Merge pull request #92 from ContextLab/google-drive

Google drive

docs/_build/html/_downloads/animate.ipynb

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\n\ndata=sio.loadmat('sample_data/weights.mat')\nw=data['weights'][0]\nw = [i for i in w]\naligned_w = hyp.tools.align(w)\n\nw1 = np.mean(aligned_w[:17],0)\nw2 = np.mean(aligned_w[18:],0)\n\nhyp.plot([w1,w2],animate=True, duration=100)"
+        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\n\ndata = hyp.tools.load('weights')\nw = [i for i in data]\naligned_w = hyp.tools.align(w)\n\nw1 = np.mean(aligned_w[:17],0)\nw2 = np.mean(aligned_w[18:],0)\n\nhyp.plot([w1,w2],animate=True)"
       ], 
       "outputs": [], 
       "metadata": {

docs/_build/html/_downloads/animate.py

@@ -16,12 +16,11 @@
 import scipy.io as sio
 import numpy as np
 
-data=sio.loadmat('sample_data/weights.mat')
-w=data['weights'][0]
-w = [i for i in w]
+data = hyp.tools.load('weights')
+w = [i for i in data]
 aligned_w = hyp.tools.align(w)
 
 w1 = np.mean(aligned_w[:17],0)
 w2 = np.mean(aligned_w[18:],0)
 
-hyp.plot([w1,w2],animate=True, duration=100)
+hyp.plot([w1,w2],animate=True)

docs/_build/html/_downloads/explore.ipynb

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\n\ndata=sio.loadmat('sample_data/weights.mat')\nw=[i for i in data['weights'][0][0:2]]\n\nhyp.plot(w,'o', explore=True)"
+        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\n\ndata = hyp.tools.load('weights_sample')\n\nhyp.plot(data, 'o', explore=True)"
       ], 
       "outputs": [], 
       "metadata": {

docs/_build/html/_downloads/explore.py

@@ -19,7 +19,6 @@
 import scipy.io as sio
 import numpy as np
 
-data=sio.loadmat('sample_data/weights.mat')
-w=[i for i in data['weights'][0][0:2]]
+data = hyp.tools.load('weights_sample')
 
-hyp.plot(w,'o', explore=True)
+hyp.plot(data, 'o', explore=True)

docs/_build/html/_downloads/plot_PPCA.ipynb

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Code source: Andrew Heusser\n# License: MIT\n\nfrom scipy.linalg import toeplitz\nimport numpy as np\nfrom copy import copy\nimport hypertools as hyp\n\nK = 10 - toeplitz(np.arange(10))\n\ndata1 = np.cumsum(np.random.multivariate_normal(np.zeros(10), K, 250), axis=0)\ndata2 = copy(data1)\n\nmissing = .1\ninds = [(i,j) for i in range(data2.shape[0]) for j in range(data2.shape[1])]\nmissing_data = [inds[i] for i in np.random.choice(len(inds), len(inds)*missing)]\nfor i,j in missing_data:\n    data2[i,j]=np.nan\n\nhyp.plot([data1,data2],linestyle=['-',':'], legend=['Original', 'PPCA'])"
+        "# Code source: Andrew Heusser\n# License: MIT\n\nfrom scipy.linalg import toeplitz\nimport numpy as np\nfrom copy import copy\nimport hypertools as hyp\n\nK = 10 - toeplitz(np.arange(10))\n\ndata1 = np.cumsum(np.random.multivariate_normal(np.zeros(10), K, 250), axis=0)\ndata2 = copy(data1)\n\nmissing = .1\ninds = [(i,j) for i in range(data2.shape[0]) for j in range(data2.shape[1])]\nmissing_data = [inds[i] for i in np.random.choice(int(len(inds)), int(len(inds)*missing))]\nfor i,j in missing_data:\n    data2[i,j]=np.nan\n\nhyp.plot([data1,data2],linestyle=['-',':'], legend=['Original', 'PPCA'])"
       ], 
       "outputs": [], 
       "metadata": {

docs/_build/html/_downloads/plot_PPCA.py

@@ -26,7 +26,7 @@
 
 missing = .1
 inds = [(i,j) for i in range(data2.shape[0]) for j in range(data2.shape[1])]
-missing_data = [inds[i] for i in np.random.choice(len(inds), len(inds)*missing)]
+missing_data = [inds[i] for i in np.random.choice(int(len(inds)), int(len(inds)*missing))]
 for i,j in missing_data:
     data2[i,j]=np.nan
 

docs/_build/html/_downloads/plot_align.ipynb

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\n\ndata=sio.loadmat('sample_data/weights.mat')\n\nw=data['weights'][0]\nw = [i for i in w]\naligned_w = hyp.tools.align(w)\n\nw1 = np.mean(aligned_w[:17],0)\nw2 = np.mean(aligned_w[18:],0)\n\nhyp.plot([w1[:100,:],w2[:100,:]])"
+        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\n\ndata = hyp.tools.load('weights')\nw = [i for i in data]\naligned_w = hyp.tools.align(w)\n\nw1 = np.mean(aligned_w[:17],0)\nw2 = np.mean(aligned_w[18:],0)\n\nhyp.plot([w1[:100,:],w2[:100,:]])"
       ], 
       "outputs": [], 
       "metadata": {

docs/_build/html/_downloads/plot_align.py

@@ -17,10 +17,8 @@
 import scipy.io as sio
 import numpy as np
 
-data=sio.loadmat('sample_data/weights.mat')
-
-w=data['weights'][0]
-w = [i for i in w]
+data = hyp.tools.load('weights')
+w = [i for i in data]
 aligned_w = hyp.tools.align(w)
 
 w1 = np.mean(aligned_w[:17],0)

docs/_build/html/_downloads/plot_basic.ipynb

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\n\ndata=sio.loadmat('sample_data/weights.mat')\nw=[i for i in data['weights'][0][0:2]]\n\nhyp.plot(w,'o')"
+        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\n\ndata = hyp.tools.load('weights_sample')\n\nhyp.plot(data,'o')"
       ], 
       "outputs": [], 
       "metadata": {

docs/_build/html/_downloads/plot_basic.py

@@ -13,10 +13,7 @@
 # License: MIT
 
 import hypertools as hyp
-import scipy.io as sio
-import numpy as np
 
-data=sio.loadmat('sample_data/weights.mat')
-w=[i for i in data['weights'][0][0:2]]
+data = hyp.tools.load('weights_sample')
 
-hyp.plot(w,'o')
+hyp.plot(data,'o')

docs/_build/html/_downloads/plot_clusters.ipynb

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport pandas as pd\n\ndata=pd.read_csv('sample_data/mushrooms.csv')\n\nhyp.plot(data,'o',n_clusters=10)"
+        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport pandas as pd\n\ndata = hyp.tools.load('mushrooms')\n\nhyp.plot(data,'o',n_clusters=10)"
       ], 
       "outputs": [], 
       "metadata": {

docs/_build/html/_downloads/plot_clusters.py

@@ -16,6 +16,6 @@
 import hypertools as hyp
 import pandas as pd
 
-data=pd.read_csv('sample_data/mushrooms.csv')
+data = hyp.tools.load('mushrooms')
 
 hyp.plot(data,'o',n_clusters=10)

docs/_build/html/_downloads/plot_customize_figure.ipynb

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndata=sio.loadmat('sample_data/weights.mat')\nw=[i for i in data['weights'][0][0:2]]\n\nfig,ax,data = hyp.plot(w,'o', ndims=2, legend=['Group A', 'Group B'], show=False, return_data=True)\n\nax.set_title('This is an example title', fontsize=20)\nax.set_ylabel('PCA Dimension 2', fontsize=15)\nax.set_xlabel('PCA Dimension 1', fontsize=15)\nlegend_text = plt.gca().get_legend().get_texts()\nplt.setp(legend_text, fontsize=15)\n\nplt.show()"
+        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndata = hyp.tools.load('weights_sample')\n\nfig,ax,data = hyp.plot(data[:2], 'o', ndims=2, legend=['Group A', 'Group B'], show=False, return_data=True)\n\nax.set_title('This is an example title', fontsize=20)\nax.set_ylabel('PCA Dimension 2', fontsize=15)\nax.set_xlabel('PCA Dimension 1', fontsize=15)\nlegend_text = plt.gca().get_legend().get_texts()\nplt.setp(legend_text, fontsize=15)\n\nplt.show()"
       ], 
       "outputs": [], 
       "metadata": {

docs/_build/html/_downloads/plot_customize_figure.py

@@ -16,10 +16,9 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-data=sio.loadmat('sample_data/weights.mat')
-w=[i for i in data['weights'][0][0:2]]
+data = hyp.tools.load('weights_sample')
 
-fig,ax,data = hyp.plot(w,'o', ndims=2, legend=['Group A', 'Group B'], show=False, return_data=True)
+fig,ax,data = hyp.plot(data[:2], 'o', ndims=2, legend=['Group A', 'Group B'], show=False, return_data=True)
 
 ax.set_title('This is an example title', fontsize=20)
 ax.set_ylabel('PCA Dimension 2', fontsize=15)

docs/_build/html/_downloads/plot_dataframe.ipynb

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport pandas as pd\n\ndata = pd.read_csv('sample_data/mushrooms.csv')\ngroup = data.pop('class')\n\nhyp.plot(data,'o', group=group)"
+        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport pandas as pd\n\ndata = hyp.tools.load('mushrooms')\n\ngroup = data.pop('class')\n\nhyp.plot(data, 'o', group=group)"
       ], 
       "outputs": [], 
       "metadata": {

docs/_build/html/_downloads/plot_dataframe.py

@@ -17,7 +17,8 @@
 import hypertools as hyp
 import pandas as pd
 
-data = pd.read_csv('sample_data/mushrooms.csv')
+data = hyp.tools.load('mushrooms')
+
 group = data.pop('class')
 
-hyp.plot(data,'o', group=group)
+hyp.plot(data, 'o', group=group)

docs/_build/html/_downloads/plot_describe.ipynb

@@ -15,7 +15,7 @@
     }, 
     {
       "source": [
-        "\n# Using describe_pca to evaluate the integrity of your visualization\n\n\nThe downside to using PCA to visualize your data is that some variance will\nlikely be removed. To help get a sense for the integrity of your low\ndimensional visualizations, we built the `describe_pca` function, which computes\nthe covariance (samples by samples) of both the raw and reduced datasets, and\nplots their correlation.  The function repeats this analysis from 2:N dimensions\nuntil the correlation reaches a local maximum.  Often times this will be less\nthan the total number of dimensions because the PCA model is whitened. \n\n"
+        "\n# Using describe_pca to evaluate the integrity of your visualization\n\n\nThe downside to using PCA to visualize your data is that some variance will\nlikely be removed. To help get a sense for the integrity of your low\ndimensional visualizations, we built the `describe_pca` function, which computes\nthe covariance (samples by samples) of both the raw and reduced datasets, and\nplots their correlation.  The function repeats this analysis from 2:N dimensions\nuntil the correlation reaches a local maximum.  Often times this will be less\nthan the total number of dimensions because the PCA model is whitened.\n\n"
       ], 
       "cell_type": "markdown", 
       "metadata": {}
@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\n\ndata=sio.loadmat('sample_data/weights.mat')\nw=[i for i in data['weights'][0][0:3]]\n\nhyp.tools.describe_pca(w)"
+        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\n\ndata = hyp.tools.load('weights_sample')\n\nhyp.tools.describe_pca(data)"
       ], 
       "outputs": [], 
       "metadata": {

docs/_build/html/_downloads/plot_describe.py

@@ -10,7 +10,7 @@
 the covariance (samples by samples) of both the raw and reduced datasets, and
 plots their correlation.  The function repeats this analysis from 2:N dimensions
 until the correlation reaches a local maximum.  Often times this will be less
-than the total number of dimensions because the PCA model is whitened. 
+than the total number of dimensions because the PCA model is whitened.
 """
 
 # Code source: Andrew Heusser
@@ -20,7 +20,6 @@
 import scipy.io as sio
 import numpy as np
 
-data=sio.loadmat('sample_data/weights.mat')
-w=[i for i in data['weights'][0][0:3]]
+data = hyp.tools.load('weights_sample')
 
-hyp.tools.describe_pca(w)
+hyp.tools.describe_pca(data)

docs/_build/html/_downloads/plot_group.ipynb

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\n\ndata=sio.loadmat('sample_data/weights.mat')\nw=[i for i in data['weights'][0][0:3]]\n\ngroup = [['a' if idx % 2 == 0 else 'b' for idx,j in enumerate(i)] for i in w]\nhyp.plot(w,'o',group=group)\n\ngroup=[]\nfor idx,i in enumerate(w):\n    tmp=[]\n    for iidx,ii in enumerate(i):\n            tmp.append(int(np.random.randint(1000, size=1)))\n    group.append(tmp)\n\nhyp.plot(w,'o',group=group)"
+        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\n\ndata = hyp.tools.load('weights_sample')\n\ngroup = [['a' if idx % 2 == 0 else 'b' for idx,j in enumerate(i)] for i in data]\nhyp.plot(data, 'o', group=group)\n\ngroup=[]\nfor idx,i in enumerate(data):\n    tmp=[]\n    for iidx,ii in enumerate(i):\n            tmp.append(int(np.random.randint(1000, size=1)))\n    group.append(tmp)\n\nhyp.plot(data, 'o', group=group)"
       ], 
       "outputs": [], 
       "metadata": {

docs/_build/html/_downloads/plot_group.py

@@ -20,17 +20,16 @@
 import scipy.io as sio
 import numpy as np
 
-data=sio.loadmat('sample_data/weights.mat')
-w=[i for i in data['weights'][0][0:3]]
+data = hyp.tools.load('weights_sample')
 
-group = [['a' if idx % 2 == 0 else 'b' for idx,j in enumerate(i)] for i in w]
-hyp.plot(w,'o',group=group)
+group = [['a' if idx % 2 == 0 else 'b' for idx,j in enumerate(i)] for i in data]
+hyp.plot(data, 'o', group=group)
 
 group=[]
-for idx,i in enumerate(w):
+for idx,i in enumerate(data):
     tmp=[]
     for iidx,ii in enumerate(i):
             tmp.append(int(np.random.randint(1000, size=1)))
     group.append(tmp)
 
-hyp.plot(w,'o',group=group)
+hyp.plot(data, 'o', group=group)

docs/_build/html/_downloads/plot_labels.ipynb

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\n\ndata=sio.loadmat('sample_data/weights.mat')\nw=[i for i in data['weights'][0][0:3]]\n\nlabels=[]\nfor idx,i in enumerate(w):\n    tmp=[]\n    for iidx,ii in enumerate(i):\n        if iidx==0:\n            tmp.append('Subject ' + str(idx))\n        else:\n            tmp.append(None)\n    labels.append(tmp)\n\nhyp.plot(w,'o',labels=labels)"
+        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\n\ndata = hyp.tools.load('weights_sample')\n\nlabels=[]\nfor idx,i in enumerate(data):\n    tmp=[]\n    for iidx,ii in enumerate(i):\n        if iidx==0:\n            tmp.append('Subject ' + str(idx))\n        else:\n            tmp.append(None)\n    labels.append(tmp)\n\nhyp.plot(data, 'o', labels=labels)"
       ], 
       "outputs": [], 
       "metadata": {

docs/_build/html/_downloads/plot_labels.py

@@ -16,11 +16,10 @@
 import scipy.io as sio
 import numpy as np
 
-data=sio.loadmat('sample_data/weights.mat')
-w=[i for i in data['weights'][0][0:3]]
+data = hyp.tools.load('weights_sample')
 
 labels=[]
-for idx,i in enumerate(w):
+for idx,i in enumerate(data):
     tmp=[]
     for iidx,ii in enumerate(i):
         if iidx==0:
@@ -29,4 +28,4 @@
             tmp.append(None)
     labels.append(tmp)
 
-hyp.plot(w,'o',labels=labels)
+hyp.plot(data, 'o', labels=labels)

docs/_build/html/_downloads/plot_legend.ipynb

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\n\ndata=sio.loadmat('sample_data/weights.mat')\nw=[i for i in data['weights'][0][0:2]]\n\nhyp.plot(w,'o', legend=['Group A', 'Group B'])"
+        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\n\ndata = hyp.tools.load('weights_sample')\n\nhyp.plot(data, 'o', legend=['Group A', 'Group B', 'Group C'])"
       ], 
       "outputs": [], 
       "metadata": {

docs/_build/html/_downloads/plot_legend.py

@@ -14,7 +14,6 @@
 import scipy.io as sio
 import numpy as np
 
-data=sio.loadmat('sample_data/weights.mat')
-w=[i for i in data['weights'][0][0:2]]
+data = hyp.tools.load('weights_sample')
 
-hyp.plot(w,'o', legend=['Group A', 'Group B'])
+hyp.plot(data, 'o', legend=['Group A', 'Group B', 'Group C'])

docs/_build/html/_downloads/plot_missing_data.ipynb

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Code source: Andrew Heusser\n# License: MIT\n\nfrom scipy.linalg import toeplitz\nimport numpy as np\nfrom copy import copy\nimport hypertools as hyp\n\nK = 10 - toeplitz(np.arange(10))\ndata1 = np.cumsum(np.random.multivariate_normal(np.zeros(10), K, 250), axis=0)\ndata2 = copy(data1)\n\nmissing = .05\ninds = [(i,j) for i in range(data1.shape[0]) for j in range(data1.shape[1])]\nmissing_data = [inds[i] for i in np.random.choice(len(inds), len(inds)*missing)]\nfor i,j in missing_data:\n    data2[i,j]=np.nan\n\ndata1_r,data2_r = hyp.tools.reduce([data1,data2],ndims=3)\n\nmissing_inds = hyp.tools.missing_inds(data2)\nmissing_data = data2_r[missing_inds,:]\n\nhyp.plot([data1_r, data2_r, missing_data], ['r','b--','b*'])"
+        "# Code source: Andrew Heusser\n# License: MIT\n\nfrom scipy.linalg import toeplitz\nimport numpy as np\nfrom copy import copy\nimport hypertools as hyp\n\nK = 10 - toeplitz(np.arange(10))\ndata1 = np.cumsum(np.random.multivariate_normal(np.zeros(10), K, 250), axis=0)\ndata2 = copy(data1)\n\nmissing = .05\ninds = [(i,j) for i in range(data1.shape[0]) for j in range(data1.shape[1])]\nmissing_data = [inds[i] for i in np.random.choice(int(len(inds)), int(len(inds)*missing))]\nfor i,j in missing_data:\n    data2[i,j]=np.nan\n\ndata1_r,data2_r = hyp.tools.reduce([data1,data2],ndims=3)\n\nmissing_inds = hyp.tools.missing_inds(data2)\nmissing_data = data2_r[missing_inds,:]\n\nhyp.plot([data1_r, data2_r, missing_data], ['r','b--','b*'])"
       ], 
       "outputs": [], 
       "metadata": {

docs/_build/html/_downloads/plot_missing_data.py

@@ -26,7 +26,7 @@
 
 missing = .05
 inds = [(i,j) for i in range(data1.shape[0]) for j in range(data1.shape[1])]
-missing_data = [inds[i] for i in np.random.choice(len(inds), len(inds)*missing)]
+missing_data = [inds[i] for i in np.random.choice(int(len(inds)), int(len(inds)*missing))]
 for i,j in missing_data:
     data2[i,j]=np.nan
 

docs/_build/html/_downloads/plot_procrustes.ipynb

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndata = sio.loadmat('sample_data/test_data.mat')\ntarget = data['spiral']\n\n# A random rotation matrix\nrot = np.array([[-0.89433495, -0.44719485, -0.01348182],\n       [-0.43426149,  0.87492975, -0.21427761],\n       [-0.10761949,  0.18578133,  0.97667976]])\n# creating new spiral with some noise\nsource = np.dot(target, rot)\n\n# Before hyperalignment\nfig,ax,data = hyp.plot([target, source], show=False, return_data=True)\nax.set_title('Before Procrustes')\nplt.show()\n\n# After hyperalignment\nfig,ax,data = hyp.plot([hyp.tools.procrustes(source, target), target], ['-','--'], show=False, return_data=True)\nax.set_title('After Procrustes')\nplt.show()"
+        "# Code source: Andrew Heusser\n# License: MIT\n\nimport hypertools as hyp\nimport scipy.io as sio\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndata = hyp.tools.load('spiral')\ntarget = data\n\n# A random rotation matrix\nrot = np.array([[-0.89433495, -0.44719485, -0.01348182],\n       [-0.43426149,  0.87492975, -0.21427761],\n       [-0.10761949,  0.18578133,  0.97667976]])\n# creating new spiral with some noise\nsource = np.dot(target, rot)\n\n# Before hyperalignment\nfig,ax,data = hyp.plot([target, source], show=False, return_data=True)\nax.set_title('Before Procrustes')\nplt.show()\n\n# After hyperalignment\nfig,ax,data = hyp.plot([hyp.tools.procrustes(source, target), target], ['-','--'], show=False, return_data=True)\nax.set_title('After Procrustes')\nplt.show()"
       ], 
       "outputs": [], 
       "metadata": {

docs/_build/html/_downloads/plot_procrustes.py

@@ -18,8 +18,8 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-data = sio.loadmat('sample_data/test_data.mat')
-target = data['spiral']
+data = hyp.tools.load('spiral')
+target = data
 
 # A random rotation matrix
 rot = np.array([[-0.89433495, -0.44719485, -0.01348182],