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 plotTotalTransitionRate(x,y, title):
fig, ax = plt.subplots(1, 1, sharey=True, figsize=(5,4))
fig.subplots_adjust(top=0.85, bottom=0.15, left=0.20, right=0.95, hspace=0.25, wspace=0.35)
ax.plot(x,y,".-", label="data")
[a,b] = fun.linearFit(x,y,0,len(x))
ax.plot(x,fun.exp(x,a,b), label=r"$f(x) = "+str(a)+r"x^{"+str(b)+r"}$")
ax.set_title(title)
ax.set_xlabel(r"$1/k_BT$")
ax.set_yscale("log")
ax.legend(loc="best")
fig.savefig(title+".svg")
plt.close(fig)
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
plot.plot2(x_volt_1,y_current_1, "Anodenspannung in $V$","Anodenstrom in $ A$","../plots/anode1.pdf", None)
for i in range(5):
plt.axhline(m.saturation_current[i], linewidth=0.2, color='c')
plt.savefig('../plots/anode1.pdf')
plt.clf()
plt.clf()
#plot.plot([0,5,9,12,15,22],m.q_gone_korr, "Teilchennummer","Korrigierte Ladung $q / C$","../plots/ladung2.pdf", None)
plot.log_plot(x_volt_1,y_current_1, "Logarithmierte Anodenspannung $\ln(U)$","Logarithmierter Anodenstrom $\ln(\mu A)$","../plots/raum.pdf", None)
x_flow = np.linspace(1,3,1000)
plt.plot(x_flow, f.linearFit(x_flow, m.raumladung[0].nominal_value,m.raumladung[1].nominal_value), 'b-', label= "Linearer Fit")
plt.tight_layout(pad=0, h_pad=1.20, w_pad=1.20)
plt.legend(loc='best')
plt.savefig('../plots/raum.pdf')
plt.clf()
plt.clf()
plot.plot4(x_volt_langmuh,y_current_langmuh, "$\ln(U_A)$","$\ln(I_A)$","../plots/langmuh.pdf", None)
x_flow = np.linspace(1,3,1000)
plt.plot(x_flow, f.linearFit(x_flow, m.raumladung[0].nominal_value,m.raumladung[1].nominal_value), 'b-', label= "Linearer Fit")
plt.tight_layout(pad=0, h_pad=1.20, w_pad=1.20)
plt.legend(loc='best')
plt.savefig('../plots/langmuh.pdf')
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