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 0.04875 0.01934 0.04791 0.05241 0.04124 ... 0.2814 0.2802 0.2784 0.28
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]]]) Coordinates: (7)
AFGain
()
int64
-14
long_name : audio-frequency gain control setting units : decibels attenuator
()
float64
5.0
long_name : radio-frequency attenuator setting units : decibels burst_number
(time)
int64
4 5
description : the number of each burst within each file chirp_num
(chirp_num)
int64
0 1 2 3 4 5 6 ... 14 15 16 17 18 19
description : the number of each chirp within each burst long_name : chirp number array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+ " 18, 19]) chirp_time
(chirp_time)
timedelta64[ns]
00:00:00 ... 00:00:01
long_name : time of samples during chirps name : time of samples during chirps array([ 0, 25000, 50000, ..., 999950000, 999975000,\n",
+ " 1000000000], dtype='timedelta64[ns]') filename
(time)
<U83
'ldeo-glaciology/GL_apres_2022/A...
description : the name of the file that contains each burst array(['ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-05-29-1406.DAT',\n",
+ " 'ldeo-glaciology/GL_apres_2022/A101/CardA/DIR2022-05-26-1536/DATA2022-05-29-1406.DAT'],\n",
+ " dtype='<U83') time
(time)
datetime64[ns]
2022-05-29T15:06:19 2022-05-29T1...
array(['2022-05-29T15:06:19.000000000', '2022-05-29T15:21:50.000000000'],\n",
+ " dtype='datetime64[ns]') Indexes: (3)
PandasIndex
PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], dtype='int64', name='chirp_num')) PandasIndex
PandasIndex(TimedeltaIndex([ '0 days 00:00:00', '0 days 00:00:00.000025',\n",
+ " '0 days 00:00:00.000050', '0 days 00:00:00.000075',\n",
+ " '0 days 00:00:00.000100', '0 days 00:00:00.000125',\n",
+ " '0 days 00:00:00.000150', '0 days 00:00:00.000175',\n",
+ " '0 days 00:00:00.000200', '0 days 00:00:00.000225',\n",
+ " ...\n",
+ " '0 days 00:00:00.999775', '0 days 00:00:00.999800',\n",
+ " '0 days 00:00:00.999825', '0 days 00:00:00.999850',\n",
+ " '0 days 00:00:00.999875', '0 days 00:00:00.999900',\n",
+ " '0 days 00:00:00.999925', '0 days 00:00:00.999950',\n",
+ " '0 days 00:00:00.999975', '0 days 00:00:01'],\n",
+ " dtype='timedelta64[ns]', name='chirp_time', length=40001, freq=None)) PandasIndex
PandasIndex(DatetimeIndex(['2022-05-29 15:06:19', '2022-05-29 15:21:50'], dtype='datetime64[ns]', name='time', freq=None)) Attributes: (3)
description : voltage from the analog-to-digital converter after the received signal has been mixed with the transmitted signal and the result has been filtered to leave only the low frequency compponent corresponding to the differences in the frequencies of the Tx and Rx signals long_name : de-ramped chirp units : volts "
+ ],
+ "text/plain": [
+ "