diff --git a/README.md b/README.md index 49e673e..0652c6e 100644 --- a/README.md +++ b/README.md @@ -8,9 +8,10 @@ Make every matrix beautiful. MatRepr formats matrices and tensors to HTML, string, and LaTeX, with Jupyter integration. +See [Jupyter notebook for examples.](doc/demo.ipynb) * **SciPy** - sparse matrices and arrays like `csr_matrix` and `coo_array` -* **NumPy** - `ndarray` +* **NumPy** - `ndarray` [(demo)](doc/demo-numpy.ipynb) * **[PyTorch](https://pytorch.org/docs/stable/sparse.html)** - dense and sparse `torch.Tensor` [(demo)](doc/demo-pytorch.ipynb) * **[TensorFlow](https://www.tensorflow.org/guide/sparse_tensor)** - `tf.Tensor` and `tf.SparseTensor` [(demo)](doc/demo-tensorflow.ipynb) * **[Python-graphblas](https://github.com/python-graphblas/python-graphblas)** - `gb.Matrix` and `gb.Vector` [(demo)](doc/demo-python-graphblas.ipynb) @@ -19,19 +20,17 @@ MatRepr formats matrices and tensors to HTML, string, and LaTeX, with Jupyter in Features: * Jupyter extension to format matrices in cell outputs. -* A `__repr__` monkey patch to format matrices in the Python shell. -* Nested sub-matrices of any supported type, including mixing packages. * Configurable float precision or format string. * Toggle row and column indices or set your own labels. +* Nested sub-matrices of any supported type, including mixing packages. * Toggle matrix description or set your own title. * String output can optionally autodetect terminal width. * Methods to directly display a matrix (`mprint`, `mdisplay` for Jupyter) * Methods to convert to string (`to_html`, `to_latex`, `to_str`). * Configurable per method call or set defaults with `matrepr.params`. +* A `__repr__` monkey patch to format matrices in the Python shell. * Fast. -See [Jupyter notebook with examples.](doc/demo.ipynb) - ## Quick Start ```shell @@ -70,13 +69,19 @@ from matrepr import mdisplay, mprint or simply `A` with monkey patching as below ### HTML -HTML screenshot + +HTML screenshot
+HTML screenshot
+4D NumPy Array
+Adjacency Matrix
`mdisplay(A)`, `to_html(A)` or simply `A` with Jupyter extension `%load_ext matrepr` + ### LaTeX -LaTeX screenshot +LaTeX screenshot
+LaTeX edgecases screenshot `mdisplay(A, 'latex')`, `to_latex(A)` or simply `A` with Jupyter extension `%load_ext matrepr.latex` diff --git a/doc/demo-numpy.ipynb b/doc/demo-numpy.ipynb index cb5c31e..22d7915 100644 --- a/doc/demo-numpy.ipynb +++ b/doc/demo-numpy.ipynb @@ -9,20 +9,22 @@ } }, "source": [ - "# MatRepr numpy" + "# MatRepr NumPy\n", + "\n", + "Compare the native NumPy repr with MatRepr's formatting." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { + "ExecuteTime": { + "end_time": "2023-09-12T06:17:06.024081Z", + "start_time": "2023-09-12T06:17:05.936502Z" + }, "collapsed": false, "jupyter": { "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-08-31T21:28:07.077311Z", - "start_time": "2023-08-31T21:28:06.917621Z" } }, "outputs": [], @@ -34,123 +36,32 @@ "import sys\n", "sys.path.insert(0, '..')\n", "\n", - "from matrepr import mdisplay" + "from matrepr import mdisplay, mprint" ] }, { "cell_type": "markdown", - "source": [ - "## NumPy\n", - "\n", - "Compare:\n", - "\n", - " * numpy's native `str`\n", - " * MatRepr `str`\n", - " * MatRepr HTML\n", - " * MatRepr LaTeX" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "def render_all(mat):\n", - " print(f\"shape: {mat.shape}\")\n", - " print(mat)\n", - " mdisplay(mat, \"str\", txt_width=100)\n", - " mdisplay(mat, \"html\")\n", - " mdisplay(mat, \"latex\")" - ], "metadata": { "collapsed": false, - "ExecuteTime": { - "end_time": "2023-08-31T21:28:07.077458Z", - "start_time": "2023-08-31T21:28:07.030507Z" + "jupyter": { + "outputs_hidden": false } - } - }, - { - "cell_type": "markdown", + }, "source": [ - "#### A long vector" - ], - "metadata": { - "collapsed": false - } + "### 1D vector" + ] }, { "cell_type": "code", - "execution_count": 3, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape: (40,)\n", - "[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33\n", - " 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49]\n" - ] - }, - { - "data": { - "text/plain": "\n 0 1 2 3 4 5 6 34 35 36 37 38 39\n[10 11 12 13 14 15 22 ... 44 45 46 47 48 49 ]" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": "", - "text/html": "
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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": "", - "text/latex": "$\\begin{bmatrix}\n \\textrm{10} & \\textrm{11} & \\textrm{12} & \\textrm{13} & \\textrm{14} & \\textrm{15} & \\textrm{16} & \\dots & \\textrm{43} & \\textrm{44} & \\textrm{45} & \\textrm{46} & \\textrm{47} & \\textrm{48} & \\textrm{49}\n\\end{bmatrix}$" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "v1 = np.arange(10, 50)\n", - "render_all(v1)" - ], + "execution_count": 2, "metadata": { - "collapsed": false, "ExecuteTime": { - "end_time": "2023-08-31T21:28:07.105279Z", - "start_time": "2023-08-31T21:28:07.039637Z" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "#### A small 2D matrix" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { + "end_time": "2023-09-12T06:17:06.050918Z", + "start_time": "2023-09-12T06:17:06.030799Z" + }, "collapsed": false, "jupyter": { "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-08-31T21:28:07.106765Z", - "start_time": "2023-08-31T21:28:07.071456Z" } }, "outputs": [ @@ -158,440 +69,1797 @@ "name": "stdout", "output_type": "stream", "text": [ - "shape: (2, 2)\n", - "[[1 2]\n", - " [3 4]]\n" + "[10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33\n", + " 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49]\n" ] - }, - { - "data": { - "text/plain": "<2×2, 4 'int64' elements, array>\n 0 1\n ┌ ┐\n0 │ 1 2 │\n1 │ 3 4 │\n └ ┘" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": "", - "text/html": "
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" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "a2 = np.array([\n", - " [[1, 2], [3, 4]],\n", - " [[10, 20], [30, 40]],\n", - "])\n", - "render_all(a2)" - ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-08-31T21:28:07.108992Z", - "start_time": "2023-08-31T21:28:07.085962Z" - } - } + "mdisplay(v1)" + ] }, { "cell_type": "markdown", - "source": [ - "#### A small 4D matrix" - ], "metadata": { - "collapsed": false - } + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "### A small 2D matrix" + ] }, { "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape: (4, 3, 2, 1)\n", - "[[[[ 0]\n", - " [ 1]]\n", - "\n", - " [[ 2]\n", - " [ 3]]\n", - "\n", - " [[ 4]\n", - " [ 5]]]\n", - "\n", - "\n", - " [[[ 6]\n", - " [ 7]]\n", - "\n", - " [[ 8]\n", - " [ 9]]\n", - "\n", - " [[10]\n", - " [11]]]\n", - "\n", - "\n", - " [[[12]\n", - " [13]]\n", - "\n", - " [[14]\n", - " [15]]\n", - "\n", - " [[16]\n", - " [17]]]\n", - "\n", - "\n", - " [[[18]\n", - " [19]]\n", - "\n", - " [[20]\n", - " [21]]\n", - "\n", - " [[22]\n", - " [23]]]]\n" - ] - }, - { - "data": { - "text/plain": "\n 0 1 2\n ┌ ┐\n0 │ [[0] [1]] [[2] [3]] [[4] [5]] │\n1 │ [[6] [7]] [[8] [9]] [[10] [11]] │\n2 │ [[12] [13]] [[14] [15]] [[16] [17]] │\n3 │ [[18] [19]] [[20] [21]] [[22] [23]] │\n └ ┘" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": "", - "text/html": "
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" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "render_all(big_2D)" + "mdisplay(big_2D)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, + "metadata": {}, "outputs": [], - "source": [], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2023-08-31T21:28:07.239045Z", - "start_time": "2023-08-31T21:28:07.228626Z" - } - } + "source": [] } ], "metadata": { @@ -731,7 +2271,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.2" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/doc/demo.ipynb b/doc/demo.ipynb index f11ac08..f4b6a80 100644 --- a/doc/demo.ipynb +++ b/doc/demo.ipynb @@ -18,13 +18,13 @@ "cell_type": "code", "execution_count": 1, "metadata": { + "ExecuteTime": { + "end_time": "2023-09-12T03:27:08.569908Z", + "start_time": "2023-09-12T03:27:08.562850Z" + }, "collapsed": false, "jupyter": { "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-09-01T19:07:01.026975Z", - "start_time": "2023-09-01T19:07:00.830991Z" } }, "outputs": [], @@ -37,7 +37,7 @@ "import sys\n", "sys.path.insert(0, '..')\n", "\n", - "from matrepr import mdisplay" + "from matrepr import mdisplay, mprint" ] }, { @@ -56,13 +56,13 @@ "cell_type": "code", "execution_count": 2, "metadata": { + "ExecuteTime": { + "end_time": "2023-09-12T03:27:08.571818Z", + "start_time": "2023-09-12T03:27:08.566180Z" + }, "collapsed": false, "jupyter": { "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-09-01T19:07:01.046299Z", - "start_time": "2023-09-01T19:07:01.023246Z" } }, "outputs": [], @@ -79,27 +79,213 @@ } }, "source": [ - "### SciPy sparse matrix" + "## SciPy sparse matrix" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { + "ExecuteTime": { + "end_time": "2023-09-12T03:27:08.627283Z", + "start_time": "2023-09-12T03:27:08.570110Z" + }, "collapsed": false, "jupyter": { "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-09-01T19:07:01.046605Z", - "start_time": "2023-09-01T19:07:01.032299Z" } }, "outputs": [ { "data": { - "text/plain": "<5x5 sparse matrix of type ''\n\twith 12 stored elements in COOrdinate format>", - "text/html": "
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5×5, 12 'float64' elements, coo
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6×6, 18 'float64' elements, coo
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" + ], + "text/plain": [ + "<6x6 sparse matrix of type ''\n", + "\twith 18 stored elements in COOrdinate format>" + ] }, "execution_count": 3, "metadata": {}, @@ -107,7 +293,887 @@ } ], "source": [ - "scipy.sparse.random(5, 5, density=0.5)" + "scipy.sparse.random(6, 6, density=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2D NumPy array" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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10.830.600.550.340.300.420.680.880.510.67
20.590.620.670.840.080.760.240.190.570.10
30.890.630.720.020.590.560.160.150.700.32
40.690.550.390.930.840.360.040.300.400.70
51.000.360.760.590.690.150.400.240.340.51
60.670.110.130.320.660.850.550.850.380.32
70.350.170.830.340.550.580.520.000.990.91
80.210.290.520.900.980.260.560.810.390.73
90.160.600.870.980.080.430.200.450.550.09
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500×25, 12500 'float64' elements, array
0123456789101112131415161718192021222324
00.300.930.570.460.750.740.050.710.840.170.780.290.310.670.110.660.890.700.440.440.770.570.080.580.81
10.340.930.750.570.750.080.860.820.910.130.080.140.400.420.560.120.200.810.470.810.010.550.930.580.21
20.720.380.670.030.640.030.740.470.120.540.070.651.000.770.570.100.700.660.050.790.520.430.790.410.48
30.180.320.850.190.420.990.240.920.920.090.460.500.310.050.240.100.240.810.890.040.300.980.540.630.01
40.480.990.380.100.460.960.340.800.800.210.440.720.410.190.970.650.870.030.270.500.070.990.240.370.21
50.110.230.300.630.280.360.010.370.530.160.600.290.630.030.890.020.130.780.050.710.970.870.710.960.43
60.870.360.930.150.940.830.850.120.600.020.720.010.080.230.880.360.540.570.230.570.660.300.420.450.93
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
4930.210.030.820.020.410.690.870.630.340.320.190.330.090.990.750.720.190.150.660.590.770.930.560.680.63
4940.710.820.710.600.610.620.700.820.050.460.310.280.360.010.520.690.260.570.050.460.660.620.370.610.14
4950.620.210.610.300.660.290.670.240.090.410.960.800.210.790.100.180.160.990.960.570.560.430.910.090.36
4960.610.910.760.410.810.580.540.150.150.080.430.510.250.920.100.660.610.460.870.740.340.260.800.130.75
4970.210.260.870.690.470.020.150.310.370.460.070.100.070.150.950.120.140.150.730.330.720.440.040.670.11
4980.470.390.520.490.710.580.140.980.350.830.180.030.930.870.980.080.740.360.700.060.890.520.500.150.95
4990.030.320.400.760.430.120.920.490.230.230.290.330.230.570.230.480.410.540.050.340.920.850.700.360.52
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mat = np.random.random((500, 25))\n", + "mdisplay(mat, floatfmt=\".2f\", max_rows=15, max_cols=25)" ] }, { @@ -119,27 +1185,211 @@ } }, "source": [ - "### 2D NumPy array with row and column labels" + "## 2D NumPy array with row and column labels" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": { + "ExecuteTime": { + "end_time": "2023-09-12T03:27:08.648723Z", + "start_time": "2023-09-12T03:27:08.623744Z" + }, "collapsed": false, "jupyter": { "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-09-01T19:07:01.050660Z", - "start_time": "2023-09-01T19:07:01.040636Z" } }, "outputs": [ { "data": { - "text/plain": "", - "text/html": "
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BostonBuffaloChicagoClevelandDallasDenver
Boston-45798363918151991
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Dallas181513879311205--801
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" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" @@ -156,7 +1406,1073 @@ " [1991, 1561, 1050, 1369, 801, None],\n", "])\n", "\n", - "mdisplay(distances, title=None, row_labels=cities, col_labels=cities, fill_value=\"-\")" + "mdisplay(distances, title=None, row_labels=cities, col_labels=cities, fill_value=\"--\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1D arrays and vectors" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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length=1000, 1000 'float64' elements, array
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0.17080.73650.41660.87640.9640.78090.1369⋯0.77040.16080.8160.0083670.28580.64230.8979
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vec = np.random.random((1000,))\n", + "mdisplay(vec)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.1708, 0.7365, 0.4166, 0.8764, 0.651, ..., 0.008367, 0.2858, 0.6423, 0.8979]\n" + ] + } + ], + "source": [ + "mprint(vec, indices=False, title=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sparse N-dimensional Tensors\n", + "\n", + "Sparse multidimensional tensors are presented as a list of tuples." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import sparse\n", + "tensor_4d = sparse.random((1000, 10, 10, 10, 10), density=0.1234)\n", + "mdisplay(tensor_4d)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " 0 1 2 3 4 val\n", + " 0 ( 0, 0, 0, 0, 1, 0.8415 )\n", + " 1 ( 0, 0, 0, 1, 6, 0.6691 )\n", + " 2 ( 0, 0, 0, 2, 1, 0.6162 )\n", + " 3 ( 0, 0, 0, 2, 2, 0.09681)\n", + " 4 ( 0, 0, 0, 4, 7, 0.4684 )\n", + " ( :, :, :, :, :, : )\n", + "1233995 (999, 9, 9, 6, 7, 0.9675 )\n", + "1233996 (999, 9, 9, 7, 0, 0.2194 )\n", + "1233997 (999, 9, 9, 7, 7, 0.188 )\n", + "1233998 (999, 9, 9, 8, 5, 0.03111)\n", + "1233999 (999, 9, 9, 9, 9, 0.1041 )\n" + ] + } + ], + "source": [ + "mprint(tensor_4d)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dense N-dimensional Numpy Arrays" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import graphblas as gb\n", + "\n", + "edges = gb.Matrix.from_coo(\n", + " [3, 0, 3, 5, 6, 0, 6, 1, 6, 2, 4, 1],\n", + " [0, 1, 2, 2, 2, 3, 3, 4, 4, 5, 5, 6],\n", + " [3, 2, 3, 1, 5, 3, 7, 8, 3, 1, 7, 4],\n", + " nrows=7, ncols=7\n", + ")\n", + "vertices = gb.Vector.from_coo([range(7)], [f\"V{i+1}\" for i in range(7)], size=7)\n", + "\n", + "mdisplay(edges, row_labels=vertices, col_labels=vertices, title=False)" ] }, { @@ -168,27 +2484,212 @@ } }, "source": [ - "### Nested sub-matrices" + "## Nested matrices" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": { + "ExecuteTime": { + "end_time": "2023-09-12T03:27:08.649007Z", + "start_time": "2023-09-12T03:27:08.624126Z" + }, "collapsed": false, "jupyter": { "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-09-01T19:07:01.074454Z", - "start_time": "2023-09-01T19:07:01.052464Z" } }, "outputs": [ { "data": { - "text/plain": "", - "text/html": "
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" + ], + "text/plain": [ + "" + ] }, "metadata": {}, "output_type": "display_data" @@ -197,11 +2698,11 @@ "source": [ "# You may mix types if the datastructure allows, as a Python list does\n", "mat = [\n", - " [scipy.sparse.random(2, 2, density=0.6), [[1, 2], [3, 4]]],\n", + " [scipy.sparse.random(2, 2, density=0.8), [[1, 2], [3, 4]]],\n", " [np.array([[1, 2], [3, 4]]), scipy.sparse.random(2, 2, density=0.6)]\n", "]\n", "\n", - "mdisplay(mat)" + "mdisplay(mat, floatfmt=\".2f\")" ] }, { @@ -213,47 +2714,747 @@ } }, "source": [ - "### Large matrices" + "## Large matrices" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "metadata": { "collapsed": false, + "is_executing": true, "jupyter": { "outputs_hidden": false - }, + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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100000000×100000000, 100000000 'float64' elements, csr
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" + ], + "text/plain": [ + "<100000000x100000000 sparse matrix of type ''\n", + "\twith 100000000 stored elements in Compressed Sparse Row format>" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scipy.sparse.eye(100_000_000, format=\"csr\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { "ExecuteTime": { - "end_time": "2023-09-01T19:07:05.210310Z", - "start_time": "2023-09-01T19:07:01.059338Z" + "start_time": "2023-09-12T03:27:08.624194Z" + }, + "collapsed": false, + "is_executing": true, + "jupyter": { + "outputs_hidden": false } }, "outputs": [], "source": [ - "r = scipy.sparse.random(10000, 10000, density=0.23421, format=\"csr\")" + "r = scipy.sparse.random(10000, 10000, density=0.23456789, format=\"csr\")" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 16, "metadata": { "collapsed": false, + "is_executing": true, "jupyter": { "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-09-01T19:07:05.214191Z", - "start_time": "2023-09-01T19:07:05.210759Z" } }, "outputs": [ { "data": { - "text/plain": "<10000x10000 sparse matrix of type ''\n\twith 23421000 stored elements in Compressed Sparse Row format>", - "text/html": "
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10000×10000, 23421000 'float64' elements, csr
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10000×10000, 23456789 'float64' elements, csr
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" + ], + "text/plain": [ + "<10000x10000 sparse matrix of type ''\n", + "\twith 23456789 stored elements in Compressed Sparse Row format>" + ] }, - "execution_count": 7, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -263,46 +3464,193 @@ ] }, { - "cell_type": "code", - "execution_count": 8, + "cell_type": "markdown", "metadata": { "collapsed": false, + "is_executing": true, "jupyter": { "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-09-01T19:07:05.478907Z", - "start_time": "2023-09-01T19:07:05.214670Z" } }, + "source": [ + "## Duplicate entries\n", + "\n", + "Some sparse formats allow multiple values with the same indices." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, "outputs": [ { "data": { - "text/plain": "<100000000x100000000 sparse matrix of type ''\n\twith 100000000 stored elements in Compressed Sparse Row format>", - "text/html": "
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100000000×100000000, 100000000 'float64' elements, csr
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3×3, 6 'float64' elements, coo
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" + ], + "text/plain": [ + "" + ] }, - "execution_count": 8, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "scipy.sparse.eye(100_000_000, format=\"csr\")" + "row = [0, 1, 1, 2, 2, 2]\n", + "col = [0, 1, 1, 2, 2, 2]\n", + "val = [1, 2.1, 2.2, 3.1, 3.2, 3.3]\n", + "dupes = scipy.sparse.coo_array((val, (row, col)), shape=(3, 3))\n", + "\n", + "mdisplay(dupes)" ] }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "ExecuteTime": { - "end_time": "2023-09-01T19:07:05.480525Z", - "start_time": "2023-09-01T19:07:05.479173Z" - } - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [] } @@ -323,7 +3671,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.2" + "version": "3.11.5" } }, "nbformat": 4,