def compplot_deviations(jsonfilelist, fignum=333, figname="figdiff", compress=10):
""" compute the deviations of the measured output from the stable
state output
"""
import matplotlib.cm as cm
fig = plt.figure(fignum)
ax1 = fig.add_subplot(111)
diffnormlist = []
collin = np.linspace(0.2, 0.7, len(jsonfilelist))
for k, jsfstr in enumerate(jsonfilelist):
jsf = dou.load_json_dicts(jsfstr)
tmesh = jsf["tmesh"]
redinds = range(0, len(tmesh), compress)
redina = np.array(redinds)
outsig = np.array(jsf["outsig"])
# two norm at the differences
outsigdiff = np.sqrt(((outsig - outsig[0, :]) * (outsig - outsig[0, :])).sum(axis=1))
diffnormlist.append((tmesh[-1] - tmesh[0]) / len(tmesh) * outsigdiff.sum())
curline, = ax1.plot(
np.array(tmesh)[redina], outsigdiff[redina], c=cm.CMRmap(collin[k]), linewidth=2.0, label="{0}".format(k)
)
handles, labels = ax1.get_legend_handles_labels()
ax1.legend(handles, labels, loc="lower right")
tikz_save(figname + "{0}".format(fignum) + ".tikz", figureheight="\\figureheight", figurewidth="\\figurewidth")
print "saved to " + figname + "{0}".format(fignum) + ".tikz"
fig.show()
print diffnormlist
print len(tmesh)
def compplotfreqresps(jsonfilelist, fignum=444, figname="freqfigdiff", compress=1):
""" plot the errors in the freqresp for various configurations
Parameters:
-----------
jsonfilelist : list
of json files as produced by `btu.compare_freqresp`
"""
import matplotlib.cm as cm
fig = plt.figure(fignum)
ax1 = fig.add_subplot(111)
collin = np.linspace(0.2, 0.7, len(jsonfilelist))
for k, jsfstr in enumerate(jsonfilelist):
jsf = dou.load_json_dicts(jsfstr)
tmesh = jsf["tmesh"]
redinds = range(0, len(tmesh), compress)
redina = np.array(redinds)
outsig = np.array(jsf["diffsysfr"])
curline, = ax1.plot(
np.array(tmesh)[redina], outsig[redina], c=cm.CMRmap(collin[k]), linewidth=2.0, label="{0}".format(k)
)
handles, labels = ax1.get_legend_handles_labels()
ax1.legend(handles, labels, loc="lower left")
tikz_save(figname + "{0}".format(fignum) + ".tikz", figureheight="\\figureheight", figurewidth="\\figurewidth")
print "saved to " + figname + "{0}".format(fignum) + ".tikz"
ax1.semilogx()
ax1.semilogy()
fig.show()
def plot_step_resp(str_to_json=None,
tmesh=None,
red_stp_rsp=None,
ful_stp_rsp=None,
inivout=None,
compress=20):
"""
compress : real, optional
factor of compressing for plot, defaults to 20
"""
from matplotlib2tikz import save as tikz_save
if str_to_json is not None:
jsdict = dou.load_json_dicts(str_to_json)
tmesh = np.array(jsdict['tmesh'])
red_stp_rsp = jsdict['red_stp_rsp']
ful_stp_rsp = jsdict['ful_stp_rsp']
inivout = jsdict['inivout']
else:
str_to_json = 'notspecified'
redinds = list(range(0, len(tmesh), compress))
redina = np.array(redinds)
for ccol in range(len(red_stp_rsp)):
# [0, b_mat.shape[1]-1]: # range(2): # b_mat.shape[1]):
fuloutp = np.array(ful_stp_rsp[ccol]) - np.array(inivout).T
redoutp = np.array(red_stp_rsp[ccol])
outdiff = fuloutp - redoutp
NY = fuloutp.shape[1] / 2
fig = plt.figure(200 + ccol)
ax1 = fig.add_subplot(131)
ax1.plot(tmesh[redina], redoutp[redina, :NY], color='b', linewidth=2.0)
ax1.plot(tmesh[redina], redoutp[redina, NY:], color='r', linewidth=2.0)
ax2 = fig.add_subplot(132)
ax2.plot(tmesh[redina], fuloutp[redina, :NY], color='b', linewidth=2.0)
ax2.plot(tmesh[redina], fuloutp[redina, NY:], color='r', linewidth=2.0)
ax3 = fig.add_subplot(133)
ax3.plot(tmesh[redina], outdiff[redina, :NY], color='b', linewidth=2.0)
ax3.plot(tmesh[redina], outdiff[redina, NY:], color='r', linewidth=2.0)
tikz_save(str_to_json + '{0}'.format(200 + ccol) + '.tikz',
figureheight='\\figureheight',
figurewidth='\\figurewidth')
print('saved to ' + str_to_json + '{0}'.format(200 + ccol) + '.tikz')
fig.show()
def plot_step_resp(str_to_json=None, tmesh=None,
red_stp_rsp=None, ful_stp_rsp=None, inivout=None,
compress=20):
"""
compress : real, optional
factor of compressing for plot, defaults to 20
"""
from matplotlib2tikz import save as tikz_save
if str_to_json is not None:
jsdict = dou.load_json_dicts(str_to_json)
tmesh = np.array(jsdict['tmesh'])
red_stp_rsp = jsdict['red_stp_rsp']
ful_stp_rsp = jsdict['ful_stp_rsp']
inivout = jsdict['inivout']
else:
str_to_json = 'notspecified'
redinds = list(range(0, len(tmesh), compress))
redina = np.array(redinds)
for ccol in range(len(red_stp_rsp)):
# [0, b_mat.shape[1]-1]: # range(2): # b_mat.shape[1]):
fuloutp = np.array(ful_stp_rsp[ccol])-np.array(inivout).T
redoutp = np.array(red_stp_rsp[ccol])
outdiff = fuloutp - redoutp
NY = fuloutp.shape[1]/2
fig = plt.figure(200 + ccol)
ax1 = fig.add_subplot(131)
ax1.plot(tmesh[redina], redoutp[redina, :NY], color='b', linewidth=2.0)
ax1.plot(tmesh[redina], redoutp[redina, NY:], color='r', linewidth=2.0)
ax2 = fig.add_subplot(132)
ax2.plot(tmesh[redina], fuloutp[redina, :NY], color='b', linewidth=2.0)
ax2.plot(tmesh[redina], fuloutp[redina, NY:], color='r', linewidth=2.0)
ax3 = fig.add_subplot(133)
ax3.plot(tmesh[redina], outdiff[redina, :NY], color='b', linewidth=2.0)
ax3.plot(tmesh[redina], outdiff[redina, NY:], color='r', linewidth=2.0)
tikz_save(str_to_json + '{0}'.format(200+ccol) + '.tikz',
figureheight='\\figureheight',
figurewidth='\\figurewidth'
)
print('saved to ' + str_to_json + '{0}'.format(200+ccol) + '.tikz')
fig.show()
