{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-09T12:31:15.585676Z",
     "start_time": "2025-07-09T12:31:15.374087Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from aaredaq.devices import BeamlineDevices\n",
    "from aaredaqlib.beamline import MXBeamline"
   ],
   "id": "d2efa08de2dbd46b",
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'aaredaq'",
     "output_type": "error",
     "traceback": [
      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
      "\u001B[31mModuleNotFoundError\u001B[39m                       Traceback (most recent call last)",
      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[1]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01maaredaq\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mdevices\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m BeamlineDevices\n\u001B[32m      2\u001B[39m \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01maaredaqlib\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mbeamline\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mimport\u001B[39;00m MXBeamline\n",
      "\u001B[31mModuleNotFoundError\u001B[39m: No module named 'aaredaq'"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "from epics import PV\n",
    "import numpy as np\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy.signal import peak_widths, find_peaks\n",
    "from scipy import asarray as ar,exp"
   ],
   "id": "f8305237c9f98964"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "d = BeamlineDevices(MXBeamline.X06DA)\n",
    "b = MXBeamline.X06DA\n",
    "beamline = b.value.upper()"
   ],
   "id": "5407e46ef4c2a26d"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "def gaus(x, *p):\n",
    "    A, mu, sigma = p\n",
    "    return A * np.exp(-(x-mu)**2/(2.*sigma**2))\n",
    "\n",
    "def sigmoid(x, *p):\n",
    "    L, x0, k, b = p\n",
    "    return L / (1 + np.exp(-k * (x -x0))) + b\n",
    "\n",
    "def unpack_data(data):\n",
    "    x_vals, y_vals = zip(*data)\n",
    "    x_vals = list(x_vals)\n",
    "    y_vals = list(y_vals)\n",
    "    return x_vals, y_vals\n",
    "\n",
    "def sigmoid_fit(x_vals, y_vals):\n",
    "    L_guess = np.max(y_vals)\n",
    "    b_guess = np.min(y_vals)\n",
    "    x0_guess = x_vals[np.argmin(np.abs(y_vals - (L_guess + b_guess) / 2))]\n",
    "    k_guess = 1 / (x_vals[-1] - x_vals[0])\n",
    "\n",
    "    s0 = [L_guess, x0_guess, k_guess, b_guess]\n",
    "    parameters, params_covariance = curve_fit(sigmoid, x_vals, y_vals, p0=s0)\n",
    "    return parameters\n",
    "\n",
    "def gauss_fit(x_vals, dydx):\n",
    "    mean = x_vals[np.argmax(dydx)]\n",
    "    sigma = (np.max(x_vals) - np.min(x_vals))/4\n",
    "    A= np.max(dydx)\n",
    "    dydx[np.isnan(dydx)] = 0\n",
    "\n",
    "    p0 = [A, mean, sigma]\n",
    "    parameters, covariance = curve_fit( gaus, x_vals, dydx, p0=p0)\n",
    "    return parameters\n",
    "\n",
    "def calc_fwhm(gauss_y, x_vals):\n",
    "    peaks = find_peaks( gauss_y )\n",
    "    fwhm_peak = peak_widths( gauss_y, peaks[0], rel_height=0.5 )\n",
    "\n",
    "    fwhm_str = x_vals[int( round( fwhm_peak[2][0], 0 ))]\n",
    "    fwhm_end = x_vals[int( round( fwhm_peak[3][0], 0 ))]\n",
    "\n",
    "    fwhm = fwhm_str - fwhm_end\n",
    "    fwhm_height = gauss_y[int( round( fwhm_peak[2][0], 0 ))]\n",
    "\n",
    "    # find 1/e2 peak width\n",
    "    full_peak = peak_widths( gauss_y, peaks[0], rel_height=0.865 )\n",
    "    full_str = x_vals[int( round( full_peak[2][0], 0 ))]\n",
    "    full_end = x_vals[int( round( full_peak[3][0], 0 ))]\n",
    "\n",
    "    full = full_str - full_end\n",
    "    full_height = gauss_y[int( round( full_peak[2][0], 0 ))]\n",
    "    return fwhm, full"
   ],
   "id": "d92115ec3d76d57d"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "def knife_edge(motor: str, d: BeamlineDevices, beamline: str, steps: int = 40, start: float = 0.0, step_size: float = 0.002, offset: float = 0.0):\n",
    "    diode = PV(f\"{beamline}-ES-BS:READOUT\")#X06DA-ES-BS:READOUT\n",
    "    d.shutter = True\n",
    "    time.sleep(1.0)\n",
    "\n",
    "    data = []\n",
    "\n",
    "    for i in range(steps): #40\n",
    "        if motor == 'X':\n",
    "            d.gmx.move(start-offset+step_size*i, wait=True)\n",
    "            time.sleep(1.0)\n",
    "            print(f\"{d.gmx.value:.3f} {diode.get()}\")\n",
    "            data.append([d.gmx.value, diode.get()])\n",
    "        else:\n",
    "            d.gmy.move(start-offset+step_size*i, wait=True)\n",
    "            time.sleep(1.0)\n",
    "            print(f\"{d.gmy.value:.3f} {diode.get()}\")\n",
    "            data.append([d.gmy.value, diode.get()])\n",
    "\n",
    "    if motor == 'X':\n",
    "        d.gmx.move(start, wait=True)\n",
    "    else:\n",
    "        d.gmy.move(start, wait=True)\n",
    "\n",
    "    d.shutter = False\n",
    "    return data\n"
   ],
   "id": "512dc280e62e4ed4"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "def plot_knife_edge(data, motor: str):\n",
    "\n",
    "    x_vals, y_vals = unpack_data(data)\n",
    "\n",
    "    plt.plot(x_vals, y_vals, label = 'edge_scan')\n",
    "    plt.ylabel('Intensity (counts)')\n",
    "    plt.xlabel('Motor position (mm)')\n",
    "\n",
    "    params=sigmoid_fit(x_vals, y_vals)\n",
    "\n",
    "    plt.plot(x_vals, y_vals)\n",
    "    plt.plot(x_vals, sigmoid(x_vals, *params))\n",
    "    plt.show()\n",
    "\n",
    "    y_fit = sigmoid(x_vals, *params)\n",
    "\n",
    "    dydx = np.gradient(y_fit, x_vals)\n",
    "\n",
    "    if motor == 'Y':\n",
    "        dydx=-dydx\n",
    "\n",
    "    parameters = gauss_fit(x_vals, dydx)\n",
    "\n",
    "    x0_op = parameters[1]\n",
    "    sigma_op = parameters[2]\n",
    "    print(parameters)\n",
    "\n",
    "    gauss_y = gaus(x_vals,*parameters)\n",
    "    fwhm = np.abs(2*np.sqrt(2*np.log(2))*sigma_op)\n",
    "    print(f\"Manually calculated FWHM to {fwhm} mm\")\n",
    "    plt.plot(x_vals, dydx, label = 'derivative')\n",
    "    plt.plot(x_vals, gauss_y,label='Gaussian fit',color ='orange')\n",
    "    plt.fill_between(x_vals,gauss_y,color='orange',alpha=0.5)\n",
    "\n",
    "    try:\n",
    "        fwhm, full = calc_fwhm(gauss_y, x_vals)\n",
    "        print( \"Scipy calculated FWHM = {0} mm\".format( fwhm ) )\n",
    "        print( \"Scipy calculated 1/e2 = {0} mm\".format( full ) )\n",
    "    except:\n",
    "        print('scipy peak finding failed')\n",
    "\n",
    "    plt.axvspan(x0_op+fwhm/2,x0_op-fwhm/2, color='green', alpha=0.75, lw=0, label='FWHM = {0} mm'.format(fwhm))\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    #plt.savefig(output_name+filename.split('/')[-1]+'FWHM_{0}.png'.format(fwhm))"
   ],
   "id": "80460b35a33f1ecf"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "x_data=knife_edge(motor='X', d=d, beamline=beamline, steps=40, start=-17.834, step_size=0.002, offset=0.006)\n",
    "plot_knife_edge(x_data, motor='X')"
   ],
   "id": "5454eb901526eb3"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "y_data=knife_edge(motor='Y', d=d, beamline=beamline, steps=40, start=0.0, step_size=0.002, offset=0.03)\n",
    "plot_knife_edge(y_data, motor='X')"
   ],
   "id": "f8e0541c9e92e4e6"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "d6dd221d89d3b71a"
  }
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