nvictus · October 20, 2016 15:47
diff --git a/tilt_matrix_diag_utils.ipynb b/tilt_matrix_diag_utils.ipynb
 {
  "cells": [
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2016-10-20T11:46:57.934835",
          "end_time": "2016-10-20T11:46:58.274596"
        },
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "%matplotlib inline\nimport matplotlib.pyplot as plt\nimport numpy as np",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2016-10-20T11:46:58.275811",
          "end_time": "2016-10-20T11:46:58.346075"
        },
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "def where_diagonal(A, k=0):\n    \"\"\"\n    Similar to ``numpy.diag_indices_from`` but works on kth diagonals as well.\n    \n    Parameters\n    ----------\n    A : 2-D array\n        Array.\n    \n    k : int, optional\n        Which diagonal to index. Default is 0 (main diagonal).\n    \n    Returns\n    -------\n    tuple of ndarrays\n    \n    \"\"\"\n    m, n = A.shape\n    if k >= 0:\n        end = min(n, m+k)\n        return np.arange(0, end-k), np.arange(k, end)\n    else:\n        end = min(m, n-k)\n        return np.arange(-k, end), np.arange(0, end+k)\n\n\ndef fill_diagonal(A, values, k=0, wrap=False, inplace=False):\n    \"\"\"\n    Based on ``numpy.fill_diagonal``, but allows for kth diagonals as well.\n    Only works on 2D arrays.\n\n    Parameters\n    ----------\n    A : 2-D array-like\n        Array whose diagonal is to be filled.\n    \n    values : scalar or 1-D array\n        Value(s) to write to diagonal. Will cycle if 1-D.\n    \n    k : int, optional\n        Which diagonal to write to. Positive and negative indices\n        imply upper and lower diagonals, respectively. \n        Default is 0 (main diagonal).\n    \n    wrap : bool, optional\n        Wrap diagonal after N columns. Only affects \"tall\" matrices.\n        Default is False.\n    \n    inplace : bool, optional\n        Write to array A in-place. \n        Only works if np.asarray returns a view.\n    \n    Returns\n    -------\n    2-D array with diagonal values inserted.\n\n    \"\"\"\n    if A.ndim != 2:\n        raise ValueError(\"array must be  2-d\")\n    if not inplace:\n        A = np.array(A)\n    else:\n        A = np.asarray(A)\n    \n    step = A.shape[1] + 1\n\n    if k >= 0:\n        start = k\n        end = None\n        if not wrap:\n            nsteps = A.shape[1] - k\n            end = nsteps*step\n    else:\n        k = -k\n        start = None\n        end = A.size - k - 1 + step\n        if not wrap:\n            nsteps = A.shape[1] - k\n            start = end - nsteps * step\n    \n    A.flat[start:end:step] = values\n    return A\n\n\ndef tilt_matrix(A, n_diags=None, pad=np.nan, direction='down'):\n    \"\"\"\n    Vertically stacks the upper diagonals of A onto a new matrix.\n    This is basically a hack to visualize a half-matrix at a 45 degree\n    angle, since AFAIK matplotlib can't rotate pixel images.\n\n    Input\n    -----\n    A: ndarray\n        Symmetric n x n matrix.\n    n_diags: integer, optional\n        Number of diagonals to copy, starting from the main diagonal.\n        The entire upper triangle is tilted by default.\n    pad: number, optional\n        Padding value for unfilled elements (default: NaN).\n    direction : {'up' | 'down'}, optional\n        Place successive diagonals of the array at the top or bottom\n        of the stack. Default is 'down'.\n\n    Returns\n    -------\n    T: \"tilted\" version of upper triangle of A.\n\n    Note that this transformation distorts the height when viewed with equal \n    aspect. When using imshow/matshow, set the aspect ratio to 1/2 or set\n    the ``extent`` coordinates appropriately.\n\n        >>> f = plt.figure()\n        >>> ax = f.add_subplot(111)\n        >>> ax.matshow(tilt_heatmap(A), aspect=0.5, origin='lower')\n\n    \"\"\"\n    n = len(A)\n    if n_diags is None:\n        n_diags = n\n\n    T = np.full((n_diags, n), pad)\n    for k in range(n_diags):\n        T[k, k//2 : n-(k+1)//2] = A.diagonal(k)\n\n    if direction.lower() == 'up':\n        T = T[::-1, :]\n    elif direction.lower() != 'down':\n        raise ValueError(\n            \"`direction` must be one of {'up', 'down'}\" +\n            \", got '{}'\".format(direction))\n\n    return T\n\n\ndef untilt_matrix(T, pad=np.nan, direction='down'):\n    \"\"\"\n    Restore a tilted matrix to a square matrix.\n    \n    Input\n    -----\n    A: ndarray\n        Rectangular m x n matrix representing the first m diagonals\n        of a n x n square symmetric matrix.\n    pad: number, optional\n        Padding value for unfilled elements (default: NaN).\n    direction : ['up' | 'down' ], optional\n        Whether the diagonals making up the rows of T were stacked up or down.\n    \n    \"\"\"\n    n_diags, n = T.shape\n    if direction.lower() == 'up':\n        T = T[::-1, :]\n    elif direction.lower() != 'down':\n        raise ValueError(\n            \"`direction` must be one of {'up', 'down'}\" +\n            \", got '{}'\".format(direction))\n    \n    A = np.full((n, n), np.nan)\n    for k in range(n_diags):\n        values = T[k, k//2 : n-(k+1)//2]\n        fill_diagonal(A, values, -k, inplace=True)\n        fill_diagonal(A, values,  k, inplace=True)\n    return A\n",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### test"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2016-10-20T11:46:58.346967",
          "end_time": "2016-10-20T11:46:58.499805"
        },
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "from scipy.linalg import toeplitz\nA = np.random.rand(50,50)\nA = toeplitz(np.arange(50))",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2016-10-20T11:46:58.501340",
          "end_time": "2016-10-20T11:46:58.779071"
        },
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "plt.matshow(A)",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.image.AxesImage at 0x7fd0074c5940>"
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x7fd00abd9400>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2016-10-20T11:46:58.780204",
          "end_time": "2016-10-20T11:46:58.944163"
        },
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "plt.matshow(tilt_matrix(A, direction='up'))",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.image.AxesImage at 0x7fd006f67048>"
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD7CAYAAABKWyniAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7fd0074902b0>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2016-10-20T11:46:58.945026",
          "end_time": "2016-10-20T11:46:59.074672"
        },
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "plt.matshow(untilt_matrix(tilt_matrix(A)))",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.image.AxesImage at 0x7fd006f45a20>"
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fDov4iT5IH/LxjG+WkBvfLCE3vllClazx3+0I8Mz+lweNzyGfDmk+2AMLEfLpwzO+WUJu\nfLOE3PhmCbnxzRKqZHPv3Dubl4u2HhZhwy9NyCfL13bDgoR8innGN0vIjW+WkBvfLKFK1vhbVi/v\n9F7fvXJahDU/OOTTySGfcZW75veMb5aQG98sITe+WUJufLOEqt/cK9K14eeQz5xzyKck09vw84xv\nlpAb3ywhN75ZQpWs8V8f9wYO+Sweh3xKMPma3zO+WUJufLOE3PhmCbnxzRKqLMDzCnD1du5kBiGf\n5jlolJnHYLohn+eab3NDY9e2a6pEe7PvvebPuKRxsHUwLyGf9SYsNcYYX7+QT2Uz/itVPdAUNc/N\nuoLxPNd8e9YljO295s9mXcL4zjdnXcG2+aW+WUJufLOEFBHlPoBU7gOYWV8RoaLzpTe+mdWPX+qb\nJeTGN0vIjW+WkBvfLCE3vllC/w+OgO1WG/hofgAAAABJRU5ErkJggg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7fd006f82978>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2016-10-20T11:46:59.075459",
          "end_time": "2016-10-20T11:46:59.259319"
        },
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "plt.matshow(untilt_matrix(tilt_matrix(A), direction='up'))",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.image.AxesImage at 0x7fd006ea63c8>"
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x7fd006ee2748>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2016-10-20T11:46:59.261805",
          "end_time": "2016-10-20T11:46:59.426956"
        },
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "plt.matshow(untilt_matrix(tilt_matrix(A, n_diags=15)))",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.image.AxesImage at 0x7fd006e7dd68>"
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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br3kRTT7/0nuPuzuDMbPtTT5jNDbi93q9pp6qNstW87LVC8tZ84svbHyxWTae6ksFMvhS\ngSJHzJFqfYKI5T8vkpZUZo7ocGog+JLax1N9qUAGXyqQwZcKZPClAhl8qUD/D6J0QzCVJIIMAAAA\nAElFTkSuQmCC\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7fd006eb6320>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2016-10-20T11:46:59.427814",
          "end_time": "2016-10-20T11:46:59.430918"
        },
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "assert np.allclose(A, untilt_matrix(tilt_matrix(A)))\nassert np.allclose(A, untilt_matrix(tilt_matrix(A, direction='up'), direction='up'))",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2016-10-20T11:46:59.431684",
          "end_time": "2016-10-20T11:47:00.022034"
        },
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "X = np.zeros((15, 15))\nX[where_diagonal(X, -4)] = -1\nX[where_diagonal(X, 4)] = 1\nplt.matshow(X)\n\nX = np.zeros((25, 15))\nX[where_diagonal(X, -4)] = -1\nX[where_diagonal(X, 4)] = 1\nplt.matshow(X)\n\nX = np.zeros((15, 25))\nX[where_diagonal(X, -4)] = -1\nX[where_diagonal(X, 4)] = 1\nplt.matshow(X)",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.image.AxesImage at 0x7fd006d75c88>"
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD7CAYAAABKWyniAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0P8DwFEj3UJ1GfkAAAAASUVORK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7fd006eb8c88>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Q/wFkqrPIa7fBVAAAAABJRU5ErkJggg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7fd006eb8b70>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAAD7CAYAAACvzHniAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7fd006e32160>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2016-10-20T11:47:00.023177",
          "end_time": "2016-10-20T11:47:01.287198"
        },
        "trusted": true,
        "scrolled": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "plt.matshow(fill_diagonal(np.zeros((15, 15)), np.arange(1,12), k=-4))\nplt.matshow(fill_diagonal(np.zeros((15, 15)), np.arange(1,12), k=4))\nplt.matshow(fill_diagonal(np.zeros((15, 25)), np.arange(1,12), k=4))\nplt.matshow(fill_diagonal(np.zeros((15, 25)), np.arange(1,12), k=-4))\nplt.matshow(fill_diagonal(np.zeros((25, 15)), np.arange(1,12), k=4))\nplt.matshow(fill_diagonal(np.zeros((25, 15)), np.arange(1,12), k=-4))\nplt.matshow(fill_diagonal(np.zeros((25, 15)), np.arange(1,12), k=4, wrap=True))\nplt.matshow(fill_diagonal(np.zeros((25, 15)), np.arange(1,12), k=-4, wrap=True))",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.image.AxesImage at 0x7fd006ab9f98>"
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": "<matplotlib.figure.Figure at 0x7fd006d1fe10>"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": "<matplotlib.figure.Figure at 0x7fd006d1f2b0>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": "<matplotlib.figure.Figure at 0x7fd006cefb70>"
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          "metadata": {}
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        {
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          "data": {
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            "text/plain": "<matplotlib.figure.Figure at 0x7fd006cc8080>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": "<matplotlib.figure.Figure at 0x7fd006c87710>"
          },
          "metadata": {}
        },
        {
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          "data": {
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            "text/plain": "<matplotlib.figure.Figure at 0x7fd006bd8550>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x7fd006cec908>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": "<matplotlib.figure.Figure at 0x7fd006b66978>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "mimetype": "text/x-python",
      "file_extension": ".py",
      "name": "python",
      "version": "3.5.2",
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "codemirror_mode": {
        "version": 3,
        "name": "ipython"
      }
    },
    "toc": {
      "toc_threshold": 6,
      "toc_window_display": false,
      "toc_cell": false,
      "toc_number_sections": true
    },
    "gist": {
      "id": "",
      "data": {
        "description": "diag utils",
        "public": false
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
 }