class CurvePlotter(object):
"""
I do the plotting.
"""
N = 100
width = 1400
height = 1400
Vgs = [0.0, 10.0]
def __init__(self):
"""
Constructs a Yampex Plotter object for a figure with two subplots.
"""
self.mList = []
# Small MOSFET in an IC
self.mList.append(MOSFET(2.5e-9, 5e17))
# High-current Power MOSFET
self.mList.append(MOSFET(4e-9, 2.7e19))
# Plotter
self.N_sp = len(self.mList)
self.pt = Plotter(self.N_sp, width=self.width, height=self.height)
self.pt.use_grid()
self.pt.set_xlabel("Vgst")
self.pt.set_ylabel("Vdsp")
self.pt.add_legend("Vgst/n")
self.pt.add_legend("(Vgst-f(x))/n")
self.pt.use_labels()
def Vdsp_1(self, m, Vgs):
Vgst = Vgs - m.VT
return Vgst / m.n(Vgs)
def Vdsp_2(self, m, Vgs, x=0.1):
Vgst = Vgs - m.VT
n = m.n(Vgs)
Pt = m.Pt
second = 2 * n * Pt * np.log((1 + np.exp(Vgst /
(2 * n * Pt)))**np.sqrt(x) -
1)
return (Vgst - second) / n
def plot(self):
"""
"""
with self.pt as sp:
for m in self.mList:
sp.set_title("Vdsp vs Vgst, computed both ways, for {}", m)
Vgs = np.linspace(self.Vgs[0], self.Vgs[1], 100)
#sp(Vgs, self.Vdsp_1(m, Vgs), self.Vdsp_2(m, Vgs))
sp(Vgs, self.Vdsp_2(m, Vgs))
self.pt.show()
class CurvePlotter(object):
"""
I do the plotting.
"""
width = 1400
height = 1200
N = 200
def __init__(self):
"""
Constructs a Yampex Plotter object for a figure with one subplot.
"""
self.pt = Plotter(1, width=self.width, height=self.height)
self.pt.use_grid()
def eta(self, Vds, smooth=False):
if smooth:
eta = 0.05 * np.log(1 + np.exp(20 * (1 - Vds)))
else:
eta = np.clip(1 - Vds, 0, None)
return eta
def Cdg(self, eta):
"""
The function for Cdg::
Cdg = W*L*Cdox*(4+28*eta+22*eta^2+6*eta^3)/(15*(1+eta)^3)
"""
result = 4 + 28 * eta + 22 * eta**2 + 6 * eta**3
return result / (15 * (1 + eta)**3)
def plot(self):
"""
Plots the curve for Cdg vs Vds, given Vds_prime = 1.
"""
self.pt.set_ylabel("Cdg")
with self.pt as sp:
sp.set_title("Cdg vs Vds")
sp.set_xlabel("Vds")
Vds = np.linspace(0, 2, self.N)
eta = self.eta(Vds, True)
Cdg = self.Cdg(eta)
ax = sp(Vds, eta, Cdg)
self.pt.show()
class SinCos(object):
funcNames = ('sin', 'cos')
filePath = "sc.png"
def __init__(self):
self.X = np.linspace(0, 4 * np.pi, 200)
self.pt = Plotter(1, 2, width=700, height=500, useAgg=True)
self.pt.set_xlabel("X")
self.pt.use_grid()
self.pt.add_annotation(0, "First")
self.pt.add_annotation(199, "Last")
def __call__(self, frequency):
self.pt.set_title("Sin and Cosine: Frequency = {:.2f}x", frequency)
with self.pt as p:
for funcName in self.funcNames:
Y = getattr(np, funcName)(frequency * self.X)
p.set_ylabel("{}(X)".format(funcName))
p(self.X, Y)
with open(self.filePath, "wb") as fh:
self.pt.show(fh=fh)
A log plot, with several plots in one subplot, all done in one
call to the subplotting tool in context.
"""
import numpy as np
from yampex import Plotter
# Construct a Plotter object for a 1000x800 pixel (100 DPI) figure
# with a single subplot.
pt = Plotter(1, width=10.0, height=8.0)
# The plots will be of different bases raised to powers.
X = np.linspace(0, 10, 100)
pt.set_title("Different Bases Raised to Power 0-10")
# The x label will say "Power" and the subplot will have a grid.
pt.set_xlabel("Power")
pt.use_grid()
# Make a subplotting context and work with the single subplot via the
# subplot tool sp. It's actually just a reference to pt, but set up
# for subplotting.
with pt as sp:
# Compute the vectors all at once
Y2 = np.power(2, X)
Y3 = np.power(3, X)
Y10 = np.power(10, X)
# Just call the subplotting tool with the X-axis vector and all
# the Ys at once.
sp.semilogy(X, Y2, Y3, Y10, legend=["Base 2", "Base 3", "Base 10"])
# Show the single subplot.
pt.show()
class CurvePlotter(object):
"""
I do the plotting.
"""
width = 1400
height = 1400
N = 100
gamma = [0.5, 1.0, 2.5]
Vgs = [0.0, 10.0]
def __init__(self):
"""
Constructs a Yampex Plotter object for a figure with two subplots.
"""
self.pt = Plotter(2, width=self.width, height=self.height)
self.pt.use_grid()
def n(self, gamma, Vgs, Tj=25):
"""
The function for n:
M{n = 1+gamma/(-gamma+2*sqrt(gamma^2/4 + Vgs + 1.10/300*(Tj+273.15)))}
"""
T = Tj + 273.15
n = gamma.copy() # Otherwise we wind up modifying gamma
n /= -gamma + 2 * np.sqrt(gamma**2 / 4 + Vgs + 1.1 / 300 * T)
n += 1
return n
def subplot(self, sp, X, aVals, bVals, semilog=False):
"""
Given the subplotting tool I{sp} and the supplied 1-D Numpy array
of I{X} values, plots the curves for each combination of I{a}
in I{aVals} and I{b} in I{bVals}.
"""
Ys = []
for a, b in zip(aVals, bVals):
Ys.append(self.func(X, a, b))
sp.add_legend("a={:.2f}, b={:.2f}", a, b)
if semilog:
sp.semilogy(X, *Ys)
else:
sp(X, *Ys)
def plot(self):
"""
Plots the curves for each combination of I{a} in I{aVals} and its
corresponding I{b} in I{bVals}, from my I{xMin} to my I{xMax}
and from zero to double my I{xMax}.
"""
self.pt.set_ylabel("n")
with self.pt as sp:
sp.set_title("n vs gamma with stepped Vgs")
sp.set_xlabel("gamma")
ax = sp()
gamma = np.linspace(self.gamma[0], self.gamma[1], self.N)
for Vgs in np.linspace(self.Vgs[0], self.Vgs[1], 5):
with sp.prevOpts():
sp.add_legend("Vgs: {:.1f}", Vgs)
n = self.n(gamma, Vgs)
ax.plot(gamma, n)
sp.set_title("n vs Vgs with stepped gamma")
sp.set_xlabel("Vgs")
ax = sp()
Vgs = np.linspace(self.Vgs[0], self.Vgs[1], self.N)
for gamma in np.linspace(self.gamma[0], self.gamma[1], 5):
with sp.prevOpts():
sp.add_legend("gamma: {:.1f}", gamma)
n = self.n(gamma, Vgs)
ax.plot(Vgs, n)
self.pt.show()
class CurvePlotter(object):
"""
I do the plotting.
"""
width = 1400
height = 1200
xMin, xMax = 5.0, 8.0
N = 100
def __init__(self):
"""
Constructs a Yampex Plotter object for a figure with two subplots.
"""
self.pt = Plotter(2, width=self.width, height=self.height)
self.pt.use_grid()
self.pt.set_title("Exponentials plotted from {:.1f} to {:.1f}",
self.xMin, self.xMax)
self.pt.set_xlabel("X")
self.pt.set_ylabel("a*exp(-b*X)")
def func(self, X, a, b):
"""
The exponential function M{a*exp(-b*X)}
"""
return a * np.exp(-b * X)
def leastDiff(self, Ys, logspace=False):
"""
Returns the index of the vectors of I{Ys} where there is the least
difference between their values.
Set I{logspace} C{True} to have the difference calculated in
logspace, for a semilog plot.
"""
Z = np.row_stack(Ys)
if logspace: Z = np.log(Z)
V = np.var(Z, axis=0)
return np.argmin(V)
def subplot(self, sp, X, aVals, bVals, semilog=False):
"""
Given the subplotting tool I{sp} and the supplied 1-D Numpy array
of I{X} values, plots the curves for each combination of I{a}
in I{aVals} and I{b} in I{bVals}.
Returns the value of I{X} where there is the least difference
between the curves.
"""
Ys = []
for a, b in zip(aVals, bVals):
Ys.append(self.func(X, a, b))
sp.add_legend("a={:.2f}, b={:.2f}", a, b)
k = self.leastDiff(Ys, semilog)
sp.add_annotation(k, X[k])
if semilog:
sp.semilogy(X, *Ys)
else:
sp(X, *Ys)
def plot(self, aVals, bVals):
"""
Plots the curves for each combination of I{a} in I{aVals} and its
corresponding I{b} in I{bVals}, from my I{xMin} to my I{xMax}
and from zero to double my I{xMax}.
"""
with self.pt as sp:
# Top subplot: The range of interest
X = np.linspace(self.xMin, self.xMax, self.N)
self.subplot(sp, X, aVals, bVals)
# Bottom subplot: Positive X surrounding the range of
# interest
X = np.linspace(0, 2 * self.xMax, self.N)
sp.add_axvline(self.xMin)
sp.add_axvline(self.xMax)
self.subplot(sp, X, aVals, bVals, semilog=True)
self.pt.show()