pytranskit.TBM package

TBM_CLOT

TBM_PLOT

Created on Mon Aug 10 11:14:01 2020

@author: Imaging and Data Science Lab

pytranskit.TBM.TBM_PLOT.L2_distance(a, b)

class pytranskit.TBM.TBM_PLOT.PLOT_CCA(n_components=2)

Bases: object

plot_cca(x_train_hat, y_train, x_test_hat, y_test, template)

visualize(mean_x_train_hat, Intensity, directions=5, points=5, SD_spread=1)

class pytranskit.TBM.TBM_PLOT.PLOT_NS_Classifier(train_sample=None, use_gpu=False)

Bases: object

classify_PLOT_NS(x_train, y_train, x_test, y_test)

fit(X, y)

Fit linear model. :param X: Training data. :type X: array-like, shape (n_samples, n_proj, n_angles)) :param y: Target values. :type y: ndarray of shape (n_samples,)

Returns:

Returns an instance of self.

Return type:

self

predict(X)

Predict using the linear model :param X: :type X: array-like, sparse matrix, shape (n_samples, n_proj, n_angles))

Returns:

Predicted target values per element in X.

Return type:

ndarray of shape (n_samples,)

score(y_test)

class pytranskit.TBM.TBM_PLOT.PLOT_PCA(n_components=2)

Bases: object

plot_pca(x_train_hat, y_train, x_test_hat, y_test, template)

visualize(mean_x_train_hat, Intensity, directions=5, points=5, SD_spread=2)

class pytranskit.TBM.TBM_PLOT.PLOT_PLDA(n_components=2)

Bases: object

plot_plda(x_train_hat, y_train, x_test_hat, y_test, template)

visualize(mean_x_train_hat, Intensity, directions=5, points=5, SD_spread=2)

pytranskit.TBM.TBM_PLOT.Visualize_LOT(Data, Intensity, Nx, Ny, scale)

class pytranskit.TBM.TBM_PLOT.batch_PLOT(Nmasses=50)

Bases: object

forward_seq(x_train, x_test, x_template)

class pytranskit.TBM.TBM_PLOT.batch_PLOT_v0(Nmasses=50)

Bases: object

forward_seq(x_train, x_test)

pytranskit.TBM.TBM_PLOT.fromInd2Coord(ind, Ny)

pytranskit.TBM.TBM_PLOT.gaussian2D(x, mean, sigma)

pytranskit.TBM.TBM_PLOT.get_particles(img, N)

pytranskit.TBM.TBM_PLOT.img2pts_Lloyd(img, Nmasses)

pytranskit.TBM.TBM_PLOT.pLOT_single(x_temp, x_targ, a_temp, a_targ)

pytranskit.TBM.TBM_PLOT.particle2image(x, a, sigma, imgshape)

This function gets a set of coordinates, x, their amplitude, a, and generates a PDF image using a gaussian kernel

pytranskit.TBM.TBM_PLOT.particleApproximation(imgs, Nmasses)

pytranskit.TBM.TBM_PLOT.particleApproximation_v0(imgs, Nmasses)

pytranskit.TBM.TBM_PLOT.sub2ind(array_shape, rows, cols)

TBM_RCDT

Created on Tue Aug 4 22:47:30 2020

@author: Imaging and Data Science Lab

class pytranskit.TBM.TBM_RCDT.RCDT_CCA(n_components=2)

Bases: object

rcdt_cca(x_train_hat, y_train, x_test_hat, y_test, template)

visualize(directions=5, points=5, thetas=array([0.0, 1.00558659, 2.01117318, 3.01675978, 4.02234637, 5.02793296, 6.03351955, 7.03910615, 8.04469274, 9.05027933, 10.05586592, 11.06145251, 12.06703911, 13.0726257, 14.07821229, 15.08379888, 16.08938547, 17.09497207, 18.10055866, 19.10614525, 20.11173184, 21.11731844, 22.12290503, 23.12849162, 24.13407821, 25.1396648, 26.1452514, 27.15083799, 28.15642458, 29.16201117, 30.16759777, 31.17318436, 32.17877095, 33.18435754, 34.18994413, 35.19553073, 36.20111732, 37.20670391, 38.2122905, 39.21787709, 40.22346369, 41.22905028, 42.23463687, 43.24022346, 44.24581006, 45.25139665, 46.25698324, 47.26256983, 48.26815642, 49.27374302, 50.27932961, 51.2849162, 52.29050279, 53.29608939, 54.30167598, 55.30726257, 56.31284916, 57.31843575, 58.32402235, 59.32960894, 60.33519553, 61.34078212, 62.34636872, 63.35195531, 64.3575419, 65.36312849, 66.36871508, 67.37430168, 68.37988827, 69.38547486, 70.39106145, 71.39664804, 72.40223464, 73.40782123, 74.41340782, 75.41899441, 76.42458101, 77.4301676, 78.43575419, 79.44134078, 80.44692737, 81.45251397, 82.45810056, 83.46368715, 84.46927374, 85.47486034, 86.48044693, 87.48603352, 88.49162011, 89.4972067, 90.5027933, 91.50837989, 92.51396648, 93.51955307, 94.52513966, 95.53072626, 96.53631285, 97.54189944, 98.54748603, 99.55307263, 100.55865922, 101.56424581, 102.5698324, 103.57541899, 104.58100559, 105.58659218, 106.59217877, 107.59776536, 108.60335196, 109.60893855, 110.61452514, 111.62011173, 112.62569832, 113.63128492, 114.63687151, 115.6424581, 116.64804469, 117.65363128, 118.65921788, 119.66480447, 120.67039106, 121.67597765, 122.68156425, 123.68715084, 124.69273743, 125.69832402, 126.70391061, 127.70949721, 128.7150838, 129.72067039, 130.72625698, 131.73184358, 132.73743017, 133.74301676, 134.74860335, 135.75418994, 136.75977654, 137.76536313, 138.77094972, 139.77653631, 140.78212291, 141.7877095, 142.79329609, 143.79888268, 144.80446927, 145.81005587, 146.81564246, 147.82122905, 148.82681564, 149.83240223, 150.83798883, 151.84357542, 152.84916201, 153.8547486, 154.8603352, 155.86592179, 156.87150838, 157.87709497, 158.88268156, 159.88826816, 160.89385475, 161.89944134, 162.90502793, 163.91061453, 164.91620112, 165.92178771, 166.9273743, 167.93296089, 168.93854749, 169.94413408, 170.94972067, 171.95530726, 172.96089385, 173.96648045, 174.97206704, 175.97765363, 176.98324022, 177.98882682, 178.99441341, 180.0]), SD_spread=1)

class pytranskit.TBM.TBM_RCDT.RCDT_NS_Classifier(train_sample=None, use_gpu=False)

Bases: object

classify_RCDT_NS(x_train, y_train, x_test, y_test)

fit(X, y)

Fit linear model. :param X: Training data. :type X: array-like, shape (n_samples, n_proj, n_angles)) :param y: Target values. :type y: ndarray of shape (n_samples,)

Returns:

Returns an instance of self.

Return type:

self

predict(X)

Predict using the linear model :param X: :type X: array-like, sparse matrix, shape (n_samples, n_proj, n_angles))

Returns:

Predicted target values per element in X.

Return type:

ndarray of shape (n_samples,)

score(y_test)

class pytranskit.TBM.TBM_RCDT.RCDT_PCA(n_components=2)

Bases: object

rcdt_pca(x_train_hat, y_train, x_test_hat, y_test, template)

visualize(directions=5, points=5, thetas=array([0.0, 1.00558659, 2.01117318, 3.01675978, 4.02234637, 5.02793296, 6.03351955, 7.03910615, 8.04469274, 9.05027933, 10.05586592, 11.06145251, 12.06703911, 13.0726257, 14.07821229, 15.08379888, 16.08938547, 17.09497207, 18.10055866, 19.10614525, 20.11173184, 21.11731844, 22.12290503, 23.12849162, 24.13407821, 25.1396648, 26.1452514, 27.15083799, 28.15642458, 29.16201117, 30.16759777, 31.17318436, 32.17877095, 33.18435754, 34.18994413, 35.19553073, 36.20111732, 37.20670391, 38.2122905, 39.21787709, 40.22346369, 41.22905028, 42.23463687, 43.24022346, 44.24581006, 45.25139665, 46.25698324, 47.26256983, 48.26815642, 49.27374302, 50.27932961, 51.2849162, 52.29050279, 53.29608939, 54.30167598, 55.30726257, 56.31284916, 57.31843575, 58.32402235, 59.32960894, 60.33519553, 61.34078212, 62.34636872, 63.35195531, 64.3575419, 65.36312849, 66.36871508, 67.37430168, 68.37988827, 69.38547486, 70.39106145, 71.39664804, 72.40223464, 73.40782123, 74.41340782, 75.41899441, 76.42458101, 77.4301676, 78.43575419, 79.44134078, 80.44692737, 81.45251397, 82.45810056, 83.46368715, 84.46927374, 85.47486034, 86.48044693, 87.48603352, 88.49162011, 89.4972067, 90.5027933, 91.50837989, 92.51396648, 93.51955307, 94.52513966, 95.53072626, 96.53631285, 97.54189944, 98.54748603, 99.55307263, 100.55865922, 101.56424581, 102.5698324, 103.57541899, 104.58100559, 105.58659218, 106.59217877, 107.59776536, 108.60335196, 109.60893855, 110.61452514, 111.62011173, 112.62569832, 113.63128492, 114.63687151, 115.6424581, 116.64804469, 117.65363128, 118.65921788, 119.66480447, 120.67039106, 121.67597765, 122.68156425, 123.68715084, 124.69273743, 125.69832402, 126.70391061, 127.70949721, 128.7150838, 129.72067039, 130.72625698, 131.73184358, 132.73743017, 133.74301676, 134.74860335, 135.75418994, 136.75977654, 137.76536313, 138.77094972, 139.77653631, 140.78212291, 141.7877095, 142.79329609, 143.79888268, 144.80446927, 145.81005587, 146.81564246, 147.82122905, 148.82681564, 149.83240223, 150.83798883, 151.84357542, 152.84916201, 153.8547486, 154.8603352, 155.86592179, 156.87150838, 157.87709497, 158.88268156, 159.88826816, 160.89385475, 161.89944134, 162.90502793, 163.91061453, 164.91620112, 165.92178771, 166.9273743, 167.93296089, 168.93854749, 169.94413408, 170.94972067, 171.95530726, 172.96089385, 173.96648045, 174.97206704, 175.97765363, 176.98324022, 177.98882682, 178.99441341, 180.0]), SD_spread=1)

class pytranskit.TBM.TBM_RCDT.RCDT_PLDA(n_components=2)

Bases: object

rcdt_plda(x_train_hat, y_train, x_test_hat, y_test, template)

visualize(directions=5, points=5, thetas=array([0.0, 1.00558659, 2.01117318, 3.01675978, 4.02234637, 5.02793296, 6.03351955, 7.03910615, 8.04469274, 9.05027933, 10.05586592, 11.06145251, 12.06703911, 13.0726257, 14.07821229, 15.08379888, 16.08938547, 17.09497207, 18.10055866, 19.10614525, 20.11173184, 21.11731844, 22.12290503, 23.12849162, 24.13407821, 25.1396648, 26.1452514, 27.15083799, 28.15642458, 29.16201117, 30.16759777, 31.17318436, 32.17877095, 33.18435754, 34.18994413, 35.19553073, 36.20111732, 37.20670391, 38.2122905, 39.21787709, 40.22346369, 41.22905028, 42.23463687, 43.24022346, 44.24581006, 45.25139665, 46.25698324, 47.26256983, 48.26815642, 49.27374302, 50.27932961, 51.2849162, 52.29050279, 53.29608939, 54.30167598, 55.30726257, 56.31284916, 57.31843575, 58.32402235, 59.32960894, 60.33519553, 61.34078212, 62.34636872, 63.35195531, 64.3575419, 65.36312849, 66.36871508, 67.37430168, 68.37988827, 69.38547486, 70.39106145, 71.39664804, 72.40223464, 73.40782123, 74.41340782, 75.41899441, 76.42458101, 77.4301676, 78.43575419, 79.44134078, 80.44692737, 81.45251397, 82.45810056, 83.46368715, 84.46927374, 85.47486034, 86.48044693, 87.48603352, 88.49162011, 89.4972067, 90.5027933, 91.50837989, 92.51396648, 93.51955307, 94.52513966, 95.53072626, 96.53631285, 97.54189944, 98.54748603, 99.55307263, 100.55865922, 101.56424581, 102.5698324, 103.57541899, 104.58100559, 105.58659218, 106.59217877, 107.59776536, 108.60335196, 109.60893855, 110.61452514, 111.62011173, 112.62569832, 113.63128492, 114.63687151, 115.6424581, 116.64804469, 117.65363128, 118.65921788, 119.66480447, 120.67039106, 121.67597765, 122.68156425, 123.68715084, 124.69273743, 125.69832402, 126.70391061, 127.70949721, 128.7150838, 129.72067039, 130.72625698, 131.73184358, 132.73743017, 133.74301676, 134.74860335, 135.75418994, 136.75977654, 137.76536313, 138.77094972, 139.77653631, 140.78212291, 141.7877095, 142.79329609, 143.79888268, 144.80446927, 145.81005587, 146.81564246, 147.82122905, 148.82681564, 149.83240223, 150.83798883, 151.84357542, 152.84916201, 153.8547486, 154.8603352, 155.86592179, 156.87150838, 157.87709497, 158.88268156, 159.88826816, 160.89385475, 161.89944134, 162.90502793, 163.91061453, 164.91620112, 165.92178771, 166.9273743, 167.93296089, 168.93854749, 169.94413408, 170.94972067, 171.95530726, 172.96089385, 173.96648045, 174.97206704, 175.97765363, 176.98324022, 177.98882682, 178.99441341, 180.0]), SD_spread=1)

class pytranskit.TBM.TBM_RCDT.batch_RCDT(thetas=array([0.0, 1.00558659, 2.01117318, 3.01675978, 4.02234637, 5.02793296, 6.03351955, 7.03910615, 8.04469274, 9.05027933, 10.05586592, 11.06145251, 12.06703911, 13.0726257, 14.07821229, 15.08379888, 16.08938547, 17.09497207, 18.10055866, 19.10614525, 20.11173184, 21.11731844, 22.12290503, 23.12849162, 24.13407821, 25.1396648, 26.1452514, 27.15083799, 28.15642458, 29.16201117, 30.16759777, 31.17318436, 32.17877095, 33.18435754, 34.18994413, 35.19553073, 36.20111732, 37.20670391, 38.2122905, 39.21787709, 40.22346369, 41.22905028, 42.23463687, 43.24022346, 44.24581006, 45.25139665, 46.25698324, 47.26256983, 48.26815642, 49.27374302, 50.27932961, 51.2849162, 52.29050279, 53.29608939, 54.30167598, 55.30726257, 56.31284916, 57.31843575, 58.32402235, 59.32960894, 60.33519553, 61.34078212, 62.34636872, 63.35195531, 64.3575419, 65.36312849, 66.36871508, 67.37430168, 68.37988827, 69.38547486, 70.39106145, 71.39664804, 72.40223464, 73.40782123, 74.41340782, 75.41899441, 76.42458101, 77.4301676, 78.43575419, 79.44134078, 80.44692737, 81.45251397, 82.45810056, 83.46368715, 84.46927374, 85.47486034, 86.48044693, 87.48603352, 88.49162011, 89.4972067, 90.5027933, 91.50837989, 92.51396648, 93.51955307, 94.52513966, 95.53072626, 96.53631285, 97.54189944, 98.54748603, 99.55307263, 100.55865922, 101.56424581, 102.5698324, 103.57541899, 104.58100559, 105.58659218, 106.59217877, 107.59776536, 108.60335196, 109.60893855, 110.61452514, 111.62011173, 112.62569832, 113.63128492, 114.63687151, 115.6424581, 116.64804469, 117.65363128, 118.65921788, 119.66480447, 120.67039106, 121.67597765, 122.68156425, 123.68715084, 124.69273743, 125.69832402, 126.70391061, 127.70949721, 128.7150838, 129.72067039, 130.72625698, 131.73184358, 132.73743017, 133.74301676, 134.74860335, 135.75418994, 136.75977654, 137.76536313, 138.77094972, 139.77653631, 140.78212291, 141.7877095, 142.79329609, 143.79888268, 144.80446927, 145.81005587, 146.81564246, 147.82122905, 148.82681564, 149.83240223, 150.83798883, 151.84357542, 152.84916201, 153.8547486, 154.8603352, 155.86592179, 156.87150838, 157.87709497, 158.88268156, 159.88826816, 160.89385475, 161.89944134, 162.90502793, 163.91061453, 164.91620112, 165.92178771, 166.9273743, 167.93296089, 168.93854749, 169.94413408, 170.94972067, 171.95530726, 172.96089385, 173.96648045, 174.97206704, 175.97765363, 176.98324022, 177.98882682, 178.99441341, 180.0]), rm_edge=False)

Bases: object

forward(X, template)

forward_seq(X, template)

fun_ircdt_batch(data)

fun_ircdt_single(Ihat)

fun_rcdt_batch(data)

fun_rcdt_single(I)

inverse(Xhat, template)

ircdt_parallel(Xhat)

rcdt_parallel(X)