diff --git a/notebooks/test_notes/custom_fft.ipynb b/notebooks/test_notes/custom_fft.ipynb new file mode 100644 index 0000000..b412b43 --- /dev/null +++ b/notebooks/test_notes/custom_fft.ipynb @@ -0,0 +1,3662 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import pandas as pd\n", + "sys.path.append(\"../../xapres_package/\")\n", + "import xapres as xa\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import xarray as xr\n", + "import importlib\n", + "import gcsfs\n", + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "d = xa.load.load_zarr().isel(time=slice(300,302))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'chirp' (time: 2, chirp_time: 40001, chirp_num: 20)>\n",
+       "array([[[ 0.04875183,  0.01934052,  0.0479126 , ...,  0.0327301 ,\n",
+       "          0.04302979,  0.03765106],\n",
+       "        [ 0.04230499,  0.00839233,  0.02979279, ...,  0.0485611 ,\n",
+       "          0.02334595,  0.027771  ],\n",
+       "        [ 0.03986359, -0.02555847, -0.07919312, ...,  0.10356903,\n",
+       "         -0.02494812, -0.01178741],\n",
+       "        ...,\n",
+       "        [ 0.00370026,  0.00400543,  0.00648499, ...,  0.00610352,\n",
+       "          0.0056076 ,  0.00297546],\n",
+       "        [ 0.01621246,  0.01522064,  0.01647949, ...,  0.00759125,\n",
+       "          0.00991821,  0.00995636],\n",
+       "        [ 0.28560638,  0.28301239,  0.2841568 , ...,  0.27935028,\n",
+       "          0.28171539,  0.28160095]],\n",
+       "\n",
+       "       [[ 0.00076294,  0.11383057,  0.02944946, ...,  0.03334045,\n",
+       "          0.01270294,  0.05149841],\n",
+       "        [ 0.03307343,  0.09578705,  0.05680084, ...,  0.00656128,\n",
+       "          0.026474  ,  0.05443573],\n",
+       "        [ 0.13248444,  0.03494263,  0.11417389, ..., -0.08838654,\n",
+       "          0.03162384,  0.02593994],\n",
+       "        ...,\n",
+       "        [ 0.00457764,  0.00385284,  0.00209808, ...,  0.01186371,\n",
+       "          0.01064301,  0.01064301],\n",
+       "        [ 0.00556946,  0.00850677,  0.00946045, ...,  0.01415253,\n",
+       "          0.01251221,  0.01079559],\n",
+       "        [ 0.27896881,  0.28079987,  0.28182983, ...,  0.28018951,\n",
+       "          0.27835846,  0.28003693]]])\n",
+       "Coordinates:\n",
+       "    AFGain        int64 -14\n",
+       "    attenuator    float64 5.0\n",
+       "    burst_number  (time) int64 4 5\n",
+       "  * chirp_num     (chirp_num) int64 0 1 2 3 4 5 6 7 ... 12 13 14 15 16 17 18 19\n",
+       "  * chirp_time    (chirp_time) timedelta64[ns] 00:00:00 ... 00:00:01\n",
+       "    filename      (time) <U83 'ldeo-glaciology/GL_apres_2022/A101/CardA/DIR20...\n",
+       "  * time          (time) datetime64[ns] 2022-05-29T15:06:19 2022-05-29T15:21:50\n",
+       "Attributes:\n",
+       "    description:  voltage from the analog-to-digital converter after the rece...\n",
+       "    long_name:    de-ramped chirp\n",
+       "    units:        volts
" + ], + "text/plain": [ + "\n", + "array([[[ 0.04875183, 0.01934052, 0.0479126 , ..., 0.0327301 ,\n", + " 0.04302979, 0.03765106],\n", + " [ 0.04230499, 0.00839233, 0.02979279, ..., 0.0485611 ,\n", + " 0.02334595, 0.027771 ],\n", + " [ 0.03986359, -0.02555847, -0.07919312, ..., 0.10356903,\n", + " -0.02494812, -0.01178741],\n", + " ...,\n", + " [ 0.00370026, 0.00400543, 0.00648499, ..., 0.00610352,\n", + " 0.0056076 , 0.00297546],\n", + " [ 0.01621246, 0.01522064, 0.01647949, ..., 0.00759125,\n", + " 0.00991821, 0.00995636],\n", + " [ 0.28560638, 0.28301239, 0.2841568 , ..., 0.27935028,\n", + " 0.28171539, 0.28160095]],\n", + "\n", + " [[ 0.00076294, 0.11383057, 0.02944946, ..., 0.03334045,\n", + " 0.01270294, 0.05149841],\n", + " [ 0.03307343, 0.09578705, 0.05680084, ..., 0.00656128,\n", + " 0.026474 , 0.05443573],\n", + " [ 0.13248444, 0.03494263, 0.11417389, ..., -0.08838654,\n", + " 0.03162384, 0.02593994],\n", + " ...,\n", + " [ 0.00457764, 0.00385284, 0.00209808, ..., 0.01186371,\n", + " 0.01064301, 0.01064301],\n", + " [ 0.00556946, 0.00850677, 0.00946045, ..., 0.01415253,\n", + " 0.01251221, 0.01079559],\n", + " [ 0.27896881, 0.28079987, 0.28182983, ..., 0.28018951,\n", + " 0.27835846, 0.28003693]]])\n", + "Coordinates:\n", + " AFGain int64 -14\n", + " attenuator float64 5.0\n", + " burst_number (time) int64 4 5\n", + " * chirp_num (chirp_num) int64 0 1 2 3 4 5 6 7 ... 12 13 14 15 16 17 18 19\n", + " * chirp_time (chirp_time) timedelta64[ns] 00:00:00 ... 00:00:01\n", + " filename (time) clip_threshold)\n", + "good_bursts = bad_chirps.max(dim='chirp_time').count(dim='chirp_num') <= 20-min_chirps\n", + "chirps = chirps.where(good_bursts)\n", + "chirps = chirps.where(abs(chirps).max(dim='chirp_time')]" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# De-mean and detrend\n", + "chirps = chirps - chirps.mean(dim='chirp_time')\n", + "p = chirps.polyfit('chirp_time', 1)\n", + "fit = xr.polyval(chirps.chirp_time, p.polyfit_coefficients)\n", + "chirps = chirps - fit\n", + "chirp_stack = chirps.mean(dim='chirp_num', skipna=True)\n", + "\n", + "chirp_stack.isel(time=0).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray (time: 2, chirp_time: 39999)>\n",
+       "array([[-2.94260054e-20, -1.04601618e-11, -2.07333192e-10, ...,\n",
+       "        -1.90670160e-10, -4.71042907e-11,  2.72057197e-19],\n",
+       "       [-2.00129287e-19,  1.31131166e-11, -1.46606128e-10, ...,\n",
+       "        -1.78433902e-10, -4.56845577e-11,  2.83514402e-19]])\n",
+       "Coordinates:\n",
+       "    AFGain        int64 -14\n",
+       "    attenuator    float64 5.0\n",
+       "    burst_number  (time) int64 4 5\n",
+       "  * chirp_time    (chirp_time) timedelta64[ns] 00:00:00 ... 00:00:00.999950\n",
+       "    filename      (time) <U83 'ldeo-glaciology/GL_apres_2022/A101/CardA/DIR20...\n",
+       "  * time          (time) datetime64[ns] 2022-05-29T15:06:19 2022-05-29T15:21:50
" + ], + "text/plain": [ + "\n", + "array([[-2.94260054e-20, -1.04601618e-11, -2.07333192e-10, ...,\n", + " -1.90670160e-10, -4.71042907e-11, 2.72057197e-19],\n", + " [-2.00129287e-19, 1.31131166e-11, -1.46606128e-10, ...,\n", + " -1.78433902e-10, -4.56845577e-11, 2.83514402e-19]])\n", + "Coordinates:\n", + " AFGain int64 -14\n", + " attenuator float64 5.0\n", + " burst_number (time) int64 4 5\n", + " * chirp_time (chirp_time) timedelta64[ns] 00:00:00 ... 00:00:00.999950\n", + " filename (time) \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray (time: 2, chirp_time: 19999)>\n",
+       "array([[-7.46950842e-03, -4.23852253e-03, -1.78573490e-03, ...,\n",
+       "        -4.23783640e-10, -1.90670160e-10, -4.71042907e-11],\n",
+       "       [-8.02440701e-03, -6.31548530e-03, -3.68531419e-03, ...,\n",
+       "        -3.95184587e-10, -1.78433902e-10, -4.56845577e-11]])\n",
+       "Coordinates:\n",
+       "    AFGain        int64 -14\n",
+       "    attenuator    float64 5.0\n",
+       "    burst_number  (time) int64 4 5\n",
+       "  * chirp_time    (chirp_time) timedelta64[ns] 00:00:00.499975 ... 00:00:00.9...\n",
+       "    filename      (time) <U83 'ldeo-glaciology/GL_apres_2022/A101/CardA/DIR20...\n",
+       "  * time          (time) datetime64[ns] 2022-05-29T15:06:19 2022-05-29T15:21:50
" + ], + "text/plain": [ + "\n", + "array([[-7.46950842e-03, -4.23852253e-03, -1.78573490e-03, ...,\n", + " -4.23783640e-10, -1.90670160e-10, -4.71042907e-11],\n", + " [-8.02440701e-03, -6.31548530e-03, -3.68531419e-03, ...,\n", + " -3.95184587e-10, -1.78433902e-10, -4.56845577e-11]])\n", + "Coordinates:\n", + " AFGain int64 -14\n", + " attenuator float64 5.0\n", + " burst_number (time) int64 4 5\n", + " * chirp_time (chirp_time) timedelta64[ns] 00:00:00.499975 ... 00:00:00.9...\n", + " filename (time) \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray (time: 2, chirp_time: 19999)>\n",
+       "array([[-2.94260054e-20, -1.04601618e-11, -2.07333192e-10, ...,\n",
+       "        -1.28747450e-02, -1.10666420e-02, -9.87842690e-03],\n",
+       "       [-2.00129287e-19,  1.31131166e-11, -1.46606128e-10, ...,\n",
+       "        -1.20086690e-02, -1.07880293e-02, -8.68428682e-03]])\n",
+       "Coordinates:\n",
+       "    AFGain        int64 -14\n",
+       "    attenuator    float64 5.0\n",
+       "    burst_number  (time) int64 4 5\n",
+       "  * chirp_time    (chirp_time) timedelta64[ns] 00:00:00 ... 00:00:00.499950\n",
+       "    filename      (time) <U83 'ldeo-glaciology/GL_apres_2022/A101/CardA/DIR20...\n",
+       "  * time          (time) datetime64[ns] 2022-05-29T15:06:19 2022-05-29T15:21:50
" + ], + "text/plain": [ + "\n", + "array([[-2.94260054e-20, -1.04601618e-11, -2.07333192e-10, ...,\n", + " -1.28747450e-02, -1.10666420e-02, -9.87842690e-03],\n", + " [-2.00129287e-19, 1.31131166e-11, -1.46606128e-10, ...,\n", + " -1.20086690e-02, -1.07880293e-02, -8.68428682e-03]])\n", + "Coordinates:\n", + " AFGain int64 -14\n", + " attenuator float64 5.0\n", + " burst_number (time) int64 4 5\n", + " * chirp_time (chirp_time) timedelta64[ns] 00:00:00 ... 00:00:00.499950\n", + " filename (time) \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray (time: 2, chirp_time: 39998)>\n",
+       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.]])\n",
+       "Coordinates:\n",
+       "    AFGain        int64 -14\n",
+       "    attenuator    float64 5.0\n",
+       "    burst_number  (time) int64 4 5\n",
+       "  * chirp_time    (chirp_time) timedelta64[ns] 00:00:00 ... 00:00:00.999925\n",
+       "    filename      (time) <U83 'ldeo-glaciology/GL_apres_2022/A101/CardA/DIR20...\n",
+       "  * time          (time) datetime64[ns] 2022-05-29T15:06:19 2022-05-29T15:21:50
" + ], + "text/plain": [ + "\n", + "array([[0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.]])\n", + "Coordinates:\n", + " AFGain int64 -14\n", + " attenuator float64 5.0\n", + " burst_number (time) int64 4 5\n", + " * chirp_time (chirp_time) timedelta64[ns] 00:00:00 ... 00:00:00.999925\n", + " filename (time) \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray (x: 8)>\n",
+       "array([7, 8, 0, 0, 0, 0, 5, 6])\n",
+       "Coordinates:\n",
+       "  * x        (x) float64 nan nan 0.0 1.0 2.0 3.0 nan nan
" + ], + "text/plain": [ + "\n", + "array([7, 8, 0, 0, 0, 0, 5, 6])\n", + "Coordinates:\n", + " * x (x) float64 nan nan 0.0 1.0 2.0 3.0 nan nan" + ] + }, + "execution_count": 201, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "arr = xr.DataArray([5, 6, 7, 8], coords=[(\"x\", [0, 1, 2, 3])])\n", + "arr.pad(x=(2, 2), constant_values=0).roll(x=4)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p = np.fft.fft(padchirp,axis=1)/Nfft * math.sqrt(2*pad)\n", + "profile = p[:,0:math.floor(Nfft/2)-1]\n", + "m = np.asarray([i for i in range(profile.shape[1])])/pad\n", + "phiref = 2*math.pi*CentreFreq*m/B - m * m * 2*math.pi * K/2/B**2\n", + "profile_ref = profile * np.exp(phiref[np.newaxis,:]*(-1j))\n", + "\n", + "n = np.argmin(profile_range<=1200)\n", + "Range = profile_range[:n]\n", + "Profile = profile_ref[:,:n]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p = xa.load.generate_xarray(directory = \"../../data/sample/polarmetric\", polarmetric=True, attended=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 205MB\n",
+       "Dimensions:          (orientation: 4, waypoint: 1, chirp_time: 40001,\n",
+       "                      chirp_num: 100, attenuator_setting_pair: 1,\n",
+       "                      profile_range: 11889)\n",
+       "Coordinates:\n",
+       "    time             (orientation, waypoint) datetime64[ns] 32B 2023-01-05T03...\n",
+       "  * chirp_time       (chirp_time) float64 320kB 0.0 2.5e-05 5e-05 ... 1.0 1.0\n",
+       "  * profile_range    (profile_range) float64 95kB 0.0 0.2103 ... 2.5e+03 2.5e+03\n",
+       "  * chirp_num        (chirp_num) int64 800B 0 1 2 3 4 5 6 ... 94 95 96 97 98 99\n",
+       "    filename         (orientation, waypoint) <U56 896B '../../data/sample/pol...\n",
+       "    burst_number     (orientation, waypoint) int64 32B 0 0 0 0\n",
+       "    AFGain           (attenuator_setting_pair) int64 8B -4\n",
+       "    attenuator       (attenuator_setting_pair) float64 8B 22.0\n",
+       "  * orientation      (orientation) <U2 32B 'HH' 'HV' 'VH' 'VV'\n",
+       "  * waypoint         (waypoint) int64 8B 1\n",
+       "Dimensions without coordinates: attenuator_setting_pair\n",
+       "Data variables:\n",
+       "    chirp            (orientation, waypoint, chirp_time, chirp_num, attenuator_setting_pair) float64 128MB ...\n",
+       "    profile          (orientation, waypoint, profile_range, chirp_num, attenuator_setting_pair) complex128 76MB ...\n",
+       "    latitude         (orientation, waypoint) float64 32B 0.0 0.0 0.0 0.0\n",
+       "    longitude        (orientation, waypoint) float64 32B 0.0 0.0 0.0 0.0\n",
+       "    battery_voltage  (orientation, waypoint) float64 32B 0.0 0.0 0.0 0.0\n",
+       "    temperature_1    (orientation, waypoint) float64 32B 502.9 503.1 2.891 2.492\n",
+       "    temperature_2    (orientation, waypoint) float64 32B 501.6 503.6 511.8 4.93
" + ], + "text/plain": [ + " Size: 205MB\n", + "Dimensions: (orientation: 4, waypoint: 1, chirp_time: 40001,\n", + " chirp_num: 100, attenuator_setting_pair: 1,\n", + " profile_range: 11889)\n", + "Coordinates:\n", + " time (orientation, waypoint) datetime64[ns] 32B 2023-01-05T03...\n", + " * chirp_time (chirp_time) float64 320kB 0.0 2.5e-05 5e-05 ... 1.0 1.0\n", + " * profile_range (profile_range) float64 95kB 0.0 0.2103 ... 2.5e+03 2.5e+03\n", + " * chirp_num (chirp_num) int64 800B 0 1 2 3 4 5 6 ... 94 95 96 97 98 99\n", + " filename (orientation, waypoint) ]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "displacement_binned(p1_stacked, p2_stacked).displacement.plot(y = 'bin_depth', yincrease=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## notes from doing the fft in numpy while writing ApRES data page:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "fd = xa.load.from_dats(max_range=2500)\n", + "root_directory = 'gs://ldeo-glaciology/apres/thwaites/'\n", + "filenames = fd.list_files(directory = root_directory, remote_load=True)\n", + "len(filenames)\n", + "\n", + "directory = os.path.dirname(filenames[20])\n", + "ds = fd.load_all(directory = directory, remote_load=True, file_numbers_to_process=[0])\n", + "ds\n", + "\n", + "chirp = ds.chirp.squeeze() # .squeeze() removes the singleton dimensions\n", + "\n", + "\n", + "def rdi(x):\n", + " \"\"\"round down to the nearest integer and return an integer\"\"\"\n", + " return int(np.floor(x))\n", + "def rdei(x):\n", + " \"\"\"round down to the nearest even integer and return an integerr\"\"\"\n", + " return int(np.floor(x/2) * 2)\n", + "\n", + "t = chirp.chirp_time.values\n", + "N = rdei(len(s_one_chirp))-2\n", + "s_one_chirp = s_one_chirp[:N]\n", + "t = t[:N]\n", + "\n", + "s_windowed = s_one_chirp * np.blackman(N)\n", + "\n", + "plt.plot(t, s_one_chirp, label = 'chirp')\n", + "plt.plot(t, s_windowed, label = 'windowed chirp')\n", + "plt.legend()\n", + "plt.title('Windowing the chirp');\n", + "pad_factor = 2\n", + "s_padded = np.pad(s_windowed, pad_width=int((N*pad_factor-N)/2))\n", + "\n", + "plt.plot(s_padded)\n", + "plt.title('padded chirp')\n", + "len(s_padded)\n", + "s_padded_rolled = np.roll(s_padded, shift = int(N*pad_factor/2))\n", + "plt.plot(s_padded_rolled)\n", + "plt.title('padded and rolled chirp');\n", + "Nt = rdei(len(s_one_chirp))\n", + "Nfft = rdi(Nt*pad_factor)\n", + "winchirp = np.multiply(s_one_chirp[0:Nt], np.blackman(Nt))\n", + "\n", + "padchirp = np.zeros(Nfft)\n", + "padchirp[0:math.floor(Nt/2)] = winchirp[math.floor(Nt/2):]\n", + "padchirp[-math.floor(Nt/2):] = winchirp[0:math.floor(Nt/2)]\n", + "all(s_padded_rolled == padchirp)\n", + "def fft(s):\n", + " no_of_samples = len(s)\n", + " S = np.fft.fft(s, axis=0)/no_of_samples \n", + " indexes = np.arange(no_of_samples) \n", + " frequencies = indexes * sampling_frequency/no_of_samples\n", + " return S, frequencies\n", + "\n", + "def range(frequencies):\n", + " return c * frequencies / (2*np.sqrt(ep)*K)\n", + "S, frequencies = fft(s_padded_rolled)\n", + "r = range(frequencies)\n", + "frequencies\n", + "profile_padded = np.fft.fft(s_padded_rolled)/Nfft * np.sqrt(2*pad_factor) \n", + "bin2m1 = c/(2*pad_factor*np.sqrt(ep)*K)\n", + "r_padded1 = np.arange(Nfft) * bin2m1\n", + "\n", + "profile_padded = np.fft.fft(s_padded_rolled)/Nfft * np.sqrt(2*pad_factor) \n", + "bin2m2 = c/(2*np.sqrt(ep)*K) * sampling_frequency/Nfft\n", + "r_padded2 = np.arange(Nfft) * bin2m2\n", + "profile_padded = np.fft.fft(s_padded_rolled)/Nfft * np.sqrt(2*pad_factor) \n", + "#profile_padded = profile_padded[0:math.floor(Nfft/2)-1]\n", + "bin2m3 = c/(2*np.sqrt(ep)*K) / Nfft / dt\n", + "r_padded3 = np.arange(Nfft) * bin2m3\n", + "sampling_frequency/Nfft\n", + "np.allclose(bin2m1, bin2m2)\n", + "np.allclose(r, r_padded3)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "full_py_env", + "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": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}