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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 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 3
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 4
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 5
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 6
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 7
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 8
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 9
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 10
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 11
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 12
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 13
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 14
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 15
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 16
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 17
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 18
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 19
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 20
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 21
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 22
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 23
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 24
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 25
def distance_curves(x, ys, q1):
"""
Distances to the curves.
:param x: x values of curves (they have to be sorted).
:param ys: y values of multiple curves sharing x values.
:param q1: a point to measure distance to.
:return:
"""
# convert curves into a series of startpoints and endpoints
xp = rolling_window(x, 2)
ysp = rolling_window(ys, 2)
r = np.nanmin(distance_line_segment(xp[:, 0], ysp[:, :, 0],
xp[:, 1], ysp[:, :, 1],
q1[0], q1[1]), axis=1)
return r Example 26
def set_marker_size(self, attr, update=True):
try:
self._size_attr = variable = self.data.domain[attr]
if len(self.data) == 0:
raise Exception
except Exception:
self._size_attr = None
self._legend_sizes = []
else:
assert variable.is_continuous
self._raw_sizes = values = self.data.get_column_view(variable)[0].astype(float)
# Note, [5, 60] is also hardcoded in legend-size-indicator.svg
self._sizes = scale(values, 5, 60).astype(np.uint8)
min = np.nanmin(values)
self._legend_sizes = self._legend_values(variable,
[min, np.nanmax(values)]) if not np.isnan(min) else []
finally:
if update:
self.redraw_markers_overlay_image(new_image=True) Example 27
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 28
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 29
def extract_img_background(img_array,
custom_limits=None,
median_diffbelow=200.0,
image_min=None):
'''
This extracts the background of the image array provided:
- masks the array to only values between the median and the min of flux
- then returns the median value in 3 x 3 stamps.
img_array = image to find the background for
custom_limits = use this to provide custom median and min limits for the
background extraction
median_diffbelow = subtract this value from the median to get the upper
bound for background extraction
image_min = use this value as the lower bound for background extraction
'''
if not custom_limits:
backmax = np.median(img_array)-median_diffbelow
backmin = image_min if image_min is not None else np.nanmin(img_array)
else:
backmin, backmax = custom_limits
masked = npma.masked_outside(img_array, backmin, backmax)
backmasked = npma.median(masked)
return backmasked
## IMAGE SECTION FUNCTIONS ## Example 30
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 31
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 32
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 33
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 Example 34
def writeBinData(out_file, i, GenotypeData, ScoreList, NumInfoSites):
num_lines = len(GenotypeData.accessions)
(likeliScore, likeliHoodRatio) = snpmatch.calculate_likelihoods(ScoreList, NumInfoSites)
if len(likeliScore) > 0:
NumAmb = np.where(likeliHoodRatio < snpmatch.lr_thres)[0]
if len(NumAmb) >= 1 and len(NumAmb) < num_lines:
try:
nextLikeli = np.nanmin(likeliHoodRatio[np.where(likeliHoodRatio > snpmatch.lr_thres)[0]])
except:
nextLikeli = 1
for k in NumAmb:
score = float(ScoreList[k])/NumInfoSites[k]
out_file.write("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n" % (GenotypeData.accessions[k], int(ScoreList[k]), NumInfoSites[k], score, likeliScore[k], nextLikeli, len(NumAmb), i+1)) Example 35
def image_as_uint8(im):
""" Convert the given image to uint8
If the dtype is already uint8, it is returned as-is. If the image
is float, and all values are between 0 and 1, the values are
multiplied by 255. In all other situations, the values are scaled
such that the minimum value becomes 0 and the maximum value becomes
255.
"""
if not isinstance(im, np.ndarray):
raise ValueError('image must be a numpy array')
dtype_str = str(im.dtype)
# Already uint8?
if dtype_str == 'uint8':
return im
# Handle float
mi, ma = np.nanmin(im), np.nanmax(im)
if dtype_str.startswith('float'):
if mi >= 0 and ma <= 1:
mi, ma = 0, 1
# Now make float copy before we scale
im = im.astype('float32')
# Scale the values between 0 and 255
if np.isfinite(mi) and np.isfinite(ma):
if mi:
im -= mi
if ma != 255:
im *= 255.0 / (ma - mi)
assert np.nanmax(im) < 256
return im.astype(np.uint8)
# currently not used ... the only use it to easly provide the global meta info Example 36
def test_masked(self):
mat = np.ma.fix_invalid(_ndat)
msk = mat._mask.copy()
for f in [np.nanmin]:
res = f(mat, axis=1)
tgt = f(_ndat, axis=1)
assert_equal(res, tgt)
assert_equal(mat._mask, msk)
assert_(not np.isinf(mat).any()) Example 37
def test_nanmin(self):
tgt = np.min(self.mat)
for mat in self.integer_arrays():
assert_equal(np.nanmin(mat), tgt) Example 38
def data(self, data):
""" :type: numppy.ndarray """
self._assert_shape(data, self._x_indexes, self._y_indexes)
data[data == -np.inf] = 0.0
data[data == np.inf] = 0.0
self._data = data
self._min_value = np.nanmin(self.data)
self._max_value = np.nanmax(self.data)
self._data_x_indexes = list(range(data.shape[0]))
self._data_y_indexes = list(range(data.shape[1]))
self._dirty = False Example 39
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 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 _evaluate(self,x):
'''
Returns the level of the function at each value in x as the minimum among
all of the functions. Only called internally by HARKinterpolator1D.__call__.
'''
if _isscalar(x):
y = np.nanmin([f(x) for f in self.functions])
else:
m = len(x)
fx = np.zeros((m,self.funcCount))
for j in range(self.funcCount):
fx[:,j] = self.functions[j](x)
y = np.nanmin(fx,axis=1)
return y Example 42
def _evaluate(self,x,y):
'''
Returns the level of the function at each value in (x,y) as the minimum
among all of the functions. Only called internally by
HARKinterpolator2D.__call__.
'''
if _isscalar(x):
f = np.nanmin([f(x,y) for f in self.functions])
else:
m = len(x)
temp = np.zeros((m,self.funcCount))
for j in range(self.funcCount):
temp[:,j] = self.functions[j](x,y)
f = np.nanmin(temp,axis=1)
return f Example 43
def _evaluate(self,x,y,z):
'''
Returns the level of the function at each value in (x,y,z) as the minimum
among all of the functions. Only called internally by
HARKinterpolator3D.__call__.
'''
if _isscalar(x):
f = np.nanmin([f(x,y,z) for f in self.functions])
else:
m = len(x)
temp = np.zeros((m,self.funcCount))
for j in range(self.funcCount):
temp[:,j] = self.functions[j](x,y,z)
f = np.nanmin(temp,axis=1)
return f Example 44
def replot(self, val):
'''
'''
# Update plot
self.cadence = int(val)
self.implot.set_data(self.images[int(val)])
self.implot.set_clim(vmin = np.nanmin(self.images[int(val)]), vmax = np.nanmax(self.images[int(val)]))
self.tracker1.set_xdata([self.time[self.cadence], self.time[self.cadence]])
self.tracker2.set_xdata([self.time[self.cadence], self.time[self.cadence]])
self.update_bkg()
self.update_lc()
self.update_lcbkg()
self.fig.canvas.draw() Example 45
def vmin(self):
return self._vmin if self._vmin else np.nanmin(self.hic_matrix) Example 46
def _plot(self, region=None, cax=None):
da_sub, regions_sub = sub_data_regions(self.da, self.regions, region)
da_sub_masked = np.ma.MaskedArray(da_sub, mask=np.isnan(da_sub))
bin_coords = np.r_[[(x.start - 1) for x in regions_sub], regions_sub[-1].end]
x, y = np.meshgrid(bin_coords, self.window_sizes)
self.mesh = self.ax.pcolormesh(x, y, da_sub_masked, cmap=self.colormap, vmax=self.vmax)
self.colorbar = plt.colorbar(self.mesh, cax=cax, orientation="vertical")
self.window_size_line = self.ax.axhline(self.current_window_size, color='red')
if self.log_y:
self.ax.set_yscale("log")
self.ax.set_ylim((np.nanmin(self.window_sizes), np.nanmax(self.window_sizes))) Example 47
def _plot(self, region=None, cax=None):
self._new_region(region)
bin_coords = [(x.start - 1) for x in self.sr]
ds = self.da_sub[self.init_row]
self.line, = self.ax.plot(bin_coords, ds)
if not self.is_symmetric:
self.current_cutoff = (self.ax.get_ylim()[1] - self.ax.get_ylim()[0]) / 2 + self.ax.get_ylim()[0]
else:
self.current_cutoff = self.ax.get_ylim()[1]/ 2
self.ax.axhline(0.0, linestyle='dashed', color='grey')
self.cutoff_line = self.ax.axhline(self.current_cutoff, color='r')
if self.is_symmetric:
self.cutoff_line_mirror = self.ax.axhline(-1*self.current_cutoff, color='r')
self.ax.set_ylim((np.nanmin(ds), np.nanmax(ds))) Example 48
def update(self, ix=None, cutoff=None, region=None, update_canvas=True):
if region is not None:
self._new_region(region)
if ix is not None and ix != self.current_ix:
ds = self.da_sub[ix]
self.current_ix = ix
self.line.set_ydata(ds)
self.ax.set_ylim((np.nanmin(ds), np.nanmax(ds)))
if cutoff is None:
if not self.is_symmetric:
self.update(cutoff=(self.ax.get_ylim()[1]-self.ax.get_ylim()[0])/2 + self.ax.get_ylim()[0],
update_canvas=False)
else:
self.update(cutoff=self.ax.get_ylim()[1] / 2, update_canvas=False)
if update_canvas:
self.fig.canvas.draw()
if cutoff is not None and cutoff != self.current_cutoff:
if self.is_symmetric:
self.current_cutoff = abs(cutoff)
else:
self.current_cutoff = cutoff
self.cutoff_line.set_ydata(self.current_cutoff)
if self.is_symmetric:
self.cutoff_line_mirror.set_ydata(-1*self.current_cutoff)
if update_canvas:
self.fig.canvas.draw() Example 49
def define_levels(self, nb_class, disc_func):
pot = self.pot
_min = np.nanmin(pot)
if not nb_class:
nb_class = int(get_opt_nb_class(len(pot)) - 2)
if not disc_func or "prog_geom" in disc_func:
levels = [_min] + [
np.nanmax(pot) / i for i in range(1, nb_class + 1)][::-1]
elif "equal_interval" in disc_func:
_bin = np.nanmax(pot) / nb_class
levels = [_min] + [_bin * i for i in range(1, nb_class+1)]
elif "percentiles" in disc_func:
levels = np.percentile(
np.concatenate((pot[pot.nonzero()], np.array([_min]))),
np.linspace(0.0, 100.0, nb_class+1))
elif "jenks" in disc_func:
levels = list(jenks_breaks(np.concatenate(
([_min], pot[pot.nonzero()])), nb_class))
levels[0] = levels[0] - _min * 0.01
elif "head_tail" in disc_func:
levels = head_tail_breaks(np.concatenate(
([_min], pot[pot.nonzero()])))
elif "maximal_breaks" in disc_func:
levels = maximal_breaks(np.concatenate(
([_min], pot[pot.nonzero()])), nb_class)
else:
raise ValueError
return levels Example 50
def set_range(self, x_data, y_data):
min_x, max_x = np.nanmin(x_data), np.nanmax(x_data)
min_y, max_y = np.nanmin(y_data), np.nanmax(y_data)
self.plotview.setRange(
QRectF(min_x, min_y, max_x - min_x, max_y - min_y),
padding=0.025)
self.plotview.replot() 