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Removed redundant parentheses
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@alchemyst
alchemyst committed Mar 29, 2015
1 parent 254e9ce commit 3b87642
Showing 1 changed file with 39 additions and 38 deletions.
77 changes: 39 additions & 38 deletions utilsplot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -207,11 +207,11 @@ def mimoBode(Gin, wStart, wEnd, Kin=None):
... [7., 8.]])
>>>
>>> def G(s):
... return(K*numpy.exp(-t1*s)/(t2*s + 1.))
... return K*numpy.exp(-t1*s)/(t2*s + 1.)
>>>
>>> def Kc(s):
... return(numpy.array([[0.1, 0.],
... [0., 0.1]])*10.)
... return numpy.array([[0.1, 0.],
... [0., 0.1]])*10.
>>> mimoBode(G, -3, 3, Kc)
Bandwidth is a tuple of wC, wB
(0.55557762223988783, 1.3650078065460138)
Expand All @@ -228,7 +228,7 @@ def mimoBode(Gin, wStart, wEnd, Kin=None):
for i in range(len(w)):
Sv1[i] = utils.sigmas(Gin(s[i]))[0]
Sv2[i] = utils.sigmas(Gin(s[i]))[-1]
if (f < 1 and Sv2[i] < 1):
if f < 1 and Sv2[i] < 1:
wC = w[i]
f = 1
ymin = numpy.min(Sv2)
Expand Down Expand Up @@ -256,7 +256,7 @@ def mimoBode(Gin, wStart, wEnd, Kin=None):
def S(s):
L = Kin(s)*Gin(s)
dim1, dim2 = numpy.shape(Gin(0))
return(numpy.linalg.inv(numpy.eye(dim1) + L)) #SVD of S = 1/(I + L)
return numpy.linalg.inv(numpy.eye(dim1) + L) #SVD of S = 1/(I + L)
w = numpy.logspace(wStart, wEnd, 1000)
s = w*1j
Sv1 = numpy.zeros(len(w), dtype=complex)
Expand All @@ -266,7 +266,7 @@ def S(s):
for i in range(len(w)):
Sv1[i] = utils.sigmas(S(s[i]))[0]
Sv2[i] = utils.sigmas(S(s[i]))[-1]
if (f < 1 and Sv1[i] > 0.707):
if f < 1 and Sv1[i] > 0.707:
wB = w[i]
f = 1
plt.figure()
Expand All @@ -286,7 +286,7 @@ def S(s):
BG.set_facecolor('white')
Bandwidth = wC, wB
print('Bandwidth is a tuple of wC, wB')
return(Bandwidth)
return Bandwidth


def mino_nyquist_plot(L, axLim, wStart, wEnd):
Expand Down Expand Up @@ -325,10 +325,10 @@ def mino_nyquist_plot(L, axLim, wStart, wEnd):
... [0., 0.1]])*6
>>>
>>> def G(s):
... return(K*numpy.exp(-t1*s)/(t2*s + 1))
... return K*numpy.exp(-t1*s)/(t2*s + 1)
...
>>> def L(s):
... return(Kc*G(s))
... return Kc*G(s)
...
>>> #MIMOnyqPlot(L, 2)
Expand All @@ -349,8 +349,8 @@ def mino_nyquist_plot(L, axLim, wStart, wEnd):
plt.ylabel('Im G(wj)')
# plotting a unit circle
x = numpy.linspace(-1, 1, 200)
y_up = numpy.sqrt(1-(x)**2)
y_down = -1*numpy.sqrt(1 - (x)**2)
y_up = numpy.sqrt(1- x**2)
y_down = -1*numpy.sqrt(1 - x**2)
plt.plot(x, y_up, 'b:', x, y_down, 'b:', lw=2)
plt.plot(0, 0, 'r*', ms=10)
plt.grid(True)
Expand Down Expand Up @@ -394,8 +394,8 @@ def sv_plot(G, w_start=-2, w_end=2, axlim=None, points=100):
plt.clf()
plt.gcf().set_facecolor('white')

plt.semilogx(w, [utils.sigmas(Gfr)[0] for Gfr in freqresp], label=('$\sigma$$_{MAX}$'), color='blue')
plt.semilogx(w, [utils.sigmas(Gfr)[-1] for Gfr in freqresp], label=('$\sigma$$_{MIN}$'), color='blue', alpha=0.5)
plt.semilogx(w, [utils.sigmas(Gfr)[0] for Gfr in freqresp], label='$\sigma$$_{MAX}$', color='blue')
plt.semilogx(w, [utils.sigmas(Gfr)[-1] for Gfr in freqresp], label='$\sigma$$_{MIN}$', color='blue', alpha=0.5)
plt.xlabel('Frequency (rad/unit time)')

plt.axhline(1., color='red', ls=':')
Expand Down Expand Up @@ -431,19 +431,19 @@ def condtn_nm_plot(G, w_start=-2, w_end=2, axlim=None, points=100):
s = w*1j

def cndtn_nm(G):
return(utils.sigmas(G)[0]/utils.sigmas(G)[-1])
return utils.sigmas(G)[0]/utils.sigmas(G)[-1]

freqresp = map(G, s)

plt.figure('Condition number')
plt.clf()
plt.gcf().set_facecolor('white')

plt.semilogx(w, [cndtn_nm(Gfr) for Gfr in freqresp], label=('$\sigma$$_{MAX}$/$\sigma$$_{MIN}$'))
plt.semilogx(w, [cndtn_nm(Gfr) for Gfr in freqresp], label='$\sigma$$_{MAX}$/$\sigma$$_{MIN}$')
plt.axis(axlim)
plt.ylabel('$\gamma$(G)', fontsize = 15)
plt.xlabel('Frequency (rad/unit time)')
plt.axhline(10., color='red', ls=':', label=('$\gamma$(G) > 10'))
plt.axhline(10., color='red', ls=':', label='$\gamma$(G) > 10')
plt.legend()
plt.show()
return
Expand Down Expand Up @@ -507,7 +507,7 @@ def rga_plot(G, w_start=-2, w_end=2, axlim=None, points=100, show=True, plot_typ

plot_No = 1

if (plot_type == 'elements'):
if plot_type == 'elements':
for i in range(dim[0]):
for j in range(dim[1]):
plt.subplot(dim[0], dim[1], plot_No)
Expand All @@ -521,7 +521,7 @@ def rga_plot(G, w_start=-2, w_end=2, axlim=None, points=100, show=True, plot_typ
if i == dim[0] - 1: # To avoid clutter only plot xlabel on the very bottom subplots
plt.xlabel('Frequency [rad/unit time]')

elif (plot_type == 'outputs'): #i
elif plot_type == 'outputs': #i
for i in range(dim[0]):
plt.subplot(dim[1], 1, plot_No)
plt.title('Output %s vs. input j' % (i + 1))
Expand All @@ -531,8 +531,8 @@ def rga_plot(G, w_start=-2, w_end=2, axlim=None, points=100, show=True, plot_typ
plt.semilogx(w, rgas, label='$\lambda$$_{%s, %s}$' % (i + 1, j + 1))
rgamax.append(max(rgas))

if (j == dim[1] - 1): #self-scaling algorithm
if (axlim != None):
if j == dim[1] - 1: #self-scaling algorithm
if axlim != None:
plt.axis(axlim)
else:
plt.axis([None, None, None, max(rgamax)])
Expand All @@ -543,7 +543,7 @@ def rga_plot(G, w_start=-2, w_end=2, axlim=None, points=100, show=True, plot_typ
plt.xlabel('Frequency [rad/unit time]')
plot_No += 1

elif (plot_type == 'inputs'): #j
elif plot_type == 'inputs': #j
for j in range(dim[1]):
plt.subplot(dim[0], 1, plot_No)
plt.title('Output i vs. input %s' % (j + 1))
Expand All @@ -553,8 +553,8 @@ def rga_plot(G, w_start=-2, w_end=2, axlim=None, points=100, show=True, plot_typ
plt.semilogx(w, rgas, label='$\lambda$$_{%s, %s}$' % (i + 1, j + 1))
rgamax.append(max(rgas))

if (i == dim[1] - 1): #self-scaling algorithm
if (axlim != None):
if i == dim[1] - 1: #self-scaling algorithm
if axlim != None:
plt.axis(axlim)
else:
plt.axis([None, None, None, max(rgamax)])
Expand All @@ -565,7 +565,7 @@ def rga_plot(G, w_start=-2, w_end=2, axlim=None, points=100, show=True, plot_typ
plt.xlabel('Frequency [rad/unit time]')
plot_No += 1

elif (plot_type == 'all'):
elif plot_type == 'all':
for i in range(dim[0]):
for j in range(dim[1]):
plt.semilogx(w, numpy.array(numpy.abs(([utils.RGA(Gfr)[i, j] for Gfr in freqresp]))))
Expand Down Expand Up @@ -642,13 +642,13 @@ def rga_nm_plot(G, pairing_list=None, pairing_names=None, w_start=-2, w_end=2, a
pairing_names ='Diagonal pairing'
else:
for pairing in pairing_list:
if (pairing.shape != dim):
if pairing.shape != dim:
print('RGA_Number_Plot on plots square n by n matrices, make sure input matrix is square')
sys.exit()

plot_No = 0

if (plot_type == 'all'):
if plot_type == 'all':
for p in pairing_list:
plt.semilogx(w, [utils.RGAnumber(Gfr, p) for Gfr in freqresp], label=pairing_names[plot_No])
plot_No += 1
Expand All @@ -657,7 +657,7 @@ def rga_nm_plot(G, pairing_list=None, pairing_names=None, w_start=-2, w_end=2, a
plt.xlabel('Frequency (rad/unit time)')
plt.legend()

elif (plot_type == 'element'):
elif plot_type == 'element':
pcount = numpy.shape(pairing_list)[0] # pairing_list.count not accessible
for p in pairing_list:
plot_No += 1
Expand All @@ -666,7 +666,7 @@ def rga_nm_plot(G, pairing_list=None, pairing_names=None, w_start=-2, w_end=2, a
plt.axis(axlim)
plt.title(pairing_names[plot_No - 1])
plt.xlabel('Frequency (rad/unit time)')
if (plot_No == 1):
if plot_No == 1:
plt.ylabel('||$\Lambda$(G) - I||$_{sum}$')
else:
print("Invalid plot_type paramter.")
Expand Down Expand Up @@ -741,8 +741,8 @@ def dis_rejctn_plot(G, Gd, S, w_start=-2, w_end=2, axlim=None, points=100):
plt.subplot(2,1,2)
for i in range(dim[1]):
plt.loglog(w, inv_norm_gd[i], label=('1/||g$_{d%s}$||$_2$' % (i+1)), color='blue', alpha=((i+1.)/dim[1]))
plt.loglog(w, s_min, label=('$\sigma$$_{MIN}$'), color='green')
plt.loglog(w, s_max, label=('$\sigma$$_{MAX}$'), color='green', alpha = 0.5)
plt.loglog(w, s_min, label='$\sigma$$_{MIN}$', color='green')
plt.loglog(w, s_max, label='$\sigma$$_{MAX}$', color='green', alpha = 0.5)
plt.xlabel('Frequency (rad/unit time)')
plt.ylabel('1/||g$_d$||$_2$')
plt.axhline(1., color='red', ls=':')
Expand Down Expand Up @@ -790,11 +790,11 @@ def freq_step_response_plot(G, K, Kc, t_end=50, t_points=100, freqtype='S', head
Ks = [(kc * K) for kc in Kc]
Ts = [utils.feedback(G * Kss, 1) for Kss in Ks]

if (freqtype=='S'):
if freqtype=='S':
Fs = [(1 - Tss) for Tss in Ts]
plt.title('(a) Sensitivity function')
plt.ylabel('Magnitude $|S|$')
elif (freqtype=='T'):
elif freqtype=='T':
Fs = Ts
plt.title('(a) Complementary sensitivity function')
plt.ylabel('Magnitude $|T|$')
Expand Down Expand Up @@ -866,7 +866,7 @@ def step_response_plot(Y, U, t_end=50, initial_val=0, timedim='sec', axlim=None,
[t,y] = utils.tf_step(U, t_end, initial_val)
plt.plot(t,y)

if (constraint == None):
if constraint == None:
plt.legend(['$y(t)$','$u(t)$'])
else:
[t,y] = utils.tf_step(U, t_end, initial_val, points, constraint, Y, method)
Expand Down Expand Up @@ -927,13 +927,14 @@ def perf_Wp_plot(S, wB_req, maxSSerror, wStart, wEnd):
... [0., 0.1]])*10
>>>
>>> def G(s):
... return(K*numpy.exp(-t1*s)/(t2*s + 1))
... return K*numpy.exp(-t1*s)/(t2*s + 1)
...
>>> def L(s):
... return(Kc*G(s))
... return Kc*G(s)
...
>>> def S(s):
... return(numpy.linalg.inv((numpy.eye(2) + L(s)))) #SVD of S = 1/(I + L)
... return numpy.linalg.inv((numpy.eye(2) + L(s))) #SVD of S =
# 1/(I + L)
...
>>> #utils.perf_Wp(S, 0.05, 0.2, -3, 1)
Expand All @@ -948,7 +949,7 @@ def perf_Wp_plot(S, wB_req, maxSSerror, wStart, wEnd):
U, Sv, V = utils.SVD(S(s[i]))
magPlotS1[i] = Sv[0]
magPlotS3[i] = Sv[-1]
if (f < 1 and magPlotS1[i] > 0.707):
if f < 1 and magPlotS1[i] > 0.707:
wB = w[i]
f = 1
for i in range(len(w)):
Expand Down Expand Up @@ -982,5 +983,5 @@ def perf_Wp_plot(S, wB_req, maxSSerror, wStart, wEnd):
BG = fig.patch
BG.set_facecolor('white')
plt.grid(True)
return(wB)
return wB

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