def task2():
x = mlab.frange(-1, 1, 0.1)
pylab.grid(True)
for i in range(len(x) - 1):
a = x[i]
b = x[i + 1]
x_local = mlab.frange(a, b, 0.01)
y_local = [hermit(j, a, b, f(a), f(b), df(a), df(b)) for j in x_local]
pylab.plot(x_local, y_local)
def _genticks(self, ta, precis='h'):
'''Generating ticks and labels according to GMT time from unix time
data, ta. One can set major and minor ticks via precis. '''
if precis[0] == 'h':
mods = [12, 6, 4, 3, 2, 1]
t = [0] * 51
# 4 ticks per inch is OK!
while len(t) > 4 * self._req.figsize[0] and len(mods):
mm = mods.pop()
gm_1hour = tmu.upper_hour(ta[0], mod=mm)
t = map(int, list(frange(gm_1hour, ta[-1], 3600 * mm)))
if precis[1] == '0':
zers = []
for tt in t:
if time.localtime(tt).tm_hour == 0:
zers.append(t.index(tt))
while len(zers):
t.pop(zers.pop())
if len(t) > 4 * self._req.figsize[0]:
l = [''] * len(t)
else:
l = [time.strftime('%H', time.localtime(int(ii))) for ii in t]
elif precis[0] == 'd':
if precis[1] == 'c': # at the center of a day
gm_1day = tmu.upper_day(ta[0])
gm_1day = gm_1day - 12 * 3600 if gm_1day - 12 * 3600 > ta[
0] else gm_1day + 12 * 3600
fmt = '%d.%m'
elif precis[1] == 'l':
gm_1day = tmu.upper_day(ta[0])
fmt = '%d'
else:
AttributeError('Precise code %s is incorrect' % precis)
print 'Building day ticks from ts.%d to ts.%d' % (ta[0], ta[-1])
print 'First tick: %d' % gm_1day
skip_days = 1
while (ta[-1] -
gm_1day) / 3600. / 24. / skip_days > self._req.figsize[0]:
skip_days += 1
#TODO: and something more curious!
print 'DAYS skipping: %d days at %d inches' % (
skip_days, self._req.figsize[0])
t = map(int, list(frange(gm_1day, ta[-1],
(3600 * 24 * skip_days))))
if len(t) > 0:
if t[-1] > ta[-1]:
t.pop()
l = [time.strftime(fmt, time.localtime(int(ii))) for ii in t]
print t, l
else:
# at least one tick :-)
t = [(ta[0] + ta[1]) / 2.]
l = [time.strftime(fmt, time.localtime(ta[0]))]
else:
raise NotImplementedError('Precis type ``%s'' not implemented' \
% precis)
return t, l
def plot(self, policy):
rows = len(policy)
cols = len(policy[0])
X,Y = meshgrid(range(rows), range(cols))
# U, V give the x and y components of the arrow vectors
U = [[0]*cols for _ in range(rows)]
V = [[0]*cols for _ in range(rows)]
for row,r in enumerate(policy):
for col,c in enumerate(r):
a = c[0]
if a == 'N':
U[row][col] = 0
V[row][col] = 1
elif a == 'S':
U[row][col] = 0
V[row][col] = -1
elif a == 'E':
U[row][col] = 1
V[row][col] = 0
elif a == 'W':
U[row][col] = -1
V[row][col] = 0
else:
raise ValueError
ax = self.fig.add_subplot(111)
ax.quiver(X, Y, U, V, pivot='middle')
ax.grid(linestyle='-')
ax.set_xticks(frange(0.5, cols))
ax.set_yticks(frange(0.5, rows))
ax.set_xticklabels([])
ax.set_yticklabels([])
xmin, xmax, ymin, ymax = ax.axis()
ax.axis([xmin-0.5, xmax-1, ymin-0.5, ymax-1])
if self.world:
#map =
cdict = {'blue': ((0.0, 0.2, 0.2),
(0.25, 1, 1),
(1.0, 0, 0)),
'green': ((0.0, 0.2, 0.2),
(0.25, 1, 1),
(1.0, 1, 1)),
'red': ((0.0, 0.2, 0.2),
(0.25, 1, 1),
(1.0, 0, 0)) }
cmap = LinearSegmentedColormap('fudge', cdict)
ax.imshow(self.world, cmap=cmap, interpolation='nearest')
def Draw(func1, func2):
# генирация точек графиков
xlist = mlab.frange(a, b, 0.01)
ylist = [func1(x) for x in xlist]
ylist2 = [func2(x) for x in xlist]
# Генирирум ось
y0 = [0 for x in xlist]
#############################################################
max1Y = max(ylist)
min1Y = min(ylist)
max2Y = max(ylist2)
min2Y = min(ylist2)
minmaxarrayY = []
minmaxarrayX = []
for i in range(len(ylist)):
if ((max1Y == ylist[i]) or (min1Y == ylist[i])):
minmaxarrayY.append(ylist[i])
minmaxarrayX.append(xlist[i])
for i in range(len(ylist2)):
if ((max2Y == ylist2[i]) or (min2Y == ylist2[i])):
minmaxarrayY.append(ylist2[i])
minmaxarrayX.append(xlist[i])
################################################################
extremumX, extremumY = converter(korn, 0, 3, 4, func1)
inflectionX, inflectionY = converter(korn1, 0, 3, 4, func1)
kornsX, kornsY = converter(table, 1, 3, 4, func1)
pylab.plot(extremumX, extremumY, 'go', label='extremum', color='red')
pylab.plot(inflectionX, inflectionY, 'go', label='inflection point', color='yellow')
pylab.plot(minmaxarrayX, minmaxarrayY, 'go', label='min/max', color='green')
pylab.plot(kornsX, kornsY, 'go', label='Korn', color='black')
pylab.plot(xlist, ylist, label='$sin(x)/x$')
pylab.plot(xlist, y0, color='pink')
pylab.plot(xlist, ylist2, label='$0.02*x* x - 4$', color='pink')
pylab.legend()
# Включаем рисование сетки
pylab.grid(True)
xlist1 = mlab.frange(float(table2[0][3]), float(table2[len(table2) - 1][3]), 0.01)
pylab.fill_between(xlist1, [func1(x) for x in xlist1], [func2(x) for x in xlist1], color='green', alpha=0.25)
# если мало разбиений, то переопереляем сетку под шаг
if ((round((b - a) / h)) < 25):
pylab.xticks([a + i * h for i in range(round((b - a) / h) + 1)])
print()
print()
print("Минимумы и максимумы:")
print("X", "Y", sep="\t")
for i in range(len(minmaxarrayY)):
print('{:3.5g}'.format(minmaxarrayX[i]), '{:3.5g}'.format(minmaxarrayY[i]), sep='\t\t')
# Рисуем фогрму с графиком
pylab.show()
def search_best_parameter(filename):
a, b, y_title, x_title, table = filehandling.bonus_file_handling(filename)
b_range = frange(b[0], b[1], b[2])
a_range = frange(a[0], a[1], a[2])
# Calculate chi for each pair of values.
chi_values = []
for b_value in b_range:
for a_value in a_range:
chi_values.append([
b_value, a_value,
fitgui.bonus_chi_squared(table, my_function,
(0, a_value, b_value))
])
# Minimize chi
best = chi_values[0]
for b_value, a_value, chi in chi_values:
if (chi < best[2]):
best = (b_value, a_value, chi)
print(
output_format.format(a_value=best[1],
a_error=a[2],
b_value=best[0],
b_error=b[2],
chi_squared=best[2],
chi_reduced=fitgui._chi_reduced(
best[2], len(table["x"]))))
# Create graph.
pyplot.figure()
pyplot.title("Generated custom fit")
pyplot.ylabel("chi2(a,b = {best_b})".format(best_b=best[0]))
pyplot.xlabel('a')
# Set data and graph.
plot_fitted_graph(min(table["x"]),
max(table["x"]), (0, best[1], best[0]),
my_function,
c="blue")
plot_data_points(table)
a_values = [a for (b, a, chi) in chi_values if b == best[0]]
chi_values = [chi for (b, a, chi) in chi_values if b == best[0]]
slope = (max(chi_values) - min(chi_values)) / (max(a_values) -
min(a_values))
intercept = max(chi_values) + (slope * max(a_values))
# save and show bonus function
save_graph("numeric_sampling")
pyplot.show()
def plot(self, policy):
rows = len(policy)
cols = len(policy[0])
X, Y = meshgrid(range(rows), range(cols))
# U, V give the x and y components of the arrow vectors
U = [[0] * cols for _ in range(rows)]
V = [[0] * cols for _ in range(rows)]
for row, r in enumerate(policy):
for col, c in enumerate(r):
a = c[0]
if a == 'N':
U[row][col] = 0
V[row][col] = 1
elif a == 'S':
U[row][col] = 0
V[row][col] = -1
elif a == 'E':
U[row][col] = 1
V[row][col] = 0
elif a == 'W':
U[row][col] = -1
V[row][col] = 0
else:
raise ValueError
ax = self.fig.add_subplot(111)
ax.quiver(X, Y, U, V, pivot='middle')
ax.grid(linestyle='-')
ax.set_xticks(frange(0.5, cols))
ax.set_yticks(frange(0.5, rows))
ax.set_xticklabels([])
ax.set_yticklabels([])
xmin, xmax, ymin, ymax = ax.axis()
ax.axis([xmin - 0.5, xmax - 1, ymin - 0.5, ymax - 1])
if self.world:
#map =
cdict = {
'blue': ((0.0, 0.2, 0.2), (0.25, 1, 1), (1.0, 0, 0)),
'green': ((0.0, 0.2, 0.2), (0.25, 1, 1), (1.0, 1, 1)),
'red': ((0.0, 0.2, 0.2), (0.25, 1, 1), (1.0, 0, 0))
}
cmap = LinearSegmentedColormap('fudge', cdict)
ax.imshow(self.world, cmap=cmap, interpolation='nearest')
def box_grid(xlimp, ylimp):
"""box_grid generate list of points on edge of box
list = box_grid([xmin xmax xnum], [ymin ymax ynum]) generates a
list of points that correspond to a uniform grid at the end of the
box defined by the corners [xmin ymin] and [xmax ymax].
"""
sx10 = frange(xlimp[0], xlimp[1], float(xlimp[1] - xlimp[0]) / xlimp[2])
sy10 = frange(ylimp[0], ylimp[1], float(ylimp[1] - ylimp[0]) / ylimp[2])
sx1 = np.hstack((0, sx10, 0 * sy10 + sx10[0], sx10, 0 * sy10 + sx10[-1]))
sx2 = np.hstack((0, 0 * sx10 + sy10[0], sy10, 0 * sx10 + sy10[-1], sy10))
return np.transpose(np.vstack((sx1, sx2)))
def box_grid(xlimp, ylimp):
"""box_grid generate list of points on edge of box
list = box_grid([xmin xmax xnum], [ymin ymax ynum]) generates a
list of points that correspond to a uniform grid at the end of the
box defined by the corners [xmin ymin] and [xmax ymax].
"""
sx10 = frange(xlimp[0], xlimp[1], float(xlimp[1]-xlimp[0])/xlimp[2])
sy10 = frange(ylimp[0], ylimp[1], float(ylimp[1]-ylimp[0])/ylimp[2])
sx1 = np.hstack((0, sx10, 0*sy10+sx10[0], sx10, 0*sy10+sx10[-1]))
sx2 = np.hstack((0, 0*sx10+sy10[0], sy10, 0*sx10+sy10[-1], sy10))
return np.transpose( np.vstack((sx1, sx2)) )
def __init__(self, funct, xmin, xmax, dx, nx=False):
self.xes = list(mlab.frange(xmin, xmax, dx))
if nx != False:
for m_y in nx:
if m_y not in self.xes:
self.xes.append(m_y)
self.yes = [funct(x) for x in self.xes]
def main(args):
# y = a*x + b
x = frange(-1, 2, 0.15)
# f(x) = x / (x+ 2) dla x > 0
# f(x) = x * x/3 dla x > 0 i x < 1
# f(x) = x / -3 dla x <= 0
y = []
for el in x:
if el <= 0:
y.append(el / -3)
elif el < 1:
y.append(el * el / 3)
else:
y.append(el / (el + 2))
print(x)
plt.plot(x, y)
plt.title('Wykres f(x')
plt.grid(True)
plt.show()
return 0
def hw_6_2():
D = []
for i in range(10000):
smp = np.random.uniform(0, 1, 50)
stat = np.max(smp)
D.append(stat)
sample = np.random.uniform(0, 1, 50)
bootstrap_D = do_bootstrap(sample, 10000, lambda array: np.max(array))
xlist = mlab.frange(0, 1, 0.001)
ylist = [magic(x) for x in xlist]
# sns.distplot(D)
# plt.hist(D, bin_number(10000), color='red', alpha=0.5)
# plt.hist(bootstrap_D, bin_number(10000), color='blue', alpha=0.5)
my_cdf(D, 'blue')
my_cdf(bootstrap_D, 'red')
plt.plot(xlist, ylist)
print('Квантиль 0.025: ', find_quantile(bootstrap_D, 0.025))
print('Квантиль 0.975: ', find_quantile(bootstrap_D, 0.975))
print(se_boot(bootstrap_D))
plt.show()
def showT():
'''Utility function to show T distributions'''
t = frange(-5, 5, 0.05)
TVals = [1,5]
normal = stats.norm.pdf(t)
t1 = stats.t.pdf(t,1)
t5 = stats.t.pdf(t,5)
plt.plot(t,normal, '--', label='normal')
plt.plot(t, t1, label='df=1')
plt.plot(t, t5, label='df=5')
plt.legend()
plt.xlim(-5,5)
plt.xlabel('X')
plt.ylabel('pdf(X)')
plt.axis('tight')
outDir = r'..\Images'
outFile = 'dist_t.png'
saveTo = os.path.join(outDir, outFile)
plt.savefig(saveTo, dpi=200)
print('OutDir: {0}'.format(outDir))
print('Figure saved to {0}'.format(outFile))
plt.show()
plt.close()
def Draw(func1, func2):
# генирация точек графика
xlist = mlab.frange(a, b, 0.01)
ylist = [func1(x) for x in xlist]
ylist2 = [func2(x) for x in xlist]
# Генирирум ось
y0 = [0 for x in xlist]
pylab.plot(xlist, ylist)
#pylab.plot(xlist, y0, label='line1', color='blue')
pylab.plot(xlist, ylist2, label='$sin(x)/x)$', color='red')
pylab.legend()
# Включаем рисование сетки
pylab.grid(True)
pylab.fill_between(xlist, ylist, ylist2, color='green', alpha=0.25)
# если мало разбиений, то переопереляем сетку под шаг
if ((round((b - a) / h)) < 25):
pylab.xticks([a + i * h for i in range(round((b - a) / h) + 1)])
# рисуем корни, промерка того что корень не содержит ошибок
for i in range(1, len(table)):
if (table[i][4] != ':-('):
pylab.scatter(table[i][3], table[i][4])
# Рисуем фогрму с графиком
pylab.show()
def showExp():
'''Utility function to show exponential distributions'''
t = frange(0, 3, 0.01)
lambdas = [0.5, 1, 1.5]
for par in lambdas:
plt.plot(t,
stats.expon.pdf(t, 0, par),
label='$\lambda={0:3.1f}$'.format(par))
plt.legend()
plt.xlim(0, 3)
plt.xlabel('X')
plt.ylabel('pdf(X)')
plt.axis('tight')
plt.legend()
outDir = r'..\Images'
outFile = 'dist_exp.png'
saveTo = os.path.join(outDir, outFile)
plt.savefig(saveTo, dpi=200)
print('OutDir: {0}'.format(outDir))
print('Figure saved to {0}'.format(outFile))
plt.show()
plt.close()
def integrate(func, mim_lim, max_lim, n):
integral = 0.0
step = (max_lim - mim_lim) / n
for x in mlab.frange(mim_lim + step / 2, max_lim - step / 2, step):
integral += step / 6 * (func(x - step / 2) + 4 * func(x) +
func(x + step / 2))
return integral
def showT():
'''Utility function to show T distributions'''
t = frange(-5, 5, 0.05)
TVals = [1, 5]
normal = stats.norm.pdf(t)
t1 = stats.t.pdf(t, 1)
t5 = stats.t.pdf(t, 5)
plt.plot(t, normal, '--', label='normal')
plt.plot(t, t1, label='df=1')
plt.plot(t, t5, label='df=5')
plt.legend()
plt.xlim(-5, 5)
plt.xlabel('X')
plt.ylabel('pdf(X)')
plt.axis('tight')
outDir = r'..\Images'
outFile = 'dist_t.png'
saveTo = os.path.join(outDir, outFile)
plt.savefig(saveTo, dpi=200)
print('OutDir: {0}'.format(outDir))
print('Figure saved to {0}'.format(outFile))
plt.show()
plt.close()
def showChi2():
'''Utility function to show Chi2 distributions'''
t = frange(0, 8, 0.05)
Chi2Vals = [1, 2, 3, 5]
for chi2 in Chi2Vals:
plt.plot(t, stats.chi2.pdf(t, chi2), label='k={0}'.format(chi2))
plt.legend()
plt.xlim(0, 8)
plt.xlabel('X')
plt.ylabel('pdf(X)')
plt.axis('tight')
outDir = r'..\Images'
outFile = 'dist_chi2.png'
saveTo = os.path.join(outDir, outFile)
plt.savefig(saveTo, dpi=200)
print('OutDir: {0}'.format(outDir))
print('Figure saved to {0}'.format(outFile))
plt.show()
plt.close()
def shifted_normal():
'''PDF, scatter plot, and histogram.'''
# Generate the data
# Plot a normal distribution: "Probability density functions"
myMean = [0,0,0,-2]
mySD2 = [0.2,1,5,0.5]
t = frange(-5,5,0.02)
sns.set_palette('husl', 4)
for mu,sigma in zip(myMean, np.sqrt(mySD2)):
y = stats.norm.pdf(t, mu, sigma)
plt.plot(t,y, label='$\mu={0}, \; \t\sigma={1:3.1f}$'.format(mu,sigma))
plt.legend()
plt.xlim([-5,5])
plt.title('Normal Distributions')
outFile = 'Normal_Distribution_PDF.png'
saveTo = os.path.join(outDir, outFile)
plt.savefig(saveTo, dpi=200)
print('OutDir: {0}'.format(outDir))
print('Figure saved to {0}'.format(outFile))
plt.show()
# Generate random numbers with a normal distribution
myMean = 0
mySD = 3
numData = 500
data = stats.norm.rvs(myMean, mySD, size = numData)
plt.scatter(np.arange(len(data)), data)
plt.title('Normally distributed data')
plt.xlim([0,500])
plt.ylim([-10,10])
plt.show()
plt.close()
def showF():
'''Utility function to show F distributions'''
t = frange(0, 3, 0.01)
d1s = [1, 2, 5, 100]
d2s = [1, 1, 2, 100]
for (d1, d2) in zip(d1s, d2s):
plot(t, stats.f.pdf(t, d1, d2), label='F({0}/{1})'.format(d1, d2))
plt.legend()
plt.xlim(0, 3)
plt.xlabel('X')
plt.ylabel('pdf(X)')
plt.axis('tight')
plt.legend()
outDir = r'..\Images'
outFile = 'dist_f.png'
saveTo = os.path.join(outDir, outFile)
plt.savefig(saveTo, dpi=200)
print('OutDir: {0}'.format(outDir))
print('Figure saved to {0}'.format(outFile))
plt.show()
plt.close()
def showChi2():
'''Utility function to show Chi2 distributions'''
t = frange(0, 8, 0.05)
Chi2Vals = [1,2,3,5]
for chi2 in Chi2Vals:
plt.plot(t, stats.chi2.pdf(t, chi2), label='k={0}'.format(chi2))
plt.legend()
plt.xlim(0,8)
plt.xlabel('X')
plt.ylabel('pdf(X)')
plt.axis('tight')
outDir = r'..\Images'
outFile = 'dist_chi2.png'
saveTo = os.path.join(outDir, outFile)
plt.savefig(saveTo, dpi=200)
print('OutDir: {0}'.format(outDir))
print('Figure saved to {0}'.format(outFile))
plt.show()
plt.close()
def showF():
'''Utility function to show F distributions'''
t = frange(0, 3, 0.01)
d1s = [1,2,5,100]
d2s = [1,1,2,100]
for (d1,d2) in zip(d1s,d2s):
plot(t, stats.f.pdf(t, d1, d2), label='F({0}/{1})'.format(d1,d2))
plt.legend()
plt.xlim(0,3)
plt.xlabel('X')
plt.ylabel('pdf(X)')
plt.axis('tight')
plt.legend()
outDir = r'..\Images'
outFile = 'dist_f.png'
saveTo = os.path.join(outDir, outFile)
plt.savefig(saveTo, dpi=200)
print('OutDir: {0}'.format(outDir))
print('Figure saved to {0}'.format(outFile))
plt.show()
plt.close()
def showExp():
'''Utility function to show exponential distributions'''
t = frange(0, 3, 0.01)
lambdas = [0.5, 1, 1.5]
for par in lambdas:
plt.plot(t, stats.expon.pdf(t, 0, par), label='$\lambda={0:3.1f}$'.format(par))
plt.legend()
plt.xlim(0,3)
plt.xlabel('X')
plt.ylabel('pdf(X)')
plt.axis('tight')
plt.legend()
outDir = r'..\Images'
outFile = 'dist_exp.png'
saveTo = os.path.join(outDir, outFile)
plt.savefig(saveTo, dpi=200)
print('OutDir: {0}'.format(outDir))
print('Figure saved to {0}'.format(outFile))
plt.show()
plt.close()
def shifted_normal():
'''PDF, scatter plot, and histogram.'''
# Generate the data
# Plot a normal distribution: "Probability density functions"
myMean = [0,0,0,-2]
mySD2 = [0.2,1,5,0.5]
t = frange(-5,5,0.02)
sns.set_palette('husl', 4)
for mu,sigma in zip(myMean, np.sqrt(mySD2)):
y = stats.norm.pdf(t, mu, sigma)
plt.plot(t,y, label='$\mu={0}, \; \t\sigma={1:3.1f}$'.format(mu,sigma))
plt.legend()
plt.xlim([-5,5])
plt.title('Normal Distributions')
outFile = 'Normal_Distribution_PDF.png'
saveTo = os.path.join(outDir, outFile)
plt.savefig(saveTo, dpi=200)
print('OutDir: {0}'.format(outDir))
print('Figure saved to {0}'.format(outFile))
plt.show()
# Generate random numbers with a normal distribution
myMean = 0
mySD = 3
numData = 500
data = stats.norm.rvs(myMean, mySD, size = numData)
plt.scatter(np.arange(len(data)), data)
plt.title('Normally distributed data')
plt.xlim([0,500])
plt.ylim([-10,10])
plt.show()
plt.close()
def generate_heatmap_data(Tmin, Tmax, Smin, Smax, Lmin, Lmax, scale, log=True):
data = dict()
if log == True:
Tmin, Tmax = np.log10(Tmin), np.log10(Tmax)
Smin, Tmax = np.log10(Smin), np.log10(Smax)
from matplotlib.mlab import frange
print(Tmin, Tmax)
for i, item in enumerate(frange(Tmin, Tmax, (Tmax - Tmin) / scale)):
for j, jtem in enumerate(frange(Lmin, Lmax, (Lmax - Lmin) / scale)):
for k, ktem in enumerate(frange(Smin, Smax,
(Smax - Smin) / scale)):
if log == True:
data[(i, j, k)] = tc_gelhar(10**(item), jtem, 10**(ktem))
else:
data[(i, j, k)] = tc_gelhar(item, jtem, ktem)
return data
def plot(func):
x_min = ARRAY_X[0]
x_max = ARRAY_X[-1]
dx = 0.001
x_list = mlab.frange(x_min, x_max, dx)
y_list = [func(arg) for arg in x_list]
pylab.plot(x_list, y_list)
pylab.show()
def plot(a, b, c):
x_min = ARGS[0]
x_max = ARGS[-1]
dx = 0.001
x_list = mlab.frange(x_min, x_max, dx)
y_list = [a * arg + b * math.exp(arg) + c for arg in x_list]
pylab.scatter(ARGS, VALUES)
pylab.plot(x_list, y_list)
pylab.show()
def search_best_parameter(filename):
a, b, y_title, x_title, table = filehandling.bonus_file_handling(filename)
b_range = frange(b[0], b[1], b[2])
a_range = frange(a[0], a[1], a[2])
# Calculate the chi for each pair.
chi_values = []
for b_value in b_range:
for a_value in a_range:
chi_values.append([
b_value, a_value,
fitgui.bonus_chi_squared(table, myfunc, (0, a_value, b_value))
])
# Find the best chi.
best = chi_values[0]
for b_value, a_value, chi in chi_values:
if chi < best[2]:
best = (b_value, a_value, chi)
print(
OUTPUT_FORMAT.format(a_value=best[1],
a_error=a[2],
b_value=best[0],
b_error=b[2],
chi_squared=best[2],
chi_reduced=fitgui._chi_reduced(
best[2], len(table["x"]))))
# Create graph.
pyplot.figure()
pyplot.title("Generated custom fit")
pyplot.ylabel(y_title)
pyplot.xlabel(x_title)
# Set the data and graph.
plot_fitted_graph(min(table["x"]),
max(table["x"]), (0, best[1], best[0]),
myfunc,
c="blue")
plot_data_points(table)
pyplot.show()
def __init__(self, name='drawSinus'):
print name, 'started!\n'
# Интервал изменения переменной по оси X
self.xmin = -20.0
self.xmax = 20.0
# Шаг между точками
self.dx = 0.01
#Создадим список координат по оси X
#на отрезке [-xmin; xmax], включая концы
self.xlist = mlab.frange (self.xmin, self.xmax, self.dx)
def task1():
rndx = [-1, -0.5, 0, 0.5, 1]
rndy = [random.uniform(0, 1) for i in range(5)]
rndlagrx = mlab.frange(-1, 1, 0.01)
rndlagry = [lagr1(x, rndx, rndy) for x in rndlagrx]
pylab.subplot(2, 3, 1)
pylab.title('1.1')
pylab.grid(True)
pylab.plot(rndlagrx, rndlagry, color='green')
pylab.plot(rndx, rndy, 'go', color='red', marker='x')
def find_dots(x1, x2, n, x, q, f):
h = (x2 - x1) / (n)
print("h = " + str(h))
xlist = mlab.frange(x1, x2, h)
print("X's:")
print(len(xlist))
print(xlist)
# y" = -(1 + x^2)y - 1
# ay" + (1 + bx^2)y = -1
yks = []
for i in range(n + 1):
yks.append(Symbol("y_d" + str(i + 1)))
q = lambdify(x, q, 'numpy')
f = lambdify(x, f, 'numpy')
system = []
# for i in range(n - 2):
# system.append((yks[i + 2] - 2 * yks[i + 1] + yks[i])/(h**2) - q(xlist[i + 1]) * yks[i + 1] - f(xlist[i + 1]))
# #system.append(yks[i] - (2 + h ** 2 * q(xlist[i + 1])) * yks[i + 1] + yks[i + 2] - h ** 2 * f(xlist[i + 1]))
#^^^^^деление на нолb
k = 1
while k != n:
system.append(yks[k - 1] - (2 - h**2 * q(xlist[k])) * yks[k] +
yks[k + 1] - f(xlist[k]) * h**2)
k += 1
print("\nGain system:")
for el in system:
print(el)
system[0] = system[0].subs(yks[0], 0)
system[-1] = system[-1].subs(yks[-1], 0)
print("\nThen we substitute the boundary points and get solvable system :")
for el in system:
print(el)
coeffs = linsolve(system, yks[1:n])
coeffs = next(iter(coeffs))
print("\nAnswer:")
print(coeffs)
ylist = [0]
for el in coeffs:
ylist.append(el)
ylist.append(0)
print("\nYk's :")
print(ylist)
return xlist, ylist
def run(self, c, path):
# c = "LSKETH"
with open(path + c + ".log", "w") as f:
print(str(self.start_date) + " " + str(self.stop_date))
time_delta = self.MAX_STEP * self.tickers["1m"]
time_spans = frange(self.start_date, self.stop_date, time_delta)
for index, span in enumerate(time_spans):
print("loop " + str(index) + "/" + str(len(time_spans)))
data = Stock.get_klines(c, "1m", span)
for e in data:
f.write(str(e) + "\n")
f.flush()
def skewness(ax):
'''Normal and skewed distribution'''
t = frange(-6,10,0.1) # generate the desirded x-values
normal = stats.norm.pdf(t,1,1.6)
chi2 = stats.chi2.pdf(t,3)
ax.plot(t, normal, '--', label='normal')
ax.plot(t, chi2, label='positive skew')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.legend()
def Shooting(N):
y0 = 0
x0 = 0
h = 1 / N
xlist = mlab.frange(0, 1, h)
#описание метода
def RungeKutta(f, x, y):
def k1():
return h * f(x, y)
def k2():
return h * f(x + h / 2, y + k1() / 2)
def k3():
return h * f(x + h / 2, y + k2() / 2)
def k4():
return h * f(x + h, y + k3())
return y + 1 / 6 * k1() + 2 / 6 * k2() + 2 / 6 * k3() + 1 / 6 * k4()
#решение краевой задачи не зависит от параметра, поэтому вынес из цикла
u = 0
w = 1
def difw(x, y):
return u
def difu(x, y):
return w
for i in range(N):
w = RungeKutta(difw, x0 + i * h, w)
u = RungeKutta(difu, x0 + i * h, u)
#решение основной задачи
def dify(x, y):
return z
def difz(x, y):
return dif2y(x, y)
for i in range(N + 1):
y = y0
z = -3.1
ylist = [y0]
for j in range(N):
z = RungeKutta(difz, x0 + j * h, z)
y = RungeKutta(dify, x0 + j * h, y)
ylist.append(y)
y0 = y0 - (y - math.e - 1 / math.e + 2) / w
return plt.plot(xlist, ylist)
def __call__(self):
'Return the locations of the ticks'
self.verify_intervals()
vmin, vmax = self.viewInterval.get_bounds()
if vmax<vmin:
vmin, vmax = vmax, vmin
vmin = self._base.ge(vmin)
locs = frange(vmin, vmax+0.001*self._base.get_base(), self._base.get_base())
return locs
def plot_graphic(linear_combination, x, xmin, xmax, dx):
f = lambdify(x, linear_combination, 'numpy')
# xmin = -10000
# xmax = 10000
# dx = 0.1
xlist = mlab.frange(xmin, xmax, dx)
ylist = [f(x) for x in xlist]
# pylab.plot(xlist, ylist)
#
# pylab.axhline(0, color='black')
# pylab.axvline(0, color='black')
# pylab.show()
return xlist, ylist
def __call__(self):
'Return the locations of the ticks'
self.verify_intervals()
vmin, vmax = self.viewInterval.get_bounds()
if vmax<vmin:
vmin, vmax = vmax, vmin
vmin = self._base.ge(vmin)
locs = frange(vmin, vmax+0.001*self._base.get_base(), self._base.get_base())
return locs
def test():
min, max, dx = 0, 16, 0.01
for d in containerDot:
a = listA(d)
b = listB(d)
h = np.linalg.solve(a, b)
h = list(h)
xlist = mlab.frange(min, max, dx)
ylist = [f(x) for x in xlist]
yrlist = [g(x, h) for x in xlist]
pylab.plot(xlist, yrlist)
pylab.plot(xlist, ylist)
pylab.show()
def test():
min, max, dx = 0, 16, 0.01
for d in containerDot:
a = listA(d)
b = listB(d)
h = np.linalg.solve(a, b)
h = list(h)
xlist = mlab.frange(min, max, dx)
ylist = [f(x) for x in xlist]
yrlist = [g(x, h) for x in xlist]
pylab.plot(xlist, yrlist)
pylab.plot(xlist, ylist)
pylab.show()
def figure():
def realfunction(x):
return math.exp(x) * math.sin(x) - 1
xlist = mlab.frange(bot, top, 0.001)
ylist = [realfunction(x) for x in xlist]
pylab.plot(xlist, ylist)
# axis
ax = pylab.gca()
ax.axhline(y=0, color='k')
ax.axvline(x=0, color='k')
ax.set_ylim([-5, 5])
pylab.show()
def func(x, d_lambda, n):
mass1 = []
xlist = mlab.frange(1, 500, 1)
w = complex(1, 0)
fi = 2 * math.pi * d_lambda * math.sin(math.pi / 180.0 * x)
for i in range(1, n + 1):
x = complex(random.normalvariate(0, 10), random.normalvariate(0, 10))
x2 = x * complex(math.cos(-math.pi / 4), math.sin(-math.pi / 4))
e = x + w * x2
w = w - 2 * 0.00005 * e * x2.conjugate()
w = w + complex(math.cos(fi * (i - 1)), math.sin(fi * (i - 1)))
a = abs(w)
mass1.append(e * e.conjugate())
return mass1
def task():
a = -1
b = 1
fa = random.uniform(-1, 1)
fb = random.uniform(-1, 1)
dfb = random.uniform(-1, 1)
x = mlab.frange(-1, 1, 0.01)
for i in range(2):
for j in range(3):
dfa = random.uniform(-1.0, 1.0)
y = [hermit(i, a, b, fa, fb, dfa, dfb) for i in x]
pylab.subplot(2, 3, i * 3 + j + 1)
pylab.title('f\'(a)=%.2f f\'(b) = %.2f' % (dfa, dfb))
pylab.grid(True)
pylab.plot(x, y)
def CheckElevation(vec):
#print vec
step = 1.0/len(vec)
vec_after_polyfit = createElevationVectorAfterPolyfit(mlab.frange(0,1-step,step),vec)
vec_diff = np.diff(np.array(vec_after_polyfit))
elChange = vec_after_polyfit[-1] - vec_after_polyfit[1];
if (elChange > ELEVATION_THRESHOLD):
#print 'DOWN'
if (mlab.find(vec_diff >= 0).size > VECTOR_SIZE*ELEVATION_TREND_PRECENT):
return elChange,HAND_DOWN
elif (elChange < -ELEVATION_THRESHOLD):
#print 'UP'
if (mlab.find(vec_diff <= 0).size > VECTOR_SIZE*ELEVATION_TREND_PRECENT):
return elChange,HAND_UP
return 0,HAND_UNKWON
def kurtosis(ax):
''' Distributions with different kurtosis'''
# Generate the data
t = frange(-3,3,0.1) # generate the desirded x-values
platykurtic = stats.laplace.pdf(t)
wigner = np.zeros(np.size(t))
wignerIndex = np.abs(t) <= 1
wigner[wignerIndex] = 2/np.pi * np.sqrt(1-t[wignerIndex]**2)
ax.plot(t, platykurtic, label='kurtosis=3')
ax.plot(t, wigner, '--', label='kurtosis=-1')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.legend()
def __call__ (self) :
binner = self.binner
delta = self.delta
phase = self.phase
vmin, vmax = self.viewInterval
wmin = binner.value (vmin, False)
wmax = binner.value (vmax, False)
if phase is not None :
wmin -= (wmin % delta - phase)
return \
[ i for i in
( binner.index_f (v)
for v in frange (wmin, wmax + 0.001 * delta, delta)
)
if vmin <= i <= vmax
]
def showChi2():
'''Utility function to show Chi2 distributions'''
t = frange(0, 8, 0.05)
Chi2Vals = [1,2,3,5]
for chi2 in Chi2Vals:
plt.plot(t, stats.chi2.pdf(t, chi2), label='k={0}'.format(chi2))
plt.legend()
plt.xlim(0,8)
plt.xlabel('X')
plt.ylabel('pdf(X)')
plt.axis('tight')
outFile = 'dist_chi2.png'
C2_8_mystyle.printout_plain(outFile)
def showExp():
'''Utility function to show exponential distributions'''
t = frange(0, 3, 0.01)
lambdas = [0.5, 1, 1.5]
for par in lambdas:
plt.plot(t, stats.expon.pdf(t, 0, par), label='$\lambda={0:3.1f}$'.format(par))
plt.legend()
plt.xlim(0,3)
plt.xlabel('X')
plt.ylabel('pdf(X)')
plt.axis('tight')
plt.legend()
outFile = 'dist_exp.png'
C2_8_mystyle.printout_plain(outFile)
def showF():
'''Utility function to show F distributions'''
t = frange(0, 3, 0.01)
d1s = [1,2,5,100]
d2s = [1,1,2,100]
for (d1,d2) in zip(d1s,d2s):
plt.plot(t, stats.f.pdf(t, d1, d2), label='F({0}/{1})'.format(d1,d2))
plt.legend()
plt.xlim(0,3)
plt.xlabel('X')
plt.ylabel('pdf(X)')
plt.axis('tight')
plt.legend()
outFile = 'dist_f.png'
C2_8_mystyle.printout_plain(outFile)
def showWeibull():
'''Utility function to show Weibull distributions'''
t = frange(0, 2.5, 0.01)
lambdaVal = 1
ks = [0.5, 1, 1.5, 5]
for k in ks:
wd = stats.weibull_min(k)
plt.plot(t, wd.pdf(t), label='k = {0:.1f}'.format(k))
plt.xlim(0,2.5)
plt.ylim(0,2.5)
plt.xlabel('X')
plt.ylabel('pdf(X)')
plt.legend()
outFile = 'Weibull_PDF.png'
showData(outFile)
def shifted_normal():
'''PDF and scatter plot'''
# Plot 3 PDFs (Probability density functions) for normal distributions ----------
# Select 3 mean values, and 3 SDs
myMean = [0,0,0,-2]
mySD = [0.2,1,5,0.5]
t = frange(-5,5,0.02)
# Plot the 3 PDFs, using the color-palette "hls"
with sns.color_palette('hls', 4):
for mu,sigma in zip(myMean, np.sqrt(mySD)):
y = stats.norm.pdf(t, mu, sigma)
plt.plot(t,y, label='$\mu={0}, \; \t\sigma={1:3.1f}$'.format(mu,sigma))
# Format the plot
plt.legend()
plt.xlim([-5,5])
plt.title('Normal Distributions')
# Show the plot, and save the out-file
outFile = 'Normal_Distribution_PDF.png'
showData(outFile)
# Generate random numbers with a normal distribution ------------------------
myMean = 0
mySD = 3
numData = 500
data = stats.norm.rvs(myMean, mySD, size = numData)
# Plot the data
plt.scatter(np.arange(len(data)), data)
# Format the plot
plt.title('Normally distributed data')
plt.xlim([0,500])
plt.ylim([-10,10])
plt.show()
plt.close()
def showT():
'''Utility function to show T distributions'''
t = frange(-5, 5, 0.05)
TVals = [1,5]
normal = stats.norm.pdf(t)
t1 = stats.t.pdf(t,1)
t5 = stats.t.pdf(t,5)
plt.plot(t,normal, '--', label='normal')
plt.plot(t, t1, label='df=1')
plt.plot(t, t5, label='df=5')
plt.legend()
plt.xlim(-5,5)
plt.xlabel('X')
plt.ylabel('pdf(X)')
plt.axis('tight')
outFile = 'dist_t.png'
C2_8_mystyle.printout_plain(outFile)
def DrawPlot(self):
from matplotlib import pyplot as plt
from matplotlib.mlab import frange
import numpy
xBegin = -1
xEnd = 1
xRange = frange(xBegin, xEnd, self._drawingStep)
y1Points = [ self._f1(x) for x in xRange ]
y2Points = [ self._f2(x) for x in xRange ]
top, = plt.plot(xRange, y1Points, color="red", linewidth=2.5)
bottom, = plt.plot(xRange, y2Points, color="red", linewidth=2.5)
plt.fill_between(xRange, y1Points, y2Points, color="red", alpha=0.25)
plt.xlim(-2, 2)
plt.title("♥")
plt.show()
for i in xrange(0, n):
array_x_y[0] = [-3.,-2.,-1.,1.,2.,4.]
array_x_y[1] = [-2.1,-1.,2.,2.,1.,-2.]
#for i in xrange(0, n):
array_x_y[0] = [-3.,-2.,0.,0.,0.,0.]
array_x_y[1] = [-2.,-1.,0.,0.,0.,0.]
array_x_y[2] = [-1.,2.,0.,0.,0.,0.]
array_x_y[3] = [1.,2.,0.,0.,0.,0.]
array_x_y[4] = [2.,1.,0.,0.,0.,0.]
array_x_y[5] = [4.,-2.,0.,0.,0.,0.]
from matplotlib import mlab
zlist = mlab.frange (-5, 5, 0.01)
res = get_coefficient(array_x_y)
import pylab
pylab.grid()
#pylab.plot(zlist, [f(x) for x in zlist ], label='График функции'.decode("utf8"))
pylab.plot(zlist, [approx_function(res, x/0.85)-2.1 for x in zlist ], label='Аппроксимация'.decode("utf8"))
#pylab.plot(glist, [approx_function(res,x) for x in glist ], "ro")
pylab.plot([-3.,-2.,-1.,1.,2.,4.], [-2.1,-1.,2.,2.,1.,-2.], "ro")
pylab.legend(loc='lower left').draggable()
pylab.show()
__author__ = 'maxim'
from math import log
from math import cos
from math import sin
from matplotlib import mlab
import pylab as plt
e1, e2, e3 = 10 ** (-3), 10 ** (-6), 10 ** (-9)
x_min = 0.0
x_max = 10.0
n = 200
h = (x_max - x_min) / n
x = mlab.frange(x_min, x_max, h)
x1 = list(x)
fig = plt.figure()
axes = fig.add_subplot(1, 1, 1)
def f(z):
return log(z ** 2 + 3 * z + 1) - cos(2 * z - 1)
y = map(lambda z: log(z ** 2 + 3 * z + 1) - cos(2 * z - 1), x)
def u(z):
return (2 * z + 3) / (z ** 2 + 3 * z + 1) + 2 * sin(2 * z - 1)
# М Е Т О Д Н Ь Ю Т О Н А
import numpy as np import math import pylab from matplotlib import mlab tmin = -10 tmax = 10 dt = 0.01 tlist = mlab.frange(tmin, tmax, dt) pylab.ion() for i in range(100): xlist = [math.sin(t + i) for t in tlist] ylist = [math.cos(2*t) for t in tlist] pylab.clf() pylab.plot(xlist, ylist) pylab.draw() pylab.close()
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from __future__ import division
from matplotlib.artist import Artist
import matplotlib.mlab as mlab
import matplotlib.font_manager as fm
import matplotlib.colors as mc
import numpy as np
import colorsys
# from http://matplotlib.org/examples/pylab_examples/demo_agg_filter.html
plot_colors = [
mc.rgb2hex(colorsys.hsv_to_rgb(x, 0.58, 1))
for x in mlab.frange(0, 1, 0.05)
]
plot_colors = plot_colors[0::2] + plot_colors[1::2]
class FilteredArtistList(Artist):
def __init__(self, artist_list, filter):
self._artist_list = artist_list
self._filter = filter
Artist.__init__(self)
def draw(self, renderer):
renderer.start_rasterizing()
renderer.start_filter()
for a in self._artist_list:
a.draw(renderer)
# functii neprerivnoy sluchaynoy velichini
pyplot.subplot(1, 1, 1)
y1.sort()
F1 = [i / float(len(y1)) for i in xrange(len(y1))]
pyplot.title("exponential function with parameter lambda : {0} and volume sample : {1}".format(_lambda, len(y1)))
pyplot.plot(y1, F1)
pyplot.plot(x, F)
pyplot.show()
pyplot.subplot(1, 1, 1)
y2.sort()
F2 = [i / float(len(y2)) for i in xrange(len(y2))]
pyplot.title("exponential function with parameter lambda : {0} and volume sample : {1}".format(_lambda, len(y2)))
pyplot.plot(y2, F2)
pyplot.plot(x, F)
pyplot.show()
# kolmogorov kriterii soglasia
F1_teor = [1 - math.e ** (-_lambda * y1[i]) for i in xrange(len(y1))]
F2_teor = [1 - math.e ** (-_lambda * y2[i]) for i in xrange(len(y2))]
print math.sqrt(n) * max(abs(F2[i] - F2_teor[i]) for i in xrange(len(y2)))
x_min = 0.01
x_max = 0.99
dx = 0.01
xlist = frange(x_min, x_max, dx)
ylist = [st.norm.ppf((i + 1) / 2) * 2 * math.sqrt(D1) / math.sqrt(n - 1) for i in xlist]
pyplot.plot(xlist, ylist, 'b')
pyplot.show()
return (exp(z) - exp(-z)) / 2.0
def ch(z):
return (exp(z) + exp(-z)) / 2.0
def ch1(v1, v):
return (exp(v1 - v) + exp(v - v1)) / 2.0
# Задание сетки по х
x_min = 0.0
x_max = 1.0
n = 20
h = (x_max - x_min) / n
x = mlab.frange(x_min, x_max, h)
N = len(x) - 1
# Задание сетки по t
t_min = 0.0
print("Хотите Задать Время Окончания Расчета (1/0) ?")
a = (input())
if a == 1:
print("t_max = ")
t_max = input()
if a == 0:
t_max = 1.0
print("Хотите Задать Шаг ? (1/0)")
a = (input())
if (k & 1):
res *= t
k = k >> 1
if k == 0:
break
t *= t
return res
def bernulli(n, k, p0):
koef = math.factorial(n) / ((math.factorial(k))*(math.factorial(n - k)))
return koef * math.pow(p0, k) * math.pow(1 - p0, (n - k))
while j < len(n):
i = math.floor((d[j] - 1)/2)
s = i + 1
summ = 0
while s < n[j]:
summ += bernulli(n[j], s, p0 = 0.01)
s += 1
result.append(summ)
j += 1
f1=open('./testfile.txt', 'w+')
print (result, f1)
xlist = mlab.frange (2, 24, 2)
ylist = result
pylab.plot (xlist, ylist)
pylab.show()
