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Example 1
def normalize_array (solution, prediction):
''' Use min and max of solution as scaling factors to normalize prediction,
then threshold it to [0, 1]. Binarize solution to {0, 1}.
This allows applying classification scores to all cases.
In principle, this should not do anything to properly formatted
classification inputs and outputs.'''
# Binarize solution
sol=np.ravel(solution) # convert to 1-d array
maxi = np.nanmax((filter(lambda x: x != float('inf'), sol))) # Max except NaN and Inf
mini = np.nanmin((filter(lambda x: x != float('-inf'), sol))) # Mini except NaN and Inf
if maxi == mini:
print('Warning, cannot normalize')
return [solution, prediction]
diff = maxi - mini
mid = (maxi + mini)/2.
new_solution = np.copy(solution)
new_solution[solution>=mid] = 1
new_solution[solution<mid] = 0
# Normalize and threshold predictions (takes effect only if solution not in {0, 1})
new_prediction = (np.copy(prediction) - float(mini))/float(diff)
new_prediction[new_prediction>1] = 1 # and if predictions exceed the bounds [0, 1]
new_prediction[new_prediction<0] = 0
# Make probabilities smoother
#new_prediction = np.power(new_prediction, (1./10))
return [new_solution, new_prediction] Example 2
def logscale_img(img_array,
cap=255.0,
coeff=1000.0):
'''
This scales the image according to the relation:
logscale_img = np.log(coeff*(img/max(img))+1)/np.log(coeff)
Taken from the DS9 scaling algorithms page at:
http://hea-www.harvard.edu/RD/ds9/ref/how.html
According to that page:
coeff = 1000.0 works well for optical images
coeff = 100.0 works well for IR images
'''
logscaled_img = np.log(coeff*img_array/np.nanmax(img_array)+1)/np.log(coeff)
return cap*logscaled_img Example 3
def _learn_from_memories(self, replay_memories, q_network, global_step):
if self._pre_learning_stage(global_step):
loss = 0.0
return loss
sampled_replay_memories = replay_memories.sample(sample_size=self.hyperparameters.REPLAY_MEMORIES_TRAIN_SAMPLE_SIZE,
recent_memories_span=self.hyperparameters.REPLAY_MEMORIES_RECENT_SAMPLE_SPAN)
consequent_states = [replay_memory.consequent_state for replay_memory in sampled_replay_memories]
max_q_consequent_states = np.nanmax(q_network.forward_pass_batched(consequent_states), axis=1)
train_bundles = [None] * self.hyperparameters.REPLAY_MEMORIES_TRAIN_SAMPLE_SIZE
discount_factor = self.hyperparameters.Q_UPDATE_DISCOUNT_FACTOR
for idx, replay_memory in enumerate(sampled_replay_memories):
target_action_q_value = float(self._q_target(replay_memory=replay_memory,
max_q_consequent_state=max_q_consequent_states[idx],
discount_factor=discount_factor))
train_bundles[idx] = q_network.create_train_bundle(state=replay_memory.initial_state,
action_index=replay_memory.action_index,
target_action_q_value=target_action_q_value)
loss = q_network.train(train_bundles, global_step - self.hyperparameters.REPLAY_MEMORIES_MINIMUM_SIZE_FOR_LEARNING)
return loss Example 4
def normalize_array (solution, prediction):
''' Use min and max of solution as scaling factors to normalize prediction,
then threshold it to [0, 1]. Binarize solution to {0, 1}.
This allows applying classification scores to all cases.
In principle, this should not do anything to properly formatted
classification inputs and outputs.'''
# Binarize solution
sol=np.ravel(solution) # convert to 1-d array
maxi = np.nanmax((filter(lambda x: x != float('inf'), sol))) # Max except NaN and Inf
mini = np.nanmin((filter(lambda x: x != float('-inf'), sol))) # Mini except NaN and Inf
if maxi == mini:
print('Warning, cannot normalize')
return [solution, prediction]
diff = maxi - mini
mid = (maxi + mini)/2.
new_solution = np.copy(solution)
new_solution[solution>=mid] = 1
new_solution[solution<mid] = 0
# Normalize and threshold predictions (takes effect only if solution not in {0, 1})
new_prediction = (np.copy(prediction) - float(mini))/float(diff)
new_prediction[new_prediction>1] = 1 # and if predictions exceed the bounds [0, 1]
new_prediction[new_prediction<0] = 0
# Make probabilities smoother
#new_prediction = np.power(new_prediction, (1./10))
return [new_solution, new_prediction] Example 5
def normalize_array (solution, prediction):
''' Use min and max of solution as scaling factors to normalize prediction,
then threshold it to [0, 1]. Binarize solution to {0, 1}.
This allows applying classification scores to all cases.
In principle, this should not do anything to properly formatted
classification inputs and outputs.'''
# Binarize solution
sol=np.ravel(solution) # convert to 1-d array
maxi = np.nanmax((filter(lambda x: x != float('inf'), sol))) # Max except NaN and Inf
mini = np.nanmin((filter(lambda x: x != float('-inf'), sol))) # Mini except NaN and Inf
if maxi == mini:
print('Warning, cannot normalize')
return [solution, prediction]
diff = maxi - mini
mid = (maxi + mini)/2.
new_solution = np.copy(solution)
new_solution[solution>=mid] = 1
new_solution[solution<mid] = 0
# Normalize and threshold predictions (takes effect only if solution not in {0, 1})
new_prediction = (np.copy(prediction) - float(mini))/float(diff)
new_prediction[new_prediction>1] = 1 # and if predictions exceed the bounds [0, 1]
new_prediction[new_prediction<0] = 0
# Make probabilities smoother
#new_prediction = np.power(new_prediction, (1./10))
return [new_solution, new_prediction] Example 6
def _get_max_sigma(self, R):
"""Calculate maximum sigma of scanner RAS coordinates
Parameters
----------
R : 2D array, with shape [n_voxel, n_dim]
The coordinate matrix of fMRI data from one subject
Returns
-------
max_sigma : float
The maximum sigma of scanner coordinates.
"""
max_sigma = 2.0 * math.pow(np.nanmax(np.std(R, axis=0)), 2)
return max_sigma Example 7
def sanitize_array(array):
"""
Replace NaN and Inf (there should not be any!)
:param array:
:return:
"""
a = np.ravel(array)
#maxi = np.nanmax((filter(lambda x: x != float('inf'), a))
# ) # Max except NaN and Inf
#mini = np.nanmin((filter(lambda x: x != float('-inf'), a))
# ) # Mini except NaN and Inf
maxi = np.nanmax(a[np.isfinite(a)])
mini = np.nanmin(a[np.isfinite(a)])
array[array == float('inf')] = maxi
array[array == float('-inf')] = mini
mid = (maxi + mini) / 2
array[np.isnan(array)] = mid
return array Example 8
def check_move_neighborhood(self, mask):
"""Given a mask block, check if any central voxels meet move threshold.
Checks whether a cube one move in each direction from the mask center
contains any probabilities greater than the move threshold.
Parameters
----------
mask : ndarray
Block of mask probabilities, usually of the shape specified by
the configured ``output_fov_shape``.
Returns
-------
bool
"""
ctr = np.asarray(mask.shape) // 2
neigh_min = ctr - self.MOVE_DELTA
neigh_max = ctr + self.MOVE_DELTA + 1
neighborhood = mask[map(slice, neigh_min, neigh_max)]
return np.nanmax(neighborhood) >= CONFIG.model.t_move Example 9
def evaluateToGT(self, Li, idxs):
"""
Evaluate the current estimate to a ground truth
:param Li: current estimates
:param idxs: idxs to evaluate
:return: mean error, max error and MD score
"""
if not isinstance(idxs, numpy.ndarray):
idxs = numpy.asarray(idxs)
if self.gt3D is not None:
gt3D_subset = self.gt3D[idxs]
if Li.shape[0] == len(idxs):
Li_subset = Li
else:
Li_subset = Li[idxs]
mean_error = numpy.mean(numpy.sqrt(numpy.square((gt3D_subset - Li_subset.reshape(gt3D_subset.shape))*self.Di_scale[idxs, None, None]).sum(axis=2)), axis=1).mean()
max_error = numpy.max(numpy.sqrt(numpy.square((gt3D_subset - Li_subset.reshape(gt3D_subset.shape))*self.Di_scale[idxs, None, None]).sum(axis=2)))
vals = [(numpy.nanmax(numpy.sqrt(numpy.square((gt3D_subset - Li_subset.reshape(gt3D_subset.shape))*self.Di_scale[idxs, None, None]).sum(axis=2)), axis=1) <= j).sum() / float(gt3D_subset.shape[0]) for j in range(0, 80)]
md_score = numpy.asarray(vals).sum() / float(80.)
return mean_error, max_error, md_score
else:
return 0., 0., 0. Example 10
def min_max(self, mask=None):
"""Get the minimum and maximum value in this data.
If a mask is provided we get the min and max value within the given mask.
Infinities and NaN's are ignored by this algorithm.
Args:
mask (ndarray): the mask, we only include elements for which the mask > 0
Returns:
tuple: (min, max) the minimum and maximum values
"""
if mask is not None:
roi = mdt.create_roi(self.data, mask)
return np.nanmin(roi), np.nanmax(roi)
return np.nanmin(self.data), np.nanmax(self.data) Example 11
def test_extrema():
for nprocs in [1, 2, 4, 8]:
ds = fake_random_ds(16, nprocs = nprocs, fields = ("density",
"velocity_x", "velocity_y", "velocity_z"))
for sp in [ds.sphere("c", (0.25, 'unitary')), ds.r[0.5,:,:]]:
mi, ma = sp.quantities["Extrema"]("density")
assert_equal(mi, np.nanmin(sp["density"]))
assert_equal(ma, np.nanmax(sp["density"]))
dd = ds.all_data()
mi, ma = dd.quantities["Extrema"]("density")
assert_equal(mi, np.nanmin(dd["density"]))
assert_equal(ma, np.nanmax(dd["density"]))
sp = ds.sphere("max", (0.25, 'unitary'))
assert_equal(np.any(np.isnan(sp["radial_velocity"])), False)
mi, ma = dd.quantities["Extrema"]("radial_velocity")
assert_equal(mi, np.nanmin(dd["radial_velocity"]))
assert_equal(ma, np.nanmax(dd["radial_velocity"])) Example 12
def mospat_plot_profile(f_Var, f_Height, c_Var, c_Net, c_Stn, t_ObsStationVerticalData):
f_lat = t_ObsStationVerticalData[c_Net][c_Stn]['f_Lat']
f_lon = t_ObsStationVerticalData[c_Net][c_Stn]['f_Lon']
f_Elev = t_ObsStationVerticalData[c_Net][c_Stn]['f_Elevation']
f_hmax = 5000.
if np.nanmax(f_Height) >= f_hmax:
i_hmax = np.where(np.array(f_Height) <= np.array(f_hmax))[0][-1]
f_Height = f_Height[:i_hmax]
f_Var = f_Var[:i_hmax]
c_var_label = ConfP.mospat_config_labels(c_Var)
c_coord = ' [lat=%0.2f lon=%0.2f elev=%0.2f]' % (f_lat, f_lon, f_Elev)
fig, ax = plt.subplots(figsize=(ConfP.f_PFSize[0], ConfP.f_PFSize[1]))
line = ax.plot(f_Var, f_Height / 1000., color=ConfP.c_ObsColor[0], marker=ConfP.c_ObsMarker,
linestyle=ConfP.c_ObsLine[0], label=c_Net)
ax.set_title(c_Stn + c_coord)
ax.set_ylabel('Height [km]')
ax.set_xlabel(c_var_label)
ax.set_ylim([0, np.nanmax(f_Height / 1000.)])
return fig, ax, line Example 13
def add_image(self, image):
"""
This function ...
:param image:
:return:
"""
# Create an animation to show the result of the source extraction step
if self.max_frame_value is None: self.max_frame_value = np.nanmax(image)
# Make a plot of the image
buf = io.BytesIO()
plotting.plot_box(image, path=buf, format="png", vmin=0.0, vmax=0.5*self.max_frame_value)
buf.seek(0)
im = imageio.imread(buf)
buf.close()
self.add_frame(im)
# ----------------------------------------------------------------- Example 14
def inpaint_biharmonic(frame, mask):
"""
This function ...
:param frame:
:param mask:
:return:
"""
maximum = np.nanmax(frame)
normalized = frame / maximum
data = inpaint.inpaint_biharmonic(normalized, mask, multichannel=False)
return data * maximum
# -----------------------------------------------------------------
# TODO: create a better inpainting function. OpenCV has one, but this is a terrible dependency because it's hard to
# install. Options:
# - The below replace_nans function can be replaced by the more up to date version at:
# https://github.com/OpenPIV/openpiv-python/blob/master/openpiv/src/lib.pyx
# We may want to keep it in cython so that it runs faster. However, this original does not have the inverse distance
# weighing as in the code below, but we can maybe add this ourselves in the cython code
# - Write our own code.
# SOLUTION: SEE FUNCTION ABOVE, GENERALLY, IT IS MUCH BETTER Example 15
def add_image(self, image):
"""
This function ...
:param image:
:return:
"""
# Create an animation to show the result of the source extraction step
if self.max_frame_value is None: self.max_frame_value = np.nanmax(image)
# Make a plot of the image
buf = io.BytesIO()
plotting.plot_box(image, path=buf, format="png", vmin=0.0, vmax=0.5*self.max_frame_value)
buf.seek(0)
im = imageio.imread(buf)
buf.close()
self.add_frame(im)
# ----------------------------------------------------------------- Example 16
def inpaint_biharmonic(frame, mask):
"""
This function ...
:param frame:
:param mask:
:return:
"""
maximum = np.nanmax(frame)
normalized = frame / maximum
data = inpaint.inpaint_biharmonic(normalized, mask, multichannel=False)
return data * maximum
# -----------------------------------------------------------------
# TODO: create a better inpainting function. OpenCV has one, but this is a terrible dependency because it's hard to
# install. Options:
# - The below replace_nans function can be replaced by the more up to date version at:
# https://github.com/OpenPIV/openpiv-python/blob/master/openpiv/src/lib.pyx
# We may want to keep it in cython so that it runs faster. However, this original does not have the inverse distance
# weighing as in the code below, but we can maybe add this ourselves in the cython code
# - Write our own code.
# SOLUTION: SEE FUNCTION ABOVE, GENERALLY, IT IS MUCH BETTER Example 17
def _calc(arr, out, ksize):
gx = arr.shape[0]
gy = arr.shape[1]
for i in range(gx):
for j in range(gy):
xmn = i-ksize
if xmn < 0:
xmn = 0
xmx = i+ksize
if xmx > gx:
xmx = gx
ymn = j-ksize
if ymn < 0:
ymn = 0
ymx = j+ksize
if ymx > gy:
ymx = gy
out[i,j] = np.nanmax(arr[xmn:xmx,ymn:ymx]) Example 18
def local_entropy(ocl_ctx, img, window_radius, num_bins=8):
""" compute local entropy using a sliding window """
mf = cl.mem_flags
cl_queue = cl.CommandQueue(ocl_ctx)
img_np = np.array(img).astype(np.float32)
img_buf = cl.Buffer(ocl_ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=img_np)
min_val = np.nanmin(img)
max_val = np.nanmax(img)
entropy = np.zeros_like(img,dtype=np.float32)
dest_buf = cl.Buffer(ocl_ctx, mf.WRITE_ONLY, entropy.nbytes)
cl_dir = os.path.dirname(__file__)
cl_filename = cl_dir + '/cl/local_entropy.cl'
with open(cl_filename, 'r') as fd:
clstr = fd.read()
prg = cl.Program(ocl_ctx, clstr).build()
prg.local_entropy(cl_queue, entropy.shape, None,
img_buf, dest_buf,
np.int32(img.shape[1]), np.int32(img.shape[0]),
np.int32(window_radius), np.int32(num_bins),
np.float32(min_val), np.float32(max_val))
cl.enqueue_copy(cl_queue, entropy, dest_buf)
cl_queue.finish()
return entropy Example 19
def minmax(X):
"""
Returns the MinMax Semivariance of sample X.
X has to be an even-length array of point pairs like: x1, x1+h, x2, x2+h, ..., xn, xn+h.
:param X:
:return:
"""
_X = np.asarray(X)
if any([isinstance(_, list) or isinstance(_, np.ndarray) for _ in _X]):
return [minmax(_) for _ in _X]
# check even
if len(_X) % 2 > 0:
raise ValueError('The sample does not have an even length: {}'.format(_X))
return (np.nanmax(_X) - np.nanmin(_X)) / np.nanmean(_X) Example 20
def test_FmtHeatmap__get_min_max_from_selected_cell_values_with_cache():
df_pn = df - 5.
cache = {}
fmt = pbtf.FmtHeatmap(cache=cache)
res = fmt._get_min_max_from_selected_cell_values(None, None, df_pn)
assert len(cache) == 1 and (None, None) in cache.keys()
assert res == (np.nanmin(df_pn), np.nanmax(df_pn))
min_value, max_value = np.nanmin(df.loc[['a'], ['aa', 'bb']]), np.nanmax(df.loc[['a'], ['aa', 'bb']])
res = fmt._get_min_max_from_selected_cell_values(['a'], ['aa', 'bb'], df)
assert len(cache) == 2 and (frozenset(['a']), frozenset(['aa', 'bb'])) in cache.keys()
assert res == (min_value, max_value)
res = fmt._get_min_max_from_selected_cell_values(['a'], ['aa', 'bb'], df)
assert len(cache) == 2 and (frozenset(['a']), frozenset(['aa', 'bb'])) in cache.keys()
assert res == (min_value, max_value) Example 21
def test_FmtHeatmap__get_min_max_from_selected_cell_values_without_cache():
df_pn = df - 5.
cache = None
fmt = pbtf.FmtHeatmap(cache=cache)
res = fmt._get_min_max_from_selected_cell_values(None, None, df_pn)
assert cache is None
assert res == (np.nanmin(df_pn), np.nanmax(df_pn))
min_value, max_value = np.nanmin(df.loc[['a'], ['aa', 'bb']]), np.nanmax(df.loc[['a'], ['aa', 'bb']])
res = fmt._get_min_max_from_selected_cell_values(['a'], ['aa', 'bb'], df)
assert cache is None
assert res == (min_value, max_value)
res = fmt._get_min_max_from_selected_cell_values(['a'], ['aa', 'bb'], df)
assert cache is None
assert res == (min_value, max_value) Example 22
def MostUnstable(self, t, p, q, td):
#Determine psfc - 300mb
minP = p[0] - 300.
diff = p - minP
ind = np.where(diff > 0)
#Determine max theta-e
vp = self.VaporPressure(td[ind])
t_lcl = self.TempLCL(t[ind]+273.15,td[ind])
thetae = self.theta[ind] * np.exp((3.376/t_lcl - 0.00254)*1000*q[ind]*(1. + 0.81 * q[ind]))
#thetae = self.theta[ind] * np.exp((3036/t_lcl - 1.78)*(q[ind]/1000.)*(1. + (0.448/1000.) * q[ind]))
indmax = np.where(thetae == np.nanmax(thetae))
#Define parcel
self.t_parcel = t[indmax]
self.q_parcel = q[indmax]
self.theta_parcel = self.theta[indmax]
self.td_parcel = td[indmax]
self.p_parcel = p[0]
#Lifted Parcel Temperature Example 23
def depth_callback(self,data):
try:
self.depth_image= self.br.imgmsg_to_cv2(data, desired_encoding="passthrough")
except CvBridgeError as e:
print(e)
# print "depth"
depth_min = np.nanmin(self.depth_image)
depth_max = np.nanmax(self.depth_image)
depth_img = self.depth_image.copy()
depth_img[np.isnan(self.depth_image)] = depth_min
depth_img = ((depth_img - depth_min) / (depth_max - depth_min) * 255).astype(np.uint8)
cv2.imshow("Depth Image", depth_img)
cv2.waitKey(5)
# stream = open("/home/chentao/depth_test.yaml", "w")
# data = {'img':depth_img.tolist()}
# yaml.dump(data, stream) Example 24
def depth_callback(self,data):
try:
self.depth_image= self.br.imgmsg_to_cv2(data, desired_encoding="passthrough")
except CvBridgeError as e:
print(e)
# print "depth"
depth_min = np.nanmin(self.depth_image)
depth_max = np.nanmax(self.depth_image)
depth_img = self.depth_image.copy()
depth_img[np.isnan(self.depth_image)] = depth_min
depth_img = ((depth_img - depth_min) / (depth_max - depth_min) * 255).astype(np.uint8)
cv2.imshow("Depth Image", depth_img)
cv2.waitKey(5)
# stream = open("/home/chentao/depth_test.yaml", "w")
# data = {'img':depth_img.tolist()}
# yaml.dump(data, stream) Example 25
def depth_callback(self,data):
try:
self.depth_image= self.br.imgmsg_to_cv2(data, desired_encoding="passthrough")
except CvBridgeError as e:
print(e)
# print "depth"
depth_min = np.nanmin(self.depth_image)
depth_max = np.nanmax(self.depth_image)
depth_img = self.depth_image.copy()
depth_img[np.isnan(self.depth_image)] = depth_min
depth_img = ((depth_img - depth_min) / (depth_max - depth_min) * 255).astype(np.uint8)
cv2.imshow("Depth Image", depth_img)
cv2.waitKey(5) Example 26
def basemap_raster_mercator(lon, lat, grid, cmin, cmax, cmap_name):
# longitude/latitude extent
lons = (np.amin(lon), np.amax(lon))
lats = (np.amin(lat), np.amax(lat))
# construct spherical mercator projection for region of interest
m = Basemap(projection='merc',llcrnrlat=lats[0], urcrnrlat=lats[1],
llcrnrlon=lons[0],urcrnrlon=lons[1])
#vmin,vmax = np.nanmin(grid),np.nanmax(grid)
masked_grid = np.ma.array(grid,mask=np.isnan(grid))
fig = plt.figure(frameon=False,figsize=(12,8),dpi=72)
plt.axis('off')
cmap = mpl.cm.get_cmap(cmap_name)
m.pcolormesh(lon,lat,masked_grid,latlon=True,cmap=cmap,vmin=cmin,vmax=cmax)
str_io = StringIO.StringIO()
plt.savefig(str_io,bbox_inches='tight',format='png',pad_inches=0,transparent=True)
plt.close()
numpy_bounds = [ (lons[0],lats[0]),(lons[1],lats[0]),(lons[1],lats[1]),(lons[0],lats[1]) ]
float_bounds = [ (float(x), float(y)) for x,y in numpy_bounds ]
return str_io.getvalue(), float_bounds Example 27
def basemap_barbs_mercator(u,v,lat,lon):
# lon/lat extents
lons = (np.amin(lon), np.amax(lon))
lats = (np.amin(lat), np.amax(lat))
# construct spherical mercator projection for region of interest
m = Basemap(projection='merc',llcrnrlat=lats[0], urcrnrlat=lats[1],
llcrnrlon=lons[0],urcrnrlon=lons[1])
#vmin,vmax = np.nanmin(grid),np.nanmax(grid)
fig = plt.figure(frameon=False,figsize=(12,8),dpi=72*4)
plt.axis('off')
m.quiver(lon,lat,u,v,latlon=True)
str_io = StringIO.StringIO()
plt.savefig(str_io,bbox_inches='tight',format='png',pad_inches=0,transparent=True)
plt.close()
numpy_bounds = [ (lons[0],lats[0]),(lons[1],lats[0]),(lons[1],lats[1]),(lons[0],lats[1]) ]
float_bounds = [ (float(x), float(y)) for x,y in numpy_bounds ]
return str_io.getvalue(), float_bounds Example 28
def _check_vals(self, vals):
"""TODO Basic check of target elements (sequence of polygons).
"""
if self.zdata is not None:
lyr = self.zdata.src.ds.GetLayerByName('src')
lyr.ResetReading()
lyr.SetSpatialFilter(None)
src_len = lyr.GetFeatureCount()
assert len(vals) == src_len, \
"Argument vals must be of length %d" % src_len
else:
imax = 0
for i in self.ix:
mx = np.nanmax(i)
if imax < mx:
imax = mx
assert len(vals) > imax, \
"Argument vals cannot be subscripted by given index values"
return vals Example 29
def _test_covariance_visual(self):
cov = self.sc.covariance
cov.epsilon = .02
cov.subsampling = 10
# l = self.sc.quadtree.leaves[0]
d = []
d.append(('Full', cov._calcCovarianceMatrix(method='full',
nthreads=0)))
d.append(('Focal', cov._calcCovarianceMatrix(method='focal')))
fig, _ = plt.subplots(1, len(d))
for i, (title, mat) in enumerate(d):
print '%s Max %f' % (title, num.nanmax(mat)), mat.shape
fig.axes[i].imshow(mat)
fig.axes[i].set_title(title)
plt.show() Example 30
def setSymColormap(self):
cmap = {'ticks':
[[0, (106, 0, 31, 255)],
[.5, (255, 255, 255, 255)],
[1., (8, 54, 104, 255)]],
'mode': 'rgb'}
cmap = {'ticks':
[[0, (172, 56, 56)],
[.5, (255, 255, 255)],
[1., (51, 53, 120)]],
'mode': 'rgb'}
lvl_min = lvl_max = 0
for plot in self.plots:
plt_min = num.nanmin(plot.data)
plt_max = num.nanmax(plot.data)
lvl_max = lvl_max if plt_max < lvl_max else plt_max
lvl_min = lvl_min if plt_min > lvl_min else plt_min
abs_range = max(abs(lvl_min), abs(lvl_max))
self.gradient.restoreState(cmap)
self.setLevels(-abs_range, abs_range) Example 31
def setSymColormap(self):
cmap = {'ticks':
[[0., (0, 0, 0, 255)],
[1e-3, (106, 0, 31, 255)],
[.5, (255, 255, 255, 255)],
[1., (8, 54, 104, 255)]],
'mode': 'rgb'}
cmap = {'ticks':
[[0., (0, 0, 0)],
[1e-3, (172, 56, 56)],
[.5, (255, 255, 255)],
[1., (51, 53, 120)]],
'mode': 'rgb'}
lvl_min = num.nanmin(self._plot.data)
lvl_max = num.nanmax(self._plot.data)
abs_range = max(abs(lvl_min), abs(lvl_max))
self.gradient.restoreState(cmap)
self.setLevels(-abs_range, abs_range) Example 32
def setArray(self, incomingArray, copy=False):
"""
You can use the self.array directly but if you want to copy from one array
into a raster we suggest you do it this way
:param incomingArray:
:return:
"""
masked = isinstance(self.array, np.ma.MaskedArray)
if copy:
if masked:
self.array = np.ma.copy(incomingArray)
else:
self.array = np.ma.masked_invalid(incomingArray, copy=True)
else:
if masked:
self.array = incomingArray
else:
self.array = np.ma.masked_invalid(incomingArray)
self.rows = self.array.shape[0]
self.cols = self.array.shape[1]
self.min = np.nanmin(self.array)
self.max = np.nanmax(self.array) Example 33
def _choose_cov(self, cov_type, **cov_config):
"""Return covariance estimator reformat clusters"""
cov_est = self._cov_estimators[cov_type]
if cov_type != 'clustered':
return cov_est, cov_config
cov_config_upd = {k: v for k, v in cov_config.items()}
clusters = cov_config.get('clusters', None)
if clusters is not None:
clusters = self.reformat_clusters(clusters).copy()
cluster_max = np.nanmax(clusters.values3d, axis=1)
delta = cluster_max - np.nanmin(clusters.values3d, axis=1)
if np.any(delta != 0):
raise ValueError('clusters must not vary within an entity')
index = clusters.panel.minor_axis
reindex = clusters.entities
clusters = pd.DataFrame(cluster_max.T, index=index, columns=clusters.vars)
clusters = clusters.loc[reindex].astype(np.int64)
cov_config_upd['clusters'] = clusters
return cov_est, cov_config_upd Example 34
def get_bbox(self):
"""
Returns boundary box for the coordinates. Useful for setting up
the map extent for plotting on a map.
:return tuple: corner coordinates (llcrnrlat, urcrnrlat, llcrnrlon,
urcrnrlon)
"""
x, y, z = zip(self)
llcrnrlat = np.nanmin(y)
urcrnrlat = np.nanmax(y)
llcrnrlon = np.nanmin(x)
urcrnrlon = np.nanmax(x)
return (llcrnrlat,
urcrnrlat,
llcrnrlon,
urcrnrlon) Example 35
def plot_counts(ax, data):
"""
"""
hourloc = mpl.dates.HourLocator()
xtickformat = mpl.dates.DateFormatter('%H:%M')
ax.xaxis.set_major_formatter(xtickformat)
ax.xaxis.set_major_locator(hourloc)
cnts = data['CPC378_counts'][:]
ix = np.where(data['WOW_IND'][:].ravel() == 1)[0]
cnts[ix,:] = np.nan
ax.plot_date(data['mpl_timestamp'][:].ravel(), cnts.ravel(), '-')
ax.set_ylim((0, np.nanmax(cnts)))
ax.set_ylabel('#')
ax.set_xlabel('Time (utc)')
ax.text(0.05, 0.98, 'CPC', axes_title_style, transform=ax.transAxes)
return ax Example 36
def visRenderedViews(self,outDir,nViews=0):
pt = Imath.PixelType(Imath.PixelType.FLOAT)
renders = sorted(glob.glob(outDir + '/render_*.png'))
if (nViews > 0) and (nViews < len(renders)):
renders = [renders[ix] for ix in range(nViews)]
for render in renders:
print render
rgbIm = scipy.misc.imread(render)
dMap = loadDepth(render.replace('render_','depth_'))
plt.figure(figsize=(12,6))
plt.subplot(121)
plt.imshow(rgbIm)
dMap[dMap>=10] = np.nan
plt.subplot(122)
plt.imshow(dMap)
print(np.nanmax(dMap),np.nanmin(dMap))
plt.show() Example 37
def derivcum2(segment, config):
"""
compute the second derivative of the cumulative function using savitzy-golay.
Does not modify the segment's stream or traces in-place
IMPORTANT NOTES:
- As this function is decorated for the gui visualization, any in-place modification to
the segment's Stream or any of its Traces will affect subsequent plots
-Being decorated with '@gui.sideplot' or '@gui.customplot', this function must return
a numeric sequence y taken at successive equally spaced points in any of these forms:
- a Trace object
- a Stream object
- the tuple (x0, dx, y) or (x0, dx, y, label), where
- x0 (numeric, `datetime` or `UTCDateTime`) is the abscissa of the first point
- dx (numeric or `timedelta`) is the sampling period
- y (numpy array or numeric list) are the sequence values
- label (string, optional) is the sequence name to be displayed on the plot legend.
(if x0 is numeric and `dx` is a `timedelta` object, then x0 will be converted
to `UTCDateTime(x0)`; if x0 is a `datetime` or `UTCDateTime` object and `dx` is
numeric, then `dx` will be converted to `timedelta(seconds=dx)`)
- a dict of any of the above types, where the keys (string) will denote each sequence
name to be displayed on the plot legend.
:return: the tuple (starttime, timedelta, values)
:raise: an Exception if `segment.stream()` is empty or has more than one trace (possible
gaps/overlaps)
"""
cum = cumulative(segment, config)
sec_der = savitzky_golay(cum.data, 31, 2, deriv=2)
sec_der_abs = np.abs(sec_der)
sec_der_abs /= np.nanmax(sec_der_abs) # FIXME: this should be sec_der_abs /= mmm
# the stream object has surely only one trace (see 'cumulative')
return segment.stream()[0].stats.starttime, segment.stream()[0].stats.delta, sec_der_abs Example 38
def find_bbox(t):
# given a table t find the bounding box of the ellipses for the regions
boxes=[]
for r in t:
a=r['Maj']/scale
b=r['Min']/scale
th=(r['PA']+90)*np.pi/180.0
dx=np.sqrt((a*np.cos(th))**2.0+(b*np.sin(th))**2.0)
dy=np.sqrt((a*np.sin(th))**2.0+(b*np.cos(th))**2.0)
boxes.append([r['RA']-dx/np.cos(r['DEC']*np.pi/180.0),
r['RA']+dx/np.cos(r['DEC']*np.pi/180.0),
r['DEC']-dy, r['DEC']+dy])
boxes=np.array(boxes)
minra=np.nanmin(boxes[:,0])
maxra=np.nanmax(boxes[:,1])
mindec=np.nanmin(boxes[:,2])
maxdec=np.nanmax(boxes[:,3])
ra=np.mean((minra,maxra))
dec=np.mean((mindec,maxdec))
size=1.2*3600.0*np.max((maxdec-mindec,(maxra-minra)*np.cos(dec*np.pi/180.0)))
return ra,dec,size Example 39
def VshGR(GRlog,itmin,itmax): # Usando o perfil GR
GRmin = np.nanmin(GRlog)
GRminInt = GRlog[(GRlog<=(GRmin*(1+itmin/100)))] # Valores do GRmin
GRminm = np.mean(GRminInt) # Media dos valores de GRmin
GRmax = np.nanmax(GRlog)
GRmaxInt = GRlog[(GRlog>=(GRmax*(1-itmax/100)))] # Valores de GRmax
GRmaxm = np.mean(GRmaxInt) # Media dos valores de GRmax
Vsh = 100*(GRlog-GRminm)/(GRmaxm-GRminm) # Volume de argila
for i in range(len(Vsh)):
if (Vsh[i] > 100):
Vsh[i] = 100
elif (Vsh[i] < 0):
Vsh[i] = 0
print GRmin, GRminm, GRmax, GRmaxm, np.nanmin(Vsh), np.nanmax(Vsh)
return Vsh Example 40
def sanitize_array(array):
''' Replace NaN and Inf (there should not be any!)'''
a=np.ravel(array)
maxi = np.nanmax((filter(lambda x: x != float('inf'), a))) # Max except NaN and Inf
mini = np.nanmin((filter(lambda x: x != float('-inf'), a))) # Mini except NaN and Inf
array[array==float('inf')]=maxi
array[array==float('-inf')]=mini
mid = (maxi + mini)/2
array[np.isnan(array)]=mid
return array Example 41
def frame_to_series(self, field, frame, columns=None):
"""
Convert a frame with a DatetimeIndex and sid columns into a series with
a sid index, using the aggregator defined by the given field.
"""
if isinstance(frame, pd.DataFrame):
columns = frame.columns
frame = frame.values
if not len(frame):
return pd.Series(
data=(0 if field == 'volume' else np.nan),
index=columns,
).values
if field in ['price', 'close']:
# shortcircuit for full last row
vals = frame[-1]
if np.all(~np.isnan(vals)):
return vals
return ffill(frame)[-1]
elif field == 'open':
return bfill(frame)[0]
elif field == 'volume':
return np.nansum(frame, axis=0)
elif field == 'high':
return np.nanmax(frame, axis=0)
elif field == 'low':
return np.nanmin(frame, axis=0)
else:
raise ValueError("Unknown field {}".format(field)) Example 42
def quickMinMax(self, data):
"""
Estimate the min/max values of *data* by subsampling.
"""
while data.size > 1e6:
ax = np.argmax(data.shape)
sl = [slice(None)] * data.ndim
sl[ax] = slice(None, None, 2)
data = data[sl]
return nanmin(data), nanmax(data) Example 43
def dataBounds(self, ax, frac=1.0, orthoRange=None):
if frac >= 1.0 and orthoRange is None and self.bounds[ax] is not None:
return self.bounds[ax]
#self.prepareGeometryChange()
if self.data is None or len(self.data) == 0:
return (None, None)
if ax == 0:
d = self.data['x']
d2 = self.data['y']
elif ax == 1:
d = self.data['y']
d2 = self.data['x']
if orthoRange is not None:
mask = (d2 >= orthoRange[0]) * (d2 <= orthoRange[1])
d = d[mask]
d2 = d2[mask]
if frac >= 1.0:
self.bounds[ax] = (np.nanmin(d) - self._maxSpotWidth*0.7072, np.nanmax(d) + self._maxSpotWidth*0.7072)
return self.bounds[ax]
elif frac <= 0.0:
raise Exception("Value for parameter 'frac' must be > 0. (got %s)" % str(frac))
else:
mask = np.isfinite(d)
d = d[mask]
return np.percentile(d, [50 * (1 - frac), 50 * (1 + frac)]) Example 44
def quickMinMax(self, data):
"""
Estimate the min/max values of *data* by subsampling.
"""
while data.size > 1e6:
ax = np.argmax(data.shape)
sl = [slice(None)] * data.ndim
sl[ax] = slice(None, None, 2)
data = data[sl]
return nanmin(data), nanmax(data) Example 45
def getMaxError(self):
"""
get max error over all joints
:return: maximum error
"""
return numpy.nanmax(numpy.sqrt(numpy.square(self.gt - self.joints).sum(axis=2))) Example 46
def getMaxErrorOverSeq(self):
"""
get max error over all joints for each image of sequence
:return: maximum error
"""
return numpy.nanmax(numpy.sqrt(numpy.square(self.gt - self.joints).sum(axis=2)), axis=1) Example 47
def getJointMaxError(self, jointID):
"""
get maximum error of one joint
:param jointID: joint ID
:return: maximum joint error
"""
return numpy.nanmax(numpy.sqrt(numpy.square(self.gt[:, jointID, :] - self.joints[:, jointID, :]).sum(axis=1))) Example 48
def getNumFramesWithinMaxDist(self, dist):
"""
calculate the number of frames where the maximum difference of a joint is within dist mm
:param dist: distance between joint and GT
:return: number of frames
"""
return (numpy.nanmax(numpy.sqrt(numpy.square(self.gt - self.joints).sum(axis=2)), axis=1) <= dist).sum() Example 49
def getMDscore(self, dist):
"""
calculate the max dist score, ie. MD=\int_0^d{\frac{|F<x|}{|F|}dx = \sum
:param dist: distance between joint and GT
:return: score value [0-1]
"""
vals = [(numpy.nanmax(numpy.sqrt(numpy.square(self.gt - self.joints).sum(axis=2)), axis=1) <= j).sum() / float(self.joints.shape[0]) for j in range(0, dist)]
return numpy.asarray(vals).sum() / float(dist) Example 50
def normalize_data(self, values):
normalized_values = copy.deepcopy(values)
data = np.array(values, dtype=float)[:, 0:5]
data_min = np.nanmin(data, 0)
data_max = np.nanmax(data, 0)
print data_min
print data_max
for i in range(len(values)):
for j in range(5):
normalized_values[i][j] = np.abs(values[i][j] - data_min[j]) / np.abs(data_max[j] - data_min[j])
return normalized_values, data_min, data_max 