def testOneShotIteratorInsideContainer(self):
components = (np.arange(7),
np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis],
np.array(37.0) * np.arange(7))
def within_container():
def _map_fn(x, y, z):
return math_ops.square(x), math_ops.square(y), math_ops.square(z)
iterator = (dataset_ops.Dataset.from_tensor_slices(components)
.map(_map_fn).repeat(14).make_one_shot_iterator())
return iterator.get_next()
server = server_lib.Server.create_local_server()
# Create two iterators within unique containers, and run them to
# make sure that the resources aren't shared.
#
# The test below would fail if cname were the same across both
# sessions.
for i in range(2):
with session.Session(server.target) as sess:
cname = "iteration%d" % i
with ops.container(cname):
get_next = within_container()
for _ in range(14):
for i in range(7):
result = sess.run(get_next)
for component, result_component in zip(components, result):
self.assertAllEqual(component[i]**2, result_component)
with self.assertRaises(errors.OutOfRangeError):
sess.run(get_next)
def showOverlapTable(modes_x, modes_y, **kwargs):
"""Show overlap table using :func:`~matplotlib.pyplot.pcolor`. *modes_x*
and *modes_y* are sets of normal modes, and correspond to x and y axes of
the plot. Note that mode indices are incremented by 1. List of modes
is assumed to contain a set of contiguous modes from the same model.
Default arguments for :func:`~matplotlib.pyplot.pcolor`:
* ``cmap=plt.cm.jet``
* ``norm=plt.normalize(0, 1)``"""
import matplotlib.pyplot as plt
overlap = abs(calcOverlap(modes_y, modes_x))
if overlap.ndim == 0:
overlap = np.array([[overlap]])
elif overlap.ndim == 1:
overlap = overlap.reshape((modes_y.numModes(), modes_x.numModes()))
cmap = kwargs.pop('cmap', plt.cm.jet)
norm = kwargs.pop('norm', plt.normalize(0, 1))
show = (plt.pcolor(overlap, cmap=cmap, norm=norm, **kwargs),
plt.colorbar())
x_range = np.arange(1, modes_x.numModes() + 1)
plt.xticks(x_range-0.5, x_range)
plt.xlabel(str(modes_x))
y_range = np.arange(1, modes_y.numModes() + 1)
plt.yticks(y_range-0.5, y_range)
plt.ylabel(str(modes_y))
plt.axis([0, modes_x.numModes(), 0, modes_y.numModes()])
if SETTINGS['auto_show']:
showFigure()
return show
def unproject_depth_image(self, depth_image, camera_space=False):
us = np.arange(depth_image.size) % depth_image.shape[1]
vs = np.arange(depth_image.size) // depth_image.shape[1]
ds = depth_image.ravel()
uvd = ch.array(np.vstack((us.ravel(), vs.ravel(), ds.ravel())).T)
xyz = self.unproject_points(uvd, camera_space=camera_space)
return xyz.reshape((depth_image.shape[0], depth_image.shape[1], -1))
def _update_datamap(self):
self._last_region = []
# Create a new grid of the appropriate size, initialize it with new
# Cell instance (of type self.celltype), and perform point insertion
# on the new data.
if self._data is None:
self._cellgrid = array([], dtype=object)
self._cell_lefts = array([])
self._cell_bottoms = array([])
else:
num_x_cells, num_y_cells = self._calc_grid_dimensions()
self._cellgrid = zeros((num_x_cells, num_y_cells), dtype=object)
for i in range(num_x_cells):
for j in range(num_y_cells):
self._cellgrid[i,j] = self.celltype(parent=self)
ll, ur = self._extents
cell_width = ur[0]/num_x_cells
cell_height = ur[1]/num_y_cells
# calculate the left and bottom edges of all the cells and store
# them in two arrays
self._cell_lefts = arange(ll[0], ll[0]+ur[0]-cell_width/2, step=cell_width)
self._cell_bottoms = arange(ll[1], ll[1]+ur[1]-cell_height/2, step=cell_height)
self._cell_extents = (cell_width, cell_height)
# insert the data points
self._basic_insertion(self.celltype)
return
def compute_dr_wrt(self, wrt):
if wrt not in (self.v, self.rt, self.t):
return
if wrt is self.t:
if not hasattr(self, '_drt') or self._drt.shape[0] != self.v.r.size:
IS = np.arange(self.v.r.size)
JS = IS % 3
data = np.ones(len(IS))
self._drt = sp.csc_matrix((data, (IS, JS)))
return self._drt
if wrt is self.rt:
rot, rot_dr = cv2.Rodrigues(self.rt.r)
rot_dr = rot_dr.reshape((3,3,3))
dr = np.einsum('abc, zc -> zba', rot_dr, self.v.r).reshape((-1,3))
return dr
if wrt is self.v:
rot = cv2.Rodrigues(self.rt.r)[0]
IS = np.repeat(np.arange(self.v.r.size), 3)
JS = np.repeat(np.arange(self.v.r.size).reshape((-1,3)), 3, axis=0)
data = np.vstack([rot for i in range(self.v.r.size/3)])
result = sp.csc_matrix((data.ravel(), (IS.ravel(), JS.ravel())))
return result
def show_plot(X, y, n_neighbors=10, h=0.2):
# Create color maps
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF','#FFAAAA', '#AAFFAA', '#AAAAFF','#FFAAAA', '#AAFFAA', '#AAAAFF','#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF','#FF0000','#FF0000','#FF0000','#FF0000','#FF0000','#FF0000','#FF0000',])
for weights in ['uniform', 'distance']:
# we create an instance of Neighbours Classifier and fit the data.
clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
clf.fit(X, y)
clf.n_neighbors = n_neighbors
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.title("3-Class classification (k = %i, weights = '%s')"
% (n_neighbors, weights))
plt.show()
def test_basic_instantiation(self):
'''
Tests the basic instantiation of the SHIFT class
'''
# Instantiatiation with float
self.model = Shift(5.0)
np.testing.assert_array_almost_equal(self.model.target_magnitudes,
np.array([5.0]))
self.assertEqual(self.model.number_magnitudes, 1)
# Instantiation with a numpy array
self.model = Shift(np.arange(5., 8., 0.5))
np.testing.assert_array_almost_equal(self.model.target_magnitudes,
np.arange(5., 8., 0.5))
self.assertEqual(self.model.number_magnitudes, 6)
# Instantiation with list
self.model = Shift([5., 6., 7., 8.])
np.testing.assert_array_almost_equal(self.model.target_magnitudes,
np.array([5., 6., 7., 8.]))
self.assertEqual(self.model.number_magnitudes, 4)
# Otherwise raise an error
with self.assertRaises(ValueError) as ae:
self.model = Shift(None)
self.assertEqual(ae.exception.message,
'Minimum magnitudes must be float, list or array')
# Check regionalisation - assuming defaults
self.model = Shift(5.0)
for region in self.model.regionalisation.keys():
self.assertDictEqual(BIRD_GLOBAL_PARAMETERS[region],
self.model.regionalisation[region])
np.testing.assert_array_almost_equal(np.log10(self.model.base_rate),
np.array([-20.74610902]))
def createContext(self, item, plot, onlimits):
self.origin = item.getOrigin()
self.scale = item.getScale()
self.data = item.getData(copy=True)
if onlimits:
minX, maxX = plot.getXAxis().getLimits()
minY, maxY = plot.getYAxis().getLimits()
XMinBound = int((minX - self.origin[0]) / self.scale[0])
YMinBound = int((minY - self.origin[1]) / self.scale[1])
XMaxBound = int((maxX - self.origin[0]) / self.scale[0])
YMaxBound = int((maxY - self.origin[1]) / self.scale[1])
XMinBound = max(XMinBound, 0)
YMinBound = max(YMinBound, 0)
if XMaxBound <= XMinBound or YMaxBound <= YMinBound:
self.data = None
else:
self.data = self.data[YMinBound:YMaxBound + 1,
XMinBound:XMaxBound + 1]
if self.data.size > 0:
self.min, self.max = min_max(self.data)
else:
self.min, self.max = None, None
self.values = self.data
if self.values is not None:
self.axes = (self.origin[1] + self.scale[1] * numpy.arange(self.data.shape[0]),
self.origin[0] + self.scale[0] * numpy.arange(self.data.shape[1]))
def test_array_richcompare_legacy_weirdness(self):
# It doesn't really work to use assert_deprecated here, b/c part of
# the point of assert_deprecated is to check that when warnings are
# set to "error" mode then the error is propagated -- which is good!
# But here we are testing a bunch of code that is deprecated *because*
# it has the habit of swallowing up errors and converting them into
# different warnings. So assert_warns will have to be sufficient.
assert_warns(FutureWarning, lambda: np.arange(2) == "a")
assert_warns(FutureWarning, lambda: np.arange(2) != "a")
# No warning for scalar comparisons
with warnings.catch_warnings():
warnings.filterwarnings("error")
assert_(not (np.array(0) == "a"))
assert_(np.array(0) != "a")
assert_(not (np.int16(0) == "a"))
assert_(np.int16(0) != "a")
for arg1 in [np.asarray(0), np.int16(0)]:
struct = np.zeros(2, dtype="i4,i4")
for arg2 in [struct, "a"]:
for f in [operator.lt, operator.le, operator.gt, operator.ge]:
if sys.version_info[0] >= 3:
# py3
with warnings.catch_warnings() as l:
warnings.filterwarnings("always")
assert_raises(TypeError, f, arg1, arg2)
assert_(not l)
else:
# py2
assert_warns(DeprecationWarning, f, arg1, arg2)
def test_no_interpolation_origin():
fig = plt.figure()
ax = fig.add_subplot(211)
ax.imshow(np.arange(100).reshape((2, 50)), origin="lower", interpolation='none')
ax = fig.add_subplot(212)
ax.imshow(np.arange(100).reshape((2, 50)), interpolation='none')
def scree_plot(pca_obj, fname=None):
'''
Scree plot for variance & cumulative variance by component from PCA.
Arguments:
- pca_obj: a fitted sklearn PCA instance
- fname: path to write plot to file
Output:
- scree plot
'''
components = pca_obj.n_components_
variance = pca.explained_variance_ratio_
plt.figure()
plt.plot(np.arange(1, components + 1), np.cumsum(variance), label='Cumulative Variance')
plt.plot(np.arange(1, components + 1), variance, label='Variance')
plt.xlim([0.8, components]); plt.ylim([0.0, 1.01])
plt.xlabel('No. Components', labelpad=11); plt.ylabel('Variance Explained', labelpad=11)
plt.legend(loc='best')
plt.tight_layout()
if fname is not None:
plt.savefig(fname)
plt.close()
else:
plt.show()
return
def main():
y, x = svmutil.svm_read_problem("char_recon_shuffled.db")
x_train = x[:1800]
y_train = y[:1800]
x_val = x[1800:]
y_val = y[1800:]
gamma_c_pairs = [GammaCPair(1.0 / (2.0 * (3.0 ** log_sigma) ** 2), 3.0 ** log_C)
for log_sigma in [7]
for log_C in [3]
]
log_log_pairs = [[log_sigma, log_C]
for log_sigma in np.arange(6, 10, 0.5)
for log_C in np.arange(0, 5, 0.5)
]
def cv(gamma_c):
return get_cross_val(x_train, y_train, x_val, y_val, gamma_c)
cross_val = []
for gamma_c in gamma_c_pairs:
cross_val.append(cv(gamma_c))
f = open("gamma_c", "w")
for i in range(len(gamma_c_pairs)):
f.write("{0} {1} {2}\n".format(log_log_pairs[i][0], log_log_pairs[i][1], cross_val[i]))
f.close()
def barplot(grouped_df, _column, statistic, levels=[0]):
means = grouped_df.groupby(level=levels).mean()
bar_width = 1.0/(len(means.index))
error_config = {'ecolor': '0.1'}
sems = grouped_df.groupby(level=levels).sem().fillna(0)
fig = plt.figure()
fig.set_size_inches(10,6)
ax = fig.add_subplot(1,1,1)
plt.bar(np.arange(0.1,(len(means.index)+0.1),1),
means[_column].fillna(0),
color= '#AAAAAA',
yerr=sems[_column].fillna(0),
error_kw=error_config,
label=list(means.index))
if means[_column].values.min() >= 0:
ax.set_ylim(0,1.1*((means[_column] + sems[_column]).values.max()))
else:
ax.set_ylim(1.1*((means[_column] - sems[_column]).values.min()),1.1*((means[_column] + sems[_column]).values.max()))
ax.set_ylabel(statistic + ' ' + u"\u00B1" + ' SEM', fontsize=20) # +/- sign is u"\u00B1"
ax.set_xticks(np.arange(0.1+bar_width/2.0,(len(means.index)+0.1+(bar_width/2.0)),1))
ax.set_xticklabels(list(means.index), rotation=90)
ax.tick_params(axis='y', labelsize=16 )
ax.set_xlabel('Group', fontsize=20)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
return fig
def all_patches(padded_brain,i,predict_patchsize,obs_patchsize,num_channels):
image = padded_brain[i]
ishape_h , ishape_w = padded_brain.shape[1:3]
#ipdb.set_trace()
#ipdb.set_trace()
half_obs_patchsize = obs_patchsize/2
half_predict_patchsize = predict_patchsize/2
extended_image = np.zeros((ishape_h+obs_patchsize-predict_patchsize,ishape_w+obs_patchsize-predict_patchsize,num_channels))
extended_image[half_obs_patchsize - half_predict_patchsize : -(half_obs_patchsize - half_predict_patchsize),half_obs_patchsize - half_predict_patchsize : -(half_obs_patchsize - half_predict_patchsize)]= image
num_patches_rows = ishape_h // predict_patchsize
num_patches_cols = ishape_w // predict_patchsize
list_patches = np.zeros((num_patches_cols*num_patches_rows, obs_patchsize, obs_patchsize, num_channels))
index = 0
h_range = np.arange(obs_patchsize/2,ishape_h+obs_patchsize/2,predict_patchsize)
#h_range = h_range[:-1]
v_range = np.arange(obs_patchsize/2,ishape_w+obs_patchsize/2,predict_patchsize)
#v_range = v_range[:-1]
#ipdb.set_trace()
for index_h in h_range:
for index_w in v_range:
patch_brian = extended_image[index_h-obs_patchsize/2: index_h+obs_patchsize/2 ,index_w-obs_patchsize/2: index_w+obs_patchsize/2,:]
#if patch_brian.shape == (38,29,4):
# ipdb.set_trace()
list_patches[index,:,:,:] = patch_brian
index += 1
#ipdb.set_trace()
assert index == num_patches_rows*num_patches_cols
return list_patches
def __unit_test_onset_function(metric):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
# First, test for a warning on empty onsets
metric(np.array([]), np.arange(10))
assert len(w) == 1
assert issubclass(w[-1].category, UserWarning)
assert str(w[-1].message) == "Reference onsets are empty."
metric(np.arange(10), np.array([]))
assert len(w) == 2
assert issubclass(w[-1].category, UserWarning)
assert str(w[-1].message) == "Estimated onsets are empty."
# And that the metric is 0
assert np.allclose(metric(np.array([]), np.array([])), 0)
# Now test validation function - onsets must be 1d ndarray
onsets = np.array([[1., 2.]])
nose.tools.assert_raises(ValueError, metric, onsets, onsets)
# onsets must be in seconds (so not huge)
onsets = np.array([1e10, 1e11])
nose.tools.assert_raises(ValueError, metric, onsets, onsets)
# onsets must be sorted
onsets = np.array([2., 1.])
nose.tools.assert_raises(ValueError, metric, onsets, onsets)
# Valid onsets which are the same produce a score of 1 for all metrics
onsets = np.arange(10, dtype=np.float)
assert np.allclose(metric(onsets, onsets), 1)
def svm_loss(x, y):
"""
Computes the loss and gradient using for multiclass SVM classification.
Inputs:
- x: Input data, of shape (N, C) where x[i, j] is the score for the jth class
for the ith input.
- y: Vector of labels, of shape (N,) where y[i] is the label for x[i] and
0 <= y[i] < C
Returns a tuple of:
- loss: Scalar giving the loss
- dx: Gradient of the loss with respect to x
"""
N = x.shape[0]
correct_class_scores = x[np.arange(N), y]
margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)
margins[np.arange(N), y] = 0
loss = np.sum(margins) / N
num_pos = np.sum(margins > 0, axis=1)
dx = np.zeros_like(x)
dx[margins > 0] = 1
dx[np.arange(N), y] -= num_pos
dx /= N
return loss, dx
def test_series(self):
# GH6407
# inferring series
# invalid type of Series
for s in [ Series(np.arange(10)),
Series(np.arange(10.))]:
self.assertRaises(TypeError, lambda : infer_freq(s))
# a non-convertible string
self.assertRaises(ValueError, lambda : infer_freq(Series(['foo','bar'])))
# cannot infer on PeriodIndex
for freq in [None, 'L', 'Y']:
s = Series(period_range('2013',periods=10,freq=freq))
self.assertRaises(TypeError, lambda : infer_freq(s))
# DateTimeIndex
for freq in ['M', 'L', 'S']:
s = Series(date_range('20130101',periods=10,freq=freq))
inferred = infer_freq(s)
self.assertEqual(inferred,freq)
s = Series(date_range('20130101','20130110'))
inferred = infer_freq(s)
self.assertEqual(inferred,'D')
def test_arrayrepr():
"""Check the array representation."""
# Check resize
clf = tree.DecisionTreeRegressor(max_depth=None)
X = np.arange(10000)[:, np.newaxis]
y = np.arange(10000)
clf.fit(X, y)
def _setup_plot(self):
self.plot.clear()
explained_ratio = self._variance_ratio
explained = self._cumulative
p = min(len(self._variance_ratio), self.maxp)
self.plot.plot(numpy.arange(p), explained_ratio[:p],
pen=pg.mkPen(QColor(Qt.red), width=2),
antialias=True,
name="Variance")
self.plot.plot(numpy.arange(p), explained[:p],
pen=pg.mkPen(QColor(Qt.darkYellow), width=2),
antialias=True,
name="Cumulative Variance")
cutpos = self._nselected_components() - 1
self._line = pg.InfiniteLine(
angle=90, pos=cutpos, movable=True, bounds=(0, p - 1))
self._line.setCursor(Qt.SizeHorCursor)
self._line.setPen(pg.mkPen(QColor(Qt.black), width=2))
self._line.sigPositionChanged.connect(self._on_cut_changed)
self.plot.addItem(self._line)
self.plot_horlines = (
pg.PlotCurveItem(pen=pg.mkPen(QColor(Qt.blue), style=Qt.DashLine)),
pg.PlotCurveItem(pen=pg.mkPen(QColor(Qt.blue), style=Qt.DashLine)))
self.plot_horlabels = (
pg.TextItem(color=QColor(Qt.black), anchor=(1, 0)),
pg.TextItem(color=QColor(Qt.black), anchor=(1, 1)))
for item in self.plot_horlabels + self.plot_horlines:
self.plot.addItem(item)
self._set_horline_pos()
self.plot.setRange(xRange=(0.0, p - 1), yRange=(0.0, 1.0))
self._update_axis()
def adwin_ultrafast_M_set_input (self, msmnt_result = [], nr_coeff = 1): self.max_k = 2**self.N discr_steps = 2*self.max_k+1 self.msmnt_phases = np.zeros((self.reps,self.N)) self.msmnt_times = np.zeros(self.N) self.msmnt_results = np.zeros((self.reps,self.N)) m = np.zeros (self.N+1) t = np.zeros (self.N+1) th = np.zeros(self.N+1) p_real = np.zeros (discr_steps) p_imag = np.zeros (discr_steps) p_real [0] = 1./(2*np.pi) t[0] = 2**self.N for n in np.arange(self.N)+1: t[n] = int(2**(self.N-n)) k_opt = int(2**(self.N-n+1)) th[n] = -0.5*np.angle(-1j*p_imag[k_opt]+p_real[k_opt]) nr_ones = msmnt_result [n-1] nr_zeros = self.M - nr_ones for mmm in np.arange(nr_ones): for j in np.arange(nr_coeff)-nr_coeff/2: if (k_opt+j*t[n]>=0): p_real, p_imag = self.adwin_update (p_real = p_real, p_imag = p_imag, meas_res = 1, phase = th[n], tn = t[n], k=k_opt+j*t[n]) for mmm in np.arange(nr_zeros): for j in np.arange(nr_coeff)-nr_coeff/2: if (k_opt+j*t[n]>=0): p_real, p_imag = self.adwin_update (p_real = p_real, p_imag = p_imag, meas_res = 0, phase = th[n], tn = t[n], k=k_opt+j*t[n]) return th[1:]
def plot_figs(fig_num, elev, azim, a):
fig = plt.figure(fig_num, figsize=(4, 3))
plt.clf()
ax = Axes3D(fig, elev=elev, azim=azim)
ax.scatter(body_mass, work_level, heat_output, c='k', marker='+')
X = np.arange(55, 85, 0.5)
Y = np.arange(90, 180, 0.5)
X, Y = np.meshgrid(X, Y)
Z = a[0] + a[1]*X + +(Y/(a[2]+a[3]*X))
ax.plot_surface(X, Y, Z,alpha=.5, antialiased=True,rstride=200, cstride=100, cmap=plt.cm.coolwarm)
ax.set_xlabel('BODY_MASS', color='b')
ax.set_ylabel('WORK_LEVEL', color='b')
ax.set_zlabel('HEAT_OUTPUT', color='b')
ax.w_xaxis.set_ticklabels([])
ax.w_yaxis.set_ticklabels([])
ax.w_zaxis.set_ticklabels([])
ax.zaxis.set_major_locator(plt.LinearLocator(10))
ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.f'))
ax.yaxis.set_major_formatter(plt.FormatStrFormatter('%.f'))
ax.zaxis.set_major_formatter(plt.FormatStrFormatter('%.f'))
def draw(data, classes, model, resolution=100):
mycm = mpl.cm.get_cmap('Paired')
one_min, one_max = data[:, 0].min()-0.1, data[:, 0].max()+0.1
two_min, two_max = data[:, 1].min()-0.1, data[:, 1].max()+0.1
xx1, xx2 = np.meshgrid(np.arange(one_min, one_max, (one_max-one_min)/resolution),
np.arange(two_min, two_max, (two_max-two_min)/resolution))
inputs = np.c_[xx1.ravel(), xx2.ravel()]
z = []
for i in range(len(inputs)):
z.append(predict(model, inputs[i])[0])
result = np.array(z).reshape(xx1.shape)
plt.contourf(xx1, xx2, result, cmap=mycm)
plt.scatter(data[:, 0], data[:, 1], s=50, c=classes, cmap=mycm)
t = np.zeros(15)
for i in range(15):
if i < 5:
t[i] = 0
elif i < 10:
t[i] = 1
else:
t[i] = 2
plt.scatter(model[:, 0], model[:, 1], s=150, c=t, cmap=mycm)
plt.xlim([0, 10])
plt.ylim([0, 10])
plt.show()
def test_sort_index_multicolumn(self):
import random
A = np.arange(5).repeat(20)
B = np.tile(np.arange(5), 20)
random.shuffle(A)
random.shuffle(B)
frame = DataFrame({'A': A, 'B': B,
'C': np.random.randn(100)})
# use .sort_values #9816
with tm.assert_produces_warning(FutureWarning):
frame.sort_index(by=['A', 'B'])
result = frame.sort_values(by=['A', 'B'])
indexer = np.lexsort((frame['B'], frame['A']))
expected = frame.take(indexer)
assert_frame_equal(result, expected)
# use .sort_values #9816
with tm.assert_produces_warning(FutureWarning):
frame.sort_index(by=['A', 'B'], ascending=False)
result = frame.sort_values(by=['A', 'B'], ascending=False)
indexer = np.lexsort((frame['B'].rank(ascending=False),
frame['A'].rank(ascending=False)))
expected = frame.take(indexer)
assert_frame_equal(result, expected)
# use .sort_values #9816
with tm.assert_produces_warning(FutureWarning):
frame.sort_index(by=['B', 'A'])
result = frame.sort_values(by=['B', 'A'])
indexer = np.lexsort((frame['A'], frame['B']))
expected = frame.take(indexer)
assert_frame_equal(result, expected)
def get_gabors(self, rf):
lams = float(rf[0])/self.sfs # lambda = 1./sf #1./np.array([.1,.25,.4])
sigma = rf[0]/2./np.pi
# rf = [100,100]
gabors = np.zeros(( len(oris),len(phases),len(lams), rf[0], rf[1] ))
i = np.arange(-rf[0]/2+1,rf[0]/2+1)
#print i
j = np.arange(-rf[1]/2+1,rf[1]/2+1)
ii,jj = np.meshgrid(i,j)
for o, theta in enumerate(self.oris):
x = ii*np.cos(theta) + jj*np.sin(theta)
y = -ii*np.sin(theta) + jj*np.cos(theta)
for p, phase in enumerate(self.phases):
for s, lam in enumerate(lams):
fxx = np.cos(2*np.pi*x/lam + phase) * np.exp(-(x**2+y**2)/(2*sigma**2))
fxx -= np.mean(fxx)
fxx /= np.linalg.norm(fxx)
#if p==0:
#plt.subplot(len(oris),len(lams),count+1)
#plt.imshow(fxx,cmap=mpl.cm.gray,interpolation='bicubic')
#count+=1
gabors[o,p,s,:,:] = fxx
plt.show()
return gabors
def test_groupby_groups_datetimeindex(self):
# GH#1430
periods = 1000
ind = pd.date_range(start='2012/1/1', freq='5min', periods=periods)
df = DataFrame({'high': np.arange(periods),
'low': np.arange(periods)}, index=ind)
grouped = df.groupby(lambda x: datetime(x.year, x.month, x.day))
# it works!
groups = grouped.groups
assert isinstance(list(groups.keys())[0], datetime)
# GH#11442
index = pd.date_range('2015/01/01', periods=5, name='date')
df = pd.DataFrame({'A': [5, 6, 7, 8, 9],
'B': [1, 2, 3, 4, 5]}, index=index)
result = df.groupby(level='date').groups
dates = ['2015-01-05', '2015-01-04', '2015-01-03',
'2015-01-02', '2015-01-01']
expected = {pd.Timestamp(date): pd.DatetimeIndex([date], name='date')
for date in dates}
tm.assert_dict_equal(result, expected)
grouped = df.groupby(level='date')
for date in dates:
result = grouped.get_group(date)
data = [[df.loc[date, 'A'], df.loc[date, 'B']]]
expected_index = pd.DatetimeIndex([date], name='date')
expected = pd.DataFrame(data,
columns=list('AB'),
index=expected_index)
tm.assert_frame_equal(result, expected)
def test_stratified_shuffle_split_init():
X = np.arange(7)
y = np.asarray([0, 1, 1, 1, 2, 2, 2])
# Check that error is raised if there is a class with only one sample
assert_raises(ValueError, next,
StratifiedShuffleSplit(3, 0.2).split(X, y))
# Check that error is raised if the test set size is smaller than n_classes
assert_raises(ValueError, next, StratifiedShuffleSplit(3, 2).split(X, y))
# Check that error is raised if the train set size is smaller than
# n_classes
assert_raises(ValueError, next,
StratifiedShuffleSplit(3, 3, 2).split(X, y))
X = np.arange(9)
y = np.asarray([0, 0, 0, 1, 1, 1, 2, 2, 2])
# Check that errors are raised if there is not enough samples
assert_raises(ValueError, StratifiedShuffleSplit, 3, 0.5, 0.6)
assert_raises(ValueError, next,
StratifiedShuffleSplit(3, 8, 0.6).split(X, y))
assert_raises(ValueError, next,
StratifiedShuffleSplit(3, 0.6, 8).split(X, y))
# Train size or test size too small
assert_raises(ValueError, next,
StratifiedShuffleSplit(train_size=2).split(X, y))
assert_raises(ValueError, next,
StratifiedShuffleSplit(test_size=2).split(X, y))
def get_new_columns(self):
if self.value_columns is None:
return self.removed_level
stride = len(self.removed_level)
width = len(self.value_columns)
propagator = np.repeat(np.arange(width), stride)
if isinstance(self.value_columns, MultiIndex):
new_levels = self.value_columns.levels + [self.removed_level]
new_names = self.value_columns.names + [self.removed_name]
new_labels = [lab.take(propagator)
for lab in self.value_columns.labels]
new_labels.append(np.tile(np.arange(stride), width))
else:
new_levels = [self.value_columns, self.removed_level]
new_names = [self.value_columns.name, self.removed_name]
new_labels = []
new_labels.append(propagator)
new_labels.append(np.tile(np.arange(stride), width))
return MultiIndex(levels=new_levels, labels=new_labels,
names=new_names)
def test_sort_index_different_sortorder(self):
A = np.arange(20).repeat(5)
B = np.tile(np.arange(5), 20)
indexer = np.random.permutation(100)
A = A.take(indexer)
B = B.take(indexer)
df = DataFrame({'A': A, 'B': B,
'C': np.random.randn(100)})
# use .sort_values #9816
with tm.assert_produces_warning(FutureWarning):
df.sort_index(by=['A', 'B'], ascending=[1, 0])
result = df.sort_values(by=['A', 'B'], ascending=[1, 0])
ex_indexer = np.lexsort((df.B.max() - df.B, df.A))
expected = df.take(ex_indexer)
assert_frame_equal(result, expected)
# test with multiindex, too
idf = df.set_index(['A', 'B'])
result = idf.sort_index(ascending=[1, 0])
expected = idf.take(ex_indexer)
assert_frame_equal(result, expected)
# also, Series!
result = idf['C'].sort_index(ascending=[1, 0])
assert_series_equal(result, expected['C'])
def test_as_float_array():
# Test function for as_float_array
X = np.ones((3, 10), dtype=np.int32)
X = X + np.arange(10, dtype=np.int32)
# Checks that the return type is ok
X2 = as_float_array(X, copy=False)
np.testing.assert_equal(X2.dtype, np.float32)
# Another test
X = X.astype(np.int64)
X2 = as_float_array(X, copy=True)
# Checking that the array wasn't overwritten
assert_true(as_float_array(X, False) is not X)
# Checking that the new type is ok
np.testing.assert_equal(X2.dtype, np.float64)
# Here, X is of the right type, it shouldn't be modified
X = np.ones((3, 2), dtype=np.float32)
assert_true(as_float_array(X, copy=False) is X)
# Test that if X is fortran ordered it stays
X = np.asfortranarray(X)
assert_true(np.isfortran(as_float_array(X, copy=True)))
# Test the copy parameter with some matrices
matrices = [
np.matrix(np.arange(5)),
sp.csc_matrix(np.arange(5)).toarray(),
sparse_random_matrix(10, 10, density=0.10).toarray()
]
for M in matrices:
N = as_float_array(M, copy=True)
N[0, 0] = np.nan
assert_false(np.isnan(M).any())
def main():
"""The main function."""
# Build data ################
x = np.arange(0, 10000, 500)
y = np.arange(0, 1, 0.05)
xx, yy = np.meshgrid(x, y)
z = np.power(xx,yy)
print "xx ="
print xx
print "yy ="
print yy
print "z ="
print z
# Plot data #################
fig = plt.figure()
ax = axes3d.Axes3D(fig)
surf = ax.plot_surface(xx, yy, z, cmap=cm.jet, rstride=1, cstride=1, color='b', shade=True)
ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.set_zlabel("Z")
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
def test_arange_to(self):
"""
順数のndarrayを作成する(終了のみ指定)
"""
vector = np.arange(5)
assert_array_equal(vector, np.array([0, 1, 2, 3, 4]))
def test_validate_tpm_nonbinary_nodes():
tpm = np.arange(3*3*2).reshape(3, 3, 2)
with pytest.raises(ValueError):
assert validate.tpm(tpm)
def test_validate_tpm_wrong_shape():
tpm = np.arange(3**3).reshape(3, 3, 3)
with pytest.raises(ValueError):
assert validate.tpm(tpm)
from hyperion.io import H5DataWriter
from hyperion.generators.sequence_batch_generator_v1 import SequenceBatchGeneratorV1 as SBG
output_dir = './tests/data_out/generators'
if not os.path.exists(output_dir):
os.makedirs(output_dir)
h5_file = output_dir + '/seqbg.h5'
key_file = output_dir + '/seqbg.scp'
num_seqs = 10
dim = 2
min_seq_length = 100
delta = 10
max_seq_length = min_seq_length + (num_seqs - 1) * delta
seq_lengths = np.arange(100, max_seq_length + 1, delta)
def create_dataset():
file_path = [str(k) for k in xrange(num_seqs)]
key = []
i = 0
j = 0
while i < num_seqs:
key_i = (j + 1) * str(j)
i += (i + 1)
j += 1
key += key_i
key = key[:num_seqs]
# excepcion de fichero
if os.path.isfile("/home/josemo/python/wavfiles/"+file_input):
filename ="/home/josemo/python/wavfiles/"+file_input
#filename ="/home/josemo/"+file_input
print('file exit')
else:
print('File not exit')
exit()
# read wave file
fs, data = wavfile.read(filename)
print(' fs', fs,' shapes ', data.shape, ' data ', data)
# tipical delay, entre 10 y 50 ms, LFO entre 5 y 14 Hz
# chorus parameters
index = np.arange(len(data))
rate1 = 7
rate2 = 10
rate3 = 12
A = 5 # amplitud
lfo1 = A*np.sin(2*np.pi*index*(rate1/fs))
lfo2 = A*np.sin(2*np.pi*index*(rate2/fs))
lfo3 = A*np.sin(2*np.pi*index*(rate3/fs))
# ganancias 1
g = 0.20
delay = int(0.040*fs) # frames
y = np.zeros(len(data))
y[:delay+5] = data[:delay+5]
for i in range (delay +5, len(data)):
def test_arange_with_step(self):
"""
順数のndarrayを作成する(開始と終了とステップを指定)
"""
vector = np.arange(0, 10, 2)
assert_array_equal(vector, np.array([0, 2, 4, 6, 8]))
def test_arange_from_to(self):
"""
順数のndarrayを作成する(開始と終了を指定)
"""
vector = np.arange(0, 5)
assert_array_equal(vector, np.array([0, 1, 2, 3, 4]))
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more
# details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import random, os, datetime, itertools, glob
import numpy
numpy.random.seed(8765432)
random.seed(8765432)
rootdir = os.path.abspath(
os.path.join(os.path.dirname(__file__),
"../remote_client/examples/solarsimulator_raw_data"))
voltages = numpy.arange(-0.1, 1.001, 0.01)
for filepath in glob.glob(os.path.join(rootdir, "measurement-*.dat")):
os.remove(filepath)
shapes_inm = {
"1": ((18, 18.5), (10, 10)),
"2": ((31.5, 18.5), (10, 10)),
"3": ((43, 18.5), (10, 10)),
"4": ((56.5, 18.5), (10, 10)),
"5": ((68, 18.5), (10, 10)),
"6": ((81.5, 18.5), (10, 10)),
"7": ((18, 32.5), (10, 5)),
"8": ((33, 32.5), (20, 5)),
"9": ((60, 32.5), (2, 5)),
"10": ((64, 32.5), (2, 5)),
def Simons_Observatory_V3_LA_noise(sensitivity_mode,
f_sky,
ell_max,
delta_ell,
N_LF=1.,
N_MF=4.,
N_UHF=2.,
survey_time=5.):
## returns noise curves in both temperature and polarization, including the impact of the beam, for the SO large aperture telescope
# sensitivity_mode:
# 1: baseline,
# 2: goal
# f_sky: number from 0-1
# ell_max: the maximum value of ell used in the computation of N(ell)
# delta_ell: the step size for computing N_ell
####################################################################
####################################################################
### Internal variables
## LARGE APERTURE
# configuration
# ensure valid parameter choices
assert (sensitivity_mode == 1 or sensitivity_mode == 2)
assert (f_sky > 0. and f_sky <= 1.)
assert (ell_max <= 2e4)
assert (delta_ell >= 1)
# ensure total is 7
if (N_LF + N_MF + N_UHF) != 7:
print("WARNING! You requested:", N_LF + N_MF + N_UHF,
" optics tubes while SO LAT design is for 7")
NTubes_LF = N_LF #default = 1
NTubes_MF = N_MF #default = 4.
NTubes_UHF = N_UHF #default = 2.
# sensitivity in uK*sqrt(s)
# set noise to irrelevantly high value when NTubes=0
# note that default noise levels are for 1-4-2 tube configuration
if (NTubes_LF == 0.):
S_LA_27 = 1.e9 * np.ones(3)
S_LA_39 = 1.e9 * np.ones(3)
else:
S_LA_27 = np.array([1.e9, 48., 35.]) * np.sqrt(
1. / NTubes_LF) ## converting these to per tube sensitivities
S_LA_39 = np.array([1.e9, 24., 18.]) * np.sqrt(1. / NTubes_LF)
if (NTubes_MF == 0.):
S_LA_93 = 1.e9 * np.ones(3)
S_LA_145 = 1.e9 * np.ones(3)
else:
S_LA_93 = np.array([1.e9, 5.4, 3.9]) * np.sqrt(4. / NTubes_MF)
S_LA_145 = np.array([1.e9, 6.7, 4.2]) * np.sqrt(4. / NTubes_MF)
if (NTubes_UHF == 0.):
S_LA_225 = 1.e9 * np.ones(3)
S_LA_280 = 1.e9 * np.ones(3)
else:
S_LA_225 = np.array([1.e9, 15., 10.]) * np.sqrt(2. / NTubes_UHF)
S_LA_280 = np.array([1.e9, 36., 25.]) * np.sqrt(2. / NTubes_UHF)
# 1/f polarization noise -- see Sec. 2.2 of SO science goals paper
f_knee_pol_LA_27 = 700.
f_knee_pol_LA_39 = 700.
f_knee_pol_LA_93 = 700.
f_knee_pol_LA_145 = 700.
f_knee_pol_LA_225 = 700.
f_knee_pol_LA_280 = 700.
alpha_pol = -1.4
# atmospheric 1/f temperature noise -- see Sec. 2.2 of SO science goals paper
C_27 = 200.
C_39 = 77.
C_93 = 1800.
C_145 = 12000.
C_225 = 68000.
C_280 = 124000.
alpha_temp = -3.5
####################################################################
## calculate the survey area and time
#survey_time = 5. #years
t = survey_time * 365.25 * 24. * 3600. ## convert years to seconds
t = t * 0.2 ## retention after observing efficiency and cuts
t = t * 0.85 ## a kludge for the noise non-uniformity of the map edges
A_SR = 4. * np.pi * f_sky ## sky areas in steradians
A_deg = A_SR * (180 / np.pi)**2 ## sky area in square degrees
A_arcmin = A_deg * 3600.
print("sky area: ", A_deg, "degrees^2")
####################################################################
## make the ell array for the output noise curves
ell = np.arange(2, ell_max, delta_ell)
####################################################################
### CALCULATE N(ell) for Temperature
## calculate the experimental weight
W_T_27 = S_LA_27[sensitivity_mode] / np.sqrt(t)
W_T_39 = S_LA_39[sensitivity_mode] / np.sqrt(t)
W_T_93 = S_LA_93[sensitivity_mode] / np.sqrt(t)
W_T_145 = S_LA_145[sensitivity_mode] / np.sqrt(t)
W_T_225 = S_LA_225[sensitivity_mode] / np.sqrt(t)
W_T_280 = S_LA_280[sensitivity_mode] / np.sqrt(t)
## calculate the map noise level (white) for the survey in uK_arcmin for temperature
MN_T_27 = W_T_27 * np.sqrt(A_arcmin)
MN_T_39 = W_T_39 * np.sqrt(A_arcmin)
MN_T_93 = W_T_93 * np.sqrt(A_arcmin)
MN_T_145 = W_T_145 * np.sqrt(A_arcmin)
MN_T_225 = W_T_225 * np.sqrt(A_arcmin)
MN_T_280 = W_T_280 * np.sqrt(A_arcmin)
Map_white_noise_levels = np.array(
[MN_T_27, MN_T_39, MN_T_93, MN_T_145, MN_T_225, MN_T_280])
print("white noise levels (T): ", Map_white_noise_levels, "[uK-arcmin]")
## calculate the atmospheric contribution for T
## see Sec. 2.2 of SO science goals paper
ell_pivot = 1000.
# handle cases where there are zero tubes of some kind
if (NTubes_LF == 0.):
AN_T_27 = 0. #irrelevantly large noise already set above
AN_T_39 = 0.
else:
AN_T_27 = C_27 * (ell / ell_pivot)**alpha_temp * A_SR / t / (2. *
NTubes_LF)
AN_T_39 = C_39 * (ell / ell_pivot)**alpha_temp * A_SR / t / (2. *
NTubes_LF)
if (NTubes_MF == 0.):
AN_T_93 = 0.
AN_T_145 = 0.
else:
AN_T_93 = C_93 * (ell / ell_pivot)**alpha_temp * A_SR / t / (2. *
NTubes_MF)
AN_T_145 = C_145 * (ell / ell_pivot)**alpha_temp * A_SR / t / (
2. * NTubes_MF)
if (NTubes_UHF == 0.):
AN_T_225 = 0.
AN_T_280 = 0.
else:
AN_T_225 = C_225 * (ell / ell_pivot)**alpha_temp * A_SR / t / (
2. * NTubes_UHF)
AN_T_280 = C_280 * (ell / ell_pivot)**alpha_temp * A_SR / t / (
2. * NTubes_UHF)
# include cross-frequency correlations in the atmosphere
# use correlation coefficient of r=0.9 within each dichroic pair and 0 otherwise
r_atm = 0.9
AN_T_27x39 = r_atm * np.sqrt(AN_T_27 * AN_T_39)
AN_T_93x145 = r_atm * np.sqrt(AN_T_93 * AN_T_145)
AN_T_225x280 = r_atm * np.sqrt(AN_T_225 * AN_T_280)
## calculate N(ell)
N_ell_T_27 = (W_T_27**2. * A_SR) + AN_T_27
N_ell_T_39 = (W_T_39**2. * A_SR) + AN_T_39
N_ell_T_93 = (W_T_93**2. * A_SR) + AN_T_93
N_ell_T_145 = (W_T_145**2. * A_SR) + AN_T_145
N_ell_T_225 = (W_T_225**2. * A_SR) + AN_T_225
N_ell_T_280 = (W_T_280**2. * A_SR) + AN_T_280
# include cross-correlations due to atmospheric noise
N_ell_T_27x39 = AN_T_27x39
N_ell_T_93x145 = AN_T_93x145
N_ell_T_225x280 = AN_T_225x280
## include the impact of the beam
LA_beams = Simons_Observatory_V3_LA_beams() / np.sqrt(
8. * np.log(2)) / 60. * np.pi / 180.
## LAT beams as a sigma expressed in radians
N_ell_T_27 *= np.exp(ell * (ell + 1) * LA_beams[0]**2.)
N_ell_T_39 *= np.exp(ell * (ell + 1) * LA_beams[1]**2.)
N_ell_T_93 *= np.exp(ell * (ell + 1) * LA_beams[2]**2.)
N_ell_T_145 *= np.exp(ell * (ell + 1) * LA_beams[3]**2.)
N_ell_T_225 *= np.exp(ell * (ell + 1) * LA_beams[4]**2.)
N_ell_T_280 *= np.exp(ell * (ell + 1) * LA_beams[5]**2.)
N_ell_T_27x39 *= np.exp(
(ell * (ell + 1) / 2.) * (LA_beams[0]**2. + LA_beams[1]**2.))
N_ell_T_93x145 *= np.exp(
(ell * (ell + 1) / 2.) * (LA_beams[2]**2. + LA_beams[3]**2.))
N_ell_T_225x280 *= np.exp(
(ell * (ell + 1) / 2.) * (LA_beams[4]**2. + LA_beams[5]**2.))
## make an array of noise curves for T
# include cross-correlations due to atmospheric noise
N_ell_T_LA = np.array([
N_ell_T_27, N_ell_T_39, N_ell_T_93, N_ell_T_145, N_ell_T_225,
N_ell_T_280, N_ell_T_27x39, N_ell_T_93x145, N_ell_T_225x280
])
####################################################################
### CALCULATE N(ell) for Polarization
## calculate the atmospheric contribution for P
AN_P_27 = (ell / f_knee_pol_LA_27)**alpha_pol + 1.
AN_P_39 = (ell / f_knee_pol_LA_39)**alpha_pol + 1.
AN_P_93 = (ell / f_knee_pol_LA_93)**alpha_pol + 1.
AN_P_145 = (ell / f_knee_pol_LA_145)**alpha_pol + 1.
AN_P_225 = (ell / f_knee_pol_LA_225)**alpha_pol + 1.
AN_P_280 = (ell / f_knee_pol_LA_280)**alpha_pol + 1.
## calculate N(ell)
N_ell_P_27 = (W_T_27 * np.sqrt(2))**2. * A_SR * AN_P_27
N_ell_P_39 = (W_T_39 * np.sqrt(2))**2. * A_SR * AN_P_39
N_ell_P_93 = (W_T_93 * np.sqrt(2))**2. * A_SR * AN_P_93
N_ell_P_145 = (W_T_145 * np.sqrt(2))**2. * A_SR * AN_P_145
N_ell_P_225 = (W_T_225 * np.sqrt(2))**2. * A_SR * AN_P_225
N_ell_P_280 = (W_T_280 * np.sqrt(2))**2. * A_SR * AN_P_280
# include cross-correlations due to atmospheric noise
# different approach than for T -- need to subtract off the white noise part to get the purely atmospheric part
# see Sec. 2.2 of the SO science goals paper
N_ell_P_27_atm = (W_T_27 * np.sqrt(2))**2. * A_SR * (
ell / f_knee_pol_LA_27)**alpha_pol
N_ell_P_39_atm = (W_T_39 * np.sqrt(2))**2. * A_SR * (
ell / f_knee_pol_LA_39)**alpha_pol
N_ell_P_93_atm = (W_T_93 * np.sqrt(2))**2. * A_SR * (
ell / f_knee_pol_LA_93)**alpha_pol
N_ell_P_145_atm = (W_T_145 * np.sqrt(2))**2. * A_SR * (
ell / f_knee_pol_LA_145)**alpha_pol
N_ell_P_225_atm = (W_T_225 * np.sqrt(2))**2. * A_SR * (
ell / f_knee_pol_LA_225)**alpha_pol
N_ell_P_280_atm = (W_T_280 * np.sqrt(2))**2. * A_SR * (
ell / f_knee_pol_LA_280)**alpha_pol
N_ell_P_27x39 = r_atm * np.sqrt(N_ell_P_27_atm * N_ell_P_39_atm)
N_ell_P_93x145 = r_atm * np.sqrt(N_ell_P_93_atm * N_ell_P_145_atm)
N_ell_P_225x280 = r_atm * np.sqrt(N_ell_P_225_atm * N_ell_P_280_atm)
## include the impact of the beam
N_ell_P_27 *= np.exp(ell * (ell + 1) * LA_beams[0]**2)
N_ell_P_39 *= np.exp(ell * (ell + 1) * LA_beams[1]**2)
N_ell_P_93 *= np.exp(ell * (ell + 1) * LA_beams[2]**2)
N_ell_P_145 *= np.exp(ell * (ell + 1) * LA_beams[3]**2)
N_ell_P_225 *= np.exp(ell * (ell + 1) * LA_beams[4]**2)
N_ell_P_280 *= np.exp(ell * (ell + 1) * LA_beams[5]**2)
N_ell_P_27x39 *= np.exp(
(ell * (ell + 1) / 2.) * (LA_beams[0]**2. + LA_beams[1]**2.))
N_ell_P_93x145 *= np.exp(
(ell * (ell + 1) / 2.) * (LA_beams[2]**2. + LA_beams[3]**2.))
N_ell_P_225x280 *= np.exp(
(ell * (ell + 1) / 2.) * (LA_beams[4]**2. + LA_beams[5]**2.))
## make an array of noise curves for P
N_ell_P_LA = np.array([
N_ell_P_27, N_ell_P_39, N_ell_P_93, N_ell_P_145, N_ell_P_225,
N_ell_P_280, N_ell_P_27x39, N_ell_P_93x145, N_ell_P_225x280
])
####################################################################
return (ell, N_ell_T_LA, N_ell_P_LA, Map_white_noise_levels)
def Rejection_Sampling_MixtureNormals(M,c):
accepted_values = []
rejected_values = []
for i in range(M):
#randomly sampling from Q(x)
x = np.random.normal(mu_q, sigma_q)
#using Q(x) function and samples x to sample from uniform
u = np.random.uniform(0,c * Q(x))
#eeccepting/rejecting
if u <= P_star(x):
accepted_values.append(x)
else:
rejected_values.append(x)
return(np.array(accepted_values))
x = np.arange(-10,150)
c = max(P_star(x) / Q(x))
X_accepted = Rejection_Sampling_MixtureNormals(M = 100000,c = c)
import matplotlib.pyplot as plt
counts, bins, ignored = plt.hist(X_accepted, x, density = True,color = 'purple', label = 'accepted samples')
plt.title("Rejection Sampling for Mixture of Normal Distribution with Unif(0,cQ(x))")
plt.ylabel("Probability")
plt.show()
return signal
if __name__ == '__main__':
myModel = TfReg()
mySession = tf.Session(graph = myModel.grafo )
#Num points must be at least grater than vary*0.1 to get a accurate model.
numpoints = 30
noiseVary = 0*numpoints
true_A = +0.87
true_B = -0.43
true_C = +1.18
x = np.arange(numpoints).reshape((numpoints))
y_0 = true_A*x*x + true_B*x + true_C
y = addNoise(signal = y_0, numpoints = numpoints, vary = noiseVary)
############################################
plt.scatter( x,
y,
label = 'Datos')
plt.xlabel('Puntos')
plt.title('Datos')
plt.ylabel('Datos')
plt.show()
#############################################
costs = train_reg( theSession = mySession,
theModel = myModel,
x_train = x,
y_train = y,
def Simons_Observatory_V3_SA_noise(sensitivity_mode, one_over_f_mode,
SAT_yrs_LF, f_sky, ell_max, delta_ell):
## returns noise curves in polarization only, including the impact of the beam, for the SO small aperture telescopes
## noise curves are polarization only
# sensitivity_mode
# 1: baseline,
# 2: goal
# one_over_f_mode
# 0: pessimistic
# 1: optimistic
# SAT_yrs_LF: 0,1,2,3,4,5: number of years where an LF is deployed on SAT
# f_sky: number from 0-1
# ell_max: the maximum value of ell used in the computation of N(ell)
# delta_ell: the step size for computing N_ell
####################################################################
####################################################################
### Internal variables
## SMALL APERTURE
# ensure valid parameter choices
assert (sensitivity_mode == 1 or sensitivity_mode == 2)
assert (one_over_f_mode == 0 or one_over_f_mode == 1)
assert (SAT_yrs_LF <= 5) #N.B. SAT_yrs_LF can be negative
assert (f_sky > 0. and f_sky <= 1.)
assert (ell_max <= 2e4)
assert (delta_ell >= 1)
# configuration
if (SAT_yrs_LF > 0):
NTubes_LF = SAT_yrs_LF / 5. + 1e-6 ## regularized in case zero years is called
NTubes_MF = 2 - SAT_yrs_LF / 5.
else:
NTubes_LF = np.fabs(
SAT_yrs_LF) / 5. + 1e-6 ## regularized in case zero years is called
NTubes_MF = 2
NTubes_UHF = 1.
NTubes_LF = 1.
NTubes_MF = 1.
# sensitivity
# N.B. divide-by-zero will occur if NTubes = 0
# handle with assert() since it's highly unlikely we want any configurations without >= 1 of each tube type
assert (NTubes_LF > 0.)
assert (NTubes_MF > 0.)
assert (NTubes_UHF > 0.)
S_SA_27 = np.array([1.e9, 21, 15]) * np.sqrt(1. / NTubes_LF)
S_SA_39 = np.array([1.e9, 13, 10]) * np.sqrt(1. / NTubes_LF)
S_SA_93 = np.array([1.e9, 3.4, 2.4]) * np.sqrt(2. / (NTubes_MF))
S_SA_145 = np.array([1.e9, 4.3, 2.7]) * np.sqrt(2. / (NTubes_MF))
S_SA_225 = np.array([1.e9, 8.6, 5.7]) * np.sqrt(1. / NTubes_UHF)
S_SA_280 = np.array([1.e9, 22, 14]) * np.sqrt(1. / NTubes_UHF)
# 1/f polarization noise
# see Sec. 2.2 of the SO science goals paper
f_knee_pol_SA_27 = np.array([30., 15.])
f_knee_pol_SA_39 = np.array([30., 15.]) ## from QUIET
f_knee_pol_SA_93 = np.array([50., 25.])
f_knee_pol_SA_145 = np.array(
[50., 25.]) ## from ABS, improvement possible by scanning faster
f_knee_pol_SA_225 = np.array([70., 35.])
f_knee_pol_SA_280 = np.array([100., 40.])
alpha_pol = np.array([-2.4, -2.4, -2.5, -3, -3, -3])
####################################################################
## calculate the survey area and time
t = 1 * 365. * 24. * 3600 ## five years in seconds
#t = 5 * 365. * 24. * 3600 ## five years in seconds
t = t * 0.2 ## retention after observing efficiency and cuts
t = t * 0.85 ## a kludge for the noise non-uniformity of the map edges
A_SR = 4 * np.pi * f_sky ## sky area in steradians
A_deg = A_SR * (180 / np.pi)**2 ## sky area in square degrees
A_arcmin = A_deg * 3600.
print("sky area: ", A_deg, "degrees^2")
####################################################################
## make the ell array for the output noise curves
ell = np.arange(2, ell_max, delta_ell)
####################################################################
### CALCULATE N(ell) for Temperature
## calculate the experimental weight
W_T_27 = S_SA_27[sensitivity_mode] / np.sqrt(t)
W_T_39 = S_SA_39[sensitivity_mode] / np.sqrt(t)
W_T_93 = S_SA_93[sensitivity_mode] / np.sqrt(t)
W_T_145 = S_SA_145[sensitivity_mode] / np.sqrt(t)
W_T_225 = S_SA_225[sensitivity_mode] / np.sqrt(t)
W_T_280 = S_SA_280[sensitivity_mode] / np.sqrt(t)
## calculate the map noise level (white) for the survey in uK_arcmin for temperature
MN_T_27 = W_T_27 * np.sqrt(A_arcmin)
MN_T_39 = W_T_39 * np.sqrt(A_arcmin)
MN_T_93 = W_T_93 * np.sqrt(A_arcmin)
MN_T_145 = W_T_145 * np.sqrt(A_arcmin)
MN_T_225 = W_T_225 * np.sqrt(A_arcmin)
MN_T_280 = W_T_280 * np.sqrt(A_arcmin)
Map_white_noise_levels = np.array(
[MN_T_27, MN_T_39, MN_T_93, MN_T_145, MN_T_225, MN_T_280])
print("white noise levels (T): ", Map_white_noise_levels, "[uK-arcmin]")
####################################################################
### CALCULATE N(ell) for Polarization
## calculate the atmospheric contribution for P
## see Sec. 2.2 of the SO science goals paper
AN_P_27 = (ell / f_knee_pol_SA_27[one_over_f_mode])**alpha_pol[0] + 1.
AN_P_39 = (ell / f_knee_pol_SA_39[one_over_f_mode])**alpha_pol[1] + 1.
AN_P_93 = (ell / f_knee_pol_SA_93[one_over_f_mode])**alpha_pol[2] + 1.
AN_P_145 = (ell / f_knee_pol_SA_145[one_over_f_mode])**alpha_pol[3] + 1.
AN_P_225 = (ell / f_knee_pol_SA_225[one_over_f_mode])**alpha_pol[4] + 1.
AN_P_280 = (ell / f_knee_pol_SA_280[one_over_f_mode])**alpha_pol[5] + 1.
## calculate N(ell)
N_ell_P_27 = (W_T_27 * np.sqrt(2))**2. * A_SR * AN_P_27
N_ell_P_39 = (W_T_39 * np.sqrt(2))**2. * A_SR * AN_P_39
N_ell_P_93 = (W_T_93 * np.sqrt(2))**2. * A_SR * AN_P_93
N_ell_P_145 = (W_T_145 * np.sqrt(2))**2. * A_SR * AN_P_145
N_ell_P_225 = (W_T_225 * np.sqrt(2))**2. * A_SR * AN_P_225
N_ell_P_280 = (W_T_280 * np.sqrt(2))**2. * A_SR * AN_P_280
## include the impact of the beam
SA_beams = Simons_Observatory_V3_SA_beams() / np.sqrt(
8. * np.log(2)) / 60. * np.pi / 180.
## SAT beams as a sigma expressed in radians
N_ell_P_27 *= np.exp(ell * (ell + 1) * SA_beams[0]**2.)
N_ell_P_39 *= np.exp(ell * (ell + 1) * SA_beams[1]**2.)
N_ell_P_93 *= np.exp(ell * (ell + 1) * SA_beams[2]**2.)
N_ell_P_145 *= np.exp(ell * (ell + 1) * SA_beams[3]**2.)
N_ell_P_225 *= np.exp(ell * (ell + 1) * SA_beams[4]**2.)
N_ell_P_280 *= np.exp(ell * (ell + 1) * SA_beams[5]**2.)
## make an array of noise curves for P
N_ell_P_SA = np.array([
N_ell_P_27, N_ell_P_39, N_ell_P_93, N_ell_P_145, N_ell_P_225,
N_ell_P_280
])
####################################################################
return (ell, N_ell_P_SA, Map_white_noise_levels)
def depth():
return np.arange(10)
para,
on_handle=None,
on_ok=None,
on_cancel=None,
preview=False,
modal=True):
dialog = ParaDialog(self, title)
dialog.init_view(view, para, preview, modal=modal, app=self)
dialog.Bind('cancel', on_cancel)
dialog.Bind('parameter', on_handle)
dialog.Bind('commit', on_ok)
return dialog.show()
if __name__ == '__main__':
import numpy as np
import pandas as pd
app = wx.App(False)
frame = MiniApp(None)
frame.Show()
frame.show_img([np.zeros((512, 512), dtype=np.uint8)], 'zeros')
#frame.show_img(None)
frame.show_table(pd.DataFrame(np.arange(100).reshape((10, 10))), 'title')
'''
frame.show_md('abcdefg', 'md')
frame.show_md('ddddddd', 'md')
frame.show_txt('abcdefg', 'txt')
frame.show_txt('ddddddd', 'txt')
'''
app.MainLoop()
def visualize(sess, dcgan, config, option):
if dcgan.dataset_name == 'mnist':
data_dir = os.path.join("./data", dcgan.dataset_name)
fd = open(os.path.join(data_dir, 't10k-images.idx3-ubyte'))
loaded = np.fromfile(file=fd, dtype=np.uint8)
teX = loaded[16:].reshape((10000, 28, 28, 1)).astype(np.float)
fd = open(os.path.join(data_dir, 't10k-labels.idx1-ubyte'))
loaded = np.fromfile(file=fd, dtype=np.uint8)
teY = loaded[8:].reshape((10000)).astype(np.float)
teY = np.asarray(teY)
X = teX
y = teY.astype(np.int)
seed = 547
np.random.seed(seed)
np.random.shuffle(X)
np.random.seed(seed)
np.random.shuffle(y)
y_vec = np.zeros((len(y), dcgan.y_dim), dtype=np.float)
for i, label in enumerate(y):
y_vec[i, y[i]] = 1.0
X = X / 255.
y = y_vec
image_frame_dim = int(math.ceil(config.batch_size**.5))
if option == 0:
import numpy
perm0 = numpy.arange(10000)
numpy.random.shuffle(perm0)
X = X[perm0]
z_sample = X[:config.batch_size]
samples, AE_loss = sess.run([dcgan.sampler, dcgan.AE_loss],
feed_dict={dcgan.inputs_e: z_sample})
print("[Test] AE_loss: %.8f" % (AE_loss))
save_images(
samples, [image_frame_dim, image_frame_dim],
'./samples/test_%s.png' % strftime("%Y%m%d%H%M%S", gmtime()))
elif option == 1:
values = np.arange(0, 1, 1. / config.batch_size)
for idx in xrange(100):
print(" [*] %d" % idx)
z_sample = np.zeros([config.batch_size, dcgan.z_dim])
for kdx, z in enumerate(z_sample):
z[idx] = values[kdx]
if config.dataset == "mnist":
y = np.random.choice(10, config.batch_size)
y_one_hot = np.zeros((config.batch_size, 10))
y_one_hot[np.arange(config.batch_size), y] = 1
samples = sess.run(dcgan.sampler,
feed_dict={
dcgan.inputs_e: z_sample,
dcgan.y: y_one_hot
})
else:
samples = sess.run(dcgan.sampler,
feed_dict={dcgan.inputs_e: z_sample})
save_images(samples, [image_frame_dim, image_frame_dim],
'./samples/test_arange_%s.png' % (idx))
elif option == 2:
values = np.arange(0, 1, 1. / config.batch_size)
for idx in [random.randint(0, 99) for _ in xrange(100)]:
print(" [*] %d" % idx)
z = np.random.uniform(-0.2, 0.2, size=(dcgan.z_dim))
z_sample = np.tile(z, (config.batch_size, 1))
#z_sample = np.zeros([config.batch_size, dcgan.z_dim])
for kdx, z in enumerate(z_sample):
z[idx] = values[kdx]
if config.dataset == "mnist":
y = np.random.choice(10, config.batch_size)
y_one_hot = np.zeros((config.batch_size, 10))
y_one_hot[np.arange(config.batch_size), y] = 1
samples = sess.run(dcgan.sampler,
feed_dict={
dcgan.z: z_sample,
dcgan.y: y_one_hot
})
else:
samples = sess.run(dcgan.sampler,
feed_dict={dcgan.z: z_sample})
try:
make_gif(samples, './samples/test_gif_%s.gif' % (idx))
except:
save_images(
samples, [image_frame_dim, image_frame_dim],
'./samples/test_%s.png' %
strftime("%Y%m%d%H%M%S", gmtime()))
elif option == 3:
values = np.arange(0, 1, 1. / config.batch_size)
for idx in xrange(100):
print(" [*] %d" % idx)
z_sample = np.zeros([config.batch_size, dcgan.z_dim])
for kdx, z in enumerate(z_sample):
z[idx] = values[kdx]
samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})
make_gif(samples, './samples/test_gif_%s.gif' % (idx))
elif option == 4:
image_set = []
values = np.arange(0, 1, 1. / config.batch_size)
for idx in xrange(100):
print(" [*] %d" % idx)
z_sample = np.zeros([config.batch_size, dcgan.z_dim])
for kdx, z in enumerate(z_sample):
z[idx] = values[kdx]
image_set.append(
sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample}))
make_gif(image_set[-1], './samples/test_gif_%s.gif' % (idx))
new_image_set = [merge(np.array([images[idx] for images in image_set]), [10, 10]) \
for idx in range(64) + range(63, -1, -1)]
make_gif(new_image_set, './samples/test_gif_merged.gif', duration=8)
def get_data(self):
idxs = np.arange(self.size())
if self.shuffle:
self.rng.shuffle(idxs)
for id in idxs:
yield [self.X[id, :], self.y[id]]
# 分析结果
frequencies = []
max_result = die_1.num_sides + die_2.num_sides
for value in range(2, max_result + 1):
frequency = results.count(value)
frequencies.append(frequency)
# 用pygal对结果进行可视化
# hist = pygal.Bar()
# hist.title = "Results of rolling two D6 1000 times."
# hist.x_labels = [i for i in range(2, 13)]
# hist.x_title = "Result"
# hist.y_title = "Frequency of Result"
# hist.add('D6 + D6', frequencies)
# hist.render_to_file('die_visual.svg')
# 用matplotlib对结果进行可视化
ind = np.arange(2, 13)
plt.bar(ind, frequencies, width=0.5)
plt.title("Results of rolling two D6 1000 times.")
plt.xlabel("Result")
plt.ylabel("Frequency of Result")
plt.xticks(ind)
plt.yticks(np.arange(0, 200, 20))
plt.savefig('die_visual2.svg', bbox_inches='tight')
if not opts.max is None: max = opts.max
else: max = d.max()
if not opts.drng is None: min = max - opts.drng
else: min = d.min()
#p.subplot(m2, m1, cnt+1)
if not opts.nogrid:
from mpl_toolkits.basemap import Basemap
xpx,ypx = d.shape
dx1 = -(xpx/2 + .5) * kwds['d_ra'] * a.img.deg2rad
dx2 = (xpx/2 - .5) * kwds['d_ra'] * a.img.deg2rad
dy1 = -(ypx/2 + .5) * kwds['d_dec'] * a.img.deg2rad
dy2 = (ypx/2 - .5) * kwds['d_dec'] * a.img.deg2rad
map = Basemap(projection='ortho', lon_0=180, lat_0=kwds['dec'],
rsphere=1, llcrnrx=dx1, llcrnry=dy1, urcrnrx=dx2,urcrnry=dy2)
map.drawmeridians(n.arange(kwds['ra']-180,kwds['ra']+180,30))
map.drawparallels(n.arange(-90,120,30))
map.drawmapboundary()
map.imshow(d, vmin=min, vmax=max, cmap=cmaps[cnt%len(cmaps)], interpolation='nearest', alpha=1-.25*cnt)
else: p.imshow(d, vmin=min, vmax=max, origin='lower', cmap=cmap, interpolation='nearest')
p.colorbar(shrink=.5, fraction=.05)
p.title(filename)
if opts.batch:
print 'Saving to', outfile
p.savefig(outfile)
p.clf()
# Add right-click functionality for finding locations/strengths in map.
cnt = 1
model = OnlinePerceptron(iters = 15)
final_weights, train_accuracies, val_accuracies, weight_history = model.train(X_train, Y_train, X_val, Y_val)
model_final = OnlinePerceptron(iters = 6)
trained_weights, trained_accuracies, val_acc, weight_hist = model_final.train(X_train, Y_train, X_val, Y_val)
oplabel = model_final.predict(X_test, trained_weights)
pd.DataFrame(oplabel).to_csv(path_or_buf='oplabel.csv', index= False, header = False)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_title('Training Accuracy vs Validation Accuracy')
ax.plot(np.arange(0,train_accuracies.shape[0]), train_accuracies, label='Train')
ax.plot(np.arange(0,val_accuracies.shape[0]), val_accuracies, label='Dev')
ax.set_xlabel('Iterations')
ax.set_ylabel('Accuracy')
ax.set_ylim([90,100])
ax.legend()
plt.show()
fig.savefig('onlinePerceptronTrainingVal.png')
# Get weights for model's best performance on validation set
idx = np.argmax(val_accuracies)
print("best weights at iteration {}".format(idx))
best_online_weights = weight_history[idx]
"""
steepness = 1.0 / 20.0 # dictates the gradient. Small values give shallower gradients.
shift = 90 # shifts the curve relative to the x-axis. Positive values move curve right.
return 1.0 / (1.0 + np.exp( -steepness * (limit - shift)))
death_rate_model = _calculate_death_rate_exp
# Lookup table for relating death rate to limiting resource level.
# The equation is computationally expensive, hence the use of a lookup table.
limiting_resource_resolution = 0.01 # Resolution of limiting resource for which death rates are calculated.
max_limiting_resource_lookup_value = 500 # Maximum value for which lookup table values should be calculated.
death_rate_lookup_table = [] # The lookup table itself.
divide_rate_lookup_table = []
# Populate lookup tables.
for limit in np.arange(0.0, max_limiting_resource_lookup_value, limiting_resource_resolution):
death_rate_lookup_table.append(death_rate_model(limit))
divide_rate_lookup_table.append(_calculate_divide_rate_logistic(limit))
def death_rate_lookup(limit):
"""
Uses look up table to estimate death rate based on limiting resource.
"""
if limit > max_limiting_resource_lookup_value:
print('WARNING: limiting resource value exceeds range of pre-calculated values : ' + str(limit))
return death_rate_model(limit)
scaled_limit = int(limit / limiting_resource_resolution)
return death_rate_lookup_table[scaled_limit]
def data():
'''
Data providing function:
- reads all the files conteined inside "./np_data/" Those are numpy.arrays containing the images previousy read using utils/data.py
- divides them by label, according to the input_labels list
- divides the whole dataset in training and validation set, with an 80/20 ratio
'''
K.set_image_dim_ordering("tf")
print("=" * 30)
print('Loading and preprocessing train data...')
print('=' * 30)
data_path = './np_data/'
trainX = {}
for label in input_labels:
trainX[label] = np.load(data_path + label + "_ordered.npy")
trainY = np.load(data_path + "diagnosis_ordered.npy")
channels = trainX["images"].shape[3]
img_cols = trainX["images"].shape[2]
img_rows = trainX["images"].shape[1]
nb_train = trainX["images"].shape[0]
trainX["images"] = trainX["images"].astype('float32')
print "Dataset has ", nb_train, " training images"
shuffled_index = np.arange(nb_train)
np.random.shuffle(shuffled_index)
mean = np.mean(trainX["images"]) # mean for data centering
std = np.std(trainX["images"]) # std for data normalization
trainX["images"] -= mean
trainX["images"] /= std
trainnum = trainX["images"].shape[0]
trainlen = int(trainnum * 0.8)
valX = {}
for label in input_labels:
trainX[label] = trainX[label][shuffled_index]
valX[label] = trainX[label][trainlen:]
trainX[label] = trainX[label][0:trainlen]
trainY = trainY[shuffled_index]
print("trainlen: ", trainlen)
print("train Images: ", trainX["images"].shape)
print("val images: ", valX["images"].shape)
print("train globules: ", trainX["globules"].shape)
valY = trainY[trainlen:]
trainY = trainY[:trainlen]
print("valY shape : ", valY.shape)
print("trainY shape: ", trainY.shape)
return trainX, trainY, valX, valY
def heatmap(data, colidx=3, labidx=0, fname='heatmap.png'):
# Based on: https://stackoverflow.com/questions/14391959/heatmap-in-matplotlib-with-pcolor
plt.rcParams.update({'figure.autolayout': True})
if data.empty:
print("Can not plot empty data set.")
return
df = pd.DataFrame()
names = list(data.columns)
label = names[labidx]
names = [label] + names[colidx:]
# A simpler dataframe with only labels and values
for name in names:
df[name] = data[name]
df = df.set_index(label)
# df = (df - df.mean()) / (df.max() - df.min())
# Transform the scale to log.
df = np.log(df + 1)
# The size of the data frame.
rnum, cnum = df.shape
# The size of the plot will grow with the row numbers.
hsize = 4 + cnum / 3
vsize = 4 + rnum / 10
fig, ax = plt.subplots(figsize=(hsize, vsize))
heatmap = ax.pcolor(df, cmap=plt.cm.Blues, alpha=0.8)
# put the major ticks at the middle of each cell
ax.set_yticks(np.arange(rnum) + 0.5, minor=False)
ax.set_xticks(np.arange(cnum) + 0.5, minor=False)
# ax.invert_yaxis()
ax.xaxis.tick_top()
# Get the vertical labels.
labels = list(df.columns)
# Simplify label names.
#labels = [label.split("_")[0] for label in labels]
ax.set_xticklabels(labels, minor=False)
ax.set_yticklabels(df.index, minor=False)
ax.grid(False)
# Turn off all the ticks
ax = plt.gca()
for t in ax.xaxis.get_major_ticks():
t.tick1line.set_visible = False
t.tick2line.set_visible = False
for t in ax.yaxis.get_major_ticks():
t.tick1line.set_visible = False
t.tick2line.set_visible = False
plt.xticks(rotation=90)
plt.savefig(f'{fname}.pdf')
if SHOW_PLOT:
# Pop a window in non-offline mode.
plt.show()
# In[5]:
# import required modules
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
get_ipython().run_line_magic('matplotlib', 'inline')
# In[6]:
class_names=[0,1] # name of classes
fig, ax = plt.subplots()
tick_marks = np.arange(len(class_names))
plt.xticks(tick_marks, class_names)
plt.yticks(tick_marks, class_names)
# create heatmap
sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap="Greens" ,fmt='g')
ax.xaxis.set_label_position("top")
plt.tight_layout()
plt.title('Confusion matrix', y=1.1)
plt.ylabel('Actual label')
plt.xlabel('Predicted label')
# In[7]:
def build(self, atoms):
"""Build the list. Modified so that the vectors are also stored."""
self.atoms = atoms
self.sndict = symbol_number(atoms)
self.positions = atoms.get_positions()
self.pbc = atoms.get_pbc()
self.cell = atoms.get_cell()
if len(self.cutoffs) > 0:
rcmax = self.cutoffs.max()
else:
rcmax = 0.0
icell = np.linalg.inv(self.cell)
scaled = np.dot(self.positions, icell)
scaled0 = scaled.copy()
N = []
for i in range(3):
if self.pbc[i]:
scaled0[:, i] %= 1.0
v = icell[:, i]
h = 1 / sqrt(np.dot(v, v))
n = int(2 * rcmax / h) + 1
else:
n = 0
N.append(n)
offsets = (scaled0 - scaled).round().astype(int)
positions0 = np.dot(scaled0, self.cell)
natoms = len(atoms)
indices = np.arange(natoms)
self.nneighbors = 0
self.npbcneighbors = 0
self.neighbors = [np.empty(0, int) for a in range(natoms)]
self.displacements = [np.empty((0, 3), int) for a in range(natoms)]
self.disp_vectors = [np.empty((0, 3), float) for a in range(natoms)]
for n1 in range(0, N[0] + 1):
for n2 in range(-N[1], N[1] + 1):
for n3 in range(-N[2], N[2] + 1):
if n1 == 0 and (n2 < 0 or n2 == 0 and n3 < 0):
continue
displacement = np.dot((n1, n2, n3), self.cell)
for a in range(natoms):
d = positions0 + displacement - positions0[a]
i = indices[(d**2).sum(1) <
(self.cutoffs + self.cutoffs[a])**2]
if n1 == 0 and n2 == 0 and n3 == 0:
if self.self_interaction:
i = i[i >= a]
else:
i = i[i > a]
self.nneighbors += len(i)
self.neighbors[a] = np.concatenate((self.neighbors[a],
i))
self.disp_vectors[a] = np.concatenate(
(self.disp_vectors[a], d[i]))
disp = np.empty((len(i), 3), int)
disp[:] = (n1, n2, n3)
disp += offsets[i] - offsets[a]
self.npbcneighbors += disp.any(1).sum()
self.displacements[a] = np.concatenate(
(self.displacements[a], disp))
if self.bothways:
neighbors2 = [[] for a in range(natoms)]
displacements2 = [[] for a in range(natoms)]
disp_vectors2 = [[] for a in range(natoms)]
for a in range(natoms):
for b, disp, disp_vec in zip(self.neighbors[a],
self.displacements[a],
self.disp_vectors[a]):
neighbors2[b].append(a)
displacements2[b].append(-disp)
disp_vectors2[b].append(-disp_vec)
for a in range(natoms):
self.neighbors[a] = np.concatenate(
(self.neighbors[a], np.array(neighbors2[a], dtype=int))
) ##here dtype should be int.Becuase np.array([]) is float64 .
self.displacements[a] = np.array(
list(self.displacements[a]) + displacements2[a])
self.disp_vectors[a] = np.array(
list(self.disp_vectors[a]) + disp_vectors2[a])
if self.sorted:
for a, i in enumerate(self.neighbors):
mask = (i < a)
if mask.any():
j = i[mask]
offsets = self.displacements[a][mask]
for b, offset in zip(j, offsets):
self.neighbors[b] = np.concatenate((self.neighbors[b],
[a]))
self.displacements[b] = np.concatenate(
(self.displacements[b], [-offset]))
self.disp_vectors[b] = np.concatenate(
(self.disp_vectors[b], [-offset]))
mask = np.logical_not(mask)
self.neighbors[a] = self.neighbors[a][mask]
self.displacements[a] = self.displacements[a][mask]
self.disp_vectors[a] = self.disp_vectors[a][mask]
self.nupdates += 1
#Loads and appends all folds all at once
trainfolds = [] # Train set
testfolds = [] # Test set (LEARNED)
testfolds_U = [] # Test set (UNLEARNED)
col_select = np.array([])
#This is an hack to test smaller windows
for i in range (spw*nmuscles,200):
col_select = np.append(col_select,i)
for i in range (0,spw*nmuscles,nmuscles):
for muscle in features_select:
col_select = np.append(col_select,muscle -1 + i)
cols=np.arange(0,spw*nmuscles+1)
if exclude_features & (not include_only_features): #delete gonio
for j in range(fold_offset,fold_offset + nfold):
print("Loading fold " + str(j))
traindata = pd.read_table(os.path.join(cwd, prefix_train + str(j)+'.csv'),sep=',',header=None,dtype=np.float32,usecols=[i for i in cols if i not in col_select.astype(int)])
trainfolds.append(traindata)
testdata = pd.read_table(os.path.join(cwd, prefix_test + str(j)+'.csv'),sep=',',header=None,dtype=np.float32, usecols=[i for i in cols if i not in col_select.astype(int)])
testfolds.append(testdata)
elif include_only_features & (not exclude_features): #only gonio
for j in range(fold_offset, fold_offset + nfold):
print("Loading fold " + str(j))
traindata = pd.read_table(os.path.join(cwd, prefix_train + str(j)+'.csv'),sep=',',header=None,dtype=np.float32,usecols=[i for i in cols if i in col_select.astype(int)])
testdata = pd.read_table(os.path.join(cwd, prefix_test + str(j)+'.csv'),sep=',',header=None,dtype=np.float32, usecols=[i for i in cols if i in col_select.astype(int)])
trainfolds.append(traindata)
testfolds.append(testdata)
#building the network to understand the generator network in VAE
network_architecture = \
dict(n_hidden_recog_1=300, # 1st layer encoder neurons
n_hidden_gener_1=300, # 1st layer decoder neurons
# n_hidden_gener_2=500, # 2nd layer decoder neurons
n_input=784, # MNIST data input (img shape: 28*28)
n_z=15) # dimensionality of latent space
vae, new_cost = train(network_architecture, training_epochs=10)
x_sample = mnist.test.next_batch(100)[0]
x_reconstruct = vae.reconstruct(x_sample)
training_epochs=10
#plotting reconstruct data
x = np.arange(0,training_epochs,1)
plt.title("Cost Graph")
plt.plot(x, new_cost)
plt.show()
#plotting the images before and after reconstruction
plt.figure(figsize=(8, 12))
for i in range(5):
plt.subplot(5, 2, 2*i + 1)
plt.imshow(x_sample[i].reshape(28, 28), vmin=0, vmax=1, cmap="gray")
plt.title("Test input")
plt.colorbar()
plt.subplot(5, 2, 2*i + 2)
plt.imshow(x_reconstruct[i].reshape(28, 28), vmin=0, vmax=1, cmap="gray")
def legacy_resonator(config_file, channel=None, noise=True):
"""
Function for loading in legacy matlab resonator data.
Args:
config_file: string
The resonator configuration file name.
channel: integer
The resonator channel for the data.
noise: boolean
If False, ignore the noise data. The default is True.
Returns:
loop_kwargs: list of dictionaries
A list of keyword arguments to send to Loop.from_file().
"""
directory = os.path.dirname(config_file)
config = loadmat(config_file, squeeze_me=True)['curr_config']
temperatures = np.arange(
config['starttemp'].astype(float), config['stoptemp'].astype(float) +
config['steptemp'].astype(float) / 2, config['steptemp'].astype(float))
attenuations = np.arange(
config['startatten'].astype(float), config['stopatten'].astype(float) +
config['stepatten'].astype(float) / 2,
config['stepatten'].astype(float))
loop_kwargs = []
for t_index, temp in enumerate(temperatures):
for a_index, atten in enumerate(attenuations):
loop_kwargs.append({
"loop_file_name": config_file,
"index": (t_index, a_index),
"data": legacy_loop,
"channel": channel
})
if config['donoise'] and noise:
group = channel // 2 + 1
# on resonance file names
on_res = glob.glob(
os.path.join(
directory,
"{:g}-{:d}a*-{:g}.ns".format(temp, group, atten)))
noise_kwargs = []
for file_name in on_res:
# collect the index for the file name
base_name = os.path.basename(file_name)
index2 = base_name.split("a")[1].split("-")[0]
index = (t_index, a_index,
int(index2)) if index2 else (t_index, a_index)
noise_kwargs.append({
"index": index,
"on_res": True,
"data": legacy_noise,
"channel": channel
})
# off resonance file names
off_res_names = glob.glob(
os.path.join(
directory,
"{:g}-{:d}b*-{:g}.ns".format(temp, group, atten)))
for file_name in off_res_names:
# collect the index for the file name
base_name = os.path.basename(file_name)
index2 = base_name.split("b")[1].split("-")[0]
index = (t_index, a_index,
int(index2)) if index2 else (t_index, a_index)
noise_kwargs.append({
"index": index,
"on_res": False,
"data": legacy_noise,
"channel": channel
})
loop_kwargs[-1].update({
"noise_file_names":
[config_file] * len(on_res + off_res_names),
"noise_kwargs":
noise_kwargs
})
if not noise_kwargs:
log.warning("Could not find noise files for '{}'".format(
config_file))
return loop_kwargs
'''
matplotlib.pyplot 画图工具
'''
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] # 指定默认字体
plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题
a = np.arange(1, 10, 0.2)
b = np.sin(a)
plt.figure(figsize=(10, 5)) # 生成画布
plt.plot(a, b, 'ro') # 准备数据
plt.figure()
plt.plot(a, b, color='r', linestyle='--', linewidth=3.0, label='sin')
c = np.cos(a)
plt.plot(a, c, 'g-.', label='cos')
plt.legend() # 显示图例(即不同线注解)
plt.xlabel('弧度') # x轴名称
plt.ylabel('正/余弦值') # y轴名称
plt.figure()
plt.scatter(a,b)
plt.show() # 显示
#Total Number of Sub regions
array_split_size_sub = [1, 4, 16, 64, 256]
for n in length_variable:
array_split_size = length_variable[n] * 25.6 #Value for blocks
array_split_size_sub = array_split_size_sub[n]
array_split_size_sub_tot = array_split_size // array_split_size_sub
arr_reshape = blockshaped(arr, array_split_size, array_split_size)
#Callable Sizing Value
array_size = np.size(arr_reshape[0])
#Plane Fitting Process
#Creating x,y coordintes for blocks of data
xvalues = np.arange(0, data_length)
yvalues = np.arange(0, data_length)
xx, yy = np.meshgrid(xvalues, yvalues)
xx = blockshaped(xx, array_split_size, array_split_size)
yy = blockshaped(yy, array_split_size, array_split_size)
#Importing Plane Fit Script here.
from plane_fit import plane_fit
#Making z of zeros for plane fit script
first_plane_sec = np.zeros((array_split_size_sub, 1, 3))
for i in range(0, array_split_size_sub):
first_plane_sec[i] = plane_fit(np.ndarray.flatten(xx[i]),
def main():
args = get_arguments()
started_datestring = "{0:%Y-%m-%dT%H-%M-%S}".format(datetime.now())
logdir = os.path.join(args.logdir, 'generate', started_datestring)
if not os.path.exists(logdir):
os.makedirs(logdir)
with open(args.wavenet_params, 'r') as config_file:
wavenet_params = json.load(config_file)
sess = tf.Session()
net = WaveNetModel(
batch_size=1,
dilations=wavenet_params['dilations'],
filter_width=wavenet_params['filter_width'],
residual_channels=wavenet_params['residual_channels'],
dilation_channels=wavenet_params['dilation_channels'],
quantization_channels=wavenet_params['quantization_channels'],
skip_channels=wavenet_params['skip_channels'],
use_biases=wavenet_params['use_biases'],
scalar_input=wavenet_params['scalar_input'],
initial_filter_width=wavenet_params['initial_filter_width'],
global_condition_channels=args.gc_channels,
global_condition_cardinality=args.gc_cardinality)
samples = tf.placeholder(tf.int32)
if args.fast_generation:
next_sample = net.predict_proba_incremental(samples, args.gc_id)
else:
next_sample = net.predict_proba(samples, args.gc_id)
if args.fast_generation:
sess.run(tf.global_variables_initializer())
sess.run(net.init_ops)
variables_to_restore = {
var.name[:-2]: var for var in tf.global_variables()
if not ('state_buffer' in var.name or 'pointer' in var.name)}
saver = tf.train.Saver(variables_to_restore)
print('Restoring model from {}'.format(args.checkpoint))
saver.restore(sess, args.checkpoint)
decode = mu_law_decode(samples, wavenet_params['quantization_channels'])
quantization_channels = wavenet_params['quantization_channels']
if args.wav_seed:
seed = create_seed(args.wav_seed,
wavenet_params['sample_rate'],
quantization_channels,
net.receptive_field)
waveform = sess.run(seed).tolist()
else:
# Silence with a single random sample at the end.
waveform = [quantization_channels / 2] * (net.receptive_field - 1)
waveform.append(np.random.randint(quantization_channels))
if args.fast_generation and args.wav_seed:
# When using the incremental generation, we need to
# feed in all priming samples one by one before starting the
# actual generation.
# TODO This could be done much more efficiently by passing the waveform
# to the incremental generator as an optional argument, which would be
# used to fill the queues initially.
outputs = [next_sample]
outputs.extend(net.push_ops)
print('Priming generation...')
for i, x in enumerate(waveform[-net.receptive_field: -1]):
if i % 100 == 0:
print('Priming sample {}'.format(i))
sess.run(outputs, feed_dict={samples: x})
print('Done.')
last_sample_timestamp = datetime.now()
for step in range(args.samples):
if args.fast_generation:
outputs = [next_sample]
outputs.extend(net.push_ops)
window = waveform[-1]
else:
if len(waveform) > net.receptive_field:
window = waveform[-net.receptive_field:]
else:
window = waveform
outputs = [next_sample]
# Run the WaveNet to predict the next sample.
prediction = sess.run(outputs, feed_dict={samples: window})[0]
# Scale prediction distribution using temperature.
np.seterr(divide='ignore')
scaled_prediction = np.log(prediction) / args.temperature
scaled_prediction = (scaled_prediction -
np.logaddexp.reduce(scaled_prediction))
scaled_prediction = np.exp(scaled_prediction)
np.seterr(divide='warn')
# Prediction distribution at temperature=1.0 should be unchanged after
# scaling.
if args.temperature == 1.0:
np.testing.assert_allclose(
prediction, scaled_prediction, atol=1e-5,
err_msg='Prediction scaling at temperature=1.0 '
'is not working as intended.')
sample = np.random.choice(
np.arange(quantization_channels), p=scaled_prediction)
waveform.append(sample)
# Show progress only once per second.
current_sample_timestamp = datetime.now()
time_since_print = current_sample_timestamp - last_sample_timestamp
if time_since_print.total_seconds() > 1.:
print('Sample {:3<d}/{:3<d}'.format(step + 1, args.samples),
end='\r')
last_sample_timestamp = current_sample_timestamp
# If we have partial writing, save the result so far.
if (args.wav_out_path and args.save_every and
(step + 1) % args.save_every == 0):
out = sess.run(decode, feed_dict={samples: waveform})
write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
# Introduce a newline to clear the carriage return from the progress.
print()
# Save the result as an audio summary.
datestring = str(datetime.now()).replace(' ', 'T')
writer = tf.summary.FileWriter(logdir)
# writer = tf.train.SummaryWriter(logdir)
tf.summary.audio('generated', decode, wavenet_params['sample_rate'])
summaries = tf.summary.merge_all()
summary_out = sess.run(summaries,
feed_dict={samples: np.reshape(waveform, [-1, 1])})
writer.add_summary(summary_out)
# Save the result as a wav file.
if args.wav_out_path:
out = sess.run(decode, feed_dict={samples: waveform})
write_wav(out, wavenet_params['sample_rate'], args.wav_out_path)
print('Finished generating. The result can be viewed in TensorBoard.')
