Python numpy.delete方法代码示例

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import delete [as 别名]
def draw_heatmap(img, heatmap, alpha=0.5):
    """Draw a heatmap overlay over an image."""
    assert len(heatmap.shape) == 2 or \
        (len(heatmap.shape) == 3 and heatmap.shape[2] == 1)
    assert img.dtype in [np.uint8, np.int32, np.int64]
    assert heatmap.dtype in [np.float32, np.float64]

    if img.shape[0:2] != heatmap.shape[0:2]:
        heatmap_rs = np.clip(heatmap * 255, 0, 255).astype(np.uint8)
        heatmap_rs = ia.imresize_single_image(
            heatmap_rs[..., np.newaxis],
            img.shape[0:2],
            interpolation="nearest"
        )
        heatmap = np.squeeze(heatmap_rs) / 255.0

    cmap = plt.get_cmap('jet')
    heatmap_cmapped = cmap(heatmap)
    heatmap_cmapped = np.delete(heatmap_cmapped, 3, 2)
    heatmap_cmapped = heatmap_cmapped * 255
    mix = (1-alpha) * img + alpha * heatmap_cmapped
    mix = np.clip(mix, 0, 255).astype(np.uint8)
    return mix 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import delete [as 别名]
def jacobian(self, params, into=None):
        params = flattest(params)
        n = len(params)
        ii = np.arange(n)
        (rs,cs,zs) = ([],[],[])
        for ((mn,mx), f) in self.pieces_with_default:
            if len(ii) == 0: break
            k = np.where((params >= mn) & (params <= mx))[0]
            if len(k) == 0: continue
            kk = ii[k]
            j = f.jacobian(params[k])
            if j.shape[0] == 1 and j.shape[1] > 1: j = repmat(j, j.shape[1], 1)
            (rj,cj,vj) = sps.find(j)
            rs.append(kk[rj])
            cs.append(kk[cj])
            zs.append(vj)
            ii = np.delete(ii, k)
            params = np.delete(params, k)
        (rs,cs,zs) = [np.concatenate(us) if len(us) > 0 else [] for us in (rs,cs,zs)]
        dz = sps.csr_matrix((zs, (rs,cs)), shape=(n,n))
        return safe_into(into, dz) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import delete [as 别名]
def sacrificeStarCbyT(self, sInds, t_dets, fZ, fEZ, WA, overheadTime):
        """Sacrifice the worst performing CbyT star
        Args:
            sInds[nStars] - indicies of stars in the list
            t_dets[nStars] - time to observe each star (in days)
            fZ[nStars] - zodiacal light for each target
            fEZ - 0 
            WA - inner working angle of the instrument
            overheadTime - overheadTime added to each observation
        Return:
            sInds[nStars] - indicies of stars in the list
            t_dets[nStars] - time to observe each star (in days)
            sacrificedStarTime - time to distribute in days       
        """
        CbyT = self.Completeness.comp_per_intTime(t_dets*u.d, self.TargetList, sInds, self.valfZmin[sInds], fEZ, WA, self.mode, self.Cb[sInds], self.Csp[sInds])/t_dets#takes 5 seconds to do 1 time for all stars

        sacrificeIndex = np.argmin(CbyT)#finds index of star to sacrifice

        #Need index of sacrificed star by this point
        sacrificedStarTime = t_dets[sacrificeIndex] + overheadTime#saves time being sacrificed
        sInds = np.delete(sInds,sacrificeIndex)
        t_dets = np.delete(t_dets,sacrificeIndex)
        return sInds, t_dets, sacrificedStarTime 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import delete [as 别名]
def create_vertex_groups(groups=['common', 'not_used'], weights=[0.0, 0.0], ob=None):
    '''Creates vertex groups and sets weights. "groups" is a list of strings
    for the names of the groups. "weights" is a list of weights corresponding 
    to the strings. Each vertex is assigned a weight for each vertex group to
    avoid calling vertex weights that are not assigned. If the groups are
    already present, the previous weights will be preserved. To reset weights
    delete the created groups'''
    if ob is None:
        ob = bpy.context.object
    vg = ob.vertex_groups
    for g in range(0, len(groups)):
        if groups[g] not in vg.keys(): # Don't create groups if there are already there
            vg.new(groups[g])
            vg[groups[g]].add(range(0,len(ob.data.vertices)), weights[g], 'REPLACE')
        else:
            vg[groups[g]].add(range(0,len(ob.data.vertices)), 0, 'ADD') # This way we avoid resetting the weights for existing groups. 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import delete [as 别名]
def create_sew_edges():

    bpy.ops.mesh.bridge_edge_loops()
    bpy.ops.mesh.delete(type='ONLY_FACE')
    return
    #highlight a sew edge
    #compare vertex counts
    #subdivide to match counts
    #distribute and smooth back into mesh
    #create sew lines
     

    



# sewing functions ---------------->>> 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import delete [as 别名]
def test_pruneFeatureMap_ShouldPruneRightParams(self):
        dropped_index = 0
        output = self.module(self.input)
        torch.autograd.backward(output, self.upstream_gradient)

        old_weight_size = self.module.weight.size()
        old_bias_size = self.module.bias.size()
        old_out_channels = self.module.out_channels
        old_weight_values = self.module.weight.data.cpu().numpy()

        # ensure that the chosen index is dropped
        self.module.prune_feature_map(dropped_index)

        # check bias size
        self.assertEqual(self.module.bias.size()[0], (old_bias_size[0]-1))
        # check output channels
        self.assertEqual(self.module.out_channels, old_out_channels-1)

        _, *other_old_weight_sizes = old_weight_size
        # check weight size
        self.assertEqual(self.module.weight.size(), (old_weight_size[0]-1, *other_old_weight_sizes))
        # check weight value
        expected = np.delete(old_weight_values, dropped_index , 0)
        self.assertTrue(np.array_equal(self.module.weight.data.cpu().numpy(), expected)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import delete [as 别名]
def test_PLinearDropInputs_ShouldDropRightParams(self):
        dropped_index = 0

        # assume input is 2x2x2, 2 layers of 2x2
        input_shape = (2, 2, 2)
        module = pnn.PLinear(8, 10)

        old_num_features = module.in_features
        old_weight = module.weight.data.cpu().numpy()
        resized_old_weight = np.resize(old_weight, (module.out_features, *input_shape))

        module.drop_inputs(input_shape, dropped_index)
        new_shape = module.weight.size()

        # ensure that the chosen index is dropped
        expected_weight = np.resize(np.delete(resized_old_weight, dropped_index, 1), new_shape)
        output = module.weight.data.cpu().numpy()
        self.assertTrue(np.array_equal(output, expected_weight))

        # ensure num features is reduced
        self.assertTrue(module.in_features, old_num_features-1) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import delete [as 别名]
def test_PBatchNorm2dDropInputChannel_ShouldDropRightParams(self):
        dropped_index = 0
        module = pnn.PBatchNorm2d(2)

        old_num_features = module.num_features
        old_bias = module.bias.data.cpu().numpy()
        old_weight = module.weight.data.cpu().numpy()

        module.drop_input_channel(dropped_index)

        # ensure that the chosen index is dropped
        expected_weight = np.delete(old_weight, dropped_index, 0)
        self.assertTrue(np.array_equal(module.weight.data.cpu().numpy(), expected_weight))
        expected_bias = np.delete(old_bias, dropped_index, 0)
        self.assertTrue(np.array_equal(module.bias.data.cpu().numpy(), expected_bias))
        # ensure num features is reduced
        self.assertTrue(module.num_features, old_num_features-1) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import delete [as 别名]
def _correct_misaligned(misaligned_idcs, peaks):

    corrected_peaks = peaks.copy()
    misaligned_idcs = np.array(misaligned_idcs)
    # Make sure to not generate negative indices, or indices that exceed
    # the total number of peaks. prev_peaks and next_peaks must have the
    # same number of elements.
    valid_idcs = np.logical_and(
        misaligned_idcs > 1, misaligned_idcs < len(corrected_peaks) - 1  # pylint: disable=E1111
    )
    misaligned_idcs = misaligned_idcs[valid_idcs]
    prev_peaks = corrected_peaks[[i - 1 for i in misaligned_idcs]]
    next_peaks = corrected_peaks[[i + 1 for i in misaligned_idcs]]

    half_ibi = (next_peaks - prev_peaks) / 2
    peaks_interp = prev_peaks + half_ibi
    # Shift the R-peaks from the old to the new position.
    corrected_peaks = np.delete(corrected_peaks, misaligned_idcs)
    corrected_peaks = np.concatenate((corrected_peaks, peaks_interp)).astype(int)
    corrected_peaks.sort(kind="mergesort")

    return corrected_peaks 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import delete [as 别名]
def _rsp_findpeaks_biosppy(rsp_cleaned, sampling_rate):

    extrema = _rsp_findpeaks_extrema(rsp_cleaned)
    extrema, amplitudes = _rsp_findpeaks_outliers(rsp_cleaned, extrema, amplitude_min=0)

    peaks, troughs = _rsp_findpeaks_sanitize(extrema, amplitudes)

    # Apply minimum period outlier-criterion (exclude inter-breath-intervals
    # that produce breathing rate larger than 35 breaths per minute.
    outlier_idcs = np.where((np.diff(peaks) / sampling_rate) < 1.7)[0]

    peaks = np.delete(peaks, outlier_idcs)
    troughs = np.delete(troughs, outlier_idcs)

    info = {"RSP_Peaks": peaks, "RSP_Troughs": troughs}
    return info 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import delete [as 别名]
def _rsp_findpeaks_outliers(rsp_cleaned, extrema, amplitude_min=0.3):

    # Only consider those extrema that have a minimum vertical distance to
    # their direct neighbor, i.e., define outliers in absolute amplitude
    # difference between neighboring extrema.
    vertical_diff = np.abs(np.diff(rsp_cleaned[extrema]))
    median_diff = np.median(vertical_diff)
    min_diff = np.where(vertical_diff > (median_diff * amplitude_min))[0]
    extrema = extrema[min_diff]

    # Make sure that the alternation of peaks and troughs is unbroken. If
    # alternation of sign in extdiffs is broken, remove the extrema that
    # cause the breaks.
    amplitudes = rsp_cleaned[extrema]
    extdiffs = np.sign(np.diff(amplitudes))
    extdiffs = np.add(extdiffs[0:-1], extdiffs[1:])
    removeext = np.where(extdiffs != 0)[0] + 1
    extrema = np.delete(extrema, removeext)
    amplitudes = np.delete(amplitudes, removeext)

    return extrema, amplitudes 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import delete [as 别名]
def nms_crnr_dist(boxes, conf, overlap_threshold):
        
    I = np.argsort(conf)
    pick = []
    while (I.size!=0):
        last = I.size
        i = I[-1]
        pick.append(i)        
        
        scores = []
        for ind in I[:-1]:
            scores.append(bbox_corner_dist_measure(boxes[i,:], boxes[ind, :]))

        I = np.delete(I, np.concatenate(([last-1], np.where(np.array(scores)>overlap_threshold)[0])))

    return pick 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import delete [as 别名]
def compute_max_min(self, ske_joints):
        max_vals, min_vals = list(), list()
        for ske_joint in ske_joints:
            zero_row = []
            if self.dataset == 'NTU':
                for i in range(len(ske_joint)):
                    if (ske_joint[i, :] == np.zeros((1, 150))).all():
                        zero_row.append(i)
                ske_joint = np.delete(ske_joint, zero_row, axis=0)
                if (ske_joint[:, 0:75] == np.zeros((ske_joint.shape[0], 75))).all():
                    ske_joint = np.delete(ske_joint, range(75), axis=1)
                elif (ske_joint[:, 75:150] == np.zeros((ske_joint.shape[0], 75))).all():
                    ske_joint = np.delete(ske_joint, range(75, 150), axis=1)

            max_val = ske_joint.max()
            min_val = ske_joint.min()
            max_vals.append(float(max_val))
            min_vals.append(float(min_val))
        max_vals, min_vals = np.array(max_vals), np.array(min_vals)

        return max_vals.max(), min_vals.min() 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import delete [as 别名]
def test_line_area_nan_series(self):
        values = [1, 2, np.nan, 3]
        s = Series(values)
        ts = Series(values, index=tm.makeDateIndex(k=4))

        for d in [s, ts]:
            ax = _check_plot_works(d.plot)
            masked = ax.lines[0].get_ydata()
            # remove nan for comparison purpose
            exp = np.array([1, 2, 3], dtype=np.float64)
            tm.assert_numpy_array_equal(np.delete(masked.data, 2), exp)
            tm.assert_numpy_array_equal(
                masked.mask, np.array([False, False, True, False]))

            expected = np.array([1, 2, 0, 3], dtype=np.float64)
            ax = _check_plot_works(d.plot, stacked=True)
            tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
            ax = _check_plot_works(d.plot.area)
            tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
            ax = _check_plot_works(d.plot.area, stacked=False)
            tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import delete [as 别名]
def deleterowcol(A, delrow, delcol):
    """Assumes that matrix is in symmetric csc form !"""
    m = A.shape[0]
    keep = np.delete(np.arange(0, m), delrow)
    A = A[keep, :]
    keep = np.delete(np.arange(0, m), delcol)
    A = A[:, keep]
    return A