def MBdist(
n, e_photon, thick
): # n: particle number, loct: start point(x-x0), scale: sigma, wl: wavelength,thick: thickness of the cathode
assert e_photon > bandgap
if e_photon - bandgap - 0.8 <= 0:
scale = e_photon - bandgap
loct = 0
else:
scale = 0.8
loct = e_photon - bandgap - scale
data = maxwell.rvs(loc=loct, scale=scale, size=n)
data_ene = np.array(data)
params = maxwell.fit(data, floc=0)
data_v = np.sqrt(2 * data_ene * ec / me) * 10**9
p2D = []
wl = ((19.82 - 27.95 * e_photon + 11.15 * e_photon**2) * 10**-3)**-1
pens = expon.rvs(loc=0, scale=wl, size=n)
penss = filter(lambda x: x <= thick, pens)
params_exp = expon.fit(pens, floc=0)
i = 0
for n in range(len(penss)):
phi = random.uniform(0, 2 * math.pi) # initial angular
poy = random.uniform(-1 * 10**6,
1 * 10**6) # initial y direction position
p2D.append([
penss[i], poy, data_v[i] * math.cos(phi),
data_v[i] * math.sin(phi), data_v[i], data[i]
]) #p2D: (z,y,vz,vy,v,ene)
i += 1
p2D = np.array(p2D)
return params, p2D, penss, params_exp
def plot_hist_and_fit_gauss(data_list,
binno=60,
normed=1,
facecolor="green",
linecolor='r--',
alpha=0.5):
"""
return (mu, sigma)
"""
# fit the data by maxwell-boltzmann distribution
# mawell.fit returns shape, loc, scale : tuple of floats
# MLEs for any shape statistics, followed by those for location and
# scale.
params = maxwell.fit(data_list)
print params
# plot the histogram of the data
#(n, bins, patches) = plt.hist(data_list, binno, range=(0, 50), normed=normed, facecolor=facecolor, alpha=alpha)
(n, bins, patches) = plt.hist(data_list,
binno,
normed=normed,
facecolor=facecolor,
alpha=alpha)
x = np.linspace(0, 25, 100)
# add a 'best fit' line
plt.plot(x, maxwell.pdf(x, *params), linecolor, lw=3)
print get_max_likelihood(data_list, params[0], params[1])
return params
def quiver_desired_velocity(crStats\
,further_conditioning = None
,grid_x=40,grid_y=40,small_incr=.00001\
,qty='vel'
,spec_type='gt'
,img_format='png'
,**kw):
x_class_v,y_class_v,u_means,v_means,u_delta,v_delta,u_all,v_all =\
sub_eval_desired_velocity_qtys(crStats
,further_conditioning\
,grid_x,grid_y,small_incr\
,qty\
,spec_type)
plt.figure()
plt.hist2d(u_delta,v_delta,**hist2d_param)
plt.title('Overall noise distribution')
plt.savefig(PATH_PREFIX + 'total_noise_%s_%s.%s' % (qty,spec_type,img_format))
rv = maxwell()
speed_delta = np.sqrt(np.asarray(u_delta)**2 + np.asarray(v_delta)**2)
loc,scale = maxwell.fit(speed_delta)
plt.figure()
plt.hist(speed_delta,**hist_param_noLog)
x = np.linspace(0,2,100)
plt.plot(x,maxwell.pdf(x,scale=scale,loc=loc))
plt.title('Overall abs noise distribution')
plt.savefig(PATH_PREFIX + 'maxwellian_noise_%s_%s.%s' % (qty,spec_type,img_format))
temperature = np.sqrt( np.asarray([np.var(us) for us in u_all]) + np.asarray([np.var(vs) for vs in v_all]) )
speed = [np.mean(np.sqrt(np.asarray(us)**2 + np.asarray(vs)**2)) for us,vs in zip(u_all,v_all) ]
plt.figure()
plt.quiver(x_class_v,y_class_v,u_means,v_means,temperature,**kw)
plt.colorbar()
plt.title('cross-realization "thermal" noise [m/s]')
plt.xlabel('x [m]')
plt.ylabel('y [m]')
plt.savefig(PATH_PREFIX + 'quiver_thermal_noise_%s_%s.%s' % (qty,spec_type,img_format))
plt.figure()
plt.quiver(x_class_v,y_class_v,u_means,v_means,speed,**kw)
plt.colorbar()
plt.title('average speed')
plt.xlabel('x [m]')
plt.ylabel('y [m]')
plt.savefig(PATH_PREFIX + 'quiver_average_modulus_%s_%s.%s' % (qty,spec_type,img_format))
def plot_hist_and_fit_gauss(data_list, binno=60, normed=1, facecolor="green", linecolor='r--', alpha=0.5):
"""
return (mu, sigma)
"""
# fit the data by maxwell-boltzmann distribution
# mawell.fit returns shape, loc, scale : tuple of floats
# MLEs for any shape statistics, followed by those for location and
# scale.
params = maxwell.fit(data_list)
print params
# plot the histogram of the data
#(n, bins, patches) = plt.hist(data_list, binno, range=(0, 50), normed=normed, facecolor=facecolor, alpha=alpha)
(n, bins, patches) = plt.hist(data_list, binno, normed=normed, facecolor=facecolor, alpha=alpha)
x = np.linspace(0, 25, 100)
# add a 'best fit' line
plt.plot(x, maxwell.pdf(x, *params), linecolor, lw=3)
print get_max_likelihood(data_list, params[0], params[1])
return params
for i in colnames:
df[i]= (df[i] - df[i].min()) / (df[i].max() - df[i].min())
print(df)
df.plot(x='Temperature')
plt.show()
y = input('Enter temperature')
d1 = df[df['Temperature'] >= int(y)]
a = input('Second temperature')
d2 = d1[d1['Temperature'] <= int(a)]
colnames2 = list(d2.columns)
for i in colnames2:
df[i] = (df[i] - df[i].min()) / (df[i].max() - df[i].min())
main = d2.plot(x='Temperature')
line = plt.axhline(y=0.5, color='black', linestyle='-')
plt.show()
maxwell = stats.maxwell
for column in d2.columns:
params= maxwell.fit(d2.columns, floc=0)
print(params)