def _pvoc(self, X_hat, Phi_hat=None, R=None):
"""
::
a phase vocoder - time-stretch
inputs:
X_hat - estimate of signal magnitude
[Phi_hat] - estimate of signal phase
[R] - resynthesis hop ratio
output:
updates self.X_hat with modified complex spectrum
"""
N = self.nfft
W = self.wfft
H = self.nhop
R = 1.0 if R is None else R
dphi = (2 * P.pi * H * P.arange(N / 2 + 1)) / N
print("Phase Vocoder Resynthesis...", N, W, H, R)
A = P.angle(self.STFT) if Phi_hat is None else Phi_hat
phs = A[:, 0]
self.X_hat = []
n_cols = X_hat.shape[1]
t = 0
while P.floor(t) < n_cols:
tf = t - P.floor(t)
idx = P.arange(2) + int(P.floor(t))
idx[1] = n_cols - 1 if t >= n_cols - 1 else idx[1]
Xh = X_hat[:, idx]
Xh = (1 - tf) * Xh[:, 0] + tf * Xh[:, 1]
self.X_hat.append(Xh * P.exp(1j * phs))
U = A[:, idx[1]] - A[:, idx[0]] - dphi
U = U - P.np.round(U / (2 * P.pi)) * 2 * P.pi
phs += (U + dphi)
t += P.randn() * P.sqrt(PVOC_VAR * R) + R # 10% variance
self.X_hat = P.np.array(self.X_hat).T
def _make_log_freq_map(self):
"""
::
For the given ncoef (bands-per-octave) and nfft, calculate the center frequencies
and bandwidths of linear and log-scaled frequency axes for a constant-Q transform.
"""
fp = self.feature_params
bpo = float(self.nbpo) # Bands per octave
self._fftN = float(self.nfft)
hi_edge = float(self.hi)
lo_edge = float(self.lo)
f_ratio = 2.0**(1.0 / bpo) # Constant-Q bandwidth
self._cqtN = float(P.floor(P.log(hi_edge / lo_edge) / P.log(f_ratio)))
self._dctN = self._cqtN
self._outN = float(self.nfft / 2 + 1)
if self._cqtN < 1: print("warning: cqtN not positive definite")
mxnorm = P.empty(self._cqtN) # Normalization coefficients
fftfrqs = self._fftfrqs #P.array([i * self.sample_rate / float(self._fftN) for i in P.arange(self._outN)])
logfrqs = P.array([
lo_edge * P.exp(P.log(2.0) * i / bpo) for i in P.arange(self._cqtN)
])
logfbws = P.array([
max(logfrqs[i] * (f_ratio - 1.0),
self.sample_rate / float(self._fftN))
for i in P.arange(self._cqtN)
])
#self._fftfrqs = fftfrqs
self._logfrqs = logfrqs
self._logfbws = logfbws
self._make_cqt()
def _pvoc2(self, X_hat, Phi_hat=None, R=None):
"""
::
alternate (batch) implementation of phase vocoder - time-stretch
inputs:
X_hat - estimate of signal magnitude
[Phi_hat] - estimate of signal phase
[R] - resynthesis hop ratio
output:
updates self.X_hat with modified complex spectrum
"""
N, W, H = self.nfft, self.wfft, self.nhop
R = 1.0 if R is None else R
dphi = P.atleast_2d((2 * P.pi * H * P.arange(N / 2 + 1)) / N).T
print("Phase Vocoder Resynthesis...", N, W, H, R)
A = P.angle(self.STFT) if Phi_hat is None else Phi_hat
U = P.diff(A, 1) - dphi
U = U - P.np.round(U / (2 * P.pi)) * 2 * P.pi
t = P.arange(0, n_cols, R)
tf = t - P.floor(t)
phs = P.c_[A[:, 0], U]
phs += U[:, idx[1]] + dphi # Problem, what is idx ?
Xh = (1 - tf) * Xh[:-1] + tf * Xh[1:]
Xh *= P.exp(1j * phs)
self.X_hat = Xh
def main(argv=None):
if argv is None:
argv = sys.argv
if len(argv) != 4:
print "Usage: " + argv[0] + " <report name> <report dir> <graph dir>"
return 1
report = argv[1]
report_dir = argv[2]
graph_dir = argv[3]
file_list = glob(os.path.join(report_dir, 'part*'))
#Copy the raw data file to the graph_dir
raw_file = os.path.join(graph_dir, report + '.tsv')
shutil.copyfile(file_list[0], raw_file)
#Process the file into a graph, ideally I would combine the two into one but for now I'll stick with two
data_file = csv.DictReader(open(raw_file, 'rb'), fieldnames = ['hour', 'Requests', 'Bytes'], delimiter="\t")
#Make an empty set will all the hours in it os if an hour is not in the data it will be 0
length = 24
requests_dict = {}
for num in range(length):
requests_dict['%0*d' % (2, num)] = (0, 0)
#add the values we have to the dictionaries
for row in data_file:
requests_dict[row['hour']] = (int(row['Requests']), int(row['Bytes']))
#Now get the lists for graphing in the right order
requests = []
num_bytes = []
requests_lists = requests_dict.items()
requests_lists.sort(key=lambda req: req[0])
for req in requests_lists:
requests.append(req[1][0])
num_bytes.append(req[1][1])
fig = pylab.figure(1)
pos = pylab.arange(length) + .5
pylab.bar(pos, requests[:length], aa=True, ecolor='r')
pylab.ylabel('Requests')
pylab.xlabel('Hour')
pylab.title('Request per hour')
pylab.grid(True)
#Save the figure
pylab.savefig(os.path.join(graph_dir, report + '_requests.pdf'), bbox_inches='tight', pad_inches=1)
#bytes listed
fig = pylab.figure(2)
pos = pylab.arange(length) + .5
pylab.bar(pos, num_bytes[:length], aa=True, ecolor='r')
pylab.ylabel('Bytes')
pylab.xlabel('Hour')
pylab.title('Bytes per hour')
pylab.grid(True)
#Save the figure
pylab.savefig(os.path.join(graph_dir, report + '_bytes.pdf'), bbox_inches='tight', pad_inches=1)
def custom_modulation(fc, bits, add_first_bit):
# this custom ASK modulation (amplitude-shift modulation) is for support
# for CDMA, in this case: 0, -2, 2. Each number will have certain amplitude
lenghtBits = len(bits)
final_time = lenghtBits / 2
global ts
ts = pyl.arange(0, final_time, sampling_period)
global lenghtChunk
lenghtChunk = int(len(ts) / lenghtBits)
global A
A = []
if (add_first_bit):
# When add_first_bit is activated, a "2" bit is added
# at the beginning of the data
ts = pyl.arange(0, final_time + 0.5, sampling_period)
for j in range(lenghtChunk):
A.append(5)
for i in range(lenghtBits):
for j in range(lenghtChunk):
A_for_Bit_i = 0
if (bits[i] == 0):
A_for_Bit_i = 0
elif (bits[i] == 2):
A_for_Bit_i = 5
elif (bits[i] == -2):
A_for_Bit_i = 1
A.append(A_for_Bit_i)
return A * pyl.sin(2.0 * pyl.pi * fc * ts)
def main():
pl.ion() # interactive mode
fig1 = pl.figure()
ax1 = fig1.add_axes([0.1, 0.1, 0.8, 0.8])
pl.draw()
ax1.plot(pl.arange(5), [30, 40, 55, 80, 100])
pl.draw()
ax1.set_xlabel('x')
ax1.set_ylabel('y')
pl.draw()
for label in ax1.get_xticklabels():
label.set_color('red')
pl.draw()
for label in ax1.get_yticklabels():
label.set_color('green')
pl.draw()
ax1.grid(True)
pl.draw()
ax1.patch.set_facecolor('yellow')
pl.draw()
fig2 = pl.figure()
pl.draw()
ax2 = fig2.add_axes([0.1, 0.1, 0.8, 0.8])
pl.draw()
t = pl.arange(0.0, 1.0, 0.01)
s = pl.sin(2 * pl.pi * t)
ax2.plot(t, s, color='blue', lw=2)
pl.draw()
print "done"
def drawimage(self):
#Find the longest text on the x axis
maxtextlen = 0
for text in self.xdata:
if len(text) > maxtextlen:
maxtextlen = len(text)
#Convert it to a proportion of the image
bottomProportion = .1 + maxtextlen*.013
heightProportion = .99 - bottomProportion
#Set the size of the subplot big enough to handle times
#[left, bottom, width, height] out of 1
self.figure.add_axes([.125,bottomProportion,.825,heightProportion],'w')
#Set the min/max of each axis
ymin = sys.maxint
ymax = -sys.maxint+1
for value in self.ydata[0]:
if value < ymin:
ymin = value
if value > ymax:
ymax = value
self.figure.get_current_axis().set_xlim([0,len(self.xdata)+1])
self.figure.get_current_axis().set_ylim([math.floor(ymin),math.ceil(ymax)])
self.figure.get_current_axis().set_xticks(matlab.arange(len(self.xdata))+0.25)
self.figure.get_current_axis().set_xticklabels(self.xdata,rotation='vertical')
originY = None
if ymin < 0 and ymax > 0:
originY = 0
self.figure.get_current_axis().bar(matlab.arange(len(self.xdata)),self.ydata[0],0.5,color=self.colours,originY=originY)
def prec_rec(ranks):
"""
::
Return precision and recall arrays for ranks array data
"""
P = (1.0 + pylab.arange(pylab.size(ranks))) / (1.0 + pylab.sort(ranks))
R = (1.0 + pylab.arange(pylab.size(ranks))) / pylab.size(ranks)
return P, R
def insert_feature_files(featureList,
powerList,
keyList,
dbName,
delta_time=None,
undo_log10=False):
"""
::
Walk the list of features, powers, keys, and, optionally, times, and insert into database
"""
if delta_time == None:
delta_time = 0.0
db = adb.get(dbName, "w")
if not db:
print("Could not open database: %s" % dbName)
return False
# FIXME: need to test if KEY (%i) already exists in db
# Support for removing keys via include/exclude keys
for feat, pwr, key in zip(featureList, powerList, keyList):
print("Processing features: %s" % key)
F = adb.read(feat)
P = adb.read(pwr)
a, b = F.shape
if (len(P.shape) == 2):
P = P.reshape(P.shape[0])
if (len(P.shape) == 1):
c = P.shape[0]
else:
print("Error: powers have incorrect shape={0}".format(P.shape))
return None
if a != c:
F = F.T
a, b = F.shape
if a != c:
print(
"Error: powers and features different lengths powers={0}*, features={1},{2}*"
.format(c, a, b))
return None
# raw features, power in Bels, and key
if undo_log10:
F = 10**F
if delta_time != 0.0:
T = pylab.c_[pylab.arange(0, a) * delta_time,
(pylab.arange(0, a) + 1) * delta_time].reshape(
1, 2 * a).squeeze()
else:
T = None
db.insert(featData=F, powerData=P, timesData=T, key=key)
return db
def find_gt_ranks(self, out_ranks, ground_truth_keys=None):
"""
::
Return ranks matrix for ground-truth columns only
"""
r = out_ranks.argsort()
lzt_keys, lzt_len = self.get_adb_lists()
gt_idx = [lzt_keys.index(s) for s in ground_truth_keys]
ranks = pylab.zeros((len(gt_idx), len(gt_idx)))
for i in pylab.arange(len(gt_idx)):
for j in pylab.arange(len(gt_idx)):
ranks[i][j] = pylab.nonzero(r[i] == gt_idx[j])[0][0]
return ranks
def _make_dct(self):
"""
::
Construct the discrete cosine transform coefficients for the
current size of constant-Q transform
"""
DCT_OFFSET = self.lcoef
nm = 1 / P.sqrt(self._cqtN / 2.0)
self.DCT = P.empty((self._dctN, self._cqtN))
for i in P.arange(self._dctN):
for j in P.arange(self._cqtN):
self.DCT[i, j] = nm * P.cos(i * (2 * j + 1) *
(P.pi / 2.0) / self._cqtN)
for j in P.arange(self._cqtN):
self.DCT[0, j] *= P.sqrt(2.0) / 2.0
def plot_prob_effector(sens, fpr, xmax=1, baserate=0.1):
"""Plots a line graph of P(effector|positive test) against
the baserate of effectors in the input set to the classifier.
The baserate argument draws an annotation arrow
indicating P(pos|+ve) at that baserate
"""
assert 0.1 <= xmax <= 1, "Max x axis value must be in range [0,1]"
assert 0.01 <= baserate <= 1, "Baserate annotation must be in range [0,1]"
baserates = pylab.arange(0, 1.05, xmax * 0.005)
probs = [p_correct_given_pos(sens, fpr, b) for b in baserates]
pylab.plot(baserates, probs, 'r')
pylab.title("P(eff|pos) vs baserate; sens: %.2f, fpr: %.2f" % (sens, fpr))
pylab.ylabel("P(effector|positive)")
pylab.xlabel("effector baserate")
pylab.xlim(0, xmax)
pylab.ylim(0, 1)
# Add annotation arrow
xpos, ypos = (baserate, p_correct_given_pos(sens, fpr, baserate))
if baserate < xmax:
if xpos > 0.7 * xmax:
xtextpos = 0.05 * xmax
else:
xtextpos = xpos + (xmax - xpos) / 5.
if ypos > 0.5:
ytextpos = ypos - 0.05
else:
ytextpos = ypos + 0.05
pylab.annotate('baserate: %.2f, P(pos|+ve): %.3f' % (xpos, ypos),
xy=(xpos, ypos),
xytext=(xtextpos, ytextpos),
arrowprops=dict(facecolor='black', shrink=0.05))
else:
pylab.text(0.05 * xmax, 0.95,
'baserate: %.2f, P(pos|+ve): %.3f' % (xpos, ypos))
def noise(noise_fun=pylab.rand, **params):
"""
::
Generate noise according to params dict
params - parameter dict containing sr, and num_harmonics elements [None=default_noise_params()]
noise_fun - the noise generating function [pylab.rand]
"""
params = _check_noise_params(**params)
noise_dB = params['noise_dB']
num_harmonics = params['num_harmonics']
num_points = params['num_points']
cf = params['cf']
bw = params['bw']
sr = params['sr']
g = 10**(noise_dB / 20.0) * noise_fun(num_points)
[b, a] = scipy.signal.filter_design.butter(4,
bw * 2 * pylab.pi / sr,
btype='low',
analog=0,
output='ba')
g = scipy.signal.lfilter(b, a, g)
# Phase modulation with *filtered* noise (side-band modulation should be narrow-band at bw)
x = pylab.sin((2.0 * pylab.pi * cf / sr) * pylab.arange(num_points) + g)
return x
def __init__(self, contact_area_percent=50.0):
######################################
# begin: parameters to be specified
self.contact_area_percent = contact_area_percent
# resistor that is in series with the taxel (Ohms)
self.r1 = 47.0
# total voltage across the taxel and r1, which are in serise (Volts)
self.vtot = 5.0
# the maximum resistance of the taxel when no pressure is applied (Ohms)
self.rtax_max = 50.0
# the minimum force that will be applied to the taxel (Newtons)
self.fz_min = 0.0
# the maximum force that will be applied to the taxel (Newtons)
self.fz_max = 45.0
# the number of bits for the analog to digital conversion
self.adc_bits = 10
# the pressure sensitive area of the taxel (meters^2)
self.taxel_area = 0.04 * 0.04
# pressure that results in minimum resistance after which
# further pressure does not result in a reduction in the
# signal, since the sensor is saturated (Pascals = N/m^2)
self.pressure_max = self.fz_max / (0.4 * self.taxel_area)
# hack to specify the minimum resistance of the taxel, which
# is associated with the maximum pressure. for now, it's
# specified as a percentage of the maximum resistance, which
# is associated with 0 applied pressure (no contact)
self.r_min_percent_of_r_no_contact = 0.001 #
# end
######################################
self.r_no_contact = self.taxel_area * self.rtax_max
self.r_min = self.r_no_contact * (self.r_min_percent_of_r_no_contact /
100.0)
self.fz_array = pl.arange(self.fz_min, self.fz_max, 0.001) # N
self.adc_range = pow(2.0, self.adc_bits)
self.volts_per_adc_unit = self.vtot / self.adc_range # V
self.contact_area = self.taxel_area * (
self.contact_area_percent / 100.0) # m^2
self.no_contact_area = self.taxel_area - self.contact_area # m^2
self.pressure_array = pl.array(
[f / self.contact_area for f in self.fz_array]) # Pascals = N/m^2
self.rtax_array = pl.array([self.rtax(f) for f in self.pressure_array])
self.vdigi_array = pl.array(
[self.output_voltage(r) for r in self.rtax_array])
self.vdigi_max = self.output_voltage(self.rtax_max)
self.adc_bias = self.vdigi_max / self.volts_per_adc_unit
self.adc_array = self.vdigi_array / self.volts_per_adc_unit
self.adc_plot = self.adc_bias - self.adc_array
def plot(self):
self._logger.debug('plotting')
colors = self._colors[:(len(self._categoryData))]
ind = pylab.arange(len(self._xData))
bar_width = 1.0 / (len(self._categoryData) + 1)
bar_groups = []
for c in range(len(self._categoryData)):
bars = pylab.bar(ind + c * bar_width,
self._yData[c],
bar_width,
color=colors[c % len(colors)])
bar_groups.append(bars)
pylab.xticks(ind + bar_width, self._xData)
if (self._usingLegend):
pylab.legend((b[0] for b in bar_groups),
self._categoryData,
title=self._legendTitle,
loc=self._legendLocation,
labelspacing=self._legendLabelSpacing,
prop=self._legendFontProps,
bbox_to_anchor=self._legendBboxToAnchor)
pylab.xlabel(self._xLabel, fontdict=self._font)
pylab.ylabel(self._yLabel, fontdict=self._font)
pylab.title(self._title, fontdict=self._font)
if (self._saveFig):
self._logger.debug('Saving plot as {}'.format(self._saveName))
pylab.savefig(self._saveName)
pylab.show()
def createZoneTransient(self):
"""make a dic of all times and values of each zone for each time"""
lVar = self.Zones.keys() * 1
dZone = {}
tlist = []
for var in lVar: # make the list of times
for z in self.getZoneList(var):
if z.getForme() != 4 and type(z.getVal()) == type([5, 6]): # if list of values : transient
slist = z.getVal()
for s in slist:
tlist.append(float(s.split(" ")[0]))
# get the list from flow model
t0 = self.model.getParm("Ecoulement", "Temps") # final time, stress period l, nsteps
tf = t0[0][1]
per = t0[1][1]
tf2 = arange(0.0, tf * 1.01, per)
tflow = [round(x, 5) for x in tf2]
tflow.extend(tlist)
tlist = list(set(tflow))
tlist.sort()
# if tlist==[]: tlist=[100.] # cas permanent on met long 500
dZone["tlist"] = tlist
dZone["Transient"] = {}
dZone["zlist"] = {}
for var in lVar: # loop into var to get transient values for each zone
dZone["Transient"][var] = False
dZone["zlist"][var] = []
dZone[var] = None
lZ = self.getZoneList(var)
if len(lZ) == 0:
continue
tbl = zeros((len(tlist), len(lZ))) + 0.0
for iz in range(len(lZ)):
# get the list,
slist = lZ[iz].getVal()
form = lZ[iz].getForme()
if type(slist) != type([5, 6]):
tbl[:, iz] = slist # only one value
elif form == 4:
tbl[:, iz] = slist[0] # variable polygon take only 1st value
else: # a list of values : transient
vprec = 0
tl2 = []
vl2 = []
dZone["Transient"][var] = True
dZone["zlist"][var].append(iz)
for s in slist:
[a, b] = s.split()
tl2.append(float(a))
vl2.append(float(b))
for it in range(len(tlist)):
t = tlist[it]
if t in tl2:
v = vl2[tl2.index(t)]
tbl[it, iz] = v
vprec = v
else:
tbl[it, iz] = vprec
dZone[var] = tbl
self.zoneTrans = dZone
def bar_chart(categories, xdata, ydata,
title, xlabel, ylabel,
font={'family':'serif','color':'black','weight':'normal','size':12,},
plot=True, saveImage=False, imageName='fig.png'):
colors = 'rgbcmyk'
colors = colors[:(len(categories))]
ind = pylab.arange(len(xdata))
bar_width = 1.0 / (len(categories) + 1)
bar_groups = []
# loop through categories and plot one bar in each category every loop (ie., one color at a time.)
fig = pylab.figure()
for c in range(len(categories)):
bars = pylab.bar(ind+c*bar_width, ydata[c], bar_width, color=colors[c % len(colors)])
bar_groups.append(bars)
fontP = FontProperties()
fontP.set_size('small')
pylab.xticks(ind+bar_width, xdata)
pylab.legend([b[0] for b in bar_groups], categories,
loc='center right', title='Flow #', labelspacing=0,
prop=fontP, bbox_to_anchor=(1.125, .7))
pylab.xlabel(xlabel, fontdict=font)
pylab.ylabel(ylabel, fontdict=font)
pylab.title(title, fontdict=font)
# save the figure
if saveImage:
pylab.savefig(imageName)
# plot the figure
if plot:
pylab.show()
def plot(self):
self._logger.debug('plotting')
colors = self._colors[:(len(self._categoryData))]
ind = pylab.arange(len(self._xData))
bar_width = 1.0 / (len(self._categoryData) + 1)
bar_groups = []
for c in range(len(self._categoryData)):
bars = pylab.bar(ind+c*bar_width, self._yData[c], bar_width, color=colors[c % len(colors)])
bar_groups.append(bars)
pylab.xticks(ind+bar_width, self._xData)
if (self._usingLegend):
pylab.legend((b[0] for b in bar_groups), self._categoryData,
title = self._legendTitle, loc = self._legendLocation,
labelspacing = self._legendLabelSpacing,
prop = self._legendFontProps, bbox_to_anchor = self._legendBboxToAnchor)
pylab.xlabel(self._xLabel, fontdict=self._font)
pylab.ylabel(self._yLabel, fontdict=self._font)
pylab.title(self._title, fontdict=self._font)
if(self._saveFig):
self._logger.debug('Saving plot as {}'.format(self._saveName))
pylab.savefig(self._saveName)
pylab.show()
def sol_tov(rho_center):
rmin = dr
rmax = 20000.0
r = pylab.arange(rmin, rmax + dr, dr)
m = pylab.zeros_like(r)
P = pylab.zeros_like(r)
m[0] = (4.0 / 3.0) * pi * rho_center * r[0]**3
P[0] = eos(rho_center)
y = pylab.array([P[0], m[0]])
i = 0
while P[i] > 0.0 and i < len(r) - 1:
y = rk4(tov, y, r[i], dr)
P[i + 1] = y[0]
m[i + 1] = y[1]
i = i + 1
rho = pylab.array(list(map(lambda p: inv_eos(p), P)))
m, r, rho, P = m[:i], r[:i], rho[:i], P[:i] # Give restriction for region
if isentropic: # Consider the isentropic case
# We use conventional finite differencing to handle this case
drhodr = get_dfdr(rho, dr)
rprime = (r[-1] - r)[::-1] # Inward integration variable
# Specific internal energy from thermo identities
u_integrand = (-P * drhodr / (rho**2))[::-1]
# Interpolation
integrand_interpol = iterp1d(rprime, u_integrand, kind='cubic')
urhs = lambda u, rprime: integrand_interpol(rprime)
u = np.zeros_like(r)
for i in range(0, len(rprime) - 1):
u[i + 1] = rk4(urhs, u[i], rprime[i], dr) # Using RK4 to integrate
u = u[::-1] # Reverse u
else:
u = np.zeros_like(r)
return m, m[-1] / Msun, r, rho, P, u # Return the mass and radius of star
def _chroma_hcqft(self):
"""
::
Chromagram formed by high-pass liftering in cepstral domain, then usual self.nbpo-BPO folding.
"""
fp = self._check_feature_params()
if not self._hcqft():
return False
a, b = self.HCQFT.shape
complete_octaves = a / self.nbpo # integer division, number of complete octaves
#complete_octave_bands = complete_octaves * self.nbpo
# column-major ordering, like a spectrogram, is in FORTRAN order
self.CHROMA = P.zeros((self.nbpo, b))
for k in P.arange(complete_octaves):
self.CHROMA += self.HCQFT[k * self.nbpo:(k + 1) * self.nbpo, :]
self.CHROMA /= complete_octaves
self._have_chroma = True
if self.verbosity:
print(
"Extracted HCQFT CHROMA: lcoef=%d, ncoef=%d, intensified=%d" %
(self.lcoef, self.ncoef, self.intensify))
self.inverse = self.ichroma
self.X = self.CHROMA
return True
def generateHist(data, bins = 20, rnge = None,
histogram = None):
""" Generate an histogram of data
:arg data: Array-like
:arg bins: Number of bins
:arg rnge: 2-tuple with min and max values,
if None it is calculated from the data
:arg histogram: Array-like updated histogram rather then create new one
If histogram is not None, also a range must be given
"""
if histogram == None:
histogram = [0]*bins
else:
if rnge == None:
raise Exception("Histogram given but not range")
bins = len(histogram)
if rnge == None:
rnge = [min(data),max(data)]
delta_bin = 1.*(rnge[1] - rnge[0])/bins
for d in data:
if (d>=rnge[0]) and (d<rnge[1]):
histogram[ int((d-rnge[0])/delta_bin) ] += 1
return ( (pl.arange(bins)+ 0.5)*delta_bin + rnge[0]), histogram
def plotimx(self, im):
if self.type == "polygon":
for reg in ("ap", "bg0", "bg1"):
ci = self.imcoords(im, reg=reg)
pl.plot(ci[:, 0], ci[:, 1])
elif self.type == "circle":
n = 33
t = pl.arange(n) * pl.pi * 2 / n
ct = pl.cos(t)
st = pl.sin(t)
for reg in ("ap", "bg0", "bg1"):
ci = self.imcoords(im, reg=reg)
#print "center of circle= %f %f" % ci[0:2]
r = ci[2] # in pix
pl.plot(ci[0] + r * ct, ci[1] + r * st)
# point north: XXX TODO make general
from astropy import wcs
w = wcs.WCS(im.header)
# use origin=0 i.e. NOT FITS convention, but pl.imshow sets origin
# to 0,0 so do that here so we can overplot on pl.imshow axes
origin = 0
c = self.bg1coords # only works below for circle
ctr = w.wcs_world2pix([c[0:2]], origin)[0]
# north
ctr2 = w.wcs_world2pix([c[0:2] + pl.array([c[2], 0])], origin)[0]
pl.plot([ctr[0], ctr2[0]], [ctr[1], ctr2[1]])
def _stft(self):
if not self._have_x:
print(
"Error: You need to load a sound file first: use self.load_audio('filename.wav')"
)
return False
fp = self._check_feature_params()
num_frames = len(self.x)
self.STFT = P.zeros((self.nfft / 2 + 1, num_frames), dtype='complex')
self.win = P.ones(self.wfft) if self.window == 'rect' else P.np.sqrt(
P.hanning(self.wfft))
x = P.zeros(self.wfft)
buf_frames = 0
for k, nex in enumerate(self.x):
x = self._shift_insert(x, nex, self.nhop)
if self.nhop >= self.wfft - k * self.nhop: # align buffer on start of audio
self.STFT[:, k - buf_frames] = P.rfft(self.win * x,
self.nfft).T
else:
buf_frames += 1
self.STFT = self.STFT / self.nfft
self._fftfrqs = P.arange(
0, self.nfft / 2 + 1) * self.sample_rate / float(self.nfft)
self._have_stft = True
if self.verbosity:
print("Extracted STFT: nfft=%d, hop=%d" % (self.nfft, self.nhop))
self.inverse = self._istftm
self.X = abs(self.STFT)
if not self.magnitude:
self.X = self.X**2
return True
def plotPerB(self, figu, stats, classstats, f_num, c_num, name, li):
ax = figu.add_subplot(1, 1, 1)
ind = pylab.arange(c_num)
width = 1
i = 0
maxi = 1
ctags = []
mytags = []
# means
means = self.defineAvg(stats, f_num, c_num)
# cumul means
cumul = self.disCumul(means)
# plot bars
self.barPlot(ax, ind, width, f_num, ctags, stats, i)
# plot labels
ax.set_title(name + '\n\n', fontstyle='normal', fontsize='12')
# setting axis
ax.axis([0, c_num, 0, maxi])
# plot class names
for c in range(c_num):
mytags.append(c + (width / 2))
ax.set_xticks(mytags)
ax.set_xticklabels(ctags, rotation='75', fontsize='12')
ax.grid(False)
# set y axis label
ax.set_ylabel("relative frequency", fontsize='12')
self.plotLegend(ax, li)
def plot_prob_effector(sens, fpr, xmax=1, baserate=0.1):
"""Plots a line graph of P(effector|positive test) against
the baserate of effectors in the input set to the classifier.
The baserate argument draws an annotation arrow
indicating P(pos|+ve) at that baserate
"""
assert 0.1 <= xmax <= 1, "Max x axis value must be in range [0,1]"
assert 0.01 <= baserate <= 1, "Baserate annotation must be in range [0,1]"
baserates = pylab.arange(0, 1.05, xmax * 0.005)
probs = [p_correct_given_pos(sens, fpr, b) for b in baserates]
pylab.plot(baserates, probs, 'r')
pylab.title("P(eff|pos) vs baserate; sens: %.2f, fpr: %.2f" % (sens, fpr))
pylab.ylabel("P(effector|positive)")
pylab.xlabel("effector baserate")
pylab.xlim(0, xmax)
pylab.ylim(0, 1)
# Add annotation arrow
xpos, ypos = (baserate, p_correct_given_pos(sens, fpr, baserate))
if baserate < xmax:
if xpos > 0.7 * xmax:
xtextpos = 0.05 * xmax
else:
xtextpos = xpos + (xmax-xpos)/5.
if ypos > 0.5:
ytextpos = ypos - 0.05
else:
ytextpos = ypos + 0.05
pylab.annotate('baserate: %.2f, P(pos|+ve): %.3f' % (xpos, ypos),
xy=(xpos, ypos),
xytext=(xtextpos, ytextpos),
arrowprops=dict(facecolor='black', shrink=0.05))
else:
pylab.text(0.05 * xmax, 0.95, 'baserate: %.2f, P(pos|+ve): %.3f' %
(xpos, ypos))
def sol_tov(rho_center):
rmin = dr
rmax = 2.e10
r = pylab.arange(rmin, rmax + dr, dr)
m = pylab.zeros_like(r)
P = pylab.zeros_like(r)
rho = pylab.zeros_like(r)
i = 0
rho[i] = rho_center
m[i] = (4.0 / 3.0) * pi * dr**3
P[i] = eos(rho_center, 0.0)
y = pylab.array([P[i], m[i]])
while P[i] > 0.0 and i < len(r) - 1:
y = rk4(tov, y, r[i], dr, rho[i])
m[i + 1] = y[1]
P[i + 1] = y[0]
rho[i + 1] = bisection(2 * rho[i], 0.1 * rho[i], 1.e-8, P[i + 1])
i = i + 1
if P[i] < 0.0:
P[i] = 0.0
rho[i] = 0.0
m, r, rho, P = m[:i], r[:i], rho[:i], P[:i] # Give restriction for region
return m, m[-1] / Msun, r, rho, P # Return the mass and radius of star
def plmyfig(df, bgname, dirname, tar, count=10):
#plot fig!
print("Starting Plot %s %s" % (dirname, bgname))
if len(df) > count:
df = df.head(count)
pos = plt.arange(len(df)) + 0.5
ytick = _getTerm(df['Term_description'], df['Term_ID'], bgname)
xs = [float(n) for n in df[' -log10(pvalue)']]
ytick.reverse()
xs.reverse()
plt.barh(pos, xs, align = 'center', height = 0.5, alpha = 1, color='orange')
plt.yticks(pos, ytick, size = 'x-small')
plt.xlabel('$-Log10(pValue)$')
plt.title('%s' % bgname)
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
try:
plt.tight_layout()
except ValueError:
pass
filename = os.path.join(tar, dirname, dirname + '_' + bgname)
plt.savefig(filename + '.png', dpi = 72)
plt.savefig(filename + '.pdf')
plt.close()
def _chroma(self):
"""
::
Chromagram, like 12-BPO CQFT modulo one octave. Energy is folded onto first octave.
"""
fp = self._check_feature_params()
lo = self.lo
self.lo = 63.5444 # set to quarter tone below C
if not self._cqft():
return False
self.lo = lo # restore original lo edge
a, b = self.CQFT.shape
complete_octaves = a / self.nbpo # integer division, number of complete octaves
#complete_octave_bands = complete_octaves * self.nbpo
# column-major ordering, like a spectrogram, is in FORTRAN order
self.CHROMA = P.zeros((self.nbpo, b))
for k in P.arange(complete_octaves):
self.CHROMA += self.CQFT[k * self.nbpo:(k + 1) * self.nbpo, :]
self.CHROMA = (self.CHROMA / complete_octaves)
self._have_chroma = True
if self.verbosity:
print("Extracted CHROMA: intensified=%d" % self.intensify)
self.inverse = self.ichroma
self.X = self.CHROMA
return True
def plotTraces():
tracesList = f.cfg['recordTraces'].keys()
tracesList.sort()
gidList = [trace for trace in f.cfg['plotCells'] if isinstance(trace, int)]
popList = [trace for trace in f.cfg['plotCells'] if isinstance(trace, str)]
if 'all' in popList:
gidList = [cell['gid'] for cell in f.net.allCells]
popList = []
duration = f.cfg['duration']
recordStep = f.cfg['recordStep']
for gid in gidList:
figure() # Open a new figure
fontsiz = 12
for itrace, trace in enumerate(tracesList):
try:
data = f.allSimData[trace]['cell_'+str(gid)]
t = arange(0, duration+recordStep, recordStep)
subplot(len(tracesList),1,itrace+1)
plot(t[:len(data)], data, linewidth=1.5)
xlabel('Time (ms)', fontsize=fontsiz)
ylabel(trace, fontsize=fontsiz)
xlim(0,f.cfg['duration'])
except:
pass
if tracesList: subplot(len(tracesList),1,1)
title('Cell %d'%(int(gid)))
for popLabel in popList:
fontsiz = 12
for pop in f.net.pops:
if pop.tags['popLabel'] == popLabel and pop.cellGids:
figure() # Open a new figure
gid = pop.cellGids[0]
for itrace, trace in enumerate(tracesList):
try:
data = f.allSimData[trace]['cell_'+str(gid)]
t = arange(0, len(data)*recordStep, recordStep)
subplot(len(tracesList),1,itrace+1)
plot(t, data, linewidth=1.5)
xlabel('Time (ms)', fontsize=fontsiz)
ylabel(trace, fontsize=fontsiz)
xlim(0,f.cfg['duration'])
except:
pass
subplot(len(tracesList),1,1)
title('Pop %s, Cell %d'%(popLabel, int(gid)))
def _phase_map(self):
self.dphi = (2 * P.pi * self.nhop *
P.arange(self.nfft / 2 + 1)) / self.nfft
A = P.diff(P.angle(self.STFT), 1) # Complete Phase Map
U = P.c_[P.angle(self.STFT[:, 0]), A - P.atleast_2d(self.dphi).T]
U = U - P.np.round(U / (2 * P.pi)) * 2 * P.pi
self.dPhi = U
return U
def update_graph(self, val):
freq = self.sfreq.val
self.theta_max = freq*math.pi*2.0
self.theta = pylab.arange(0.0, self.theta_max, self.theta_max/len(self.r))
self.theta = self.theta[:len(self.r)]
self.myplot.set_xdata(self.theta)
draw()
def movie_b(dataMatrix, showFrame = 0, nbrLoop = 1):
matrixSize = dataMatrix.shape[1]
ax = pl.subplot(111)
line, = pl.plot(dataMatrix[:,1])
if showFrame:
pl.title("Movie - Frame: " + str(1))
for j in pl.arange(nbrLoop):
for i in pl.arange(matrixSize):
line.set_ydata(dataMatrix[:,i])
maxValue = dataMatrix[:,i].max()
ax.set_ylim((0,maxValue))
if showFrame:
pl.title("Movie - Frame: " + str(i))
pl.draw()
def update_graph(self, val):
freq = self.sfreq.val
self.theta_max = freq * math.pi * 2.0
self.theta = pylab.arange(0.0, self.theta_max,
self.theta_max / len(self.r))
self.theta = self.theta[:len(self.r)]
self.myplot.set_xdata(self.theta)
draw()
def __init__(self, contact_area_percent=50.0):
######################################
# begin: parameters to be specified
self.contact_area_percent = contact_area_percent
# resistor that is in series with the taxel (Ohms)
self.r1 = 47.0
# total voltage across the taxel and r1, which are in serise (Volts)
self.vtot = 5.0
# the maximum resistance of the taxel when no pressure is applied (Ohms)
self.rtax_max = 50.0
# the minimum force that will be applied to the taxel (Newtons)
self.fz_min = 0.0
# the maximum force that will be applied to the taxel (Newtons)
self.fz_max = 45.0
# the number of bits for the analog to digital conversion
self.adc_bits = 10
# the pressure sensitive area of the taxel (meters^2)
self.taxel_area = 0.04 * 0.04
# pressure that results in minimum resistance after which
# further pressure does not result in a reduction in the
# signal, since the sensor is saturated (Pascals = N/m^2)
self.pressure_max = self.fz_max/(0.4 * self.taxel_area)
# hack to specify the minimum resistance of the taxel, which
# is associated with the maximum pressure. for now, it's
# specified as a percentage of the maximum resistance, which
# is associated with 0 applied pressure (no contact)
self.r_min_percent_of_r_no_contact = 0.001 #
# end
######################################
self.r_no_contact = self.taxel_area * self.rtax_max
self.r_min = self.r_no_contact * (self.r_min_percent_of_r_no_contact/100.0)
self.fz_array = pl.arange(self.fz_min, self.fz_max, 0.001) # N
self.adc_range = pow(2.0, self.adc_bits)
self.volts_per_adc_unit = self.vtot/self.adc_range # V
self.contact_area = self.taxel_area * (self.contact_area_percent/100.0) # m^2
self.no_contact_area = self.taxel_area - self.contact_area # m^2
self.pressure_array = pl.array([f/self.contact_area for f in self.fz_array]) # Pascals = N/m^2
self.rtax_array = pl.array([self.rtax(f) for f in self.pressure_array])
self.vdigi_array = pl.array([self.output_voltage(r) for r in self.rtax_array])
self.vdigi_max = self.output_voltage(self.rtax_max)
self.adc_bias = self.vdigi_max/self.volts_per_adc_unit
self.adc_array = self.vdigi_array/self.volts_per_adc_unit
self.adc_plot = self.adc_bias - self.adc_array
def plotLog(self, figu, stats, classstats, f_num, c_num, name):
ax2 = figu.add_subplot(1, 2, 2)
myind = pylab.arange(c_num)
ind = myind
log = log10(ind + 1)
logind = log
# avg means
fmeans = self.defineAvg2(stats, f_num, c_num)
fmax = fmeans[len(fmeans) - 1]
fmin = fmeans[0]
print "###################################################"
print fmax, "= max avg freq"
print log10(fmax), "= max avg freq (log)"
print fmin, "= min avg freq"
print log10(fmin), "= min avg freq (log)"
# avg cumul means
fcumul = self.disCumul(fmeans)
cumax = fcumul[len(fcumul) - 1]
print "###################################################"
print cumax, "= max avg cumul freq"
print log10(cumax), "= max avg cumul freq (log)"
# plot log powerlaw (cumul)
ax2.plot(logind, self.powerLog(ind, fcumul)[1], '-', color='k')
ax2.plot(logind, self.powerLog(ind, fmeans)[1], '--', color='k')
ax2.plot(logind,
log10((scipy.array(fcumul) + 0.01) * 100),
'o',
color='k')
ax2.plot(logind,
log10((scipy.array(fmeans) + 0.01) * 100),
'x',
color='k')
# title
ax2.set_title(name + ' (log-log best fit)\n\n',
fontstyle='normal',
fontsize='12')
# setting axis
ax2.axis([log10(c_num) + 0.5, -0.1, -0.1, log10(cumax) + 3.0])
ax2.grid(False)
ax2.set_xlabel("log rank", fontsize='12')
ax2.set_ylabel("log frequency", fontsize='12')
# make the y, x-axis ticks formatted to 1 decimal places
ax2.xaxis.set_major_formatter(FormatStrFormatter('%.1f'))
ax2.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
# plot legend
formc = self.powerLog(ind, fcumul)[0]
formm = self.powerLog(ind, fmeans)[0]
li = ('y=' + '%.2f' % formc['(sl,int)'][1] + "-" +
'%.2f' % formc['(sl,int)'][0] + 'x, R2=' + '%.2f' % formc['R^2'],
'y=' + '%.2f' % formm['(sl,int)'][1] + "-" +
'%.2f' % formm['(sl,int)'][0] + 'x, R2=' + '%.2f' % formm['R^2'])
self.plotLegend(ax2, li)
def display():
word_freqs = nltk.FreqDist(brown.words(categories="news")).most_common()
words_by_freq = [w for (w, _) in word_freqs]
cfd = nltk.ConditionalFreqDist(brown.tagged_words(categories="news"))
sizes = 2 ** pylab.arange(15)
perfs = [performance(cfd, words_by_freq[:size]) for size in sizes]
pylab.plot(sizes, perfs, "-bo")
pylab.title("Lookup Tagger Performance with Varying Model Size")
pylab.xlabel("Model Size")
pylab.ylabel("Performance")
pylab.show()
def makeMSFrame(dirname,msname,ra0,dec0,nchan):
msstokes='RR LL';
feedtype='perfect R L';
## Directory for the MS
if(not os.path.exists(dirname)):
cmd = 'mkdir ' + dirname;
os.system(cmd);
vx = [41.1100006, -34.110001, -268.309998, 439.410004, -444.210022]
vy = [3.51999998, 129.8300018, +102.480003, -182.149994, -277.589996]
vz = [0.25, -0.439999998, -1.46000004, -3.77999997, -5.9000001]
d = [25.0, 25.0, 25.0, 25.0, 25.0]
an = ['VLA1','VLA2','VLA3','VLA4','VLA5'];
nn = len(vx)*2.0;
x = 0.5*(vx - (sum(pl.array(vx))/(nn)));
y = 0.5*(vy - (sum(pl.array(vy))/(nn)));
z = 0.5*(vz - (sum(pl.array(vz))/(nn)));
#### This call will get locations for all 27 vla antennas.
#d, an, x, y, z = getAntLocations()
obspos = me.observatory('EVLA');
#obspos = me.position('ITRF', '-0.0m', '0.0m', '3553971.510m');
## Make MS Frame
sm.open(ms=msname);
sm.setconfig(telescopename='EVLA',x=x.tolist(),y=y.tolist(),z=z.tolist(),dishdiameter=d,
mount=['alt-az'], antname=an,
coordsystem='local',referencelocation=obspos);
sm.setspwindow(spwname="CBand",freq="6.0GHz",deltafreq='500MHz',
freqresolution='2MHz',nchannels=nchan,stokes=msstokes);
sm.setfeed(mode=feedtype,pol=['']);
sm.setfield( sourcename="fake",sourcedirection=me.direction(rf='J2000',v0=ra0,v1=dec0) );
sm.setlimits(shadowlimit=0.01, elevationlimit='10deg');
sm.setauto(autocorrwt=0.0);
sm.settimes(integrationtime='1800s', usehourangle=True,
referencetime=me.epoch('UTC','2013/05/10/00:00:00'));
# Every 30 minutes, from -3h to +3h
ostep = 0.5
for loop in pl.arange(-3.0,+3.0,ostep):
starttime = loop
stoptime = starttime + ostep
print starttime, stoptime
for ch in range(0,nchan):
sm.observe(sourcename="fake",spwname='CBand',
starttime=str(starttime)+'h', stoptime=str(stoptime)+'h');
sm.close();
listobs(vis=msname)
return d
def bar_chart(categories,
xdata,
ydata,
title,
xlabel,
ylabel,
font={
'family': 'serif',
'color': 'black',
'weight': 'normal',
'size': 12,
},
plot=True,
saveImage=False,
imageName='fig.png'):
colors = 'rgbcmyk'
colors = colors[:(len(categories))]
ind = pylab.arange(len(xdata))
bar_width = 1.0 / (len(categories) + 1)
bar_groups = []
# loop through categories and plot one bar in each category every loop (ie., one color at a time.)
fig = pylab.figure()
for c in range(len(categories)):
bars = pylab.bar(ind + c * bar_width,
ydata[c],
bar_width,
color=colors[c % len(colors)])
bar_groups.append(bars)
fontP = FontProperties()
fontP.set_size('small')
pylab.xticks(ind + bar_width, xdata)
pylab.legend([b[0] for b in bar_groups],
categories,
loc='center right',
title='Flow #',
labelspacing=0,
prop=fontP,
bbox_to_anchor=(1.125, .7))
pylab.xlabel(xlabel, fontdict=font)
pylab.ylabel(ylabel, fontdict=font)
pylab.title(title, fontdict=font)
# save the figure
if saveImage:
pylab.savefig(imageName)
# plot the figure
if plot:
pylab.show()
def campana(f0, A0, I0, duracion, fsampl):
ts = pyl.arange(0, duracion, 1 / fsampl) # creo el espacio temporal
Tao = duracion / 3
fc = f0
fm = 2 * f0
I_t = I0 * pyl.exp(-ts / Tao) # para la campana
A_t = A0 * pyl.exp(-ts / Tao) # para la campana
ym = pyl.sin(2 * pyl.pi * fm * ts) # función modulada
# Señal FM final con sus componentes que varían en el tiempo
yc = A_t * pyl.sin(2 * pyl.pi * fc * ts + (I_t * ym))
return yc
def getdata(self):
chartdata = self.charttable.gettablerows(self.attribs.get('filter',None))
xlabels = [str(row[self.xcolumn]) for row in chartdata]
self.ydata = [[self.mapfromNone(row[ycolumn]) for row in chartdata] for ycolumn in self.ycolumns]
self.legendlabels = [str(ycolumn) for ycolumn in self.ycolumns]
#x axis tick labels should be uniformly distributed in this case
startVal = self.attribs.get('startval', 0)
endVal = self.attribs.get('endval', len(self.xdata))
self.xvalues = matlab.arange(startVal, endVal, float(endVal - startVal)/len(xlabels))
self.xMajorLocator = ticker.LinearLocator(self.attribs.get('numticks',10))
self.xMajorFormatter = ticker.FixedFormatter(xlabels)
def draw_snd(data, freq):
timA = arange(0, len(data), 1)
timA = timA / freq
#plot
#print(timA.shape)#
pyplot.plot(timA, data, color='k', alpha=0.5)
ylabel = ('Amp')
xlabel('Time (ms)')
show()
def plotCurve(self,figu,stats,c_num,name,pos):
'''
curve chart plotter
feature x precision, recall, f-measure
'''
ax = figu.add_subplot(1,1,pos)
ind = pylab.arange(c_num)
width = 1 # 1cm per unit of length
maxi = 1 # y-axis ranges from 0 to 0.6
# methods, annotators
ctags = stats[0]
pre = stats[1]
rec = stats[2]
fme = stats[3]
acc = stats[4]
# line identifiers
li = ("MetaMap","BabelFly","TagMe","WordNet")
# plot 1
ax.plot((ind+width/2),pre,'o--',# change line type
color='r',linewidth=3,label="MetaMap") # change color
# plot 1
ax.plot((ind+width/2),rec,'x--',# change line type
color='k',linewidth=3,label="BabelFly") # change color
# plot 2
ax.plot((ind+width/2),fme,'x-',# change line type
color='b',linewidth=1,label="TagMe") # change color
# plot 3
ax.plot((ind+width/2),acc,'o-',# change line type
color='g',linewidth=3,label="WordNet") # change color
ax.axis([0,c_num,0,maxi])
# set name of y-axis
ax.set_ylabel(name+" ",fontsize='12')
# plot feature names
mytags = []
for c in range(c_num):
mytags.append(c+(width/2))
ax.set_xticks(mytags)
ax.set_xticklabels(ctags,rotation='45',fontsize='12')
ax.grid(False)
# plot legend
self.plotLegend(ax,li)
def sinusoid(**params):
"""
::
Generate a sinusoidal audio signal from signal_params: see default_signal_params()
**params - signal_params dict, see default_signal_params()
"""
params = _check_signal_params(**params)
t = pylab.arange(params['num_points'])
x = pylab.sin(TWO_PI * params['f0'] / params['sr'] * t +
params['phase_offset'])
return x
def _cqft_intensified(self):
"""
::
Constant-Q Fourier transform using only max abs(STFT) value in each band
"""
if not self._have_stft:
if not self._stft():
return False
self._make_log_freq_map()
r, b = self.Q.shape
b, c = self.STFT.shape
self.CQFT = P.zeros((r, c))
for i in P.arange(r):
for j in P.arange(c):
self.CQFT[i, j] = (self.Q[i, :] *
P.absolute(self.STFT[:, j])).max()
self._have_cqft = True
self._is_intensified = True
self.inverse = self.icqft
self.X = self.CQFT
return True
def plotLog(self, figu, stats, classstats, f_num, c_num, name):
ax2 = figu.add_subplot(1, 2, 2)
myind = pylab.arange(c_num)
ind = myind
log = log10(ind + 1)
logind = log
width = 0.1
i = 0
ctags = []
mytags = []
# means
fmeans = self.defineAvg2(stats, f_num, c_num)
fmax = fmeans[len(fmeans) - 1]
print fmax, "is max freq"
# cumul means
fcumul = self.disCumul(fmeans)
cumax = log10(fcumul[len(fcumul) - 1])
print "###################################################"
print cumax, "is max cumul freq"
# plot log powerlaw (cumul)
ax2.plot(logind, self.powerLog(ind, fcumul)[1], '-', color='k')
ax2.plot(logind, self.powerLog(ind, fmeans)[1], '--', color='k')
ax2.plot(logind,
log10((scipy.array(fcumul) + 0.01) * 100),
'o',
color='k')
ax2.plot(logind,
log10((scipy.array(fmeans) + 0.01) * 100),
'x',
color='k')
# title
ax2.set_title('log-log best fit (' + name + ')\n\n',
fontstyle='normal',
fontsize='12')
# setting axis
ax2.axis([1.05, -0.1, 0, 7])
ax2.grid(False)
# plot legend
formc = self.powerLog(ind, fcumul)[0]
formm = self.powerLog(ind, fmeans)[0]
li = (
'best fit (cum.), y = ' + '%.2f' % formc['(sl,int)'][1] + " - " +
'%.2f' % formc['(sl,int)'][0] + 'x, r^2 = ' +
'%.2f' % formc['R^2'],
'best fit (incr.), y = ' + '%.2f' % formm['(sl,int)'][1] + " - " +
'%.2f' % formm['(sl,int)'][0] + 'x, r^2 = ' +
'%.2f' % formm['R^2'],
#'averages (cum.)',
#'averages (inc..)'
)
self.plotLegend(ax2, li)
def plotSigmoid():
t = plab.arange(-60.0, 60.3, 0.1)
s = 1 / (1 + plab.exp(-t))
ax = plab.subplot(211)
ax.plot(t, s)
ax.axis([-5, 5, 0, 1])
plt.xlabel('x')
plt.ylabel('Sigmoid(x)')
ax = plab.subplot(212)
ax.plot(t, s)
ax.axis([-60, 60, 0, 1])
plt.xlabel('x')
plt.ylabel('Sigmoid(x)')
plab.show()
def movielog(dataMatrix, showFrame):
matrixSize = dataMatrix.shape[1]
ax = pl.subplot(111)
line, = pl.semilogy(dataMatrix[:,1])
if showFrame:
pl.title("Movie - Frame: " + str(1))
for i in pl.arange(matrixSize):
line.set_ydata(dataMatrix[:,i])
ax.relim()
ax.autoscale_view()
if showFrame:
pl.title("Movie - Frame: " + str(i))
pl.draw()
def plotCurve(self,figu,stats,c_num,name,pos):
ax = figu.add_subplot(1,1,pos)
ind = pylab.arange(c_num)
width = 1 # 1cm per unit of length
maxi = 0.5 # y-axis ranges from 0 to 0.5
# methods, annotators
ctags = stats[0]
mmap = stats[1] # MMap
bfly = stats[2] # BFly
tagm = stats[3] # TGMe
wnet = stats[4] # Wnet
# line identifiers
li = ("MetaMap","BabelFly","TagMe","WordNet")
# plot 0
ax.plot((ind+width/2),mmap,'x--',# change line type
color='k',linewidth=3,label=li[3]) # change color
# plot 1
ax.plot((ind+width/2),bfly,'o--',# change line type
color='r',linewidth=3,label=li[2]) # change color
# plot 2
ax.plot((ind+width/2),tagm,'x-',# change line type
color='b',linewidth=1,label=li[1]) # change color
# plot 3
ax.plot((ind+width/2),wnet,'o-',# change line type
color='g',linewidth=3,label=li[0]) # change color
ax.axis([0,c_num,0,maxi])
# set name of y-axis
ax.set_ylabel(name+" (avg.)",fontsize='12')
# plot feature names
mytags = []
for c in range(c_num):
mytags.append(c+(width/2))
ax.set_xticks(mytags)
ax.set_xticklabels(ctags,rotation='45',fontsize='12')
ax.grid(False)
# plot legend
self.plotLegend(ax,li)
def mat_plot(funclist,suplist,lab1=None,lab2=None,ftype='continuous'):
"""
Procedure Name: mat_plot
Purpose: Create a matplotlib plot of a random variable
Arguments: 1. RVar: A random variable
2. suplist: The support of the plot
Output: 1. A plot of the random variable
"""
# if the random variable is continuous, plot the function
if ftype=='continuous':
for i in range(len(funclist)):
if funclist[i]=='0':
continue
if 'x' in funclist[i]:
x=arange(suplist[i],suplist[i+1],0.01)
s=eval(funclist[i])
plot(x,s,linewidth=1.0,color='green')
else:
plot([suplist[i],suplist[i+1]],
[funclist[i],funclist[i]],
linewidth=1.0,color='green')
if lab1=='idf':
xlabel('s')
else:
xlabel('x')
if lab1!=None:
ylabel(lab1)
if lab2!=None:
title(lab2)
grid(True)
# If the random variable is discrete, plot the function
if ftype=='discrete':
plot(suplist,funclist,'ro')
if lab1=='F-1(s)':
xlabel('s')
else:
xlabel('x')
if lab1!=None:
ylabel(lab1)
if lab2!=None:
title(lab2)
grid(True)
def plot_mc_results(output, dz = 1):
Z,N = output.shape
x = dz*np.arange(Z)
f, ax = pl.subplots(figsize=(12,4))
ax.imshow(abs(output.T)**2, aspect='auto', interpolation='none', cmap='Reds')
ax.set_xlabel('Propagation dx')
f, [ax1, ax2] = pl.subplots(1,2, figsize=(12,3))
ax1.plot(x,abs(output[:,N//2])**2) # Plot few channels
ax1.plot(x,abs(output[:,N//2-1])**2)
ax1.plot(x,abs(output[:,N//2+1])**2) # Plot few channels
ax1.plot(x,abs(output[:,N//2-2])**2)
ax1.plot(x,abs(output[:,N//2+2])**2) # Plot few channels
ax1.plot(x,abs(output[:,N//2-3])**2)
ax1.plot(x,abs(output[:,N//2+3])**2) # Plot few channels
ax1.plot(x,np.sum(abs(output)**2,1), c = 'k', ls = '--') # Total Energy
ax1.set_xlabel('Propagation dx')
ax2.bar(pl.arange(N)-N/2,abs(output[-1])**2)
def bar_freq(self,fre,n,fig,k,i,id,li):
words = fre.keys()[:n]
freqs = []
for word in words:
freqs.append(fre.freq(word))
ax = fig.add_subplot(k,1,i)
ind = pylab.arange(len(freqs))
width = 1
ax.bar(ind,freqs,width,facecolor='gray')
ax.set_title('Word relative frequencies (top ' + `n` + ' words)\n\n',
fontstyle='normal',fontsize='12')
max = fre.freq(fre.max())
ax.axis([0,len(words),0,max])
tags = []
for c in range(len(words)):
tags.append(width+c)
ax.set_xticks(tags)
self.plotticks(ax,words)
ax.grid(False)
self.plotLegend(ax, li)
def main(argv=None):
if argv is None:
argv = sys.argv
if len(argv) != 4:
print "Usage: " + argv[0] + " <report name> <report dir> <graph dir>"
return 1
report = argv[1]
report_dir = argv[2]
graph_dir = argv[3]
file_list = glob(os.path.join(report_dir, 'part*'))
#Copy the raw data file to the graph_dir
raw_file = os.path.join(graph_dir, report + '.tsv')
shutil.copyfile(file_list[0], raw_file)
#Process the file into a graph, ideally I would combine the two into one but for now I'll stick with two
data_file = csv.DictReader(open(raw_file, 'rb'), fieldnames = ['IP', 'Requests', 'Bytes'], delimiter="\t")
ips = []
requests = []
num_bytes = []
for row in data_file:
ips.append(row['IP'])
requests.append(int(row['Requests']))
num_bytes.append(int(row['Bytes']))
if len(ips) > 25:
length = 25
else:
length = len(ips)
fig = pylab.figure(1)
pos = pylab.arange(length) + .5
pylab.barh(pos, requests[:length], align='center', aa=True, ecolor='r')
pylab.yticks(pos, ips[:length])
pylab.xlabel('Requests')
pylab.title('Top %d ips ordered by # of requests' % length)
pylab.grid(True)
#Save the figure
pylab.savefig(os.path.join(graph_dir, report + '.pdf'), bbox_inches='tight', pad_inches=1)
def prob_plot(Sample,Fitted,plot_type):
"""
Procedure Name: prob_plot
Purpose: Create a mat plot lib plot to compare sample distributions
with theoretical models
Arguments: 1. Sample: Data sample quantiles
2. Model: Model quantiles
Output: 1. A probability plot that compares data with a model
"""
plot(Fitted,Sample,'ro')
x=arange(min(min(Sample),min(Fitted)),
max(max(Sample),max(Fitted)),0.01)
s=x
plot(x,s,linewidth=1.0,color='red')
if plot_type=='QQ Plot':
xlabel('Model Quantiles')
ylabel('Sample Quantiles')
elif plot_type=='PP Plot':
xlabel('Model CDF')
ylabel('Sample CDF')
title(plot_type)
grid(True)
def plotFigPerTrace(subGids):
for itrace, trace in enumerate(tracesList):
figs['_trace_'+str(trace)] = figure() # Open a new figure
fontsiz = 12
for igid, gid in enumerate(subGids):
if 'cell_'+str(gid) in sim.allSimData[trace]:
data = sim.allSimData[trace]['cell_'+str(gid)][int(timeRange[0]/recordStep):int(timeRange[1]/recordStep)]
t = arange(timeRange[0], timeRange[1]+recordStep, recordStep)
tracesData.append({'t': t, 'cell_'+str(gid)+'_'+trace: data})
color = colorList[igid%len(colorList)]
if not overlay:
subplot(len(subGids),1,igid+1)
color = 'blue'
ylabel(trace, fontsize=fontsiz)
plot(t[:len(data)], data, linewidth=1.5, color=color, label='Cell %d, Pop %s '%(int(gid), gidPops[gid]))
xlabel('Time (ms)', fontsize=fontsiz)
xlim(timeRange)
title('Cell %d, Pop %s '%(int(gid), gidPops[gid]))
if overlay:
maxLabelLen = 10
subplots_adjust(right=(0.9-0.012*maxLabelLen))
legend(fontsize=fontsiz, bbox_to_anchor=(1.04, 1), loc=2, borderaxespad=0.)
def main(argv=None):
if argv is None:
argv = sys.argv
if len(argv) != 4:
print "Usage: " + argv[0] + " <report name> <report dir> <graph dir>"
return 1
report = argv[1]
report_dir = argv[2]
graph_dir = argv[3]
file_list = glob(os.path.join(report_dir, "part*"))
# Copy the raw data file to the graph_dir
raw_file = os.path.join(graph_dir, report + ".tsv")
shutil.copyfile(file_list[0], raw_file)
# Process the file into a graph, ideally I would combine the two into one but for now I'll stick with two
data_file = csv.DictReader(open(raw_file, "rb"), fieldnames=["page", "avgTime"], delimiter="\t")
pages = []
avg_time = []
for row in data_file:
pages.append(row["page"])
avg_time.append(int(row["avgTime"]))
if len(pages) > 25:
length = 25
else:
length = len(pages)
fig = pylab.figure(1)
pos = pylab.arange(length) + 0.5
pylab.barh(pos, avg_time[:length], align="center", aa=True, ecolor="r")
pylab.yticks(pos, pages[:length])
pylab.xlabel("Average Time Taken")
pylab.title("Top %d pages ordered by average time taken" % length)
pylab.grid(True)
# Save the figure
pylab.savefig(os.path.join(graph_dir, report + ".pdf"), bbox_inches="tight", pad_inches=2)
def plot_per_position_fragment_analysis(self, position, target_axis = None):
from matplotlib import pylab
position = int(position)
if position < 0 or position >= self.selected_fragment_rmsds.shape[0]:
raise ValueError("Invalid fragment position specified.")
if target_axis is None:
target_axis = pylab.gca()
fragment_rmsds = numpy.sort(self.selected_fragment_rmsds[position], axis=-1)
target_axis.set_title("Fragment %s rmsd distribution." % position)
target_axis.hist(fragment_rmsds, bins=50, normed=True)
target_axis.grid(False, axis="y")
target_axis.set_xlabel("RMSD")
target_axis = target_axis.twinx()
target_axis.set_yscale("symlog")
target_axis.plot(fragment_rmsds, pylab.arange(len(fragment_rmsds)), label="Fragment count at RMSD.", color="red")
target_axis.legend()
return target_axis
def compute_initial_figure(self,dados):
self.axes.grid(True)
self.dias = dados
d = []
a = []
for i in self.dias.keys():
for j in self.dias.get(i).keys():
a.append(self.dias.get(i).get(j))
d.append( copy(a) )
del a[:]
data = d
a = 1
if (a==0):
dim = len(data[0])
w = 0.75
dimw = w / dim
x = pylab.arange(len(data))
cor = ['r','y', 'b','p','c','w']
g = []
for i in range(len(data[0])) :
y = [d[i] for d in data]
g.append( self.axes.bar(x + i * dimw, y, dimw, color=cor[i], bottom=0.001) )
# self.axes.legend( (g[0][0],g[1][0],g[2][0]), ('Men', 'Women','Both') )
else:
t = range( len(data) )
s = []
def millions(x, t):
'The two args are the value and tick position'
return 'maria'
formatter = FuncFormatter(millions)
#self.axes.xaxis.set_major_formatter(formatter)
#para cada um dos nutrientes
for k in range(len(data[0])):
#para cada um dos dias
for a in range(len(data)):
s.append( data[ a ][k])
self.axes.plot(t, s)
del s[:]
w = 0.75
x = pylab.arange(len(data))
vb = self.dias.keys()
self.axes.set_xticklabels(vb)
vb = arange(len(vb))
self.axes.set_xticks(vb)
print x + w / 2
import csv
import numpy as np
import matplotlib.pylab as plt
class_dict = {}
class_list = []
with open("data.txt", 'rb') as csvfile:
spamreader = csv.reader(csvfile, delimiter=',')
for row in spamreader:
class_dict[ int (row[4]) ] = int (row[5])
#class_list.append( row[4] )
print class_dict.keys()
print "###########################"
print class_dict
plt.xlim(0, len(class_dict)+1)
plt.ylim(0,4)
#x = np.linspace(0, 15, len(class_list))
plt.plot(class_dict.keys(),class_dict.values(), "gp")
x = plt.arange(100)
##plt.plot(20*plt.ones(100), x , 'ro', label = 'x = 10')
plt.show()
def plot_data(data_without_orig,data_with_orig,title,x_axis,x_axis2,
filename):
data_without = []
data_with = []
for idx in range(len(data_without_orig)):
avg = numpy.mean(data_without_orig[idx])
data_without.append( data_without_orig[idx] / avg )
data_with.append( data_with_orig[idx] / avg )
index = numpy.arange(len(data_with))
y_min = 1
y_max = 1
for row in data_without:
if numpy.min(row) < y_min:
y_min = numpy.min(row)
if numpy.max(row) > y_max:
y_max = numpy.max(row)
for row in data_with:
print numpy.min(row)
if numpy.min(row) < y_min:
y_min = numpy.min(row)
if numpy.max(row) > y_max:
y_max = numpy.max(row)
print (y_min,y_max)
plt.figure()
axes = plt.axes()
# ax = fig.add_axes([0,len(data_without), y_min, y_max])
plot_mean = axes.plot([numpy.average(x) for x in data_without],
"-",
color="black",
label='Uninstrumented Mean')
axes.plot([numpy.max(x) for x in data_without],
"+--",
color="black",
label="Uninstrumented Max.")
axes.plot([numpy.min(x) for x in data_without],
".--",
color="black",
label="Uninstrumented Min.")
pylab.plot([numpy.mean(x) for x in data_with], "o")
axes.errorbar(range(len(data_with)),
[numpy.mean(x) for x in data_with],
[numpy.std(x) for x in data_with],
fmt="o",
color="red",
label="With LO-PHI")
#
# axes.boxplot(data_with,
# sym='')
# pylab.errorbar(range(len(read_data_with)),
# [numpy.mean(x) for x in data_without],
# [numpy.std(x) for x in data_without],
# fmt="k")
plt.xlim(-1,len(x_axis))
plt.title(title, fontsize=50)
plt.ylabel("Normalized Throughput", fontsize=60)
plt.xlabel("Total Size (KB) : Record Size(B)",labelpad=20, fontsize=60)
plt.xticks(pylab.arange(len(x_axis)), x_axis, rotation=45)
#
# # axes2.spines['bottom']
# axes.set_xticks(x_ticks,minor=False)
# axes.set_xticklabels(x_axis)
# axes.minorticks_on()
plt.setp(axes)
plt.tick_params(axis='x', which='major', labelsize=20)
for key in x_axis2:
plt.annotate(key, (x_axis2[key],0), (8, -25),
xycoords='axes fraction',
textcoords='offset points',
va='top')
plt.legend(
loc='upper right',
frameon=False,
prop={'size':20})
plt.show()
