def fitAndPlotLinear(x, y, rngt, ax, alfa, showPlot, labelAlfa, p):
markers=["o", "s","D","^","d","h","p","o"]
labelRange = ['low', 'med', 'high']
labelRange = labelRange+list([str(i) for i in rngt])
cm = plt.get_cmap('Set1')
cm1 = plt.get_cmap('Set3')
slopes = []
if showPlot:
inf.smallerFont(ax, 8)
ax.scatter(x, y, color=cm(abs(alfa/9)), alpha=0.75, edgecolors='none', marker=markers[alfa])#, "o", lw=0.5)
arrow = dict(arrowstyle="-", connectionstyle="arc3", ls="--", color="gray")
putLabels(ax, p.calc, alfa)
for i in range(0,len(rngt)-1):
a, b = fun.linearFit(x, y, rngt[i], rngt[i+1])
slopes.append(b)
if showPlot:
ax.semilogy(x[rngt[i]:rngt[i+1]+1], np.exp(fun.linear(x[rngt[i]:rngt[i+1]+1], a, b)), ls="-", color=cm1((i+abs(alfa)*3)/12))
xHalf = (x[rngt[i]]+x[rngt[i+1]]+1)/2
text = "{:03.3f}".format(-b)
yHalf = np.exp(fun.linear(xHalf, a, b))
if alfa == -1:
ax.text(xHalf, 2e1, r"$"+roman.toRoman(i+1)+r"$", color="gray", ha="right", va="center")#, transform=axarr[i].transAxes)
xHalf *= 1.15
yHalf *= 5
text = r"$E_a="+text+r"$"
bbox_props = dict(boxstyle="round", fc="w", ec="1", alpha=0.6)
ax.text(xHalf,yHalf, text, color=cm(abs(alfa/9)), bbox=bbox_props, ha="center", va="center", size=6)
if showPlot and alfa > -1:
locator = LogLocator(100,[1e-1])
ax.yaxis.set_major_locator(locator)
return slopes
def linear_act_forward(A, W, b, act):
"""
Implements the linear and activation functions of a single node
Also, returns the original values for storage in cache
"""
if act == "sigmoid":
Z, lin_cache = linear(A, W, b)
A, act_cache = sigmoid(Z)
elif act == "relu":
Z, lin_cache = linear(A, W, b)
A, act_cache = relu(Z)
elif act == "softmax":
Z, lin_cache = linear(A, W, b)
A, act_cache = softmax(Z)
cache = (lin_cache, act_cache)
return A, cache
def get_value(self, onset):
elapsed = (float(onset) + self.shift) % self.length
if self.interp == 'linear':
val = float(linear(elapsed / self.length, self.arr))
elif self.interp == 'nearest':
val = float(nearest(elapsed / self.length, self.arr))
if self.bounds:
ymin, ymax = self.bounds
s, y, n = rescale(self.arr, ymin, ymax)
val = (val - y) * s + n
inv_qnt = 1.0 / self.quantize
val = floor(val * inv_qnt) / inv_qnt
return val
def fitAndPlotLinear(x, y, rngt, axis, alfa, showPlot, labelAlfa):
labelRange = ['low', 'med', 'high']
labelRange = labelRange+list([str(i) for i in rngt])
cm = plt.get_cmap('Set1')
slopes = []
if showPlot:
axis.scatter(x, y, color=cm(abs(alfa/9)), alpha=0.75, edgecolors='none')#, "o", lw=0.5)
for i in range(0,len(rngt)-1):
a, b = fun.linearFit(x, y, rngt[i], rngt[i+1])
slopes.append(b)
if showPlot:
axis.semilogy(x[rngt[i]:rngt[i+1]+1], np.exp(fun.linear(x[rngt[i]:rngt[i+1]+1], a, b)), ls="-", label="{} {} {:03.3f} ".format(labelAlfa[alfa],labelRange[i],b))
if i == len(rngt)-2:
if alfa == -1:
axis.legend(prop={'size': 8}, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
else:
axis.legend(prop={'size': 5.1}, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., ncol=2)
return slopes
def localAvgAndPlotLinear(x,
y,
ax,
alfa,
sp,
co2,
first=False,
total=False,
verbose=False):
showPlot = sp # and (alfa == 4 or alfa == 5)
markers = ["o", "s", "D", "^", "d", "h", "p"]
cm = plt.get_cmap('tab20')
cm1 = plt.get_cmap('hsv')
slopes = []
l = 1
if showPlot:
#inf.smallerFont(ax, 8)
y = [float("NaN") if v < 1e-50 else v
for v in y] # remove almost 0 multiplicities
ax.scatter(x,
y,
color=cm(abs((alfa % 20) / 20)),
alpha=0.75,
edgecolors=getMec(alfa),
marker=markers[alfa % 7])
arrow = dict(arrowstyle="-",
connectionstyle="arc3",
ls="--",
color="gray")
a = alfa
if first:
a = 0
putLabels(ax, co2 + 1, a, total)
# all
s = allSlopes(x, y)
if verbose and (any(np.isinf(s)) or any(np.isnan(s))):
print("error fitting", alfa)
for i in range(0, len(x)):
a, b = fitA(x, y, s, i)
slopes.append(b)
if showPlot:
ax.set_yscale("log")
ax.plot(x[i - 1:i + 2],
np.exp(fun.linear(x[i - 1:i + 2], a, b)),
ls="-",
color="gray",
alpha=0)
xHalf = x[i]
text = "{:03.3f}".format(-b)
yHalf = np.exp(fun.linear(xHalf, a, b))
if alfa == -1:
xHalf *= 1
yHalf *= 5
text = r"$E_a=" + text + r"$"
bbox_props = dict(boxstyle="round", fc="w", ec="1", alpha=0.6)
if alfa == -10:
ax.text(xHalf,
yHalf,
text,
color=cm(abs(alfa / 9)),
bbox=bbox_props,
ha="center",
va="center",
size=6)
return slopes
def forward(self, input):
"""Applies the forward pass by returning a linear function wx + b,
where x is the input, w the weights and b the bias and saves the input
for the backward pass to compute its computations."""
self.input = input
return functions.linear(self.input, self.weight, self.bias)
def plot(ax, ax2, data, i, alp=1):
marker = ["o", "s", "H", "D", "^", "d", "h", "p", "o"]
cm = plt.get_cmap("Accent")
alpha = 0.5
mew = 0
data = np.array(data)
try:
flux = data[0, 0]
except IndexError:
raise
factor = data[0, 6]
x = 1 / kb / data[:, 1] + np.log(flux**factor)
lw = 3
lg1, = ax.plot(x,
data[:, 3],
label=r"$F=$" + fun.base10(flux),
lw=lw,
marker="",
ls="-",
mew=mew,
ms=12,
alpha=alpha,
color=cm(i / 8))
ax.plot(x,
data[:, 4],
label=r"$N_h$" + fun.base10(flux),
lw=lw + 1,
ls="-.",
mew=1,
color=cm(i / 8),
ms=7,
alpha=1)
ax2.plot(x,
data[:, 3] / data[:, 4],
label="aa",
color=cm(i / 8),
ls="",
marker="o",
ms=2,
mec=cm(i / 8),
mfc=cm(i / 8),
alpha=alp)
arrow = dict(arrowstyle="->",
connectionstyle="arc3",
ls="-",
color=cm(i / 8))
if str(flux)[0] == "5":
ax.annotate(fun.base10(flux),
xy=(x[2], data[:, 4][2]),
color=cm(i / 8),
xytext=(0.9, 0.52 - 0.05 * i),
textcoords="axes fraction",
arrowprops=arrow)
if flux == 5e4:
#Fit
rngt = inf.defineRanges("AgUc", "simple", data[:, 1])
for i in range(len(rngt) - 2, -1, -1):
y = data[:, 4]
a, b = fun.linearFit(x, y, rngt[i], rngt[i + 1])
ax.semilogy(x[rngt[i]:rngt[i + 1] + 1],
np.exp(fun.linear(x[rngt[i]:rngt[i + 1] + 1], a, b)),
ls="-",
color="lightgray",
zorder=+200)
xHalf = (x[rngt[i]] + x[rngt[i + 1]] + 1) / 2
text = "{:03.3f}".format(-b)
yHalf = np.exp(fun.linear(xHalf, a, b)) * 10
text = r"$" + text + r"$"
ax.text(xHalf,
yHalf,
text,
color="black",
ha="center",
va="center",
size=10,
zorder=+400)
i = 0 # print "interpolation" to the rest of lower x values
ax.semilogy(x[0:rngt[i + 1] + 1],
np.exp(fun.linear(x[0:rngt[i + 1] + 1], a, b)),
ls=":",
color="lightgray",
zorder=+300)
#Fit f
for i in range(len(rngt) - 2, -1, -1):
y = data[:, 3] / data[:, 4]
a, b = fun.linearFit(x, y, rngt[i], rngt[i + 1])
ax2.plot(x[rngt[i]:rngt[i + 1] + 1],
np.exp(fun.linear(x[rngt[i]:rngt[i + 1] + 1], a, b)),
ls="-",
color="lightgray",
zorder=+200)
xHalf = (x[rngt[i]] + x[rngt[i + 1]] + 1) / 2
text = "{:03.3f}".format(-b)
yHalf = np.exp(fun.linear(xHalf, a, b)) * 1.02
text = r"$" + text + r"$"
ax2.text(xHalf,
yHalf,
text,
color="gray",
ha="center",
va="center",
size=10,
zorder=+400)
i = 0 # print "interpolation" to the rest of lower x values
ax2.plot(x[0:rngt[i + 1] + 1],
np.exp(fun.linear(x[0:rngt[i + 1] + 1], a, b)),
ls=":",
color="lightgray",
zorder=+300)
if flux == 3.5e0:
rngt = inf.defineRanges("basic", "simple", data[:, 1])
for i in range(len(rngt) - 2, -1, -1): #reverse iteration
y = data[:, 4]
a, b = fun.linearFit(x, y, rngt[i], rngt[i + 1])
ax.semilogy(x[rngt[i]:rngt[i + 1] + 1],
np.exp(fun.linear(x[rngt[i]:rngt[i + 1] + 1], a, b)),
ls="-",
color="lightgray",
zorder=+200)
xHalf = (x[rngt[i]] + x[rngt[i + 1]] + 1) / 2
text = "{:03.3f}".format(-b)
yHalf = np.exp(fun.linear(xHalf, a, b)) * 10
text = r"$" + text + r"$"
ax.text(xHalf,
yHalf,
text,
color="black",
ha="center",
va="center",
size=10,
zorder=+400)
# print "interpolation" to the rest of lower x values
ax.semilogy(np.arange(120, 70, -1),
np.exp(fun.linear(np.arange(120, 70, -1), a, b)),
ls=":",
color="lightgray",
zorder=+300)
#Fit f
for i in range(len(rngt) - 2, -1, -1): #reverse iteration
y = data[:, 3] / data[:, 4]
a, b = fun.linearFit(x, y, rngt[i], rngt[i + 1])
ax2.plot(x[rngt[i]:rngt[i + 1] + 1],
np.exp(fun.linear(x[rngt[i]:rngt[i + 1] + 1], a, b)),
ls="-",
color="lightgray",
zorder=+200)
xHalf = (x[rngt[i]] + x[rngt[i + 1]] + 1) / 2
text = "{:03.3f}".format(-b)
yHalf = np.exp(fun.linear(xHalf, a, b)) * 1.02
text = r"$" + text + r"$"
ax2.text(xHalf,
yHalf,
text,
color="gray",
ha="center",
va="center",
size=7,
zorder=+400)
return lg1
def forward(self, x):
if self.unified:
return linearUnified(self.k)(x, self.w, self.b)
else:
return linear(self.k)(x, self.w, self.b)
])
plt.errorbar(x,
mag,
yerr=err,
ls='',
marker='.',
markersize=1,
color='r',
elinewidth=0.5,
capsize=2.5,
ecolor='r',
label='Without Accept/Reject')
# Linear fit
best, covar = curve_fit(f.linear, x, mag, sigma=err, p0=(0.375, -1.0))
plt.plot(x, f.linear(x, *best), ls='--', c='red', linewidth=0.65)
deg = len(mag) - len(best) #Here and below: degrees of freedom for the
#reduced chi-squared
#Saving the value of the parameter so that it can be used as an estimate in the following fit
mag_no_AR = best[0]
#Print the results of the fit
print("WITHOUT ACCEPT/REJECT")
print('Parameters: ', best)
print("Parameters' Std Error: ", np.sqrt(np.diag(covar)))
print('Covariance: ', covar)
print('Reduced chi-squared: ',
f.red_chi_sq(mag, f.linear(x, *best), err, dof=deg))
print('\n')
dist_lidar_data = [] for sensor in dist_lidar_sense: i = 0 data_buff = [] while i < 20: data_buff.append(str(functions.constant(100, 0))) i += 1 dist_lidar_data.append([sensor] + data_buff) ####linear pressure_brake_data = [] for sensor in pressure_brake_sense: i = 0 data_buff = [] while i < 20: data_buff.append(str(functions.linear(1, 0, i, 0.3))) i += 1 pressure_brake_data.append([sensor] + data_buff) thermo_brake_data = [] for sensor in thermo_brake_sense: i = 0 data_buff = [] while i < 20: data_buff.append(str(functions.linear(2, 0, i, 0.4))) i += 1 thermo_brake_data.append([sensor] + data_buff) ####exponential pressure_sense_data = []
import functions
print("This is the test for the linear function")
print("\n")
#This is the test for the function of solving linear equations.
print('x =', functions.linear())
print("\n")
#This is the test for the function of solving quadratic equations.
print("This is the test for the function of quadratic equations")
print("\n")
print(functions.quadratic())
#This is the test for graphing a 2D equation.
functions.graph(4, 8, 50)
print("CORRELATORS")
print("Parameters: ", best)
print("Errors of the parameters: ", np.sqrt(np.diag(c)))
print("Covariance: ", c)
fig2 = plt.figure(2)
plt.errorbar(N,
t_metr,
yerr=err_m,
ls='',
marker='.',
markersize=1,
elinewidth=0.5,
capsize=2.5,
mec='red',
ecolor='red')
best, c = curve_fit(f.linear, N, t_metr, p0=(1, 1), sigma=err_m)
plt.plot(x, f.linear(x, *best), ls='-', color='b', linewidth=0.9)
plt.grid(linestyle=':')
plt.title("Behavior of the computational times of the Metropolis")
plt.xlabel("N")
plt.ylabel(r"$t_{metr}$ (s)")
print("\nMETROPOLIS")
print("Parameters: ", best)
print("Errors of the parameters: ", np.sqrt(np.diag(c)))
print("Covariance: ", c)
fig1.savefig("graphs/correlators_times.png", dpi=(200), bbox_inches='tight')
fig2.savefig("graphs/metropolis_times.png", dpi=(200), bbox_inches='tight')
