{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial: `spec_mod`\n", "\n", "## Sample data\n", "Sample imagery captured from a [Resonon](https://resonon.com/) Pika II VIS-NIR line scanning imager and ancillary sample files can be downloaded from this [link](https://drive.google.com/drive/folders/1KpOBB4-qghedVFd8ukQngXNwUit8PFy_?usp=sharing).\n", "\n", "Before trying this tutorial on your own machine, please download the [sample files](https://drive.google.com/drive/folders/1KpOBB4-qghedVFd8ukQngXNwUit8PFy_?usp=sharing) and place into a local directory of your choosing (and do not change the file names). Indicate the location of your sample files by modifying `data_dir`:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "data_dir = r'F:\\\\nigo0024\\Documents\\hs_process_demo'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "## Confirm your environment\n", "\n", "Before trying the tutorials, be sure `hs_process` and its dependencies are [properly installed](installation.html#). If you installed in a *virtual environment*, first check we are indeed using the Python instance that was installed with the virtual environment:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python install location: C:\\Users\\nigo0024\\Anaconda3\\envs\\hs_process\\python.exe\n", "Version: 0.0.4\n" ] } ], "source": [ "import sys\n", "import hs_process\n", "print('Python install location: {0}'.format(sys.executable))\n", "print('Version: {0}'.format(hs_process.__version__))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The *spec* folder that contains `python.exe` tells me that the activated Python instance is indeed in the `spec` environment, just as I intend. If you created a virtual environment, but your `python.exe` is not in the `envs\\spec` directory, you either did not properly create your virtual environment or you are not pointing to the correct Python installation in your IDE (e.g., Spyder, Jupyter notebook, etc.). \n", "\n", "***\n", "\n", "## `spec_mod.load_spyfile`\n", "Loads a `SpyFile` (Spectral Python object) for data access and/or manipulation by the `hstools` class. [[API]](api/hs_process.spec_mod.html#hs_process.spec_mod.load_spyfile)\n", "\n", "Load and initialize the `hsio` and `spec_mod` modules" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import os\n", "from hs_process import hsio\n", "from hs_process import spec_mod\n", "\n", "fname_in = os.path.join(data_dir, 'Wells_rep2_20180628_16h56m_pika_gige_7-Radiance Conversion-Georectify Airborne Datacube-Convert Radiance Cube to Reflectance from Measured Reference Spectrum.bip.hdr')\n", "io = hsio(fname_in)\n", "my_spec_mod = spec_mod(io.spyfile)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load datacube using ``spec_mod.load_spyfile``" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\tData Source: 'F:\\\\nigo0024\\Documents\\hs_process_demo\\Wells_rep2_20180628_16h56m_pika_gige_7-Radiance Conversion-Georectify Airborne Datacube-Convert Radiance Cube to Reflectance from Measured Reference Spectrum.bip'\n", "\t# Rows: 617\n", "\t# Samples: 1300\n", "\t# Bands: 240\n", "\tInterleave: BIP\n", "\tQuantization: 32 bits\n", "\tData format: float32" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_spec_mod.load_spyfile(io.spyfile)\n", "my_spec_mod.spyfile" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "## `spec_mod.spec_derivative`\n", "Calculates the numeric derivative spectra from spyfile_spec. [[API]](api/hs_process.hstools.html#hs_process.spec_mod.spec_derivative)\n", "\n", "Load and initialize `hsio`" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import os\n", "from hs_process import hsio\n", "from hs_process import spec_mod\n", "fname_hdr_spec = os.path.join(data_dir, 'Wells_rep2_20180628_16h56m_pika_gige_7_plot_611-cube-to-spec-mean.spec.hdr')\n", "io = hsio()\n", "io.read_spec(fname_hdr_spec)\n", "my_spec_mod = spec_mod(io.spyfile_spec)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the numeric derivative." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "spec_dydx, metadata_dydx = my_spec_mod.spec_derivative(io.spyfile_spec) # Be sure this is the spec spyfile and not a full array" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the numeric derivative spectra and compare against the original spectra." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'API Example: `spec_mod.spec_derivative`')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import seaborn as sns\n", "sns.set_style(\"ticks\")\n", "wl_x = np.array([float(i) for i in metadata_dydx['wavelength']])\n", "y_ref = io.spyfile_spec.open_memmap()[0,0,:]*100\n", "ax1 = sns.lineplot(x=wl_x, y=y_ref)\n", "ax2 = ax1.twinx()\n", "ax2 = sns.lineplot(x=wl_x, y=0, ax=ax2, color='gray')\n", "ax2 = sns.lineplot(x=wl_x, y=spec_dydx[0,0,:]*100, ax=ax2, color=sns.color_palette()[1])\n", "ax2.set(ylim=(-0.8, 1.5))\n", "ax1.set_xlabel('Wavelength (nm)', weight='bold')\n", "ax1.set_ylabel('Reflectance (%)', weight='bold')\n", "ax2.set_ylabel('Reflectance derivative (%)', weight='bold')\n", "ax1.set_title(r'API Example: `spec_mod.spec_derivative`', weight='bold')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save the derivative spectra to file." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "fname_spec_der = os.path.join(data_dir, 'spec_mod', 'Wells_rep2_20180628_16h56m_pika_gige_7_plot_611-spec-derivative.spec.hdr')\n", "io.write_spec(fname_spec_der, spec_dydx, df_std=None, metadata=metadata_dydx, force=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "## `spec_mod.spectral_clip`\n", "Removes/clips designated wavelength bands from the hyperspectral datacube. [[API]](api/hs_process.spec_mod.html#hs_process.spec_mod.spectral_clip)\n", "\n", "Load and initialize the `hsio` and `spec_mod` modules" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import os\n", "from hs_process import hsio\n", "from hs_process import spec_mod\n", "\n", "fname_hdr = os.path.join(data_dir, 'Wells_rep2_20180628_16h56m_pika_gige_7-Radiance Conversion-Georectify Airborne Datacube-Convert Radiance Cube to Reflectance from Measured Reference Spectrum.bip.hdr')\n", "io = hsio()\n", "io.read_cube(fname_hdr)\n", "my_spec_mod = spec_mod(io.spyfile)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using `spec_mod.spectral_clip`, clip all spectral bands below *420 nm* and above *880 nm*, as well as the bands near the oxygen absorption (i.e., *760-776 nm*) and water absorption (i.e., *813-827 nm*) regions." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "array_clip, metadata_clip = my_spec_mod.spectral_clip(wl_bands=[[0, 420], [760, 776], [813, 827], [880, 1000]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the spectra of the unclippe hyperspectral image and compare to that of the clipped image for a single pixel." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'API Example: `spec_mod.spectral_clip`')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "from ast import literal_eval\n", "\n", "spy_hs = my_spec_mod.spyfile.open_memmap() # datacube before smoothing\n", "meta_bands = list(io.tools.meta_bands.values())\n", "meta_bands_clip = sorted([float(i) for i in literal_eval(metadata_clip['wavelength'])])\n", "\n", "ax = sns.lineplot(x=meta_bands, y=spy_hs[200][800]*100, label='Before spectral clipping', linewidth=3)\n", "ax = sns.lineplot(x=meta_bands_clip, y=array_clip[200][800]*100, label='After spectral clipping', ax=ax)\n", "ax.set_xlabel('Wavelength (nm)', weight='bold')\n", "ax.set_ylabel('Reflectance (%)', weight='bold')\n", "ax.set_title(r'API Example: `spec_mod.spectral_clip`', weight='bold')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save the clipped datacube" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "\n", "Path(os.path.join(data_dir, 'spec_mod')).mkdir(parents=True, exist_ok=True)\n", "fname_hdr_clip = os.path.join(data_dir, 'spec_mod', 'Wells_rep2_20180628_16h56m_pika_gige_7-clip.bip.hdr')\n", "io.write_cube(fname_hdr_clip, array_clip, metadata_clip, force=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "## `spec_mod.spectral_mimic`\n", "Mimics the response of a multispectral sensor based on transmissivity of sensor bands across a range of wavelength values by calculating its weighted average response and interpolating the hyperspectral response. [[API]](api/hs_process.spec_mod.html#hs_process.spec_mod.spectral_mimic)\n", "\n", "Load and initialize the `hsio` and `spec_mod` modules" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "import os\n", "from hs_process import hsio\n", "from hs_process import spec_mod\n", "\n", "fname_hdr = os.path.join(data_dir, 'Wells_rep2_20180628_16h56m_pika_gige_7-Radiance Conversion-Georectify Airborne Datacube-Convert Radiance Cube to Reflectance from Measured Reference Spectrum.bip.hdr')\n", "io = hsio()\n", "io.read_cube(fname_hdr)\n", "\n", "my_spec_mod = spec_mod(io.spyfile)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use `spec_mod.spectral_mimic` to mimic the [Sentinel-2A spectral response function](https://sentinel.esa.int/web/sentinel/document-library/content/-/article/sentinel-2a-spectral-responses)." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [], "source": [ "array_s2a, metadata_s2a = my_spec_mod.spectral_mimic(sensor='sentinel-2a', center_wl='weighted')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the spectra of the hyperspectral image and compare to that of the mimicked Sentinel-2A for a single pixel." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'API Example: `spec_mod.spectral_mimic`')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "\n", "spy_hs = my_spec_mod.spyfile.open_memmap() # datacube before smoothing\n", "meta_bands = list(io.tools.meta_bands.values())\n", "meta_bands_s2a = sorted([float(i) for i in literal_eval(metadata_s2a['wavelength'])])\n", "\n", "ax = sns.lineplot(x=meta_bands, y=spy_hs[200][800]*100, label='Hyperspectral (Pika II)', linewidth=3)\n", "ax = sns.lineplot(x=meta_bands_s2a, y=array_s2a[200][800]*100, label='Sentinel-2A \"mimic\"', marker='o', ms=6, ax=ax)\n", "ax.set_xlabel('Wavelength (nm)', weight='bold')\n", "ax.set_ylabel('Reflectance (%)', weight='bold')\n", "ax.set_title(r'API Example: `spec_mod.spectral_mimic`', weight='bold')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use `spec_mod.spectral_mimic` to mimic the [Sentera 6x spectral configuration](https://sentera.com/6x/) and compare to both hyperspectral and Sentinel-2A." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'API Example: `spec_mod.spectral_mimic`')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "array_6x, metadata_6x = my_spec_mod.spectral_mimic(sensor='sentera_6x', center_wl='peak')\n", "meta_bands_6x = sorted([float(i) for i in literal_eval(metadata_6x['wavelength'])])\n", "\n", "ax = sns.lineplot(x=meta_bands, y=spy_hs[200][800]*100, label='Hyperspectral (Pika II)', linewidth=3)\n", "ax = sns.lineplot(x=meta_bands_s2a, y=array_s2a[200][800]*100, label='Sentinel-2A \"mimic\"', marker='o', ms=6, ax=ax)\n", "ax = sns.lineplot(x=meta_bands_6x, y=array_6x[200][800]*100, label='Sentera 6X \"mimic\"', marker='o', ms=8, ax=ax)\n", "ax.set_xlabel('Wavelength (nm)', weight='bold')\n", "ax.set_ylabel('Reflectance (%)', weight='bold')\n", "ax.set_title(r'API Example: `spec_mod.spectral_mimic`', weight='bold')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And finally, mimic the [Micasense RedEdge-MX](https://micasense.com/rededge-mx/) and compare to hyperspectral, Sentinel-2A, and Sentera 6X." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'API Example: `spec_mod.spectral_mimic`')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "array_re3, metadata_re3 = my_spec_mod.spectral_mimic(sensor='micasense_rededge_3', center_wl='peak')\n", "meta_bands_re3 = sorted([float(i) for i in literal_eval(metadata_re3['wavelength'])])\n", "\n", "ax = sns.lineplot(x=meta_bands, y=spy_hs[200][800]*100, label='Hyperspectral (Pika II)', linewidth=3)\n", "ax = sns.lineplot(x=meta_bands_s2a, y=array_s2a[200][800]*100, label='Sentinel-2A \"mimic\"', marker='o', ms=6, ax=ax)\n", "ax = sns.lineplot(x=meta_bands_6x, y=array_6x[200][800]*100, label='Sentera 6X \"mimic\"', marker='o', ms=8, ax=ax)\n", "ax = sns.lineplot(x=meta_bands_re3, y=array_re3[200][800]*100, label='Micasense RedEdge 3 \"mimic\"', marker='o', ms=8, ax=ax)\n", "ax.set_xlabel('Wavelength (nm)', weight='bold')\n", "ax.set_ylabel('Reflectance (%)', weight='bold')\n", "ax.set_title(r'API Example: `spec_mod.spectral_mimic`', weight='bold')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save the mimicked datacubes using `hsio.write_cube`" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "fname_hdr_mimic_s2a = os.path.join(data_dir, 'spec_mod', 'Wells_rep2_20180628_16h56m_pika_gige_7-mimic-s2a.bip.hdr')\n", "fname_hdr_mimic_6x = os.path.join(data_dir, 'spec_mod', 'Wells_rep2_20180628_16h56m_pika_gige_7-mimic-6x.bip.hdr')\n", "fname_hdr_mimic_re3 = os.path.join(data_dir, 'spec_mod', 'Wells_rep2_20180628_16h56m_pika_gige_7-mimic-re3.bip.hdr')\n", "io.write_cube(fname_hdr_mimic_s2a, array_s2a, metadata_s2a, force=True)\n", "io.write_cube(fname_hdr_mimic_6x, array_6x, metadata_6x, force=True)\n", "io.write_cube(fname_hdr_mimic_re3, array_re3, metadata_re3, force=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "## `spec_mod.spectral_resample`\n", "Performs pixel-wise resampling of spectral bands via binning (calculates the mean across all bands within each `bandwidth` region for each image pixel). [[API]](api/hs_process.spec_mod.html#hs_process.spec_mod.spectral_resample)\n", "\n", "Load and initialize the `hsio` and `spec_mod` modules" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "import os\n", "from hs_process import hsio\n", "from hs_process import spec_mod\n", "\n", "fname_hdr = os.path.join(data_dir, 'Wells_rep2_20180628_16h56m_pika_gige_7-Radiance Conversion-Georectify Airborne Datacube-Convert Radiance Cube to Reflectance from Measured Reference Spectrum.bip.hdr')\n", "io = hsio()\n", "io.read_cube(fname_hdr)\n", "\n", "my_spec_mod = spec_mod(io.spyfile)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use `spec_mod.spectral_resample` to \"bin\" the datacube to bands with *20 nm* bandwidths." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "array_bin, metadata_bin = my_spec_mod.spectral_resample(bandwidth=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the spectra for both the hyperspectral image and that of the binned image bands (for a single \"vegetation\" pixel)." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'API Example: `spec_mod.spectral_resample`')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Ulkj2giAIlYAYjSMIglAJiGQvCIJQCbxWHbSurq5lHYIgCEK5lN9gg9cq2YMYHSEIglBYBSmUS6QZJy0tjdGjR9OsWTMCAgK4ffs2kZGRWvO0F3V+bUEQBKHwSqSy37VrFyEhIWzdupXRo0czb948unbtioWFhWZ2v8JMIyAIgiC8mhKp7N977z127NiBvb09urq6KBQKLl68SFZWFv7+/owcObLQ82ULgiAIRVdio3EsLS0JCAjgxo0bfPjhh9SsWZPBgwezaNEiEhMTi3XOEkEQBOHlSrSDdv78+fzvf/9jxIgRHDhwgKysLAwNDWndurXW6juCIAhCySqRyn7NmjUMGjQIuVyOnp4eaWlpDBo0iA8++IC7d+9y6tQpGjVqVBKHFgRBEHJRIsm+W7duAPTs2ZNz584xb948pkyZQlZWFt27d8fCwoLJkyeXxKEFQRCK1S9no/hsYyiXopPyv/PrTHqN1K1bt6xDeME///wj1a1bV7p+/bpmm7e3t/Tjjz+WYVRFk5OTI23dulW6f/9+gR+zbds2qW7dulJGRsYLvxsxYoS0b98+zX3q1q0r1a9fX/L29pY2bdokSZIkjRs3TgoICJAkSZI6dOggfffdd4WOu27dutKGDRu0YnnrrbekkJCQQu9LEAojOiFNchr3q+Q07lfJe87hYttvjlIl7QyNkn49Hy2pVKpX3l9BcqeYLqESOXv2LBMnTiQlJeWV93X69GlCQ0O11k79+++/NWuhfv3116SmpjJ16tQX1iEtDgEBAQVe21cQiupcRILm9t2E9GLb76//RfP5pnOM2BDC3gv5r+xWHF67K2hfZvmxm/xw6CqpWUVbdi83xgodRnWqy7B2LoV+7KJFiwgODuavv/5CLpfTpk0bhgwZQkZGBnv27MHFxYUzZ87QuXNnZs2aRWpqKkFBQZw4cQJnZ2dmzZpFgwYN8PHxwdbWlmvXrvH1119z4MABjh07ho6ODr1792bixIksXLiwUPv8559/mDlzJhERETRo0IB58+YRFBQEQNeuXTl8+DAdO3akefPmhIWFsXbtWrZv387u3btRKpW0bduWBQsW5Hnu69evp02bNlorS5mamqJQKDTT3ero6DBt2jRu3rzJli1bNPc7cOAAY8aMYc6cObRt25aRI0cSGhqKoaEhH3zwAcOHD8/3uW/fvj0zZ84kPDxcTLMhlBhdHe16WKWSkMtzX1v4CaVKYv0/d0jJzGFYWxcUui/W1D8evqa5/emGULo3tiuegF+iXFX2y4/fLNZED5CapWT58Zv53i8gIABPT088PT2JjY3VbEtJSeGvv/7ixIkTJCUlaVavj4iIoH///poVc/bv38/SpUsJDw9n9+7dtGvXTpN8Qb3m57Zt23BxcWH//v1MmzaNBQsWYG5uTmpqaqH2KUmS5grmvXv3Ur16dc6cOcPUqVMB2Lp1K3Z26jeXubk527Ztw8nJCUNDQ1atWsWkSZM4ePAgt2/fzvP5CAkJwcHBQWtb69atady4Mdu3b2fs2LEYGBi88LjQ0FDGjh3L5MmTeeutt7h//z5ubm7s27ePjh07sm7dunxfCwA7Ozt0dHQICQkp0P0FoSgysrXzTWJ6/st9Hr78gKm7w/juQDgLj1zL9T7ZSu2Z5ZWqkp9pvlxV9sPaupRIZT+sbf5V/eLFizXJ7Z133gGgatWqtGvXjr1796Knp0e7du00C3SbmppqFl22srLi5s2bhIeHc+/ePXr37o1SqSQtLU2TyJs0aYKzszOSJDFq1Ch++uknYmJieOONNzRL6hV0n/fu3SM2Npa2bdvi4ODA/PnzATh16pT6nI2NNYtDN2/eHBcXFzIyMkhLS+P777/H1NQUIM8FPwASEhKwsbHR2rZlyxaMjIywtLREX18/18f9+++/GBkZkZSk7uwyMTHhxo0bTJkyhbS0NDIzM/N9LUD9rcHKyorExMQC3V8QiiIpQzu5x6ZmYWn88qv/94c9XeRl4ZHrjOxYB73nviEYKXS0fr4UnUQje/NXjPblyleyb+dSpOaW4mBra4u9vT2AVtNFQEAAY8eORUdHhzlz5mi2JyUlceTIEUxNTYmLi6NWrVokJydz+/Ztpk+fTkhICAkJCZqk+OT/y5cvExYWxsyZM5Ekif79+9O1a9dC7dPGxoYqVapw9OhR6tWrx9SpU/Hw8KBly5YAPHjwQPPB9eS4f/31F5s2bWLLli2EhoZy5MgRpJesa2NpaUlycrLWtho1auSZ5J945513aNy4Md988w09evQgODiYa9eusXbtWn744QeuXcu9EspNamoqVapUKfD9BaGwktJztH6OTcmktq3JSx+jr6udyA9fjqGLWzUAspUqrj1I4W68dvv/3zcelXiyL1fNOK+j9u3bY2pqip6eHu3bt9dsNzMzY8uWLXz00Uf07t0bPz8/Pv74Y1xdXfn000/ZsmULbm5uLyzUXLt2bUxNTfnwww/58MMPefPNN2ndunWh9qmnp8e8efMICQmha9eupKen4+/vT+3atXF2dubzzz8nMjJS67geHh40bNiQwMBAjh8/jp6e3gv3eZanpyc3btwo9PP15MpqJycn5s6di6+vLzk5OfTs2ZOEhARSU1MLNJVGVFQUaWlpNG3atNAxCBVDjlLFwUsP+C8qQbMtMS2bL7eeZ9LOC2Tl5L7IfGE8X9nHpeb9bVcTQ7r2fX45GwkqFTmJ94m9e4P0+Ghq2xpp3WfT6QiSM/JvInoVr9WyhK6uruVqiuOsrCxiY2N5++236dmzJ+PGjQPUq9tv2rSJv//+u9iOVRL7fBV///03QUFB/Pnnn5omodK0adMmdu3axcaNG0v92MLrYe2J20zdHYZMBr+PakedqqYsOXqdb/erc8iXfq6M6FD7lY4xcccFfj4Vofn5615uBLZ0eulj+iw9yelbTwsWv/o2/ORnhGzTe5AQARaOxL61lqH7UwmNfDp2/51m9nwf4F6kOAuSO0Vl/wrOnj2Lr68vdnZ2DBs2rKzDKVWtW7embt26HD16tEyO/8svv/Dll1+WybGF18PU3WEASBLM3X8FQJPoAb47EP7K1X1ShnYzzrJjN/jjSkyeTZw5ShWPUrT7nT5uYf400QMkRFBlz/tMaq/d53Xw0oOXNp2+KlHZC4JQLjkH7dXcbupowfZPWtN81iEeJj9NtnIZDGzlzLQeDYt0jPdXnebPqw9f2P52U3tm+7tptc9/d+AKy4/dIkup/QFz8qOaVF/b6oV9RL5/mnmn0mhgZ8aav28z0NuZ4W/UKlKcorIXBKFSeDKU0cpIe6SMSoI1J24Tk5yhtf1+YgYpmdpVe26eb7N/YltIFKv+uq35+WFyJov/uPFCou8m/wfbjFtg4ai9AwtHYtIkqlsY8mG7WpwY37HIib6gRLIXBKHcy36cZGPz6EANi0qAlAeQEEl8TBQjfj5Ds68P8tnGUJYdu8GDpIxcH5f0knH1R6480Nw+dPmB1u/0yGGKbjCLFT9y7fgW4t9a/TThP26zn3n0IXbmL16LUlLK1dBLQRCE3GQrVShVEnGpL16n4eFgRnOj+7AiEBIisLRwZNlbaxm6H/acj2bP+Wj2XbzPjk9aax6TmpGFcU48696x416KiplHH2p1pgJcuZeMJEnIZDL2X7yPh4MZk9rbUN1Iwiz9LiYnHrHqdhfm3PTHLTudSb5bcTDTJTIph5n71fvr56XzfLglRiR7QRDKnfTnLqxMycwhNjWT3C5EndTeBpMdAS90kH7XeQ1frz9ONjooo3TIvq1DulLOpvNx9HG3hj2DsUuIwM7CkVVvrebL/Un8FxmP+hAyVJky7kVHYmqoQJ6VyKouVljuCdCMuEnusYI9+7LIjkwiNDKJt9cl8aWfK6duqX+Wy6ClS+ldJ1LxmnFUKs3XNVIeqH9+BZIkMXfuXHx8fGjSpAl9+vTh4sWLxRQsJCcnF3qisKCgIPr06VNsMZSEhQsXaq4PKIjLly/To0cPANatW0ebNm3w9PRk+vTpSJKESqViypQpNGvWDH9/f27dulVssT548AAfHx/NVb3C6y/hubHssSlZxCRpV/WuVdVXgtsZqZ4mes0OIqhtlM5axVw2KGazWfE1emv8MFvny4f1s7HYM1jrw8Fyz2BW+Kg4bTCCMwYjOGPwCWcNPsZueSNMf3Rl9RsZWO4ZpPUY091DXxhxU6+aKV/3bEg/L0f+r08THKy0x9uXpIpV2atUEHMJnhnPSt+NYNsAijgW/K+//mLVqlUsX74cV1dXJkyYwIQJE9i9e3exhLx27Vo2btzI4MGDi2V/5dXs2bPp06cP9+7dY9asWcyaNQtra2s+/PBD2rVrR1ZWFjt27GDjxo3Mnz+fuXPn8tNPPxXLsatWrUrDhg1Zvnw5Y8aMKZZ9CiUrPlW7LT1HJXH1wdMrulvXsuJnXxXhO5ZQPWO4Ohc8m/AtHLmeYUaw9Xyu3I1DV6bE19WKv8LvMdfACetcPhziDBz5LnsItiZ6xD4eXtmwmgkJaVn0NXDAMpfH2BppT5pmZ2GIUxVjZvcu/cWbyleyP7cRQtfn/fs3voLdn2p9urLpPeixCP78NvfHeAyAJu/lucsn88SsXLmSN998k2nTpmkmEQsMDEShUPDw4UMePHjAyJEj6d+/P+Hh4YwfP55bt27h5eXFnDlzsLS0JDg4mOXLl5OSkoKvry/Dhw9n4cKFAPj4+DBnzhwGDhxIq1atuH79Ojt37mT06NEFmhEyKiqKjh074uXlxcWLF9m/fz/r1q1j8+bNGBsb88UXX9CjRw+2b9/O/PnzSUhIoH79+syfP58aNWowbdq0F2a8XLJkCb/++is1a9bk33//JSAggLt373LixAl8fX2ZPXs248eP5+bNm+jq6nLt2jX69evHF198oRVbXs/HE1evXuX06dPMnj0ba2tr9u/fT40aNYiIUL+OSqWSkJAQnJ2dcXNzw9vbm2XLlr3wHPj4+NC0aVMuXbpEbGwsY8eOJSAgAB8fH9zc3AgLC0OpVDJs2DCCg4NJTU1l2rRpdOrUiTfeeIPvv/+eL774okwuEhMKTpIkIuLSXtj+86kIdFDiJz/DuNjfYc0V6hpa8UfIZZq8teZp5f24g3T9RRnZdp6cjooECS7c1CVZZcedNAXWuXw43ErT55RlDwa3dmbBLvUYf+6q/2uRZkCzXB4Tk6bdrmRnbljMz0bBVax3tcI4169rKIyLvMsmTZrw9ddfc//+faZOnYqvry8//vij5vfXr1/nhx9+YNCgQcyaNYsHDx4wefJk7O3t2bdvH5mZmSxevJirV68ye/ZsxowZQ3BwMLGxsaSnpzNs2DCqVKmi9U3B29ubjRs38ujRo0LPCFmnTh127NjBlStXWL58Of/73/+YOnUqEydOJCYmhuDgYJo1a8Yvv/xCu3btePToEUlJSXnOeHnnzh3ef/99AgICWLVqFe3bt+fzzz9n+/bt3L2rfqdfu3aNCRMmMHHiRH766SfOnz+vFVNuz8ezQkJC0NXVxc7ODj09PZydnYmPj+ezzz7D2dmZdu3akZycrJlF08DA4IV5eZ64ceMGS5YsoWHDhqxatUqzPTo6mtWrV6Orq8vy5cv56aefcHBwYO3atQA4ODgQHx9frM1DQvHLylHxzk8nWfrnNbYF1uL4R7XZFliLlg4GNIzaxB+K0SxR/IiplALd/g/ZF2F4vzuGPxJsOeu7lcj3T3PWdytD96ciIcOpytPckPx4KObMow+JfWut1uiZxJ7ByExs+eVjbxrZW7wQ18yjD0nqFaz1mLge6hE3TxgrdDAzLLv6unxV9k3ee2kVTsqDXL+uYeEAg/fm/biXuHLlClZWVhw4cIDo6GgWL17MTz/9xIABAwBwc3PDxcWFzp0788MPPxAREUF4eDhXrlzhxIkTZGZmkpiYSNOmTZEkiY4dO2JqasrKlSsBOHLkCDKZDBOTp5MrPZmtMioqqtAzQrZq1QonJyd+//13JEnio48+AtRTO4SFhTFx4kQWLVpE3759qVWrFp6enigUijxnvLS0tKRVq1bcu6deYKFLly5cuHABQBOPk5MTbm5u1KlTh3Hjxr2QMHN7Pp6VkJCApaWlZoK5Bw8eMHDgQDIyMli7di0KhQITExMyMtTD4zIyMjRxPq9ly5Y4OztTv359rSmaW7RogaOjI9WrV8fS0pKaNWvi5OTEzZvq6a2fzFYqZtF8ve0+H41KpWRFF2OqPO4MdbBwZEOPRciPhHE2woJZWQPw7NCPYc3rAqAP+Ddz0JpKAaCXhwNVzV6cuC80MonlV6zw9d2KrZGMmDSJX//LZmpP9ToNhno6mBvqaU13HBqZRFiOK4Z+26hiIBGTJnE4XEVo5NOFSewsDJHJXj4XfkkqX8k+P0Y26jb659vsjWzyf2weLl26xKRJk/jxxx9xd3fHxMQEAwMDDA3VX8fOnTvH9evXOXLkCLq6ujg6OuLi4kLVqlUZMmQIe/bswdXVlTp16iCTyThw4ABubm588cUXBAUFIZfLycrK4sGDp+N0FQr1hSFFmRHyyWNdXFzQ1dVl+vTpZGVlceLECerWrcvSpUtp06YN33zzDZ9++inBwcGkpKTkOePl800az874+cTNmzc5f/68ZuI0FxcXrUnUcns+nmVlZaVZPSsnJ4ePPvqIhIQE1q5di5WVFVlZWbi7u7Nx40YuX77MiRMn8PDwyPX8n8T3/B/Vs+eR2zk8Ob61tXWu+xWKV0xSBkpJonohmzX2XbjHpPY2mkQPQEIE8t2fcqXzOt6+rv622cXsxY7P1rWsgafJ3sXGGGuTF5O9qYEuvZs54vfDMc22ro2qaW4bKnRYFtiMA2EP6Na4GrEpWejpymlV25aQCD3e33KeOlVN6O/lxJI/nxY+dhZl14QDFS3Zy+XqztihhyAnC3QV6kT/Cm2wvXv35tatW0ybNo2EhAQcHByYN2+ephKvVq0aX3zxBTExMUycOJGqVasyZ84cJk2axNChQ3F0dKRPnz7UqVOH8ePH88MPP5Camkrnzp1p1aoVxsbGBAcH8/777zN9+nStY/v6+rJ//3569uyJh4dHgWeEBHX79ZOmpczMTPz9/bGzs6NFixbMnz+fBQsWULNmTYYNG4ajo6NmxsvmzZvnO+Pl86pVq8acOXO4fv06w4cPp3Hjxvz555+a3+f2fDyrWbNmpKenc/fuXS5fvszly5cBNAvBTJs2jT59+nD69GkGDBiAk5MT8+bNK3B8BXHt2jVsbGxwdHTM/87CK7kUnUTPxX+Ro5L4eagX3rXy/4C9HpPMxE0nqfVgP43a98m1udbY8GkyrWL8YhJ3q6E9hXAdW9Ncm1Xc7MxxrWaKu70556PU3/TMDPS07uPlUgWvXIZNNnW05MjY9gCERWt/SyzrZF8ic+OkpaUxadIk/vzzT1xcXPjuu++QJIkxY8Zw584dunXrxrRp016oGsvb3DiBgYFYW1trFgepjIKCgl5YdrAo+vbtS69evejbt28xRVY4o0ePxsHB4YXOZaH4fb4plF3nogGwNNIjdErnvO8sSRAdwqH139Iq7Q+MZZmk9dmK0e9jXmiuPeu7lbfXqafdPv5Vh1yHNe4Mvcu8g+H0alKDMZ3V3zBbzDpEzDPz6XzUzoXxXeuz8XQE47ermywndavP0AIscvSs6IR0vL85ovl5jG9dPutYp1D7KKiC5M4Sqex37dpFSEgIW7duZfTo0cybN4/s7GwsLS2ZOXMmffr0oXXr1vj5+ZXE4YVy6KuvvmLWrFllkuzj4uI4d+4c06ZNK/VjV0YX7j6teOPTsknPzMYwO07r2/it6Pvs3/gjXTIPUDPnJt6SPruVrdik9EH2hyEr3lpLlT3va5prE3sFk5ZlRcd6ybSubZ3n+PVeHjXo5VFDa1tTR0vN6lIGenLeclePtuvb3IGUjBxSMnN4r0Xhv/FZGGl/G6heESt7gPj4eIyNjenXrx9OTk4cP36c4cOH88EHH9C9e3fatGmjtQYrlL/KXhCEwmv9zRHuJqhXavJwMGNTT3P0f+mvSdyS/3Kyfp+KftQ/XFQ5s1Hpwy6lNymoE/j7rZz4LyqeSe1tsDWSYWFqgqlV9SI318alZrHxdARWxgo61rPF1qx45quRJIma43/T/LxqkCc+9aoWy76fV2aVPahHcfTs2ZOIiAhmzZrFb7/9VqChc4IgVAySJHE+KpHatiaY6KtTTXJGtibRg3oqA/1ftDtbZduHkdR5MYPXneeipN10svA9DzydLWk15whvr1Nf8bx/VFvqvUK/nJWx4pUXOcnN84ME7C1L72rZ3JToOPv58+fTqVMnRowYgYmJiWao3suGzgmCUL5Fxadx8W4is/Zeptfiv3lzwTEyc9Rz2VyLSdG6bw0jZa6drRmGVV9I9GYGunSqX5Vqz1XeuXXGvi6m92iIgZ6ct5vaU7dq2ea8Eqns16xZw9GjR5k2bRp6enqkpaXRuHFjTpw4QYsWLbhz5w6fffZZSRxaEIQydONhCm/+cFxrXvfIuHT+uBJDF7fqXL2v/kavTxaf6u6kakZArtfGxKRJDG7tzNjOruwIvUvInXje8bTHUKEeNvvt24357vdw3m5qj43p65vs3/d2pp+XI3o6ZX/9aokk+27dunH06FF69uxJjRo1mDdvHtWqVWPs2LEEBgbSu3dvfH19S+LQgiCUoWm7w15YwAMgOkF9QVz4g2S8ZJeZrbeCWvJ7SPfroerzM/ItT9vs43us5VacEVO6OyCTyRjQ0okBz6372qe5A32aO5TKOb2q1yHRg1iWUBCEYlR34r5ck72/Rw0Cm1gQtyuIjmn7iFDZENN+Lp4+b6snMEx7WGzXxlRGZdpBKwhC5aJSSbkmepBIP7+dGpfW0phEliq78UPO2+xr3EX9a7kcTEpmlIrwlEj2giAUixsP1Z2vT1ZssjWSEZ+WTcZfS2hx72cuqpwZnP0lYVJNDPTkpTqXuyCSvSAIxSQkIh4PB7MXJimTeixize+OfH27AUrUHax1bE3RkZfdpGCVkUj2giC8kkOXHnD6dhyX7yXlOkmZbPenNPLdivL2Dc1jynoYYmUkkr0gCEV2NyGd4evPkvN48dfZPha5jpt/fsUm12omCKVLdHkLglBkf197pEn0gHplJovn5pHJZcUmUdmXPpHsBUEosvAH2tOezDz6kJweS7RWbIp9S3vFJgDXaiLZlzbRjCMIQpFdvKs9Z3tM5A10j6wjqvMyJEMrYtIkopKM+b5PXQauPM3dhHRqWhu/MOWBUPJEshcEoUhUKomw6CStbR11QiDqXwLXhXFLqg6oJyqrZWPC6sHN2XfhPt0aVyvT5fkqK5HsBUEoktuxqaQ8XqT7iR7650gxqsmtjOqabU8mKqtb1VS01Zch0WYvCEKRPLsISVNHC5YH1KYZYSQ5a688ZWWsKO3QhFyIZC8IQpH8eztec7t1bWt8FReQqXKQ6r6pdT9x8dTrQSR7QRCK5Pi1pyNsWtWqAuG/gZE1Ndza0a1RdeQy+KqLaxlGKDxLtNkLglBoEbFp3I5NA8BQT4dm9saw5RDUfwvkOizu35TUzByM9UWKeV2Iyl4QhEI7fv1pVe/lYoV+1D+QmQj1umq2i0T/ehHJXhCEQvvjytNk37aODYTvA10DcGlfdkEJLyWSvSAIhXI/MYM/wmM0P3eoa61ur3fpAArjMoxMeBmR7AVBKJSNpyNQPp4Px6umFS6q25AYqdWEI7x+RLIXBKHAJEli85lIzc+BrZzgym+ADOp2KbvAhHyJZC8IQoE9SsnifpJ68XBjhQ6dG1SD8L1g3xxMbMs4OuFlRLIXBKHAIuPTNLcdqxijSL0H986D65sveZTwOij2ZJ+SksLHH3+Mh4cHnTp14ujRo0RGRuLq6qr5N2XKlOI+rCAIpSAqPl1z28HSUN0xC1CvWxlFJBRUsQ+EDQ4O5sKFC+zYsYOlS5cSFBTE1KlTsbCw4ODBgwAoFGKuDEEojyLjnlb2DlZG6iGXVi5gXbcMoxIKotgr+8DAQDZv3oyzszNVqlRBqVRy8eJFsrKy8Pf3Z+TIkcTFxRX3YQVBKAVRzzTjuJgq4dYxcO0KYsri116xJ3tTU1Nq1KjB/v37Wb16NQMHDqRmzZoMHjyYRYsWkZiYyMyZM4v7sIIglIJnm3EaZZwFVbY62QuvvRK5nnnPnj2MGzeON998k08++QSArKwsDA0Nad26Nfv37y+JwwqCUMKebcZxfnQUDK3AwavsAhIKrNgr+3PnzhEUFESHDh2YPHkyqampDBo0iA8++IC7d+9y6tQpGjVqVNyHFQShhKlUEncT1JW9LjmYRh6Bun6gI+bAKQ+KPdkvW7aMnJwcDh06hJeXF82bN2fKlClkZWXRvXt3LCwsmDx5cnEfVhCEEvYgOYNspfrKWR+jm8gyEkUTTjlS7B/JS5YsyXX7tm3bivtQgiCUomfb67srQiFbH2r5lGFEQmGIi6oEQSiQ+4kZj29JtMo5DS5vgL5JmcYkFJxI9oIgFMjD5EwAXGWR2OTcE0045YxI9oIgFMjDFHWy7yQPUW8QE5+VKyLZC4JQIE8qe1+df4k1bwRm1cs4IqEwRLIXBKFAHiZnYks8TeQ3iXfoVNbhCIWU72iclJQUzp07R2JiIlWqVMHNzQ0TE9EpIwiVxW8X7vH1r5e4l5hBPx11E46yjpjlsrzJM9nfvXuX//u//+PAgQPk5ORotisUCrp06cLIkSOxt7cvlSAFQSg7n/wcorndSX6WCJUNpo7iwsjyJs9mnMDAQGxtbVmxYgWHDx/mwoULnDx5kuXLl1O1alU++OCD0oxTEIQyZkQGreVhHFR5UsVUv6zDEQopz8p+37596Otrv6CWlpZ4eXnh5eXFyJEjSzw4QRDKlurxWrMAbeX/oS/L5qRuC4bo6pRhVEJR5FnZP5vod+/ejbe3N82bN2fZsmUA6OnplXx0giCUqaSMbM3tzjpnSZCMiTR1L8OIhKLKM9lHR0drbm/cuJG1a9cyZ84cfvrpp1IJTBCEsheXmgWADkp85KH8oWqClalxGUclFEWezTiDBw/Gx8eH4cOH06RJEwYOHEhOTg4tWrQozfgEQShD8WnZeDiY8U0HEyyNNuOeZsDFGwZlHZZQBHkm+z179rB+/Xreeecd3n33XVasWIFcLqdevXqlGZ8gCGVIpVKyoosxVfYMhIQIXCwcGdkrGFQqkIvLdMqTPF8thUJBr169+OWXX0hOTmbs2LGEh4cjE8uPCUKl4WKYTpU970NChHpDQgTmOwdC2sOyDUwotDyT/ciRI2ndujVDhgzhiy++YM2aNYSEhODv71+a8QmCUIYUZD9N9E8kREBOVtkEJBRZnsn++PHjfP/991y+fJm4uDiqVq3KjBkzmDdvXmnGJwhCGUrKloOFo/ZGC0fQVZRNQEKR5Zns/f39mThxIu3atcPKykqzvWbNmqUSmCAIZW/39WzSeix7mvAtHKHvRjCyKdvAhELLs4P2448/funygXFxcVofAoIgVDz/3U3i4tV1NOr8PZK1K0ZGhupELzpny508k/17771HgwYN6NChA/b29tjY2JCcnMz169c5fvw458+f59ChQ6UZqyAIpSw+LQu76AMc3HgTq4HraWNrXdYhCUX00qGXK1as4LvvviM2NhaZTIYkSVhbW/P2228zbdq0UgxTEISykJMSh73sEetVnXjLWFw1X57lmewNDAz49NNPGTFiBLdv3yYhIQErKyucnJxKMz5BEMqQbdpVAMIkZwYaiU7Z8izfhjeZTEbNmjXx8PAoUKJPSUnh448/xsPDg06dOnH06FFu3bqFv78/zZo1Y8qUKahUqmIJXhCEkiNJEvaZNwC4rHLCUiT7cq3Ye1mCg4O5cOECO3bsoHnz5gQFBTF37lwsLS1Zt24d27dv5+DBg8V9WEEQillalhJXbvNAsiBZ1xJDhZjpsjwrcLJPT08vUEUeGBjI5s2bcXZ2pkqVKiiVSkJCQmjdujUNGjTA2dmZ0NDQVwpaEISSFxWfTgPZbS6pnKhiLKr68i7fZH/lyhV69uxJ06ZN+eabb9i1a9dL729qakqNGjXYv38/q1evZuDAgSQnJ2NgoJ48ycDAgOTk5OKJXhCEEvPv9Whqy6K5JDnRyN68rMMRXlG+yX7ChAm0b98eAwMDzM3NmT9/fr473bNnD6NHj6ZLly588sknmJiYkJmpXpk+IyMDU1PTV49cEIQSFXk1FD2ZkksqZ1q6VCnrcIRXlG+yv3PnDgEBAejp6dG0adN8q/Jz584RFBREhw4dmDx5MqmpqTRu3JgTJ04QFhbGnTt38PDwKLYTEASh+EmSRFbUeQAuSU541RTJvrzLN9m3bNmSDz74gLS0NKZNm4a3t/dL779s2TJycnI4dOgQXl5eNG/enC+//JK4uDgCAwPp3bs3vr6+xXYCgiAUvxsPU3HIukGqpE+8vj31qolv4+VdnuPsn5g7dy5Lly4lPDwcFxcXPv7445fef8mSJblu37ZtW9EiFASh1IXciaeB/A6XJSc8XayRy8XU5uVdvpW9JEm0bNmSZcuWUadOHXR0xPArQajorj1IpIHsDpdUTjSqITpnK4J8k/3YsWNZt24dAJs2beLLL78s8aAEQShbidHXMZWlc0lyoratSVmHIxSDfJP9qVOnCAoKAuD777/nn3/+KfGgXkV8ahYfrfuXj9efJTE9u6zDEYRySfEoDIBLKpHsK4p82+xtbGz4/fffeeONNzh06BBVqrzevfKzfrvMgbAHAFib6PN1L7cyjkgQypf0LCVV066SoyPnGg44WxuVdUhCMci3sv/iiy9YvHgxPXv2ZNmyZYwePbo04iqyX85GaW5v/jeyDCMRhPLpxsMU6svucEOyo1oVS/R1RT9dRZBvZd+1a1e8vb2JiIjA0dERCwuLUgireIgBBIJQeDceptBcfodTqvrUshFNOBVFvsn+2LFjrF+/noyMDEA9C+batWtLPLDiIJeJbC8IhXX3bhQ9ZXGivb6CyTfZT5w4EQsLC2rXro2snCVPkewFofAyo/4D1FfO9hbJvsLIN9nr6emxZMkSHBwcSiOeYiVyvSAUnv6ji4B6Dvvx4srZCiPfZN+mTRtmzpxJ+/btkcvlyGQy+vTpUxqxvTJR2QtC4SRnZFM94xr35FYkyc2pU1VU9hVFvsl+y5YtAPz5558A5SrZi1wvCIVz5X4yDWR3CHvcXi9G4lQc+Sb7K1eulEYcJUJU9oJQOOGRMfSVRXNA5UmD6mZlHY5QjAqU7H/77TcyMzORJImoqKg8Jzt73Yihl4JQOHG3/0NXpuKSyhlPO5HsK5J8k/2oUaNITk5GJpOhUqmoVq1aacRVLMrb6CFBKGuy+xcA9UicgaKyr1DyvYI2KiqKjRs30qRJE1avXk1qamppxFUsRGUvCIVjlRJOsmRIpGSDqxiJU6HkW9nb2tpy8uRJ4uPj2blzJzExMaURV7EQbfaCUHApmTm4Sre4jCMKXV2sxCLjFUq+lf3UqVPR09OjX79+bNu2jX79+pVGXMVCJHtBKLiYxDTqP57D3tZMXzSDVjD5Vvbx8fH06NEDXV1dzcyX5YV4rwpCwSXevYqLLJMwyRlbU4OyDkcoZnkm+9OnT3Pr1i2mTZtGUlIS+vr6xMfHs2zZMnr16lWKIRadSPaCUHA50Y8XGFc54WiqX8bRCMXtpZX91KlTAZg9ezYAcrmcjh07lnxUxUQ04whCwek9DCNb0uG6VANPkewrnDyTfYsWLbhy5Qrjx49n2rRp6OuXvxdfJHtBKDiT+Mtcl+zIRIGtmWjGqWjy7aB96623WLhwIQDDhw/n5MmTBd55cHAwrq6uZGZmEhkZiaurq+bflClTih51AYlcLwgFZ5MaziXJSX1bVPYVTr4dtNOnT8fPzw8AZ2dnZsyYwb59+176mIyMDBYsWMCaNWs02y5evIiFhQUHDx4EQKEo+WFdorIXhAJKicE8J5ZLKnWytxXJvsLJt7KPiYmhb9++AAQGBnL//v18d/rw4UNu3brFJ598otl28eJFsrKy8Pf3Z+TIkcTFxb1C2AUjLqoShALSXDnrDEBV0YxT4eSb7N3d3fn888/55ptv+Oyzz3B3d893pw4ODvz000/UqFFDs61mzZoMHjyYRYsWkZiYyMyZM18t8gIQlb0gFNCTZC8q+wor32Q/Z84cqlevzvHjx3F0dGTOnDlFOlDv3r0ZNmwY9erVo3Xr1ly9erVI+ykMcVGIIBSM8t5/REnWJGKCrlyGpZG4eraiybfNvnr16kyZMoWwsDBcXV2LPBHaoEGDyMnJ4fvvv+fUqVM0atSoSPspDNGMIwgFo4r+j8uqp52zcvHHU+HkW9nv2bMHHx8fhg8fzvz584tc2U+ZMoWsrCy6d++OhYUFkydPLtJ+CkMU9oJQAFlp6Cbc0IzEEcMuK6Z8k/38+fNZuHAhJiYmtGrViu3btxd45/7+/oSHh6Ovr0+dOnXYtm0boaGhLF++HCsrq1cKvCBEm70gFEDMJWSSStNe72JtXMYBCSUh32SfmpqKmZkZMpkMhUJRKkMmi0qpkso6BEEof+7/B0DY48q+tq1Yd7YiyjfZ9+nTh379+pGUlMRXX32Fv79/acRVJDkqldbPIvkLQgHcv0CqzJgoyQaAWjaisq+I8u2gHTNmDC1atCA8PBwXFxd8fHxKI64iyVFqJ3eR7AWhAO5fIBxnQN3sKSr7iinPZL9582atn01NTXn48CFbtmyhT58+JR5YUYhkLwiFpFIiPQjjXHY7AHTkMhytRGVfEeWZ7J/MePk8mUz22ib77OebcSSR7AXhpeJuIstO04zEcbIyQqGbb+uuUA7lmeznz5+Pj48Pjx49ws7OrlxcoPR8Za8Slb0gvNzjztknI3FqiSacCivPj/CJEydy+vRpOnXqxKVLl7h165bm3+sqWykqe0EolHv/kSPT5ZpkD4j2+oosz8q+fv36DBs2DIB33nkHAEmSkMlkXL58uXSiK6Qc1fOVfRkFIgjlRHb0f1xT2ZP9OBX4NqhaxhEJJSXPZL98+XIuXbrEgAED+N///oeRkVFpxlUkOc9X9qIZRxBeKufuecKUDQGoX90MDweLsg1IKDF5NuMYGRnh6enJlStXqFq1KteuXcPMzAwPD4/SjK9Qsp8fjSOacQQhb8kPMMyK1XTO9vNyLBd9c0LR5DvOfvXq1cyfP5+cnBzefPNN5HI53333XWnEVmjPV/Kig1YQ8pYRdQ4D4JLKGYCubkWb5FAoH/IdY7Vy5Uo2bNiAsbExXbt25dChQ6URV5GIoZeCUHD3w88AcFlypG5VE6qYiDnsK7ICDaiNjIwE4P79+5iZmZVoQK9CXFQlCAWXGRlKpMqGJIxpUbPkJyYUyla+yf6jjz5i9OjRJCcnM3PmTIYMGVIacRXJ8x20ohlHEPJmmnBZ017vVbNKGUcjlLR82+wDAwNp1aoV165dw8XFBRcXl9KIq0iyVaKDVhAKIjs9iWo50WxStQLAS1T2FV6elf3Dhw+ZN28eCxYswNramjfffJPMzEx69epViuEVzouVfRkFIgivuXtXQ5DLJC5JTtiZG4gFSyqBPCv70aNHExoaio6ODpcvX8bHx4fp06fj5ORUmvEVihh6KQgFk3grBFBPk1C3mmkZRyOUhjyT/YULF9iyZQs2NjZ07NiRv//+m7fffpsJEyaUZnyFIuazF4SCke79R4JkTDRV6CamSKgU8kz2GRkZODo6YmJigkKh4JNPPuGDDz4ozdgK7fnROKDupBWLJwuCNtOEy48nP5NRp6qo7CuDl3bQ3rlzRzNNQoMGDTSToNWsWbPkIyuC5+fGAXVTjhyR7AVBQ5mDXeZNDksdAagrkn2l8NJk/+wEaIMHD379J0JTvtgjq1RJ6OmUQTCC8JrKirmKPlmEPb5yto5oxqkU8kz2wcHBr7zz4OBgZs2axX///Ud0dDRjxozhzp07dOvWjWnTpiGXF+8iCc8PvQRQiU5aQXhKpUKSVDBoL8PSzEg7k4Gxfr4jsIUKIM9XuUWLFkXeaUZGBgsWLGDNmjWabXPnzsXS0pKZM2fSp08fWrdujZ+fX5GPkZu8KntBEFAn+phL6G95DxIiaGDhyPe9g9VjlIu58BJePyXyCj98+JBbt27xySefaLaFhITQunVrGjRogLOzM6GhocV+3Nw7aIv9MIJQPqU9RLZJnegBSIjAdMdASHtYtnEJpaJEkr2DgwM//fQTNWrU0GxLTk7GwEB94YaBgQHJycnFftznJ0IDMdZeEDRysp4m+icSItTbhQqv1L67mZiYkJmZCaibeUxNi38EQG6VvWjGEYTHdBVg4ai9zcJRvV2o8Eot2Tdu3JgTJ04QFhbGnTt3SmQRlNza7EUHrSCoxWGKssfipwnfwhH6bgQjm7INTCgVpdYNP2nSJMaOHUtgYCC9e/fG19e32I+R22gcUdkLglrkmX1Y3VjCrc6rMDK1oKqlmTrRi87ZSqFEk72/vz/+/v6A+kKsbdu2leThck3sItkLgprJxWAePbqKX/Bd3mtlyPSeYnHxyqRCfaRni2YcQchd4l1qxh5jq/INstDDrYZ5WUcklLIKlexFB60g5C7tn1UgSWxQ+gDQyF4k+8qmYiX7XIZeispeqPSU2Sj/XcMxVWMiparUr26Gq5gPp9KpUMn++fnsAXJp2RGESiU9bC+m2Y9Yr+wEwBed6iCTickBK5sKNSmGmC5BEF6UcWI58ZIVf6iaUNvWBN8GomO2MqpYlb2YCE0QtMXewPL+X2zM8UGJDl41rURVX0lVqGQvKntBeEqpkrh7aAlK5GxWdgCgiYNF2QYllJkKlexzHWcvKnuhklp/PBzDS5s4oPQkBktAJPvKrEIl+9w6aFWishcqqQenNmMlS+FnZUfNtlo2YqGSyqpiddDmMvQyt6UKBaGiU6okOqT8yk2qcULVEAAPRwuxHnMlJip7QaiAoq6cobn8KhuUHZEe/5n38XQo46iEslSxKvvcOmhFm71QCSlPryJT0uMXZTusTfT5PqAxb9QVs1tWZhWqss+tyUaMxhEqm4PnbmBzaye/qrxIwJR+LRxo72orhlxWchUq2WdkK1/YJsbZC5XJ7UepHP1lCaaydH7OUV8x28BOzIMjVLBkn5mT2zj7MghEEMrIsasxvCc/xGWVIyFSHQAa2pmVcVTC66BCJfvcKnvRjCNUJg8u/42b/PbjeXBk+NSzxd7SsKzDEl4DFaqDNiNbzHpZ1iJi0zgXlUCb2tZYGSvYf/E+Nx4k8VYdPcz0JGS6Csyr2InVkUqAJEm4Rv1CimTATmVrVg9uTvu6NqKtXgAqWLLPzBGVfUlRqiQS07OxMs59cerkjGzGbj3PgbAHANSrZsoXvnVZ+uc1VnQxpsr29yEhAiwcSXt7PXvvW9C6ji12FqLqLC63IqPorPqLX5Tt0DU04406ItELT1WYZC9JkqjsS0hmjpI+P53kfFQi8wIa8barPlJOFg/TJeb8+Yi/b8QRk5yp9Zgr95OYtO4QPw9sSJU9A9WJHiAhAqNtA3Dx3UrATyfZO7INFka5f4AIhRN7Yi0usmx+VnaieR0rcQGVoKXCJPusPHpiRWX/6o5ffcT5qEQ8HMxob/EIVryPLCECWwtHJr21lslxqWSmnqe2LJpasmhqy+9SWxaNmSwNjPY+TfRPJERQ1UjG3YR0msw4SMd6tnzzdmNsTPXL5gQrAknC7tpGzqrqcFlyok/tKmUdkfCaqTANp7lV9SCSfXH48+pDACa1t6HKnve1qvQqe95nSQc5KxXzGK+3kS4GF8iQFOxUtmZq9vtcTTMCC0ftHVo4Yptxk091dmBJEoevxDAs+N9cO9iFgrn/30FqKKP4OacjOnIZ3RvblXVIwmumVCv7/v378++//wKgUCi4cOFCse07t/Z6EM04r0qSJI5ejQGgmhG5VulKy1pcfnMbxjXq41zDjkn/9yc3H6YC4BhrTt2+G2HTe5o2+0z/1cT8FcxYva18qruT7cq2rIx6k4Er5SwZ0BRrE1HhF1bSsaXoSybsVbXkjXo24luS8IJSS/YqlYpLly4xf/582rRpU+wdR5l5VvbFephKRaWSWPzHdSLj0vGQXcM6Q0ddpT+b8C0c0TGpgptXA82mr3u68fmmc9Svbsq7LZxATw5DD0FOFugq0DeywaGfJw9vDCZy33z8H/5GP90jHL3rzsTvu9O953t0r6NAys4kIUtGjkEVbM2NyuAZKB+k5Pu4xP7BaqUfmSh4u6l9WYckvIZKrRnn1q1bpKWlsWDBAgICAjh+/Hix7j+vyl7MjVM0kiTx2aZQ5h0MZ7DOPjYrZpB2ciWqgOCnzTIWjtB3Ixhpz7nSurY1/07qxLohXpjo66qHWZpUBQsH9f+Ph13a1PKg6afBbGqzn3k5ATSQ32Gp/QHeNL+NbEUn5AsaYbWxK3pxV8jIyi7tp+D1p1JBygNUKQ/RfXct5+zexUihQ8f6tmUdmfAaKrXKXpIk+vTpQ+/evTl+/Djjxo2jRYsWWFtbF8v+82qzF7NeFs3Kv25x7L8bLNZbRjed0xxUNgX3r/GtXlerSsfI5pXHzA/y9eSo42z67erDCm8LnHcP0eoXsNz1Pne7/8z2a0rqOdmhVEkYKnS4E5vKg6QMPBws6VDPFp3KNPpEpYKYS7DpPXQeN4/NeGstq64ZYKCnU9bRCa+hUkv2tWvXZty4cZiYmGBubs6SJUu4fft2sSX7PCt7kewLJDUzh73/3aOpkwVxqdns2H+A3Yr5OMgeEmwyBLeASTR1slLf2aT4F6xu72qL9+hOpMTcyrVfoIZuEp/8053wk46EqGpzVlWXEKkOd6SqeDiY46ifQp0qCmS5fADdT8zgn5ux1Ktuypdb/0Mul/HdO42pW9W02M+j1KQ9fNoPAprO8iHv/Va2cQmvrVJL9jt27GDixIls2LCB06dPY2hoSK1atYpt/3lW9qIZp0A+3RDCH+EPAYk+OkfZpruGBEyYYvEN0z4bhkK35Fv8FLpyLM1Mcu0XuJZmxF5lb5rKrtFD5wQDdA8DkFDjDYw6jkOxu5emAzjrnfWobBpgoK9HYno2vRb/zf2kDK1jzdpzkcW9ndBRZYGOAkOLavl+Q8nvwrLSolJJKDPT0cvlQ9FCId7vQu5KLdm/9dZbnD17lg8++AAbGxsWLFiApaVlse1fVPZFpFKREn+fGe3NedTClPi/VuBzbwV/KRsyVW806wZ3K5VE/4TMyEbdD/DM6J3Yt9by1f5UQnPeoUF1M2xMdDFOvI677CrvtvVFsXu4VoWr+GUAKr85xG79nOgcE+ZI5sTqmfNIMueRZIapfUMG+9hgss5Pc4ycPhu4pKzB3osPaFfHhtjULKIT0mnvasOav29jZ65Pe3sZlvoSKToK7Owc0NUtm8tU1mzdRt9GZujl8qEo1xOjcITcldq7VVdXl5kzZzJz5swS2X+e4+xFZZ+3x+2+JpvewyQhAgcLR6Qei9j8uwXjbzdh7cCWpT+dgVwOtg00/QIpSjmfbbtDUoaSP79sj1MV48d3bAWAlBCZa7NPioEdh7MaYi1LpIosibqyKKxJRF+WA+3Xw3P9Arpb+lGr8//heWI2d/62IAYLYiRLvjtggZW9K4FNamG5Z7DmwyG5VzC61d0wUOiikiid/oLsdHIOz+L9S4tISGqPssdKTJ+cx+NpKIyMxAIlQu4qzBW0eY6zF5V93nJp95Xt/pTavlsZVUeHtnXKKHE8Gb0DmADrh9qhkiR0dV78hiHTVeTZ7PNVzkfP3VvChHT+MquLRS4fEHIjc2rIYvGQX8dalvT0dx3Ww5NE//i+pjsHcqXrViYciuV8VCITu9ZnQEsngrb/x61Hqczxb0S9asU4tfCdE7DrU3TjbrBR2YE5N/pRKyuTSb5bsTWSEZMmYahTjQZigjkhDxUm2ed9BW0pB1JO3Lz3iGqqhxjlkvTq2RjQtIFT2QSWC7lchpw8Kudcmn2kvhuxN3Rk4Xtm7DkfTXq2ksT0bP6LSkRSmGJkbJ7rB8SlVDPezpoDgC45WJOIrSyBVQbOWOfyPLkqHjHl/mcckzfm9/3u3I3ryPaQuwB0+eE4g1o58lEzM6qZyHPtOC6QzGQ4NB3OLAcLR9bUXsC0i+oP4dDIJN5e9/RD6erMuoXbt1CpVJhkn/nMpfYeDmZMam+DrZEMHT2Furmiklc88alZfL75HPqZcXTP+o3WcTsw6rsk16RnbGQI5WW2xOeafdBVIDOyoapczlvuRrzlrp42IDUzh30X79OohjkKM+MXPiCSegVjol+dK1/XYff5aCyNFBy58oCdodFkK8xyfZ7upclRosMInV2MlO0k6ey3tNBryHFVIx7V6MBnbtlU+aW75hgpvdcRrnKgWc3c562RVEpkaY+eDmuNvwO/DIHESJQtPmKtQSAzfn8ag3MVI27HpgHQwdWmVPtWhPJHJkmvT6O2q6sr4eHhRXrsT3/e4Jt9V/BwMFNPqbvn6ZS69N2oTgiVIOFn5ijR1306zvri3UT2nI/m3s0wmt/fyDs6xzCUZXFY6cFRuyGM6tywcj5XKpW6Gasg1ws8M6b9yfOU/vZ6+u1OIjQyCTNS8JZfop38PO10LmAvewTvrocDE1789tD5Z8xNzYhTGXHidjJd3Kpx8kYs/9x4xFQvsNz9zGvRYxHxx5Zyr/4g5lw05/i1R5pdmejrcmK8DzN/vaRpNqptW46HkgqvpCC5swJV9ur2GvVkXQFa7atsek9d+ZXA+PDXhVIl8dG6f4lPzWB+d3sczHTJknTZuvcorSJXMk7+L9k6OuxQtmGFsivXJXvcleZczK6B1/u/YyDLKbaLpMqFZ/oFCnTf5749GBrZMO7NeJIzcjh46T5b/jVhv6oF5Ei4yO6xzcABy1yafhoYJcHKttQAXCR94o+Y0kgypmvf+ZjtHqX9vt39Kbd9t9B73U3gkdauPm5fCzMDPb59x/1VnwmhkqgwyT7jcQetrZEs19EZqvQk7uWYUaMCLJaRrVTx1S//ceZ2HFPfaohvg6psPB1BbErG44VCekBCBPoWjkzvsYikwwYsvtOT4JzOPMQCgMb25mz+qJW42rKgcvlwaOmibo7xqWdLEwdLfvrzBtYmCr4PaI9MSsi16edmmgGrsgdjQQoWshQsZKlYkExtI5Nc37fWRtofvEPb1GRI25pUNy//72OhdFWYZP+kso9Jk3DIbfzxoysc+TkI53e/pW2j4ruY61VFxqVx+V4SzZwsqWKiT3xqFunZSvWQR5WKnJQYMtLTSc6Ro2tqS0xKFiuP32JH6F3kqJixbi//OaSTeDecpf0DXpiCmN2fcsN3C/Nu3ATgg9Y1cbExppdHDZHoi4mOXEY/L0f6eT0zlbPK6IV+gbgeaxmzL5VQpa/W4/V15RzVq0b1XN63MWnqVtYaFoZM66H+YBeEoqgwyf5JZT/z6ENW9wzGYtdAzR9Zau9gDu/bRj+dwzz8pSPoLoL63Us3wMdtxMrsTBIyZchMbHiYko3/kr9JzVIil4GpgfqKT7kM5vq78aZtPCY7AjFJiMDEwpHUHsu5/vuvdL37FyMU93GQxaCQKSEG0AOV0bu5Vof25no0tDOjsb05E7rWy3UIo1DMcmn6Ueha0rLWTRysTLAyVvBhOxfC7ydTv7oZ1UwVL3w40HcjHjZ1ODS6Bo5WxqIDVnglFaaDdsyW82wLiQLgbY/q9HMzwtZIhqSjYNm/Saw/HUVj2Q3m6i2nvjwC6r8Fb34HZtWL8xQAiEvNwkihQ2xqFmduxdGgugm1pQjkm/s9U+WtYfz+aB5GXsdGloiNLAFbWTy2JGAjS6TlexMw/n3MC5We0u8brm4M4pZUjdtSNW5LVbmtqsYtqRrLBjbH4/d3XniMNPQQsgrcX1FhFKbTWBCeUak6aDOeuagqIj6Dt9fdA2BAS0fiM9S/+0+qxVtZM7nU5RryY98iXTuKbpevkTV9X+uPKitHRVxqFram+nmu45mTk4Mq5RF6ZGuNod50OoIJOy6gkkCfLGrL7rIo0Av57x9oNa9Y7R7EUr/ZsHmaZp9KSUYs5sRIFsiNLHKt0u8b1uF9/fnYmunj6WTFkf/u0djenHneztRzssh1zLlMXFVZPhSm01gQCqnCJPtnFy8xUjw9LaVKPb3yEznoMvlRZ06lWzBbdyXev46CC1uh1/+QdA1ISE7hdkIOM/6IoYqJIcsHNkMmk5GRreRA2H3sLY3488oDBtVJw+qZYXIpPVezfP8pFHf/YanuXerIonCUxSCXSXmuwxpn4MjorC/p26E5uhbVWXAijnvJ2TxKyWJbqhnNcmnDtatizumJnTSbpvVoqL3fXMaci+pQEISKk+yfqeyNFE87HlUqifhU7YUvNv8bCVSnX/ZEAlR/MlN1BUX8bWS7P8UyIQJLC0fWvLWK7w5c4frxG9QyVbLxj/PExz4iRpbGwH4fY7V7hFalbrJrMF/4zSZ702RuSdW4KDmzQ9mGq5I9X2RYUDeXxK00sOSzj0bQzEk9IVyn5upf7btwjxP3knDvswHdLf202nBlxvlU6aI6FAQhFxUn2edV2UsS8WlZeTxKxlZlewJbDqLx7r5aydt8zwfM9JsNmwcAMBhAD1IlffSNRubexGJQi7aZq8lGl7Z1rDE31GNcZ1ecrQzB4sXONxtbe2xyqbrfbFQdGlVXt+EW80IhgiBUThUm2We8pLJPSHv5knaWRrq5Ju8Yg5p8kDmTJIxJkoxIwZAcdNmeZkXTXCp1A2MzRvk1xK9h1RevZnyueaVAiVtU6YIgFJMKk+y1Knv9p8n+5ZU9zH/XHbluTq4XwESm6XJRctFsqmFhyKJ+HtQw1891mJyFtR0jOuSRwEXiFgShDFWYZK9V2es9Pa3UzBwyc3Kf+rKxvTm9PezVzSXPJe/sPj+z8nAyHo4W1LIxwVBPhw/a1KSm9eP51E2LUKkLgiCUkQqT7J+t7I2fqewfpeRd1TtaGalv5HIBjJ6RDUsCX5K8RaUuCEI5UmGS/bOVveEzbfaxqZl5PsapitHTH0TyFgShAqsw7Q5alf0zo3HiXlLZO1kZ5/k7QRCEiqRCJHtJkvKs7FOznm73dNJe4LyGpZg5UBCEyqFCJPtspcSTi2R15TIUeUz0VaeqCfWqqYdEKnTl1K9ejGuECoIgvMZKLdmrVCqmTJlCs2bN8Pf359atW8W279uxqZrbhgqdPOezsTBSMK+PO/5NazC/TxOsjBXFFoMgCMLrrNSS/cGDB9mxYwdr167F0tKSuXPnFtu+Vx5/+sHRyqUKOnmsn2pppEdDO3P+r08TujUu/tkuBUEQXlelluxDQkJwdnbGzc0Nb29vQkNDi2W/j1Iy2RF6V/PzsHYueQ53tzASlbwgCJVTqSX75ORkDAwMADAwMCA5OblY9nsnNo0spXokjoejBZ5Olnk2z1SEJQkFQRCKotTG2ZuYmJCRkQFARkYGpqam+TyiYBrbm/N2U3tux6by7TuNkclk1KtmxiBvZ3797x4qSUIGdKhnS6vHa4YKgiBUNqWW7N3d3dm4cSOXL1/mxIkTeHh4FMt+9XTkzOvj/sL2aT0avjjXuyAIQiVVasm+S5cunD59mgEDBuDk5MS8efNK69CCIAiVXoVZg1YQBKGyKkjurBAXVQmCIAgvJ5K9IAhCJSCSvSAIQiUgkr0gCEIlIJK9IAhCJfDaLV7i6upa1iEIgiBUOK/V0EtBEAShZIhmHEEQhEpAJHtBEIRKoFwn+//++w83Nze2b9/OrVu38Pf3p1mzZkyZMgWVSj0T5ubNm2nVqhXt27fnyJEjZRxx8ejfvz+urq64urrSqFGjSnPuS5cuxdvbmzZt2vDbb79VivMOCgrSvNZP/p05c6bCn3dWVhaff/45Hh4e+Pn5cfbs2Urxeufk5DB+/HiaNWtGQEAAN27cKL7zlsqphIQEycfHR6pbt660bds26aOPPpI++OADKSwsTGrYsKG0f/9+6eHDh1KDBg2kLVu2SAsWLJBatGghZWVllXXor0SpVEpNmjSR9u7dKyUmJkpJSUmV4tyPHTsmNWnSRLp8+bK0adMmadmyZZXivNPS0qTExEQpPj5e6tmzpzRjxoxKcd4HDx6UGjRoIIWHh0sTJkyQAgICKsV5b968WfLw8JCuXbsmffjhh1Lv3r2L7bzLbWUfFBSEn5+f5ueQkBBat25NgwYNcHZ2JjQ0lPPnz5OTk0OnTp3o0KEDCQkJ3Lx5swyjfnW3bt0iLS2NBQsWEBAQwPHjxyvFuZ84cQJzc3OmTZtGcHAw7u7uleK8DQ0NMTMzY//+/SQkJDB27NhKcd61a9fGyMgIW1tbzMzM0NPTqxTnfenSJZydnalduzadOnUiLCyMs2fPFst5l8tkv2rVKmJjY/niiy8023JbHOXJAimGhoYYGhpq7leeSZJEnz59mDNnDl27dmXcuHEkJiZW+HNPSkri4cOHjBw5End3d0aPHl1pXnOVSsXKlSsZOnQohoaGleK8zczMcHR0pE2bNqxdu5ZRo0ZVivO2t7cnMjKSxMRErly5AkBKSkqxnPdrN86+INavX09sbCytWrUCYPr06ahUKjIzM4Gni6OYmJhofk5PTwcotkVTykrt2rUZN24cJiYmmJubs2TJEoAKf+4mJiZYW1vj7e1Neno627ZtAyr+eQNcuHCB6OhounXrBqifi4p+3itXruTOnTts2LCBXbt28fnnn1eK8+7bty/79u3D29sbe3t7ZDJZsZ13uazsN2zYwN69e9m5cycAI0eOpE2bNpw4cYKwsDDu3LmDh4cHjRo1QkdHhyNHjvDHH39gYWGBi4tL2Qb/inbs2EGLFi04d+4chw8fxtDQEG9v7wp/7s2bNycmJobTp0/z77//Ym1tXWle81OnTlG3bl0sLS0BaNy4cYU/b2NjYxQKBfr6+piYmJCYmEjDhg0r/HnHxMTQuXNnNm/eTNu2bWnSpEnxvd6l0+1Qcp500N68eVPy9/eXPDw8pMmTJ0tKpVKSJEnatGmT5O3tLb3xxhvS4cOHyzjaV5ednS1NnDhR8vDwkDp37iwdPXq00pz7vHnzpBYtWkidOnWSTpw4UWnOe/r06dLIkSM1P1eG805NTZU+++wzqUmTJlK7du0qzd94QkKCNGDAAKlx48ZS3759pdu3bxfbeYsraAVBECqBctmMIwiCIBSOSPaCIAiVgEj2giAIlYBI9oIgCJWASPaCIAiVgEj2wmvBz8+PoKAgAM6ePYurqyuBgYEA3L59G1dXVw4dOvTKxzl16hSurq7cuHHjlff1RHJyMqtXrwYgKioKV1dXjh079tLHXL58mR49erzScR88eICPjw9JSUmvtB+hchDJXngttGjRgtDQUAD+/vtvdHR0OHfuHBkZGZw7dw65XE7z5s3LOMrcrV27lhUrVhTqMbNnz6ZPnz6vdNyqVavSsGFDli9f/kr7ESoHkeyF10Lz5s25ffs2cXFx/P333/j7+5OTk0NoaCihoaG4urpiaGiomRvHw8OD6dOnk5aWhqenJ2vXrgXg559/xtPTk/T0dPbt20eHDh3w8vLim2++4flLSpKTkxkxYgQeHh707t2bS5cuARAYGMjw4cMJCAjA09OTRYsWAeoptbt160bLli2ZMmWK5hvCwoULefToET4+Ppp979q1C29vb/z8/DRznDxx9epVTp8+zRtvvAGAj48PY8eOpWvXrnh5ebF161bN9pEjR9KxY0fat2/Pzz//jJ+fH23atNF8y3njjTfYunWrZtpbQciLSPbCa6FFixYAHD9+nAsXLtCtWzfq16/PP//8Q2hoKC1atODhw4fY2dmxfft2Bg0axIYNG5DL5fTq1Yvt27cDsG3bNnr16kVmZiZBQUEMGDCAjRs3smfPnheagZYuXUp4eDi7d++mXbt2mmYkUDezzJ49G19fX5YtWwbA1KlTsbW1ZceOHeTk5ADqiemGDRtGlSpV2L17t+bxhoaGbNq0idTUVLZs2aJ13JCQEHR1dbGzs9Nsu3HjBkuWLKFhw4asWrVKsz06OprVq1ejq6vL8uXL+emnn3BwcNB8uDk4OBAfH8+tW7de+TUQKjaR7IXXQrVq1XBwcGDp0qXo6enRrFkz2rRpw5EjR7h27RotWrTAyMiImJgYpk+fzrlz5wD1Ihf9+vUjPDycnTt3EhYWxnvvvUdERAQZGRksWrSIPn36EB8fT0hIiNYxw8PDuXfvHr179yY4OJjw8HBSU1MBcHd3p06dOjRq1EgzCdWNGzd44403qF69umZ6bZlMhr6+vmbCqid8fX1xdHTEzs5OM1HVEwkJCVhaWqKjo6PZ1rJlS5ydnalfv77meKD+EHR0dKR69eo0btyYmjVr4uTkpLmPra0tAImJicXxMggVmEj2wmujefPm3LhxA09PTxQKBd7e3ly9elXzu927d3PkyBGmT59OvXr1AHVl7eLiQsuWLZkxYwZeXl7UqlULe3t79PT0GDhwIAsXLsTPz4927dppHc/FxQU7Ozt+/PFHhgwZQmBgIPr6+gCaRCyTybTuf/z4ce7fv8/+/fs12+VyOVlZWTx48ECz7dnHPd98ZGVlRUpKita23I73ZN/P3+dZT/ZjbW394hMqCM8QyV54bTxpymnTpg0ATZs2xcjICFdXV8zNzWnTpg1Vq1ald+/emoUaIiMjAfVSjampqfTr1w9QJ9S5c+eyd+9ePvroI5KTk6lfv77W8T7++GNcXV359NNP2bJlC25ubujq5j3r94wZM4iOjqZ3795kZ2cD6mTs5eWFXC7n/fffL9B5NmvWjPT0dO7evVuIZyd3165dw8bGBkdHx1fel1CxiYnQBKGAfvjhB1QqFe+99x7btm1jyZIlnDp1qkjzp/ft25devXrRt2/fV4pp9OjRODg4aC3kIwi5EZW9IBSQu7s7Bw4cwNfXly1btjBx4sQiL5Tx1VdfaUbdFFVcXBznzp1jyJAhr7QfoXIQlb0gCEIlICp7QRCESkAke0EQhEpAJHtBEIRKQCR7QRCESkAke0EQhEpAJHtBEIRK4P8BVMqpdjqmOZEAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "from ast import literal_eval\n", "\n", "spy_hs = my_spec_mod.spyfile.open_memmap() # datacube before smoothing\n", "meta_bands = list(io.tools.meta_bands.values())\n", "# meta_bands_bin = sorted([float(i) for i in metadata_bin['wavelength']])\n", "meta_bands_bin = sorted([float(i) for i in literal_eval(metadata_bin['wavelength'])])\n", "\n", "ax = sns.lineplot(x=meta_bands, y=spy_hs[200][800]*100, label='Hyperspectral (Pika II)', linewidth=3)\n", "ax = sns.lineplot(x=meta_bands_bin, y=array_bin[200][800]*100, label='Spectral resample (20 nm)', marker='o', ms=6, ax=ax)\n", "ax.set_xlabel('Wavelength (nm)', weight='bold')\n", "ax.set_ylabel('Reflectance (%)', weight='bold')\n", "ax.set_title(r'API Example: `spec_mod.spectral_resample`', weight='bold')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save the spectrally resampled datacube using `hsio.write_cube`" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "fname_hdr_bin = os.path.join(data_dir, 'spec_mod', 'Wells_rep2_20180628_16h56m_pika_gige_7-bin-20.bip.hdr')\n", "io.write_cube(fname_hdr_bin, array_bin, metadata_bin, force=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "## `spec_mod.spectral_smooth`\n", "Performs Savitzky-Golay smoothing on the spectral domain. [[API]](api/hs_process.spec_mod.html#hs_process.spec_mod.spectral_smooth)\n", "\n", "Load and initialize the `hsio` and `spec_mod` modules" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "import os\n", "from hs_process import hsio\n", "from hs_process import spec_mod\n", "\n", "fname_hdr = os.path.join(data_dir, 'Wells_rep2_20180628_16h56m_pika_gige_7-Radiance Conversion-Georectify Airborne Datacube-Convert Radiance Cube to Reflectance from Measured Reference Spectrum.bip.hdr')\n", "io = hsio()\n", "io.read_cube(fname_hdr)\n", "my_spec_mod = spec_mod(io.spyfile)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use `spec_mod.spectral_smooth` to perform a *Savitzky-Golay* smoothing operation across the hyperspectral spectral signature." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "array_smooth, metadata_smooth = my_spec_mod.spectral_smooth(window_size=11, order=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the spectra of an *individual pixel* to visualize the result of the smoothing procedure. " ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'API Example: `spec_mod.spectral_smooth`')" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "from ast import literal_eval\n", "\n", "spy_hs = my_spec_mod.spyfile.open_memmap() # datacube before smoothing\n", "meta_bands = list(io.tools.meta_bands.values())\n", "meta_bands_smooth = sorted([float(i) for i in metadata_smooth['wavelength']])\n", "\n", "ax = sns.lineplot(x=meta_bands, y=spy_hs[200][800]*100, label='Before spectral smoothing', linewidth=3)\n", "ax = sns.lineplot(x=meta_bands_smooth, y=array_smooth[200][800]*100, label='After spectral smoothing', ax=ax)\n", "ax.set_xlabel('Wavelength (nm)', weight='bold')\n", "ax.set_ylabel('Reflectance (%)', weight='bold')\n", "ax.set_title(r'API Example: `spec_mod.spectral_smooth`', weight='bold')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save the smoothed datacube using `hsio.write_cube`" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "fname_hdr_smooth = os.path.join(data_dir, 'spec_mod', 'Wells_rep2_20180628_16h56m_pika_gige_7-smooth.bip.hdr')\n", "io.write_cube(fname_hdr_smooth, array_smooth, metadata_smooth, force=True)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }