def go(self):
var = self.metadata['ceof']['var']
if ('Lat' not in self.keys()) or ('Lon' not in self.keys()):
self.data['Lon'], self.data['Lat'] = numpy.meshgrid(self.data['lon'],self.data['lat'])
if 'prefilter' in self.metadata:
print "Filtering in time"
if self.metadata['prefilter']['type'] == 'bandpass':
self.filter(var,l=self.metadata['prefilter']['l'],type=self.metadata['prefilter']['type'], l2=self.metadata['prefilter']['l2'],)
else:
self.filter(var,l=self.metadata['prefilter']['l'],type=self.metadata['prefilter']['type'])
# ---- Normalize -----------------------------------------------------
#self.data['ssh']=self.data['ssh']-self.data['ssh'].mean()
# --------------------------------------------------------------------
ind = ma.getmaskarray(self.data[var]).any(axis=0) == False
I, J, K = self.data[var].shape
if 'ceof_coord' in self.metadata:
coord = self.metadata['ceof_coord']
assert type(coord) == list
from shapely.geometry import Point, Polygon
polygon = Polygon(coord)
for j in range(J):
for k in range(K):
ind[j,k] = ind[j,k] & polygon.intersects(
Point(self.data['Lon'][j,k],
self.data['Lat'][j,k]))
#ind = ind&tmp
N = int((numpy.ones(ind.shape)[ind]).sum())
grid_index = ma.masked_all((N,2), dtype='i')
n = -1
for j in range(J):
for k in range(K):
if ind[j, k]:
n += 1
grid_index[n, :] = [j, k]
self.grid_index = grid_index
data2D = numpy.zeros((I,N), dtype=self.data[var].dtype)
for n, ind in enumerate(self.grid_index):
data2D[:, n] = self.data[var][:,ind[0], ind[1]]
print "Running CEOF_2D()"
output = CEOF_2D(data2D, cfg=self.metadata['ceof'])
nmodes = len(output['lambdas'])
for k in [k for k in output.keys() if k is not 'ceof']:
self.data[k] = output[k]
self.data['eofs'] = ma.masked_all((J, K, nmodes), dtype='complex128')
for n,ind in enumerate(self.grid_index):
self.data['eofs'][ind[0],ind[1],:] = output['eofs'][n,:]
# ----
self.set_wavelenght()
#for k in self.data.keys():
# print k, self.data[k].shape, type(self.data[k])
if 'figs' in self.metadata:
print "Creating figures for %s modes" % nmodes
#for nmode in range(5):
#for n in range(10):
for n in range(nmodes):
#eof2D=ceof['eofs'][:,nmode]
#pc2D=ceof['pcs'][:,nmode]
##
##eof=ma.masked_all(self.data['ssh'].shape[1:],dtype=eofs.dtype)
#eof=ma.masked_all(self.data['ssh'].shape[1:],dtype='complex128')
##pc=ma.masked_all(self.data['ssh'].shape[1:],dtype=pcs.dtype)
#pc=ma.masked_all(self.data['ssh'].shape[1:],dtype='complex128')
##
##for n,i in enumerate(ind):
##
## ---- 2D back to grid ----
#for n,ind in enumerate(self.data2D['grid_index']):
# #print n,ind
# eof[ind[0],ind[1]] = eof2D[n]
# #pc[ind[0],ind[1]] = pc2D[n]
#pc = pc2D
#print pc2D.shape
#varfrac = round(getvariancefraction(lambdas)[nmode]*1e2)
#varfrac = ceof['variancefraction'][nmode]
#fig = self.plot(eof_amp,eof_phase,pc_amp,pc_phase,nmode,varfrac)
if 'suffix' in self.metadata['figs']:
filename="../figs/CEOF_%s_mode%s.eps" % (self.metadata['figs']['suffix'],(n+1))
else:
filename="../figs/CEOF_mode%s.eps" % (n+1)
limits={'LatIni':-5, 'LatFin':15, 'LonIni':-60, 'LonFin':-25}
#self.plot(self['eofs'][:,:,n],self['pcs'][:,n],(n+1),self['variancefraction'][n],filename=filename,limits=limits,cumvarfrac=self['variancefraction'][:(n+1)].sum())
import graphics
graphics.plot(self['eofs'][:,:,n], self['pcs'][:,n],
(n+1),self['variancefraction'][n],
filename=filename,
data = self.data,
limits=limits,
cumvarfrac=self['variancefraction'][:(n+1)].sum())
@property
def zbars(self):
return self._zbars
if __name__ == '__main__':
import graphics
import color
standardObserver = color.CIEStandardObserver()
graphics.plot([standardObserver.wavelengths,
standardObserver.wavelengths,
standardObserver.wavelengths],
[standardObserver.xbars,
standardObserver.ybars,
standardObserver.zbars],
title='1931 2-degree Standard Observer',
xlabel='Wavelength [nm]',
ylabel='Response',
ylim=[0, 2.5],
label=['x-bar', 'y-bar', 'z-bar'])
standardObserver = color.CIEStandardObserver('10deg')
graphics.plot([standardObserver.wavelengths,
standardObserver.wavelengths,
standardObserver.wavelengths],
[standardObserver.xbars,
standardObserver.ybars,
standardObserver.zbars],
title='1964 10-degree Standard Observer',
xlabel='Wavelength [nm]',
