def Compare_Dihedral(Dihedral):
dx = 0.0001
N = len(Dihedral)
print N
Dih = np.arange(0, 2*3.1415, dx)
U1 = np.full_like(Dih, 0.0)
U2 = np.full_like(Dih, 0.0)
U3 = np.full_like(Dih, 0.0)
for i in range(N):
U1 += Dihedral[i]*np.cos(Dih)**i
for i in range(5):
print i
U2 += Dihedral[i]*np.cos(Dih)**i
Popt, Pcov = curve_fit(Multi, Dih, U1)
for i in range(5):
U3 += Popt[i]*np.cos(Dih)**i
print Popt
plt.figure()
plt.plot(Dih, U1, label = 'Full Potential')
plt.plot(Dih, U2, label = 'Truncated Approximation')
plt.plot(Dih, U3, label = 'Optimized Approximation')
plt.ylim((U2.min(), U2.max()))
plt.xlim((0.0, 2*3.1415))
plt.title('P2P1P1P2', fontsize = 30)
plt.xlabel('Dihedral Angle (radians)', fontsize = 20)
plt.ylabel('Energy (Kcal/mol)', fontsize = 20)
plt.legend()
plt.show()
return
def Gen_PDF_CDF_OPLS( V, Beta ): """ This function takes in a numpy array V that containes the energetic coefficients for the OPLS style dihedral potential of the form: U = (1/2)V1(1+cos(phi)) + (1/2)V2(1-cos(2phi)) + (1/2)V3(1+cos(3phi)) + .... It then uses Boltzmann statistics along with the inverse temperature Beta to generate a PDF and CDF of the dihedral angle The output is two numpy arrays that represent the PDF and CDF associated with this potential energy function """ dx = 0.0001 x = np.arange(0, 6.28, dx) # Generate discretized values for x (phi) U = np.full_like(x, 0.0) # Initialize potential energy array PDF = np.full_like(x, 0.0) # Initialize PDF array CDF_NN = np.full_like(x, 0.0) # Initialize non-normalized CDF array CDF = np.full_like(x, 0.0) # Initialize normalized CDF array norm = 0 L = len(x.tolist()) U = 0.5*(V[0]*(1 + np.cos(x)) + V[1]*(1 - np.cos(2*x)) + V[2]*(1 + np.cos(3*x)) + V[3]*(1 - np.cos(4*x))) PDF = np.exp(-U*Beta) for i in range(L-1): CDF_NN[i+1] = CDF_NN[i] + PDF[i]*dx for i in range(L): PDF[i] = PDF[i]/CDF_NN[-1] norm += PDF[i]*dx for i in range(L-1): CDF[i+1] = CDF[i] + PDF[i]*dx return PDF, CDF
def Compare_Angle( Angle):
dx = 0.0001
M = [0, 2 , 3, 4 , 5 , 6, 8, 10, 12, 14, 16, 18, 20, 22, 24]
N = len(Angle) - 1
Th0 = Angle[0]*(3.1415/180.)
dTh = 2.0
Th = np.arange(Th0-dTh, Th0 + dTh, dx)
U1 = np.full_like(Th, 0.0)
U2 = np.full_like(Th, 0.0)
for i in range(1, N+1):
U1 += Angle[i]*(Th - Th0)**M[i-1]
#U2 = Angle[2]*(Th - Th0)**2 + Angle[3]*(Th - Th0)**3 + Angle[4]*(Th-Th0)**4
U2 =1000.*(Th - Th0)**2
plt.figure()
plt.plot(Th, U1, label = 'Full Potential')
plt.plot(Th, U2, label = 'Harmonic Approximation')
plt.ylim((U1.min(), U2.max() ))
plt.xlim((Th0-dTh, Th0 + dTh))
plt.title('P1P1P2')
plt.xlabel('Angle (Radians)', fontsize = 20)
plt.ylabel('Potential Energy (Kcal/mol)', fontsize = 20)
plt.legend()
plt.show()
return
def Compare_Bond( Bond , title):
dx = 0.0001
N = len(Bond) - 1
DR = .25
r = np.arange(Bond[0]-DR, Bond[0]+DR, dx)
U1 = np.full_like(r, 0.0)
U2 = np.full_like(r, 0.0)
for i in range(1,N+1):
U1 += Bond[i]*(r - Bond[0])**(i+1)
print i+1
U2 = Bond[1]*(r - Bond[0])**2 + Bond[2]*(r-Bond[0])**3 + Bond[3]*(r-Bond[0])**4
plt.figure()
plt.plot(r, U1, label = 'Full Potential')
plt.plot(r, U2, label = 'Class2 Approximation')
plt.xlim((Bond[0]-DR,Bond[0]+DR))
plt.ylim((U1.min(), U1.max()))
plt.title('%s' % title, fontsize = 30)
plt.xlabel('Bond Length (Angstrom)', fontsize = 20)
plt.ylabel('Potential Energy (Kcal/mol)', fontsize = 20)
plt.legend()
plt.show()
return
def assignReads(anchor, highestPeak, clusterSize, blockCount):
global tagCount
global clusterStart
readMeans = np.empty(tagCount)
readHeights = np.empty(tagCount)
readMeans = np.full_like(readMeans, -1, dtype=np.double)
readHeights = np.full_like(readHeights, -1, dtype=np.double)
meanCounter = 0
counterNew = 0
counterOld = -1
while counterOld != counterNew:
dev = stddev(readMeans, readHeights, tagCount)
counterOld = counterNew
for start in anchor:
if start.block == -1:
mean = ((start.start + start.end) / 2) - clusterStart
variance = args.sizescale * (abs(start.end - start.start) / 2)
if (((mean - variance - dev) <= highestPeak and (mean + variance + dev) >= highestPeak) or (mean >= (highestPeak - args.merge) and mean <= (highestPeak + args.merge))):
readMeans[meanCounter] = mean
readHeights[meanCounter] = start.height
meanCounter += 1
start.block = blockCount
counterNew += 1
return counterNew
def calculate_risk(vector):
init_dollars = 100 # change this if you want
DOB = days_of_bettin = 82 # change this if you want
num_runs = 4096 # change this if you want
risks = np.array(np.linspace(0.01,0.99,99)).T # change this too, but be careful
risks = risks.reshape((risks.size, 1))
prob, odds = vector[0], vector[1]
dollars = np.full_like(risks, init_dollars)
talley = np.full_like(risks, 0)
multiplier = odds_to_pct(odds)
if prob*multiplier - (1-prob) < 0:
return 0
else:
for _ in xrange(num_runs):
for day in xrange(DOB):
outcome = np.random.random()
if outcome < prob:
dollars += dollars * multiplier * risks
else:
dollars -= dollars * risks
if talley.all() == np.full_like(risks, 0).all():
talley = dollars
else:
talley = np.c_[talley, dollars]
dollars = np.full_like(risks, init_dollars)
expectation = np.median(talley, axis=1)
index = np.argmax(expectation)
return risks[index][0]
def test_manual_bounds(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
# get a test module
train_x = torch.tensor([[1.0, 2.0, 3.0]], device=device, dtype=dtype)
train_y = torch.tensor([4.0], device=device, dtype=dtype)
likelihood = GaussianLikelihood()
model = ExactGP(train_x, train_y, likelihood)
model.covar_module = RBFKernel(ard_num_dims=3)
model.mean_module = ConstantMean()
model.to(device=device, dtype=dtype)
mll = ExactMarginalLogLikelihood(likelihood, model)
# test the basic case
x, pdict, bounds = module_to_array(
module=mll, bounds={"model.covar_module.raw_lengthscale": (0.1, None)}
)
self.assertTrue(np.array_equal(x, np.zeros(5)))
expected_sizes = {
"likelihood.noise_covar.raw_noise": torch.Size([1]),
"model.covar_module.raw_lengthscale": torch.Size([1, 3]),
"model.mean_module.constant": torch.Size([1]),
}
self.assertEqual(set(pdict.keys()), set(expected_sizes.keys()))
for pname, val in pdict.items():
self.assertEqual(val.dtype, dtype)
self.assertEqual(val.shape, expected_sizes[pname])
self.assertEqual(val.device.type, device.type)
lower_exp = np.full_like(x, 0.1)
for p in ("likelihood.noise_covar.raw_noise", "model.mean_module.constant"):
lower_exp[_get_index(pdict, p)] = -np.inf
self.assertTrue(np.equal(bounds[0], lower_exp).all())
self.assertTrue(np.equal(bounds[1], np.full_like(x, np.inf)).all())
def test_hpxgeom_coord_to_idx(nside, nested, coordsys, region, axes):
import healpy as hp
geom = HpxGeom(nside, nested, coordsys, region=region, axes=axes)
lon = np.array([112.5, 135.0, 105.0])
lat = np.array([75.3, 75.3, 74.6])
coords = make_test_coords(geom, lon, lat)
zidx = tuple([ax.coord_to_idx(t) for t, ax in zip(coords[2:], geom.axes)])
if geom.nside.size > 1:
nside = geom.nside[zidx]
else:
nside = geom.nside
phi, theta = coords.phi, coords.theta
idx = geom.coord_to_idx(coords)
assert_allclose(hp.ang2pix(nside, theta, phi), idx[0])
for i, z in enumerate(zidx):
assert_allclose(z, idx[i + 1])
# Test w/ coords outside the geometry
lon = np.array([0.0, 5.0, 10.0])
lat = np.array([75.3, 75.3, 74.6])
coords = make_test_coords(geom, lon, lat)
zidx = [ax.coord_to_idx(t) for t, ax in zip(coords[2:], geom.axes)]
idx = geom.coord_to_idx(coords)
if geom.region is not None:
assert_allclose(np.full_like(coords[0], -1, dtype=int), idx[0])
idx = geom.coord_to_idx(coords, clip=True)
assert np.all(np.not_equal(np.full_like(coords[0], -1, dtype=int), idx[0]))
def optimize_img(init_img, solver_type, solver_param, max_iter, display, root_dir, net,
all_target_blob_names, targets, target_data_list):
ensuredir(root_dir)
solver_param.update({
'maxiter': max_iter,
'disp': True,
})
# Set initial value and reshape net
set_data(net, init_img)
x0 = np.ravel(init_img).astype(np.float64)
mins = np.full_like(x0, -128)
maxs = np.full_like(x0, 128)
bounds = zip(mins, maxs)
display_func = DisplayFunctor(net, root_dir, display)
opt_res = optimize.minimize(
objective_func,
x0,
args=(net, all_target_blob_names, targets, target_data_list),
bounds=bounds,
method=solver_type,
jac=True,
callback=display_func,
options=solver_param,
)
print opt_res
def test_flux_unit_conversion():
# By default the flux units should be set to Jy
s = Spectrum1D(flux=np.array([26.0, 44.5]), spectral_axis=np.array([400, 500]) * u.nm)
assert np.all(s.flux == np.array([26.0, 44.5]) * u.Jy)
assert s.flux.unit == u.Jy
# Simple Unit Conversion
s = Spectrum1D(flux=np.array([26.0, 44.5]) * u.Jy, spectral_axis=np.array([400, 500])*u.nm)
converted_value = s.to_flux(unit=u.uJy)[0]
assert ((26.0 * u.Jy).to(u.uJy) == converted_value)
# Make sure incompatible units raise UnitConversionError
with pytest.raises(u.UnitConversionError):
converted_value = s.to_flux(unit=u.m)
# Pass custom equivalencies
s = Spectrum1D(flux=np.array([26.0, 44.5]) * u.Jy, spectral_axis=np.array([400, 500]) * u.nm)
eq = [[u.Jy, u.m,
lambda x: np.full_like(np.array(x), 1000.0, dtype=np.double),
lambda x: np.full_like(np.array(x), 0.001, dtype=np.double)]]
converted_value = s.to_flux(unit=u.m, equivalencies=eq)[0]
assert 1000.0 * u.m == converted_value
# Check if suppressing the unit conversion works
s = Spectrum1D(flux=np.array([26.0, 44.5]) * u.Jy, spectral_axis=np.array([400, 500]) * u.nm)
s.to_flux("uJy", suppress_conversion=True)
assert s.flux[0] == 26.0 * u.uJy
def fill_array( var1, var2 ):
"""
fix fill_array such that it returns two numpy arrays of equal size
use numpy.full_like
"""
var1_a = np.asarray( var1 )
var2_a = np.asarray( var2 )
if var1_a.shape==():
var1_a = np.asarray( [var1] )
if var2_a.shape==():
var2_a = np.asarray( [var2] )
# Begin try/except block to handle all cases for filling an array
while True:
try:
assert var1_a.shape == var2_a.shape
break
except: pass
try:
var1_a = np.full_like( var2_a, var1_a )
break
except: pass
try:
var2_a = np.full_like( var1_a, var2_a )
break
except: pass
# If none of the cases properly handle it, throw error
assert False, 'var1 and var2 must both be equal shape or size=1'
return var1_a, var2_a
def _extract_current_results(data, curr, data_time):
grid = data['models']['simulationGrid']
plate_spacing = _meters(grid['plate_spacing'])
zmesh = np.linspace(0, plate_spacing, grid['num_z'] + 1) #holds the z-axis grid points in an array
beam = data['models']['beam']
if data.models.simulationGrid.simulation_mode == '3d':
cathode_area = _meters(grid['channel_width']) * _meters(grid['channel_height'])
else:
cathode_area = _meters(grid['channel_width'])
RD_ideal = sources.j_rd(beam['cathode_temperature'], beam['cathode_work_function']) * cathode_area
JCL_ideal = sources.cl_limit(beam['cathode_work_function'], beam['anode_work_function'], beam['anode_voltage'], plate_spacing) * cathode_area
if beam['currentMode'] == '2' or (beam['currentMode'] == '1' and beam['beam_current'] >= JCL_ideal):
curr2 = np.full_like(zmesh, JCL_ideal)
y2_title = 'Child-Langmuir cold limit'
else:
curr2 = np.full_like(zmesh, RD_ideal)
y2_title = 'Richardson-Dushman'
return {
'title': 'Current for Time: {:.4e}s'.format(data_time),
'x_range': [0, plate_spacing],
'y_label': 'Current [A]',
'x_label': 'Z [m]',
'points': [
curr.tolist(),
curr2.tolist(),
],
'x_points': zmesh.tolist(),
'y_range': [min(np.min(curr), np.min(curr2)), max(np.max(curr), np.max(curr2))],
'y1_title': 'Current',
'y2_title': y2_title,
}
def watershed(image):
hsv_image = color.rgb2hsv(image)
low_res_image = rescale(hsv_image[:, :, 0], SCALE)
local_mean = mean(low_res_image, disk(50))
local_minimum_flat = np.argmin(local_mean)
local_minimum = np.multiply(np.unravel_index(local_minimum_flat, low_res_image.shape), round(1 / SCALE))
certain_bone_pixels = np.full_like(hsv_image[:, :, 0], False, bool)
certain_bone_pixels[
local_minimum[0] - INITIAL_WINDOW_SIZE/2:local_minimum[0]+INITIAL_WINDOW_SIZE/2,
local_minimum[1] - INITIAL_WINDOW_SIZE/2:local_minimum[1]+INITIAL_WINDOW_SIZE/2
] = True
certain_non_bone_pixels = np.full_like(hsv_image[:, :, 0], False, bool)
certain_non_bone_pixels[0:BORDER_SIZE, :] = True
certain_non_bone_pixels[-BORDER_SIZE:-1, :] = True
certain_non_bone_pixels[:, 0:BORDER_SIZE] = True
certain_non_bone_pixels[:, -BORDER_SIZE:-1] = True
smoothed_hsv = median(hsv_image[:, :, 0], disk(50))
threshold = MU * np.median(smoothed_hsv[certain_bone_pixels])
possible_bones = np.zeros_like(hsv_image[:, :, 0])
possible_bones[smoothed_hsv < threshold] = 1
markers = np.zeros_like(possible_bones)
markers[certain_bone_pixels] = 1
markers[certain_non_bone_pixels] = 2
labels = morphology.watershed(-possible_bones, markers)
return labels
def prob3():
"""Define the matrices A and B as arrays. Calculate the matrix product ABA,
change its data type to np.int64, and return it.
"""
A = np.triu(np.ones((7,7)))
B = np.full_like(A, 5) - np.tril(np.full_like(A, 6))
return np.dot(np.dot(A, B), A).astype(np.int64)
def resample(self, bin_count=120):
start_i = int(self.t0 * self.sample_rate)
end_i = util.clip(start_i + int(self.dt * self.sample_rate),
start_i, sys.maxsize)
bin_size = (end_i - start_i) // bin_count
if bin_size < 1:
bin_size = 1
bin_count = len(np.arange(start_i, end_i, bin_size))
data = np.empty((self.data.shape[1], 2*bin_count, 4), dtype=np.float32)
for i, column in enumerate(self.data):
v = mea.min_max_bin(self.data[column].values[start_i:end_i],
bin_size, bin_count+1)
col, row = mea.coordinates_for_electrode(column)
row = 12 - row - 1
x = np.full_like(v, col, dtype=np.float32)
y = np.full_like(v, row, dtype=np.float32)
t = np.arange(0, bin_count, 0.5, dtype=np.float32)
data[i] = np.column_stack((x, y, t, v))
# Update shader
self.program['a_position'] = data.reshape(
2*self.data.shape[1]*bin_count, 4)
self.program['u_width'] = bin_count
def march(x,u_e,nu):
dx = numpy.diff(x)
du_e = numpy.gradient(u_e,numpy.gradient(x))
delta = numpy.full_like(x,0.)
lam = numpy.full_like(x,lam0)
# Initial conditions must be a stagnation point. If u_e[0]>0
# assume stagnation is at x=0 and integrate from x=0..x[0].
if u_e[0]<0.01: # stagnation point
delta[0] = numpy.sqrt(lam0*nu/du_e[0])
elif x[0]>0: # just downstream
delta[0] = numpy.sqrt(lam0*nu*x[0]/u_e[0])
delta[0] += 0.5*x[0]*g_pohl(delta[0],0,u_e,du_e,nu)
lam[0] = delta[0]**2*du_e[0]/nu
else:
raise ValueError('x=0 must be stagnation point')
# march!
for i in range(len(x)-1):
delta[i+1] = heun(g_pohl,delta[i],i,dx[i],
u_e,du_e,nu) # ...additional arguments
lam[i+1] = delta[i+1]**2*du_e[i+1]/nu
if lam[i+1] < -12: i-=1; break # separation condition
return delta,lam,i+1 # return with separation index
def plot_energy(run_summary, x_axis):
plt.figure()
if x_axis == "time":
x_variable = run_summary.times / yr
xlabel = "Time [yr]"
xscale = "linear"
plt.xscale(xscale)
xfmt = plt.gca().get_xaxis().get_major_formatter() # needs to be set AFTER plt.xscale()
if xscale == "log":
mask = x_variable > 1
elif xscale == "linear":
mask = np.full_like(x_variable, True, dtype=bool)
xfmt.set_powerlimits((-2, 2)) # force scientific notation outside this range
elif x_axis == "checkpoints":
x_variable = np.arange(len(run_summary.times))
xlabel = "Checkpoint"
xscale = "linear"
mask = np.full_like(x_variable, True, dtype=bool)
plt.xscale(xscale)
xfmt = plt.gca().get_xaxis().get_major_formatter() # needs to be set AFTER plt.xscale()
else:
raise NotImplementedError("can't recognize x_axis value: " + x_axis)
E_err = (run_summary.E_tot - run_summary.E_tot[0]) / run_summary.E_tot[0]
plt.plot(x_variable[mask], E_err[mask])
plt.xscale(xscale)
plt.xlabel(xlabel)
plt.gca().xaxis.set_major_formatter(xfmt)
plt.ylabel("Fractional Change (Energy)")
SNe_distplot(run_summary, x_axis)
plt.figure()
plt.plot(x_variable[mask], run_summary.E_tot[mask], label="E_tot" )
plt.plot(x_variable[mask], run_summary.E_kin[mask], label="E_kin" )
plt.plot(x_variable[mask], run_summary.E_int[mask], label="E_int" )
plt.legend(loc="best")
plt.xscale(xscale)
plt.xlabel(xlabel)
plt.gca().xaxis.set_major_formatter(xfmt)
plt.ylabel("Energy [erg]")
SNe_distplot(run_summary, x_axis)
plt.figure()
plt.plot(x_variable[mask], run_summary.E_R_tot[mask], label="E_Remnant" )
plt.legend(loc="best")
plt.xscale(xscale)
plt.xlabel(xlabel)
plt.gca().xaxis.set_major_formatter(xfmt)
plt.ylabel("Energy [erg]")
SNe_distplot(run_summary, x_axis)
if x_axis == "checkpoints":
plt.xlim(xmin=0)
def plot(self):
self.graph.clearPlot()
self.validindices = numpy.empty((0,), dtype=int)
self.current_selection = []
group, target_indices = self.selected_split()
self.warning([0, 1])
self.error(1)
if self.data and group is not None and target_indices:
X = self.data.X
I1 = grouputils.group_selection_mask(
self.data, group, target_indices)
I2 = ~I1
if isinstance(group, grouputils.RowGroup):
X = X.T
N1, N2 = numpy.count_nonzero(I1), numpy.count_nonzero(I2)
if not N1 or not N2:
self.error(
1, "Target labels most exclude/include at least one value."
)
if N1 < 2 and N2 < 2:
self.warning(
0, "Insufficient data to compute statistics. "
"More than one measurement per class should be provided"
)
X1, X2 = X[:, I1], X[:, I2]
if numpy.any(X1 < 0.0) or numpy.any(X2 < 0):
self.error(
"Negative values in the input. The inputs cannot be in "
"ratio scale."
)
X1 = numpy.full_like(X1, numpy.nan)
X2 = numpy.full_like(X2, numpy.nan)
with numpy.errstate(divide="ignore", invalid="ignore"):
fold = numpy.log2(numpy.mean(X1, axis=1) /
numpy.mean(X2, axis=1))
# TODO: handle missing values better (mstats)
_, P = scipy.stats.ttest_ind(X1, X2, axis=1, equal_var=True)
logP = numpy.log10(P)
if numpy.isscalar(logP):
# ttest_ind does not preserve output shape if either
# a or b is empty
logP = numpy.full(fold.shape, numpy.nan)
mask = numpy.isfinite(fold) & numpy.isfinite(logP)
self.validindices = numpy.flatnonzero(mask)
self.graph.setPlotData(numpy.array([fold[mask], -logP[mask]]).T)
self.infoLabel.setText("%i genes on input" % len(fold))
# ("{displayed} displayed, {undef} with undefined ratio "
# "or t-statistics.")
if not len(numpy.flatnonzero(mask)):
self.warning(1, "Could not compute statistics for any genes!")
def add_location_data(ds, lat, lon):
lat = lat if lat else LAT_FILL
lon = lon if lon else LON_FILL
lat_array = np.full_like(ds.time.values, lat)
lon_array = np.full_like(ds.time.values, lon)
ds['lat'] = ('obs', lat_array, {'axis': 'Y', 'units': 'degrees_north', 'standard_name': 'latitude'})
ds['lon'] = ('obs', lon_array, {'axis': 'X', 'units': 'degrees_east', 'standard_name': 'longitude'})
def distance_to(self, source):
src_lats = num.full_like(self.lats, fill_value=source.lat)
src_lons = num.full_like(self.lons, fill_value=source.lon)
target_coords = self.get_latlon()
target_lats = target_coords[:, 0]
target_lons = target_coords[:, 1]
return distance_accurate50m_numpy(
src_lats, src_lons, target_lats, target_lons)
def test_basic(self):
# Check derivative at endpoints
n1_10 = np.arange(1, 10)
dataset0 = np.column_stack([n1_10, np.full_like(n1_10, 0), np.full_like(n1_10, -1)])
FuncData(_smirnovp, dataset0, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
n2_10 = np.arange(2, 10)
dataset1 = np.column_stack([n2_10, np.full_like(n2_10, 1.0), np.full_like(n2_10, 0)])
FuncData(_smirnovp, dataset1, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
def march(x,u_e,du_e,nu):
delta0 = numpy.sqrt(lam0*nu/du_e[0]) # set delta0
delta = numpy.full_like(x,delta0) # delta array
lam = numpy.full_like(x,lam0) # lambda array
for i in range(len(x)-1): # march!
delta[i+1] = heun(g_pohl,delta[i],i,x[i+1]-x[i], # integrate BL using...
u_e,du_e,nu) # additional arguments
lam[i+1] = delta[i+1]**2*du_e[i+1]/nu # compute lambda
if abs(lam[i+1])>12: break # check stop condition
return delta,lam,i # return with separation index
def test_single_percentile_data():
n = 1000
x = np.arange(n, dtype=np.float)
y = np.ones(n)
s = scaling.lin_cdf_match(y, x)
nptest.assert_almost_equal(s, np.full_like(s, np.nan))
s = scaling.cdf_match(y, x)
nptest.assert_almost_equal(s, np.full_like(s, np.nan))
def h_to_rgb(h, sat=1, val=255.0):
# Local variation of hsv_to_rgb that only cares about a variable
# hue, with (s,v) assumed to be constant
# h should be a numpy array with values between 0.0 and 1.0
# hsv_to_rgb returns an array of uints between 0 and 255.
s = np.full_like(h, sat)
v = np.full_like(h, val)
hsv = np.dstack((h, s, v))
return hsv_to_rgb(hsv)
def test_jam_axi_vel():
"""
Usage example for jam_axi_vel().
It takes about 5s on a 2.5 GHz computer
"""
np.random.seed(123)
xbin, ybin = np.random.uniform(low=[-55, -40], high=[55, 40], size=[1000, 2]).T
inc = 60. # assumed galaxy inclination
r = np.sqrt(xbin**2 + (ybin/np.cos(np.radians(inc)))**2) # Radius in the plane of the disk
a = 40 # Scale length in arcsec
vr = 2000*np.sqrt(r)/(r+a) # Assumed velocity profile
vel = vr * np.sin(np.radians(inc))*xbin/r # Projected velocity field
sig = 8700/(r+a) # Assumed velocity dispersion profile
rms = np.sqrt(vel**2 + sig**2) # Vrms field in km/s
surf = np.array([39483., 37158., 30646., 17759., 5955.1, 1203.5, 174.36, 21.105, 2.3599, 0.25493])
sigma = np.array([0.153, 0.515, 1.58, 4.22, 10, 22.4, 48.8, 105, 227, 525])
qObs = np.full_like(sigma, 0.57)
distance = 16.5 # Assume Virgo distance in Mpc (Mei et al. 2007)
mbh = 1e8 # Black hole mass in solar masses
beta = np.full_like(surf, 0.3)
surf_lum = surf # Assume self-consistency
sigma_lum = sigma
qobs_lum = qObs
surf_pot = surf
sigma_pot = sigma
qobs_pot = qObs
sigmapsf = 0.6
pixsize = 0.8
goodbins = r > 10 # Arbitrarily exclude the center to illustrate how to use goodbins
# First the M/L is determined by fitting the Vrms.
# In general beta_z and the inclination will also be
# fitted at this stage as described in Cappellari (2008)
rmsModel, ml, chi2, flux = jam_axi_rms(
surf_lum, sigma_lum, qobs_lum, surf_pot, sigma_pot, qobs_pot,
inc, mbh, distance, xbin, ybin, plot=True, rms=rms, goodbins=goodbins,
sigmapsf=sigmapsf, beta=beta, pixsize=pixsize, tensor='zz')
plt.pause(0.01)
# The velocity is fitted at the best fitting M/L, beta_z and
# inclination determined at the previous stage
surf_pot *= ml # Scale the density by the best fitting M/L
velModel, kappa, chi2, flux = jam_axi_vel(
surf_lum, sigma_lum, qobs_lum, surf_pot, sigma_pot, qobs_pot,
inc, mbh, distance, xbin, ybin, plot=True, vel=vel, goodbins=goodbins,
sigmapsf=sigmapsf, beta=beta, pixsize=pixsize, component='z')
plt.pause(0.01)
def detection_efficiency(mes, mesthresh, version):
"""Gives the Kepler pipeline detection efficiency for a given
MES. In other words whats the propability that a transit
signal of a given MES is recovered by the kepler pipeline.
There are a variety of functional forms, use the version
argument to select the one you want.
INPUT:
mes - Multiple even statistic
mesthresh - Mes threshold. Normally 7.1 unless TPS
times out at higher value
version - Integer specifying functional form
0 = Standard Theory prob=0.5 at MES=7.1 follows erf
1 = Fressin et al. (2013) linear ramp 6<MES<16
2 = Christiansen et al. (2015) gamma distribution
for Q1-Q16 pipeline run
OUTPUT:
prob - probability for detection [0.0-1.0]
"""
# Each version has some hard coded parameters
parm1s = [0.0, 6.0, 4.65]
parm2s = [1.0, 16.0, 0.98]
# Get parameters for specified version
p1 = parm1s[version]
p2 = parm2s[version]
# Do verion 0 which is erf form
if version == 0:
muoffset = p1
sig = p2
prob = np.full_like(mes, 1.0)
abssnrdiff = np.abs(mes - mesthresh - muoffset);
prob = np.where(abssnrdiff < 9.0, \
0.5 + (0.5*spec.erf( \
abssnrdiff / np.sqrt(2.0*sig**2))),\
prob)
prob = np.where(mes < (mesthresh + muoffset), 1.0 - prob, prob)
# Do version 1 which is linear ramp
elif version == 1:
mesmin = p1
mesmax = p2
slope = 1.0 / (mesmax - mesmin)
prob = (mes - mesmin) * slope
prob = np.where(prob < 0.0, 0.0, prob)
prob = np.where(prob > 1.0, 1.0, prob)
# Do version 2 which is gamma cdf
elif version == 2:
a = p1
b = p2
usemes = mes - 4.1 - (mesthresh - 7.1)
usemes = np.where(usemes < 0.0, 0.0, usemes)
gammawant = stat.gamma(a,loc=0.0,scale=b)
prob = gammawant.cdf(usemes)
else:
prob = np.full_like(mes, 0.0)
return prob
def add_location(dic):
df0 = dic[dic.keys()[0]]
df0['location'] = np.full_like(df0['datetime'].astype(str), dic.keys()[0])
for key in dic.keys():
if key == dic.keys()[0]:
pass
else:
df1 = dic[key]
df1['location'] = np.full_like(df1.datetime.astype(str), key)
df0 = pd.concat([df0, df1])
return dic
def firlp_lowpass1(numtaps, deltap, deltas, cutoff, width, fs, tol=None):
# Edges of the transition band, expressed as radians per sample.
wp = np.pi*(cutoff - 0.5*width)/(0.5*fs)
ws = np.pi*(cutoff + 0.5*width)/(0.5*fs)
# Grid density.
density = 16*numtaps/np.pi
# Number of grid points in the pass band.
numfreqs_pass = int(np.ceil(wp*density))
# Number of grid points in the stop band.
numfreqs_stop = int(np.ceil((np.pi - ws)*density))
# Grid of frequencies in the pass band.
wpgrid = np.linspace(0, wp, numfreqs_pass)
# Remove the first; the inequality associated with this frequency
# will be replaced by an equality constraint.
wpgrid = wpgrid[1:]
# Grid of frequencies in the pass band.
wsgrid = np.linspace(ws, np.pi, numfreqs_stop)
# wgrid is the combined array of frequencies.
wgrid = np.concatenate((wpgrid, wsgrid))
# The array of weights in the linear programming problem.
weights = np.concatenate((np.full_like(wpgrid, fill_value=1/deltap),
np.full_like(wsgrid, fill_value=1/deltas)))
# The array of desired frequency responses.
desired = np.concatenate((np.ones_like(wpgrid),
np.zeros_like(wsgrid)))
R = (numtaps - 1)//2
C = np.cos(wgrid[:, np.newaxis] * np.arange(R+1))
V = 1/weights[:, np.newaxis]
A = np.block([[C, -V], [-C, -V]])
b = np.block([[desired, -desired]]).T
c = np.zeros(R+2)
c[-1] = 1
# The equality constraint corresponding to H(0) = 1.
A_eq = np.ones((1, R+2))
A_eq[:, -1] = 0
b_eq = np.array([1])
print("numfreqs_pass ="******" numfreqs_stop =", numfreqs_stop)
print("R =", R)
print("c.shape =", c.shape)
print("A.shape =", A.shape)
print("b.shape =", b.shape)
print("A_eq.shape =", A_eq.shape)
print("b_eq.shape =", b_eq.shape)
taps_lp = solve_linprog(c, A, b, A_eq=A_eq, b_eq=b_eq, tol=tol)
return taps_lp
def addAxisTicks(self, painter, cont, dirn, linecoords, tickprops,
ticklabelsprop, tickvals):
"""Add ticks for the vals and tick properties class given.
linecoords: coordinates of start and end points of lines
labelprops: properties of label, or None
cont: container to add ticks
dirn: 'x', 'y', 'z' for axis
"""
ticklen = tickprops.length * 1e-3
tfracs = self.dataToLogicalCoords(tickvals, scaling=False)
outstart = []
outend = []
for op1, op2 in self.getAutoMirrorCombs():
# where to draw tick from
op1pts = N.full_like(tfracs, op1)
op2pts = N.full_like(tfracs, op2)
# where to draw tick to
op1pts2 = N.full_like(tfracs, op1+ticklen*(1 if op1 < 0.5 else -1))
op2pts2 = N.full_like(tfracs, op2+ticklen*(1 if op2 < 0.5 else -1))
# swap coordinates depending on axis direction
if dirn == 'x':
ptsonaxis = (tfracs, op1pts, op2pts)
ptsoff1 = (tfracs, op1pts2, op2pts)
ptsoff2 = (tfracs, op1pts, op2pts2)
elif dirn == 'y':
ptsonaxis = (op1pts, tfracs, op2pts)
ptsoff1 = (op1pts2, tfracs, op2pts)
ptsoff2 = (op1pts, tfracs, op2pts2)
else:
ptsonaxis = (op1pts, op2pts, tfracs)
ptsoff1 = (op1pts2, op2pts, tfracs)
ptsoff2 = (op1pts, op2pts2, tfracs)
outstart += [N.ravel(N.column_stack(ptsonaxis)),
N.ravel(N.column_stack(ptsonaxis))]
outend += [N.ravel(N.column_stack(ptsoff1)),
N.ravel(N.column_stack(ptsoff2))]
# add labels for ticks and axis label
if ticklabelsprop is not None:
self.addLabels(
cont, linecoords, ticklabelsprop, tfracs, tickvals,
self.settings.label, self.settings.Label)
# add ticks themselves
if not tickprops.hide:
startpts = threed.ValVector(N.concatenate(outstart))
endpts = threed.ValVector(N.concatenate(outend))
lineprop = tickprops.makeLineProp(painter)
cont.addObject(threed.LineSegments(startpts, endpts, lineprop))
def make_meshes(self):
# X-axis
xcenters = self.get_xdata()
# Z-axis
zvalues = self.get_zdata()
print 'z-valus: {0}'.format(zvalues)
if self.xerr_asym:
xerr_lo, xerr_hi = self.get_xerr()
else:
xerr_lo, xerr_hi = self.get_xerr(), self.get_xerr()
xlowedges, xupedges = xcenters - xerr_lo, xcenters + xerr_hi
list_xgrid = [xlowedges[0]]
list_zvalues = []
list_zmask = []
for ix, x in enumerate(xcenters):
#if ix==0:
# list_xgrid.append(xlowedges[ix])
if abs(xlowedges[ix]-list_xgrid[-1])<0.01:
list_xgrid.append(xupedges[ix])
elif xlowedges[ix]>list_xgrid[-1]:
list_zvalues.append(np.full_like(list_zvalues[0], float(sys.maxint)))
list_zmask.append(np.full_like(zvalues[0], True, dtype=bool))
list_xgrid.append(xlowedges[ix])
list_xgrid.append(xupedges[ix])
else:
logging.critical('X bin edge (No.{0}) {1} is smaller than the previous one {2}!!!'.format(ix, xlowedges[ix], list_xgrid[-1]))
logging.critical('Difference: {0}'.format(xlowedges[ix]-list_xgrid[-1]))
logging.critical('Filled grids: {0}'.format(list_xgrid))
logging.critical('Lower edges: {0}'.format(xlowedges))
logging.critical('Upper edges: {0}'.format(xupedges))
sys.exit(1)
list_zvalues.append(zvalues[ix])
list_zmask.append(np.full_like(zvalues[0], False, dtype=bool))
xedges = np.array(list_xgrid)
self.z_mesh = np.ma.array(list_zvalues, mask=list_zmask).T
print 'Z mesh: {0}'.format(self.z_mesh)
# Y-axis
for iys, ys in enumerate(self.lst_ydata):
if any(ys!=self.lst_ydata[0]):
logging.critical('Y bin edges do NOT match!!!')
logging.critical('In the first time bin: {0}'.format(self.lst_ydata[0]))
logging.critical('In the {0}th time bin: {1}'.format(iys, ys))
sys.exit(1)
yedges = self.lst_ydata[0]
# Y-X mesh
self.x_mesh, self.y_mesh = np.meshgrid(xedges, yedges)
print 'X mesh: {0}'.format(self.x_mesh.shape)
print 'Y mesh: {0}'.format(self.y_mesh.shape)
print 'Z mesh: {0}'.format(self.z_mesh.shape)
Returns
-------
None
"""
try:
dr = DataReader("test_data1.csv")
except ValueError:
pytest.fail("validate_csv_data failed to correctly determine that "
"the time and voltage arrays are of the same length.")
@pytest.mark.parametrize("test_array, expected_can_interp", [
(np.append(np.zeros(100), np.full_like(
np.zeros(1), np.nan, dtype=np.double)), True),
(np.append(np.zeros(10), np.full_like(
np.zeros(10), np.nan, dtype=np.double)), False),
(np.append(np.zeros(9), np.full_like(np.zeros(1), np.nan,
dtype=np.double)), True),
(np.append(np.zeros(8), np.full_like(np.zeros(1), np.nan,
dtype=np.double)), False),
])
def test_can_interp(dr, test_array, expected_can_interp):
"""Tests the can_interp function to determine if it returns true for
arrays were more than 90% of values are defined, and returns false for
arrays where less than 90% of values are defined
Parameters
----------
dr: DataReader
def _calculate_signal(settings, t):
# pylint: disable=too-many-branches
x = t * settings.frequency + settings.phase / 360.0
if settings.func == DwfAnalogOutFunction.DC:
# All-zero, independent of the symmetry value.
y = np.zeros_like(x)
elif settings.func == DwfAnalogOutFunction.Sine:
# The angle of which the sine is taken varies as a three-part piecewise linear function.
angle = _waveform_triangle(settings.symmetry, x) * (0.5 * np.pi)
y = np.sin(angle)
elif settings.func == DwfAnalogOutFunction.Square:
# Amplitude in range -1 .. 1
q = np.clip(settings.symmetry / 100.0, 0.0, 1.0)
if q == 0.0:
y = np.full_like(x, -1.0)
else:
x = np.mod(x, 1)
y = np.sign(q - x)
elif settings.func == DwfAnalogOutFunction.Triangle:
y = _waveform_triangle(settings.symmetry, x)
elif settings.func == DwfAnalogOutFunction.RampUp:
q = np.clip(settings.symmetry / 100.0, 0.0, 1.0)
if q == 0.0:
y = np.ones_like(x)
else:
x = np.mod(x, 1.0)
y = (x - np.abs(x - q)) / q
elif settings.func == DwfAnalogOutFunction.RampDown:
q = np.clip(settings.symmetry / 100.0, 0.0, 1.0)
if q == 1.0:
y = np.ones_like(x)
else:
x = np.mod(x, 1.0)
y = ((x - 1) + np.abs(x - q)) / (q - 1)
elif settings.func == DwfAnalogOutFunction.Pulse:
# Amplitude in range 0 .. 1
y = 0.5 * (1.0 + _waveform_square(settings.symmetry, x))
elif settings.func == DwfAnalogOutFunction.Trapezium:
x = np.mod(x, 1.0)
q = np.clip(0.25 * (settings.symmetry / 100.0), 0.000000001, 0.25)
y = (-1 + 2*x - np.abs(q - x) + np.abs(q - x + 0.5) + np.abs(q + x - 1.0) - np.abs(q + x - 0.5)) / (2 * q)
elif settings.func == DwfAnalogOutFunction.SinePower:
plain_old_sine = np.sin(x * 2 * np.pi)
# In the SinePower wave function, the 'symmetry' value is abused
# to indicate and exponent between 1.0 and 0.0.
exponent_setting = np.clip(settings.symmetry, -99.999999999, 100.000) / 100.0
if exponent_setting >= 0:
exponent = (1.0 - exponent_setting)
else:
exponent = 1.0 / (1.0 + exponent_setting)
y = np.copysign(np.abs(plain_old_sine) ** exponent, plain_old_sine)
return y
def attenuation(frequency):
return np.full_like(frequency, value)
def constant_function(x, y, z):
return np.full_like(x, percent_polarized)
def SFMSimpleResp(S, channel, stimParams = []):
'''
# S is the cellStructure
# channel is a dictionary with the parameters specifying the filter/model
# returns object with simpleResp and other things
# SFMSimpleResp Computes response of simple cell for sfmix experiment
# SFMSimpleResp(varargin) returns a simple cell response for the
# mixture stimuli used in sfMix. The cell's receptive field is the n-th
# derivative of a 2-D Gaussian that need not be circularly symmetric.
# 1/23/17 - Edits: Added stimParamsm, make_own_stim so that I can set what
# stimuli I want when simulating from model
'''
make_own_stim = 0;
if stimParams: # i.e. if we actually have non-empty stimParams
make_own_stim = 1;
if not 'template' in stimParams:
stimParams['template'] = S;
if not 'repeats' in stimParams:
stimParams['repeats'] = 10; # why 10? To match experimental #repetitions
# Load the data structure
T = S.get('sfm');
# Get preferred stimulus values
prefSf = channel.get('pref').get('sf'); # in cycles per degree
# CHECK LINE BELOW
prefTf = round(numpy.nanmean(T.get('exp').get('trial').get('tf')[0])); # in cycles per second
# Get directional selectivity - removed 7/18/17
# Get derivative order in space and time
dOrdSp = channel.get('dord').get('sp');
dOrdTi = channel.get('dord').get('ti');
# Get aspect ratio in space - removed 7/18/17
# Get spatial coordinates
xCo = 0; # in visual degrees, centered on stimulus center
yCo = 0; # in visual degrees, centered on stimulus center
# Store some results in M
M = dict();
pref = dict();
dord = dict();
pref.setdefault('sf', prefSf);
pref.setdefault('tf', prefTf);
pref.setdefault('xCo', xCo);
pref.setdefault('yCo', yCo);
dord.setdefault('sp', dOrdSp);
dord.setdefault('ti', dOrdTi);
M.setdefault('pref', pref);
M.setdefault('dord', dord);
# Pre-allocate memory
z = T.get('exp').get('trial');
nSf = 1;
nGratings = 7;
nFrames = 120;
if make_own_stim == 1:
nTrials = stimParams.get('repeats'); # to keep consistent with number of repetitions used for each stim. condition
else: # CHECK THIS GUY BELOW
nTrials = len(z['num']);
# set it zero
M['simpleResp'] = numpy.zeros((nFrames, nTrials));
# Compute simple cell response for all trials
for p in range(nTrials):
# Set stim parameters
if make_own_stim == 1:
all_stim = makeStimulus(stimParams.get('stimFamily'), stimParams.get('conLevel'), \
stimParams.get('sf_c'), stimParams.get('template'));
stimOr = all_stim.get('Ori');
stimTf = all_stim.get('Tf');
stimCo = all_stim.get('Con');
stimPh = all_stim.get('Ph');
stimSf = all_stim.get('Sf');
else:
stimOr = numpy.empty((nGratings,));
stimTf = numpy.empty((nGratings,));
stimCo = numpy.empty((nGratings,));
stimPh = numpy.empty((nGratings,));
stimSf = numpy.empty((nGratings,));
for iC in range(nGratings):
stimOr[iC] = z.get('ori')[iC][p] * math.pi/180; # in radians
stimTf[iC] = z.get('tf')[iC][p]; # in cycles per second
stimCo[iC] = z.get('con')[iC][p]; # in Michelson contrast
stimPh[iC] = z.get('ph')[iC][p] * math.pi/180; # in radians
stimSf[iC] = z.get('sf')[iC][p]; # in cycles per degree
if numpy.count_nonzero(numpy.isnan(stimOr)): # then this is a blank stimulus, no computation to be done
continue;
# I. Orientation, spatial frequency and temporal frequency
# Compute orientation tuning - removed 7/18/17
# Compute spatial frequency tuning
sfRel = stimSf / prefSf;
s = pow(stimSf, dOrdSp) * numpy.exp(-dOrdSp/2 * pow(sfRel, 2));
sMax = pow(prefSf, dOrdSp) * numpy.exp(-dOrdSp/2);
sNl = s/sMax;
selSf = sNl;
# Compute temporal frequency tuning
tfRel = stimTf / prefTf;
t = pow(stimTf, dOrdTi) * numpy.exp(-dOrdTi/2 * pow(tfRel, 2));
tMax = pow(prefTf, dOrdTi) * numpy.exp(-dOrdTi/2);
tNl = t/tMax;
selTf = tNl;
# II. Phase, space and time
omegaX = stimSf * numpy.cos(stimOr); # the stimulus in frequency space
omegaY = stimSf * numpy.sin(stimOr);
omegaT = stimTf;
P = numpy.empty((nFrames, 3)); # nFrames for number of frames, two for x and y coordinate, one for time
P[:,0] = 2*math.pi*xCo*numpy.ones(nFrames,); # P is the matrix that contains the relative location of each filter in space-time (expressed in radians)
P[:,1] = 2*math.pi*yCo*numpy.ones(nFrames,); # P(:,0) and p(:,1) describe location of the filters in space
# Pre-allocate some variables
if nSf == 1:
respSimple = numpy.zeros(nFrames,);
else:
respSimple = numpy.zeros(nFrames, nSf);
for iF in range(nSf):
if isinstance(xCo, int):
factor = 1;
else:
factor = len(xCo);
linR1 = numpy.zeros((nFrames*factor, nGratings)); # pre-allocation
linR2 = numpy.zeros((nFrames*factor, nGratings));
linR3 = numpy.zeros((nFrames*factor, nGratings));
linR4 = numpy.zeros((nFrames*factor, nGratings));
computeSum = 0; # important constant: if stimulus contrast or filter sensitivity equals zero there is no point in computing the response
for c in range(nGratings): # there are up to nine stimulus components
selSi = selSf[c]*selTf[c]; # filter sensitivity for the sinusoid in the frequency domain
if selSi != 0 and stimCo[c] != 0:
computeSum = 1;
# Use the effective number of frames displayed/stimulus duration
stimPos = numpy.asarray(range(nFrames))/float(nFrames) + \
stimPh[c] / (2*math.pi*stimTf[c]); # 120 frames + the appropriate phase-offset
P3Temp = numpy.full_like(P[:, 1], stimPos);
#P3Temp = repmat(stimPos, 1, len(xCo));
P[:,2] = 2*math.pi*P3Temp; # P(:,2) describes relative location of the filters in time.
omegas = numpy.vstack((omegaX[c], omegaY[c], omegaT[c])); # make this a 3 x len(omegaX) array
rComplex = selSi*stimCo[c]*numpy.exp(1j*numpy.dot(P, omegas));
linR1[:,c] = rComplex.real.reshape(linR1[:,c].shape); # four filters placed in quadrature
linR2[:,c] = -1*rComplex.real.reshape(linR2[:,c].shape);
linR3[:,c] = rComplex.imag.reshape(linR3[:,c].shape);
linR4[:,c] = -1*rComplex.imag.reshape(linR4[:,c].shape);
if computeSum == 1:
respSimple1 = numpy.maximum(0, linR1.sum(1)); # superposition and half-wave rectification,...
respSimple2 = numpy.maximum(0, linR2.sum(1));
respSimple3 = numpy.maximum(0, linR3.sum(1));
respSimple4 = numpy.maximum(0, linR4.sum(1));
# if channel is tuned, it is phase selective...
if nSf == 1:
if channel.get('dord').get('sp') != 0:
respSimple = respSimple1;
elif channel.get('dord').get('sp') == 0:
respComplex = pow(respSimple1, 2) + pow(respSimple2, 2) \
+ pow(respSimple3, 2) + pow(respSimple4, 2);
respSimple = numpy.sqrt(respComplex);
else:
if channel.get('dord').get('sp') != 0:
respSimple[iF, :] = respSimple1;
elif channel.get('dord').get('sp') == 0:
respComplex = pow(respSimple1, 2) + pow(respSimple2, 2) \
+ pow(respSimple3, 2) + pow(respSimple4, 2);
respSimple[iF, :] = numpy.sqrt(respComplex);
#pdb.set_trace();
# Store response in desired format
M['simpleResp'][:,p] = respSimple;
return M;
def test_arithmetic():
"""Test evoked arithmetic."""
ev = read_evokeds(fname, condition=0)
ev20 = EvokedArray(np.ones_like(ev.data), ev.info, ev.times[0], nave=20)
ev30 = EvokedArray(np.ones_like(ev.data), ev.info, ev.times[0], nave=30)
tol = dict(rtol=1e-9, atol=0)
# test subtraction
sub1 = combine_evoked([ev, ev], weights=[1, -1])
sub2 = combine_evoked([ev, -ev], weights=[1, 1])
assert np.allclose(sub1.data, np.zeros_like(sub1.data), atol=1e-20)
assert np.allclose(sub2.data, np.zeros_like(sub2.data), atol=1e-20)
# test nave weighting. Expect signal ampl.: 1*(20/50) + 1*(30/50) == 1
# and expect nave == ev1.nave + ev2.nave
ev = combine_evoked([ev20, ev30], weights='nave')
assert np.allclose(ev.nave, ev20.nave + ev30.nave)
assert np.allclose(ev.data, np.ones_like(ev.data), **tol)
# test equal-weighted sum. Expect signal ampl. == 2
# and expect nave == 1/sum(1/naves) == 1/(1/20 + 1/30) == 12
ev = combine_evoked([ev20, ev30], weights=[1, 1])
assert np.allclose(ev.nave, 12.)
assert np.allclose(ev.data, ev20.data + ev30.data, **tol)
# test equal-weighted average. Expect signal ampl. == 1
# and expect nave == 1/sum(weights²/naves) == 1/(0.5²/20 + 0.5²/30) == 48
ev = combine_evoked([ev20, ev30], weights='equal')
assert np.allclose(ev.nave, 48.)
assert np.allclose(ev.data, np.mean([ev20.data, ev30.data], axis=0), **tol)
# test zero weights
ev = combine_evoked([ev20, ev30], weights=[1, 0])
assert ev.nave == ev20.nave
assert np.allclose(ev.data, ev20.data, **tol)
# default comment behavior if evoked.comment is None
old_comment1 = ev20.comment
ev20.comment = None
ev = combine_evoked([ev20, -ev30], weights=[1, -1])
assert_equal(ev.comment.count('unknown'), 2)
assert ('-unknown' in ev.comment)
assert (' + ' in ev.comment)
ev20.comment = old_comment1
with pytest.raises(ValueError, match="Invalid value for the 'weights'"):
combine_evoked([ev20, ev30], weights='foo')
with pytest.raises(ValueError, match='weights must be the same size as'):
combine_evoked([ev20, ev30], weights=[1])
# grand average
evoked1, evoked2 = read_evokeds(fname, condition=[0, 1], proj=True)
ch_names = evoked1.ch_names[2:]
evoked1.info['bads'] = ['EEG 008'] # test interpolation
evoked1.drop_channels(evoked1.ch_names[:1])
evoked2.drop_channels(evoked2.ch_names[1:2])
gave = grand_average([evoked1, evoked2])
assert_equal(gave.data.shape, [len(ch_names), evoked1.data.shape[1]])
assert_equal(ch_names, gave.ch_names)
assert_equal(gave.nave, 2)
with pytest.raises(TypeError, match='All elements must be an instance of'):
grand_average([1, evoked1])
gave = grand_average([ev20, ev20, -ev30]) # (1 + 1 + -1) / 3 = 1/3
assert_allclose(gave.data, np.full_like(gave.data, 1. / 3.))
# test channel (re)ordering
evoked1, evoked2 = read_evokeds(fname, condition=[0, 1], proj=True)
data2 = evoked2.data # assumes everything is ordered to the first evoked
data = (evoked1.data + evoked2.data) / 2.
evoked2.reorder_channels(evoked2.ch_names[::-1])
assert not np.allclose(data2, evoked2.data)
with pytest.warns(RuntimeWarning, match='reordering'):
evoked3 = combine_evoked([evoked1, evoked2], weights=[0.5, 0.5])
assert np.allclose(evoked3.data, data)
assert evoked1.ch_names != evoked2.ch_names
assert evoked1.ch_names == evoked3.ch_names
import numpy as np
import cv2
import matplotlib.pyplot as plt
img = cv2.imread('/Users/nami/Documents/tester/boxies/image_062.png')
# img = img[...,::-1]
matte = cv2.imread(
'/Users/nami/Documents/background_removal_test/indexnet_matting/examples/mattes/image_062.png'
)
h, w, _ = img.shape
bg = np.full_like(img, 255) #white background
img = img.astype(float)
bg = bg.astype(float)
matte = matte.astype(float) / 255.0
img = cv2.multiply(img, matte)
bg = cv2.multiply(bg, 1.0 - matte)
outImage = cv2.add(img, bg)
# plt.subplot(1,2,1)
# plt.imshow(img)
# plt.subplot(1,2,2)
# plt.imshow(outImage/255)
cv2.imwrite(
'/Users/nami/Documents/background_removal_test/indexnet_matting/examples/images/63.png',
outImage)
# cv2.imshow('image', outImage/255)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# plt.show()
def _decompress(data, mask, dtype):
data_blank = np.full_like(mask, np.nan, dtype=dtype)
data_blank[mask] = data
data_blank.shape = mask.shape
return data_blank
plt.figure(figsize=(10, 7))
G = gridspec.GridSpec(2, 3)
ax1 = plt.subplot(G[0, :])
ax2 = plt.subplot(G[1, 0])
ax3 = plt.subplot(G[1, 1])
ax4 = plt.subplot(G[1, 2])
# Reachability plot
colors = ['g.', 'r.', 'b.', 'y.', 'c.']
for klass, color in zip(range(0, 5), colors):
Xk = space[labels == klass]
Rk = reachability[labels == klass]
ax1.plot(Xk, Rk, color, alpha=0.3)
ax1.plot(space[labels == -1], reachability[labels == -1], 'k.', alpha=0.3)
ax1.plot(space, np.full_like(space, 2., dtype=float), 'k-', alpha=0.5)
ax1.plot(space, np.full_like(space, 0.5, dtype=float), 'k-.', alpha=0.5)
ax1.set_ylabel('Reachability (epsilon distance)')
ax1.set_title('Reachability Plot')
# OPTICS
colors = ['g.', 'r.', 'b.', 'y.', 'c.']
for klass, color in zip(range(0, 5), colors):
Xk = X[clust.labels_ == klass]
ax2.plot(Xk[:, 0], Xk[:, 1], color, alpha=0.3)
ax2.plot(X[clust.labels_ == -1, 0], X[clust.labels_ == -1, 1], 'k+', alpha=0.1)
ax2.set_title('Automatic Clustering\nOPTICS')
# DBSCAN at 0.5
colors = ['g', 'greenyellow', 'olive', 'r', 'b', 'c']
for klass, color in zip(range(0, 6), colors):
# Calculate the posterior probabilities of the point being from distr a or b
bi = (likelihood(x, mu_b, sig_b) * pb) / (
likelihood(x, mu_b, sig_b) * pb + likelihood(x, mu_a, sig_a) * pa)
ai = 1 - bi
#print(i, x, bi, ai)
posteriors_b.append(bi)
posteriors_a.append(ai)
# Calculate new estimates for the parameters using the posteriors as weights in a weighted average
new_mu_a = np.average(n, weights=posteriors_a)
new_mu_b = np.average(n, weights=posteriors_b)
new_sig_a = np.average((n - np.full_like(n, mu_a))**2,
weights=posteriors_a)
new_sig_b = np.average((n - np.full_like(n, mu_b))**2,
weights=posteriors_b)
new_sig_a = np.sqrt(new_sig_a)
new_sig_b = np.sqrt(new_sig_b)
# Upload parameter lists with new parameters
guesses_mu_a.append(new_mu_a)
guesses_mu_b.append(new_mu_b)
guesses_sig_a.append(new_sig_a)
guesses_sig_b.append(new_sig_b)
print('%.2f %.2f' % (new_mu_a, new_mu_b))
print('%.2f %.2f' % (new_sig_a, new_sig_b))
def updateInfoAndCounters(cobra, xNew, yNewEval, conNewEval, phase):
cobra['Fsteepness'] = [0] * cobra['nObj']
cobra['A'] = np.vstack((cobra['A'], xNew))
cobra['lastX'] = xNew
cobra['Fres'] = np.vstack((cobra['Fres'], yNewEval))
cobra['Gres'] = np.vstack((cobra['Gres'], conNewEval))
FresStandardized = np.full_like(cobra['Fres'], 0)
FresStandardizedMean = np.zeros(cobra['nObj'])
FresStandardizedStd = np.zeros(cobra['nObj'])
FresPlogStandardized = np.full_like(cobra['Fres'], 0)
FresPlogStandardizedMean = np.zeros(cobra['nObj'])
FresPlogStandardizedStd = np.zeros(cobra['nObj'])
for obji in range(cobra['nObj']):
res, mean, std = standardize_obj(cobra['Fres'][:, obji])
FresStandardized[:, obji] = res
FresStandardizedMean[obji] = mean
FresStandardizedStd[obji] = std
plogFres = plog(cobra['Fres'][:, obji])
res, mean, std = standardize_obj(plogFres)
FresPlogStandardized[:, obji] = res
FresPlogStandardizedMean[obji] = mean
FresPlogStandardizedStd[obji] = std
cobra['FresStandardized'] = FresStandardized
cobra['FresStandardizedMean'] = FresStandardizedMean
cobra['FresStandardizedStd'] = FresStandardizedStd
cobra['lastF'] = FresStandardized[-1]
cobra['FresPlogStandardized'] = FresPlogStandardized
cobra['FresPlogStandardizedMean'] = FresPlogStandardizedMean
cobra['FresPlogStandardizedStd'] = FresPlogStandardizedStd
GresRescaled = np.full_like(cobra['Gres'], 0)
GresRescaledDivider = np.zeros(cobra['nConstraints'])
GresPlogRescaled = np.full_like(cobra['Gres'], 0)
GresPlogRescaledDivider = np.zeros(cobra['nConstraints'])
for coni in range(cobra['nConstraints']):
GresRescaled[:, coni], GresRescaledDivider[coni] = rescale_constr(
cobra['Gres'][:, coni])
plogGres = plog(cobra['Gres'][:, coni])
GresPlogRescaled[:, coni], GresPlogRescaledDivider[
coni] = rescale_constr(plogGres)
cobra['GresRescaled'] = GresRescaled
cobra['GresRescaledDivider'] = GresRescaledDivider
cobra['GresPlogRescaled'] = GresPlogRescaled
cobra['GresPlogRescaledDivider'] = GresPlogRescaledDivider
pff = paretofrontFeasible(cobra['Fres'], cobra['Gres'])
pf = cobra['Fres'][pff]
cobra['paretoFrontier'] = pf
cobra['paretoFrontierFeasible'] = pff
hv = hypervolume(pf, cobra['ref'])
cobra['currentHV'] = hv
newNumViol = np.sum(conNewEval > 0)
newMaxViol = max(0, max(conNewEval))
if newNumViol == 0:
cobra['hypervolumeProgress'] = np.append(
cobra['hypervolumeProgress'], hv)
else:
cobra['hypervolumeProgress'] = np.append(
cobra['hypervolumeProgress'], cobra['hypervolumeProgress'][-1])
cobra['numViol'] = np.append(cobra['numViol'], newNumViol)
cobra['maxViol'] = np.append(cobra['maxViol'], newMaxViol)
cobra['phase'].append(phase)
for ci in range(cobra['nConstraints']):
if conNewEval[ci] <= 0:
cobra['EPS'][ci] = cobra['EPS'][ci] * 0.9
else:
cobra['EPS'][ci] = np.minimum(1.1 * cobra['EPS'][ci],
cobra['epsilonMax'][ci])
return (cobra)
mp_face = mp.solutions.face_mesh
mesh = mp_face.FaceMesh()
#OPENCV image bgr default mediapipe rgb
img = cv2.imread(
"train/testface.jpg"
) ##########just change these 2 lines and read your images and run the script. If you don't want the blurred(gauss) output go the line 180
img2 = cv2.imread("train/otherface.jpg")
if img.shape != img2.shape:
print("img1 shape: ", img.shape)
print("img2 shape:", img2.shape)
raise ValueError("images must be same shape!")
mask = np.full_like(img, 255)
height, weight, channel = img.shape
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_gaus = cv2.GaussianBlur(img_rgb, (7, 7), 60)
result = mesh.process(img_rgb)
indexes_triangles = []
# img2=cv2.resize(img2,(500,500))
uz, gen, kan = img2.shape
img_rgb2 = cv2.cvtColor(img2, cv2.COLOR_BGR2RGB)
img2_gray = cv2.cvtColor(img2, cv2.COLOR_RGB2GRAY)
result2 = mesh.process(img_rgb2)
def plot_graphical_explanation(saxdt_model,
x,
rule,
title,
legend_label,
figsize,
dpi,
fontsize,
text_height,
labelfontsize,
loc,
frameon,
is_factual_for_counterexemplar,
contained_subsequences,
fixed_contained_subsequences=True,
forced_y_lim=None,
return_y_lim=False,
draw_on=None,
print_word=True,
enhance_not_contained=False,
no_axes_labels=False):
fig, ax = plt.subplots(figsize=figsize, dpi=dpi)
ax.set_title(title, fontsize=fontsize)
ax.plot(x.ravel().T if draw_on is None else draw_on.ravel().T,
c="royalblue",
alpha=0.2,
lw=3,
label=legend_label)
#ax.plot(x.ravel().T if draw_on is None else draw_on.T, c="royalblue", alpha=0.2, lw=3, label=legend_label)
for i, idx_word in enumerate(rule["features"][:-1]):
feature = saxdt_model.name_dictionary[idx_word]
threshold_sign = rule["thresholds_signs"][i]
dummy_ts = np.full_like(x.ravel(), np.nan)
if idx_word in contained_subsequences:
if fixed_contained_subsequences:
subsequence = contained_subsequences[idx_word][0]
else:
if is_factual_for_counterexemplar and (len(
contained_subsequences[idx_word]) == 2):
subsequence = contained_subsequences[idx_word][1]
else:
subsequence = contained_subsequences[idx_word][0]
else:
if enhance_not_contained:
maximum = 0
subseq = None
for subsequence in saxdt_model.subsequence_dictionary[
idx_word][:, :, 0]:
dist = sliding_window_euclidean(x.ravel(), subsequence)
if dist > maximum:
maximum = dist
subseq = subsequence
subsequence = subseq
else:
subsequence = compute_medoid(
saxdt_model.subsequence_dictionary[idx_word][:, :, 0])
best_alignment_start_idx = sliding_window_distance(
x.ravel(), subsequence)
best_alignment_end_idx = best_alignment_start_idx + len(subsequence)
start_idx = best_alignment_start_idx
end_idx = best_alignment_end_idx
if end_idx == len(x.ravel()):
end_idx -= 1
subsequence = subsequence[:-1]
dummy_ts[start_idx:end_idx] = subsequence
if threshold_sign == "contained":
ax.plot(dummy_ts, c="#2ca02c", alpha=0.5, lw=5, label="contained")
plt.text(
(start_idx + end_idx) / 2,
# np.nanmin(dummy_ts) + text_height + ((np.nanmin(dummy_ts) + np.nanmax(dummy_ts))/2),
text_height + np.mean(subsequence),
str(idx_word) if not print_word else str(idx_word) + " (" +
feature.decode("utf-8") + ")",
fontsize=fontsize - 2,
c="#2ca02c",
horizontalalignment='center',
verticalalignment='center',
weight='bold',
path_effects=[
patheffects.Stroke(linewidth=3,
foreground='white',
alpha=0.6),
patheffects.Normal()
])
else:
ax.plot(dummy_ts,
c="#d62728",
alpha=0.5,
lw=5,
linestyle="--",
label="not-contained")
plt.text(
(best_alignment_start_idx + best_alignment_end_idx) / 2,
# np.nanmin(dummy_ts) + text_height + ((np.nanmin(dummy_ts) + np.nanmax(dummy_ts))/2),
text_height + np.mean(subsequence),
str(idx_word) if not print_word else str(idx_word) + " (" +
feature.decode("utf-8") + ")",
fontsize=fontsize - 2,
c="#d62728",
horizontalalignment='center',
verticalalignment='center',
weight='bold',
path_effects=[
patheffects.Stroke(linewidth=3,
foreground='white',
alpha=0.6),
patheffects.Normal()
])
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.tick_params(axis='both', which='major', labelsize=fontsize)
if not no_axes_labels:
plt.xlabel("time-steps", fontsize=fontsize)
plt.ylabel("value", fontsize=fontsize)
plt.legend(by_label.values(),
by_label.keys(),
frameon=frameon,
fontsize=labelfontsize,
loc=loc)
if forced_y_lim is not None:
plt.gca().set_ylim(forced_y_lim)
if return_y_lim:
y_lim = plt.gca().get_ylim()
plt.show()
if return_y_lim:
return y_lim
def bag_of_words_sum(
input_text, compression, number_of_sentence
): #input_text：入力テキスト,compression：圧縮率,number_of_sentence：文字数制限
#一番初めに専門用語の配列を作成
"""
wordlist = []
path = "dataset.txt"
with open(path) as f:
for s_line in f:
#print(s_line)
s_line = s_line.strip("\n")
wordlist.append(s_line)
"""
#input_text=("経路制御技術（ルーティング）とは、ネットワーク層が行う処理で目的のパケットの宛先のIPアドレスまでどのようにIPを経由して送られるかについて制御を行う技術である。\nインターネットにおける経路制御技術は、直接転送と間接転送がある。直接転送では、同一のネットワーク上のホストに転送を行い、ルータを経由する必要がない。一方間接転送は、異なるネットワーク上のホストへの転送を行う。\n経路選択には、事前に経路を決定する静的経路制御とルータ間でルーティングプロトコルを使用し、経路表を作成する動的経路制御の２種類がある。静的経路制御では、経路表が事前に作られているため、経路が安定する。しかし経路数が多くなった場合は設定が複雑になることやルータの障害の際に再設定が必要である。一方、動的経路制御はルータが経路を計算するため、経路計算の負荷や冗長性のある経路選択が行われるなどのデメリットがあるが、経路表を自動で作るため、障害迂回の際の経路変更が自動で行われるなどのメリットがある。今回の課題では、動的経路制御の一種であるOSPFのプロトコルについて調査を行う。\n")
input_text_word = input_text
input_text = input_text.strip("\n")
input_text = input_text.split('。')
input_text_copy = input_text
#形態素分析（pip3 install janomeが必要）
tokenslist = []
tokenslist_dic = []
for i in range(len(input_text) - 1):
tokenslist1 = []
t = Tokenizer() # 字句解析器の作成
tokens = t.tokenize(input_text[i]) # 形態素解析tokens[]のなかに一つづつ含まれる。
for token in tokens:
tokenslist1.append(token.surface)
tokenslist_dic.append(token.surface)
tokenslist.append(tokenslist1)
#print(tokens[0].surface) # 結果の表示(.surfaceを使うと文字の表示ができる)
#print(tokenslist)
#辞書を作る
tokenslist_dic = list(set(tokenslist_dic)) #重複を消してあげる。
vec = np.zeros((len(tokenslist), len(tokenslist_dic))) #配列の用意
#bags of words を作る！！
for i in range(len(tokenslist)):
for j in range(len(tokenslist_dic)):
vec[i][j] = tokenslist[i].count(tokenslist_dic[j])
#print(vec)
#print(cosinSimilarity(vec[0],vec[0]))
graph = np.zeros((len(tokenslist), len(tokenslist)), dtype="float32")
for i in range(len(tokenslist)):
for j in range(len(tokenslist)):
graph[i][j] = cosinSimilarity(vec[i], vec[j])
#print(graph) #cos類似度の対応関係の配列ができる！！
#隣接行列を作ってあげる
para = 0.3 #比較のパラメータ
compare = np.full_like(graph, para) #比較用配列
adjacency = graph > compare #閾値処理を行う
#確率行列を作る
rundom_graph = np.zeros_like(graph)
for i in range(len(tokenslist)):
sum_one = np.sum(adjacency[i])
for j in range(len(tokenslist)):
rundom_graph[i][j] = adjacency[i][j] / sum_one #和が１になる行列を作る
ratings = power_method(rundom_graph, len(tokenslist), 0.01)
#print((ratings)) #どの文が重要かを示してくれてる！
#文字列の出力
compression = compression / 100 #圧縮率（%)
#number_of_sentence = len(tokenslist) # 文字数
sort_index = np.argsort(ratings) #大きい順にソートした時のインデックス
output = []
output_index = []
for i in range(int(len(tokenslist) / 2)):
output_index.append(sort_index[i])
output_index.sort
output_index = np.array(output_index)
output_index = np.sort(output_index) #文章番号のソート
for i in range(len(output_index)):
output.append(input_text_copy[output_index[i]])
# リストから文章に変更する。
output_text = ""
for i in range(len(output)):
output_text = output_text + output[i] + "。"
#重要語句の抽出
wordlist = Extract_ImportantWords(output_text)
return output_text, wordlist
def fit(self, X, y):
"""
Fits this ``SelfTrainingClassifier`` to a dataset.
Parameters
----------
X : array-like, shape = (n_samples, n_features)
array representing the data
y : array-like, shape = (n_samples,)
array representing the labels. Unlabeled samples should have the
label -1.
Returns
-------
self : object
returns an instance of self.
"""
# we need row slicing support for sparce matrices
X, y = self._validate_data(X,
y,
accept_sparse=['csr', 'csc', 'lil', 'dok'])
if self.base_estimator is None:
raise ValueError("base_estimator cannot be None!")
self.base_estimator_ = clone(self.base_estimator)
if self.max_iter is not None and self.max_iter < 0:
raise ValueError("max_iter must be >= 0 or None,"
f" got {self.max_iter}")
if not (0 <= self.threshold < 1):
raise ValueError("threshold must be in [0,1),"
f" got {self.threshold}")
if self.criterion not in ['threshold', 'k_best']:
raise ValueError(f"criterion must be either 'threshold' "
f"or 'k_best', got {self.criterion}.")
if y.dtype.kind in ['U', 'S']:
raise ValueError("y has dtype string. If you wish to predict on "
"string targets, use dtype object, and use -1"
" as the label for unlabeled samples.")
has_label = y != -1
if np.all(has_label):
warnings.warn("y contains no unlabeled samples", UserWarning)
if self.criterion == 'k_best' and (self.k_best >
X.shape[0] - np.sum(has_label)):
warnings.warn(
"k_best is larger than the amount of unlabeled "
"samples. All unlabeled samples will be labeled in "
"the first iteration", UserWarning)
self.transduction_ = np.copy(y)
self.labeled_iter_ = np.full_like(y, -1)
self.labeled_iter_[has_label] = 0
self.n_iter_ = 0
while not np.all(has_label) and (self.max_iter is None
or self.n_iter_ < self.max_iter):
self.n_iter_ += 1
self.base_estimator_.fit(X[safe_mask(X, has_label)],
self.transduction_[has_label])
if self.n_iter_ == 1:
# Only validate in the first iteration so that n_iter=0 is
# equivalent to the base_estimator itself.
_validate_estimator(self.base_estimator)
# Predict on the unlabeled samples
prob = self.base_estimator_.predict_proba(X[safe_mask(
X, ~has_label)])
pred = self.base_estimator_.classes_[np.argmax(prob, axis=1)]
max_proba = np.max(prob, axis=1)
# Select new labeled samples
if self.criterion == 'threshold':
selected = max_proba > self.threshold
else:
n_to_select = min(self.k_best, max_proba.shape[0])
if n_to_select == max_proba.shape[0]:
selected = np.ones_like(max_proba, dtype=bool)
else:
# NB these are indicies, not a mask
selected = \
np.argpartition(-max_proba, n_to_select)[:n_to_select]
# Map selected indices into original array
selected_full = np.nonzero(~has_label)[0][selected]
# Add newly labeled confident predictions to the dataset
self.transduction_[selected_full] = pred[selected]
has_label[selected_full] = True
self.labeled_iter_[selected_full] = self.n_iter_
if selected_full.shape[0] == 0:
# no changed labels
self.termination_condition_ = "no_change"
break
if self.verbose:
print(f"End of iteration {self.n_iter_},"
f" added {selected_full.shape[0]} new labels.")
if self.n_iter_ == self.max_iter:
self.termination_condition_ = "max_iter"
if np.all(has_label):
self.termination_condition_ = "all_labeled"
self.base_estimator_.fit(X[safe_mask(X, has_label)],
self.transduction_[has_label])
self.classes_ = self.base_estimator_.classes_
return self
# plt.show()
# plt.close()
# pmra/pmdec density estimation plot
pmra_vals = np.linspace(-12, 9, 350)
pmdec_vals = np.linspace(-14, 4, 350)
xx, yy = np.meshgrid(pmra_vals, pmdec_vals)
pos = np.empty(xx.shape + (2,))
pos[:, :, 0] = xx
pos[:, :, 1] = yy
file_density = 'density_{:.2f}_{:.2f}.pkl'.format(mean_parallax, d_parallax)
if path.isfile(file_density):
total_density = joblib.load(file_density)
else:
total_density = np.full_like(xx, fill_value=0.)
for i_s, star in enumerate(gaia_data):
if i_s % 500 == 0:
print i_s
cov = np.array([[star['pmra_error'], 0], [0, star['pmdec_error']]])
mean = np.array([star['pmra'], star['pmdec']])
total_density += multivariate_normal.pdf(pos, mean=mean, cov=cov)
joblib.dump(total_density, file_density)
plt.imshow(total_density, interpolation='none', origin='lower', cmap='viridis',
extent=[-12, 9, -14, 4],
vmin=0, # np.percentile(total_density, 0),
vmax=1300, # np.percentile(total_density, 100)
)
plt.colorbar()
plt.contour(xx, yy, total_density, [0, 50, 200, 350, 500, 650, 800, 950, 1100, 1250, 1400, 1550, 1700],
def compute_shape(segmentation,
angle_correction=True,
param_centerline=None,
verbose=1):
"""
Compute morphometric measures of the spinal cord in the transverse (axial) plane from the segmentation.
The segmentation could be binary or weighted for partial volume [0,1].
:param segmentation: input segmentation. Could be either an Image or a file name.
:param angle_correction:
:param param_centerline: see centerline.core.ParamCenterline()
:param verbose:
:return metrics: Dict of class Metric(). If a metric cannot be calculated, its value will be nan.
:return fit_results: class centerline.core.FitResults()
"""
# List of properties to output (in the right order)
property_list = [
'area',
'angle_AP',
'angle_RL',
'diameter_AP',
'diameter_RL',
'eccentricity',
'orientation',
'solidity',
]
im_seg = Image(segmentation).change_orientation('RPI')
# Getting image dimensions. x, y and z respectively correspond to RL, PA and IS.
nx, ny, nz, nt, px, py, pz, pt = im_seg.dim
# Extract min and max index in Z direction
data_seg = im_seg.data
X, Y, Z = (data_seg > 0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
# Initialize dictionary of property_list, with 1d array of nan (default value if no property for a given slice).
shape_properties = {
key: np.full_like(np.empty(nz), np.nan, dtype=np.double)
for key in property_list
}
if angle_correction:
# compute the spinal cord centerline based on the spinal cord segmentation
# here, param_centerline.minmax needs to be False because we need to retrieve the total number of input slices
_, arr_ctl, arr_ctl_der, fit_results = get_centerline(
im_seg, param=param_centerline, verbose=verbose)
else:
fit_results = None
# Loop across z and compute shape analysis
for iz in tqdm(range(min_z_index, max_z_index + 1),
unit='iter',
unit_scale=False,
desc="Compute shape analysis",
ascii=True,
ncols=80):
# Extract 2D patch
current_patch = im_seg.data[:, :, iz]
if angle_correction:
# Extract tangent vector to the centerline (i.e. its derivative)
tangent_vect = np.array([
arr_ctl_der[0][iz - min_z_index] * px,
arr_ctl_der[1][iz - min_z_index] * py, pz
])
# Normalize vector by its L2 norm
tangent_vect = tangent_vect / np.linalg.norm(tangent_vect)
# Compute the angle about AP axis between the centerline and the normal vector to the slice
v0 = [tangent_vect[0], tangent_vect[2]]
v1 = [0, 1]
angle_AP_rad = np.math.atan2(np.linalg.det([v0, v1]),
np.dot(v0, v1))
# Compute the angle about RL axis between the centerline and the normal vector to the slice
v0 = [tangent_vect[1], tangent_vect[2]]
v1 = [0, 1]
angle_RL_rad = np.math.atan2(np.linalg.det([v0, v1]),
np.dot(v0, v1))
# Apply affine transformation to account for the angle between the centerline and the normal to the patch
tform = transform.AffineTransform(scale=(np.cos(angle_RL_rad),
np.cos(angle_AP_rad)))
# Convert to float64, to avoid problems in image indexation causing issues when applying transform.warp
current_patch = current_patch.astype(np.float64)
# TODO: make sure pattern does not go extend outside of image border
current_patch_scaled = transform.warp(
current_patch,
tform.inverse,
output_shape=current_patch.shape,
order=1,
)
else:
current_patch_scaled = current_patch
angle_AP_rad, angle_RL_rad = 0.0, 0.0
# compute shape properties on 2D patch
shape_property = _properties2d(current_patch_scaled, [px, py])
if shape_property is not None:
# Add custom fields
shape_property['angle_AP'] = angle_AP_rad * 180.0 / math.pi
shape_property['angle_RL'] = angle_RL_rad * 180.0 / math.pi
# Loop across properties and assign values for function output
for property_name in property_list:
shape_properties[property_name][iz] = shape_property[
property_name]
else:
logging.warning('\nNo properties for slice: {}'.format(iz))
""" DEBUG
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
fig = Figure()
FigureCanvas(fig)
ax = fig.add_subplot(111)
ax.imshow(current_patch_scaled)
ax.grid()
ax.set_xlabel('y')
ax.set_ylabel('x')
fig.savefig('tmp_fig.png')
"""
metrics = {}
for key, value in shape_properties.items():
# Making sure all entries added to metrics have results
if not value == []:
metrics[key] = Metric(data=np.array(value), label=key)
return metrics, fit_results
import numpy as np
import pandas as pd
# set the date range of output files
st = '20190724 18:00'
et = '20190724 23:55'
dt = 5 # unit: min
# get basic file
datadir = './lda/'
base = 'wrflda_d03_2019-07-25_00:00:00'
ds = xr.open_dataset(datadir + base)
# load data and create zero DataArray
da = ds['LDACHECK']
ds['LDACHECK'] = da.copy(data=np.full_like(da, 0.))
# generate output filenames based on the date range
dr = pd.date_range(st, et, freq=str(dt) + 'T')
filenames = [d.strftime(f'{base[:10]}_%Y-%m-%d_%H:%M:%S') for d in dr]
# create files (zero values) based on filenames
for tindex, f in enumerate(filenames):
# generate 'Times' variable
Times = xr.DataArray(np.array([dr[tindex]], dtype=np.dtype(('S', 19))),
dims=['Time'])
ds['Times'] = Times
ncfile = datadir + f
print('Saving to {}'.format(ncfile))
ds.to_netcdf(ncfile,
# 创建一个shape生成的数组，每个元素的值为value
full = np.full((2, 3), 2)
print('full-->', full)
# 创建一个正方的n*n的单位矩阵，对角线为1 其余为0
eye = np.eye(4)
print('eye--->', eye)
# 根据数组a的形状生成全1数组
one_like = np.ones_like(full)
print('one_like--->', one_like)
# 根据数组a的形状生成全0数组
zero_like = np.zeros_like(full)
print('zero_like--->', zero_like)
# 根据数组a的形状生成全value数组
full_like = np.full_like(z, 3)
print('full_like--->', full_like)
linespace1 = np.linspace(2.0, 3.0, num=5)
print('linespace1----->', linespace1)
linespace2 = np.linspace(1, 10, 4)
print('linespace2----->', linespace2)
linespace3 = np.linspace(1, 10, 4, endpoint=False)
print('linespace3----->', linespace3)
concatenate = np.concatenate((x, y))
print('concatenate----->', concatenate)
plt.ylabel('Frequency')
plt.title('Histogram of village foundations in different periods')
# 4.1.2 Density
# KDE
x = np.array(df_vil_wgs84['AD'])
x_d = np.linspace(1200, 1400, 201)
kde = KernelDensity(bandwidth=5, kernel='gaussian')
kde.fit(x[:, None])
# score_samples returns the log of the probability density
logprob = kde.score_samples(x_d[:, None])
plt.fill_between(x_d, np.exp(logprob), alpha=0.5)
plt.plot(x, np.full_like(x, -0.01), '|k', markeredgewidth=1)
plt.ylim(0, 0.025)
plt.xlim(1220, 1380)
plt.xlabel('Time A.D.')
plt.ylabel('Density of village foundation')
# 4.1.3 Distance between Events Concept
plt.plot(df_vil_wgs84["AD"], df_vil_wgs84["id"], color='gray')
plt.scatter(df_vil_wgs84["AD"], df_vil_wgs84["id"], color='darkgray')
plt.grid(axis='y', alpha=0.75)
plt.grid(axis='x', alpha=0.75)
plt.xlabel('Time A.D.')
plt.ylabel('id')
# Interval
def compute_cj_estimate(posterior_sample,
lnlikefunc,
lnpriorfunc,
param_post,
nsamples,
qprob=None,
lnlikeargs=(),
lnpriorargs=(),
lnlike_post=None,
lnprior_post=None):
"""
Computes the Chib & Jeliazkov estimate of the bayesian evidence.
The estimation is based on an posterior sample with n elements
(indexed by s, with s = 0, ..., n-1), and a sample from the proposal
distribution used in MCMC (qprob) of size nsample. Note that if qprob is
None, it is estimated as a multivariate Gaussian.
:param array posterior_sample:
A sample from the parameter posterior distribution. Dimensions are
(n x k), where k is the number of parameters.
:param callable lnlikefunc:
Function to compute ln(likelihood) on the marginal samples.
:param callable lnpriorfunc:
Function to compute ln(prior density) on the marginal samples.
:param array param_post:
Posterior parameter sample used to obtained fixed point needed by the
algorithm.
:param int nsamples:
Size of sample drawn from proposal distribution.
:param object or None qprob:
Proposal distribution function. If None, it will be estimated as a
multivariate Gaussian. If not None, it must possess the methods pdf and
rvs. See scipy.stats.rv_continuous.
:param tuple lnlikeargs:
Extra arguments passed to the likelihood function.
:param tuple lnpriorargs:
Extra arguments passed to the lnprior function.
:param array lnlike_post:
log(likelihood) computed over a posterior sample. 1-D array of length n.
:param array lnprior_post:
log(prior) computed over a posterior sample. 1-D array of length n.
:raises AttributeError:
if instace qprob does not have method 'pdf' or 'rvs'.
:raises TypeError:
if methods 'pdf' or 'rvs' from instance qprob are not callable.
:returns: Natural logarithm of estimated Bayesian evidence.
References
----------
Chib & Jeliazkov (2001): Journal of the Am. Stat. Assoc.; Mar 2001; 96, 453
"""
#Find fixed point on which to estimate posterior ordinate.
if lnlike_post is not None:
#Pass values of log(likelihood) in posterior sample.
arg_fp = [
lnlike_post,
]
else:
#Pass function that computes log(likelihood).
arg_fp = [
lnlikefunc,
]
if lnlike_post is not None:
#Pass values of log(prior) in posterior sample.
arg_fp.append(lnprior_post)
else:
#Pass function that computes log(prior).
arg_fp.append(lnpriorfunc)
fp, lnpost0 = get_fixed_point(posterior_sample,
param_post,
lnlikefunc,
lnpriorfunc,
lnlikeargs=lnlikeargs,
lnpriorargs=lnpriorargs)
#If proposal distribution is not given, define as multivariate Gaussian.
if qprob is None:
#Get covariance from posterior sample
k = np.cov(posterior_sample.T)
qprob = lib.MultivariateGaussian(fp, k)
else:
#Check that qprob has the necessary attributes
for method in ('pdf', 'rvs'):
try:
att = getattr(qprob, method)
except AttributeError:
raise AttributeError('qprob does not have method '
'\'{}\''.format(method))
if not callable(att):
raise TypeError('{} method of qprob is not '
'callable'.format(method))
#Compute proposal density in posterior sample
q_post = qprob.pdf(posterior_sample)
#If likelihood over posterior sample is not given, compute it
if lnlike_post is None:
lnlike_post = lnlikefunc(posterior_sample, *lnlikeargs)
#Idem for prior
if lnprior_post is None:
lnprior_post = lnpriorfunc(posterior_sample, *lnpriorargs)
#Compute Metropolis ratio with respect to fixed point over posterior sample
lnalpha_post = metropolis_ratio(lnprior_post + lnlike_post, lnpost0)
#Sample from the proposal distribution with respect to fixed point
proposal_sample = qprob.rvs(nsamples)
#Compute likelihood and prior on proposal_sample
lnprior_prop = lnpriorfunc(proposal_sample, *lnpriorargs)
if np.all(lnprior_prop == -np.inf):
raise ValueError('All samples from proposal density have zero prior'
'probability. Increase nsample.')
#Now compute likelihood only on the samples where prior != 0.
lnlike_prop = np.full_like(lnprior_prop, -np.inf)
ind = lnprior_prop != -np.inf
lnlike_prop[ind] = lnlikefunc(proposal_sample[ind, :], *lnlikeargs)
#Get Metropolis ratio with respect to fixed point over proposal sample
lnalpha_prop = metropolis_ratio(lnpost0, lnprior_prop + lnlike_prop)
#Compute estimate of posterior ordinate (see Eq. 9 from reference)
num = log_sum(lnalpha_post + q_post) - log(len(posterior_sample))
den = log_sum(lnalpha_prop) - log(len(proposal_sample))
lnpostord = num - den
#Return log(Evidence) estimation
return lnpost0 - lnpostord
def _set_animation(
pp_sampled_vals,
ax,
dtype=None,
kind="density",
alpha=None,
drawstyle=None,
linewidth=None,
height=None,
markersize=None,
plot_kwargs=None,
):
if kind == "kde":
length = len(pp_sampled_vals)
if dtype == "f":
y_vals, lower, upper = _fast_kde(pp_sampled_vals[0])
x_vals = np.linspace(lower, upper, len(y_vals))
max_max = max(
[max(_fast_kde(pp_sampled_vals[i])[0]) for i in range(length)])
ax.set_ylim(0, max_max)
(line, ) = ax.plot(x_vals, y_vals, **plot_kwargs)
def animate(i):
y_vals, lower, upper = _fast_kde(pp_sampled_vals[i])
x_vals = np.linspace(lower, upper, len(y_vals))
line.set_data(x_vals, y_vals)
return line
else:
vals = pp_sampled_vals[0]
y_vals, x_vals = histogram(vals, bins="auto")
(line, ) = ax.plot(x_vals[:-1], y_vals, **plot_kwargs)
max_max = max([
max(histogram(pp_sampled_vals[i], bins="auto")[0])
for i in range(length)
])
ax.set_ylim(0, max_max)
def animate(i):
y_vals, x_vals = histogram(pp_sampled_vals[i], bins="auto")
line.set_data(x_vals[:-1], y_vals)
return (line, )
elif kind == "cumulative":
x_vals, y_vals = _empirical_cdf(pp_sampled_vals[0])
(line, ) = ax.plot(x_vals,
y_vals,
alpha=alpha,
color="C5",
drawstyle=drawstyle,
linewidth=linewidth)
def animate(i):
x_vals, y_vals = _empirical_cdf(pp_sampled_vals[i])
line.set_data(x_vals, y_vals)
return line
elif kind == "scatter":
x_vals = pp_sampled_vals[0]
y_vals = np.full_like(x_vals, height, dtype=np.float64)
(line, ) = ax.plot(x_vals,
y_vals,
"o",
zorder=2,
color="C5",
markersize=markersize,
alpha=alpha)
def animate(i):
line.set_xdata(np.ravel(pp_sampled_vals[i]))
return line
def init():
if kind != "scatter":
line.set_data([], [])
else:
line.set_xdata([])
return line
return animate, init
def make_summary(
input_text, compression, number_of_sentence
): #input_text：入力テキスト,compression：圧縮率,number_of_sentence：文字数制限
input_text = input_text.strip("\n")
input_text = input_text.split('。')
#print(input_text)
input_text_copy = input_text
#形態素分析（pip3 install janomeが必要）
tokenslist = []
tokenslist_dic = []
#print(tokenslist)
for i in range(len(input_text) - 1):
tokenslist1 = []
t = Tokenizer() # 字句解析器の作成
tokens = t.tokenize(input_text[i]) # 形態素解析tokens[]のなかに一つづつ含まれる。
for token in tokens:
#print(token) # 結果の表示
tokenslist1.append(token.surface)
tokenslist_dic.append(token.surface)
tokenslist.append(tokenslist1)
#print(tokens[0].surface) # 結果の表示(.surfaceを使うと文字の表示ができる)
#print(tokenslist)
#辞書を作る
#print(tokenslist_dic)
tokenslist_dic = list(set(tokenslist_dic)) #重複を消してあげる。
#print(tokenslist_dic)
#print(len(tokenslist_dic))
vec = np.zeros((len(tokenslist), len(tokenslist_dic))) #配列の用意
#print(vec.shape)
#bags of words を作る！！
for i in range(len(tokenslist)):
for j in range(len(tokenslist_dic)):
#print(tokenslist[i].count(tokenslist_dic[j]))
vec[i][j] = tokenslist[i].count(tokenslist_dic[j])
#print(vec)
#print(cosinSimilarity(vec[0],vec[0]))
graph = np.zeros((len(tokenslist), len(tokenslist)), dtype="float32")
for i in range(len(tokenslist)):
for j in range(len(tokenslist)):
graph[i][j] = cosinSimilarity(vec[i], vec[j])
#print(graph) #cos類似度の対応関係の配列ができる！！
#隣接行列を作ってあげる
para = 0.3 #比較のパラメータ
compare = np.full_like(graph, para) #比較用配列
adjacency = graph > compare
#print(adjacency)
#確率行列を作る
rundom_graph = np.zeros_like(graph)
for i in range(len(tokenslist)):
sum_one = np.sum(adjacency[i])
for j in range(len(tokenslist)):
rundom_graph[i][j] = adjacency[i][j] / sum_one
#print(rundom_graph)
ratings = power_method(rundom_graph, len(tokenslist), 0.01)
#print((ratings)) #どの文が重要かを示してくれてる！
#文字列の出力
compression = compression / 100 #圧縮率（%)
#number_of_sentence = len(tokenslist) # 文字数
sort_index = np.argsort(ratings) #大きい順にソートした時のインデックス
#print(sort_index)
output = []
output_index = []
for i in range(int(len(tokenslist) / 2)):
output_index.append(sort_index[i])
output_index.sort
output_index = np.array(output_index)
output_index = np.sort(output_index)
#print(output_index)
for i in range(len(output_index)):
#print(input_text_copy[output_index[i]])
output.append(input_text_copy[output_index[i]])
# リストから文章に変更する。
output_text = "。".join(line for line in output if line)
return output_text
def simulation(pts, net_layout, cost_dict, netsim_dict):
'''
Generates a network of paths.
Parameters
----------
pts: dataframe
contains the identifier, row and column of each location
net_layout: dataframe
dataframe specifying origin and destination of each path in the network
cost_dict: dictionary
contains parameters used for ``calculate_iwdt()``
netsim_dict: dictionary
contains parameters needed to execute network simulation
Returns
-------
Gt: 2D numpy array
final ground potential
paths: 2D numpy array
sum of all network paths
paths_dict: dictionary
dictionary with the track of every path in network
Notes
-----
``simulate()`` generates a network of paths using the ``net_layout`` dataframe generated with the
functions from the **generate** module.
The ``netsim_dict{}`` must contain the following entries:
- 'i: ' - float
effect of new path. *Default:* 1.0
- 'Gmax: ' - float
maximum ground potential.
- 'T: ' - float
1/T represents the *residuality* of a path.
- 'alpha: ' - float
coefficient calculated from :math:`\\alpha_1 = \\frac {d_0} {ln(1 - NC_0)}`
While the ``cost_dict{}`` must contain the same entries as ``iwdt_dict{}`` see **cost** module
'''
# unpack netsim dictionary
i = netsim_dict['i']
Gmax = netsim_dict['Gmax']
T = netsim_dict['T']
alpha = netsim_dict['alpha']
# initialize netsim outputs
Gt = np.zeros_like(cost_dict['dem'])
Gt_1 = np.zeros_like(Gt)
paths = np.zeros_like(Gt)
# initialize paths dictionary
path_lst = [] #paths_dict= {}#OrderedDict()
for pth_id, pth_def in net_layout.iterrows():
# origin & destination?
o = pth_def['origin']
d = pth_def['destination']
# retrieve location @ origin & destination
origin = [[
pts.loc[pts['id'] == o, 'r'].values[0], pts.loc[pts['id'] == o,
'c'].values[0]
]]
destination = [[
pts.loc[pts['id'] == d, 'r'].values[0], pts.loc[pts['id'] == d,
'c'].values[0]
]]
# initialize ACS
acs = np.full_like(Gt, 999999.0)
for r, c in origin:
acs[r, c] = 0.0
# calculated influence weighted distance transform
acs, blx, bly = calculate_iwdt(acs, cost_dict)
# create new path to destination
path_t, path_info = pt.create_paths(blx,
bly,
origin,
destination,
start_path=pth_id)
# update paths
paths += path_t
path_lst.append(path_info) #paths_dict.update(path_info)
# update ground potential
Gt = Gt_1 - (Gt_1 / T) + path_t * i * (1 - (Gt_1 / Gmax))
# update network cost
d = np.full_like(Gt, 99999.0)
d[Gt >= 1.0] = 0.0
d = calculate_dt(d, cost_dict['cellsize'], option=2)
cost_dict['netcost'] = 1.0 - np.exp(d / alpha)
# update Gt_1
Gt_1 = np.copy(Gt)
return Gt, paths, path_lst #paths_dict
def from_shuffled(cls, shuffled_results: ShuffledResults):
return _FinalResults(
theta=np.full_like(shuffled_results.theta[:, 0], np.nan),
skeletons=np.full_like(shuffled_results.scores[:, 0], np.nan),
scores=np.full_like(shuffled_results.skeletons[:, 0], np.nan),
)
def test_bounds(simulated_problem, method):
"""Test that non-binding bounds on parameters in simulated problems do not affect estimates and that binding bounds
are respected.
"""
simulation, _, problem, _ = simulated_problem
# all problems will be solved with the same optimization method starting as close to the true parameters as possible
solve = lambda s, p: problem.solve(
np.minimum(np.maximum(simulation.sigma, s[0]), s[1]),
np.minimum(np.maximum(simulation.pi, p[0]), p[1])
if simulation.pi is not None else None,
sigma_bounds=s,
pi_bounds=p,
steps=1,
optimization=Optimization(method))
# solve the problem when unbounded
unbounded_sigma_bounds = (np.full_like(simulation.sigma, -np.inf),
np.full_like(simulation.sigma, +np.inf))
unbounded_pi_bounds = None
if simulation.pi is not None:
unbounded_pi_bounds = (np.full_like(simulation.pi, -np.inf),
np.full_like(simulation.pi, +np.inf))
unbounded_results = solve(unbounded_sigma_bounds, unbounded_pi_bounds)
# choose an element in each parameter matrix and identify its estimated value
sigma_index = (simulation.sigma.nonzero()[0][0],
simulation.sigma.nonzero()[1][0])
sigma_value = unbounded_results.sigma[sigma_index]
pi_index = None
if simulation.pi is not None:
pi_index = (simulation.pi.nonzero()[0][0],
simulation.pi.nonzero()[1][0])
pi_value = unbounded_results.pi[pi_index]
# use different types of binding bounds and skip types that fix all parameters
for lb_scale, ub_scale in [(+np.inf, -0.1), (-0.1, +np.inf), (+1, -0.1),
(-0.1, +1), (0, 0)]:
binding_sigma_bounds = (np.full_like(simulation.sigma, -np.inf),
np.full_like(simulation.sigma, +np.inf))
binding_sigma_bounds[0][
sigma_index] = sigma_value - lb_scale * np.abs(sigma_value)
binding_sigma_bounds[1][
sigma_index] = sigma_value + ub_scale * np.abs(sigma_value)
if simulation.pi is None:
binding_pi_bounds = None
if np.array_equal(*map(np.abs, binding_sigma_bounds)):
continue
else:
binding_pi_bounds = (np.full_like(simulation.pi, -np.inf),
np.full_like(simulation.pi, +np.inf))
binding_pi_bounds[0][
pi_index] = pi_value - lb_scale * np.abs(pi_value)
binding_pi_bounds[1][
pi_index] = pi_value + ub_scale * np.abs(pi_value)
if np.array_equal(
*map(np.abs, binding_sigma_bounds)) and np.array_equal(
*map(np.abs, binding_pi_bounds)):
continue
# solve the problem with binding bounds and test that they are essentially respected
binding_results = solve(binding_sigma_bounds, binding_pi_bounds)
assert_array_less = lambda a, b: np.testing.assert_array_less(
a, b + 1e-14, verbose=True)
assert_array_less(binding_sigma_bounds[0], binding_results.sigma)
assert_array_less(binding_results.sigma, binding_sigma_bounds[1])
if simulation.pi is not None:
assert_array_less(binding_pi_bounds[0], binding_results.pi)
assert_array_less(binding_results.pi, binding_pi_bounds[1])
# for methods other than TNC, which works differently with bounds, test that non-binding bounds furnish results that
# are similar to their unbounded counterparts
if method != 'tnc':
unbinding_sigma_bounds = (simulation.sigma -
1e10 * np.abs(simulation.sigma),
simulation.sigma +
1e10 * np.abs(simulation.sigma))
unbinding_pi_bounds = None
if simulation.pi is not None:
unbinding_pi_bounds = (simulation.pi -
1e10 * np.abs(simulation.pi),
simulation.pi +
1e10 * np.abs(simulation.pi))
unbinding_results = solve(unbinding_sigma_bounds, unbinding_pi_bounds)
np.testing.assert_allclose(unbounded_results.sigma,
unbinding_results.sigma,
atol=0,
rtol=0.1)
if simulation.pi is not None:
np.testing.assert_allclose(unbounded_results.pi,
unbinding_results.pi,
atol=0,
rtol=0.1)
import numpy as np #All 0s matrix a = np.zeros([5, 5]) #print(a) #All 0s matrix b = np.ones([4, 4], dtype='int32') #print(b) #any other number #print(np.full((2,2) , 99)) c = np.full_like(a, 4) #print(c) #random decimal numbers random = np.random.rand(4, 2) print(random) #random integer values # lower , upper , size random = np.random.randint(0, 10, size=(3, 3)) print(random) identity = np.identity(5) print(identity) #repeat an array
def plot_ppc(
data,
kind="kde",
alpha=None,
mean=True,
figsize=None,
textsize=None,
data_pairs=None,
var_names=None,
coords=None,
flatten=None,
flatten_pp=None,
num_pp_samples=None,
random_seed=None,
jitter=None,
animated=False,
animation_kwargs=None,
legend=True,
ax=None,
):
"""
Plot for posterior predictive checks.
Parameters
----------
data : az.InferenceData object
InferenceData object containing the observed and posterior
predictive data.
kind : str
Type of plot to display (kde, cumulative, or scatter). Defaults to kde.
alpha : float
Opacity of posterior predictive density curves. Defaults to 0.2 for kind = kde
and cumulative, for scatter defaults to 0.7
mean : bool
Whether or not to plot the mean posterior predictive distribution. Defaults to True
figsize : tuple
Figure size. If None it will be defined automatically.
textsize: float
Text size scaling factor for labels, titles and lines. If None it will be
autoscaled based on figsize.
data_pairs : dict
Dictionary containing relations between observed data and posterior predictive data.
Dictionary structure:
Key = data var_name
Value = posterior predictive var_name
For example, `data_pairs = {'y' : 'y_hat'}`
If None, it will assume that the observed data and the posterior
predictive data have the same variable name.
var_names : list
List of variables to be plotted. Defaults to all observed variables in the
model if None.
coords : dict
Dictionary mapping dimensions to selected coordinates to be plotted.
Dimensions without a mapping specified will include all coordinates for
that dimension. Defaults to including all coordinates for all
dimensions if None.
flatten : list
List of dimensions to flatten in observed_data. Only flattens across the coordinates
specified in the coords argument. Defaults to flattening all of the dimensions.
flatten_pp : list
List of dimensions to flatten in posterior_predictive. Only flattens across the coordinates
specified in the coords argument. Defaults to flattening all of the dimensions.
Dimensions should match flatten excluding dimensions for data_pairs parameters.
If flatten is defined and flatten_pp is None, then `flatten_pp=flatten`.
num_pp_samples : int
The number of posterior predictive samples to plot. For `kind` = 'scatter' and
`animation = False` if defaults to a maximum of 5 samples and will set jitter to 0.7
unless defined otherwise. Otherwise it defaults to all provided samples.
random_seed : int
Random number generator seed passed to numpy.random.seed to allow
reproducibility of the plot. By default, no seed will be provided
and the plot will change each call if a random sample is specified
by `num_pp_samples`.
jitter : float
If kind is "scatter", jitter will add random uniform noise to the height
of the ppc samples and observed data. By default 0.
animated : bool
Create an animation of one posterior predictive sample per frame. Defaults to False.
animation_kwargs : dict
Keywords passed to `animation.FuncAnimation`.
legend : bool
Add legend to figure. By default True.
ax : axes
Matplotlib axes. Defaults to None.
Returns
-------
axes : matplotlib axes
Examples
--------
Plot the observed data KDE overlaid on posterior predictive KDEs.
.. plot::
:context: close-figs
>>> import arviz as az
>>> data = az.load_arviz_data('radon')
>>> az.plot_ppc(data)
Plot the overlay with empirical CDFs.
.. plot::
:context: close-figs
>>> az.plot_ppc(data, kind='cumulative')
Use the coords and flatten parameters to plot selected variable dimensions
across multiple plots.
.. plot::
:context: close-figs
>>> az.plot_ppc(data, coords={'observed_county': ['ANOKA', 'BELTRAMI']}, flatten=[])
Plot the overlay using a stacked scatter plot that is particularly useful
when the sample sizes are small.
.. plot::
:context: close-figs
>>> az.plot_ppc(data, kind='scatter', flatten=[],
>>> coords={'observed_county': ['AITKIN', 'BELTRAMI']})
Plot random posterior predictive sub-samples.
.. plot::
:context: close-figs
>>> az.plot_ppc(data, num_pp_samples=30, random_seed=7)
"""
for group in ("posterior_predictive", "observed_data"):
if not hasattr(data, group):
raise TypeError(
'`data` argument must have the group "{group}" for ppcplot'.
format(group=group))
if kind.lower() not in ("kde", "cumulative", "scatter"):
raise TypeError(
"`kind` argument must be either `kde`, `cumulative`, or `scatter`")
if data_pairs is None:
data_pairs = {}
if animation_kwargs is None:
animation_kwargs = {}
if platform.system() == "Linux":
animation_kwargs.setdefault("blit", True)
else:
animation_kwargs.setdefault("blit", False)
if animated and animation_kwargs["blit"] and platform.system() != "Linux":
_log.warning(
"If you experience problems rendering the animation try setting"
"`animation_kwargs({'blit':False}) or changing the plotting backend (e.g. to TkAgg)"
)
if alpha is None:
if animated:
alpha = 1
else:
if kind.lower() == "scatter":
alpha = 0.7
else:
alpha = 0.2
if jitter is None:
jitter = 0.0
assert jitter >= 0.0
observed = data.observed_data
posterior_predictive = data.posterior_predictive
if var_names is None:
var_names = list(observed.data_vars)
var_names = _var_names(var_names, observed)
pp_var_names = [data_pairs.get(var, var) for var in var_names]
pp_var_names = _var_names(pp_var_names, posterior_predictive)
if flatten_pp is None and flatten is None:
flatten_pp = list(posterior_predictive.dims.keys())
elif flatten_pp is None:
flatten_pp = flatten
if flatten is None:
flatten = list(observed.dims.keys())
if coords is None:
coords = {}
if random_seed is not None:
np.random.seed(random_seed)
total_pp_samples = posterior_predictive.sizes[
"chain"] * posterior_predictive.sizes["draw"]
if num_pp_samples is None:
if kind == "scatter" and not animated:
num_pp_samples = min(5, total_pp_samples)
else:
num_pp_samples = total_pp_samples
if (not isinstance(num_pp_samples, Integral) or num_pp_samples < 1
or num_pp_samples > total_pp_samples):
raise TypeError("`num_pp_samples` must be an integer between 1 and " +
"{limit}.".format(limit=total_pp_samples))
pp_sample_ix = np.random.choice(total_pp_samples,
size=num_pp_samples,
replace=False)
for key in coords.keys():
coords[key] = np.where(np.in1d(observed[key], coords[key]))[0]
obs_plotters = filter_plotters_list(
list(
xarray_var_iter(observed.isel(coords),
skip_dims=set(flatten),
var_names=var_names,
combined=True)),
"plot_ppc",
)
length_plotters = len(obs_plotters)
pp_plotters = [
tup for _, tup in zip(
range(length_plotters),
xarray_var_iter(
posterior_predictive.isel(coords),
var_names=pp_var_names,
skip_dims=set(flatten_pp),
combined=True,
),
)
]
rows, cols = default_grid(length_plotters)
(figsize, ax_labelsize, _, xt_labelsize, linewidth,
markersize) = _scale_fig_size(figsize, textsize, rows, cols)
if ax is None:
fig, axes = _create_axes_grid(length_plotters,
rows,
cols,
figsize=figsize)
else:
axes = np.ravel(ax)
if len(axes) != length_plotters:
raise ValueError(
"Found {} variables to plot but {} axes instances. They must be equal."
.format(length_plotters, len(axes)))
if animated:
fig = axes[0].get_figure()
if not all([ax.get_figure() is fig for ax in axes]):
raise ValueError(
"All axes must be on the same figure for animation to work"
)
for i, ax_i in enumerate(axes):
var_name, selection, obs_vals = obs_plotters[i]
pp_var_name, _, pp_vals = pp_plotters[i]
dtype = posterior_predictive[pp_var_name].dtype.kind
# flatten non-specified dimensions
obs_vals = obs_vals.flatten()
pp_vals = pp_vals.reshape(total_pp_samples, -1)
pp_sampled_vals = pp_vals[pp_sample_ix]
if kind == "kde":
plot_kwargs = {
"color": "C5",
"alpha": alpha,
"linewidth": 0.5 * linewidth
}
if dtype == "i":
plot_kwargs["drawstyle"] = "steps-pre"
ax_i.plot([],
color="C5",
label="Posterior predictive {}".format(pp_var_name))
if dtype == "f":
plot_kde(
obs_vals,
label="Observed {}".format(var_name),
plot_kwargs={
"color": "k",
"linewidth": linewidth,
"zorder": 3
},
fill_kwargs={"alpha": 0},
ax=ax_i,
legend=legend,
)
else:
bins = get_bins(obs_vals)
hist, bin_edges = histogram(obs_vals, bins=bins)
hist = np.concatenate((hist[:1], hist))
ax_i.plot(
bin_edges,
hist,
label="Observed {}".format(var_name),
color="k",
linewidth=linewidth,
zorder=3,
drawstyle=plot_kwargs["drawstyle"],
)
pp_densities = []
pp_xs = []
for vals in pp_sampled_vals:
vals = np.array([vals]).flatten()
if dtype == "f":
pp_density, lower, upper = _fast_kde(vals)
pp_x = np.linspace(lower, upper, len(pp_density))
pp_densities.append(pp_density)
pp_xs.append(pp_x)
else:
bins = get_bins(vals)
hist, bin_edges = histogram(vals, bins=bins)
hist = np.concatenate((hist[:1], hist))
pp_densities.append(hist)
pp_xs.append(bin_edges)
if animated:
animate, init = _set_animation(pp_sampled_vals,
ax_i,
dtype=dtype,
kind=kind,
plot_kwargs=plot_kwargs)
else:
if dtype == "f":
ax_i.plot(np.transpose(pp_xs), np.transpose(pp_densities),
**plot_kwargs)
else:
for x_s, y_s in zip(pp_xs, pp_densities):
ax_i.plot(x_s, y_s, **plot_kwargs)
if mean:
if dtype == "f":
rep = len(pp_densities)
len_density = len(pp_densities[0])
new_x = np.linspace(np.min(pp_xs), np.max(pp_xs),
len_density)
new_d = np.zeros((rep, len_density))
bins = np.digitize(pp_xs, new_x, right=True)
new_x -= (new_x[1] - new_x[0]) / 2
for irep in range(rep):
new_d[irep][bins[irep]] = pp_densities[irep]
ax_i.plot(
new_x,
new_d.mean(0),
color="C0",
linestyle="--",
linewidth=linewidth,
zorder=2,
label="Posterior predictive mean {}".format(
pp_var_name),
)
else:
vals = pp_vals.flatten()
bins = get_bins(vals)
hist, bin_edges = histogram(vals, bins=bins)
hist = np.concatenate((hist[:1], hist))
ax_i.plot(
bin_edges,
hist,
color="C0",
linewidth=linewidth,
label="Posterior predictive mean {}".format(
pp_var_name),
zorder=2,
linestyle="--",
drawstyle=plot_kwargs["drawstyle"],
)
ax_i.tick_params(labelsize=xt_labelsize)
ax_i.set_yticks([])
elif kind == "cumulative":
drawstyle = "default" if dtype == "f" else "steps-pre"
ax_i.plot(*_empirical_cdf(obs_vals),
color="k",
linewidth=linewidth,
label="Observed {}".format(var_name),
drawstyle=drawstyle,
zorder=3)
if animated:
animate, init = _set_animation(
pp_sampled_vals,
ax_i,
kind=kind,
alpha=alpha,
drawstyle=drawstyle,
linewidth=linewidth,
)
else:
pp_densities = np.empty(
(2 * len(pp_sampled_vals), pp_sampled_vals[0].size))
for idx, vals in enumerate(pp_sampled_vals):
vals = np.array([vals]).flatten()
pp_x, pp_density = _empirical_cdf(vals)
pp_densities[2 * idx] = pp_x
pp_densities[2 * idx + 1] = pp_density
ax_i.plot(*pp_densities,
alpha=alpha,
color="C5",
drawstyle=drawstyle,
linewidth=linewidth)
ax_i.plot([],
color="C5",
label="Posterior predictive {}".format(pp_var_name))
if mean:
ax_i.plot(
*_empirical_cdf(pp_vals.flatten()),
color="C0",
linestyle="--",
linewidth=linewidth,
drawstyle=drawstyle,
label="Posterior predictive mean {}".format(pp_var_name))
ax_i.set_yticks([0, 0.5, 1])
elif kind == "scatter":
if mean:
if dtype == "f":
plot_kde(
pp_vals.flatten(),
plot_kwargs={
"color": "C0",
"linestyle": "--",
"linewidth": linewidth,
"zorder": 3,
},
label="Posterior predictive mean {}".format(
pp_var_name),
ax=ax_i,
legend=legend,
)
else:
vals = pp_vals.flatten()
bins = get_bins(vals)
hist, bin_edges = histogram(vals, bins=bins)
hist = np.concatenate((hist[:1], hist))
ax_i.plot(
bin_edges,
hist,
color="C0",
linewidth=linewidth,
label="Posterior predictive mean {}".format(
pp_var_name),
zorder=3,
linestyle="--",
drawstyle="steps-pre",
)
_, limit = ax_i.get_ylim()
limit *= 1.05
y_rows = np.linspace(0, limit, num_pp_samples + 1)
jitter_scale = y_rows[1] - y_rows[0]
scale_low = 0
scale_high = jitter_scale * jitter
obs_yvals = np.zeros_like(obs_vals, dtype=np.float64)
if jitter:
obs_yvals += np.random.uniform(low=scale_low,
high=scale_high,
size=len(obs_vals))
ax_i.plot(
obs_vals,
obs_yvals,
"o",
color="C0",
markersize=markersize,
alpha=alpha,
label="Observed {}".format(var_name),
zorder=4,
)
if animated:
animate, init = _set_animation(
pp_sampled_vals,
ax_i,
kind=kind,
height=y_rows.mean() * 0.5,
markersize=markersize,
)
else:
for vals, y in zip(pp_sampled_vals, y_rows[1:]):
vals = np.ravel(vals)
yvals = np.full_like(vals, y, dtype=np.float64)
if jitter:
yvals += np.random.uniform(low=scale_low,
high=scale_high,
size=len(vals))
ax_i.plot(vals,
yvals,
"o",
zorder=2,
color="C5",
markersize=markersize,
alpha=alpha)
ax_i.plot([],
"C5o",
label="Posterior predictive {}".format(pp_var_name))
ax_i.set_yticks([])
if var_name != pp_var_name:
xlabel = "{} / {}".format(var_name, pp_var_name)
else:
xlabel = var_name
ax_i.set_xlabel(make_label(xlabel, selection), fontsize=ax_labelsize)
if legend:
if i == 0:
ax_i.legend(fontsize=xt_labelsize * 0.75)
else:
ax_i.legend([])
if animated:
ani = animation.FuncAnimation(fig,
animate,
np.arange(0, num_pp_samples),
init_func=init,
**animation_kwargs)
return axes, ani
else:
return axes
def par_trend(n, input_param):
"""
:param n: int: parallel index
:param input_param: type(dict):
{'dt': time-date array
'path2data': path where to read data series for which calculating the trend
'path2slopes': path where to save the calculated trend infos
'data_mask': mask array, set the index of valid pixels (not-nan)
'head': head defining the name of temporary data to read
'step': used to define the index of the matrix-chunk relative to the current loop
'nloops': parallel loop index number
'dbg': enables debug mode
'fid': object of kind open(filename) points to the log_file where to write
debug output
'frequency' frequency of the timeseries, i.e. how many observations per year
:return:
Save a npy temporary file into the save_path directory for each loop of parallel cycle
"""
dt = input_param['dt']
d_path = input_param['path2data']
s_path = input_param['path2slopes']
head = input_param['head']
wm = input_param['data_mask']
step = input_param['step']
nloops = input_param['nloops']
dbg = input_param['dbg']
fid = input_param['fid']
frequency = input_param['frequency']
threshold = input_param['threshold']
if dbg:
fid.writelines('start parallel loop index ' + str(n) + '\n')
sl_name = s_path + head + '-' + str(n).zfill(2) + '.npy'
if not os.path.exists(sl_name):
i0 = step * n
i1 = (n + 1) * step
if n + 1 == nloops:
i1 = None
# reading temporary data chunk as saved in previous step
if dbg:
fid.writelines('reading chunk ' + str(n) + '\n')
data = np.load(d_path + head + '-' + str(n).zfill(2) + '.npy')
wm = wm[i0:i1]
ind_good = np.where(wm != 0)
slopes = np.full_like(wm, fill_value=np.nan)
interc = np.full_like(wm, fill_value=np.nan)
pvalue = np.full_like(wm, fill_value=np.nan)
for k in ind_good[0]:
d = data[k, :]
ts = pd.Series(d, index=pd.to_datetime(dt))
trend_out = mk.seasonal_test(ts, period=frequency, alpha=threshold)
slopes[k] = trend_out.slope / frequency
interc[k] = trend_out.intercept
pvalue[k] = trend_out.p
np.save(sl_name, [slopes, interc, pvalue])
if dbg:
fid.writelines('end parallel loop index ' + str(n) + '\n')
d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
print(d)
print(d[0, :, 0])
print('replace')
d[:, 0, :] = [[0, 0], [0, 0]]
print(d)
print('Different types of Arrays:')
print('All 0s matrix')
print(np.zeros((2, 3, 3)))
print('All ones')
print(np.ones((3, 3)))
print('Any other values:')
print(np.full((4, 2), 55))
print('Any other values(full_like):')
print(np.full_like(b, 44))
print(' or:')
print(np.full(b.shape, 33, b.dtype))
print('Random decimal(десятичная дробь) numbers:')
print(np.random.rand(3, 4))
print(np.random.random_sample(b.shape))
print('Random integer numbers:')
print(np.random.randint(5, 100, size=(3, 4)))
print(np.random.randint(10, size=(10, 5)))
print('The identity matrix (единичная)')
print(np.identity(5))
print('repeat an array')
arr = np.array([[1, 2, 3]])