Foward_Model_Notebook.ipynb

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      "name": "Foward_Model_Notebook.ipynb",
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        "<a href=\"https://colab.research.google.com/github/dopplerchase/Leinonen_Python_Forward_Model/blob/master/Foward_Model_Notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Forward Modeling Z from a PSD (SNOW)\n",
        "\n",
        "Here is an example of calculating Z at common microwave wavelengths from a measured PSD. \n",
        "\n",
        "First up, download the repository. It has the code, sample data to play with and Leinonen and Szyrmer (2015)'s results. "
      ]
    },
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          "height": 119
        },
        "outputId": "a71899d5-b1d7-4ed5-b48c-2111767d6859"
      },
      "source": [
        "!git clone https://github.com/dopplerchase/Leinonen_Python_Forward_Model.git"
      ],
      "execution_count": 1,
      "outputs": [
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          "text": [
            "Cloning into 'Leinonen_Python_Forward_Model'...\n",
            "remote: Enumerating objects: 24, done.\u001b[K\n",
            "remote: Counting objects: 100% (24/24), done.\u001b[K\n",
            "remote: Compressing objects: 100% (23/23), done.\u001b[K\n",
            "remote: Total 33 (delta 9), reused 0 (delta 0), pack-reused 9\u001b[K\n",
            "Unpacking objects: 100% (33/33), done.\n"
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          "name": "stdout"
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      "source": [
        "Install netCDF4 to read the sample PSD in and pytmatrix for coefs in the calc"
      ]
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        "outputId": "1d69534d-2c54-4c8e-f60a-d9364c05be42"
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      "source": [
        "!pip install netCDF4\n",
        "!pip install pytmatrix\n",
        "\n",
        "# comment out if you dont want my style plots \n",
        "!pip install git+https://github.com/dopplerchase/DRpy.git\n",
        "import drpy"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: netCDF4 in /usr/local/lib/python3.6/dist-packages (1.5.3)\n",
            "Requirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.6/dist-packages (from netCDF4) (1.18.4)\n",
            "Requirement already satisfied: cftime in /usr/local/lib/python3.6/dist-packages (from netCDF4) (1.1.3)\n",
            "Requirement already satisfied: pytmatrix in /usr/local/lib/python3.6/dist-packages (0.3.2)\n",
            "Collecting git+https://github.com/dopplerchase/DRpy.git\n",
            "  Cloning https://github.com/dopplerchase/DRpy.git to /tmp/pip-req-build-3pajtsht\n",
            "  Running command git clone -q https://github.com/dopplerchase/DRpy.git /tmp/pip-req-build-3pajtsht\n",
            "Building wheels for collected packages: DRpy\n",
            "  Building wheel for DRpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for DRpy: filename=DRpy-0.0.0-cp36-none-any.whl size=49344 sha256=7cd9b22be396420b0b09fa9168c55eede5afeb5a29eda7b55f47b3c8f4ec873c\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-gy468c69/wheels/38/6d/b7/910e8e07b5b2348384992db25ecb85a0a60d51b873db4cf749\n",
            "Successfully built DRpy\n",
            "Installing collected packages: DRpy\n",
            "Successfully installed DRpy-0.0.0\n"
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      "source": [
        "import packages "
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      "source": [
        "import netCDF4 \n",
        "import xarray as xr \n",
        "import scipy.interpolate\n",
        "import pandas as pd \n",
        "import os \n",
        "\n",
        "#change dir \n",
        "os.chdir('/content/Leinonen_Python_Forward_Model/')\n",
        "\n",
        "from forward import *\n",
        "\n",
        "%pylab inline \n",
        "\n",
        "\n"
      ],
      "execution_count": 3,
      "outputs": [
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            "Populating the interactive namespace from numpy and matplotlib\n"
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      "source": [
        "psd = xr.open_dataarray('./SAMPLE_OLYMPEX.nc')\n",
        "psd"
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      "execution_count": 4,
      "outputs": [
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              "               0.      ,        0.      ,        0.      ],\n",
              "       [       0.      ,        0.      ,        0.      , ...,\n",
              "               0.      ,        0.      ,        0.      ]])</pre></div></li><li class='xr-section-item'><input id='section-7612da89-0247-45cc-9d1f-abdd95132db8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7612da89-0247-45cc-9d1f-abdd95132db8' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(n_sample)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2dd9c66a-cf1e-476b-9d17-e9087e401a81' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2dd9c66a-cf1e-476b-9d17-e9087e401a81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d2cc532-5315-4239-977f-e7f2fb6f63af' class='xr-var-data-in' type='checkbox'><label for='data-0d2cc532-5315-4239-977f-e7f2fb6f63af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([&#x27;2015-12-01T22:46:00.000000000&#x27;, &#x27;2015-12-01T22:46:05.000000000&#x27;,\n",
              "       &#x27;2015-12-01T22:46:10.000000000&#x27;, ..., &#x27;2015-12-02T01:32:25.000000000&#x27;,\n",
              "       &#x27;2015-12-02T01:32:30.000000000&#x27;, &#x27;2015-12-02T01:32:35.000000000&#x27;],\n",
              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>D</span></div><div class='xr-var-dims'>(D_bin)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d4e8242-6a6e-4c12-9b18-b02de39691a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d4e8242-6a6e-4c12-9b18-b02de39691a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f80c321-33e6-4500-b733-df44d644d3f7' class='xr-var-data-in' type='checkbox'><label for='data-0f80c321-33e6-4500-b733-df44d644d3f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([5.000e-05, 7.000e-05, 9.000e-05, 1.125e-04, 1.375e-04, 1.750e-04,\n",
              "       2.250e-04, 2.750e-04, 3.250e-04, 3.750e-04, 4.375e-04, 5.125e-04,\n",
              "       5.875e-04, 6.625e-04, 7.500e-04, 8.500e-04, 9.500e-04, 1.100e-03,\n",
              "       1.300e-03, 1.500e-03, 1.700e-03, 2.000e-03, 2.400e-03, 2.800e-03,\n",
              "       3.200e-03, 3.600e-03, 4.000e-03, 4.400e-03, 4.800e-03, 5.500e-03,\n",
              "       6.500e-03, 7.500e-03, 8.500e-03, 9.500e-03, 1.100e-02, 1.300e-02,\n",
              "       1.500e-02, 1.700e-02, 1.900e-02, 2.250e-02, 2.750e-02])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>dD</span></div><div class='xr-var-dims'>(D_bin)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-19665c34-e39b-4207-ba51-2d7189ef494c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19665c34-e39b-4207-ba51-2d7189ef494c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-552cc6c3-d33a-4522-92b8-c281001d031c' class='xr-var-data-in' type='checkbox'><label for='data-552cc6c3-d33a-4522-92b8-c281001d031c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><pre class='xr-var-data'>array([2.0e-05, 2.0e-05, 2.0e-05, 2.5e-05, 2.5e-05, 5.0e-05, 5.0e-05, 5.0e-05,\n",
              "       5.0e-05, 5.0e-05, 7.5e-05, 7.5e-05, 7.5e-05, 7.5e-05, 1.0e-04, 1.0e-04,\n",
              "       1.0e-04, 2.0e-04, 2.0e-04, 2.0e-04, 2.0e-04, 4.0e-04, 4.0e-04, 4.0e-04,\n",
              "       4.0e-04, 4.0e-04, 4.0e-04, 4.0e-04, 4.0e-04, 1.0e-03, 1.0e-03, 1.0e-03,\n",
              "       1.0e-03, 1.0e-03, 2.0e-03, 2.0e-03, 2.0e-03, 2.0e-03, 2.0e-03, 5.0e-03,\n",
              "       5.0e-03])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-24ee60ef-5502-44c6-91ef-807769b3f31c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-24ee60ef-5502-44c6-91ef-807769b3f31c' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m^-4</dd><dt><span>standard_name :</span></dt><dd>Sample N(D) from OLYMPEX 01 Dec 2015. 5-s averages. D is in m, dD is in m</dd></dl></div></li></ul></div></div>"
            ],
            "text/plain": [
              "<xarray.DataArray (n_sample: 2000, D_bin: 41)>\n",
              "array([[       0.      ,        0.      ,        0.      , ...,\n",
              "                    nan,             nan,             nan],\n",
              "       [            nan,             nan,             nan, ...,\n",
              "                    nan,             nan,             nan],\n",
              "       [            nan,             nan,             nan, ...,\n",
              "                    nan,             nan,             nan],\n",
              "       ...,\n",
              "       [30738672.269088,        0.      ,        0.      , ...,\n",
              "               0.      ,        0.      ,        0.      ],\n",
              "       [       0.      , 62776142.268432,  9493922.750473, ...,\n",
              "               0.      ,        0.      ,        0.      ],\n",
              "       [       0.      ,        0.      ,        0.      , ...,\n",
              "               0.      ,        0.      ,        0.      ]])\n",
              "Coordinates:\n",
              "    time     (n_sample) datetime64[ns] ...\n",
              "    D        (D_bin) float64 ...\n",
              "    dD       (D_bin) float64 ...\n",
              "Dimensions without coordinates: n_sample, D_bin\n",
              "Attributes:\n",
              "    units:          m^-4\n",
              "    standard_name:  Sample N(D) from OLYMPEX 01 Dec 2015. 5-s averages. D is ..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hxon5WdFemxN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "daf824ec-beb0-430e-f107-867a1fa6f154"
      },
      "source": [
        "#initialize class\n",
        "Z = forward_Z()\n",
        "#git the class the PSD\n",
        "Z.set_PSD(PSD = np.asarray(psd.values,dtype=float),D=psd.D.values,dD=psd.dD.values)\n",
        "#load the leinonen output\n",
        "Z.load_split_L15()\n",
        "#fit the backscatter cross-sections \n",
        "Z.fit_sigmas()\n",
        "#calculate Z\n",
        "Z.calc_Z()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Leinonen_Python_Forward_Model/forward.py:186: RuntimeWarning: divide by zero encountered in log10\n",
            "  Z_x = 10*np.log10(coef*np.nansum(self.sigma_x*self.PSD*self.dD,axis=1))\n",
            "/content/Leinonen_Python_Forward_Model/forward.py:187: RuntimeWarning: divide by zero encountered in log10\n",
            "  Z_ku = 10*np.log10(coef2*np.nansum(self.sigma_ku*self.PSD*self.dD,axis=1))\n",
            "/content/Leinonen_Python_Forward_Model/forward.py:188: RuntimeWarning: divide by zero encountered in log10\n",
            "  Z_ka = 10*np.log10(coef3*np.nansum(self.sigma_ka*self.PSD*self.dD,axis=1))\n",
            "/content/Leinonen_Python_Forward_Model/forward.py:189: RuntimeWarning: divide by zero encountered in log10\n",
            "  Z_w = 10*np.log10(coef4*np.nansum(self.sigma_w*self.PSD*self.dD,axis=1))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3XHn5c5SfUwh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "outputId": "e83135b9-faee-42a1-b17e-1f95bd48924a"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "labels = ['L00','L01','L02','L05','L10','L20']\n",
        "for i in np.arange(0,6):\n",
        "  plt.plot(psd.time.values,Z.Z_x[:,i],'-k',lw=(i+1)/4,label=labels[i])\n",
        "\n",
        "plt.legend()\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Z, [dBZ]')"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Z, [dBZ]')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}