# Python numpy.nanmin() 使用实例

The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.

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:

Returns:
tuple: (min, max) the minimum and maximum values
"""
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'))

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:
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.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)
fig = plt.figure(frameon=False,figsize=(12,8),dpi=72)
plt.axis('off')
cmap = mpl.cm.get_cmap(cmap_name)

str_io = StringIO.StringIO()
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.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.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.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:
"""
if copy:
self.array = np.ma.copy(incomingArray)
else:
else:
self.array = incomingArray
else:

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
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

## 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])

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:
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)
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)
for f in [np.nanmin]:
res = f(mat, axis=1)
tgt = f(_ndat, axis=1)
assert_equal(res, tgt)
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.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.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)

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
([_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),