Python numpy.uint函数代码示例

def test_uint_indexing():
    """
    Test that accessing a row with an unsigned integer
    works as with a signed integer.  Similarly tests
    that printing such a row works.

    This is non-trivial: adding a signed and unsigned
    integer in numpy results in a float, which is an
    invalid slice index.

    Regression test for gh-7464.
    """
    t = table.Table([[1., 2., 3.]], names='a')
    assert t['a'][1] == 2.
    assert t['a'][np.int(1)] == 2.
    assert t['a'][np.uint(1)] == 2.
    assert t[np.uint(1)]['a'] == 2.

    trepr = ['<Row index=1>',
             '   a   ',
             'float64',
             '-------',
             '    2.0']

    assert repr(t[1]).splitlines() == trepr
    assert repr(t[np.int(1)]).splitlines() == trepr
    assert repr(t[np.uint(1)]).splitlines() == trepr

 def get_panorama_col_from_azimuth(self, azimuth):
     '''
     @param azimuth: The target azimuth angle (in radians) for which the corresponding col in the panoramic image is to be found.
     @retval col: The valid col number (first col is index 0 and last is width-1) in the panorama where the azimuth maps to.
     '''
     azimuth_filtered = np.mod(azimuth, 2.0 * np.pi)  # Filter input azimuth so values are only positive angles between 0 and 2PI
     arc_length = self.cyl_radius * azimuth_filtered
     col = np.uint(self.cols - 1 - np.uint(arc_length / self.pixel_size))
     return col

    def __init__(self,timeSeries=None,
                 lenSeries=2**18,
                 numChannels=1,
                 fMin=400,fMax=800,
                 sampTime=None,
                 noiseRMS=0.1):
        """ Initializes the AmplitudeTimeSeries instance. 
        If a array is not passed, then a random whitenoise dataset is generated.
        Inputs: 
        Len -- Number of time data points (usually a power of 2) 2^38 gives about 65 seconds 
        of 400 MHz sampled data
        The time binning is decided by the bandwidth
        fMin -- lowest frequency (MHz)
        fMax -- highest frequency (MHz)
        noiseRMS -- RMS value of noise (TBD)
        noiseAlpha -- spectral slope (default is white noise) (TBD)
        ONLY GENERATES WHITE NOISE RIGHT NOW!
        """
        self.shape = (np.uint(numChannels),np.uint(lenSeries))
        self.fMax = fMax
        self.fMin = fMin        
        
        if sampTime is None:
            self.sampTime = np.uint(numChannels)*1E-6/(fMax-fMin)
        else:
            self.sampTime = sampTime

        if timeSeries is None:
            # then use the rest of the data to generate a random timeseries
            if VERBOSE:
                print "AmplitudeTimeSeries __init__ did not get new data, generating white noise data"

            self.timeSeries = np.complex64(noiseRMS*(np.float16(random.standard_normal(self.shape))
                                                     +np.float16(random.standard_normal(self.shape))*1j)/np.sqrt(2))
            
        else:
            if VERBOSE:
                print "AmplitudeTimeSeries __init__ got new data, making sure it is reasonable."

            if len(timeSeries.shape) == 1:
                self.shape = (1,timeSeries.shape[0])
                
            else:
                self.shape = timeSeries.shape

            self.timeSeries = np.reshape(np.complex64(timeSeries),self.shape)
            
            self.fMin = fMin
            self.fMax = fMax

            if sampTime is None:
                self.sampTime = numChannels*1E-6/(fMax-fMin)
            else:
                self.sampTime = sampTime

        return None

def TipDetector(I):
    
    I.flags.writeable = True
    
    # Convert RGB to YUV
    Y=0.3*I[:,:,2]+0.6*I[:,:,1]+0.1*I[:,:,0]
    V=0.4375*I[:,:,2]-0.375*I[:,:,1]-0.0625*I[:,:,0]
    U=-0.15*I[:,:,2]-0.3*I[:,:,1]+0.45*I[:,:,0]

    # Find pink
    M=np.ones((np.shape(I)[0], np.shape(I)[1]), np.uint8)*255
    for i in range(0,np.shape(I)[0]):
        for j in range(0,np.shape(I)[1]):
            if V[i,j]>15 and U[i,j]>-7:
                M[i,j]=0
    kernel = np.ones((5,5),np.uint8)   
    M = cv2.morphologyEx(M, cv2.MORPH_OPEN, kernel)
    M=cv2.GaussianBlur(M,(7,7),8)
    
    # find Harris corners in pink mask
    dst = cv2.cornerHarris(M,5,3,0.04)
    dst = cv2.dilate(dst,None)
    ret, dst = cv2.threshold(dst,0.7*dst.max(),255,0)
    dst = np.uint8(dst)
    E = np.where(dst > 0.01*dst.max())
    
    # find Harris corners in image
    gray1 = cv2.cvtColor(I,cv2.COLOR_BGR2GRAY)
    gray1 = np.float32(gray1)
    dst1 = cv2.cornerHarris(gray1,3,3,0.04)
    dst1 = cv2.dilate(dst1,None)
    ret1, dst1 = cv2.threshold(dst1,0.01*dst1.max(),255,0)
    dst1 = np.uint8(dst1)
    E1 = np.where(dst1 > 0.01*dst1.max())

    # no tip identified  
    if not E or not E1:
        return [0,0]
    
    # Rearrange the coordinates in more readable format
    ind1 = np.lexsort((E1[1],E1[0]))
    C1=[(E1[1][i],E1[0][i]) for i in ind1]
    ind = np.lexsort((E[1],E[0]))
    C=[(E[1][i],E[0][i]) for i in ind]
    
    # Identify the tip
    D=[]
    for i in range(1,np.shape(C1)[0]):
        for j in range(1,np.shape(C)[0]):
       	    if abs(C1[i][0]-C[j][0])<5 and abs(C1[i][1]-C[j][1])<5:
                D.append([int(np.uint(C1[i][0]*2)), int(np.uint(C1[i][1]*2))])
    if not D:
        return [0,0]
    else:
        return count(D)

def fun_select_image_area(image_data):
    """The function select idealy the area whith information in it.

        Basically I'm defining a grid and take only the center as important area.
    """

    ss = np.shape(image_data)
    h = np.uint(np.linspace(0, ss[0], 6))
    v = np.uint(np.linspace(0, ss[1], 6))
    image_data_area = image_data[h[2] : h[3], v[2] : v[3], :]
    # image_data_area = image_data[h[1]:h[4],v[1]:v[4],:]

    return image_data_area

def max_32_val():
	x = 0
	numpy.uint(x)
	x = 0xFFFFFFFFFF #Can go up to 10 byte representation? long int?
	#x = 0x800000000; # Mask for 1000 0000 0000.... 0000
	#x = x >> 32; Interestingly... it uses logic right shift, not arith.
	print x
	x = x/8
	print("Number of bytes: ", x)
	x = x/1024
	print("Number of Kilobytes: ", x)
	x = x/1024
	print("Number of Megabytes: ", x)
	x = x/1024
	print("Number of Gigabytes: ", x)

def downsample(image, scale):
    """Downsample an image down to a smaller image by a factor of `scale`, by
    averaging the bins."""
    result_shape = np.uint(np.array(image.shape) // scale)
    result = np.zeros(result_shape, dtype=image.dtype)
    num_avg = scale**2
    if len(result_shape) == 2:
        # 2d downsample
        # XXX: I know this is topography, so scale down the z-axis, too.
        ylim, xlim = result_shape
        for y in range(ylim):
            for x in range(xlim):
                xmin, xmax = x * scale, (x+1) * scale
                ymin, ymax = y * scale, (y+1) * scale
                result[y,x] = np.mean(image[ymin:ymax, xmin:xmax] / scale)
    elif len(result_shape) == 3:
        # 3d downsample
        # XXX: I know this is price data, so sum it instead of averaging.
        zlim, ylim, xlim = result_shape
        for z in range(zlim):
            for y in range(ylim):
                for x in range(xlim):
                    zmin, zmax = z * scale, (z+1) * scale
                    xmin, xmax = x * scale, (x+1) * scale
                    ymin, ymax = y * scale, (y+1) * scale
                    result[z,y,x] = np.sum(image[zmin:zmax, ymin:ymax, xmin:xmax])
    return result

 def __init__(self, opt):
     # option (dictionary) contains
     # mandatory:
     # season, batter/pitcher
     self.season = []
     if len(opt['season']) == 1:
         self.season.append(str(opt['season'][0]))
     else:            
         self.season = np.linspace(opt['season'][0],
                                   opt['season'][1], 
                                   opt['season'][1]-opt['season'][0]+1)
         for i in range(len(self.season)):
             self.season[i] = np.uint(self.season[i])
     # batter or pitcher
     self.type = opt['type']
     # options:
     # position, file path, etc (tbd)
     if 'postion' in opt:
         self.position = opt['position']
     else:
         self.position = 'NULL'
     if 'file' in opt:
         self.fp = opt['file']
     else:
         self.fp = []
     # initialize DB
     # DB structure
     # [season] -> [each files] -> [each line]
     self.db = []

def parse_text(file_name):
    """Parse data from Ohio State University text mocap files (http://accad.osu.edu/research/mocap/mocap_data.htm)."""

    # Read the header
    fid = open(file_name, 'r')
    point_names = np.array(fid.readline().split())[2:-1:3]
    fid.close()
    for i in range(len(point_names)):
        point_names[i] = point_names[i][0:-2]

    # Read the matrix data
    S = np.loadtxt(file_name, skiprows=1)
    field = np.uint(S[:, 0])
    times = S[:, 1]
    S = S[:, 2:]

    # Set the -9999.99 markers to be not present
    S[S==-9999.99] = np.NaN

    # Store x, y and z in different arrays
    points = []
    points.append(S[:, 0:-1:3])
    points.append(S[:, 1:-1:3])
    points.append(S[:, 2:-1:3])

    return points, point_names, times

def sanitize_refreq(origin, dest):

    dest.create_dataset(name="data", data=origin["Data"].value.transpose((2,0,1,3)))
    dest["data"].attrs.create('__complex__', "1")

    dest.create_group(name="indices")
    exec("indL = %s"%origin["IndicesL"].value)
    exec("indR = %s"%origin["IndicesR"].value)
    indL = [ str(i) for i in indL ]
    indR = [ str(i) for i in indR ]
    dest["indices"].create_dataset(name="left", data=indL)
    dest["indices"].create_dataset(name="right", data=indR)

    dest.create_group(name="singularity")
    dest["singularity"].create_dataset(name="data", data=origin["Tail"]["array"].value.transpose((2,0,1,3)))
    dest["singularity"]["data"].attrs.create('__complex__', "1")
    dest["singularity"].create_dataset(name="omin", data=origin["Tail"]["OrderMinMIN"].value)
    mask = numpy.zeros( dest["singularity"]["data"].shape[0:2], numpy.integer )
    mask.fill(origin["Tail"]["OrderMax"].value)
    dest["singularity"].create_dataset(name="mask", data=mask)

    dest.create_group(name="mesh")
    size = numpy.uint(len(origin["Mesh"]["array"].value))
    min_w = origin["Mesh"]["array"].value[0]
    max_w = origin["Mesh"]["array"].value[-1]
    dest["mesh"].create_dataset(name="kind", data=1)
    dest["mesh"].create_dataset(name="min", data=min_w)
    dest["mesh"].create_dataset(name="max", data=max_w)
    dest["mesh"].create_dataset(name="size", data=size)

    return ['Data', 'IndicesL', 'IndicesR', 'Mesh', 'Name', 'Note', 'Tail']

def sanitize_imfreq(origin, dest):

    dest.create_dataset(name="data", data=origin["Data"].value.transpose((2,0,1,3)))
    dest["data"].attrs.create('__complex__', "1")

    dest.create_group(name="indices")
    exec("indL = %s"%origin["IndicesL"].value)
    exec("indR = %s"%origin["IndicesR"].value)
    indL = [ str(i) for i in indL ]
    indR = [ str(i) for i in indR ]
    dest["indices"].create_dataset(name="left", data=indL)
    dest["indices"].create_dataset(name="right", data=indR)

    dest.create_group(name="singularity")
    dest["singularity"].create_dataset(name="data", data=origin["Tail"]["array"].value.transpose((2,0,1,3)))
    dest["singularity"]["data"].attrs.create('__complex__', "1")
    dest["singularity"].create_dataset(name="omin", data=origin["Tail"]["OrderMinMIN"].value)
    mask = numpy.zeros( dest["singularity"]["data"].shape[0:2], numpy.integer )
    mask.fill(origin["Tail"]["OrderMax"].value)
    dest["singularity"].create_dataset(name="mask", data=mask)

    dest.create_group(name="mesh")
    beta = origin["Mesh"]["Beta"].value
    pi = numpy.arccos(-1)
    size = numpy.uint(len(origin["Mesh"]["array"].value))
    dest["mesh"].create_dataset(name="kind", data=2)
    dest["mesh"].create_dataset(name="min", data=pi/beta)
    dest["mesh"].create_dataset(name="max", data=(2*size+1)*pi/beta)
    dest["mesh"].create_dataset(name="size", data=size)
    dest["mesh"].create_group(name="domain")
    dest["mesh"]["domain"].create_dataset(name="beta", data=beta)
    dest["mesh"]["domain"].create_dataset(name="statistic", data={"Fermion":"F", "Boson":"B"}[origin["Mesh"]["Statistic"].value] )

    return ['Data', 'IndicesL', 'IndicesR', 'Mesh', 'Name', 'Note', 'Tail']

def deterministic_shuffle(list_, seed=1):
    r"""
    Args:
        list_ (list):
        seed (int):

    Returns:
        list: list_

    CommandLine:
        python -m utool.util_numpy --test-deterministic_shuffle

    Example:
        >>> # ENABLE_DOCTEST
        >>> from utool.util_numpy import *  # NOQA
        >>> list_ = [1,2,3,4,5,6]
        >>> seed = 1
        >>> list_ = deterministic_shuffle(list_, seed)
        >>> result = str(list_)
        >>> print(result)
        [4, 6, 1, 3, 2, 5]
    """
    rand_seed = np.uint32(np.random.rand() * np.uint(0 - 2) / 2)
    if not isinstance(list_, (np.ndarray, list)):
        list_ = list(list_)
    seed_ = len(list_) + seed
    np.random.seed(seed_)
    np.random.shuffle(list_)
    np.random.seed(rand_seed)  # reseed
    return list_

def _load_MNIST(datafile, labelfile):
    #Get training data
    df = open(datafile, 'rb')

    magic = int(binascii.hexlify(df.read(4)), 16)
    assert magic == 2051
    num_examples = int(binascii.hexlify(df.read(4)), 16)
    i = int(binascii.hexlify(df.read(4)), 16)
    j = int(binascii.hexlify(df.read(4)), 16)

    #I only have to work with the feature matrix in terms of its rows,
    #so I store it as a list of <train.num_examples> rows.
    one = np.array([np.uint(255)])
    features = []
    for example in range(0, num_examples):
        #Create a numpy uint8 array of pixels. We set the first attribute to 1, because it corresponds to a y-intercept term in [theta].
        features.append(np.concatenate((one, np.fromfile(df, dtype='u1', count=i*j))))


    lf = open(labelfile, 'rb')
    assert (int(binascii.hexlify(lf.read(4)), 16)) == 2049                  #check magic
    images = (int(binascii.hexlify(lf.read(4)), 16))
    labels = np.fromfile(lf, dtype='u1', count=images)

    data = Data(features=features, labels=labels, theta=np.zeros((10, 785)),
                num_features=i*j, num_labels=10, num_examples=num_examples,
                alpha=0.01, epsilon = 0.01)
    return data

    def __get_excit_wfm(filepath):
        """
        Returns the excitation BE waveform present in the more parms.mat file
        
        Parameters
        ------------
        filepath : String / unicode
            Absolute filepath of the .mat parameter file
        
        Returns
        -----------
        ex_wfm : 1D numpy float array
            Band Excitation waveform

        """
        if not path.exists(filepath):
            warn('BEPSndfTranslator - NO more_parms.mat file found')
            return np.zeros(1000, dtype=np.float32)

        if 'more_parms' in filepath:
            matread = loadmat(filepath, variable_names=['FFT_BE_wave'])
            fft_full = np.complex64(np.squeeze(matread['FFT_BE_wave']))
            bin_inds = None
            fft_full_rev = None
        else:
            matread = loadmat(filepath, variable_names=['FFT_BE_wave', 'FFT_BE_rev_wave', 'BE_bin_ind'])
            bin_inds = np.uint(np.squeeze(matread['BE_bin_ind'])) - 1
            fft_full = np.complex64(np.squeeze(matread['FFT_BE_wave']))
            fft_full_rev = np.complex64(np.squeeze(matread['FFT_BE_rev_wave']))

        return fft_full, fft_full_rev, bin_inds

 def initialize_cost_map(self):
     ''' Performs all the neccessary initialization
     and creation of the cost map graph.
     '''
     self.params['stripWidth'] = np.uint(np.double(self.costm.shape) \
         / self.params['pixels'])
     #self._add_image_strips()
     self.graph = build_graph(self.costm)