def plotResults(results):
plt.interactive(True)
plt.subplot(131)
plt.plot(results.xpos, color='b',label='X Position')
plt.plot(results.ypos, linestyle='--', color='r', label='Y Position')
plt.plot(results.zpos, linestyle='-', color='y', label='Z Position')
plt.xlabel('Time')
plt.ylabel('Distance')
plt.title('Distance Traveled')
plt.legend()
plt.show()
ts = range(0,len(results),6)
plt.subplot(131)
pt = plt.plot(0,results.zpos[0], 'ro', markersize=4)
for t in ts:
plt.subplot(131)
pt[0].set_ydata(results.zpos[t])
pt[0].set_xdata(t)
ax1 = plt.subplot(132)
ax1.clear()
plt.bar(range(6), results[t].lowerleglinearmag)
ax2 = plt.subplot(133)
ax2.clear()
plt.bar(range(6), results[t].upperleglinearmag)
plt.draw()
time.sleep(0.01)
def print_all(fnames, freq, spec1, spec2, rms1, rms2, bw=(0,1600)):
'''
Print all power spectra to PDF files
'''
# Construct path for saving figures and notify
base_dir = os.path.join(os.getcwd(), 'figures')
if not os.path.exists(base_dir):
os.mkdir(base_dir)
print('\nsaving figures to {s} ... '.format(s=base_dir), flush=True)
# Plot and save figures for each channel's spectrum
on = plt.isinteractive()
plt.ioff()
for chan in range(spec1.shape[-1]):
fig = comp_spectra(freq, spec1, spec2, channel=chan, bw=bw)
plt.title('Channel {0:d}'.format(chan))
plt.xlabel('Frequency (Hz)')
plt.ylabel('Power')
plt.legend(('Before', 'After'))
print('figure {:02d}'.format(chan), flush=True)
plt.savefig(os.path.join(base_dir, 'channel{0:02d}.png'.format(chan)), format='png')
plt.close(fig)
# Plot and save figure showing RMS ratio
fig = plt.figure()
plt.plot(rms2 / rms1, 'o')
plt.title('RMS ratio (after / before)')
plt.xlabel('Channel')
plt.savefig(os.path.join(base_dir, 'rms_ratio.png'), format='png')
plt.close(fig)
# Notify
plt.interactive(on)
print('done.')
def lvmTwoDPlot(X, lbl=None, symbol=None):
"""Helper function for plotting the labels in 2-D.
Description:
lvmTwoDPlot(X, lbl, symbol) helper function for plotting an
embedding in 2-D with symbols.
Arguments:
X - the data to plot.
lbl - the labels of the data point.
symbol - the symbols to use for the different labels.
See also
lvmScatterPlot, lvmVisualise
Copyright (c) 2004, 2005, 2006, 2008, 2009 Neil D. Lawrence
"""
if lbl=='connect':
connect = True
lbl = None
else:
connect = False
if symbol is None:
if lbl is None:
symbol = ndlutil.getSymbols(1)
else:
symbol = ndlutil.getSymbols(lbl.shape[1])
axisHand = pp.gca()
returnVal = []
holdState = axisHand.ishold()
intState = pp.isinteractive()
pp.interactive(False)
for i in range(X.shape[0]):
if i == 1:
axisHand.hold(True)
if lbl is not None:
labelNo = np.flatnonzero(lbl[i])
else:
labelNo = 0
try:
returnVal.append(axisHand.plot([X[i, 0]], [X[i, 1]], symbol[labelNo], markersize=10, linewidth=2))
if connect:
if i>0:
axisHand.plot([X[i-1, 0], X[i, 0]], [X[i-1, 1], X[i, 1]], 'r')
except(NotImplementedError):
raise NotImplementedError('Only '+ str(len(symbol)) + ' labels supported (it''s easy to add more!)')
axisHand.hold(holdState)
if intState:
pp.show()
pp.interactive(intState)
return returnVal
def plot_gamma_1_storage(): plt.figure() ts=get_mismatch(1.0) dummy,storage_capacity,storage_level=get_policy_2_storage(ts,return_storage_filling_time_series=True) plt.plot(storage_level) plt.interactive(1) plt.show()
def plot_path2d(data1, data2, data3, data4):
x1=[x for [x, y, z] in data1]
y1=[y for [x, y, z] in data1]
z1=[z for [x, y, z] in data1]
x2=[x for [x, y, z] in data2]
y2=[y for [x, y, z] in data2]
z2=[z for [x, y, z] in data2]
mx1=[x for [x, y, z] in data3]
my1=[y for [x, y, z] in data3]
mz1=[z for [x, y, z] in data3]
mx2=[x for [x, y, z] in data4]
my2=[y for [x, y, z] in data4]
mz2=[z for [x, y, z] in data4]
pltxy=plt.plot(x1,y1, 'ro-',label='xy')
pltuv=plt.plot(x2,y2, 'bs-',label='uv')
mpltxy=plt.plot(mx1,my1, 'go-',label='cut_xy')
mpltuv=plt.plot(mx2,my2, 'ks-',label='cut_uv')
plt.legend()
plt.axis('equal')
plt.axis([min([min(x1),min(x2),min(mx1),min(mx2)]),\
max([max(x1),max(x2),max(mx1),max(mx2)]),\
min([min(y1),min(y2),min(my1),min(my2)]),\
max([max(y1),max(y2),max(my1),max(my2)])])
plt.grid(True)
plt.interactive(True)
plt.show(block=False)
# plt.show()
plt.hold(True)
def cosp_plot_column_2D(fnc, varname='equivalent_reflectivity_factor', level=0, column = 0, time = 0):
"""
Function that plots one column of lat/lon data.
"""
plt.interactive(True)
fig=plt.figure()
ax = fig.add_subplot(111)
# Read cube
z=iris.load(fnc)
z=z[0]
# Get coords
c = z.coord('column')
t = z.coord('time')
# Select time and column
y=z.extract(iris.Constraint(column=c.points[column]))
y=y.extract(iris.Constraint(time=t.points[time]))
# Select level. Not managed to make constrain with 'atmospheric model level'
y=y[level]
color_map = mpl_cm.get_cmap('Paired')
qplt.pcolormesh(y,cmap=color_map,vmin=-20,vmax=20)
plt.gca().coastlines()
return
def plot_data_dict(data_dict, plots = None, mode = 'static', hang = True, figure = None, size = None, **plot_preference_kwargs):
"""
Make a plot of data in the format defined in data_dict
:param data_dict: dict<str: plottable_data>
:param plots: Optionally, a dict of <key: IPlot> identifying the plot objects to use (keys should
be the same as those in data_dict).
:return: The plots (same ones you provided if you provided them)
"""
assert mode in ('live', 'static')
if isinstance(data_dict, list):
assert all(len(d) == 2 for d in data_dict), "You can provide data as a list of 2 tuples of (plot_name, plot_data)"
data_dict = OrderedDict(data_dict)
if plots is None:
plots = {k: get_plot_from_data(v, mode = mode, **plot_preference_kwargs) for k, v in data_dict.items()}
if figure is None:
if size is not None:
from pylab import rcParams
rcParams['figure.figsize'] = size
figure = plt.figure()
n_rows, n_cols = vector_length_to_tile_dims(len(data_dict))
for i, (k, v) in enumerate(data_dict.items()):
plt.subplot(n_rows, n_cols, i + 1)
plots[k].update(v)
plots[k].plot()
plt.title(k, fontdict = {'fontsize': 8})
oldhang = plt.isinteractive()
plt.interactive(not hang)
plt.show()
plt.interactive(oldhang)
return figure, plots
def plot_paths(results, which_to_label=None):
import matplotlib
import matplotlib.pyplot as plt
plt.clf()
interactive_state = plt.isinteractive()
xvalues = -np.log(results.lambdas[1:])
for index, path in enumerate(results.coefficients):
if which_to_label and results.indices[index] in which_to_label:
if which_to_label[results.indices[index]] is None:
label = "$x_{%d}$" % results.indices[index]
else:
label = which_to_label[results.indices[index]]
else:
label = None
if which_to_label and label is None:
plt.plot(xvalues, path[1:], ':')
else:
plt.plot(xvalues, path[1:], label=label)
plt.xlim(np.amin(xvalues), np.amax(xvalues))
if which_to_label is not None:
plt.legend(loc='upper left')
plt.title('Regularization paths ($\\rho$ = %.2f)' % results.balance)
plt.xlabel('$-\log(\lambda)$')
plt.ylabel('Value of regression coefficient $\hat{\\beta}_i$')
plt.show()
plt.interactive(interactive_state)
def get_windows(image):
"""Display the given image and record user selected points.
Parameters
----------
image : M,N,3 ndarray
The image to be displayed.
Returns
-------
array : n_points,2
An array of coordinates in the image. Each row corresponds to the x, y
coordinates of one point. If an odd number of points are specified, the
last one will be discarded.
"""
plt.interactive(True)
plt.imshow(image)
plt.show()
crop = plt.ginput(0)
plt.close()
plt.interactive(False)
# remove last point if an odd number selected
crop = crop[:-1] if np.mod(len(crop), 2) else crop
return np.vstack(crop).astype('int')[:, [1, 0]]
def createHistogramOfOrientedGradientFeatures(sourceImage, numOrientations, pixelsPerCell):
# Returns an nxd matrix, n pixels and d the HOG vector length.
# H is a matrix NBLOCKS_Y x NBLOCKS_X x CPB_Y x CPB_X x ORIENTATIONS
# Here CPB == 1
H,Himg = myhog.hog( sourceImage, numOrientations, pixelsPerCell, cells_per_block=(1,1), flatten=False, visualise=True )
hog_image_rescaled = skimage.exposure.rescale_intensity( Himg )#, in_range=(0, 0.2))
plt.interactive(True)
plt.figure()
plt.subplot(1,2,1)
plt.imshow(sourceImage)
plt.subplot(1,2,2)
plt.imshow( hog_image_rescaled, cmap=plt.cm.gray )
plt.title('HOG')
plt.waitforbuttonpress()
# Reduce to non-singleton dimensions, BY x BX x ORIENT
H = H.squeeze()
assert H.ndim == 3
assert H.max() <= 1.0
# resize to image pixels rather than grid blocks
hogImg = np.zeros( ( sourceImage.shape[0], sourceImage.shape[1], numOrientations ), dtype=float )
for o in range(numOrientations):
hogPerOrient = H[:,:,o].astype(np.float32)
hpoAsPil = pil.fromarray( hogPerOrient, mode='F' )
hogImg[:,:,o] = np.array( hpoAsPil.resize( (sourceImage.shape[1], sourceImage.shape[0]), pil.NEAREST ) )
return hogImg.reshape( ( sourceImage.shape[0]*sourceImage.shape[1], numOrientations ) )
def train_agent(mdp, agent, max_episodes, epsilon_decay=0.9, plot=False):
'''
Trains an agent on the given MDP for the specified number of episodes.
:param mdp: The mdp which implements the domain
:param agent: The RL agent to train
:param max_episodes: The maximum number of episodes to run
:param epsilon_decay: The per-episode decay rate of the epsilon parameter
:param plot: If true, plot the reward results online.
'''
episode_rewards = []
for i in range(max_episodes):
episode_rewards.append(run_episode(mdp, agent, kbd_ctl=False))
if i % 1 == 0:
agent.epsilon *= epsilon_decay
if plot:
plt.interactive(True)
plt.clf()
plt.ylabel('Reward per episodes')
plt.xlabel('Episodes')
plt.plot(episode_rewards)
plt.draw()
print "[episode %d] episode reward: %f. Epsilon now: %f" %\
(i, episode_rewards[-1], agent.epsilon)
return episode_rewards
def main(argv=None):
# Permit interactive use
if argv is None:
argv = sys.argv
# Parse and check incoming command line arguments
outsuffix = None
try:
try:
opts, args = getopt.getopt(argv[1:], "h", ["help"])
except getopt.error as msg:
raise Usage(msg)
for o, a in opts:
if o in ("-h", "--help"):
print(__doc__)
return 0
elif o == "-o":
outsuffix = a
except Usage as err:
print(err.msg, file=sys.stderr)
return 2
# Push interactive mode off (in case we get used from IPython)
was_interactive = plt.isinteractive()
plt.interactive(False)
# If not saving, then display.
if not outsuffix:
plt.show()
# Pop interactive mode
plt.interactive(was_interactive)
def draw_2D_slice_interactive(self, p_vals, x_variable, y_variable,
range_x, range_y, slider_ranges,
**kwargs):
previous = plt.isinteractive()
plt.ioff()
number_of_sliders = len(slider_ranges)
slider_block = 0.03*number_of_sliders
fig = plt.figure()
plt.clf()
ax = plt.axes([0.1, 0.2+slider_block, 0.8, 0.7-slider_block])
c_axs = list()
cdict = dict()
if 'color_dict' in kwargs:
cdict.update(kwargs['color_dict'])
j = 0
sliders = dict()
for i in slider_ranges:
slider_ax = plt.axes([0.1, 0.1+j*0.03, 0.8, 0.02])
slider = Slider(slider_ax, i,
log10(slider_ranges[i][0]), log10(slider_ranges[i][1]),
valinit=log10(p_vals[i]), color='#AAAAAA'
)
j += 1
sliders[i] = slider
update = SliderCallback(self, sliders, c_axs, cdict,
ax, p_vals, x_variable, y_variable, range_x, range_y,
**kwargs)
update(1)
for i in sliders:
sliders[i].on_changed(update)
plt.show()
plt.interactive(previous)
def __init__(self, client_pars=None, plot_template=None, interactive=True, **kwargs):
self.client=Client(client_pars)
self.connect()
# initialize data containers
self.pr=u.Param()
self.ob=u.Param()
self.err=[]
self.ferr=None
# initialize the plotter
from matplotlib import pyplot
self.interactive = interactive
self.pp = pyplot
pyplot.interactive(interactive)
#self.template_default = default_template
self.templates = templates
#self.template = u.Param()
self.p = u.Param(DEFAULT)
#self.update_plot_layout(plot_template=plot_template)
# save as 'cmd': tuple(ticket,buffer,key)
# will call get cmds with 'cmd', save the ticket in <ticket> and
# save the resulting data in buffer[key]
self.cmd_dct = {}
self.server_dcts={}
def generate_single_funnel_test_data( excitation_angles, emission_angles, \
md_ex=0, md_fu=1, \
phase_ex=0, phase_fu=0, \
gr=1.0, et=1.0 ):
ex, em = np.meshgrid( excitation_angles, emission_angles )
alpha = 0.5 * np.arccos( .5*(((gr+2)*md_ex)-gr) )
ph_ii_minus = phase_ex - alpha
ph_ii_plus = phase_ex + alpha
print ph_ii_minus
print ph_ii_plus
Fnoet = np.cos( ex-ph_ii_minus )**2 * np.cos( em-ph_ii_minus )**2
Fnoet += gr*np.cos( ex-phase_ex )**2 * np.cos( em-phase_ex )**2
Fnoet += np.cos( ex-ph_ii_plus )**2 * np.cos( em-ph_ii_plus )**2
Fnoet /= (2+gr)
Fet = .25 * (1+md_ex*np.cos(2*(ex-phase_ex))) \
* (1+md_fu*np.cos(2*(em-phase_fu-phase_ex)))
Fem = et*Fet + (1-et)*Fnoet
import matplotlib.pyplot as plt
plt.interactive(True)
plt.matshow( Fem, origin='bottom' )
plt.colorbar()
def interactive(b):
b_prev = plt.isinteractive()
plt.interactive(b)
try:
yield
finally:
plt.interactive(b_prev)
def plot_patches(patches, fignum=None, low=0, high=0):
"""
Given a stack of 2D patches indexed by the first dimension, plot the
patches in subplots.
'low' and 'high' are optional arguments to control which patches
actually get plotted. 'fignum' chooses the figure to plot in.
"""
try:
istate = plt.isinteractive()
plt.ioff()
if fignum is None:
fig = plt.gcf()
else:
fig = plt.figure(fignum)
if high == 0:
high = len(patches)
pmin, pmax = patches.min(), patches.max()
dims = np.ceil(np.sqrt(high - low))
for idx in xrange(high - low):
spl = plt.subplot(dims, dims, idx + 1)
ax = plt.axis('off')
im = plt.imshow(patches[idx], cmap=matplotlib.cm.gray)
cl = plt.clim(pmin, pmax)
plt.show()
finally:
plt.interactive(istate)
def show_result(lr, title):
fig, axarr = plt.subplots(2, 1)
fig.suptitle(title, fontsize=16)
# Draw line
num_samples = 1000
x = lr.features[:, :-1]
min_val_x, max_val_x = np.floor(np.amin(x)), np.ceil(np.amax(x))
x_continue = np.linspace(min_val_x, max_val_x, num_samples).reshape(num_samples, 1)
features = np.hstack((x_continue, np.ones((num_samples, 1))))
y_continue = lr.hypothesise(features)
axarr[0].set_title("Draw line.")
axarr[0].plot(lr.features[:, :-1], lr.labels, 'bo')
axarr[0].plot(x_continue, y_continue, 'r-')
axarr[0].axis([min_val_x, max_val_x, np.amin(y_continue), np.amax(y_continue)])
# History of the Cost
num_iters = len(lr.loss_history)
axarr[1].set_title("History of the Cost")
axarr[1].plot(np.linspace(1, num_iters, num_iters), lr.loss_history, 'b-')
axarr[1].axis([0, num_iters, 0, np.max(lr.loss_history)])
plt.interactive(False)
plt.show(block=True)
plt.show()
def __init__(self, i):
#TO-DO: work the i argument
plt.interactive(True)
self._figure = plt.figure()
ax = f.add_subplot() #<- TO-DO: depending on intended axes and geometries
'''
def main():
plt.interactive(False)
#analyseSimpleBatchGradientDescent()
#analyseSimpleBatchGradientDescentRegularization()
#analyseSimpleBatchGradientDescentMNIST()
analyseBatchVersusMiniBatch()
#analyseOneVersusAll()
return
def rotating_sequence(show_frames=True, save_frames=True,
save_ani=False, show_ani=False,
ani_path='./puffer.mp4',
frames_basename='./puffer_frame_',
airtemp_cubes=None,
precip_cubes=None,
n_steps_round=20,
tilt_angle=21.7
):
plt.interactive(show_frames)
figure = make_puffersphere_figure()
# per_image_artists = []
for (i_plt, lon_rotate) in enumerate(np.linspace(0.0, 360.0, n_steps_round, endpoint=False)):
print 'rotate=', lon_rotate,' ...'
axes = make_puffersphere_axes(
projection_kwargs={'central_longitude': lon_rotate, 'central_latitude': tilt_angle})
image = axes.stock_img()
coast = axes.coastlines()
# data = istk.global_pp()
data = airtemp_cubes[i_plt]
transparent_blue = (0.0, 0.0, 1.0, 0.25)
transparent_red = (1.0, 0.0, 0.0, 0.25)
cold_thresh = -10.0
cold_fill = iplt.contourf(
data,
levels=[cold_thresh, cold_thresh],
colors=[transparent_blue],
extend='min')
cold_contour = iplt.contour(
data,
levels=[cold_thresh], colors=['blue'],
linestyles=['solid'])
data = precip_cubes[i_plt]
precip_thresh = 0.0001
precip_fill = iplt.contourf(
data,
levels=[precip_thresh, precip_thresh],
colors=[transparent_red],
extend='max')
precip_contour = iplt.contour(
data,
levels=[precip_thresh], colors=['red'],
linestyles=['solid'])
gridlines = draw_gridlines(n_meridians=6)
# artists = []
# artists += [coast]
# artists += [image]
# artists += cold_fill.collections
# artists += precip_fill.collections
# for gridline in gridlines:
# artists += gridline
if show_frames:
plt.draw()
if save_frames:
save_path = frames_basename+str(i_plt)+'.png'
save_figure_for_puffersphere(figure, save_path)
# per_image_artists.append(artists)
print ' ..done.'
def plot(self):
plt.interactive(True)
min_stamp = None
max_stamp = None
no_valid_data = True
for abag in self.bag_file:
with rosbag.Bag(abag) as bag:
info = yaml.load(bag._get_yaml_info())
message_num = sum([topic["messages"] for topic in info["topics"]
if topic["topic"] in self.all_topics])
widgets = [Fore.GREEN + "%s: " % (abag) + Fore.RESET, Percentage(), Bar()]
pbar = ProgressBar(maxval=message_num, widgets=widgets).start()
counter = 0
for topic, msg, timestamp in bag.read_messages(topics=self.all_topics):
pbar.update(counter)
for topic_data in self.topic_data:
topic_data.addValue(topic, msg)
no_valid_data = False
if min_stamp:
if min_stamp > msg.header.stamp:
min_stamp = msg.header.stamp
else:
min_stamp = msg.header.stamp
if max_stamp:
if max_stamp < msg.header.stamp:
max_stamp = msg.header.stamp
else:
max_stamp = msg.header.stamp
counter = counter + 1
pbar.finish()
if no_valid_data:
print Fore.RED + "Cannot find valid data in bag files, valid topics are:\n%s" % ", ".join(self.all_topics) + Fore.RESET
return
title = ("""Plot from [%s] to [%s] (%d secs)""" %
(str(time.ctime(min_stamp.to_sec())),
str(time.ctime(max_stamp.to_sec())),
(max_stamp - min_stamp).to_sec()))
start_time = rospy.Duration(self.start_time) + min_stamp
if self.duration:
end_time = start_time + rospy.Duration(self.duration)
else:
end_time = max_stamp
for topic_data in self.topic_data:
topic_data.filter(start_time, end_time)
fig = plt.figure(facecolor="1.0")
self.fig = fig
fig.suptitle(title)
self.show_legend = True
fig.canvas.mpl_connect('key_press_event', self.keyPress)
# Compute layout
self.start_time = start_time
self.plotAll(fig, start_time, self.show_legend)
self.printUsage()
self.runp = True
while self.runp:
plt.pause(1)
def display(self, covariances=None, logalpha=None, name=None, figurenum=1):
"""Display covariances and alphas with matplotlib."""
if PLOTTING_AVAILABLE:
try:
if covariances == None:
covariances = self._covariances
if logalpha == None:
logalpha = self._logalpha
if name == None:
name = "mixture"
# Save state of interactive mode and turn it off.
istate = pyplot.isinteractive()
pyplot.ioff()
# Create figure instance.
pyplot.figure(figurenum)
pyplot.clf()
# Calculate necessary subplot dimensions for a roughly square
rows = np.floor(np.sqrt(self._ncomponent()))
cols = np.ceil(np.sqrt(self._ncomponent()))
# Add one more row if we have a perfect square, for the
# preferences bar chart
while rows * cols <= self._ncomponent():
rows = rows + 1
for clust in xrange(self._ncomponent()):
pyplot.subplot(rows, cols, clust + 1)
self._display_covariance_matrix(clust)
title = "Covariance for %s component %d" % (name, clust + 1)
pyplot.title(title, fontsize="small")
pyplot.colorbar()
# Plot a bar graph of the mixing proportions in an extra subplot
pyplot.subplot(rows, cols, self._ncomponent() + 1)
# Do the bar graph and make it look reasonable
pyplot.bar(
bottom=np.zeros(self._ncomponent()),
left=np.arange(1, self._ncomponent() + 1) - 0.5,
height=np.exp(self._logalpha),
width=1,
)
# xticks for each bar in the bar graph.
pyplot.xticks(np.arange(1, self._ncomponent() + 1))
pyplot.title("Mixing proportions")
# Display figure.
pyplot.show()
finally:
# Restore previous interactivity state.
pyplot.interactive(istate)
def draw_plot(self, x_label, y_label, x_data, y_data):
x_data.sort()
y_data.sort()
import matplotlib.pyplot as plt
plt.xlabel(x_label)
plt.ylabel(y_label)
title = '{0} {1} RELATIONSHIP'.format(x_label, y_label)
plt.title(title)
plt.interactive(False)
plt.plot(x_data, y_data, label="")
plt.show()
def plot_em(T):
problem=r'2b: Plot lnp'
w=W(T)
lnp=[probit(w,t,Xtrain, ytrain) for t in range(T)]
x=[t+1 for t in range(T)]
plt.plot(x, lnp)
plt.title(problem)
plt.xlabel(r't')
plt.ylabel(r'ln[p(w_t, y | X)]')
plt.interactive(True)
plt.savefig(problem.replace(':','').replace(' ', '_'))
def plot_map2d(k, coordinates, unit_statistic_homogeneous_significant, vmin=None, vmax=None):
"""Plots 2d sensor (unit) map related to simulate_2d() data.
"""
map2d = np.zeros((k, k))
for i, (x, y) in enumerate(coordinates):
map2d[x, y] = unit_statistic_homogeneous_significant[i]
plt.interactive(True)
plt.figure()
plt.imshow(map2d, interpolation='nearest', vmin=vmin, vmax=vmax)
plt.colorbar()
def plot_data(dist, fset, dset, qset): plt.interactive(False) plt.rcParams['font.size'] = 17.0 dist_params = pdata.dist_map[dist] ext = dist_params["ext"] typ = dset["type"] emp_key = "emp_" + typ emp_file = fset[emp_key] dist_key = ext + "_" + typ dist_file = fset[dist_key] p1 = read_data(emp_file) p2 = read_data(dist_file) xlabel = dset["xlabel"] ylabel = dset["ylabel"] l1 = dset["legend1"] l2 = dset["legend2"].replace(pdata.dist_replace_string, dist_params["legend"]) loc = dset["loc"] if ( typ == "cdf" ): plt.plot(p1[:,0], p1[:,1], 'k-', label=l1) plt.plot(p2[:,0], p2[:,1], 'k--', label=l2) else: plt.loglog(p1[:,0], p1[:,1], 'k-', label=l1) plt.loglog(p2[:,0], p2[:,1], 'k--', label=l2) if "xlim" in dset: plt.xlim(dset["xlim"]) if "ylim" in dset: plt.ylim(dset["ylim"]) if "xticks" in dset: plt.xticks(dset["xticks"]) if "yticks" in dset: plt.yticks(dset["yticks"]) plt.grid() plt.xlabel(xlabel) plt.ylabel(ylabel) plt.legend(loc=loc, frameon=False) dir = qset["dir"] fname = qset["file"] eps_file = dset["tag"].lower() + "_" + typ + "_" + ext + ".eps" plot_dir = os.path.join(dir, pdata.plot_dir) image_file = os.path.join(plot_dir, eps_file) prepare_file(image_file) plt.savefig(image_file) print "\t Created plot", image_file plt.close()
def plot_uncertainty_bounds_s(self, multiplier =200, *args, **kwargs):
'''
Plots complex uncertainty bounds plot on smith chart.
This function plots the complex uncertainty of a NetworkSet
as circles on the smith chart. At each frequency a circle
with radii proportional to the complex standard deviation
of the set at that frequency is drawn. Due to the fact that
the `markersize` argument is in pixels, the radii can scaled by
the input argument `multiplier`.
default kwargs are
{
'marker':'o',
'color':'b',
'mew':0,
'ls':'',
'alpha':.1,
'label':None,
}
Parameters
-------------
multipliter : float
controls the circle sizes, by multiples of the standard
deviation.
'''
default_kwargs = {
'marker':'o',
'color':'b',
'mew':0,
'ls':'',
'alpha':.1,
'label':None,
}
default_kwargs.update(**kwargs)
if plb.isinteractive():
was_interactive = True
plb.interactive(0)
else:
was_interactive = False
[self.mean_s[k].plot_s_smith(*args, ms = self.std_s[k].s_mag*multiplier, **default_kwargs) for k in range(len(self[0]))]
if was_interactive:
plb.interactive(1)
plb.draw()
plb.show()
def compare_sarsa_qlearning(render):
'''
The top-level method for running RL experiments. Performs runs on the two
defined agents, and plots the results.
:param render: If true, render while training
'''
sarsa_rewards = run_experiment(SARSAAgent, render)
qlearning_rewards = run_experiment(QLearningAgent, render)
plt.interactive(False)
plot_sarsa_vs_qlearning(sarsa_rewards, qlearning_rewards)
def main():
plt.interactive(False)
iris = sklearn.datasets.load_iris()
data = iris['data']
labels = iris['target']
pca = sklearn.decomposition.PCA(2)
data_pca = pca.fit_transform(data)
estimated_labels, centers = kmeans(data_pca, 3, 0.001)
plot_groups(data_pca, estimated_labels, {0: 'o', 1: 's', 2: '^'}, figsize=(4, 4))
plt.show()
def density_plot(x):
p = sns.kdeplot(x)
p = plt.show(block=True)
p = plt.interactive(False)
return p
# Run redmonster on a cluster using multiprocessesing # # Tim Hutchinson, University of Utah, August 2014 # [email protected] from time import gmtime, strftime import numpy as n import matplotlib.pyplot as p p.interactive(True) import multiprocessing as mp from astropy.io import fits from redmonster.sandbox import yanny as y from redmonster.datamgr import spec, io from redmonster.physics import zfinder, zfitter, zpicker def parallel_rm(xxx_todo_changeme): (plate, mjd, fiberid) = xxx_todo_changeme specs = spec.Spec(plate=plate, mjd=mjd, fiberid=fiberid) zssp = zfinder.ZFinder(fname='ndArch-ssp_em_galaxy-v000.fits', npoly=4, zmin=-0.01, zmax=1.2) zssp.zchi2(specs.flux, specs.loglambda, specs.ivar) # Write chi2 file with zbase prihdu = fits.PrimaryHDU(zssp.zchi2arr) col1 = fits.Column(name='ZBASE', format='E', array=zssp.zbase) cols = fits.ColDefs([col1])
def show_model_output(model, dataset, indices, num_rows):
plt.interactive(False)
num_cols = 3
plt.rcParams.update({'font.size': 8})
for i_sample, index in enumerate(indices):
sample = dataset.__getitem__((index, True))
x, y, question, _, full_sequence = sample
if question != -1:
y_pred, latent_stuff = model(torch.unsqueeze(x, 0),
torch.unsqueeze(question, 0))
else:
y_pred, latent_stuff = model(torch.unsqueeze(x, 0),
torch.Tensor([-1]))
y_pred = torch.sigmoid(y_pred)
to_pil = transforms.ToPILImage()
f, axes = plt.subplots(num_rows, num_cols)
# f.suptitle("\nSample {}, question: {}".format(i_sample, question), fontsize=16)
f.suptitle("\nQuestion: {}".format(question), fontsize=16)
if num_rows > 1:
for i_frame in range(num_rows):
# Plot ground truth
axes[i_frame, 0].imshow(to_pil(y[i_frame]), cmap='gray')
# Plot prediction
axes[i_frame, 1].imshow(to_pil(y_pred[0, i_frame]),
cmap='gray')
# Plot Deviation
diff = abs(y_pred[0, i_frame] - y[i_frame])
axes[i_frame, 2].imshow(to_pil(diff), cmap='gray')
else:
for i_frame in range(num_rows):
# Plot ground truth
if question != -1:
im = torch.zeros_like(full_sequence[0])
if question > 0:
for tmp_im in full_sequence[:question.int()]:
tmp_im[tmp_im > 0.5] = 1
tmp_im[tmp_im < 0.5] = 0
im += tmp_im
im = im.clamp(0, 0.5)
im = (im + full_sequence[question.int()]).clamp(0, 1)
axes[0].imshow(to_pil(im), cmap='gray')
else:
axes[0].imshow(to_pil(y), cmap='gray')
# Plot prediction
axes[1].imshow(to_pil(y_pred[0]), cmap='gray')
# Plot Deviation
diff = abs(y_pred[0] - y)
axes[2].imshow(to_pil(diff), cmap='gray')
# Remove axis ticks
for ax in axes.reshape(-1):
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_tick_params(which='both',
length=0,
labelleft=False)
# Label rows
labels = {0: 'Ground truth', 1: 'Prediction', 2: 'Deviation'}
for i_col in range(num_cols):
if num_rows > 1:
plt.sca(axes[0, i_col])
axes[0, i_col].set_title(labels[i_col], rotation=0, size=14)
else:
plt.sca(axes[i_col])
axes[i_col].set_title(labels[i_col], rotation=0, size=14)
f.tight_layout()
plt.show(block=True)
def plot_joint_distribution_3dbar_chart(joint_data,
X_field,
Y_field,
chart_field,
lower_color=(0., 0., .85, 1.),
greater_color=(.85, 0., 0., 1.),
neutral_color=(0., .25, .0, 0.25),
add_x_label=False,
add_y_label=False):
plt.interactive(True)
fig_3d = plt.figure(figsize=(10, 10))
ax_3d = fig_3d.add_subplot(111, projection='3d')
joint_data.sort_values([Y_field, chart_field, X_field],
ascending=[True, False, True],
inplace=True)
x_values = list(joint_data[X_field].drop_duplicates())
joint_data[X_field +
'_idx'] = joint_data[X_field].apply(lambda x: x_values.index(x))
y_values = list(joint_data[Y_field].drop_duplicates())
joint_data[Y_field +
'_idx'] = joint_data[Y_field].apply(lambda y: y_values.index(y))
x_data = joint_data[X_field + '_idx'].as_matrix()
y_data = joint_data[Y_field + '_idx'].as_matrix()
z_data = joint_data[chart_field].as_matrix()
colors = joint_data['favored hypothesis'].apply(
lambda x: greater_color if x == 'significantly_greater' else
lower_color if x == 'significantly_lower' else neutral_color)
joint_data['color'] = colors
ax_3d.bar3d(x_data,
y_data,
np.zeros(len(joint_data)),
1,
1,
z_data,
color=colors,
linewidth=1.)
_, x_labels_texts = plt.xticks(range(len(x_values)),
x_values,
rotation=90,
fontsize=8)
_, y_labels_texts = plt.yticks(range(len(y_values)),
y_values,
rotation=-90,
fontsize=8)
for x_index, x_label_text in enumerate(x_labels_texts):
avg_color_of_value = tuple(
map(
np.mean,
zip(*joint_data[joint_data[X_field].astype(str) ==
x_label_text._text]['color'])))
x_label_text.set_color(avg_color_of_value)
for y_index, y_label_text in enumerate(y_labels_texts):
avg_color_of_value = tuple(
map(
np.mean,
zip(*joint_data[joint_data[Y_field].astype(str) ==
y_label_text._text]['color'])))
y_label_text.set_color(avg_color_of_value)
if add_x_label:
ax_3d.set_xlabel(X_field)
if add_y_label:
ax_3d.set_ylabel(Y_field)
plt.show()
import matplotlib
import pandas
from matplotlib import pyplot
from matplotlib2tikz import save as tikz_save
from psutil import virtual_memory
import numpy
matplotlib.style.use('ggplot')
pyplot.interactive(False)
def to_min_secs(x, pos):
x = int(x)
minutes = x // 60
seconds = x % 60
return '{:02d}:{:02d}'.format(minutes, seconds)
def system_performance():
total_RAM_mb = virtual_memory().total / (1024 * 1024)
c = [
'timestamp', 'escrituras hdd', 'lecturas hdd', 'total I/o',
'% freq procesador', '% rendimiento procesador', '% de uso procesador',
'% tiempo procesador', 'MB disponibles RAM'
]
df = pandas.read_csv('SystemStats.csv', encoding='ISO-8859-1')
df.columns = c
df['timestamp'] = pandas.to_datetime(df['timestamp'])
starttime = df['timestamp'].min()
from scipy.signal import convolve2d
from matplotlib.path import Path
from matplotlib.patches import PathPatch
import matplotlib.colors as mcolors
import matplotlib.cm as cm
from matplotlib.colors import Normalize
import matplotlib.patches as patches
import math
import copy
import pickle
import os
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import matplotlib.pylab as py
import mpl_toolkits.mplot3d.art3d as art3d
plt.interactive(True);
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import datetime
from descartes.patch import PolygonPatch
import matplotlib.patheffects as PathEffects
import matplotlib.gridspec as gridspec
from matplotlib import rcParams
import h5py
import matplotlib.pyplot as plt
import matplotlib.pylab as pl
import scipy
def MFP_process (component, init, NSAMP, win_size, XINIT, YINIT, GRDX, GRDY, XEND, YEND, DEPTHS, MFP, rx, ry, VEL, depth_start, sampl_freq):
# load init data
import re
import sys
import numpy as np
import lsst.afw.geom as afwGeom
import lsst.afw.cameraGeom as afwCG
import lsst.geom as geom
import lsst.daf.persistence as dafPersist
try:
plt
except NameError:
import matplotlib.pyplot as plt
from matplotlib.patches import Circle, PathPatch
from matplotlib.path import Path
plt.interactive(1)
def main(butler,
visits,
fields,
fieldRadius,
showCCDs=False,
aitoff=False,
alpha=0.2,
byFilter=False,
byVisit=False,
title="",
verbose=False):
ra, dec = [], []
filters = {}
def plotMap(map2d_, path_, title_ =''):
'''Plots a map (image) of a 2d matrix with a path from start point to the goal point.
cells with a value of 0: Free cell;
-1: Obstacle;
-2: Start point;
-3: Goal point;
Parameters:
-----------
map2d_ : array-like
an array with Real Numbers
path_ : list of lists
a list of the points (2d corrdinates) in the format of [[x0, x1, x2, x3 ...], [y0, y1, y2, y3 ...]]
title_ : string
information/description of the plot
Returns:
--------
'''
import matplotlib.cm as cm
plt.interactive(False)
greennumber = map2d_.max()
#greennumber = len(np.unique(map2d_))
#print(greennumber)
colors = cm.winter(np.linspace(0, 1, greennumber))
colorsMap2d = [[[] for x in range(map2d_.shape[1])] for y in range(map2d_.shape[0])]
# Assign RGB Val for starting point and ending point
locStart, locEnd = np.where(map2d_ == -2), np.where(map2d_ == -3)
colorsMap2d[locStart[0][0]][locStart[1][0]] = [.0, .0, .0, 1.0] # black
colorsMap2d[locEnd[0][0]][locEnd[1][0]] = [.0, .0, .0, 1.0] # white
# Assign RGB Val for obstacle
locObstacle = np.where(map2d_ == -1)
for iposObstacle in range(len(locObstacle[0])):
colorsMap2d[locObstacle[0][iposObstacle]][locObstacle[1][iposObstacle]] = [1.0, .0, .0, 1.0]
# Assign 0
locZero = np.where(map2d_ == 0)
for iposZero in range(len(locZero[0])):
colorsMap2d[locZero[0][iposZero]][locZero[1][iposZero]] = [1.0, 1.0, 1.0, 1.0]
# Assign Expanded nodes
locExpand = np.where(map2d_>0)
for iposExpand in range(len(locExpand[0])):
colorsMap2d[locExpand[0][iposExpand]][locExpand[1][iposExpand]] = colors[int(map2d_[locExpand[0][iposExpand]][locExpand[1][iposExpand]])-1]
for irow in range(len(colorsMap2d)):
for icol in range(len(colorsMap2d[irow])):
if colorsMap2d[irow][icol] == []:
colorsMap2d[irow][icol] = [1.0, 0.0, 0.0, 1.0]
fig = plt.figure()
plt.title(title_)
plt.imshow(colorsMap2d, interpolation='nearest')
plt.colorbar()
plt.plot(path_[:][0],path_[:][1], color='magenta',linewidth=2.5)
plt.ylim(0,map2d_.shape[0])
plt.xlim(0,map2d_.shape[1])
plt.draw()
#plt.savefig()
#plt.show()
## Example
## Map description
## 0 - Free cell
## -1 - Obstacle
## -2 - Start point
## -3 - Goal point
#_map_ = generateMap2d([60,60])
#plt.clf()
#plt.imshow(_map_)
# map with rotated H shape obstacle and obstacles randomly distributed
#map_h_object, info = generateMap2d_obstacle([60,60])
#plt.clf()
#plt.imshow(map_h_object)
## solved_map description
## 0 - unexpanded cell
## -1 - obstacle
## -2 - start point
## -3 - goal point
## positive_number - one of the values described in lab2 description (heuristic cost, travel cost, cell total cost,...)
#example_solved_map = map_h_object
#x_corr, y_corr = range(30), range(30)[::-1]
#example_solved_path = [x_corr, y_corr]
#pp.plotMap(example_solved_map,example_solved_path)
def mk_plots(
res,
event=None,
issrs=True,
Q="auto",
drms=None,
inc0rb=True,
fmt="pdf",
onepdf=True,
layout=(2, 3),
figsize=(11, 8.5),
showall=None,
showboth=False,
cases=None,
direc="srs_plots",
tight_layout_args=None,
plot=plt.plot,
show_figures=False,
):
"""
Make SRS or response history plots
Parameters
----------
res : :class:`DR_Results` instance
Subclass of dict containing categories with results (see
:class:`DR_Results`). For example, ``results['SC_atm'].ext``.
event : string or None; optional
String for plot titles and file names (eg: 'Liftoff'). If
None, `event` is determined from `res[drm].event`.
issrs : bool; optional
True if plotting SRS data; False otherwise.
Q : scalar or iterable or 'auto'; optional
The Q value(s) to plot. If 'auto', all the Q values for
each category are plotted. Must be a scalar if `showall`
is True (see below).
drms : list of data recovery categories or None; optional
Data recovery categories to plot. If None, plot all
available. See also input `inc0rb`.
inc0rb : bool; optional
If True, the '_0rb' versions of each data recovery
category are automatically included.
fmt : string or None; optional
If `fmt` == "pdf", all plots are written to one PDF file,
unless `onepdf` is set to False. If `fmt` is some other
string, it is used as the `format` parameter in
:func:`matplotlib.pyplot.savefig`. If None, no figures
will be saved. Typical values for `fmt` are (from
``fig.canvas.get_supported_filetypes()``)::
'eps': 'Encapsulated Postscript',
'jpeg': 'Joint Photographic Experts Group',
'jpg': 'Joint Photographic Experts Group',
'pdf': 'Portable Document Format',
'pgf': 'PGF code for LaTeX',
'png': 'Portable Network Graphics',
'ps': 'Postscript',
'raw': 'Raw RGBA bitmap',
'rgba': 'Raw RGBA bitmap',
'svg': 'Scalable Vector Graphics',
'svgz': 'Scalable Vector Graphics',
'tif': 'Tagged Image File Format',
'tiff': 'Tagged Image File Format'
File naming conventions: if 'SC_atm' is a category, then
example output filenames could be::
'SC_atm_srs.pdf'
'SC_atm_eqsine.pdf'
'SC_atm_srs_0.png', 'SC_atm_srs_1.png', ...
'SC_atm_eqsine_0.png', 'SC_atm_eqsine_1.png', ...
onepdf : bool or string; optional
If `onepdf` evaluates to True and `fmt` is 'pdf', all plots
are written to one PDF file where the name is:
======== =======================================
`onepdf` PDF file name
======== =======================================
string All plots saved in: `onepdf` + ".pdf"
True If `issrs`: all plots saved in:
`event` + "_srs.pdf"
True If not `issrs`: all plots saved in:
`event` + "_??.pdf", where "??" is
either 'hist', 'psd', or 'frf' as
appropriate.
======== =======================================
If False, each figure is saved to its own file named as
described above (see `fmt`).
layout : 2-element tuple/list; optional
Subplot layout, eg: (2, 3) for 2 rows by 3 columns. See also
`figsize`.
figsize : 2-element tuple/list; optional
Define page size in inches. See also `layout`.
showall : bool or None; optional
If True, show all SRS curves for all cases; otherwise just
plot envelope. If None and `showboth` is True, `showall`
is set to True.
showboth : bool; optional
If True, shows all SRS curves and the envelope; otherwise
just plot which ever `showall` indicates.
direc : string; optional
Directory name to put all output plot files; will be
created if it doesn't exist.
cases : tuple/list of case names to plot or None; optional
If None, all cases are plotted. This option is ignored if
plotting SRS curves and `showall` is True.
tight_layout_args : dict or None; optional
Arguments for :func:`matplotlib.pyplot.tight_layout`. If None,
defaults to::
{'pad': 3.0,
'w_pad': 2.0,
'h_pad': 2.0,
'rect': (0.3 / figsize[0],
0.3 / figsize[1],
1.0 - 0.3 / figsize[0],
1.0 - 0.3 / figsize[1])}
plot : function; optional
The function that will draw each curve. Defaults to
:func:`matplotlib.pyplot.plot`. As an example, for a plot with
a linear X-axis but a log Y-axis, set `plot` to
:func:`matplotlib.pyplot.semilogy`. You can also use a custom
function of your own devising, but it is expected to accept
the same arguments as :func:`matplotlib.pyplot.plot`.
show_figures : bool; optional
If True, plot figures will be displayed on the screen for
interactive viewing. Warning: there may be many figures.
Notes
-----
Set the `onepdf` parameter to a string to specify the name of the
PDF file.
Used by :func:`DR_Results.srs_plots` and
:func:`DR_Results.resp_plots` for plot generation.
"""
if tight_layout_args is None:
tight_layout_args = {
"pad": 3.0,
"w_pad": 2.0,
"h_pad": 2.0,
"rect": (
0.3 / figsize[0],
0.3 / figsize[1],
1.0 - 0.3 / figsize[0],
1.0 - 0.3 / figsize[1],
),
}
if showboth and showall is None:
showall = True
if not os.path.exists(direc):
os.mkdir(direc)
rows = layout[0]
cols = layout[1]
perpage = rows * cols
# The following removed in v0.95.5:
# orientation = "landscape" if figsize[0] > figsize[1] else "portrait"
if drms is None:
alldrms = sorted(res)
else:
alldrms = copy.copy(drms)
if inc0rb:
for name in drms:
if name + "_0rb" in res:
alldrms.append(name + "_0rb")
pdffile = None
imode = plt.isinteractive()
plt.interactive(show_figures)
cur_fig = None
try:
for name in alldrms:
if name not in res:
raise ValueError(f"category {name} does not exist.")
if issrs:
if "srs" not in res[name].__dict__:
if drms and name in drms:
warnings.warn(f"no SRS data for {name}", RuntimeWarning)
continue
Qs = _get_Qs(Q, res, name, showall)
if Qs is None:
continue
x = res[name].srs.frq
y = None
xlab = "Frequency (Hz)"
ylab = None
ptype = "srs"
else:
if "hist" in res[name].__dict__:
x = res[name].time
y = res[name].hist
xlab = "Time (s)"
ylab = "Response"
ptype = "hist"
elif "psd" in res[name].__dict__:
x = res[name].freq
y = res[name].psd
xlab = "Frequency (Hz)"
ylab = "PSD"
ptype = "psd"
elif "frf" in res[name].__dict__:
x = res[name].freq
y = abs(res[name].frf)
xlab = "Frequency (Hz)"
ylab = "FRF"
ptype = "frf"
else:
if drms and name in drms:
warnings.warn(f"no response data for {name}", RuntimeWarning)
continue
(labels, rowpv, maxlen, sname, srstype, lbl, units, _cases) = _set_vars(
issrs, res, name, event, showall, showboth, cases
)
if fmt == "pdf" and onepdf and pdffile is None:
if isinstance(onepdf, str):
fname = os.path.join(direc, onepdf)
if not fname.endswith(".pdf"):
fname = fname + ".pdf"
else:
fname = os.path.join(direc, f"{sname}_{ptype}.pdf")
pdffile = PdfPages(fname)
filenum = 0
uj = 0 # units index
nplots = len(rowpv)
maxcol = cols if nplots > cols else nplots
sub = perpage
prefix = None
leg_info = [None, 0.0]
for j in range(nplots):
sub, filenum, prefix, cur_fig = _prep_subplot(
rows,
cols,
sub,
perpage,
filenum,
nplots,
fmt,
name,
lbl,
sname,
figsize,
prefix,
onepdf,
show_figures,
cur_fig,
)
label = " ".join(labels[j].split())
if issrs:
for q in Qs:
if showall:
_plot_all(
issrs,
plot,
res[name],
q,
x,
y,
showboth,
_cases,
sub,
cols,
maxcol,
name,
label,
maxlen,
sname,
rowpv,
j,
leg_info,
figsize,
tight_layout_args,
)
else:
_plot_ext(
plot,
res[name],
q,
Qs,
x,
sub,
cols,
maxcol,
name,
label,
maxlen,
sname,
rowpv,
j,
)
else:
_plot_all(
issrs,
plot,
res[name],
None,
x,
y,
showboth,
_cases,
sub,
cols,
maxcol,
name,
label,
maxlen,
sname,
rowpv,
j,
leg_info,
figsize,
tight_layout_args,
)
_add_xy_labels(issrs, units, uj, xlab, ylab, nplots, sub, srstype)
if j + 1 == nplots or (j + 1) % perpage == 0:
_legend_layout(leg_info, tight_layout_args)
if fmt == "pdf" and onepdf:
pdffile.savefig()
# orientation=orientation,
# papertype='letter')
elif fmt:
fname = os.path.join(direc, prefix + "." + fmt)
# The following removed in v0.95.5:
# if fmt != "pdf":
# kwargs = dict(
# orientation=orientation, dpi=200, bbox_inches="tight"
# )
# else:
# kwargs = {}
plt.savefig(fname, format=fmt) # , **kwargs)
if not show_figures:
plt.close(cur_fig)
cur_fig = None
finally:
plt.interactive(imode)
if pdffile:
pdffile.close()
if cur_fig:
plt.close(cur_fig)
def interactive_matplotlib_context(on=True):
old_mode = plt.isinteractive()
plt.interactive(on)
yield
plt.interactive(old_mode)
#para la clasificacion en redes neuronales es recomendable codificar las clases con una neurona de salida
#por clase, esto sucede porque se usa la funcion de activacion SoftMaxLayer que lo requiere de esa manera 1 neurona por salida
trndata._convertToOneOfMany()
tstdata._convertToOneOfMany()
validata._convertToOneOfMany()
print trndata.indim, trndata.outdim, tstdata.indim, tstdata.outdim
#configuracion de la red neuronal para este caso es una red neuronal difusa o FNN con 4 entradas
#3 neuronas ocultas y 3 neuronas de salida.
net = buildNetwork(4, 5, 3, outclass=SoftmaxLayer)
#y tenemos que configurar el algoritmo de entrenamiento backpropagation
trainer = BackpropTrainer(net,
dataset=trndata,
momentum=0.1,
verbose=True,
weightdecay=0.01)
#ahora para entrenar lo que inicializamos utilizamos la siguiente linea
trnerr, valerr = trainer.trainUntilConvergence(dataset=trndata, maxEpochs=100)
pl.interactive(False)
pl.plot(trnerr, 'b', valerr, 'r')
pl.show()
out = net.activateOnDataset(tstdata)
out = np.argmax(out, axis=1)
output = np.array([net.activate(x) for x, _ in validata])
output = output.argmax(axis=1)
eror = percentError(output, validata['class'])
print 100 - eror
print output
print validata['class']
print type(validata)
import os
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
plt.interactive("True")
# Parse the testSet and dbSet
script_dir = os.path.dirname(__file__)
dbSet = open(os.path.join(script_dir, "db.txt"), 'r').read().split("\n")
testSet = open(os.path.join(script_dir, "test.txt"), 'r').read().split("\n")
dbSet = [data.split(";") for data in dbSet]
dbSet = dbSet[0:len(dbSet) - 1]
testSet = [data.split(";") for data in testSet]
testSet = testSet[0:len(testSet) - 1]
# Parse matches.txt
matches = open(os.path.join(script_dir, "matches.txt"), 'r').read().split("\n")
matches = [data.split(":") for data in matches]
# print(dbSet[0:10])
# print(testSet[0:10])
# print(matches)
imagePaths = []
for match in matches:
imagePaths.append([testSet[int(match[0])][0], dbSet[int(match[1])][0]])
# print(imagePaths)
fig = plt.figure()
for imagePath in imagePaths:
Script uses microphone to receive audio input stream.
"""
# TO DO: convert to use wave
# TO DO: convert to input .wav file
# TO DO: extract MFCC from FFT on a .wav file
# https://people.csail.mit.edu/hubert/pyaudio/docs/
import time
import pyaudio
import struct # allows conversion of audio data to ints
import numpy as np
import matplotlib.pyplot as plt
from scipy.fftpack import fft
plt.interactive(True) # ensures matplotlib opens a new window
# format constants
CHUNK = 1024 * 2
FORMAT = pyaudio.paInt16
CHANNELS = 1 # MONO
RATE = 44100 # Hz
# instance of pyaudio
p = pyaudio.PyAudio()
# audio input stream object
stream = p.open(
format=FORMAT,
channels=CHANNELS,
rate=RATE,
def show_result_jupyter(input1, input2, variable_file=False):
fasit, long_results, short_results, df_tot, chosen_p, chosen_r2, r2_modelled, r2_modelled_long, prediction, tipping_ps, short_period, nb_weeks_tipping = input1
fasit_key, ant_kandidater, max_p, reg_end, read_start = input2
"""This function prints out and plots the results from the regression."""
plt.interactive(False)
reg_start = (pd.to_datetime(time.strftime(reg_end), format="%Y.%m.%d") -
Timedelta(days=short_period * 7)).strftime('%Y.%m.%d')
print(
'\n-----------------------------------------------------------------------'
)
print('RESULTATER FOR %s\n' % fasit_key)
print('Regresjonsperiode brukt til setup for siste tipping: %s til: %s.' %
(read_start, reg_end))
if variable_file:
print(
'Input variablene (reg_period og ant_kandidater) ble hentet fra: ',
variable_file)
else:
print(
'Input variablene (reg_period og ant_kandidater) ble funnet ved tuning.'
)
print(
'Regresjonsperiode brukt på modellen for siste tipping: %s til: %s, satt til %d uker.'
% (reg_start, reg_end, int(short_period)))
print(
'Valgte %d kandidater til regresjonen utifra korrelasjon med fasitserien.'
% (int(ant_kandidater)))
print('Valgte så ut de med p-value < %.5f, som var %i stk.' %
(max_p, len(long_results.pvalues)))
print('R2 for regresjonen (kort periode): %.5f' % r2_modelled)
print('R2 mellom fasit og tipping: %.5f\n' %
(reg.calc_R2(fasit[fasit_key].loc[tipping_ps.index[0]:],
tipping_ps[:fasit[fasit_key].index[-1]])))
print('Fasit:\n', fasit[fasit_key][-4:])
print('\nModdelert/Tippet:\n', tipping_ps[-5:])
if fasit_key[-3:] == '105':
color_tipping = 'blue'
elif fasit_key[-3:] == '132':
color_tipping = 'lightblue'
else:
sys.exit("The fasit key should end with 105 or 132")
# Plot with regression:
plt.figure(figsize=(16, 10))
if (0 <= today.weekday() <= 1) or (today.weekday() == 2 and
today.hour < 14): # True for tipping
plt.plot(fasit[fasit_key].loc[:reg_end],
color='k',
linewidth=2.0,
label='fasit')
else:
plt.plot(fasit[fasit_key].loc[:],
color='k',
linewidth=2.0,
label='fasit')
plt.plot(short_results.predict(df_tot[chosen_p].loc[reg_start:reg_end]),
color='orange',
label='regresjon på historie(kort periode)')
plt.plot(long_results.predict(df_tot[chosen_p].loc[:reg_start]),
color='cyan',
label='regresjon på historie (lang periode)')
plt.plot(short_results.predict(df_tot[chosen_p].loc[:reg_start]),
color='deeppink',
label='modell på historie (kort periode)')
plt.plot(tipping_ps, label='tipping', color=color_tipping) # , marker='o')
plt.title('Regresjon for: %s' % fasit_key)
plt.legend()
# Plot just prediction:
plt.figure(figsize=(16, 10))
plt.plot(fasit[fasit_key].loc[tipping_ps.index[0]:],
color='k',
linewidth=2.0,
label='fasit')
plt.plot(tipping_ps, label='tipping', color=color_tipping) # , marker='o')
plt.title('Tipping for: %s' % fasit_key)
plt.legend()
# Plot input series:
plt.figure(figsize=(16, 10))
plt.plot(fasit[fasit_key], color='k', linewidth=3.0, label='fasit')
for key in chosen_p:
if fasit_key[-3:] == '105':
sfac = df_tot[fasit_key].mean() / df_tot[key].mean()
plt.plot(df_tot[key] * sfac, label=key) # , marker='o')
elif fasit_key[-3:] == '132':
plt.plot(df_tot[key], label=key) # , marker='o')
plt.plot(tipping_ps, label='tipping', color=color_tipping) # , marker='o')
plt.title('Regresjonsserier for: %s' % fasit_key)
plt.legend()
plt.show()
def animate(self, nframes=100, duration=10000, line_styles=None):
"""
Return animation of one or more evolutionary processes.
If the `nframes` is 0, then a static figure is returned instead of
an animation.
"""
if nframes is 0:
stride = None
else:
if nframes > len(self):
nframes = len(self)
stride = len(self) // nframes
if duration < nframes:
duration = nframes
interval = round(duration / nframes)
processes = self.processes
labels = [str(p) for p in processes]
if line_styles is None:
line_styles = ['-' for p in processes]
g = [p.growth_rates() for p in processes]
p = [processes[:], [p.normalized() for p in processes]]
n = len(self)
if not stride is None:
for i in range(2):
p[i] = [
np.concatenate((y[:n:stride], [y[n - 1]])) for y in p[i]
]
# for y in p[i]:
# y[y == 0] = np.nan
n_frames = len(p[0][0])
lines = np.empty((2, len(p[0])), dtype=object)
is_interactive = plt.isinteractive()
plt.interactive(False)
fig, ax = plt.subplots(2, sharex=True)
for n in range(2):
for i in range(len(g)):
w = p[n][i][-1] > 0
lines[n][i], = ax[n].plot(g[i][w],
p[n][i][-1][w],
label=labels[i],
ls=line_styles[i],
lw=2,
zorder=10)
################################################################################
for n in range(2):
for i in range(len(g)):
w = p[n][i][0] > 0
ax[n].plot(g[i][w],
p[n][i][0][w],
c='black',
lw=0.5,
alpha=0.5,
zorder=3)
w = p[n][i][-1] > 0
ax[n].plot(g[i][w],
p[n][i][-1][w],
c=lines[n][i].get_c(),
lw=1,
alpha=0.7)
fig.suptitle('Evolution for {0} Years{1}'.format(
len(self) - 1, self.subtitle))
ax[1].set_xlabel('Malthusian Growth Factor')
ax[0].set_yscale('log')
ax[0].set_ylabel('Frequency')
ax[1].set_ylabel('Proportion')
ax[0].legend(loc='best')
plt.interactive(is_interactive)
def initializer():
for n in range(2):
for line, x, y in zip(lines[n], g, p[n]):
line.set_xdata(x[y[0] > 0])
line.set_ydata(y[0][y[0] > 0])
line.set_zorder(2)
return lines.flatten()
def animator(i):
for n in range(2):
for line, x, y in zip(lines[n], g, p[n]):
line.set_xdata(x[y[i] > 0])
line.set_ydata(y[i][y[i] > 0])
if i == 1:
line.set_zorder(10)
return lines.flatten()
if stride is None:
out = fig
else:
out = animation.FuncAnimation(fig,
animator,
init_func=initializer,
frames=n_frames,
interval=interval,
blit=True,
repeat_delay=2000)
return out
def DisplayLearningCurve(plot):
plt.plot(plot)
plt.interactive(False)
plt.show(block=True)
Required packages:
1. Numpy
2. Scipy
3. Matplotlib
Required modules:
1. f_stft
2. f_istft
"""
import numpy as np
from f_stft import f_stft
from f_istft import f_istft
import matplotlib.pyplot as plt
plt.interactive('True')
def rpca_ss(x, fs, specparam=None, mask=False, maskgain=0):
"""
The function rpca_ss uses the RPCA method to decompose the power spectral
density of a mixture into its background (assumed to be low-rank) and
foreground (assumed to be sparse). A binary mask is formed using the results
of RPCA and applied to the mixture to perform background/foreground separation.
x: M by N numpy array containing M channel mixtures, each of length N time
samples
fs: sampling frequency of the audio signal
specparam(optional): list containing do_STFT parameters including in order,
the window length, window type, overlap in # of samples,
and # of fft points.
import pandas as pd
from pandas import DataFrame
import numpy as np
from matplotlib import pyplot as plt
plt.interactive(True)
ReadCsv = pd.read_csv(r'num_videos_per_gloss.csv', sep=';', header='infer')
df = DataFrame(ReadCsv)
df.to_csv('num_videos_per_gloss_3.csv', sep=',', index=False)
#!/usr/bin/env python
# Copyright (c) 2011-2020, wradlib developers.
# Distributed under the MIT License. See LICENSE.txt for more info.
import sys
import tempfile
import unittest
import numpy as np
import matplotlib.pyplot as pl
pl.interactive(True) # noqa
from wradlib import georef, util, vis
cartopy = util.import_optional('cartopy')
class PolarPlotTest(unittest.TestCase):
def setUp(self):
img = np.zeros((360, 10), dtype=np.float32)
img[2, 2] = 10 # isolated pixel
img[5, 6:8] = 10 # line
img[20, :] = 5 # spike
img[60:120, 2:7] = 11 # precip field
self.r = np.arange(0, 100000, 10000)
self.az = np.arange(0, 360)
self.el = np.arange(0, 90)
self.th = np.zeros_like(self.az)
self.az1 = np.ones_like(self.el) * 225
self.img = img
self.proj = georef.create_osr("dwd-radolan")
def preprocess(X, Y, PLOT_COV = False, PLOT_COV_CAT = False,
PRINT_MISSING_DATA = True, SIMPLIFY_FEATURES=True,
percentage_variance_explained_PCA=1,
high_feature_correlation = 0.8,
low_output_correlation = 0.01,
kernel_pca_components=0,
kernel_for_pca="linear"):
if PRINT_MISSING_DATA:
print_misssing(X)
categorical_data = categorical_encoding(X, 5)
if PLOT_COV_CAT:
plt.interactive(True)
plt.matshow(categorical_data.corr())
plt.ioff()
plt.show()
plt.interactive(False)
X = normalize(X, categorical_data)
corr_X = X.corr()
if PLOT_COV:
plt.interactive(True)
plt.matshow(corr_X)
plt.ioff()
plt.show()
plt.interactive(False)
corr = pd.concat([Y, X], axis=1).corr().values
corr_X = corr[1:, 1:]
corr_Y = corr[1:, 0]
if SIMPLIFY_FEATURES:
n_high_corr_X = ((corr_X>high_feature_correlation).sum() - corr_X.shape[0])/2
print("Number of features with correlation of %d or more: %d\n\n" % (high_feature_correlation, n_high_corr_X))
n_low_corr_Y = ((low_output_correlation > corr_Y) & (corr_Y > -low_output_correlation))
print("Number of features with %d output correlation: %d\n\n" % (low_output_correlation, n_low_corr_Y.sum()))
corr_X_upper_tri = np.triu(corr_X, 1)
delete_features_corr_X = (corr_X_upper_tri>high_feature_correlation).sum(axis=0) != 0
delete_features = np.logical_or(n_low_corr_Y, delete_features_corr_X)
for i in range(X.shape[1]-len(delete_features)):
delete_features = np.append(delete_features, False)
X_new = np.array(X)[:, np.invert(delete_features)]
print("%d features were deleted." % (X.shape[1]-X_new.shape[1]))
X = X_new
pca = 0
if percentage_variance_explained_PCA != 1:
X, pca = pca_function(X, percentage_variance_explained_PCA)
kernel_pca = 0
if kernel_pca_components != 0:
kernel_pca = KernelPCA(n_components=kernel_pca_components, kernel=kernel_for_pca)
X = kernel_pca.fit_transform(X)
return X, delete_features, pca, kernel_pca
if not args.json:
print("Need output filename!")
sys.exit(1)
g = get_subgraph_for_node(args.single_sankey_json)
yang_dict = {}
with open(args.dict_file, "r") as df:
for line in df:
yang_model, yang_auth_email = line.partition(":")[::2]
yang_dict[yang_model.strip()] = yang_auth_email
print_dependency_tree_as_json(graph=g,
filename=args.json,
yang_dict=yang_dict)
elif args.graph:
# Set matplotlib into non-interactive mode
plt.interactive(False)
ng = prune_standalone_nodes()
plt.figure(1, figsize=(20, 20))
print('Plotting the overall dependency graph...')
plot_module_dependency_graph(ng)
plt.savefig("modules.png")
print(' Done.')
plt.show()
else:
plot_num = 2
for node in args.sub_graphs:
# Set matplotlib into non-interactive mode
plt.interactive(False)
plt.figure(plot_num, figsize=(20, 20))
plot_num += 1
def plot(self, obstype='ADT'):
IDS=np.unique(self.OBSID)
plt.interactive(True)
fig = plt.figure(num=1, figsize=(16,14), facecolor='w')
for ids in IDS:
#I = np.where( (self.OBSID==ids) & (self.qc==1) & (np.abs(self.lat)<90.0) & (np.abs(self.lev)<300.0) )
#I = np.where( (self.OBSID==ids) & (np.abs(self.lev)<2000.0) & (np.abs(self.lev)>0.0) & (np.abs(self.omf)<100.0) & (np.abs(self.lat)<90.0) )
I = np.where( (self.OBSID==ids) & (np.abs(self.lev)<300.0) & (self.fcst!=0.0) & (np.abs(self.lat)<90.0) & (np.abs(self.omf)<10.0))
if ( (ids == 3073) | (ids == 5521) ):
if ids==3073:
vmin=-3.
vmax=3.
#ax = fig.add_subplot(411)
#ax2=ax.twinx()
plt.subplot(611)
plt.plot_date(self.date, np.mean(np.abs(self.omf[I])),'ok',markersize=8, alpha=self.alpha, marker=self.marker)
plt.plot_date(self.date, np.mean(np.abs(self.oma[I])),'or',markersize=8, alpha=self.alpha, marker=self.marker)
#plt.plot_date(self.date, np.sqrt(np.mean((self.omf[I])**2)),'k',markersize=8, alpha=self.alpha, marker=self.marker)
#plt.plot_date(self.date, np.mean(np.abs(self.oma[I])),'r',markersize=8, alpha=self.alpha, marker=self.marker)
#ax2.plot_date(self.date, len(I[0]),marker='.',color='gray',markersize=8)
plt.grid(True)
#ax2.grid(False)
#ax2.locator_params(axis='y',nbins=6)
print 'Nobs=',len(I[0])
#plt.grid(True)
#plt.subplot(812)
#plt.plot_date(self.date, np.mean(self.omf[I]),'ok',markersize=8)
#plt.grid(True)
if ids==5521:
vmin=-1
vmax=1
plt.subplot(612)
plt.plot_date(self.date, np.mean(np.abs(self.omf[I])),'ok',markersize=8, alpha=self.alpha, marker=self.marker)
plt.plot_date(self.date, np.mean(np.abs(self.oma[I])),'or',markersize=8, alpha=self.alpha, marker=self.marker)
plt.grid(True)
#plt.subplot(814)
#plt.plot_date(self.date, np.mean(self.omf[I]),'ok',markersize=8)
#plt.grid(True)
#plt.plot(self.omf[I], -self.lev[I],'.k',alpha=0.2)
#plt.xlim((vmin,vmax))
#plt.ylim((-3000,0))
if (ids == 5525):
if ids==5525:
vmin=-1.
vmax=1.
if ids==5351:
vmin=-.2
vmax=.2
plt.subplot(613)
plt.plot_date(self.date, np.mean(np.abs(self.omf[I])),'ok',markersize=8, alpha=self.alpha, marker=self.marker)
plt.plot_date(self.date, np.mean(np.abs(self.oma[I])),'or',markersize=8, alpha=self.alpha, marker=self.marker)
plt.grid(True)
plt.title(obsid_dict[ids],fontweight='bold')
#plt.subplot(816)
#plt.plot_date(self.date, np.mean(self.omf[I]),'ok',markersize=8)
#plt.grid(True)
if (ids == 5351):
plt.subplot(614)
plt.plot_date(self.date, np.mean(np.abs(self.omf[I])),'ok',markersize=8, alpha=self.alpha, marker=self.marker)
plt.plot_date(self.date, np.mean(np.abs(self.oma[I])),'or',markersize=8, alpha=self.alpha, marker=self.marker)
plt.grid(True)
plt.title(obsid_dict[ids],fontweight='bold')
if (ids == 6000):
IN = np.where( (self.OBSID==ids) & (self.lat>55.0) )#& (np.abs(self.lev)<2000.0) & (self.fcst!=0.0) & (self.lat>55.0) & (np.abs(self.omf)<40.0))
plt.subplot(615)
plt.plot_date(self.date, np.mean(np.abs(self.omf[IN])),'ok',markersize=8, alpha=self.alpha, marker=self.marker)
plt.plot_date(self.date, np.mean(np.abs(self.oma[IN])),'or',markersize=8, alpha=self.alpha, marker=self.marker)
plt.grid(True)
plt.title('Arctic '+obsid_dict[ids],fontweight='bold')
IS = np.where( (self.OBSID==ids) & (self.lat<-55.0) )#& (np.abs(self.lev)<2000.0) & (self.fcst!=0.0) & (self.lat<-55.0) & (np.abs(self.omf)<40.0))
plt.subplot(616)
plt.plot_date(self.date, np.mean(np.abs(self.omf[IS])),'ok',markersize=8, alpha=self.alpha, marker=self.marker)
plt.plot_date(self.date, np.mean(np.abs(self.oma[IS])),'or',markersize=8, alpha=self.alpha, marker=self.marker)
plt.grid(True)
plt.title('Antarctic '+obsid_dict[ids],fontweight='bold')
#plt.subplot(818)
#plt.plot_date(self.date, np.mean(self.omf[I]),'ok',markersize=8)
#plt.grid(True)
'''
def plot_mb_group_activity_healthy_vs_mb(marray_data, cat):
"""
Grouped bar chart showing the absolute expression levels of marker genes in the healthy samples and later passage MB
xenografts.
:param marray_data:
:param cat:
:return:
"""
from matplotlib import pyplot as plt
import seaborn as sns
plt.interactive(True)
sns.set_style('white')
marray_ann = add_gene_symbol_column(marray_data, cat)
REF_GROUPS = (
(
'WNT',
('WIF1', 'TNC', 'GAD1', 'DKK2', 'EMX2'),
),
(
'SHH',
('PDLIM3', 'EYA1', 'HHIP', 'ATOH1', 'SFRP1'),
),
(
'Group C',
('IMPG2', 'GABRA5', 'EGFL11', 'NRL', 'MAB21L2', 'NPR3'),
), # EYS = EGFL11
('Group D', ('KCNA1', 'EOMES', 'KHDRBS2', 'RBM24', 'UNC5D', 'OAS1')),
)
CEREBELLUM_SAMPLE_NAMES = [
'NT-1197',
'NCb-1',
'NCb-2',
'A911105',
'A508112',
'A508285',
]
CEREBELLUM_SAMPLE_NAMES += [t + '-R' for t in CEREBELLUM_SAMPLE_NAMES]
XENOGRAFT_SAMPLE_NAMES = [
'ICb1299-III',
'ICb1299-IV',
]
XENOGRAFT_SAMPLE_NAMES += [t + '-R' for t in XENOGRAFT_SAMPLE_NAMES]
# add new grouping column
marray_ann['mb_group'] = [np.nan] * len(marray_ann.index)
for grp, arr in REF_GROUPS:
marray_ann.loc[marray_ann.gene_symbol.isin(arr), 'mb_group'] = grp
# compare cerebellum sample median with passage III and IV
grp_data = marray_ann.groupby(['gene_symbol', 'mb_group'])
xeno_data = collections.defaultdict(dict)
heal_data = collections.defaultdict(dict)
for (gene, grp), arr in grp_data:
xeno_data[grp][gene] = np.nanmedian(
arr.loc[:, XENOGRAFT_SAMPLE_NAMES].values)
heal_data[grp][gene] = np.nanmedian(
arr.loc[:, CEREBELLUM_SAMPLE_NAMES].values)
fig, axs = plt.subplots(ncols=len(xeno_data), sharey=True, figsize=(10, 5))
WIDTH = 0.8
MAX_N_X = max([len(b) for a, b in heal_data.items()])
dw = WIDTH / 2. # each slot has 2 bars
for i in range(len(xeno_data)):
grp = REF_GROUPS[i][0]
x = xeno_data[grp]
h = heal_data[grp]
ax = axs[i]
x0 = np.arange(len(h))
l0, y0 = zip(*h.items())
ax.bar(x0, y0, dw, facecolor='k', edgecolor='none')
x1 = x0 + dw
y1 = [x[l] for l in l0]
ax.bar(x1, y1, dw, facecolor='b', edgecolor='none')
ax.set_xticks(x1)
ax.set_xticklabels(l0, rotation=90)
ax.set_xlabel(grp)
ax.xaxis.set_label_coords(0.5, -.21)
ax.set_xlim([WIDTH - 1, MAX_N_X])
axs[-1].legend(['Healthy cerebellum', 'MB'])
ylim = list(axs[-1].get_ylim())
ylim[0] = -10
axs[-1].set_ylim(ylim)
plt.subplots_adjust(left=0.1,
right=0.99,
bottom=0.2,
top=0.95,
wspace=0.08,
hspace=0.)
return fig # useful for programmatic saving
def visualise_engine(__C):
# Load parameters
dataset = DatasetLoader(__C).DataSet()
if __C.CKPT_PATH is not None:
print('Warning: you are now using CKPT_PATH args, '
'CKPT_VERSION and CKPT_EPOCH will not work')
path = __C.CKPT_PATH
else:
path = __C.CKPTS_PATH + \
'/ckpt_' + __C.CKPT_VERSION + \
'/epoch' + str(__C.CKPT_EPOCH) + '.pkl'
print('Loading ckpt from: {}'.format(path))
state_dict = torch.load(path)['state_dict']
print('Finish!')
if __C.N_GPU > 1:
state_dict = ckpt_proc(state_dict)
# Store the prediction list
# qid_list = [ques['question_id'] for ques in dataset.ques_list]
ans_ix_list = []
pred_list = []
data_size = dataset.data_size
token_size = dataset.token_size
ans_size = dataset.ans_size
pretrained_emb = dataset.pretrained_emb
net = ModelLoader(__C).Net(
__C,
pretrained_emb,
token_size,
ans_size,
dataset.token_to_ix
)
net.cuda()
net.eval()
if __C.N_GPU > 1:
net = nn.DataParallel(net, device_ids=__C.DEVICES)
net.load_state_dict(state_dict)
batch_size = 1
dataloader = Data.DataLoader(
dataset,
batch_size=batch_size,
shuffle=True,
num_workers=__C.NUM_WORKERS,
pin_memory=__C.PIN_MEM
)
for step, (
frcn_feat_iter,
grid_feat_iter,
bbox_feat_iter,
ques_ix_iter,
ans_iter, image_id, question, words, target_ans
) in enumerate(dataloader):
print("\rEvaluation: [step %4d/%4d]" % (
step,
int(data_size / __C.EVAL_BATCH_SIZE),
), end=' ')
frcn_feat_iter = frcn_feat_iter.cuda()
grid_feat_iter = grid_feat_iter.cuda()
bbox_feat_iter = bbox_feat_iter.cuda()
ques_ix_iter = ques_ix_iter.cuda()
pred, img_attention_map, txt_attention_map = net(
frcn_feat_iter,
grid_feat_iter,
bbox_feat_iter,
ques_ix_iter
)
img_attention_map = img_attention_map[:, :, :, 1:]
txt_attention_map = txt_attention_map[:, :, :, 1:len(words) + 1]
pred_np = pred.cpu().data.numpy()
pred_argmax = np.argmax(pred_np, axis=1)
ans = dataset.ix_to_ans[pred_argmax[0]]
plt.interactive(False)
visualise_img(question['image_filename'][0], question['question'][0], img_attention_map.cpu().data.numpy()[0],
ans, target_ans[0])
visualise_txt(words, txt_attention_map.cpu().data.numpy()[0])
def setPlotFile(fileName):
global plotFile
plotFile = PdfPages(fileName)
plt.interactive(False)
def __init__(self, normalized_worm, motion_mode=None, interactive=False):
"""
Initialize the animation of the worm's attributes.
Parameters
---------------------------------------
normalized_worm: NormalizedWorm
the NormalizedWorm object to be plotted.
motion_mode: 1-dimensional numpy array (optional)
The motion mode of the worm over time. Must have
length = normalized_worm.num_frames
interactive: boolean (optional)
if interactive is set to False, then:
suppress the drawing of the figure, until an explicit plt.show()
is called. this allows WormPlotter to be instantiated without
just automatically being displayed. Instead, the user must call
WormPlotter.show() to have plt.show() be called.
Notes
---------------------------------------
To initialize the animation, we must do six things:
1. set up the data to be used from the normalized_worm
2. create the figure
3. create subplots in the figure, assigning them Axis handles
4. create Artist objects for all objects in the subplots
5. assign the Artist objects to the correct Axis handle
6. call the base class __init__
"""
# A WormPlotter instance can be instantiated to be interactive,
# or by default it is set to NOT interactive, which means that
# WormPlotter.show() must be called to get it to display.
plt.interactive(interactive)
# 1. set up the data to be used
self.motion_mode = motion_mode
self.motion_mode_options = {-1: 'backward', 0: 'paused', 1: 'forward'}
self.motion_mode_colours = {
-1: 'b', # blue
0: 'r', # red
1: 'g'
} # green
self.normalized_worm = normalized_worm
# TODO: eventually we'll put this in a nicer place
self.vulva_contours = self.normalized_worm.data_dict['vulva_contours']
self.non_vulva_contours = self.normalized_worm.data_dict[
'non_vulva_contours']
self.skeletons = self.normalized_worm.data_dict['skeletons']
self.skeletons_centred = self.normalized_worm.translate_to_centre()
self.skeleton_centres = self.normalized_worm.centre()
self.orientation = self.normalized_worm.angle()
self.skeletons_rotated = self.normalized_worm.rotate_and_translate()
# 2. create the figure
fig = plt.figure(figsize=(5, 5))
# We have blit=True, so the animation only redraws the elements that have
# changed. This means that if the window is resized, everything other
# than the plot area will be black. To fix this, here we have matplotlib
# explicitly redraw everything if a resizing event occurs.
# DEBUG: this actually doesn't appear to work.
def refresh_plot(event):
fig.canvas.draw()
self.refresh_connection_id = \
fig.canvas.mpl_connect('resize_event', refresh_plot)
fig.suptitle('C. elegans attributes', fontsize=20)
# 3. add the subplots
ax1 = plt.subplot2grid((3, 3), (0, 0), rowspan=2, colspan=2)
ax1.set_title('Position')
ax1.set_xlabel('x')
ax1.set_ylabel('y')
ax1.set_xlim(self.normalized_worm.position_limits(0))
ax1.set_ylim(self.normalized_worm.position_limits(1))
#ax1.set_aspect(aspect='equal', adjustable='datalim')
#ax1.set_autoscale_on()
self.annotation1a = ax1.annotate("(motion mode data not available)",
xy=(-10, 10),
xycoords='axes points',
horizontalalignment='right',
verticalalignment='top',
fontsize=10)
self.annotation1b = ax1.annotate("bottom right (points)",
xy=(-10, 10),
xycoords='axes points',
horizontalalignment='right',
verticalalignment='bottom',
fontsize=10)
# DEBUG: this doesn't appear to do anything,
#it was an attempt to get it to refresh with diff titles
#self.annotation1.animated = True
ax2 = plt.subplot2grid((3, 3), (2, 0))
ax2.set_title('Morphology')
ax2.set_xlim(
(-500, 500)) # DON'T USE set_xbound, it changes dynmically
ax2.set_ylim((-500, 500))
ax2.set_aspect(aspect='equal', adjustable='datalim')
self.annotation2 = ax2.annotate("Worm head",
xy=(0, 0),
xycoords='data',
xytext=(10, 10),
textcoords='data',
arrowprops=dict(
arrowstyle="fancy",
connectionstyle="arc3,rad=.2"))
ax3 = plt.subplot2grid((3, 3), (2, 1))
ax3.set_title('Orientation-free')
ax3.set_xlim(
(-500, 500)) # DON'T USE set_xbound, it changes dynmically
ax3.set_ylim((-500, 500))
ax3.set_aspect(aspect='equal', adjustable='datalim')
ax4 = plt.subplot2grid((3, 3), (2, 2))
#self.annotation4 = ax4.annotate("Segmentation status: ",
# xy=(0,0), xycoords='data',
# xytext=(10, 10), textcoords='data',
# arrowprops=dict(arrowstyle="fancy",
# connectionstyle="arc3,rad=.2"))
# 4. create Artist objects
self.line1W = Line2D([], [],
color='green',
linestyle='point marker',
marker='o',
markersize=5)
self.line1W_head = Line2D([], [],
color='red',
linestyle='point marker',
marker='o',
markersize=7)
self.line1C = Line2D([], [],
color='yellow',
linestyle='point marker',
marker='o',
markersize=5)
self.patch1E = Ellipse(xy=(0, 0),
width=1000,
height=500,
angle=0,
alpha=0.3)
self.line2W = Line2D([], [], color='black', marker='o', markersize=5)
self.line2W_head = Line2D([], [],
color='red',
linestyle='point marker',
marker='o',
markersize=7)
self.line2C = Line2D([], [], color='blue')
self.line2C2 = Line2D([], [], color='orange')
self.line3W = Line2D([], [], color='black', marker='o', markersize=5)
self.line3W_head = Line2D([], [],
color='red',
linestyle='point marker',
marker='o',
markersize=7)
self.artists_to_be_drawn = \
[self.line1W, self.line1C, self.line1W_head, self.patch1E, self.annotation1a, self.annotation1b,
self.line2W, self.line2C, self.line2C2, self.line2W_head, self.annotation2,
self.line3W, self.line3W_head]
# 5. assign Artist objects to the relevant subplot
ax1.add_line(self.line1W)
ax1.add_line(self.line1W_head)
ax1.add_line(self.line1C)
ax1.add_artist(self.patch1E)
ax2.add_line(self.line2W)
ax2.add_line(self.line2W_head)
ax2.add_line(self.line2C)
ax2.add_line(self.line2C2)
ax3.add_line(self.line3W)
ax3.add_line(self.line3W_head)
# So labels don't overlap:
plt.tight_layout()
# 6. call the base class __init__
# TimedAnimation draws a new frame every *interval* milliseconds.
# so this is how we convert from FPS to interval:
interval = 1000 / config.FPS
return animation.TimedAnimation.__init__(self,
fig,
interval=interval,
blit=True)
def closePlotFile():
global plotFile
if plotFile is not None:
plotFile.close()
plotFile = None
plt.interactive(True)
def set_mpl_interactive():
'''Ensure matplotlib is in interactive mode.'''
import matplotlib.pyplot as plt
if not plt.isinteractive():
plt.interactive(True)
def rptpct1(
mxmn1,
mxmn2,
filename,
*,
title="PERCENT DIFFERENCE REPORT",
names=("Self", "Reference"),
desc=None,
filterval=None,
labels=None,
units=None,
ignorepv=None,
uf_reds=None,
use_range=True,
numform=None,
prtbad=None,
prtbadh=None,
prtbadl=None,
flagbad=None,
flagbadh=None,
flagbadl=None,
dohistogram=True,
histogram_inc=1.0,
domagpct=True,
magpct_options=None,
doabsmax=False,
shortabsmax=False,
roundvals=-1,
rowhdr="Row",
deschdr="Description",
maxhdr="Maximum",
minhdr="Minimum",
absmhdr="Abs-Max",
perpage=-1,
tight_layout_args=None,
show_figures=False,
align_by_label=True,
):
"""
Write a percent difference report between 2 sets of max/min data
Parameters
----------
mxmn1 : 2d array_like or SimpleNamespace
The max/min data to compare to the `mxmn2` set. If 2-column
array_like, its columns are: [max, min]. If SimpleNamespace,
it must be as defined in :class:`DR_Results` and have these
members:
.. code-block:: none
.ext = [max, min]
.drminfo = SimpleNamespace which has (at least):
.desc = one line description of category
.filterval = the filter value; (see `filterval`
description below)
.labels = a list of descriptions; one per row
.ignorepv = these rows will get 'n/a' for % diff
.units = string with units
.uf_reds = uncertainty factors
Note that the inputs `desc`, `labels`, etc, override the
values above.
mxmn2 : 2d array_like or SimpleNamespace
The reference set of max/min data. Format is the same as
`mxmn1`.
.. note::
If both `mxmn1` and `mxmn2` are SimpleNamespaces and have
the ``.drminfo.labels`` attribute, this routine will, by
default, use the labels to align the data sets for
comparison. To prevent this, set the `align_by_label`
parameter to False.
filename : string or file_like or 1 or None
Either a name of a file, or is a file_like object as returned
by :func:`open` or :class:`io.StringIO`. Input as integer 1 to
write to stdout. Can also be the name of a directory or None;
in these cases, a GUI is opened for file selection.
title : string; must be named; optional
Title for the report
names : list/tuple; must be named; optional
Two (short) strings identifying the two sets of data
desc : string or None; must be named; optional
A one line description of the table. Overrides
`mxmn1.drminfo.desc`. If neither are input,
'No description provided' is used.
filterval : scalar, 1d array_like or None; must be named; optional
Numbers with absolute value <= than `filterval` will get a
'n/a' % diff. If vector, length must match number of rows in
`mxmn1` and `mxmn2` data. Overrides `mxmn1.drminfo.filterval`.
If neither are input, `filterval` is set to 1.e-6.
labels : list or None; must be named; optional
A list of strings briefly describing each row. Overrides
`mxmn1.drminfo.labels`. If neither are input,
``['Row 1','Row 2',...]`` is used.
units : string or None; must be named; optional
Specifies the units. Overrides `mxmn1.drminfo.units`. If
neither are input, 'Not specified' is used.
ignorepv : 1d array or None; must be named; optional
0-offset index vector specifying which rows of `mxmn1` to
ignore (they get the 'n/a' % diff). Overrides
`mxmn1.drminfo.ignorepv`. If neither are input, no rows are
ignored (though `filterval` is still used).
.. note::
`ignorepv` applies *before* any alignment by labels is
done (when `align_by_label` is True, which is the
default).
uf_reds : 1d array or None; must be named; optional
Uncertainty factors: [rigid, elastic, dynamic, static].
Overrides `mxmn1.drminfo.uf_reds`. If neither is input,
'Not specified' is used.
use_range : bool; must be named, optional
If True, the denominator of the % diff calc for both the max
& min for each row is the absolute maximum of the reference
max & min for that row. If False, the denominator is the
applicable reference max or min. A quick example shows why
``use_range=True`` might be useful:
.. code-block:: none
If [max1, min1] = [12345, -10] and
[max2, min2] = [12300, 50]
Then:
% diff = [0.37%, 0.49%] if use_range is True
% diff = [0.37%, 120.00%] if use_range is False
Note that the sign of the % diff is defined such that a
positive % diff means an exceedance: where ``max1 > max2`` or
``min1 < min2``.
`use_range` is ignored if `doabsmax` is True.
numform : string or None; must be named; optional
Format of the max & min numbers. If None, it is set internally
to be 13 chars wide and depends on the range of numbers to
print:
- if range is "small", numform='{:13.xf}' where "x" ranges
from 0 to 7
- if range is "large", numform='{:13.6e}'
prtbad : scalar or None; must be named; optional
Only print rows where ``abs(%diff) > prtbad``. For example, to
print rows off by more than 5%, use ``prtbad=5``. `prtbad`
takes precedence over `prtbadh` and `prtbadl`.
prtbadh : scalar or None; must be named; optional
Only print rows where ``%diff > prtbadh``. Handy for showing
just the exceedances. `prtbadh` takes precedence over
`prtbadl`.
prtbadl : scalar or None; must be named; optional
Only print rows where ``%diff < prtbadl``. Handy for showing
where reference rows are higher.
flagbad : scalar or None; must be named; optional
Flag % diffs where ``abs(%diff) > flagbad``. Works similar to
`prtbad`. The flag is an asterisk (*).
flagbadh : scalar or None; must be named; optional
Flag % diffs where ``%diff > flagbadh``. Works similar to
`prtbadh`. Handy for flagging exceedances. `flagbadh` takes
precedence over `flagbadl`.
flagbadl : scalar or None; must be named; optional
Flag % diffs where ``%diff < flagbadl``. Works similar to
`prtbadl`.
dohistogram : bool; must be named; optional
If True, plot the histograms. Plots will be written to
"`filename`.histogram.png".
histogram_inc : scalar; must be named; optional
The histogram increment; defaults to 1.0 (for 1%).
domagpct : bool; must be named; optional
If True, plot the percent differences versus magnitude via
:func:`magpct`. Plots will be written to
"`filename`.magpct.png". Filtering for the "magpct" plot is
controlled by the ``magpct_options['filterval']`` and
``magpct_options['symlogy']`` options. By default, all percent
differences are shown, but the larger values (according to the
`filterval` filter) are emphasized by using a mixed linear/log
y-axis. The percent differences for the `ignorepv` rows are
not plotted.
magpct_options : None or dict; must be named; optional
If None, it is internally reset to::
magpct_options = {'filterval': 'filterval'}
Use this parameter to provide any options to :func:`magpct`
but note that the `filterval` option for :func:`magpct` is
treated specially. Here, in addition to any of the values that
:func:`magpct` accepts, it can also be set to the string
"filterval" as in the default case shown above. In that case,
``magpct_options['filterval']`` gets internally reset to the
initial value of `filterval` (which is None by default).
.. note::
The call to :func:`magpct` is *after* applying `ignorepv`
and doing any data aligning by labels.
.. note::
The two filter value options (`filterval` and
``magpct_options['filterval']``) have different defaults:
None and 'filterval`, respectively. They also differ on how
the ``None`` setting is used: for `filterval`, None is
replaced by 1.e-6 while for `magpct_filterval`, None means
that the "magpct" plot will not have any filters applied at
all.
.. note::
The above means that, if you accept the default values for
`filterval` and for ``magpct_options['filterval']``, then
tables and the histogram plots will use a `filterval` of
1.e-6 while the "magpct" plots will use no filter (it
compares everything except perfect zeros).
doabsmax : bool; must be named; optional
If True, compare only absolute maximums.
shortabsmax : bool; must be named; optional
If True, set ``doabsmax=True`` and do not print the max1 and
min1 columns.
roundvals : integer; must be named; optional
Round max & min numbers at specified decimal. If negative, no
rounding.
rowhdr : string; must be named; optional
Header for row number column
deschdr : string; must be named; optional
Header for description column
maxhdr : string; must be named; optional
Header for the column 1 data
minhdr : string; must be named; optional
Header for the column 2 data
absmhdr : string; must be named; optional
Header for abs-max column
perpage : integer; must be named; optional
The number of lines to write perpage. If < 1, there is no
limit (one page).
tight_layout_args : dict or None; must be named; optional
Arguments for :func:`matplotlib.pyplot.tight_layout`. If None,
defaults to ``{'pad': 3.0}``.
show_figures : bool; must be named; optional
If True, plot figures will be displayed on the screen for
interactive viewing. Warning: there may be many figures.
align_by_label : bool; must be named; optional
If True, use labels to align the two sets of data for
comparison. See note above under the `mxmn2` option.
Returns
-------
pdiff_info : dict
Dictionary with 'amx' (abs-max), 'mx' (max), and 'mn' keys:
.. code-block:: none
<class 'dict'>[n=3]
'amx': <class 'dict'>[n=5]
'hsto' : float64 ndarray 33 elems: (11, 3)
'mag' : [n=2]: (float64 ndarray: (100,), ...
'pct' : float64 ndarray 100 elems: (100,)
'prtpv': bool ndarray 100 elems: (100,)
'spct' : [n=100]: [' -2.46', ' -1.50', ...
'mn' : <class 'dict'>[n=5]
'hsto' : float64 ndarray 33 elems: (11, 3)
'mag' : [n=2]: (float64 ndarray: (100,), ...
'pct' : float64 ndarray 100 elems: (100,)
'prtpv': bool ndarray 100 elems: (100,)
'spct' : [n=100]: [' 1.55', ' 1.53', ...
'mx' : <class 'dict'>[n=5]
'hsto' : float64 ndarray 27 elems: (9, 3)
'mag' : [n=2]: (float64 ndarray: (100,), ...
'pct' : float64 ndarray 100 elems: (100,)
'prtpv': bool ndarray 100 elems: (100,)
'spct' : [n=100]: [' -2.46', ' -1.50', ...
Where:
.. code-block:: none
'hsto' : output of :func:`histogram`: [center, count, %]
'mag' : inputs to :func:`magpct`
'pct' : percent differences
'prtpv' : rows to print partition vector
'spct' : string version of 'pct'
Examples
--------
>>> import numpy as np
>>> from pyyeti import cla
>>> ext1 = [[120.0, -8.0],
... [8.0, -120.0]]
>>> ext2 = [[115.0, -5.0],
... [10.0, -125.0]]
Run :func:`rptpct1` multiple times to get a more complete picture
of all the output (the table is very wide). Also, the plots will
be turned off for this example.
First, the header:
>>> opts = {'domagpct': False, 'dohistogram': False}
>>> dct = cla.rptpct1(ext1, ext2, 1, **opts) # doctest: +ELLIPSIS
PERCENT DIFFERENCE REPORT
<BLANKLINE>
Description: No description provided
Uncertainty: Not specified
Units: Not specified
Filter: 1e-06
Notes: % Diff = +/- abs(Self-Reference)/max(abs(Reference...
Sign set such that positive % differences indicate...
Date: ...
...
Then, the max/min/absmax percent difference table in 3 calls:
>>> dct = cla.rptpct1(ext1, ext2, 1, **opts) # doctest: +ELLIPSIS
PERCENT DIFFERENCE REPORT
...
Self Reference ...
Row Description Maximum Maximum % Diff ...
------- ----------- ------------- ------------- ------- ...
1 Row 1 120.00000 115.00000 4.35 ...
2 Row 2 8.00000 10.00000 -1.60 ...
...
>>> dct = cla.rptpct1(ext1, ext2, 1, **opts) # doctest: +ELLIPSIS
PERCENT DIFFERENCE REPORT
...
... Self Reference ...
Row Description ... Minimum Minimum % Diff ...
------- ----------- ...------------- ------------- ------- ...
1 Row 1 ... -8.00000 -5.00000 2.61 ...
2 Row 2 ... -120.00000 -125.00000 -4.00 ...
...
>>> dct = cla.rptpct1(ext1, ext2, 1, **opts) # doctest: +ELLIPSIS
PERCENT DIFFERENCE REPORT
...
... Self Reference
Row Description ... Abs-Max Abs-Max % Diff
------- ----------- ...------------- ------------- -------
1 Row 1 ... 120.00000 115.00000 4.35
2 Row 2 ... 120.00000 125.00000 -4.00
...
Finally, the histogram summaries:
>>> dct = cla.rptpct1(ext1, ext2, 1, **opts) # doctest: +ELLIPSIS
PERCENT DIFFERENCE REPORT
...
No description provided - Maximum Comparison Histogram
<BLANKLINE>
% Diff Count Percent
-------- -------- -------
-2.00 1 50.00
4.00 1 50.00
<BLANKLINE>
0.0% of values are within 1%
50.0% of values are within 2%
100.0% of values are within 5%
<BLANKLINE>
% Diff Statistics: [Min, Max, Mean, StdDev] = [-1.60, 4.35,...
<BLANKLINE>
<BLANKLINE>
No description provided - Minimum Comparison Histogram
<BLANKLINE>
% Diff Count Percent
-------- -------- -------
-4.00 1 50.00
3.00 1 50.00
<BLANKLINE>
0.0% of values are within 1%
100.0% of values are within 5%
<BLANKLINE>
% Diff Statistics: [Min, Max, Mean, StdDev] = [-4.00, 2.61,...
<BLANKLINE>
<BLANKLINE>
No description provided - Abs-Max Comparison Histogram
<BLANKLINE>
% Diff Count Percent
-------- -------- -------
-4.00 1 50.00
4.00 1 50.00
<BLANKLINE>
0.0% of values are within 1%
100.0% of values are within 5%
<BLANKLINE>
% Diff Statistics: [Min, Max, Mean, StdDev] = [-4.00, 4.35,...
"""
if tight_layout_args is None:
tight_layout_args = {"pad": 3.0}
if magpct_options is None:
magpct_options = {"filterval": "filterval"}
else:
magpct_options = magpct_options.copy()
# magpct_options['filterval'] get special treatment:
magpct_filterval = magpct_options["filterval"]
del magpct_options["filterval"]
if isinstance(magpct_filterval, str):
if magpct_filterval != "filterval":
raise ValueError("``magpct_options['filterval']`` is an invalid "
f"string: {magpct_filterval!r} (can only "
"be 'filterval' if a string)")
# copy the initial `filterval` setting:
magpct_filterval = filterval
infovars = (
"desc",
"filterval",
"magpct_filterval",
"labels",
"units",
"ignorepv",
"uf_reds",
)
dct = locals()
infodct = {n: dct[n] for n in infovars}
del dct
# check mxmn1:
if isinstance(mxmn1, SimpleNamespace):
sns = mxmn1.drminfo
for key, value in infodct.items():
if value is None:
infodct[key] = getattr(sns, key, None)
del sns
mxmn1 = mxmn1.ext
else:
mxmn1 = np.atleast_2d(mxmn1)
row_number = np.arange(1, mxmn1.shape[0] + 1)
# check mxmn2:
if isinstance(mxmn2, SimpleNamespace) and getattr(mxmn2, "drminfo", None):
labels2 = mxmn2.drminfo.labels
mxmn2 = mxmn2.ext
if align_by_label:
# use labels and labels2 to align data; this is in case
# the two sets of results recover some of the same items,
# but not all
mxmn1, mxmn2, row_number = _align_mxmn(mxmn1, mxmn2, labels2,
row_number, infodct)
else:
mxmn2 = np.atleast_2d(mxmn2)
desc = infodct["desc"]
if desc is None:
desc = "No description provided"
R = mxmn1.shape[0]
if R != mxmn2.shape[0]:
raise ValueError(
f"`mxmn1` and `mxmn2` have a different number of rows: "
f"{R} vs {mxmn2.shape[0]} for category with `desc` = {desc}")
filterval = infodct["filterval"]
magpct_filterval = infodct["magpct_filterval"]
labels = infodct["labels"]
units = infodct["units"]
ignorepv = infodct["ignorepv"]
uf_reds = infodct["uf_reds"]
del infodct
if filterval is None:
filterval = 1.0e-6
filterval = _proc_filterval(filterval, R, "filterval")
magpct_filterval = _proc_filterval(magpct_filterval, R,
"magpct_options['filterval']")
if labels is None:
labels = [f"Row {i + 1:6d}" for i in range(R)]
elif len(labels) != R:
raise ValueError("length of `labels` does not match number"
f" of rows in `mxmn1`: {len(labels)} vs {R} for "
f"category with `desc` = {desc}")
if units is None:
units = "Not specified"
if numform is None:
numform = _get_numform(mxmn1)
pdhdr = "% Diff"
nastring = "n/a "
comppv = np.ones(R, bool)
if ignorepv is not None:
comppv[ignorepv] = False
# for row labels:
w = max(11, len(max(labels, key=len)))
frm = f"{{:{w}}}"
# start preparing for writer.formheader:
print_info = SimpleNamespace(
headers1=["", ""],
headers2=[rowhdr, deschdr],
formats=["{:7d}", frm],
printargs=[row_number, labels],
widths=[7, w],
seps=[0, 2],
justs=["c", "l"],
)
if shortabsmax:
doabsmax = True
if doabsmax:
use_range = False
if roundvals > -1:
mxmn1 = np.round(mxmn1, roundvals)
mxmn2 = np.round(mxmn2, roundvals)
prtbads = (prtbad, prtbadh, prtbadl)
flagbads = (flagbad, flagbadh, flagbadl)
# compute percent differences
pctinfo = {}
kwargs = dict(
names=names,
mxmn1=mxmn1,
comppv=comppv,
histogram_inc=histogram_inc,
numform=numform,
prtbads=prtbads,
flagbads=flagbads,
maxhdr=maxhdr,
minhdr=minhdr,
absmhdr=absmhdr,
pdhdr=pdhdr,
nastring=nastring,
doabsmax=doabsmax,
shortabsmax=shortabsmax,
print_info=print_info,
)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", r"All-NaN (slice|axis) encountered")
mx1 = np.nanmax(abs(mxmn1), axis=1)
mx2 = np.nanmax(abs(mxmn2), axis=1)
if not doabsmax:
max1, min1 = mxmn1[:, 0], mxmn1[:, 1]
max2, min2 = mxmn2[:, 0], mxmn2[:, 1]
mxmn_b = mxmn2 if use_range else None
prtpv = np.zeros(R, bool)
for i in zip(
("mx", "mn", "amx"),
(max1, min1, mx1),
(max2, min2, mx2),
(True, False, True),
(maxhdr, minhdr, absmhdr),
):
lbl, ext1, ext2, ismax, valhdr = i
pctinfo[lbl] = _proc_pct(
ext1,
ext2,
filterval,
magpct_filterval,
mxmn_b=mxmn_b,
ismax=ismax,
valhdr=valhdr,
**kwargs,
)
prtpv |= pctinfo[lbl]["prtpv"]
prtpv &= comppv
else:
pctinfo["amx"] = _proc_pct(
mx1,
mx2,
filterval,
magpct_filterval,
mxmn_b=None,
ismax=True,
valhdr=absmhdr,
**kwargs,
)
prtpv = pctinfo["amx"]["prtpv"]
hu, frm = writer.formheader(
[print_info.headers1, print_info.headers2],
print_info.widths,
print_info.formats,
sep=print_info.seps,
just=print_info.justs,
)
# format page header:
misc = _get_filtline(filterval) + _get_noteline(use_range, names, prtbads,
flagbads)
hdrs = _get_rpt_headers(desc=desc, uf_reds=uf_reds, units=units, misc=misc)
header = title + "\n\n" + hdrs + "\n"
imode = plt.isinteractive()
plt.interactive(show_figures)
try:
if domagpct:
_plot_magpct(
pctinfo,
names,
desc,
doabsmax,
filename,
magpct_options,
use_range,
maxhdr,
minhdr,
absmhdr,
show_figures,
tight_layout_args,
)
if dohistogram:
_plot_histogram(
pctinfo,
names,
desc,
doabsmax,
filename,
histogram_inc,
maxhdr,
minhdr,
absmhdr,
show_figures,
tight_layout_args,
)
finally:
plt.interactive(imode)
# write results
@guitools.write_text_file
def _wtcmp(f, header, hu, frm, printargs, perpage, prtpv, pctinfo, desc):
prtpv = prtpv.nonzero()[0]
if perpage < 1:
# one additional in case size is zero
perpage = prtpv.size + 1
pages = (prtpv.size + perpage - 1) // perpage
if prtpv.size < len(printargs[0]):
for i, item in enumerate(printargs):
printargs[i] = [item[j] for j in prtpv]
tabhead = header + hu
pager = "\n" # + chr(12)
for p in range(pages):
if p > 0:
f.write(pager)
f.write(tabhead)
b = p * perpage
e = b + perpage
writer.vecwrite(f, frm, *printargs, so=slice(b, e))
f.write(pager)
for lbl, hdr in zip(("mx", "mn", "amx"), (maxhdr, minhdr, absmhdr)):
if lbl in pctinfo:
f.write(_get_histogram_str(desc, hdr, pctinfo[lbl]))
_wtcmp(filename, header, hu, frm, print_info.printargs, perpage, prtpv,
pctinfo, desc)
return pctinfo
def plot_qbc_example():
dpi = 100
color_palette = ("tab:blue", "tab:orange", "tab:green", "tab:red", "tab:purple", "tab:brown")
plot_path = os.path.join(dr(dr(dr(abspath(__file__)))), 'results', 'plots', 'for_ppt')
plt.interactive(True)
# prepare figure
figure = plt.subplots(nrows=2, ncols=2, figsize=(12, 6), dpi=dpi)
ax_all = figure[0].axes[0]
ax_gt = figure[0].axes[1]
ax_random = figure[0].axes[2]
ax_active = figure[0].axes[3]
# random points, [0, :] for x; [1, :] for y, sorted for x
random_all = np.random.rand(2, 200)
random_all[0, :] = np.sort(random_all[0, :])
random_all[1, :] = random_all[1, np.argsort(random_all[0, :])]
# plot random unlabelled
ax_all.scatter(x=random_all[0, :], y=random_all[1, :], c='#606060', marker='x')
ax_all.set_title('Unlabeled data', fontsize=16)
ax_all.set_autoscale_on(False)
# make it pretty
# plot all labelled
idx_cat1 = np.argwhere(random_all[0, :] < 0.5)
idx_cat2 = np.argwhere(random_all[0, :] > 0.5)
# give overlap
idx_temp = idx_cat1[-6:-1].copy()
idx_cat1[-6:-1] = idx_cat2[0:5]
idx_cat2[0:5] = idx_temp.copy()
# plot
ax_gt.scatter(x=random_all[0, idx_cat1], y=random_all[1, idx_cat1], c=color_palette[0], marker="o")
ax_gt.scatter(x=random_all[0, idx_cat2], y=random_all[1, idx_cat2], c=color_palette[1], marker="s")
# plot boundary
ax_gt.set_autoscale_on(False)
# ax_gt.axvline(x=0.5, c=color_palette[2])
ax_gt.set_title('If All Data Labeled', fontsize=16)
# make it pretty
# plot random training, skew towards cat 1
skewer_cat1 = idx_cat1[np.intersect1d(np.argwhere(random_all[0, idx_cat1] > 0.35),
np.argwhere(random_all[1, idx_cat1] < 0.25))].squeeze()
skewer_cat2 = idx_cat2[np.intersect1d(np.argwhere(random_all[0, idx_cat2] < 0.65),
np.argwhere(random_all[1, idx_cat2] > 0.75))].squeeze()
idx_cat1_random = np.random.randint(low=0, high=idx_cat1.max(), size=30-min(skewer_cat1.size, 8))
idx_cat2_random = np.random.randint(low=idx_cat2.min(), high=idx_cat2.max(), size=30-min(skewer_cat2.size, 8))
idx_cat1_rand = np.concatenate([idx_cat1_random, skewer_cat1[0:(min(skewer_cat1.size, 8))]]) # 5 skewer data
idx_cat2_rand = np.concatenate([idx_cat2_random, skewer_cat2[0:(min(skewer_cat2.size, 8))]]) # 5 skewer data
# plot unlabeled
ax_random.scatter(x=random_all[0, np.setdiff1d(range(200), [idx_cat1_rand, idx_cat2_rand])],
y=random_all[1, np.setdiff1d(range(200), [idx_cat1_rand, idx_cat2_rand])],
c='#606060', marker='x')
# plot labeled
ax_random.scatter(x=random_all[0, idx_cat1_rand], y=random_all[1, idx_cat1_rand],
c=color_palette[0], marker="o")
ax_random.scatter(x=random_all[0, idx_cat2_rand], y=random_all[1, idx_cat2_rand],
c=color_palette[1], marker="s")
# plot boundary
ax_random.set_autoscale_on(False)
# ax_random.plot([0.55, 0.45], [-0.1, 1.1], c=color_palette[2])
ax_random.set_title('Trained with random', fontsize=16)
# make it pretty
# plot active learning
idx_al1 = idx_cat1[-30:].squeeze()
idx_al2 = idx_cat2[0:30].squeeze()
# plot unlabeled
ax_active.scatter(x=random_all[0, np.setdiff1d(range(200), [idx_al1, idx_al2])],
y=random_all[1, np.setdiff1d(range(200), [idx_al1, idx_al2])], c='#606060', marker='x')
# plot labeled
ax_active.scatter(x=random_all[0, idx_al1], y=random_all[1, idx_al1], c=color_palette[0], marker="o")
ax_active.scatter(x=random_all[0, idx_al2], y=random_all[1, idx_al2], c=color_palette[1], marker="s")
# plot boundary
ax_active.set_autoscale_on(False)
# ax_active.plot([0.49, 0.51], [-0.1, 1.1], c=color_palette[2])
ax_active.set_title('Trained with active learning', fontsize=16)
# make it pretty
plt.tight_layout()
ax_all.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)
ax_gt.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)
ax_random.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)
ax_active.tick_params(axis='both', which='both', bottom=False, left=False, labelbottom=False, labelleft=False)
# save fig
name = 'random_vs_al_example'
file_name = os.path.join(plot_path, (name + '.png'))
plt.savefig(file_name, format='png', dpi=300)
