Python numpy.ma 模块，masked_where() 实例源码

def numpy_band_stats(ds, tif, band_no, partitions=100):
    band = ds.GetRasterBand(band_no)
    data = band.ReadAsArray()
    no_data_val = band.GetNoDataValue()
    data_type = get_datatype(band)
    mask_data = ma.masked_where(data == no_data_val, data)

    if data_type is 'Categorical':
        no_categories = np.max(mask_data) - np.min(mask_data) + 1
    else:
        no_categories = np.nan

    image_source = geoio.RasterioImageSource(tif)
    l = [basename(tif), band_no, no_data_val,
         ds.RasterYSize, ds.RasterXSize,
         np.min(mask_data), np.max(mask_data),
         np.mean(mask_data), np.std(mask_data),
         data_type, float(no_categories),
         image_nans(image_source, partitions)]
    ds = None
    return [str(a) for a in l]

def test_mem_masked_where(self,level=rlevel):
        # Ticket #62
        from numpy.ma import masked_where, MaskType
        a = np.zeros((1, 1))
        b = np.zeros(a.shape, MaskType)
        c = masked_where(b, a)
        a-c

def test_mem_masked_where(self,level=rlevel):
        # Ticket #62
        from numpy.ma import masked_where, MaskType
        a = np.zeros((1, 1))
        b = np.zeros(a.shape, MaskType)
        c = masked_where(b, a)
        a-c

def test_mem_masked_where(self,level=rlevel):
        # Ticket #62
        from numpy.ma import masked_where, MaskType
        a = np.zeros((1, 1))
        b = np.zeros(a.shape, MaskType)
        c = masked_where(b, a)
        a-c

def test_mem_masked_where(self,level=rlevel):
        # Ticket #62
        from numpy.ma import masked_where, MaskType
        a = np.zeros((1, 1))
        b = np.zeros(a.shape, MaskType)
        c = masked_where(b, a)
        a-c

def test_mem_masked_where(self,level=rlevel):
        # Ticket #62
        from numpy.ma import masked_where, MaskType
        a = np.zeros((1, 1))
        b = np.zeros(a.shape, MaskType)
        c = masked_where(b, a)
        a-c

def plot_distribution(ax, lon_samples, lat_samples, to_plot='d', resolution=30, **kwargs):

    if 'cmap' in kwargs:
        cmap = kwargs.pop('cmap')
    else:
        cmap = next(cmaps)

    artists = []

    if 'd' in to_plot:
        lon_grid, lat_grid, density = density_distribution(
            lon_samples, lat_samples, resolution)
        density = ma.masked_where(density <= 0.05*density.max(), density)
        a = ax.pcolormesh(lon_grid, lat_grid, density, cmap=cmap,
                          transform=ccrs.PlateCarree(), **kwargs)
        artists.append(a)

    if 'e' in to_plot:
        lon_grid, lat_grid, cumulative_density = cumulative_density_distribution(
            lon_samples, lat_samples, resolution)
        a = ax.contour(lon_grid, lat_grid, cumulative_density, levels=[
                       0.683, 0.955], cmap=cmap, transform=ccrs.PlateCarree())
        artists.append(a)

    if 's' in to_plot:
        a = ax.scatter(lon_samples, lat_samples, color=cmap(
            [0., 0.5, 1.])[-1], alpha=0.1, transform=ccrs.PlateCarree(), edgecolors=None, **kwargs)
        artists.append(a)

    return artists

def test_mem_masked_where(self,level=rlevel):
        # Ticket #62
        from numpy.ma import masked_where, MaskType
        a = np.zeros((1, 1))
        b = np.zeros(a.shape, MaskType)
        c = masked_where(b, a)
        a-c

def test_mem_masked_where(self,level=rlevel):
        # Ticket #62
        from numpy.ma import masked_where, MaskType
        a = np.zeros((1, 1))
        b = np.zeros(a.shape, MaskType)
        c = masked_where(b, a)
        a-c

def import_all_year_data(tile):
    temp = np.zeros([46, 2400*2400], np.dtype(float))
    if int(tile[5:6])<2:
        temp[:]=np.nan
    for doy in range(1, 369, 8):
        evifile = buildVrtFile(root, doy, tile, 'evi')
        cloudfile = buildVrtFile(root, doy, tile, 'cloudmask')
        aerosolfile = buildVrtFile(root, doy, tile, 'aerosolmask')
        #if no file found for this DOY
        if evifile == 0: continue
        #doyList.append(doy)
        #build vrt for EVI
        vrtEVI = os.path.join(os.path.dirname(evifile), str(1000+doy)[1:]+tile+'EVI_vrt.vrt')
        print "Building the vrt file: ", evifile
        os.system('gdalbuildvrt -separate -input_file_list '+evifile+' '+vrtEVI)
        inEVI = gdal.Open(vrtEVI)
        EVI = inEVI.ReadAsArray()
        #build vrt for cloudmask
        vrtcloud = os.path.join(os.path.dirname(cloudfile), str(1000+doy)[1:]+tile+'cloud_vrt.vrt')
        print "Building the vrt file: ", cloudfile
        os.system('gdalbuildvrt -separate -input_file_list '+cloudfile+' '+vrtcloud)
        incloud = gdal.Open(vrtcloud)
        cloud = incloud.ReadAsArray()
        #build vrt for aerosol
        vrtaerosol = os.path.join(os.path.dirname(aerosolfile), str(1000+doy)[1:]+tile+'aerosol_vrt.vrt')
        print "Building the vrt file: ", aerosolfile
        os.system('gdalbuildvrt -separate -input_file_list '+aerosolfile+' '+vrtaerosol)
        inaerosol = gdal.Open(vrtaerosol)
        aerosol = inaerosol.ReadAsArray()
        global rows, cols, geoProj, geoTran
        rows = 2400
        cols = 2400
        geoTran = inEVI.GetGeoTransform()
        geoProj = inEVI.GetProjection()
        #mask for bad quality
        EVIgood = ma.masked_where((cloud != 1)|(aerosol == 0)|(EVI < 0)|(EVI > 10000), EVI)
        EVIgood = EVIgood.reshape(EVIgood.size/2400/2400, 2400*2400)
        medianEVI = np.nanmedian(EVIgood, axis=0)
        EVI = None
        aerosol = None
        cloud = None
        EVIgood = None
        #assign to the 46 layer of matrix
        temp[(doy-1)/8, :] = medianEVI
        meanEVI = None
    return temp

def barbs(self, x, y, u, v, *args, **kwargs):
        """
        Make a wind barb plot (u, v) with on the map.
        (see matplotlib.pyplot.barbs documentation).

        If ``latlon`` keyword is set to True, x,y are intrepreted as
        longitude and latitude in degrees.  Data and longitudes are
        automatically shifted to match map projection region for cylindrical
        and pseudocylindrical projections, and x,y are transformed to map
        projection coordinates. If ``latlon`` is False (default), x and y
        are assumed to be map projection coordinates.

        Extra keyword ``ax`` can be used to override the default axis instance.

        Other \*args and \**kwargs passed on to matplotlib.pyplot.barbs

        Returns two matplotlib.axes.Barbs instances, one for the Northern
        Hemisphere and one for the Southern Hemisphere.
        """
        if _matplotlib_version < '0.98.3':
            msg = dedent("""
            barb method requires matplotlib 0.98.3 or higher,
            you have %s""" % _matplotlib_version)
            raise NotImplementedError(msg)
        ax, plt = self._ax_plt_from_kw(kwargs)
        # allow callers to override the hold state by passing hold=True|False
        b = ax.ishold()
        h = kwargs.pop('hold',None)
        if h is not None:
            ax.hold(h)
        lons, lats = self(x, y, inverse=True)
        unh = ma.masked_where(lats <= 0, u)
        vnh = ma.masked_where(lats <= 0, v)
        ush = ma.masked_where(lats > 0, u)
        vsh = ma.masked_where(lats > 0, v)
        try:
            retnh =  ax.barbs(x,y,unh,vnh,*args,**kwargs)
            kwargs['flip_barb']=True
            retsh =  ax.barbs(x,y,ush,vsh,*args,**kwargs)
        except:
            ax.hold(b)
            raise
        ax.hold(b)
        # Because there are two collections returned in general,
        # we can't set the current image...
        #if plt is not None and ret.get_array() is not None:
        #    plt.sci(retnh)
        # clip for round polar plots.
        if self.round:
            retnh,c = self._clipcircle(ax,retnh)
            retsh,c = self._clipcircle(ax,retsh)
        # set axes limits to fit map region.
        self.set_axes_limits(ax=ax)
        return retnh,retsh

def barbs(self, x, y, u, v, *args, **kwargs):
        """
        Make a wind barb plot (u, v) with on the map.
        (see matplotlib.pyplot.barbs documentation).

        If ``latlon`` keyword is set to True, x,y are intrepreted as
        longitude and latitude in degrees.  Data and longitudes are
        automatically shifted to match map projection region for cylindrical
        and pseudocylindrical projections, and x,y are transformed to map
        projection coordinates. If ``latlon`` is False (default), x and y
        are assumed to be map projection coordinates.

        Extra keyword ``ax`` can be used to override the default axis instance.

        Other \*args and \**kwargs passed on to matplotlib.pyplot.barbs

        Returns two matplotlib.axes.Barbs instances, one for the Northern
        Hemisphere and one for the Southern Hemisphere.
        """
        if _matplotlib_version < '0.98.3':
            msg = dedent("""
            barb method requires matplotlib 0.98.3 or higher,
            you have %s""" % _matplotlib_version)
            raise NotImplementedError(msg)
        ax, plt = self._ax_plt_from_kw(kwargs)
        # allow callers to override the hold state by passing hold=True|False
        b = ax.ishold()
        h = kwargs.pop('hold',None)
        if h is not None:
            ax.hold(h)
        lons, lats = self(x, y, inverse=True)
        unh = ma.masked_where(lats <= 0, u)
        vnh = ma.masked_where(lats <= 0, v)
        ush = ma.masked_where(lats > 0, u)
        vsh = ma.masked_where(lats > 0, v)
        try:
            retnh =  ax.barbs(x,y,unh,vnh,*args,**kwargs)
            kwargs['flip_barb']=True
            retsh =  ax.barbs(x,y,ush,vsh,*args,**kwargs)
        except:
            ax.hold(b)
            raise
        ax.hold(b)
        # Because there are two collections returned in general,
        # we can't set the current image...
        #if plt is not None and ret.get_array() is not None:
        #    plt.sci(retnh)
        # clip for round polar plots.
        if self.round:
            retnh,c = self._clipcircle(ax,retnh)
            retsh,c = self._clipcircle(ax,retsh)
        # set axes limits to fit map region.
        self.set_axes_limits(ax=ax)
        return retnh,retsh

def get_vowel_segments(media_path, n_fft=2048):
    downsample = 1
    samplerate = 44100 // downsample

    win_s = n_fft // downsample # fft size
    hop_s = n_fft  // downsample # hop size

    s = source(media_path, samplerate, hop_s)
    samplerate = s.samplerate

    tolerance = 0.6

    pitch_o = pitch("yin", win_s, hop_s, samplerate)
    pitch_o.set_unit("Hz")
    pitch_o.set_tolerance(tolerance)

    pitches = []
    confidences = []

    # total number of frames read
    total_frames = 0
    samples=[]
    pitches=[]
    while True:
        samples, read = s()
        pitch_ = pitch_o(samples)[0]
        #pitch = int(round(pitch))
        confidence = pitch_o.get_confidence()
        #print("%f %f %f" % (total_frames / float(samplerate), pitch, confidence))
        pitches += [pitch_]
        confidences += [confidence]
        total_frames += read
        if read < hop_s: break

    pitches = np.array(pitches)
    confidences = np.array(confidences)

    cleaned_pitches = ma.masked_where(confidences < tolerance, pitches)
    cleaned_pitches = ma.masked_where(cleaned_pitches > 1000, cleaned_pitches)

    try: output = list(np.logical_not(cleaned_pitches.mask))
    except: output = []

    return output