write and read a matrix

write and read a matrix.ipynb

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      "source": "A = randn(5, 5)",
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          "execution_count": 1,
          "data": {
            "text/plain": "5×5 Matrix{Float64}:\n -0.240283   1.60532   -1.15529    0.260169   1.2585\n  0.423185  -0.867211  -1.2176    -0.512242  -1.04863\n  0.341219  -0.140905  -0.03313   -1.7478    -0.598576\n  0.333095   0.424912  -0.576262  -2.0013    -0.671772\n  1.95515   -0.338912   1.11586    1.74802   -0.0456715"
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      "source": "string(A)",
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          "data": {
            "text/plain": "\"[-0.2402826849352379 1.6053180905943534 -1.1552904597891978 0.26016872756648324 1.2584979744077427; 0.42318527856955884 -0.8672106347900607 -1.2175988774418558 -0.5122421299004011 -1.048628660584414; 0.3412186857086955 -0.1409045074904742 -0.03312995769217814 -1.7478019236951658 -0.598576214513245; 0.3330952595045313 0.42491234364889346 -0.5762617588060361 -2.001300594425213 -0.6717715695671819; 1.955147023461729 -0.3389123482008798 1.115860107519043 1.7480203496645637 -0.04567149678748327]\""
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      "cell_type": "code",
      "source": "write(\"A.txt\", string(A))",
      "execution_count": 3,
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      "cell_type": "code",
      "source": "read(\"A.txt\", String)",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "\"[-0.2402826849352379 1.6053180905943534 -1.1552904597891978 0.26016872756648324 1.2584979744077427; 0.42318527856955884 -0.8672106347900607 -1.2175988774418558 -0.5122421299004011 -1.048628660584414; 0.3412186857086955 -0.1409045074904742 -0.03312995769217814 -1.7478019236951658 -0.598576214513245; 0.3330952595045313 0.42491234364889346 -0.5762617588060361 -2.001300594425213 -0.6717715695671819; 1.955147023461729 -0.3389123482008798 1.115860107519043 1.7480203496645637 -0.04567149678748327]\""
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      "cell_type": "code",
      "source": "Meta.parse(read(\"A.txt\", String))",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": ":([-0.2402826849352379 1.6053180905943534 -1.1552904597891978 0.26016872756648324 1.2584979744077427; 0.42318527856955884 -0.8672106347900607 -1.2175988774418558 -0.5122421299004011 -1.048628660584414; 0.3412186857086955 -0.1409045074904742 -0.03312995769217814 -1.7478019236951658 -0.598576214513245; 0.3330952595045313 0.42491234364889346 -0.5762617588060361 -2.001300594425213 -0.6717715695671819; 1.955147023461729 -0.3389123482008798 1.115860107519043 1.7480203496645637 -0.04567149678748327])"
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    {
      "metadata": {
        "trusted": true
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      "cell_type": "code",
      "source": "A_read = eval(Meta.parse(read(\"A.txt\", String)))",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "5×5 Matrix{Float64}:\n -0.240283   1.60532   -1.15529    0.260169   1.2585\n  0.423185  -0.867211  -1.2176    -0.512242  -1.04863\n  0.341219  -0.140905  -0.03313   -1.7478    -0.598576\n  0.333095   0.424912  -0.576262  -2.0013    -0.671772\n  1.95515   -0.338912   1.11586    1.74802   -0.0456715"
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      "cell_type": "code",
      "source": "A_read == A",
      "execution_count": 7,
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          "output_type": "execute_result",
          "execution_count": 7,
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