{
"cells": [
{
"cell_type": "code",
"execution_count": 24,
"id": "0dc0eac1-8214-4d13-b3d6-55d28ed5d86a",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy.optimize import root_scalar\n",
"import xraylib\n",
"import matplotlib.pyplot as plt\n",
"import epics\n",
"import time\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "07b6fc70-6c8a-44bb-a25d-afd559cd9817",
"metadata": {},
"outputs": [],
"source": [
"# Beamline input block\n",
"energy = 15000.0 # Energy in eV\n",
"energy_keV = energy/1000.0 # Energy in keV\n",
"wl = 1239.84 / (energy * 10**9)\n",
"d_StoL1 = 51.9 # Source-to-CRL1 distance, in m\n",
"d_StoL2 = 62.1 # Source-to-CRL2 distance, in m\n",
"d_Stof = 66.2 # Source-to-focus distance, in m\n",
"\n",
"#slit1_H = 500.0e-6 # H slit size before CRL 1\n",
"#slit1_V = 300.0e-6 # V slit size before CRL 1\n",
"#slit2_H = 500.0e-6 # H slit size before CRL 2\n",
"#slit2_V = 300.0e-6 # V slit size before CRL 2"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f7317f0f-e9f5-461c-8576-8f7d03113fd4",
"metadata": {},
"outputs": [],
"source": [
"# CRL input block\n",
"d_min = 3.0e-5 # Minimum thickness at the apex in m\n",
"stack_d = 50.0e-3 # Stack thickness in m\n",
"L1_n = np.array([1, 1, 1, 1, 1, 1, 2, 4, 8, 16]) # CRL1 number of lenses in each stack\n",
"L1_R = np.array([2.0e-3, 1.0e-3, 5.0e-4, 3.0e-4, 2.0e-4, 1.0e-4, 1.0e-4, 1.0e-4, 1.0e-4, 1.0e-4]) # CRL1 lens radius in each stack\n",
"L1_mater= np.array([\"Be\", \"Be\", \"Be\", \"Be\", \"Be\", \"Be\", \"Be\", \"Be\", \"Be\", \"Be\"]) # CRL1 lens material in each stack\n",
"L1_loc = np.array([4.5, 3.5, 2.5, 1.5, 0.5, -0.5, -1.5, -2.5, -3.5, -4.5])*stack_d # CRL1 lens stack location relative to center stack, positive means upstream\n",
"L1_HE = np.array([1.0e-6, 1.0e-6, 1.0e-6, 1.0e-6, 1.0e-6, 1.0e-6, 1.4e-6, 2.0e-6, 2.8e-6, 4.0e-6]) # CRL1 lens RMS thickness error\n",
"L2_n = np.array([1, 1, 1, 1, 1, 1, 2, 4, 8, 16]) # CRL2 number of lenses in each stack\n",
"L2_R = np.array([2.0e-3, 1.0e-3, 5.0e-4, 3.0e-4, 2.0e-4, 1.0e-4, 1.0e-4, 1.0e-4, 1.0e-4, 1.0e-4]) # CRL2 lens radius in each stack\n",
"L2_mater= np.array([\"Be\", \"Be\", \"Be\", \"Be\", \"Be\", \"Be\", \"Be\", \"Be\", \"Be\", \"Be\"]) # CRL2 lens material in each stack\n",
"L2_loc = np.array([4.5, 3.5, 2.5, 1.5, 0.5, -0.5, -1.5, -2.5, -3.5, -4.5])*stack_d # CRL2 lens stack location relative to center stack, positive means upstream\n",
"L2_HE = np.array([1.0e-6, 1.0e-6, 1.0e-6, 1.0e-6, 1.0e-6, 1.0e-6, 1.4e-6, 2.0e-6, 2.8e-6, 4.0e-6]) # CRL2 lens RMS thickness error\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "c1d1228a-2bdd-47d4-ba9e-0d6e79c635a4",
"metadata": {},
"outputs": [],
"source": [
"# Source size input block\n",
"L_und = 4.7 # undulator length\n",
"sigmaH_e = 14.8e-6 # Sigma electron source size in H direction in m\n",
"sigmaV_e = 3.7e-6 # Sigma electron source size in V direction in m\n",
"sigmaHp_e = 2.8e-6 # Sigma electron divergence in H direction in rad\n",
"sigmaVp_e = 1.5e-6 # Sigma electron divergence in V direction in rad\n",
"sigmaH = (sigmaH_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaV = (sigmaV_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaHp = (sigmaHp_e**2 + wl/L_und/2)**0.5\n",
"sigmaVp = (sigmaVp_e**2 + wl/L_und/2)**0.5\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b6ff87f5-75b3-43bd-94fd-ec8fd880ab7f",
"metadata": {},
"outputs": [],
"source": [
"# Lookup table where each entry is a tuple (column1, column2)\n",
"Lens_diameter_table = [\n",
" (50, 450.0),\n",
" (100, 632.0),\n",
" (200, 894.0),\n",
" (300, 1095.0),\n",
" (500, 1414.0),\n",
" (1000, 2000.0),\n",
" (1500, 2450.0),\n",
"]\n",
"\n",
"# Convert the lookup table to a dictionary for faster lookup\n",
"Lens_diameter_dict = {int(col1): col2 for col1, col2 in Lens_diameter_table}\n",
"\n",
"def lookup_diameter(lens_radius):\n",
" # Convert the input float to an integer\n",
" input_int = int(round(lens_radius*1.0e6))\n",
" return Lens_diameter_dict.get(input_int, (lens_radius*1.0e6)**0.5*63.222+ 0.73)/1.0e6\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "401b6573-1cbb-4503-bbda-e864bd8c5969",
"metadata": {},
"outputs": [],
"source": [
"def index_to_binary_list(index, length):\n",
" \"\"\"\n",
" Converts an index number to its binary representation as a list of digits,\n",
" and pads the list with zeros in front to achieve the desired length.\n",
"\n",
" Parameters:\n",
" index (int): The index number to be converted.\n",
" length (int): The desired length of the binary list.\n",
"\n",
" Returns:\n",
" list: A list of digits representing the binary representation of the index.\n",
" \"\"\"\n",
" # Convert the index to a binary string and remove the '0b' prefix\n",
" binary_str = bin(index)[2:]\n",
"\n",
" # Pad the binary string with zeros in front to achieve the desired length\n",
" #padded_binary_str = binary_str.zfill(length)\n",
"\n",
" # Reverse the binary string\n",
" reversed_binary_str = binary_str[::-1]\n",
"\n",
" # Convert the reversed binary string to a list of integers\n",
" binary_list = [int(digit) for digit in reversed_binary_str]\n",
"\n",
" # Pad the list with zeros at the end to achieve the desired length\n",
" while len(binary_list) < length:\n",
" binary_list.append(0)\n",
" return binary_list"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "7c6792ad-6436-4864-bdb9-68ad571d13ba",
"metadata": {},
"outputs": [],
"source": [
"def binary_list_to_index(binary_list, length):\n",
" \"\"\"\n",
" Converts a list of binary digits in reverse order to its integer representation,\n",
" padding the list with zeros at the end to have a fixed number of elements.\n",
"\n",
" Parameters:\n",
" binary_list (list): A list of digits representing the binary number in reverse order.\n",
" length (int): The fixed number of elements the list should have.\n",
"\n",
" Returns:\n",
" int: The integer representation of the binary number.\n",
" \"\"\"\n",
" # Pad the list with zeros at the end to achieve the desired length\n",
" while len(binary_list) < length:\n",
" binary_list.append(0)\n",
"\n",
" # Convert the binary list to an integer\n",
" index = 0\n",
" for i, digit in enumerate(binary_list):\n",
" index += digit * 2**i\n",
"\n",
" return index"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "620e8ed0-0098-463f-8035-0e69ee7bc216",
"metadata": {},
"outputs": [],
"source": [
"def materials_to_deltas(material_list, energy):\n",
" \"\"\"\n",
" Convert a list of material names to a list of delta values at a given energy.\n",
"\n",
" Parameters:\n",
" material_list (list): A list of material names.\n",
" energy (float): The energy in keV.\n",
"\n",
" Returns:\n",
" list: A list of delta values for the given materials at the given energy.\n",
" \"\"\"\n",
" # The list to store delta values\n",
" delta_list = []\n",
"\n",
" # Iterate through each material in the input list\n",
" for material in material_list:\n",
" # Compute the delta value for the current material at the given energy\n",
" Z = xraylib.SymbolToAtomicNumber(material)\n",
" density = xraylib.ElementDensity(Z)\n",
" delta = 1.0-xraylib.Refractive_Index_Re(material, energy, density)\n",
"\n",
" # Add the delta value to the delta list\n",
" delta_list.append(delta)\n",
"\n",
" return delta_list"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "a5c53be6-af6f-485d-838f-3a0936060e6e",
"metadata": {},
"outputs": [],
"source": [
"def materials_to_linear_attenuation(material_list, energy):\n",
" \"\"\"\n",
" Convert a list of material names to a list of linear attenuation coefficients at a given energy.\n",
"\n",
" Parameters:\n",
" material_list (list): A list of material names.\n",
" energy (float): The energy in keV.\n",
"\n",
" Returns:\n",
" list: A list of linear attenuation coefficient values (in m^-1) for the given materials at the given energy.\n",
" \"\"\"\n",
" # The list to store linear attenuation coefficient values\n",
" mu_list = []\n",
"\n",
" # Iterate through each material in the input list\n",
" for material in material_list:\n",
" # Compute the delta value for the current material at the given energy\n",
" Z = xraylib.SymbolToAtomicNumber(material)\n",
" density = xraylib.ElementDensity(Z)\n",
" # Compute the mass attenuation coefficient in cm^2/g\n",
" #mass_attenuation = xraylib.CS_Photo(Z, energy)\n",
" mass_attenuation = xraylib.CS_Total(Z, energy)\n",
" # Convert mass attenuation coefficient to linear attenuation coefficient in m^-1\n",
" mu = mass_attenuation * density * 100.0\n",
" # Add the linear attenuation coefficient value to the list\n",
" mu_list.append(mu)\n",
"\n",
" return mu_list\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "3719d6b7-f3e1-439d-93fd-a7e546c5b747",
"metadata": {},
"outputs": [],
"source": [
"def absorptionaperture(x, n1mud, sigma, n1mur):\n",
" numerator = np.exp(-(x**2/(2*sigma**2))) * np.exp(-n1mur*(x**2) - n1mud)\n",
" denominator = np.exp(-n1mud)\n",
" return numerator / denominator - 0.5"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "25851616-c4ae-4d00-b5de-bb2395c60074",
"metadata": {},
"outputs": [],
"source": [
"def find_levels(array, levels, direction='forward'):\n",
" \"\"\"\n",
" Find the first indices at which the array crosses specified levels and the corresponding crossed values.\n",
"\n",
" Parameters:\n",
" array (numpy.ndarray): An array of numbers.\n",
" levels (float or numpy.ndarray): A number or an array of levels to find crossings.\n",
" direction (str, optional): The searching direction. Defaults to 'forward'.\n",
" Can be either 'forward' or 'backward'.\n",
"\n",
" Returns:\n",
" tuple: A tuple containing two arrays:\n",
" - An array of first indices at which the array crosses the specified levels.\n",
" - An array of first crossed values at the corresponding indices.\n",
" \"\"\"\n",
"\n",
" # Convert a single level to a numpy array\n",
" if isinstance(levels, (int, float)):\n",
" levels = np.array([levels])\n",
"\n",
" indices = []\n",
" values = []\n",
"\n",
" # Compute the max and min of the array ignoring NaNs\n",
" max_val = np.nanmax(array)\n",
" min_val = np.nanmin(array)\n",
"\n",
" for level in levels:\n",
" # If level is out of bounds\n",
" if level > max_val or level < min_val:\n",
" indices.append(-1)\n",
" values.append(np.nan)\n",
" continue\n",
"\n",
" crossings = []\n",
"\n",
" if direction == 'forward':\n",
" for i in range(1, len(array)):\n",
" if np.isnan(array[i - 1]) or np.isnan(array[i]):\n",
" continue\n",
" if (array[i - 1] < level <= array[i]) or (array[i - 1] > level >= array[i]):\n",
" crossings.append(i - 1)\n",
" break\n",
" elif direction == 'backward':\n",
" for i in range(len(array) - 2, -1, -1):\n",
" if np.isnan(array[i + 1]) or np.isnan(array[i]):\n",
" continue\n",
" if (array[i + 1] < level <= array[i]) or (array[i + 1] > level >= array[i]):\n",
" crossings.append(i)\n",
" break\n",
" else:\n",
" raise ValueError(\"Invalid direction. It should be either 'forward' or 'backward'.\")\n",
"\n",
" if len(crossings) > 0:\n",
" idx = crossings[0]\n",
" indices.append(idx)\n",
" values.append(array[idx])\n",
" else:\n",
" # In case no crossing is found within the range\n",
" indices.append(-1)\n",
" values.append(np.nan)\n",
"\n",
" return np.array(indices), np.array(values)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "a34dc832-bc2c-4cb8-8cf8-e4632a151b66",
"metadata": {},
"outputs": [],
"source": [
"def Zoom_CRL2D_control(fsize):\n",
"\n",
" L1_D = np.zeros(L1_R.size) # CRL1 diameters for each stack\n",
" for i in range(L1_R.size):\n",
" L1_D[i] = lookup_diameter(L1_R[i])\n",
" L1_delta = materials_to_deltas(L1_mater, energy_keV) # delta values for CRL1 stacks\n",
" L1_mu = materials_to_linear_attenuation(L1_mater, energy_keV) # mu values for CRL1 stacks\n",
" L1_Feq = L1_R/(2*L1_n*L1_delta)+L1_loc # CRL1 equivalent f in m for each stack\n",
"\n",
" L2_D = np.zeros(L2_R.size) # CRL2 diameters for each stack\n",
" for i in range(L2_R.size):\n",
" L2_D[i] = lookup_diameter(L2_R[i])\n",
" L2_delta = materials_to_deltas(L2_mater, energy_keV) # Delta values for CRL2 stacks\n",
" L2_mu = materials_to_linear_attenuation(L2_mater, energy_keV) # mu values for CRL2 stacks\n",
" L2_Feq = L2_R/(2*L2_n*L2_delta)+L2_loc # CRL2 equivalent f in m for each stack\n",
"\n",
" L1_index_n = 2**L1_Feq.size # Total number of combinations for CRL1\n",
" L1_invF_list = np.zeros(L1_index_n) # List of equivalent 1/f in m^-1 for CRL1\n",
" for i in range(L1_index_n):\n",
" L1_invF_list[i] = np.sum(index_to_binary_list(i, L1_Feq.size)/L1_Feq)\n",
" # Sort the L1_invF list (to avoid zigzagging)\n",
" L1_invF_list_sort_indices = np.argsort(L1_invF_list)\n",
" L1_invF_list_sorted = L1_invF_list[L1_invF_list_sort_indices]\n",
"\n",
" q1_list = 1/(L1_invF_list_sorted - 1/d_StoL1) # focal position of CRL1 for all configurations (sorted)\n",
" # may be limit to cases q1 beyond sample???\n",
"\n",
" L2_index_n = 2**L2_Feq.size # Total number of combinations for CRL2\n",
" L2_invF_list = np.zeros(L2_index_n) # List of equivalent 1/f in m^-1 for CRL2\n",
" for i in range(L2_index_n):\n",
" L2_invF_list[i] = np.sum(index_to_binary_list(i, L2_Feq.size)/L2_Feq)\n",
" # Sort the L2_invF list (to avoid zigzagging)\n",
" L2_invF_list_sort_indices = np.argsort(L2_invF_list)\n",
" L2_invF_list_sorted = L2_invF_list[L2_invF_list_sort_indices]\n",
"\n",
" # Start generating focal size list as a function of CRL1 setting\n",
" sigma1H = (sigmaH**2 + (sigmaHp*d_StoL1)**2)**0.5 # sigma beam size before CRL1\n",
" sigma1V = (sigmaV**2 + (sigmaVp*d_StoL1)**2)**0.5 # sigma beam size before CRL1\n",
" L1_n1mud_list = np.zeros(L1_index_n) # List of n1*mu*d_min for all possible CRL1 configurations\n",
" L1_n1muR_list = np.zeros(L1_index_n) # List of n1*mu/R for all possible CRL1 configurations\n",
" aperL1H_list = np.zeros(L1_index_n) # absorption H aperture of CRL1 for all configurations\n",
" aperL1V_list = np.zeros(L1_index_n) # absorption V aperture of CRL1 for all configurations\n",
" diameter1_list = np.zeros(L1_index_n) # CRL1 diameter for all possible configurations\n",
" FWHM1H_list = np.zeros(L1_index_n) # H focal size at the focus of CRL1\n",
" FWHM1V_list = np.zeros(L1_index_n) # V focal size at the focus of CRL1\n",
" sigma2H_list = np.zeros(L1_index_n) # sigma beam size before CRL2\n",
" sigma2V_list = np.zeros(L1_index_n) # sigma beam size before CRL2\n",
" Strehl1_list = np.zeros(L1_index_n) # Strehl ratio based on lens thickness error\n",
"\n",
" for i in range(L1_index_n):\n",
" # absorption aperture is a function of CRL absorption/physical aperture, incident beam size, and physical slits\n",
" L1_n1mud_list[i] = np.sum(index_to_binary_list(L1_invF_list_sort_indices[i], L1_Feq.size)*np.array(L1_mu*L1_n*d_min))\n",
" L1_n1muR_list[i] = np.sum(index_to_binary_list(L1_invF_list_sort_indices[i], L1_Feq.size)*np.array(L1_mu*L1_n/L1_R))\n",
" solution = root_scalar(absorptionaperture, args=(L1_n1mud_list[i], sigma1H, L1_n1muR_list[i]), bracket=[0.0, 2*sigma1H], method='bisect')\n",
" aperL1H_list[i] = solution.root*2.0\n",
" solution = root_scalar(absorptionaperture, args=(L1_n1mud_list[i], sigma1V, L1_n1muR_list[i]), bracket=[0.0, 2*sigma1V], method='bisect')\n",
" aperL1V_list[i] = solution.root*2.0\n",
" mask = (np.array(index_to_binary_list(L1_invF_list_sort_indices[i], L1_Feq.size)) == 1)\n",
" if np.all(mask == False):\n",
" diameter1_list[i] = np.inf\n",
" else:\n",
" diameter1_list[i] = np.min(L1_D[mask])\n",
" aperL1H_list[i] = min(aperL1H_list[i], diameter1_list[i], slit1_H)\n",
" aperL1V_list[i] = min(aperL1V_list[i], diameter1_list[i], slit1_V)\n",
" phase_error_tmp = np.linalg.norm(index_to_binary_list(L1_invF_list_sort_indices[i], L1_Feq.size)*np.array(L1_HE*L1_delta)*2*np.pi/wl) # RMS phase error of the lens stack\n",
" Strehl1_list[i] = np.exp(-phase_error_tmp**2)\n",
"\n",
" # FWHMbeam size at CRL1 focus\n",
" FWHM1H_list = ((0.88*wl*q1_list/aperL1H_list)**2 + (2.355*sigmaH*q1_list/d_StoL1)**2)**0.5\n",
" FWHM1V_list = ((0.88*wl*q1_list/aperL1V_list)**2 + (2.355*sigmaV*q1_list/d_StoL1)**2)**0.5\n",
" if flag_HE:\n",
" FWHM1H_list *= (Strehl1_list)**(-0.5)\n",
" FWHM1V_list *= (Strehl1_list)**(-0.5)\n",
" # Sigma beam size before CRL2\n",
" sigma2H_list = (((0.88*wl*(d_StoL2-d_StoL1))/aperL1H_list)**2 + (aperL1H_list*(1-(d_StoL2-d_StoL1)/q1_list))**2)**0.5/2.355\n",
" sigma2V_list = (((0.88*wl*(d_StoL2-d_StoL1))/aperL1V_list)**2 + (aperL1V_list*(1-(d_StoL2-d_StoL1)/q1_list))**2)**0.5/2.355\n",
"\n",
" p2_list = d_StoL2 - d_StoL1 - q1_list # p2 for CRL2 for all possible CRL1 configurations\n",
" invf2_list = 1.0/p2_list + 1/(d_Stof - d_StoL2) # f2^-1 for CRL2 to match CRL1 for all possible CRL1 configurations\n",
" #L2_config_index = np.zeros(L1_index_n) # CRL2 configueration index to match CRL1\n",
"\n",
" #invf2_indices, invf2_values = find_closest_values_auto(L2_invF_list_sorted, invf2_list)\n",
" #invf2_indices, invf2_values = find_levels_left(L2_invF_list_sorted, invf2_list)\n",
" invf2_indices, invf2_values = find_levels(L2_invF_list_sorted, invf2_list, direction = 'forward')\n",
"\n",
" nan_positions = np.where(invf2_indices == -1)\n",
" invf2_values[nan_positions] = np.nan # only f2^-1 values that can be matched with CRL1\n",
" q2_list = 1/(invf2_values - 1/p2_list)\n",
" dq2_list = q2_list - (d_Stof - d_StoL2)\n",
"\n",
" L2_n2mud_list = np.zeros(L1_index_n) # List of n2*mu*d_min for all possible CRL1 configurations\n",
" L2_n2muR_list = np.zeros(L1_index_n) # List of n2*mu/R for all possible CRL1 configurations\n",
" aperL2H_list = np.zeros(L1_index_n) # absorption H aperture of CRL2 for all CRL1 configurations\n",
" aperL2V_list = np.zeros(L1_index_n) # absorption V aperture of CRL2 for all CRL1 configurations\n",
" diameter2_list = np.zeros(L1_index_n) # CRL2 diameter for all possible CRL1 configurations\n",
" FWHM2H_list = np.zeros(L1_index_n) # H focal size at the focus of CRL2 matching all possible CRL1 configurations\n",
" FWHM2V_list = np.zeros(L1_index_n) # V focal size at the focus of CRL2 matching all possible CRL1 configurations\n",
" FWHM_list = np.zeros(L1_index_n) # Focal size sqrt(H*V) at the focus of CRL2 matching all possible CRL1 configurations\n",
" Strehl2_list = np.zeros(L1_index_n) # Strehl ratio based on lens thickness error\n",
"\n",
" for i in range(L1_index_n):\n",
" if invf2_indices[i] != -1:\n",
" # absorption aperture is a function of CRL absorption/physical aperture, incident beam size, and physical slits\n",
" L2_n2mud_list[i] = np.sum(index_to_binary_list(L2_invF_list_sort_indices[invf2_indices[i]], L2_Feq.size)*np.array(L2_mu*L2_n*d_min))\n",
" L2_n2muR_list[i] = np.sum(index_to_binary_list(L2_invF_list_sort_indices[invf2_indices[i]], L2_Feq.size)*np.array(L2_mu*L2_n/L2_R))\n",
" solution = root_scalar(absorptionaperture, args=(L2_n2mud_list[i], sigma2H_list[i], L2_n2muR_list[i]), bracket=[0.0, 2*sigma2H_list[i]], method='bisect')\n",
" aperL2H_list[i] = solution.root*2.0\n",
" solution = root_scalar(absorptionaperture, args=(L2_n2mud_list[i], sigma2V_list[i], L2_n2muR_list[i]), bracket=[0.0, 2*sigma2V_list[i]], method='bisect')\n",
" aperL2V_list[i] = solution.root*2.0\n",
" mask = (np.array(index_to_binary_list(L2_invF_list_sort_indices[invf2_indices[i]], L2_Feq.size)) == 1)\n",
" if np.all(mask == False):\n",
" diameter2_list[i] = np.inf\n",
" else:\n",
" diameter2_list[i] = np.min(L2_D[mask])\n",
" aperL2H_list[i] = min(aperL2H_list[i], diameter2_list[i], slit2_H)\n",
" aperL2V_list[i] = min(aperL2V_list[i], diameter2_list[i], slit2_V)\n",
" phase_error_tmp = np.linalg.norm(index_to_binary_list(L2_invF_list_sort_indices[invf2_indices[i]], L2_Feq.size)*np.array(L2_HE*L2_delta)*2*np.pi/wl)\n",
" Strehl2_list[i] = np.exp(-phase_error_tmp**2)\n",
" aperL2H_list[nan_positions] = np.nan\n",
" aperL2V_list[nan_positions] = np.nan\n",
" Strehl2_list[nan_positions] = np.nan\n",
"\n",
" # FWHMbeam size at focus\n",
" FWHM2H_list = ((0.88*wl*q2_list/aperL2H_list)**2 + (FWHM1H_list*q2_list/p2_list)**2)**0.5\n",
" FWHM2V_list = ((0.88*wl*q2_list/aperL2V_list)**2 + (FWHM1V_list*q2_list/p2_list)**2)**0.5\n",
" if flag_HE:\n",
" FWHM2H_list *= (Strehl2_list)**(-0.5)\n",
" FWHM2V_list *= (Strehl2_list)**(-0.5)\n",
" FWHM_list = (FWHM2H_list*FWHM2V_list)**0.5\n",
"\n",
" #index, value = find_closest_values_auto(FWHM_list, fsize)\n",
" #indices, values = find_levels_left(FWHM_list, fsize)\n",
" indices, values = find_levels(FWHM_list, fsize, direction = 'forward')\n",
" index = indices[0]\n",
" if index == -1:\n",
" print(f\"Cannot achieve the focal size {fsize*1.0e6:.2f} μm\")\n",
" return\n",
"\n",
" # Print results\n",
" print(\"======== Find size at focus ========================================\")\n",
" print(f\"Energy: {energy_keV} keV\")\n",
" print(f\"CRL1 configuration index in sorted list is {index}\")\n",
" print(f\"CRL1 configuration index is {L1_invF_list_sort_indices[index]} or {index_to_binary_list(L1_invF_list_sort_indices[index], L1_Feq.size)}\")\n",
" print(f\"CRL1 f is {1/L1_invF_list_sorted[index]:.2f} m, focus at q1 = {q1_list[index]:.2f} m ({q1_list[index]-(d_Stof-d_StoL1):.2f} m from sample)\")\n",
" #print(f\"CRL1 f is {1/L1_invF_list[L1_invF_list_sort_indices[index]]} m\")\n",
" print(f\"CRL2 configuration index in sorted list is {invf2_indices[index]}\")\n",
" print(f\"CRL2 configuration index is {L2_invF_list_sort_indices[invf2_indices[index]]} or {index_to_binary_list(L2_invF_list_sort_indices[invf2_indices[index]], L2_Feq.size)}\")\n",
" print(f\"CRL2 f is {1/invf2_values[index]:.2f} m\")\n",
" print(f\"Focal size is {FWHM2H_list[index]*1.0e6:.2f} μm x {FWHM2V_list[index]*1.0e6:.2f} μm at the focal point ({dq2_list[index]*1e3:.1f} mm from sample)\")\n",
"\n",
" FWHM2H_atsample_list = (FWHM2H_list**2 + (aperL2H_list*dq2_list/q2_list)**2)**0.5\n",
" FWHM2V_atsample_list = (FWHM2V_list**2 + (aperL2V_list*dq2_list/q2_list)**2)**0.5\n",
" FWHM_atsample_list = (FWHM2H_atsample_list*FWHM2V_atsample_list)**0.5\n",
" #indices, values = find_levels_left(FWHM_atsample_list, fsize)\n",
" indices, values = find_levels(FWHM_atsample_list, fsize, direction = 'forward')\n",
" index2 = indices[0]\n",
" if index2 == -1:\n",
" print(f\"Cannot achieve the bame size {fsize*1.0e6:.2f} μm at sample\")\n",
" return\n",
" print(\"======== Find size at sample =======================================\")\n",
" print(f\"CRL1 configuration index in sorted list is {index2}\")\n",
" print(f\"CRL1 configuration index is {L1_invF_list_sort_indices[index2]} or {index_to_binary_list(L1_invF_list_sort_indices[index2], L1_Feq.size)}\")\n",
" print(f\"CRL1 f is {1/L1_invF_list_sorted[index2]:.2f} m, focus at q1 = {q1_list[index2]:.2f} m ({q1_list[index2]-(d_Stof-d_StoL1):.2f} m from sample)\")\n",
" print(f\"CRL2 configuration index in sorted list is {invf2_indices[index2]}\")\n",
" print(f\"CRL2 configuration index is {L2_invF_list_sort_indices[invf2_indices[index2]]} or {index_to_binary_list(L2_invF_list_sort_indices[invf2_indices[index2]], L2_Feq.size)}\")\n",
" print(f\"CRL2 f is {1/invf2_values[index2]:.2f} m\")\n",
" print(f\"Beam size is {FWHM2H_atsample_list[index2]*1.0e6:.2f} μm x {FWHM2V_atsample_list[index2]*1.0e6:.2f} μm at the sample position)\")\n",
"\n",
" return"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "07e34dff-84dd-48d7-bc91-6ed16507ccf5",
"metadata": {},
"outputs": [],
"source": [
"def Zoom_CRL2D_focuscal(index1, index2):\n",
" # Preparation block\n",
" L1_D = np.zeros(L1_R.size) # CRL1 diameters for each stack\n",
" for i in range(L1_R.size):\n",
" L1_D[i] = lookup_diameter(L1_R[i])\n",
" L1_delta = materials_to_deltas(L1_mater, energy_keV) # delta values for CRL1 stacks\n",
" L1_mu = materials_to_linear_attenuation(L1_mater, energy_keV) # mu values for CRL1 stacks\n",
" L1_Feq = L1_R/(2*L1_n*L1_delta) + L1_loc # CRL1 equivalent f in m for each stack\n",
" L2_D = np.zeros(L2_R.size) # CRL2 diameters for each stack\n",
" for i in range(L2_R.size):\n",
" L2_D[i] = lookup_diameter(L2_R[i])\n",
" L2_delta = materials_to_deltas(L2_mater, energy_keV) # Delta values for CRL2 stacks\n",
" L2_mu = materials_to_linear_attenuation(L2_mater, energy_keV) # mu values for CRL2 stacks\n",
" L2_Feq = L2_R/(2*L2_n*L2_delta) + L2_loc # CRL2 equivalent f in m for each stack\n",
"\n",
" # Calculation block\n",
" L1_invF = np.sum(index_to_binary_list(index1, L1_Feq.size)/L1_Feq) # f^-1 for CRL1\n",
" L2_invF = np.sum(index_to_binary_list(index2, L2_Feq.size)/L2_Feq) # f^-1 for CRL1\n",
" q1 = 1/(L1_invF - 1/d_StoL1) # focal position of CRL1\n",
" sigma1H = (sigmaH**2 + (sigmaHp*d_StoL1)**2)**0.5 # sigma beam size before CRL1\n",
" sigma1V = (sigmaV**2 + (sigmaVp*d_StoL1)**2)**0.5 # sigma beam size before CRL1\n",
"\n",
" # absorption aperture is a function of CRL absorption/physical aperture, incident beam size, and physical slits\n",
" L1_n1mud = np.sum(index_to_binary_list(index1, L1_Feq.size)*np.array(L1_mu*L1_n*d_min))\n",
" L1_n1muR = np.sum(index_to_binary_list(index1, L1_Feq.size)*np.array(L1_mu*L1_n/L1_R))\n",
" solution = root_scalar(absorptionaperture, args=(L1_n1mud, sigma1H, L1_n1muR), bracket=[0.0, 2*sigma1H], method='bisect')\n",
" aperL1H = solution.root*2.0\n",
" solution = root_scalar(absorptionaperture, args=(L1_n1mud, sigma1V, L1_n1muR), bracket=[0.0, 2*sigma1V], method='bisect')\n",
" aperL1V = solution.root*2.0\n",
" mask = (np.array(index_to_binary_list(index1, L1_Feq.size)) == 1)\n",
" if np.all(mask == False):\n",
" diameter1 = np.inf\n",
" else:\n",
" diameter1 = np.min(L1_D[mask])\n",
" aperL1H = min(aperL1H, diameter1, slit1_H)\n",
" aperL1V = min(aperL1V, diameter1, slit1_V)\n",
" phase_error_tmp1 = np.linalg.norm(index_to_binary_list(index1, L1_Feq.size)*np.array(L1_HE*L1_delta)*2*np.pi/wl)\n",
" Strehl1 = np.exp(-phase_error_tmp1**2)\n",
"\n",
" # FWHMbeam size at CRL1 focus\n",
" FWHM1H = ((0.88*wl*q1/aperL1H)**2 + (2.355*sigmaH*q1/d_StoL1)**2)**0.5\n",
" FWHM1V = ((0.88*wl*q1/aperL1V)**2 + (2.355*sigmaV*q1/d_StoL1)**2)**0.5\n",
" if flag_HE:\n",
" FWHM1H *= (Strehl1)**(-0.5)\n",
" FWHM1V *= (Strehl1)**(-0.5)\n",
"\n",
" # Sigma beam size before CRL2\n",
" sigma2H = (((0.88*wl*(d_StoL2-d_StoL1))/aperL1H)**2 + (aperL1H*(1-(d_StoL2-d_StoL1)/q1))**2)**0.5/2.355\n",
" sigma2V = (((0.88*wl*(d_StoL2-d_StoL1))/aperL1V)**2 + (aperL1V*(1-(d_StoL2-d_StoL1)/q1))**2)**0.5/2.355\n",
"\n",
" p2 = d_StoL2 - d_StoL1 - q1 # p2 for CRL2\n",
" q2 = 1/(L2_invF - 1/p2) # q2 for CRL2 calculated from CRL2 index and p2\n",
" dq2 = q2 - (d_Stof - d_StoL2) # off focus distance\n",
"\n",
" L2_n2mud = np.sum(index_to_binary_list(index2, L2_Feq.size)*np.array(L2_mu*L2_n*d_min))\n",
" L2_n2muR = np.sum(index_to_binary_list(index2, L2_Feq.size)*np.array(L2_mu*L2_n/L2_R))\n",
" solution = root_scalar(absorptionaperture, args=(L2_n2mud, sigma2H, L2_n2muR), bracket=[0.0, 2*sigma2H], method='bisect')\n",
"\n",
" aperL2H = solution.root*2.0\n",
" solution = root_scalar(absorptionaperture, args=(L2_n2mud, sigma2V, L2_n2muR), bracket=[0.0, 2*sigma2V], method='bisect')\n",
" aperL2V = solution.root*2.0\n",
" mask = (np.array(index_to_binary_list(index2, L2_Feq.size)) == 1)\n",
" if np.all(mask == False):\n",
" diameter2 = np.inf\n",
" else:\n",
" diameter2 = np.min(L2_D[mask])\n",
" aperL2H = min(aperL2H, diameter2, slit2_H)\n",
" aperL2V = min(aperL2V, diameter2, slit2_V)\n",
" phase_error_tmp2 = np.linalg.norm(index_to_binary_list(index2, L2_Feq.size)*np.array(L2_HE*L2_delta)*2*np.pi/wl)\n",
" Strehl2 = np.exp(-phase_error_tmp2**2)\n",
"\n",
" FWHM2H = ((0.88*wl*q2/aperL2H)**2 + (FWHM1H*q2/p2)**2)**0.5\n",
" FWHM2V = ((0.88*wl*q2/aperL2V)**2 + (FWHM1V*q2/p2)**2)**0.5\n",
" if flag_HE:\n",
" FWHM2H *= (Strehl2)**(-0.5)\n",
" FWHM2V *= (Strehl2)**(-0.5)\n",
"\n",
" FWHM2H_atsample = (FWHM2H**2 + (aperL2H*dq2/q2)**2)**0.5\n",
" FWHM2V_atsample = (FWHM2V**2 + (aperL2V*dq2/q2)**2)**0.5\n",
"\n",
" # print(\"====================================================================\")\n",
" # print(f\"Energy: {energy_keV} keV\")\n",
" # print(f\"CRL1 configuration index is {index1} or {index_to_binary_list(index1, L1_Feq.size)}\")\n",
" # print(f\"CRL1 f is {1/L1_invF:.2f} m, focus at q1 = {q1:.2f} m ({q1-(d_Stof-d_StoL1):.2f} m from sample)\")\n",
" # print(f\"CRL2 configuration index is {index2} or {index_to_binary_list(index2, L2_Feq.size)}\")\n",
" # print(f\"CRL2 f is {1/L2_invF:.2f} m\")\n",
" # print(f\"Focal size is {FWHM2H*1.0e6:.2f} μm x {FWHM2V*1.0e6:.2f} μm at the focal point ({dq2*1e3:.1f} mm from sample)\")\n",
" # print(f\"Beam size is {FWHM2H_atsample*1.0e6:.2f} μm x {FWHM2V_atsample*1.0e6:.2f} μm at sample position\")\n",
"\n",
" return (FWHM2H_atsample*FWHM2V_atsample)**0.5\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fc2652f3-e27a-4710-a35c-cf4abe05afd9",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 16,
"id": "353a4c4c-3b5a-4944-b4a8-ce3ae8f70480",
"metadata": {},
"outputs": [],
"source": [
"def Zoom_CRL2D_lookup():\n",
"\n",
" L1_D = np.zeros(L1_R.size) # CRL1 diameters for each stack\n",
" for i in range(L1_R.size):\n",
" L1_D[i] = lookup_diameter(L1_R[i])\n",
" L1_delta = materials_to_deltas(L1_mater, energy_keV) # delta values for CRL1 stacks\n",
" L1_mu = materials_to_linear_attenuation(L1_mater, energy_keV) # mu values for CRL1 stacks\n",
" L1_Feq = L1_R/(2*L1_n*L1_delta)+L1_loc # CRL1 equivalent f in m for each stack\n",
"\n",
" L2_D = np.zeros(L2_R.size) # CRL2 diameters for each stack\n",
" for i in range(L2_R.size):\n",
" L2_D[i] = lookup_diameter(L2_R[i])\n",
" L2_delta = materials_to_deltas(L2_mater, energy_keV) # Delta values for CRL2 stacks\n",
" L2_mu = materials_to_linear_attenuation(L2_mater, energy_keV) # mu values for CRL2 stacks\n",
" L2_Feq = L2_R/(2*L2_n*L2_delta)+L2_loc # CRL2 equivalent f in m for each stack\n",
"\n",
" L1_index_n = 2**L1_Feq.size # Total number of combinations for CRL1\n",
" L1_invF_list = np.zeros(L1_index_n) # List of equivalent 1/f in m^-1 for CRL1\n",
" for i in range(L1_index_n):\n",
" L1_invF_list[i] = np.sum(index_to_binary_list(i, L1_Feq.size)/L1_Feq)\n",
" # Sort the L1_invF list (to avoid zigzagging)\n",
" L1_invF_list_sort_indices = np.argsort(L1_invF_list)\n",
" L1_invF_list_sorted = L1_invF_list[L1_invF_list_sort_indices]\n",
"\n",
" q1_list = 1/(L1_invF_list_sorted - 1/d_StoL1) # focal position of CRL1 for all configurations (sorted)\n",
" # may be limit to cases q1 beyond sample???\n",
"\n",
" L2_index_n = 2**L2_Feq.size # Total number of combinations for CRL2\n",
" L2_invF_list = np.zeros(L2_index_n) # List of equivalent 1/f in m^-1 for CRL2\n",
" for i in range(L2_index_n):\n",
" L2_invF_list[i] = np.sum(index_to_binary_list(i, L2_Feq.size)/L2_Feq)\n",
" # Sort the L2_invF list (to avoid zigzagging)\n",
" L2_invF_list_sort_indices = np.argsort(L2_invF_list)\n",
" L2_invF_list_sorted = L2_invF_list[L2_invF_list_sort_indices]\n",
"\n",
" # Start generating focal size list as a function of CRL1 setting\n",
" sigma1H = (sigmaH**2 + (sigmaHp*d_StoL1)**2)**0.5 # sigma beam size before CRL1\n",
" sigma1V = (sigmaV**2 + (sigmaVp*d_StoL1)**2)**0.5 # sigma beam size before CRL1\n",
" L1_n1mud_list = np.zeros(L1_index_n) # List of n1*mu*d_min for all possible CRL1 configurations\n",
" L1_n1muR_list = np.zeros(L1_index_n) # List of n1*mu/R for all possible CRL1 configurations\n",
" aperL1H_list = np.zeros(L1_index_n) # absorption H aperture of CRL1 for all configurations\n",
" aperL1V_list = np.zeros(L1_index_n) # absorption V aperture of CRL1 for all configurations\n",
" diameter1_list = np.zeros(L1_index_n) # CRL1 diameter for all possible configurations\n",
" FWHM1H_list = np.zeros(L1_index_n) # H focal size at the focus of CRL1\n",
" FWHM1V_list = np.zeros(L1_index_n) # V focal size at the focus of CRL1\n",
" sigma2H_list = np.zeros(L1_index_n) # sigma beam size before CRL2\n",
" sigma2V_list = np.zeros(L1_index_n) # sigma beam size before CRL2\n",
" Strehl1_list = np.zeros(L1_index_n) # Strehl ratio based on lens thickness error\n",
"\n",
" for i in range(L1_index_n):\n",
" # absorption aperture is a function of CRL absorption/physical aperture, incident beam size, and physical slits\n",
" L1_n1mud_list[i] = np.sum(index_to_binary_list(L1_invF_list_sort_indices[i], L1_Feq.size)*np.array(L1_mu*L1_n*d_min))\n",
" L1_n1muR_list[i] = np.sum(index_to_binary_list(L1_invF_list_sort_indices[i], L1_Feq.size)*np.array(L1_mu*L1_n/L1_R))\n",
" solution = root_scalar(absorptionaperture, args=(L1_n1mud_list[i], sigma1H, L1_n1muR_list[i]), bracket=[0.0, 2*sigma1H], method='bisect')\n",
" aperL1H_list[i] = solution.root*2.0\n",
" solution = root_scalar(absorptionaperture, args=(L1_n1mud_list[i], sigma1V, L1_n1muR_list[i]), bracket=[0.0, 2*sigma1V], method='bisect')\n",
" aperL1V_list[i] = solution.root*2.0\n",
" mask = (np.array(index_to_binary_list(L1_invF_list_sort_indices[i], L1_Feq.size)) == 1)\n",
" if np.all(mask == False):\n",
" diameter1_list[i] = np.inf\n",
" else:\n",
" diameter1_list[i] = np.min(L1_D[mask])\n",
" aperL1H_list[i] = min(aperL1H_list[i], diameter1_list[i], slit1_H)\n",
" aperL1V_list[i] = min(aperL1V_list[i], diameter1_list[i], slit1_V)\n",
" phase_error_tmp = np.linalg.norm(index_to_binary_list(L1_invF_list_sort_indices[i], L1_Feq.size)*np.array(L1_HE*L1_delta)*2*np.pi/wl) # RMS phase error of the lens stack\n",
" Strehl1_list[i] = np.exp(-phase_error_tmp**2)\n",
"\n",
" # FWHMbeam size at CRL1 focus\n",
" FWHM1H_list = ((0.88*wl*q1_list/aperL1H_list)**2 + (2.355*sigmaH*q1_list/d_StoL1)**2)**0.5\n",
" FWHM1V_list = ((0.88*wl*q1_list/aperL1V_list)**2 + (2.355*sigmaV*q1_list/d_StoL1)**2)**0.5\n",
" if flag_HE:\n",
" FWHM1H_list *= (Strehl1_list)**(-0.5)\n",
" FWHM1V_list *= (Strehl1_list)**(-0.5)\n",
" # Sigma beam size before CRL2\n",
" sigma2H_list = (((0.88*wl*(d_StoL2-d_StoL1))/aperL1H_list)**2 + (aperL1H_list*(1-(d_StoL2-d_StoL1)/q1_list))**2)**0.5/2.355\n",
" sigma2V_list = (((0.88*wl*(d_StoL2-d_StoL1))/aperL1V_list)**2 + (aperL1V_list*(1-(d_StoL2-d_StoL1)/q1_list))**2)**0.5/2.355\n",
"\n",
" p2_list = d_StoL2 - d_StoL1 - q1_list # p2 for CRL2 for all possible CRL1 configurations\n",
" invf2_list = 1.0/p2_list + 1/(d_Stof - d_StoL2) # f2^-1 for CRL2 to match CRL1 for all possible CRL1 configurations\n",
" #L2_config_index = np.zeros(L1_index_n) # CRL2 configueration index to match CRL1\n",
"\n",
" #invf2_indices, invf2_values = find_closest_values_auto(L2_invF_list_sorted, invf2_list)\n",
" #invf2_indices, invf2_values = find_levels_left(L2_invF_list_sorted, invf2_list)\n",
" invf2_indices, invf2_values = find_levels(L2_invF_list_sorted, invf2_list, direction = 'forward')\n",
"\n",
" nan_positions = np.where(invf2_indices == -1)\n",
" invf2_values[nan_positions] = np.nan # only f2^-1 values that can be matched with CRL1\n",
" q2_list = 1/(invf2_values - 1/p2_list)\n",
" dq2_list = q2_list - (d_Stof - d_StoL2)\n",
"\n",
" L2_n2mud_list = np.zeros(L1_index_n) # List of n2*mu*d_min for all possible CRL1 configurations\n",
" L2_n2muR_list = np.zeros(L1_index_n) # List of n2*mu/R for all possible CRL1 configurations\n",
" aperL2H_list = np.zeros(L1_index_n) # absorption H aperture of CRL2 for all CRL1 configurations\n",
" aperL2V_list = np.zeros(L1_index_n) # absorption V aperture of CRL2 for all CRL1 configurations\n",
" diameter2_list = np.zeros(L1_index_n) # CRL2 diameter for all possible CRL1 configurations\n",
" FWHM2H_list = np.zeros(L1_index_n) # H focal size at the focus of CRL2 matching all possible CRL1 configurations\n",
" FWHM2V_list = np.zeros(L1_index_n) # V focal size at the focus of CRL2 matching all possible CRL1 configurations\n",
" FWHM_list = np.zeros(L1_index_n) # Focal size sqrt(H*V) at the focus of CRL2 matching all possible CRL1 configurations\n",
" Strehl2_list = np.zeros(L1_index_n) # Strehl ratio based on lens thickness error\n",
"\n",
" for i in range(L1_index_n):\n",
" if invf2_indices[i] != -1:\n",
" # absorption aperture is a function of CRL absorption/physical aperture, incident beam size, and physical slits\n",
" L2_n2mud_list[i] = np.sum(index_to_binary_list(L2_invF_list_sort_indices[invf2_indices[i]], L2_Feq.size)*np.array(L2_mu*L2_n*d_min))\n",
" L2_n2muR_list[i] = np.sum(index_to_binary_list(L2_invF_list_sort_indices[invf2_indices[i]], L2_Feq.size)*np.array(L2_mu*L2_n/L2_R))\n",
" solution = root_scalar(absorptionaperture, args=(L2_n2mud_list[i], sigma2H_list[i], L2_n2muR_list[i]), bracket=[0.0, 2*sigma2H_list[i]], method='bisect')\n",
" aperL2H_list[i] = solution.root*2.0\n",
" solution = root_scalar(absorptionaperture, args=(L2_n2mud_list[i], sigma2V_list[i], L2_n2muR_list[i]), bracket=[0.0, 2*sigma2V_list[i]], method='bisect')\n",
" aperL2V_list[i] = solution.root*2.0\n",
" mask = (np.array(index_to_binary_list(L2_invF_list_sort_indices[invf2_indices[i]], L2_Feq.size)) == 1)\n",
" if np.all(mask == False):\n",
" diameter2_list[i] = np.inf\n",
" else:\n",
" diameter2_list[i] = np.min(L2_D[mask])\n",
" aperL2H_list[i] = min(aperL2H_list[i], diameter2_list[i], slit2_H)\n",
" aperL2V_list[i] = min(aperL2V_list[i], diameter2_list[i], slit2_V)\n",
" phase_error_tmp = np.linalg.norm(index_to_binary_list(L2_invF_list_sort_indices[invf2_indices[i]], L2_Feq.size)*np.array(L2_HE*L2_delta)*2*np.pi/wl)\n",
" Strehl2_list[i] = np.exp(-phase_error_tmp**2)\n",
" aperL2H_list[nan_positions] = np.nan\n",
" aperL2V_list[nan_positions] = np.nan\n",
" Strehl2_list[nan_positions] = np.nan\n",
"\n",
" # FWHMbeam size at focus\n",
" FWHM2H_list = ((0.88*wl*q2_list/aperL2H_list)**2 + (FWHM1H_list*q2_list/p2_list)**2)**0.5\n",
" FWHM2V_list = ((0.88*wl*q2_list/aperL2V_list)**2 + (FWHM1V_list*q2_list/p2_list)**2)**0.5\n",
" if flag_HE:\n",
" FWHM2H_list *= (Strehl2_list)**(-0.5)\n",
" FWHM2V_list *= (Strehl2_list)**(-0.5)\n",
" FWHM_list = (FWHM2H_list*FWHM2V_list)**0.5\n",
"\n",
" FWHM2H_atsample_list = (FWHM2H_list**2 + (aperL2H_list*dq2_list/q2_list)**2)**0.5\n",
" FWHM2V_atsample_list = (FWHM2V_list**2 + (aperL2V_list*dq2_list/q2_list)**2)**0.5\n",
" FWHM_atsample_list = (FWHM2H_atsample_list*FWHM2V_atsample_list)**0.5\n",
"\n",
" return FWHM_atsample_list, L1_invF_list_sort_indices, invf2_indices"
]
},
{
"cell_type": "markdown",
"id": "546e0e8f-0659-440a-acc1-a98018f48312",
"metadata": {},
"source": [
"# Testing XS lookup table function"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "f930757a-7c93-4600-99aa-2bcf08572338",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Beamline input block\n",
"energy = 8000.0 # Energy in eV\n",
"energy_keV = energy/1000.0 # Energy in keV\n",
"wl = 1239.84 / (energy * 10**9)\n",
"sigmaH = (sigmaH_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaV = (sigmaV_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaHp = (sigmaHp_e**2 + wl/L_und/2)**0.5\n",
"sigmaVp = (sigmaVp_e**2 + wl/L_und/2)**0.5\n",
"\n",
"flag_HE = True\n",
"slit1_H = 500.0e-6 # H slit size before CRL 1\n",
"slit1_V = 300.0e-6 # V slit size before CRL 1\n",
"slit2_H = 500.0e-6 # H slit size before CRL 2\n",
"slit2_V = 300.0e-6 # V slit size before CRL 2\n",
"\n",
"lookup_table, L1_inF_list_sort_indices, index1to2 = Zoom_CRL2D_lookup()\n",
"\n",
"plt.plot(np.linspace(0,1023,1024), lookup_table)\n",
"plt.title(str(energy_keV)+' keV lookup table')\n",
"plt.yscale('log')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "8dd42de5-a353-41a7-b375-19ecb8051aae",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Beamline input block\n",
"energy = 12000.0 # Energy in eV\n",
"energy_keV = energy/1000.0 # Energy in keV\n",
"wl = 1239.84 / (energy * 10**9)\n",
"sigmaH = (sigmaH_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaV = (sigmaV_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaHp = (sigmaHp_e**2 + wl/L_und/2)**0.5\n",
"sigmaVp = (sigmaVp_e**2 + wl/L_und/2)**0.5\n",
"\n",
"flag_HE = True\n",
"slit1_H = 500.0e-6 # H slit size before CRL 1\n",
"slit1_V = 300.0e-6 # V slit size before CRL 1\n",
"slit2_H = 500.0e-6 # H slit size before CRL 2\n",
"slit2_V = 300.0e-6 # V slit size before CRL 2\n",
"\n",
"lookup_table, L1_inF_list_sort_indices, index1to2 = Zoom_CRL2D_lookup()\n",
"\n",
"plt.plot(np.linspace(0,1023,1024), lookup_table)\n",
"plt.title(str(energy_keV)+' keV lookup table')\n",
"plt.yscale('log')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "5accb0db-239b-4791-8900-d15989aacdad",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Beamline input block\n",
"energy = 16000.0 # Energy in eV\n",
"energy_keV = energy/1000.0 # Energy in keV\n",
"wl = 1239.84 / (energy * 10**9)\n",
"sigmaH = (sigmaH_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaV = (sigmaV_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaHp = (sigmaHp_e**2 + wl/L_und/2)**0.5\n",
"sigmaVp = (sigmaVp_e**2 + wl/L_und/2)**0.5\n",
"\n",
"flag_HE = True\n",
"slit1_H = 500.0e-6 # H slit size before CRL 1\n",
"slit1_V = 300.0e-6 # V slit size before CRL 1\n",
"slit2_H = 500.0e-6 # H slit size before CRL 2\n",
"slit2_V = 300.0e-6 # V slit size before CRL 2\n",
"\n",
"lookup_table, L1_inF_list_sort_indices, index1to2 = Zoom_CRL2D_lookup()\n",
"\n",
"plt.plot(np.linspace(0,1023,1024), lookup_table)\n",
"plt.title(str(energy_keV)+' keV lookup table')\n",
"plt.yscale('log')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "4dc3c27b-a0a4-4910-ba35-2e6b24cfc0a1",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Beamline input block\n",
"energy = 20000.0 # Energy in eV\n",
"energy_keV = energy/1000.0 # Energy in keV\n",
"wl = 1239.84 / (energy * 10**9)\n",
"sigmaH = (sigmaH_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaV = (sigmaV_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaHp = (sigmaHp_e**2 + wl/L_und/2)**0.5\n",
"sigmaVp = (sigmaVp_e**2 + wl/L_und/2)**0.5\n",
"\n",
"flag_HE = True\n",
"slit1_H = 500.0e-6 # H slit size before CRL 1\n",
"slit1_V = 300.0e-6 # V slit size before CRL 1\n",
"slit2_H = 500.0e-6 # H slit size before CRL 2\n",
"slit2_V = 300.0e-6 # V slit size before CRL 2\n",
"\n",
"lookup_table, L1_inF_list_sort_indices, index1to2 = Zoom_CRL2D_lookup()\n",
"\n",
"plt.plot(np.linspace(0,1023,1024), lookup_table)\n",
"plt.title(str(energy_keV)+' keV lookup table')\n",
"plt.yscale('log')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "422f3760-6db2-4cc9-9bc6-d9bf59082c94",
"metadata": {},
"source": [
"# Comparing XS lookup table function to IOC"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "4ce455b1-54b0-48db-b665-1c618d8dc52e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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aRVhYGCEhIQwYMIDbb7/d2yF3SpK8eIPTCUvehH2rfB2JEEKINtDr9bz//vv88ssvfPTRR+7jN910E5GRkTz44IMAzJs3j4svvpjzzz+fZcuWsXLlSh5//HGsVquvQu9UJHnxguLlH8Evd8M7x/s6FCGE8AvlVnujX5U2R7uWbak+ffrw5JNPctNNN7F//36+/fZbPv30U95//31MJhMAP/zwA8cccwx33nkn/fr1o2/fvkyaNIlXX321RY+1cOFCRo4cidlspmvXrtxzzz3Y7bUxV1VVcfPNNxMTE4PFYuGYY4455MSViooKTj/9dEaPHk1+fj5Tp05l0qRJHmVuvfVWxo8f7/55/Pjx3Hjjjdx4442Eh4cTFRXFv//9b5RSLXou7UnWefGC4l1rCPV1EEII4UdSH5zT6Lnj+3Vh1pUj3T8Pe2weFbaGtxYY1SOS2deNcf98zNO/k1/m2fqx66nTWxzfTTfdxNdff83kyZNZt24dDz74IIMHD3afj4uL4+OPP2b9+vWkpaW1uH6Affv2cdpppzF16lQ++OADNm3axDXXXIPFYuHhhx8G4K677uLLL7/k/fffp3v37jzzzDNMnDiRbdu2ERkZ6VFfUVERZ5xxBhaLhfnz5xMUFNTsWN5//32mTZvG0qVLWbFiBddeey3du3fnmmuuadVzaytpefEGWTFSCCE6FU3TeOONN5g/fz6xsbHcc889HudvuukmRowYwcCBA0lOTubiiy/mv//9L1VVVc1+jNdff53ExERee+01UlJSmDRpEo888gjPP/88TqeTsrIy3njjDZ599llOPfVUUlNTeeeddwgICODdd9/1qCs7O5tx48YRExPDjz/+2KLEBSAxMZEXX3yRfv36cdlll3HTTTfx4osvtqiO9iQtL16wM2gwCcAaZ08G+zoYIYTwA+mPTmz0nO6gD4QrHzip2WX/urv9uu//+9//EhgYyM6dO9m7dy/Jycnuc0FBQfz4449s376d33//nSVLlnD77bfz8ssvs3jx4matKLxx40bGjBnjsbDb0UcfTWlpKXv37qWwsBCbzcbRRx/tPm80Ghk5ciQbN270qOukk05ixIgRfPbZZ61a2Xb06NEecYwZM4bnn38eh8PRLhsttpS0vHjBvoIyAJTcbiGEaJZAk6HRL4tR365lW2Px4sW8+OKLfPvtt4wZM4Zp06Y1OAakV69eXH311fzf//0fq1atIj09ndmzZzfrMZRS9VakrXkMTdM8vm/qutNPP50///yT9PR0j+M6na5e3DabrVnx+ZK8m3qDcgLgRLqPhBDC31VUVDBlyhSuu+46TjrpJP7v//6P5cuX89Zbbx3yuuTkZAIDAykrK2vW46SmprJo0SKP5GLRokWEhITQrVs3evfujclk4q+//nKft9lsrFixgv79+3vU9dRTTzFlyhROPPFEjwSmS5cuZGZmepRtaJ2ZJUuW1Pu5rZsrtoUkL14QW74NgGG6rT6ORAghRFvdc889OJ1Onn76aQCSkpJ4/vnnufPOO9m1axcADz/8MHfddRcLFixg586drF69mquuugqbzcbJJ5/crMe54YYbyMjI4KabbmLTpk18++23PPTQQ8yYMQOdTkdQUBD/+te/uPPOO/nll19IT0/nmmuuoby8nGnTptWr77nnnuOyyy7jhBNOYNOmTQCccMIJrFixgg8++ICtW7fy0EMPsX79+nrXZmRkMGPGDDZv3swnn3zCq6++yi233NLKO9h2nWbMy8yZM5k5cyYOR8Mjzn0p0F7o6xCEEEK0g4ULFzJz5kwWLFjgMej1mmuu4YsvvmDatGnMmzePcePGMXPmTCZPnkx2djYREREMGTKEX3/9lX79+jXrsbp168ZPP/3EnXfeyaBBg4iMjGTatGkeC+E99dRTOJ1OrrjiCkpKShg+fDhz5swhIiKiwTpffPFFHA4HJ5xwAgsWLGDixIk88MAD3HXXXVRWVnLVVVe5Z1DVNXnyZCoqKhg5ciR6vZ6bbrqJa6+9thV3sH1oypcTtQ+D4uJiwsLCKCoqIjS0Y0xQXvLJE4ze7MrQebjIt8EIIUQHUVlZyc6dO+nRowcWi8XX4YhGjB8/nsGDB/PSSy+1ua5D/c5b8v4t3UZeYAyNAWCDaZCPIxFCCCH8nyQvXqCqB+wqWe9FCCGEaLNOM+alI4vqMYhl20/HEZro61CEEEKIFlmwYIGvQ6hHWl68oKqsmJEFP5K45xtfhyKEEEL4PUlevKG6u0ijU42NFkIIIXxCkhcvKM10rfPSTWX7OBIhhBDC/0ny4gXOnX/6OgQhhBCi05DkxQuKDdG+DkEIIYToNCR58YKNIaMB2KskiRFCCCHaSpIXL5ANGYUQQrTF+PHjufXWW9u1zuTk5HZZNdcXJHnxguhgMyCzjYQQojOYOnUqkyZN8jiWkZHBtGnTiI+Px2Qy0b17d2655Rby8vLqXb9t2zauvPJKEhISMJvN9OjRg0suuYQVK1Z46Rn4P0levGB43vcAdNPq/xELIYTwbzt27GD48OFs2bKFTz75hG3btvHmm28yf/58xowZQ35+vrvsihUrGDZsGFu2bOGtt94iPT2dr7/+mpSUFG6//XYfPgv/IsmLF+icVb4OQQgh/Iu1rOVfDnvt9Q6765itoul622j69OmYTCZ+/fVXxo0bR1JSEqeeeirz5s1j37593H///QAopZg6dSp9+vThzz//5PTTT6dXr14MHjyYhx56iG+//bbZj1lQUMDkyZOJiIggMDCQU089la1bt3qU+fLLLxkwYABms5nk5GSef/75Q9Y5a9YswsLCmDt3LgsWLEDTNAoLC93n16xZg6Zp7Nq1C4D33nuP8PBwvvnmG/r27YvFYuHkk08mIyOj2c+jtWR7AC+IDjL6OgQhhPAvT8S3/JoL3oMB57i+3/Q9fD4Vuh8DV/5YW+algVB+UCv4w0WtjZL8/HzmzJnD448/TkBAgMe5uLg4LrvsMmbPns3rr7/OmjVr2LBhAx9//DE6Xf22g/Dw8GY/7tSpU9m6dSvfffcdoaGh3H333Zx22mmkp6djNBpZuXIlF154IQ8//DAXXXQRixYt4oYbbiAqKoqpU6fWq++5557jySefZM6cOYwePbrZWwKUl5fz+OOP8/7772Mymbjhhhu4+OKL+fvvv5v9XFpDkhcvkOG6QgjROW3duhWlFP3792/wfP/+/SkoKODAgQPulpGUlJQ2P+Z3333H33//zdixYwH46KOPSExM5JtvvuGCCy7ghRde4MQTT+SBBx4AoG/fvqSnp/Pss8/WS17uvfde3n//fRYsWMDAgQNbFIvNZuO1115j1KhRALz//vv079+fZcuWMXLkyDY9z0PpNMnLzJkzmTlzJg6Hw9eh1CMDdYUQooXu29/ya/Tm2u9TznTVoR3UwnHrurbF1UJKuV7/NU3z+L4tNm7ciMFgcCcMAFFRUfTr14+NGze6y5x99tke1x199NG89NJLOBwO9Ho9AM8//zxlZWWsWLGCnj17tjgWg8HA8OHD3T+npKQQHh7Oxo0bD2vy0mnGvEyfPp309HSWL1/u61DqyTInA/C9Y7RvAxFCCH9hCmr5l77O53G9wXXMGNB0vW3Qu3dvNE0jPT29wfObNm0iIiKC6Oho+vbtC+BOMFqrJglq6HhNYlT3+0Ndd+yxx+JwOPjss888jtd0a9W9xmazNfi4DSVjbU3QmtJpkpeObHNWCQBlyuLjSIQQQrSnqKgoTj75ZF5//XUqKjwHB2dlZfHRRx9x0UUXoWkagwcPJjU1leeffx6n01mvrrqDYw8lNTUVu93O0qVL3cfy8vLYsmWLu/sqNTWVv/76y+O6RYsW0bdvX3erC8DIkSP55ZdfeOKJJ3j22Wfdx7t06QJAZmam+9iaNWvqxWK32z2meG/evJnCwsI2d401RZIXL6jpNnLK7RZCiE7ntddeo6qqiokTJ/LHH3+QkZHBL7/8wsknn0y3bt14/PHHAVdrxKxZs9iyZQvHHXccP/30Ezt27OCff/7h8ccfr9fN05g+ffpw9tlnc8011/DXX3+xdu1aLr/8crp16+au4/bbb2f+/Pk89thjbNmyhffff5/XXnuNO+64o159Y8aM4eeff+bRRx/lxRdfBFwtSomJiTz88MNs2bKFH3/8scHZSkajkZtuuomlS5eyatUqrrzySkaPHn1Yu4xAkheviLDlAHCp4TcfRyKEEKK99enThxUrVtCrVy8uuugievXqxbXXXsvxxx/P4sWLiYyMdJcdOXKku+w111xD//79Oeuss9iwYUOLVrudNWsWw4YN44wzzmDMmDEopfjpp58wGl2zW4cOHcpnn33Gp59+SlpaGg8++CCPPvpogzONwDUe5scff+SBBx7glVdewWg08sknn7Bp0yYGDRrE008/zX/+85961wUGBnL33Xdz6aWXMmbMGAICAvj0009bdP9aQ1ONdZ75qeLiYsLCwigqKiI0NNTX4QCw6rkzGVr6h+uHNkzJE0KIzqSyspKdO3fSo0cPLBbpVvc37733Hrfeemuzu7vg0L/zlrx/S8uLF9jMkU0XEkIIIUSzSPLiBU5LOABLulzg20CEEEKITkCSF29Q1aPKD15vQAghhPBTU6dObVGXUXvqNIvUdWShaaeyrCIPLbKHr0MRQggh/J40BXhBaJdujCz4kf6bXvF1KEII0eF0snkj4hDa63ctyYs31HQXyf9PIYRwq5nWW15e7uNIhLdYrVYAj4XyWkO6jbwgf88GEoFQTf6DCiFEDb1eT3h4ODk5rrWwAgMDD/uy8sJ3nE4nBw4cIDAwEIOhbemHJC/esPxdX0cghBAdUlxcHIA7gRGdm06nIykpqc1JqiQvXlCgj/Z1CEII0SFpmkbXrl2JiYlpdOM/0XmYTCb3po9tIcmLF/wQejHjS3+iTJlp2/6lQgjROen1+jaPgxBHDhmw6wU1e4dKT64QQgjRdpK8eEGQyTWiXpPpRkIIIUSbSfLiBecUzgIgQLP6OBIhhBDC/0ny4gXBtnxfhyCEEEJ0GpK8eEF0sMnXIQghhBCdhiQvXhBkktsshBBCtBd5V/WGml2lhRBCCNFmkrx4QYk+AoBZ9ok+jkQIIYTwf5K8eMHm7FIAdqtYH0cihBBC+L9Ok7zMnDmT1NRURowY4etQ6qlZ38Upy9QJIYQQbdZpkpfp06eTnp7O8uXLfR1KPYHOMgDuMsz2cSRCCCGE/+s0yUtHZlYVAARrlT6ORAghhPB/krx4gV1nBqBEBfg4EiGEEML/SfLiBQ69BYCNR93t40iEEEII/yfJixdo1eu8aJrcbiGEEKKtDL4O4EhgSz2PZTuiMQZHoZxONJ0kMUIIIURrybuoF/QZO4mRBT8yZNF0HA67r8MRQggh/JokL94g3UVCCCFEu5F3VS9TSvk6BCGEEMKvSfLiDVrtyrqSvAghhBBtI8mLF2geyYvsMC2EEEK0hSQvXqBJy4sQQgjRbiR58YK6yQuSvAghhBBtIsmLFxgNtbdZJxtLCyGEEG0iyYsXmA21awGa9JK9CCGEEG0hyYtXSLeREEII0V4kefGCuumK0ymzjYQQQoi2kOTFC6octelLuc3hw0iEEEII/yfJi5fJVGkhhBCibWRXaS/QDCYmVT0KwAfGAB9HI4QQQvg3SV68QNP0rFG9AVCa3HIhhBCiLaTbyAvqrlGH9BoJIYQQbSLNAF6gKSfX6H9AQ6FsxwBhvg5JCCGE8FuSvHiBBtxv/BiAQtvDSPIihBBCtJ4kL16g0+lYHjYBgP4ms4+jEUIIIfybJC9eoOl0jLjtc1+HIYQQQnQKMmBXCCGEEH5FWl68pLK8FKUUloAgNJ3kjEIIIURrybuol5ieTiDg2QTycvb5OhQhhBDCr0ny4nWy0IsQQgjRFpK8eIk7ZXFK8iKEEEK0hSQvXqLQqv+V5EUIIYRoC0levMSdvMiu0kIIIUSbSPLiJTUpi1JOn8YhhBBC+LsOm7yUl5fTvXt37rjjDl+H0k6k5UUIIYRoDx02eXn88ccZNWqUr8NoN5rOlbxYDB32lgshhBB+oUO+k27dupVNmzZx2mmn+TqUdmPU6wEID5B1AYUQQoi2aHHy8scff3DmmWcSHx+Ppml888039cq8/vrr9OjRA4vFwrBhw/jzzz9b9Bh33HEHTz75ZEtD6+BcLS9It5EQQgjRJi1OXsrKyhg0aBCvvfZag+dnz57Nrbfeyv3338/q1as59thjOfXUU9mzZ4+7zLBhw0hLS6v3tX//fr799lv69u1L3759W/+sOiJN83UEQgghRKfQ4j6MU089lVNPPbXR8y+88ALTpk3j6quvBuCll15izpw5vPHGG+7WlJUrVzZ6/ZIlS/j000/5/PPPKS0txWazERoayoMPPthg+aqqKqqqqtw/FxcXt/QpeUW51UGgBvsLK4iP8HU0QgghhP9q1zEvVquVlStXMmHCBI/jEyZMYNGiRc2q48knnyQjI4Ndu3bx3HPPcc011zSauNSUDwsLc38lJia26TkcbjLbSAghhGibdk1ecnNzcTgcxMbGehyPjY0lKyurPR/K7d5776WoqMj9lZGRcVgep61uU7dxlfUOHIHRvg5FCCGE8GuHZeqLdtD4DqVUvWPNMXXq1CbLmM1mzGZzi+v2tr+1oZQ67TxoCPR1KEIIIYRfa9eWl+joaPR6fb1WlpycnHqtMUeamtRNOo2EEEKItmnX5MVkMjFs2DDmzp3rcXzu3LmMHTu2PR/K75zKX5yn+wMqi3wdihBCCOHXWtxtVFpayrZt29w/79y5kzVr1hAZGUlSUhIzZszgiiuuYPjw4YwZM4a3336bPXv2cP3117dr4P7m39q7hJrK2FN+CRDv63CEEEIIv9Xi5GXFihUcf/zx7p9nzJgBwJQpU3jvvfe46KKLyMvL49FHHyUzM5O0tDR++uknunfv3n5RN2DmzJnMnDkTh8NxWB+ntbYFD8NgryAmINjXoQghhBB+TVOdbO5ucXExYWFhFBUVERoa6utwhBBCCNEMLXn/7pB7GwkhhBBCNEaSFyGEEEL4FUlevCTz4d6UPxTD7o2Nb40ghBBCiKZJ8uIlFqoI1KpwKqevQxFCCCH8miQvXuIeFS3JixBCCNEmnSZ5mTlzJqmpqYwYMcLXoTRIVa+x27nmdgkhhBDe12mSl+nTp5Oens7y5ct9HUqDapMXyV6EEEKItug0yYvfkG4jIYQQok0kefESaXkRQggh2ockL15i1LtutcUot1wIIYRoC3kn9ZLwQDMACeEWH0cihBBC+DdJXrxF03wdgRBCCNEpSPLibTLmRQghhGiTTpO8dPR1XnJKrQBsP1Dq40iEEEII/9ZpkpeOvs6Ls3qGtMMpU6WFEEKItjD4OoAjxUzjFCrLi5kclODrUIQQQgi/JsmLl/xuOJq9jgouCYjydShCCCGEX+s03UYdXc1kIxmvK4QQQrSNtLx4yWDHBnrpitBXpQIRvg5HCCGE8FuSvHjJ3dbXSDBlsalgJNDD1+EIIYQQfkuSFy85ENCLiqpgjAHBvg5FCCGE8GuSvHjJkLt+8nUIQgghRKfQaQbsdvRF6oQQQgjRPjSlOtf8l+LiYsLCwigqKiI0NNTX4QghhBCiGVry/t1pWl46unVPjmf/I33YvOI3X4cihBBC+DVJXrwk1JZLvMrBXlXh61CEEEIIvybJi5co97+yt5EQQgjRFpK8eI0ssSuEEEK0B0levMShuWalK4fdx5EIIYQQ/k2SFy+pSV4ctiofRyKEEEL4N0levMSumQBw2iV5EUIIIdpCkhcvMZjMAJg16TYSQggh2kKSFy/p2zUSgAExAT6ORAghhPBvnSZ56fDbAxhcLS84rL6NQwghhPBznSZ5mT59Ounp6SxfvtzXoTRMb3T9K8mLEEII0SadJnnp6DZkVwKwZleOjyMRQggh/JskL15S5nDd6soK2R5ACCGEaAuDrwM4UmyMGM/8nFCSQ4cx2tfBCCGEEH5MWl68ZFfE0bzlOJM9gQN8HYoQQgjh1yR58RKTwXWrbXbZmFEIIYRoC0levCTckc8gbRvB5Xt8HYoQQgjh1yR58ZLBB77jW/ODjM360NehCCGEEH5Nkhcv0QVGkkUXnKZQX4cihBBC+DVNKaV8HUR7Ki4uJiwsjKKiIkJDJVEQQggh/EFL3r+l5UUIIYQQfkWSFyGEEEL4lU6TvHT0jRnX//UdW/4zgmUvX+rrUIQQQgi/1mmSl46+MaO9opS+9i2ElWz3dShCCCGEX+s0yUtHpzcHAGB0Vvo4EiGEEMK/SfLiJUZzEAAmVeXjSIQQQgj/JsmLlxgskrwIIYQQ7UGSFy8xBbiSFzOSvAghhBBtIcmLl5iqW14syurjSIQQQgj/JsmLl1gCggEwazacdruPoxFCCCH8lyQvXhIeFub+XueQGUdCCCFEa0ny4i0GS+33tgrfxSGEEEL4OUlevEWnq01gbGW+jUUIIYTwY5K8eFG5w3W7swtLfRyJEEII4b8kefEiq3Ld7soqmXEkhBBCtJbB1wEcST7VTsNpq2SCKdzXoQghhBB+S5IXL/qv4SJyKqoYb4n2dShCCCGE35JuIy/S6zQAHE7l40iEEEII/yUtL14URx4mrQSnrQIIa7K8EEIIIeqT5MWLXrP+m27mbDbldocecb4ORwghhPBLnabbaObMmaSmpjJixAhfh9KoSl0gpSrA12EIIYQQfk1TSnWqARjFxcWEhYVRVFREaGior8MRQgghRDO05P2707S8CCGEEOLIIMmLEEIIIfyKJC9etOT1a/jnqRPYvOI3X4cihBBC+C1JXrwoMn8NR1WupKIw29ehCCGEEH5LkhcvcqIHQDkdPo5ECCGE8F+SvHiRU3MlLzhsvg1ECCGE8GOSvHiR0ly32yktL0IIIUSrSfLiRTXJi3QbCSGEEK0nyYsXOTXXbgySvAghhBCtJ8mLF9W0vGiSvAghhBCtJsmLFx2VGAnA8CTZtkAIIYRoLUlevEjTVW/iraTlRQghhGgtSV68SVc9VVq6jYQQQohWk+TFi3bkVQKwObPQt4EIIYQQfkySFy/Kr3C1uBSUVvg4EiGEEMJ/GXwdwJFkR+BgtufbCQns4etQhBBCCL8lLS9etDzqLO62X8uuiDG+DkUIIYTwW5K8eJFepwHgdCofRyKEEEL4L0levMiEjWDKwVbp61CEEEIIvyXJixednvka6y1XMzxjlq9DEUIIIfyWJC9epDTXOi9KFqkTQgghWk1TSnWqARjFxcWEhYVRVFREaGjHWobfWlWJ0+nAaDSjN8hELyGEEKJGS96/O03Ly8yZM0lNTWXEiBG+DqVRJrMFS0CQJC5CCCFEG0jLixBCCCF8riXv39IE4EWr57yPY8P3qJ7jGTHpRl+HI4QQQvilTtNt5A8q921gePFcnBnLfB2KEEII4bckefEizRQAgM4u67wIIYQQrSXJixdpxkAA9A7ZmFEIIYRoLUlevEgz1SQv0vIihBBCtJYkL16kq05eDJK8CCGEEK0myYsXGcxBABidkrwIIYQQrSXJixcZLK6WF5MkL0IIIUSrSfLiRX0TYgBICtV8HIkQQgjhvyR58SKTJRgAnU1mGwkhhBCtJcmLNxld67wgyYsQQgjRapK8eFGp0wiAw1qOcjp9HI0QQgjhnyR58SLN4tpoSo+D/AP7fRyNEEII4Z9kY0YvCgoJZ7FhFDsqg+myI5sJsQm+DkkIIYTwO9Ly4mXfpDzH/fZpbCo/9HbfQgghhGiYJC9eFhrgauwqq7L7OBIhhBDCP0ny4mXBJgMWqqgqK/J1KEIIIYRfkuTFy0YdmM0my5WcsecZX4cihBBC+CVJXrxMXz3jyGgv8XEkQgghhH/SlFLK10G0p+LiYsLCwigqKiI0tOMNiq2sKMNusxIYFIpOr/d1OEIIIUSH0JL3b5kq7WWWgCAICPJ1GEIIIYTfkm4jIYQQQvgVaXnxstz9u9k5+y6UzsDIWz7ydThCCCGE35HkxcuqKksZUfQLZcri61CEEEIIvyTdRl5msrjGu5ix+jgSIYQQwj9J8uJlJksgAAbNic1a5eNohBBCCP8jyYuXmauTF3BNmxZCCCFEy0jy4mV1k5eqynIfRiKEEEL4J0levEzT6ahURgBsldLyIoQQQrSUJC8+UKWZALBWVvg4EiGEEML/SPLSzkqLC1g95322rf270TLBwSEAJAZ7KyohhBCi85B1XtpZcX4OujUfUeG0waDfGyyjNwYAoHPIbCMhhBCipSR5aWfxyf2Iv/vXQxeqTl6wS7eREEII0VLSbeQDOdU5y67sfN8GIoQQQvihDpm8GAwGBg8ezODBg7n66qt9HU67y6103fa8giIfRyKEEEL4nw7ZbRQeHs6aNWt8HUarLHvlclLz5rG+342MvvTfDZbJCEpjV14gyhnm5eiEEEII/9chW178md5WSrBWAZrWaJlVfW/lBtutrFQpXoxMCCGE6BxanLz88ccfnHnmmcTHx6NpGt988029Mq+//jo9evTAYrEwbNgw/vzzzxY9RnFxMcOGDeOYY45h4cKFLQ3Rp94Lv4njql4kK+nsRsskRLpW2d2TLyvsCiGEEC3V4m6jsrIyBg0axJVXXsl5551X7/zs2bO59dZbef311zn66KN56623OPXUU0lPTycpKQmAYcOGUVVVf5rwr7/+Snx8PLt27SI+Pp7169dz+umns27dOkJDQxuMp6qqyqOu4uLilj6ldpVjD2SPisUQEtlomYTwAEBRUCgDdoUQQoiWanHycuqpp3Lqqac2ev6FF15g2rRp7oG2L730EnPmzOGNN97gySefBGDlypWHfIz4+HgA0tLSSE1NZcuWLQwfPrzBsk8++SSPPPJIS5/GYVNhcwAQaNI3WqZbxRa2mieTVxABbPdSZEIIIUTn0K5jXqxWKytXrmTChAkexydMmMCiRYuaVUdBQYG7JWXv3r2kp6fTs2fPRsvfe++9FBUVub8yMjJa/wTawaTij7nT8Cnh1uxGywSHR2PUHISrIpTT6cXohBBCCP/XrrONcnNzcTgcxMbGehyPjY0lKyurWXVs3LiR6667Dp1Oh6ZpvPzyy0RGNt4FYzabMZvNbYq7PZ1u/YVYQx5bndc2WiYmoSfZV68iLDIWTSdjpoUQQoiWOCxTpbWDZtoopeoda8zYsWNZt27d4QjLK0J0VeCE4JDGp0EbjCZiE3p5MSohhBCi82jXj/3R0dHo9fp6rSw5OTn1WmM6JaUIpBKArtFRPg5GCCGE6JzaNXkxmUwMGzaMuXPnehyfO3cuY8eObc+H6pgcVnDaXd+bgg5ZdMnHj7H8xQvZuvoPLwQmhBBCdB4t7jYqLS1l27Zt7p937tzJmjVriIyMJCkpiRkzZnDFFVcwfPhwxowZw9tvv82ePXu4/vrr2zXwg82cOZOZM2ficDgO6+MckrWs9vsmkpeg3fMZWLWaFRnHw5DjDnNgQgghROfR4uRlxYoVHH/88e6fZ8yYAcCUKVN47733uOiii8jLy+PRRx8lMzOTtLQ0fvrpJ7p3795+UTdg+vTpTJ8+neLiYsLCfLPsfnZuHrFAFUbMeuMhy1pNEVAF9pID3gnuCFFUbuP9xbs4Z0g3EqsXA/QX1qpKsjO2kth7oK9DEUKIDq3Fycv48eNRSh2yzA033MANN9zQ6qD8VWWZa4G8Csw0Nf/JbomEElDleYc/sCPIT4tX8du8JXw2L5Sv77uULiEdZyZaU9a+MZURhT/zj2UExuPvJGXEyTIbTQghGiCvjO0oc+G7AFQ1mbqAM8A1oNdSsPWwxnRIm3+BH2aAvf5qx/4qbseXfGN+kBv133DTJ6uosvuwG7GF4orWAnBU5XL6/3whvHM8bP4ZmviwIIQQRxpJXtrR6KyPAIil6dYUU2xfAAaVLSJ7b8tW2bVWVVKY27x1cw7pk4tgxbuoBU+3va4OwmJ0/UlrgGXXfP74dpZvA2qBEoNrPaNigrDrLGiZa+CTi1H/dxJ70pf4NjghhOhAJHk5DHJV02NuBk+cyk5dMjpNsfOPj1tU//6nhxP+Wj8yd29ubYgeytd97/HzkjeuY/G7t7dL3d42PCkCgHNNS3jP9Cyp65/BYbf7OKrm0XC1sGwb/QSG29Ph6FvBGIi2bwVJn01k2UuXUF5e6tsghRCiA5DkpR3dbL0RgD2GpCbL6g0G8pNPAyCqbFsTpV3W/v45m/8zimSnawuE3X9/3spIPQUVbXF/n5e9l9HZnzIm4/8oKfK/jSONetdiiFrqWeQSzp6Es3A6/aXryJW8aJoGQVFw8iOom1ayNvhYAHLy8jnmuUU8/mM6ewtkR3IhxJHrsKyw6wu+nipttTsJ1FwL1DkNzZvlMuyoo2AH9DEXNqt8wW+vMl6/qfaA09bSMJuk09VuKFmYk0FIWONbM3RIyrVXlMEchGXGGsaERvg4oOYrN8ewz56H3hzsPqaFxjPojh9Yv3Quz823kV9s450/d7Lh7x+4O2wuxuNmkDqm8Y1ShRCiM+o0yYuvp0pXWB0E4hr4alGVzbsoLMH1b9HeZhUvIcDjZ11QdLPja4hTaeg0z8GgdVspig/sgz6D2vQY3rbjQAk9gV355ST7UeICMOyObwHo1sC5tFEnM2+4kwWbD/Deol2cu3shgyqWsXbh8yDJixDiCCPdRm2wecVv7oGzlQUZPGj8HwCh57/SvAqqkxdVtLdZu0uHW/QePxsCw5sfbAOytC4APGm7hEqbK2kJCqmtsyLPtzt0t0Z2sStxzCqxuo+t+OFtNj4+hn07NvgqrHZh0Os4KTWWD68exfArHmdJlwvoNrV2QLLsUC6EOFJI8tJK6/74mn4/nAOvDQOgqnqNl2ICSeo7uFl1OELicaKh2SspOLC/yfKD1CaPn/WOZrbwNMKmMwGwVvXiQEl1q1FgMOsiJwAQYsttU/2+oFVPK9ao3QjUuP4z+tvSyfh1pq/CandJfQczevr/ER2XCLgSl+WvXMbi9+/H6ctVpoUQwgskeWmlsh3LAQjHNfsjyuSa0WIMCGl2HXqjmVxcXRt5+3c0WT7U7jkFu39E29b/qJnd4lQaheW142cGpqQA0C+wrMHrOrLaBRRrkxc14moAUrK+pbIDz9ZZ8cL5bHtsKOmLf27xtev/+p6RhT8xZudr7P/PALau+fMwRCiEEB2DJC+tFNxnDAC7dK5Pvgd+/A8AlqqCFtXzSciV3GS9kfTsCpa/eFGL3nTK929s0WMdLDNyNA6lMUn/N9ZcV/JUWVFG/k7XYmmUZLapft+o7jrRapOXgePOJ5MuhFPKP3M67rovYeW76O3Yjq2iqMXXph1zJktT76dCmUhQmXT/ehJLZz8tXUlCiE5JkpdW0lXvXaRXrib65AO/A6A5rY1e05AtXc/ge+dYIv9+lBFFvxD19SWeBUoPwEsD4fcn+ccyvO2B1zHqxv+y1ZjCpYbf0LJd40EKczOJzFwIgPLH5KW65UXVaXnRGwzs6um6rxEb3vdJWM3xVuB1TLHeTUlkywdJazodoy68C+vN61kdeDQmzc6ojU/wz7OnsHPD0sMQrRBC+I4kL62kM1QnL7RtAbRu4a4ZRBXVWwq8ZT/D43z5b89A4R5Y+BRadaJkU66Bu+tL2r7xYEHcGFaGHE9gZBwASle7oeT+jJ1trt/7qruN6rS8APQ75XqsykAf+1Z2b1rlg7iatsmYwkLnIGzVW0e0RlhULIPv+IElfWZgU3oGVSwl4fPTcM59BPL98fcphBD1dZrkZebMmaSmpjJixAivPF7hii8BiFc5AJQrV/KRfc6XLaqnZ0A5J+pWMkG/EoCy0B4e53MLS9zf66rXMKlJdEpL2z4mZczVLzLs9m9IGXESAI7ALhxT9RIAUc48/+t2cA8D8kxeImO6kaWPBaCsINu7MTVTzXAd7dDFmqTpdIy+7CH2nPcD/wSNxYgd3d8vwCuDqZh1Dqt+nkVlhf+NZxJCiBqyzksLLf3sGcjegKbV5n2V5aUEaq7ZOoHJQ1tUXz/bRi42Pe/+eWuV56fuSmttN9QAq2ssSqhWziz7RMrNKS2Ov659j/SlqzOHrWd+Rb/hJwCuN9AcFcFDtilkqQieKbcSFmxp0+N4U+qFD5FZfCMpQfX/BkwmM1RCkKFjbnQ4pmoRafoDmCuSgJg219frqLEw8CfY8BWs+gB2LCRg928M3f0bu5Y9y96L53F0366uFX2FEMKPdJqWF28Zlf44o/K+QZez3n0sd/8u9/fBddZJaY7whH5s0/dy//yI/SXsttqEpbCs4enQw3Wb2WPo0eC55tIpJzpNoZTT/UlcKbBi5H3HROY4R5Jd1v6r+B5OYRHRdO3ej/DouHrn4sJdK9d2Dzd5O6xmubTiY542vkNQUfO2i2gWTYO082Dyt3DTSlYlTuEAEXxsPY7LZ63mkneWsDtPWmGEEP5FkpcWqLt+RhSudV0qtEASPjzafVzTteyW9kgdQbcZC90/p+gyKCmsnRKtqYa7bQbqduF0tC2xuNr8LP+1n0LKj+fxz9vXAqAryeB30238YLoPgKyitq0l06Hoq5MWR8sGVXuLQedqATHoD9N/y6heDJ32CpbbVlE27HpMeh1LduTzxosPs/bpCaz749vD87hCCNHOJHlpAZ1ez/IhT3ocC1C1G+Qt6XVLq+otrbMB4tLUf2MJrN3bZre5HwA/O+qP5YnQte0Tcz6h7FGu7gmDzTW2Rtmt9NBlk6bbxVm6RZSsb/maI760Zt4nLHnjetb+9mn9k9UzxA7HnlDtISnSNXg7Nf7wbm8REhbJ4+cOZt6McRzdO4oLdb8xqGIpZXs65kBmIYQ4mCQvLWQKdS2p31OX5T42sPL/OE89y+grHm1VneXFrpaWSmVk1IV3EhBUu9CdplyzmWwY+McyzOO6C3Jfb9Xj1VAKSpRrxpLB7lq8LdxSOwzqFdNrJG75oE2P4W2VWxcwOvsTyrf+Ve/cxpwKAFbtzPF2WM3TwAJ7h1NSVCAfXT2a6MvfZXH8ZHqfNM19btmXL7Hsy5dktV4hRIfUaQbsekNVZTl6Q/3xEiUEUBYR2+p6K7+4HgCLVr9FQFfdSmDDQEWP8bBxZe3JNo47vc7+IROMri4rc3XyElInedlgGkhZRL+2PYiX7Qwexpp9pUSGDmHMQeds1X/uDlvH7Daqnebt3c8USX0Hk9T3VY9jgVu+Jq1qDZs2fkpx/4sZMGGqx75XQgjhS9Ly0gIb//qWtPmT6x034HCv19IaelW7Vsz6P7+luM6Yly7JqQCM1DaBzjPX7HH97FY/JsCZzt/oprkey+Ks7v6qGWMTEMGA+/5izHX+tR/QlvCjecp+Kbsijq537pewi5luvZmsiJbNCPOW7CLX72BHXnkTJQ+/0u4nU6mMpNg3MnLdQ6jnUlj20iVsWvqrr0MTQghJXlrCVprf4PGHDe9zrO6fVteb2/Ns9/dp8yezf+tq98/xya7p0AE6G0GZSzyuK65qXpO+cjrZv3NTvTVbTPra7okAp2v8TKXVlUjZ/Wx5lxqq4TXqANgePIQfnaMpMsd7N6hmqrK7fp+VNt/f/NGX/puCq/5mSfJ0MrR4grUKRhb+RMrPF5D+xuWQu9XXIQohjmCSvLTA5tjTGFD5br3jlxvm08+W3up6R1x0P0sHPIBduX4dDmuV+1xkXDJL+9/LjkF3kJY/1+M6TadvVv1L/vdv4t8fxdL/3uFxPNRce32wciUvWcWucSEl1YmRw273mLp9OCz99ElWP3t6uyycFmArIEHLwWIvqXeuZhaPzeH75KAh2qEyLx/o2r0fo6c+QcIDG0if+CnLw08FIDX7e3h9DMx/FMobTuiFEOJw6jTJizdW2K1yQBkB2DRjvXMh4dGtrldvMDDqgjvYbUgGwGGrnZ5cnJ9FUMIAEoZMqHfd7nenNlrnx0v38PmKDADG7HR1/Yzee3DiVTtoJlCrQjls7tYZJxrrnxyH9lg06xd80Ypn1XyjNj3FkLK/SF/wWZvrGp/5Ln+Zb2V45if1zvWs2szpuiUEF29v8+McTh1t0ThNpyN1zKmMuPVTMs/9Gnoe75qx9efz8EwP1j49gfl/L3K3HAkhxOHWaZKX6dOnk56ezvLlyw/bY7ib9auX568rrWdSm+u3VydFTltty0vm3FdJm3s5u+e9yYPGGR7lU8uXNVhPUbmN+75ex51f/EO59RB7Lx20hoxWVYKqs7GhQzOi0xS2kgMNXr5vxwa2rFrQ1NNq0s+68fzhGIilW1qb66pVPwE4ruhbZppeISHn93Z8nPajVSeTmpdmG7VG16NOgCu+hklvYg3qCsCgiqU888NaTnv5T1bubtmu6kII0Roy26gFEjN/5Tnjz4So0vonLW1fm8Ohc81kctgq3MeKlWsgsJa3lQNlQ6HOZCeT5kA5nVRVVWCtqmTrou9IHXc+Djucr1+IUhqlFcfT2PaNxRU2QuseqJO8AFjNEVAJjrK8etcCxL5/DAbNya6A+ST3b/2O1/8x3cK+8gq+jejT6jpqDOwWCrkwKDG83jl7ZB/Sy/diCO/W5sc5PDpWt1GjNA0GX4LxqItYv/hHKpZ/hC4/gO0HyjjvjUXc3m0DZ0+cQFLfwb6OVAjRSUny0gJditczWv9HwycDwttcv6OBlpfvdCdyMv/jqPJlOKifIKx45VJGFP5MvhbDMJXDsi2/0ueyF3jO+BYAewqvx64ZMaiGFmbznGvtqChCOR3VZzQclkgoAlXecPJi0FwtN5l/fdim5MVZnTBVlhUB4a2uByDQ6GpMDDTX79obM/mxNtV9uGn+krxU03Q60o4+E44+k0/KrTz6fTpL16xlWu5zmD96guxrVhKb0KvpioQQooU6TbeRN0RX70/4gf1ktjg9P73vKqv/ZtlSDr2rO0rZa5OXmkG8epw8bXyn3jUjCl0r4Nbsbj2y8CeP7iBb0X53d9TBarYecCrXm+Xm3fuoSWgUGgS4NonUVxx6UGZI7upDnm+Ks7KYv8w3M/yTQZSXFrWpLm8v9NaeqrQAypQFnb7tf0veFh5o4oWLBjP7ujFsCziKPfruHonLmnmfkJXRjns2CSGOaJK8tEDvCNfsnCh9OX11+zzOBbVhkboacRGuTpyk0NpZQDWzZnXUJiS/OjxX2j1YREBtg1pAaDSbA4Y0WK7m7b1Ec3UsWcsKoXrArkJDC3IlL0Zr4SEfL7Zq9yHPN+Uz7T4StFz0mmL7yvltqmtvgWvGUkZhRRMlO56eD64l6JFs+g4d5+tQWi0huS9H3TOP2Bm1LZSZuzfT989bCP+/0Sx95Qryc/YdogYhhGiaJC8tUd0i4l7Qrdo2fS+6xCe3ufq4SNe4mbig2laDt/OmAGDUHFj0rlaF/7NcSZVyfTqv+ddDTcuLzkB8cj/UkCsA2GLo61GsppviTdMUrrTeyf7gAYTHdmdx8r/Y2W8aplDXDKoAW2GD8e6f6howHKaKqaps/cJqQdReW7mqbQvv7S9w1bW3oP6Gkks+eID8hxNZ/NZNbXoM0bTA4NoxYHablT3Gnlg0G6Pyv8Pw+giWvnIFG/7+EYdsPyCEaAVJXlrAaXV9mh9u8JxqWxzQTgNAa7YeqOk2KtqLrs64lJqF5EbrN7mXus/TRdSvR1W/IVQvM2/tMpBbrDfwX/MVHsVqkpctgUP53TmEPBVKdHx3xkx9itGX3E9YlGs2SRddAwOUga5JfbCawjFpDkx5m1v+fKuF1FlvZkjhr5QUtWXtkMbHjSh7JZEUo7MWt6F+0VKJvQfS775FrD/xA3bquhNKGaPyv2PA3EvJe6w32V/dC5Vt7C4UQhxRJHlppl0bV6Db9B3gammoyxrUPiu2ljldCUlRcXX9ZbkNlptR+RrBmiuRMuk836SdmoHc0uouE4eV4sI8bKYQfnMOZbGt4ZaXoOrBrcWVntOqeya5pn9H6eov+AauAZumhMGu77PWNvX0GmWus9KvQXOyffmcVtd1yCV2q8eSaI6OubfRihfO45+nTiBj2zpfh9LuNJ2OtGPPJum+Vaw/8QOWRZxOMUHEkE/sP6/Dq8NhwVNgbftChUKIzk+Sl2YKDuvi/n5twEiPc4kTb26Xx1hX4eqm2bVhseuAvX7Xx8GiHdkA2DTXYN/d3U7Haq1tik//4VXKvr6NdZarObXsa49rt1kGsNE4gL6GbM7T/UFY3mrKS4vYtvZvdm9aBYGuMS9UFIKj/noxBQ8nwI4Frh/2r2n+Ez1YdTfXroABAFTuao+1euonL1pN8qIOsfaNDyUVr+KoypVUlXXeVgi9wUDasWcz8paPMd+zjZ0nvAGRPaEsBxY8SeEzg1n81nS2bdng61CFEB1Yp0leDvcKu5GxCe7vKxLHuWfo7Ll0Id16DmiXx4hKHQ9AUsVGAEpKGm7xaIhRubqa8orLcDprkxdVVUIwriRogn6FxzUD711A//sXMYx0nje9SZ/sX9iTvozeX5+GfvYlEBCBKwlQVJXk1HvM0Drr3ezftLTZsR6sZrn+kjDXPk7mgtZ3QdXMoGpwZ2adK3mp2am7o3lOP43brP/CHpLo61C8wmwJpMdxl8K/FsOkNym3xBFuz2FM5ofMmDWfU176g1fnb6WsqmMmm0II3+k0ycvhXmFXp6+zj5DTRhWuN0KD0dJujxHeYxj9K//LsKo3UEqxPbPhbqOhlW+ywxnncSzbnMxcx1B2m/ugnLUv9pq1lI9DrwJgoLazwfqMsSn8YxkB0X1x6gxkq3AKtTDQG8gyuMa9bFm7pN51J1mfY6r1LgCiS7e0etBuhbU6mYh17aDdT8toVT0uNavU1mcMc92zuNJ0nB1woOjvutF87TwWp6WBcUydmdECgy+BG5excuQL/BZ2Lht1vdiUVcLzc7fw5jN3suS9+6goa34yL4To3DpN8uINS3rdwlZDH/qffhMBmmvcRN6C19ut/kCLiQosOJWOSpsTh7XhZMCBzj1gN1uFAxBbtYsELZdf91lwOmsH+eqsJTjN4axzJvOXM63BN+3hZ/2Lo+6Zx6iL7qYsehCjql7n5sBnANgX0A+Asp2erTZKKXaqrixwDmKNsyc/OUeyaVcrp8BWj1OJ6jXUdR9K90Ajz70Zlbn+aWDMS/9xF1CiAuimsklf9EMr6z98Oti+jF4XGBzGsNOmccJts1h+/8k8e/5RjAor5Eb7/xi9ayab/v7W1yEKIToIWWG3BUZf8SjwqMexAHv7jU8IMNa27pRZ7TitDY95+dN8Cx85TmK9SqaPto9YrRCA/ro9hGplUKfbSG8rQxcUyZnWJ3jozFSOr9OCdODhZDQU2r/+Jqq6W8yd+FS/gW7uNY2Xl40kxTKS0XViqN1FQOPRuNdYtaeQh/MMDGrF866ZUaWCYyEwGspz4cBG6Hbo9Wwa0m3SY2wpuJHeccn1zgUGh7Ei5lSGH/iKpF2fw7FntyLaw2eMcxUVukr0tsHguXHDESc80MQFwxM5o/9ZrP3lAM7sDYyecLmvwxJCdBCSvLSSHQMG7GTGHEfvdqpTp9N4yjyLEGcRlfmpOG0NL7QWqlXQT8vgKttdfGh8HICfHSP4yHESW53duKHOCrsGexnjCr7kRONqgvInAz3c57rg2kQvt2YzRqeTg9enNXQbzB9OHVq555up0+ngTsOnONGRH3czq/bAxszWNevXLsCnQ3UbhrZ1DpvmvUfKlJYnL/E9UqBHSqPnh597K7z1FaE7f3HN5gpq/W7g7e0R56tEmorZWXoq0FH3X/KugKAQRp53q6/DEEJ0MNJt1ErbcbVUFNhNTZRsmZO05ZyuX4a1OAfVSPICcIJ+DQDH6F2zMtY5e/Ku8VleML7hUc7kKKNX+T+co/+bsIo9qJoVdJViYtVTnFL1FFvXLaH0oTi2PT6CwNz1fGl6iHsrXwCga7hrTE9mkWcsToeD6YbvuMnwDWmxZjScHNjnuf5NS2k6Henx5wAQvvNnd6ztqusg6DoYnDZY+0n7198GNQmjdqT2GzXB6XCw/slxbHjiWIoLG95vSwhxZJDkpZVq1mTZe6AtC6rVtz31Rpb2v5ewLt3cyct8xxDGVb1Qr6yZ2vVKyrBg1uyYNBs9Ukew+7QPAehishEZ6OoqGpX+OCu+cyU3SsFmlcQmlYTOFECwVoHFWY7OWsQw3VZ6O1yDe+MDFefq/uCUwtmeyUSd1p1B4RWsM1/N6/nXYLe1fA2VmpYXTYMeI07DqvTsdkY1OmD5UNbM/ZjFHzzA1tWNbKAJMGwqAPl/vnN4EqRWqtnWITkq2MeRdEyappFWtYYB1n+wVfnf9g9CiPYjyUsrddNcb6xd9e27JkfKSVOJ2vIZW358Bc3mGvOyT0WTpSKZm+C5nsxmy1T393qcvGw/hwNHPwJA966umTXRRivJkQHuco7qhe/q7idtDnQt5W5RFXQJdrUkhQa4/u0WEcgzxreZoX1Ezv7a2UqmOpOvevbog861lSM5e3e0+Dm7Wxx0egKDw3gs8R0usj7IByvqT89uiuOfzxiz4xXyNv7ZaJmKfudQrsxEVuxm09I2LIjX7o7wEbtN0HQ6HNVLFKgOOFtMCOE9MuallTKPeZLM1R8w7uxp7Vpv+uePMsaxnd57trPx1M9YbAxg3uYgqjDR/7x74eVXGrzuQeP/AFjN8a4DJtdmi1jLPFpJVJVrBVOnw84t+i9RaASYrgUgQFUSHOJa7C4y2NVdZAkMZkXo8ThMwSTVXaiuTp06g5HCqX8Qk9CTeGPLu9HWDn4QHHYGhLvGn4wbewz/27aCP7e2vOVlS8BgdhQ4CA3u0WiZgJBwlkWeTGL+In5cup7eIydi1HeAPN59TyV5aYwDHXocOFXHaTETQnifJC+tNPjkS+HkS9u93oSsee7v+4+aCKMmsvnBS5noXIFWNtCjbOmt2wh+yXO4sMFgZPfmNVg+v4RYQDmsKKe9tonN5kpelNPBbcYvAdhtcW1UGEglyumofuusfQMdfvtX9QNVddpuNJ1roGwrjTzHs0VpZM9I9DqN0tx9bNmyib59m1/3X2Fn8eOeETwSfeiFA8MnPcUJ76ylIksjbnkGl4/u3qrY21OF1U4AkFtuo+MMI+5YFDrAgbOBFZ+FEEeODvBxU9SVFZrm/t5mda2ae772G5cZ5rN/xfceZU3mwHrXd1nzGhXFucTa9wNQWVnJ+r2F7vM6W/X6KXU+uQaFhrvOaYqSElc3mM1Zt2OpvtKq2rEtlfb2/RQcajHyaMIK/jLfTP78F1t0rbM6qWqq56Vv90TuPMWV4Pxv8W6UOvTz9QZ79UrD5VbpEmmMo/olqyMuMiiap9Lm8FiLSojWkOSlgxk8/UP39wUvjWXN3I9Z13MaK0OOZ8Sa+zzKWhsYHBtXsoHK4CSesV0IgBF77ZL5gK665cVUZzPEyLBw93YHm3ZnArA733NAZEVZCblZtSvfOhx16tTpyc/Zx/IXL2Tj42NbPAj2n9+/4J8FX3qs0Nuve1fMmp2A4oZXBW6MwVFFIJXN2gLgvGEJRBgqOSr3e7auX9Fk+cOt5jeia2hrAwGAs/olSylJXvxRUYWN0U/O55J36q/YLURLSLdRB2M0md3fx5Rvw7roERIe2sy29NXs+jSdZF22+/zWNy9jSAN1VJmjeMdxBiuc/bCj51HzN+5zentNy0udVXj1Bip0FgJUBQa7a78iVafbaPWvHzJk0XR2G/oT/e/qF506CZGm0xEYEs6QwrkYNCc5mbuJ6db4mBMPSnHUQte4obz+GzBbXK1JKceez/4BxzEouV/z6ql2Vc7jvGr5iyX7/g3ceciyYQFG3o74iBEl89n67e84+i9Bb/Ddf4maXb41nYx5aYyqblKTlhf/tGNfNkkVmyjaZeS3H3fTLbYLSf1HEhAU4uvQhJ+R5KWD2xAwggSgLKQHZ1tf5B/zNEI1V6vIkPK/G7zGqcCGgWWqPwCqTqIRbqhurak74FHTCAgKhdIKwnSV7mM1gqJda9pE2bPcx+q2rug0HcaAILK1CGLJozC7+cmLUk42OF0r/cYbagf7BodGEBzaij1+arqNmjnoNe7sR9jx8XaKhlyHTufbFg/NPQdMWl4aExJghopyEsPbb08x4T0hZbv4zvwA+1UkzmU6ErRcNjo+c43vA9bM+4SqTXNxBsdgCO1KQEwPIhP6EZvQy6cfLETH02n+GmbOnMnMmTNxdIJPZBW9TiNg+08AzEm6jaOWf8vAH6dyoX4advRNXA36ijyO162mVAWwXKV4JCo9Q11v6uVWGzUjZirtCospyHWtrX7LS3xv16L/XSig4EAmEV26euxcXfOmX2SIItaeR1nu3mY/Vyc6zrA+AcCagPAGyxTlHyAsskuz6usTEwy7oH9885bXT+w9EPXv1fT0ceICdVpeZKp042q61KTbyC/VfOjRgAJLIqYqJ7E9asf5VW5dyOjcL+GgiYZ2pSNbiyA0JomAqAQIiWe/M4L9znBMMX0I7jWauDALgaZO85YmmtBpftPTp09n+vTpFBcXExYW5utw2sR48QdMeehZ9qpozo4OpuuPkwF4xvgOB1TTzy0gfyOzTM8C8JjtMizOOpscWl1jXhx1B8xpGphcC6Ppa2Yj1UlegkMj2KvFkaCy2Ld5ORFdzsLp9Ow2Aig3RYEdbEWZzX6uzjrdVwe3ljgdDla+cjGDCn9jz2XzSeo7uMn6gs2u5K5mnZrm0OokLptWzCcmKYXIGO8vz6/JOi9N06qTd6ckL/6oR5RrzamYsGC6zlhQ73xQ6gQWG8zoy3KwVB4g3LqfOEc2Js1OLHmQkwc5qwGIr/5a6kzhLOuDAIRYDHygfwyDwUjUJW8SX93tvHvTKipKC4hO6EtUTDeP//PCP3Wa5KUzMRiN6PqcREVWCVeM6QF/1Z7rojW9KJ5y1k4jfcD4EVnOOq0WVtf+Q3W7knQ6PVsLFX2A7NwDJAIHrzWSb+5GQmUW5Qd21zwKAE6luTs5qixdoBwcxdk010Ezrj3o9HqCKjIxaXay0/9uVvJSW2HLE4DNK34j8ftL2Ro0hIg7fvL6C1zt9gBefVi/UlTlJAzYX1hGfJyvoxEtZaj+29Y3sq7SwHHnwrhzPY457HZysvdSkL2b3pYi9CVZULKf7Tu2UpG3l13OZILQU2Z1UF5ZxVHm9egdilxTbddi5vzXGX3gcwDKlZlcfTQlhmgqzNHYA2MY2L+/q3s8pCul5i4YQrtiCZSVrjsySV46qFlXjsTucGI46D95vgpmrmM45+sXotcanm6YElX7a10RehJOnQl78VoSnPsoLjhAKKDqtLxomkYFrv/o7paXgzKJKnMXqARHsatVRafTs13vGtfSq7qMMygW8kFX1vzkxWmrZKHpVhQamnUpWDzHuZSE9IK8ddgPbGlWfQdKKugCZJdUEdvsKFyMliDMWBlcvpjl37zKiHNvaWENbXTvHiqVIs4c0HTZI1SlXREGlFW2fBsK0QHUfGhqwYw6vcFATLdkYrolexyved1JAy4CSiptZBeWkr7lfSrzMxhap/VUBUaRRRdiVC6BWhVJzn1g3QdWoASo85JVk7IsZBhPhD1MbJiF2BAzZ5XMxhQcTv8J0wgNj2rZ8xbtTpKXDuzgxAVgp+rK3fZr2ReUwoyqNxu+zlHdTRQ/lOHXuhaiK8jNgtf6EUqZa/2YurOFNI3fIy/g3V0jGJ6cSFV5AoQnedRpD4qBItCVuv6XR8Z0I/KBNR5ldKGuj8LBthasjKscdNe5tgEoa+h0VB/IA3Nh8zZ93F9YThdgT35Fi5OXnmmjWLz0KsZk/B+h6R+Dl5OXmplWonG/6sehVRUxxNSKwdzC5zILy+gKFFTYae/fYIjFSEhcBMSdXe/cmCufBp6mqrKcA3t3UJi9k8r8/diLMqEki1HRVWglWVCSibVwHyZlpcBhZnN2CZuzSzBg52nz6+g0Re5xlwCu5GXJG9fTM3sORYYoSk1dsAbE4AxNwNSlByFxfUjuPQBTSJQ0px4Gkrz4mWRjEUNtW4igpNEy7oXoqj/dKKeTsIguPGq/glxnKP8uq8Lk9Ow22h0+im+dCfTvkcLocTfUqzMyNgn2Q5KxuNHHHTYgBTbAgJDmb5pXZ7kZTMb6f46B8f1hC0RW7K53rkHV3UYHtxw1V5/Tb8Hxxrv0s28iY9s6EnsPbPoi4TXvmCezp6ycL4O8PyZJtF1BWRVdgeIqZ7snL81htgSS0DuNhN5pjZYxOp0UFeaRVljCB/YQsoorKSjIZ/nmczBV5DCoS7y7rKlsPzHkE2PPB/tWKAfygLrLU5lDIaI7+4hlrz2U/MjB5PacRHSQiahgM3H2vUTEJBASFnnYnndnJMmLH1gScyGjcz4DIMqRw1fmh8lTjb947zuQSzdgf1Ele1++jOSCRZhvWsK35rPJK7Nyc/ZOElc95y6vaRqB1TstNra6a7++/WE1dFWNdwkZwrq6vilpfreRsU6OYWxgKmR0sutFJt6xH4fd3uR0Sa2B71oiOi6JfwKGc1Tlcg58cx/dbvsGnb7pGV7tYfmLF6ApJ32mvtns2VVHGn31GjgdYUVk0XK1Syx03JYITacjLLILYZFdqN18JRFOnlWvbPcrXmfr/h2U5e6lqmAfzuL9GEr2Ely+l2h7Fl1UPlQVQ9Y6urGObsCX2Tk88I+rZhM2tlimAJB/Q7p7osDyr17GuX8tzqAu6IJjMIXFEhARR2h0ApGxCTIeB0le/MLoG97hwwcLuVz3q/tYlHWfR5nc8KMILN9HoDWPnNx8ugHxpeuJZ72rwKt96We9j0WkEf/TVMzFrt2fM7R4EnU6ujn3c7JuBcE5xeTsM2I0WYjo0rX2AWJca8ZwYDM4HWTv34Xt3dOp0lnoVdN9FFzdUVOWA04nNGfAq8cGe/Vf0OISe1OhTARoVjJ2biCxz6CmKnTV1IZmWtMJd+P88QKGlv7Bih/fZvhZ/2p1XS0xrHCuq1m6qvktV0eaYMqIoBinrcrXoYhWqFkZWXXg5KUlomITiIpNaLyArQIK90DBbtZv+IfSvP1U6ZOZaIglr9SKsySb0rIATNiIiK59vdVvn8eIsj/qTRmv4TSHoguOheBY9liD2G8PpSh6KAU9ziAm1ExsqIV4UwXhkTGddmaVJC9+om7i0pBoi2J/kYFAoKq84a6dAdouAnRVBFYnLgCJD20EYHDBXP5leoeirUGEbS1jg+koIu77s/biiGSUMRDNVk5F9lbsVkWCyqTMXjuiXwV1cb0kOe0U5mURXqd5tTF2h9P9R+hEq7c8m06vZ5+lN72r0tFnroImkhf3VghtSF5SRp7M4vXXMmbPW3Tf+gHgneTlcftlaCiuM8unqsa8Wn43yZYM1ud8CH3O9HU4ooVqJgqoI2UhRmMAdOkHXfqR1ncCAKMBzy19z6GirART3SRj4HkszuiJvjwXU1UegdY8gh2FRDoLsGg2dFXFrhadvK0kAUnAF1nZ3LPatcFsTYuOVempmrHN3SW1/JvXcGSlQ3AX9MExmMNiCYzsSmh0POHRXTGZ/WfxR0le/MwBFcrRVa8yxryT97WHa084bGwx9CGnKojUzK/rNWIscAziRP0a7td93GC9hqhkNmf2Q6/sBNp34Tx4MTydnj2OKLpTzvbtWwjpfTTnVT2ExWTko+oimsFMAaFEUEx+dkazkpeySis1K9c4VMNry/YeegIsTif+wN/AlU3U2D4LvQ05dwbq5f+jS0k65G6D6N5NX9RG7zpOA+D66gUDRX2q+u9SOVq2f5boIKpbXpwygNXDwdsjDD1lKjC1XjnldFJSnE+ILQ9Kc6A0mw1bt1Ocu5dyXS9OMMSQXVyJVpQBDgCN4JBw9/WGrT8zouyvevW66zeHoQXHQFAX9lQFsd8RTEHUMHJ7nOUeo5NADjEJPTEYm7+W1uEgyYufWNL3DkZveY6VuqOwYiTTGgDmOgUObELvSGOd6slgw45615dioTAwmS12O33t9acdj5h0I0y6kW/X7OOkT9dwdGKUOylx12GIBGsGlQWZBOrNrFT9CNE8/4TmmCdSWFbJEJuZngddb3M4MR40g6ru2AWdrpGxJWnnweLXYP1XcPoLYGl89dzaJfbb9uJoCY+DXifAtrmw7G047Zk21dcU5bFYn2hM4r3Lcer0DPTSOCTRvmrGvBwxLS/tTNPpCAmPBqJdLTrAgOo5BWOAyXXKVlVOojA3k9g6LTqOfmeyZF8ChopczFX5BNoLCHUUEqGKMGhOtKoiqCryaNH5LCufB9a6Xs0DqGSj5So2GlPpf/9ibzzlRkny4ieUw7VL8inqL340ZbDO2YM/HANJ1HLoUb1Z47H69XRzNtxJqtBIuPgl+iZHkvFIfxLVfgC2PTaU3g+scpdzHmJvoK/jZzB9cy5TQsZybPV7re6gT1A/xlzDn1tzec4Zzag6x//amsu095fz0BmpXDq6e+3jeWwz0MjbdrehqPAktMI9FGxfSsSAkxsuB5gmPsLawhyS+o1otEyzjboets1FLXubDaHHkHbMWW2vsxHKqRiquZJKzTkez8xU1PD1pz3RNjWLY3aWMS8dmdkSSGxCL49jw8+6vsGyToeD4sJcQp2FUHYAyg6QvnUbxbmZWA09mah3jdHRivdRVW6kLKDpVvXDTZIXf+GoXZRrgG43A3S76Vv5PmnaTr4yPwzALdYbOFm/kp5k1bv8TP0S1pXuBSLZbepFYpUreUmye05BrmkAaKhVV0X3ZdcmI5nlGrrKfK7S/4xGADDBXSYhwrVWyeYsz3E382a/wmL9uyz6MQ1Gz3Ef99ge4BDTm9eq3gxmD5tWLGDMIZKXvkPHN3quxfqcxNbE8+mT8QUx826hqP8owqJaunpM8yjlcP8eC62XANJ1JDqfvqNOZX/PZYRJEtqh6PR6QqNigVh3i07qANe50cDldco6HefSv7zxpTq8Rdru/MSgnf+td+w03VK+Mj/MXhXNMVUvM0K3mTP0Sxutw1TpapWpSRLmOIZztfNeANKX/ELOwz0497sBvGN8nknFH9a7PjbU1Rqwv7ACXVkODxr/x43qU48yo3tGEk0Rto2/eBzXG81EaqV00Qo9K63+JOZU2iEH2VaE93XVU1C/S+xw6nbRC2Ro8aQ7Enn8h3WHbYpu3V26ZWPGxi35vxmseu4stqxa6OtQRCsEBIUQn9yv2bvOi45Hp9cTVGccja9Iy4ufWBd9CqPyvvU4ZtZcXUkbnd3Zq7oQpFW6z/3gGE1ahIPk4uXuYxWL3iLrh2s5mgMA/OkcyGJSAQiJ7kYM+QCcrF/J2qr6eW1KUBk3678iYQ/oh7qaHw9uLRkWWsIKy7+oKDGhnLe6p+kZeh/PhBVRDEvr79GdVNsHfmilKRdwxpYEIowp/O8Q5db+9hlVRVkkDJno3pStLQKDw9h9yfdc/d5m7BurGLNmH+cMOcTUyFaqOxQoxCKfShsTnrWIFPtGVh24wNehCCF8SFpe/MTga97kn/H/ZW/0Me5jTxvfASBKK+IU3TLWO5P5QzcSADs6uneN8agj2OAkrjpxAfiPcRZ6hyvhiU/uT6Uyus9FBtUfc9EjVDHD+AVnVnxHt1DXG2x4oOcbbXTXRCqUiVxdNKUlhe7jI4t/5WXjTI4v95zyXdOS4WziT7FrUh/Wq55sLDhkMYyLX2bk2gfITF906IIt0L9vb2450dXy8+C3G9iXW9huddeo29aia2TTOlG7crJWZ/NR4T92bljKkjdvYOlnh3cAvOj85FXST5gtgRw1/jww1V8DZKhuG2+aXuIB40cEBrlm4kzSL8J50KA4ZXRduzroWPexK/Wu8Sd6g4Fsfe02vYmR9cdcdE3uz5IuF7A28QpsVZ5bENSwBARheSibxIc2eix3HWg9QH/dHkLteR7lA4LDWNxjOit6HnotleRoVzy5pVaKK22NlttiSuE3x2CsAe27Qu2/xvdibIKJB+wzyfj01natG/DcXlsGMzbKWf335lQyVdofFexJZ3TWR4Rs/bbpwkIcQqfpNpo5cyYzZ87E4Wh4eftOQ1f7KyvtfzHBGz3HnDjModRse6Tf/IPHOVWd+OgclSyNmsSovG+4wzAbeAuAKl0AuFfvrv8GajCaGD39/1w/7F9TXa5+/tvQio7dw82QCaOzP2Hvthvde4uEhEUyZsoTh3rGAASbDVwV9BfJVVvJ3BZLaNrwBst9GDyNFXkFvBE9tMk6W8Kg13HPwFKOyl1IQW4oNmsVRlP7zQiy2h3UtGFV2p34z1JR3lazX1cn/3/eSZUEJ/OW/XQMod2rO6yFaJ1O0/Iyffp00tPTWb58edOF/dj+iNo37WJj/Y28RuV+1ei1BQ7Xm63RUUHSaXcAUKHV7mRs0wW4v3eoQ3/635Pv2gM6r7zxVpC64kNru5dytq1o1jUHO1//J5MNcyna2vj6Au5VXg7DoNfUsaezNPo89p7wWuNr0rRS3aTbIdv2NKqm5QWntLz4o6LQfjxpv4x5wfV3fhaiJTpNy8uRYvikm1luMBM/cBwZc14lHtgRNpqeRUs8ylUpI/mE0FXLrz0Y6NrGPdW2nozPXCvVBltq/wRs+trk5Z99RQxp4PHzc/ZRUnCAkgLXzCVbAx+Al3z0KFE7vyO/zwWMuuhu10FVW9CaVbtIXlVlORmbV6HTG+mZNurgqjyU9D6LxYVDiU8Z22gZ9zo1h6HnxWA0MerG+rO+2oOi9s1Y18odsY8ESqteYVdaXvxSzRi3TrrdjvAiSV78jE6vZ8TZNwAQmzUbNOhSsrFeudfsZ7NK9eEj05PuY4aQ2nEgiVVbAVBVpe4RFuaAYHDvCdjwu3/O2+eRYt/I4njXWo4NLfOtSrPpY9/Kkrzt7mOlFZXUjNYJ3f+H+3ju/p30/vp0ypQF0g69G/WoC+865HmAxwruord5M+mZr8GAi5ss3xbN2eW6uZzOOuvdyCv7IVT/vSlJXvyRZislQTtAuFNWSBZtI6+SfsyguT6thziL6p270fAtBjyb1i1h9RdY0+q8CfRPqh2wqxr59G81VI+bqXI9ZkMrZWrVLTyGytpWn/R9tdOEelVtwumo2V1WT6aKJIeIBh+vpUzKikWz1dkmoP3lZu1h5XNns//xge7n0VYe68fIOi+NGpDg6iodkhjWREnREXXdN4+/zLdwbf5zvg5F+DlJXjops2bjaN16j2MRMU0s6WwKrPNDw2+gtuoZS4aqokbL6YJdLTwma515zXWa+c2ajdysPQBYQxIYU/UaZ2kvHzq2auWlRayZ/ylrf/u0wfNdw1xDXWtmJx0OIeHR9CldRqLaz6Zlh97tu7k89jaS5KVRer2rpevgxFz4B9UOu74LAZK8+LUduuRDnr/W8KPnAVMwGA4xj8VYJ3lp5LXFbnTtfmq0u6Y0NbQ+iznUlbwE2mqTF+2gZv7cPZuA2gG2B++R1Jj1c99n8J/XEbio4U9uIWZXc3RE4OHbG8hsCWRT+HgAHAue9lgdt7VUM7dJOOLVdKnJVGm/lBbvev3oGystZ6Jt5FXSj0VN/5U1R7/R7PK2ilIIqD9DqcbqLGudnxpOJpTJ9eJjqU5eVANJhyXMtThesKN2f6ODk5eKHNd4GPcAvmZ+EOs5yjVLoY99K3nZexsIsH12lW5K9Em3UqFMDKxazfKvX2l7hdLy0iy7812LKm7PKW6ipOiIAg2uv+0As7GJkkIcmiQvfiwsKpbBJ1/KsojTAcgn9JDlI+N7kFnZ+ADTKl1tq0xjY16UxfUYJmclB4igVF9/rEpwZFdXfKp2LM7BycuwKNcUa13RHr42PcirzqbXegGIju/ONr1rp9QdS76rd76kegG7wormTeFurZ5po1iTfBUAA/95nMzdm9tUX0hYJFnTVpI1bSUGg7ywNya33LWy7oHich9HIlrF3WImCbpoG0leOoGBV7/FskGPYbuw4XEgBYRSeONmQsIiKVWNd6fkxozlZ8cIMlUktviRDZbRWVzNvfmByXR5eBep9/9dr0xo9c7LAZqVqgpXC01N8mKrSZBKXdsUaLZyhui2kcr2evU05kCca4Vgbdu8eudyil3TpfbkV9Q7195GTX6CjcYBBGhWiv43pU3dRzq9nrjE3sQl9pbZRoewJyCVnx0jKLJ083UoohX2Vq8PlVlS5eNIhL+TV8lOICAohJHn3Exsj4bXrCzTggiPds0k+iX43EbrsUb151+227gr6VNGX3Jfg2ViurjGs3SzNL63TEhoBOhdi9KZq1zjXrTqT1wVlur9lkpd06JDLa4xKhZj81sbwgaeCkCvkmU47AfHUd394oWuF51eT9hlsyhXZlLsG7EtfPawP+aR7o+oC/mX7Tb2RB3n61BEK+wrqE5eiqxNlBTi0CR56UzMYfX2MwJwaLVrKqyNOJm9KrrBywNNrnLl1san//bo5pqxFGs+xCcnTYOg6jVlSrJcMaScxeJuU8mMcbWa7N6zC4CoQFfSEmhu/nopfYYeT4kKIIIStv/j2fKj1Ywd8dKg1/jkfuwefj8Apr9fgOwNraqntLiAJa9fy5I3rm/P8DqdmoHdDiXLEPslmW0k2okkL52JTkeJcq2Su8MZx2ZDPwC6O2sHtgaaDBhoODkJoYxxurX0abBFo1r1mBf2rWTTf0az/KVGFoKL6Q+AyvwHgOFnXseYa17G2nOi63hpjqtcK5INo8nMtuBhABSv/8XjXM1Lojdn7PQ/42bodSLYK2H+Y62qo7K8lNE5sxmd/Uk7R9e51AzsdjglefFHNVOlGxtTJ0RzyV9QJxOmuQYybu13PYWh/eqdj9dyidPqrL8y4hr3t5Fl23jf9DRPlT/EqlcvbbB+R/VsI4AU+0YiSrY0WG67sQ8AK5f87nE8MLYHvzqGsdA5CIAqmytJsjexl9LBug47A4BhtlUHnTl82wM0StPgFNdKxs6tv5K7f3eLq1DGAN6wn8kb9jPbO7pO5bzM59lhvoyhu/7P16GI1qhJXuStR7SR/AV1MhtMRwGQOuokLGmuWUiF7oX5YXzBlx7lS0pqZwQFhNbtTmpkewBb7Yyk66y3MStoWoPlCgJ7uOosdb2RZ+7ezJ4tazCGxnKt7XYeqrwEm8PJnrxSAHJLW9YHHjfU9dy0vcuhorBO1D5qlu7Sj83GVHTKwdZ5LX9jVcZgnrZfwnPOhpNG4aJpoNOUrPPir6TbSLQT2duok0m563eKivJIjIoloecANgZFENfzqNoCoV2hzhZCW/dmMbT6++T+w3nruCXM/HUdZ/RPYkQD9YdH167Su8g5gJ6mrg3GYek+gpeWnkuOsTdPAPkfXc0A6z+sGP4cel03HE5FfpnVvc5LQ9sMHFJ4EkT3hdwtsHsRpJwG1Em5fPDiWNz/YmxrH2HWjlASC8pJiAhs+qJqNb0g8pJ+aCmXv0Bu1X8YFOya9eZ0ONDpZZ8cvyHdRqKdyF9QJ6M3GAirnqqs6XT0HzWRiC61Ccboyx6ivM506aGlf3hc79AZKSYIe50dpusKCArhYud/OKbqJcqwNLqgWkyPVF6yn8+npYOx2p1UagEUqUA0g4XIQCOhlJFbWOzeHbihxe6asi+gLwDr1i5zH8sffQ/LBv2HuB5HNXbZYZM24UqeCriNXytTeXne1hZdq5w2umtZJGmH3pzySBcWEU10XBKBwWFUVpSR8fggVrxwHuv/+g67TWawdHju5QTkrUe0jbS8HIH0dfaFyVbh1N2u0T1+9hBtAJkhaTjyMjhTt4gIazxwdL0yXYLNhAUYKaqwsTGzmMfDHmJVUSFvJgzjw+Vn0M+ylbXb30Uf4lrkrsUtL8AeutINqMiqHXczZMLlLa6nvQQEh3LaJTfy7huL+OGfTB45ewCBpub9F9NKs1lonoFVGYArD2+gncS6Oe8xwplB9+IMmDeP8rlmNln6UxI7ktCU4+k/aqK0ynQwvU+6mi1ZJxEf3vCMRyGaS5KXI5CuOnl5zHYZO1VX/lt93GatYvrCYUy3wGfZNwMNt15EBZmILtjBy6bX2VSeCtxUr4ymaRybZKJ063L2rK7AoVytP3qdRpUxFBxgLc7BHFSzx0krOkwShvPDrnVkOPrQ8JJ63jc0KZzkSAvRBWtYsUjjuPETm3dha7vPjmBDz7iO9V2SKFv1Of3z5hGqlZFWtQb2rIE9b1P0ZxJhQ86GtPMgfoivwxW4VsiOju/u6zBEJyDJyxGmpCifEM3VVfOjYzRZRLnP1V2Wvl/p0kbrGGXZw6WGD4FD78NzGT8zxvQmy3achlM/3fUYOo2kqz+mMjCYEYHBbFzq2pW5oQ0emxKYegqX/B5Clwoz/6o+tv6v77BXldNj8PHu7jNv0jSNR6PncVz566xeNhaambxEBbnuvdEgLQXNpTcYSDv6TDj6TJwOBzs3ryJn/e/oM5bQr2QxYRV7YNGrsOhVSgO6scmYSmXyicQefTl9YkOafgAhRIclycsRJjgknHxCUWj85/IT6Bdfu1Fj3WXpw3SNL68/LmAniTrX8v794hrfHdYcn8bOjO44gmKYfuBlgoz7CSz8D+Epx1NZUYa1qrLOug8tb3FIjg4C4EBJFSWVNkIsRsLn30mCymJT8OeERU1ocZ3tIWnMeZTsnoXVEo1yOpu13L+xegGT5u6uLTzp9Hp6pI6gR6prmLmtohi2/gJb5sDG7wiu2Mfwin18v7qEy5clMSghjJNTYzk9oZIefQf6OPojx6alv1K4+U+Ckocy8LhzfB2O8GOSvBxhNJ2OoHs247DbOCm48cQjVlfS6Lluw0+naOd7hFmzD7m43NCJV8DEK+gB7Hz0KHrod7Pe5toNeNuq34idexOW82ezJPZiVGAUSS18LmEBRqKDjASX72F3RgZpfXqyXZdMri0QkymohbW1n+T+w6m6dwejLM2fbeTNbQ2OBMaAUDjqQtdXZTEbV/xG4eY/2VreDVOWjrV7iyjdl86N5jvZauhD7/uWyZ5SXlCw/lfGZLzD0qJJIMmLaANJXo5A5ma8qeor8xo9l9hnEJz1JHxxVbPfbMMtOiiHqFBXUlG59H3CVAmFtipG/+ut5gXegNdNrzDS8SdLV90Hfe7m3+a7ySir4MvIhvd58paae1xwIJM9/51CtynvEh2X2Gj54vIqQgGrQ2HyUoxHDEso/Y+ZBMdMYgwwpbSKH/7JxLbqH6wH9FQYQj0Sl2UvXYIjsAuxYy+jZ9oon4XdGR0I7M0XjuPQgtKQOyvaQpIX0SBTdQtJY7ZkFtEX2JVfQXITdTnsdsI014ZsXcNdyUto+W5Mmp2y3L2HurRJjugUyvcswxDsGrtTM1tK1wEaMHKzMoh+M40IwPrGYKz37cNktjRYtrjCVp28IMnLYRYVbGbK2GQYey9FBdcQmZflPrdr4wpGFv4EhcAX75P1RRf2B6VQFTOIiNTj6TfsBGmhaYMtkcfzmi2RqdHJnOfrYIRf6zT/C2fOnElqaiojRjS0tJpob/k5rqTDXlZwyHJLP3uGqsfi0ZVV72Wkcw1ILTO5pkpWZm0iK2MbuVkZrYoj+aRrKJz6B8NOvxqoM9W7A3S/RMclsjjRFZdJs/PPr+83WlZRM9tIeFNYRDQJvdPcP0fF92TZoMdYFXQcDqURxwGGlv3JmJ2vkfLjeeQ9Pwp+fxIyloGz8Q1MRcOcygfbd4hOqdMkL9OnTyc9PZ3ly5f7OhS/lj7xU/Zpsawdd+gl7kdvfR6A3o7thyxnDOlCoFa7A7VNuf7krAExrnq2v0zcu8MofvuMVsXbtXs/4nukuH9+q+pu/jbfREBeeqvqa2+jr3yWbfpeAJjXf9p4wTYMXBbtJyQskpHn3MzQO7+n4F//sGrMayyOn8I/lmFUKiPRZVtg4VPw7slYn+jOhieO5a83b+KX9ZkUV9p8HX6HpzmsBFCJQUniJ9pGuo2Eh9Qxp8KYU+nWRLkqZcSsNf1i3XPEqVQsuYsAzbX66a78SvokAbEDIO8bdzm7Zmy4ghaoKCuhi8ojVstjayM7Z3ubptMRePnH8P4oBlSuZv+uzcQn198w0+neJVmSl44iOi6J6LgrgCsAKCvIhh2/wPb5sGMBpsoiBvAPf+xTXP/hKkx6HYOTwrk2+C+S08bSY8Ao9AZ5ia3r2Iw3udPyEYv3Xwq84etwhB/rNC0vwrt2Gns3q1x4dBxrY852/6zTu17MR55/h/vYWVWPcVvYi62OZfOK39j/SB/2vTCOEIur/uiQhseW+EJ8jxTWmYei0xRZX9yB09FAYuXebVd0VEERsTBsClz4Ady5gz0XzGHZoP+ws+80ekYHYXU4ydi5lZO2/ofkr87Abq/drmDr6j/Yt2MjynmEbyjp3phR3npE28jHAtEqoZe9x87/XciBgdc0ubpt9zPuhFmfA2CsHrCq6XRkBfYlrnwLNxi+Y6Y29FBVHFJ4XHdiVQ52ey56o2vxsYjAjjXsVXfcHTD3UoaW/kHh/OcIn3C3ZwFZYde/6A0kDRhN0oDRjASmANtySti6bhlrl4/A5Cijf51Zfc4f76CbfTOZdGFv+HBU97F06TeGpL5DjqjWmdSuIZAN/buG+zoU4eeOnP81ol3F90iBB/+hRzPKdu3ejxXDnsGWu4PRvWu3HIjr1gO2buEU/XLepPWfSGMTepFDJDFaPlQWVR/tWEnAgKNPZ2/enSSsepbwla/B0VdDUO3qxrUDdjtW3KL5eseE0PvEE+HEEz2OO+x27DozNqWnq3aAroU/Q+HPsBbKlZn9gSn0Hnws9DjO9WVseFPUziDU7BqwHx5kbqKkEIcmyYvwiuFnXlf/4LG3w9Y5ANxe9CRwXKvrzwgZREzJ7+6fK2xOOtpbQMIZ98H+XyBrHc6f7kCd8477U3dc9xS2nfMjms5AhI/jFO1LbzAw4L4/KS8tYuOKeZRt/o2wvLUkV20hUKuid8VaWLwWFr+GMgaSHjCM0sTj6Tr6AhITEjvEzLl2I91Gop1I8iJ8J2kUewzJJNl3EUtum6oKn3APji8WoNdcLRiZxVX0bI8Y25NOB6c8hXr/THQbvuKf7buJuext4hJ7YwkMpvegY3wdoTiMAoPDOGr8eTDetcKJw25n15bVmHPW0LVkPWydh1a8lwG2P2HDn5y9ykBOaBpjekZxUlw5/SJ1xPccQECQ/+7LlFVUThyQVWIlztfBCL8m6a/wqaJjH2JZ+GmEX/lZm+rpNXA0y5JrW3eCI2LaGtrhkXwMu45/jUpl5KjKlbz0zru8tXA7dscRPpDzCKQ3GEhOHUHX8dfAmS/DbevJu3w+i5OuY415GFt0vcgsquSr1fsonvs0vb6cyNrPn3RfX5SXzYof3mb35jU47HYfPpPm251bCsDO3Mb3ThOiOaTlRfjUwHHnwrhz26WuMVc+zY7151JRnMeAbs0ZjeMbPY67jN1dUtjw/ct8mn80/LyJL35fxvPqWcr7TmL0ZQ/6OkThC5pGVO/hjOk9HIBVVgcrdxewaHsu3dZAYWUwxphe7uI71/zO8BV3wgqwKj15Whj5xjhKAhNwhPckKWUo8b0HQ2RP0Ld9KYL2Id1Gon1I8iI6FX/Zi6Z7/2EkpbzPMyv38sRPG1EVxRxl3k7W1vcBSV4EBJj0HNMnmmP6RMMp3wIwpM40e02nZ5OhP91t2wnQrMSQT4wtH4rSoQjYDcwBdAYqLTHssoZRYunKj33/Q9cwC8nRQfQNKicqPIyQ0AivbHuguce8dKJxPMInJHkRwkc0TePC4Ymc3D+WOT8eYPWusdh7nyJjAUSjdHq9+/tBx18Ax1+Aw24nK3MXxQf2UZK1DVvOdvQFWxlozsZSuB2spVjK95PCfvaWHOC9Rbvcdcw2PUoP3Sa2nfMTvQcdDcCGRT9RsvZbVEAEusBI9EERmIOjsIRGExgWRXSXOMxBEa4xXC1Vk7zI/lCijSR5EcLHIoJMXHzhZcBlvg5F+CG9wUBcYm/iEnsD4zxPKgXF+8nZt4N9e7ZTVFbJdYE9ySysZGduGV1zXXuTleXtc19SvOUPxmQfYisLADQwBVPl1Mi3m9lnSOKZLo9j0GmYDTouLXqHCGc+0afeS3J/VzfYhkU/EVe0pvpySV5E20jyIoQQnZWmQVg3YsK6EZN6LADj65xWzk2UlBTS11A7Jiak91iWVBajqyrEaC3CZCsmwF5MkLOEEFVavVeZAmsJZqArUGS1sGxnvruO+0x/00e3j/X5U93HSjP+YYDTlSTpAsIP1zMWRwhJXoQQ4gil6XSEhEV6HEs75iw45qzGL7JXQUUhVJVQWF5Jdk4OZVV2XgtJw+FUVNmcZO+5gbzKfHp0T3VfFtFnDIvLrkMLCGPgKVcfpmckjhSaUqpTbadSXFxMWFgYRUVFhIaG+jocIYQQQjRDS96/peNRCCGEEH5FkhchhBBC+BVJXoQQQgjhVyR5EUIIIYRfkeRFCCGEEH5FkhchhBBC+BVJXoQQQgjhVyR5EUIIIYRfkeRFCCGEEH5FkhchhBBC+BVJXoQQQgjhVyR5EUIIIYRfkeRFCCGEEH7F4OsA2lvNJtnFxcU+jkQIIYQQzVXzvl3zPn4onS55KSkpASAxMdHHkQghhBCipUpKSggLCztkGU01J8XxI06nk/379xMSEoKmae1ad3FxMYmJiWRkZBAaGtqudQu5v4eb3N/DS+7v4SX39/Dz9T1WSlFSUkJ8fDw63aFHtXS6lhedTkdCQsJhfYzQ0FD5z3MYyf09vOT+Hl5yfw8vub+Hny/vcVMtLjVkwK4QQggh/IokL0IIIYTwK5K8tIDZbOahhx7CbDb7OpROSe7v4SX39/CS+3t4yf09/PzpHne6AbtCCCGE6Nyk5UUIIYQQfkWSFyGEEEL4FUlehBBCCOFXJHkRQgghhF+R5EUIIYQQfkWSl2Z6/fXX6dGjBxaLhWHDhvHnn3/6OiS/8OSTTzJixAhCQkKIiYlh0qRJbN682aOMUoqHH36Y+Ph4AgICGD9+PBs2bPAoU1VVxU033UR0dDRBQUGcddZZ7N2715tPpcN78skn0TSNW2+91X1M7m3b7du3j8svv5yoqCgCAwMZPHgwK1eudJ+Xe9x6drudf//73/To0YOAgAB69uzJo48+itPpdJeR+9t8f/zxB2eeeSbx8fFomsY333zjcb697mVBQQFXXHEFYWFhhIWFccUVV1BYWHiYn91BlGjSp59+qoxGo3rnnXdUenq6uuWWW1RQUJDavXu3r0Pr8CZOnKhmzZql1q9fr9asWaNOP/10lZSUpEpLS91lnnrqKRUSEqK+/PJLtW7dOnXRRReprl27quLiYneZ66+/XnXr1k3NnTtXrVq1Sh1//PFq0KBBym63++JpdTjLli1TycnJ6qijjlK33HKL+7jc27bJz89X3bt3V1OnTlVLly5VO3fuVPPmzVPbtm1zl5F73Hr/+c9/VFRUlPrhhx/Uzp071eeff66Cg4PVSy+95C4j97f5fvrpJ3X//ferL7/8UgHq66+/9jjfXvfylFNOUWlpaWrRokVq0aJFKi0tTZ1xxhneeppKKaUkeWmGkSNHquuvv97jWEpKirrnnnt8FJH/ysnJUYBauHChUkopp9Op4uLi1FNPPeUuU1lZqcLCwtSbb76plFKqsLBQGY1G9emnn7rL7Nu3T+l0OvXLL7949wl0QCUlJapPnz5q7ty5aty4ce7kRe5t2919993qmGOOafS83OO2Of3009VVV13lcezcc89Vl19+uVJK7m9bHJy8tNe9TE9PV4BasmSJu8zixYsVoDZt2nSYn1Ut6TZqgtVqZeXKlUyYMMHj+IQJE1i0aJGPovJfRUVFAERGRgKwc+dOsrKyPO6v2Wxm3Lhx7vu7cuVKbDabR5n4+HjS0tLkdwBMnz6d008/nZNOOsnjuNzbtvvuu+8YPnw4F1xwATExMQwZMoR33nnHfV7ucdscc8wxzJ8/ny1btgCwdu1a/vrrL0477TRA7m97aq97uXjxYsLCwhg1apS7zOjRowkLC/Pq/e50u0q3t9zcXBwOB7GxsR7HY2NjycrK8lFU/kkpxYwZMzjmmGNIS0sDcN/Dhu7v7t273WVMJhMRERH1yhzpv4NPP/2UVatWsXz58nrn5N623Y4dO3jjjTeYMWMG9913H8uWLePmm2/GbDYzefJkucdtdPfdd1NUVERKSgp6vR6Hw8Hjjz/OJZdcAsjfcHtqr3uZlZVFTExMvfpjYmK8er8leWkmTdM8flZK1TsmDu3GG2/kn3/+4a+//qp3rjX390j/HWRkZHDLLbfw66+/YrFYGi0n97b1nE4nw4cP54knngBgyJAhbNiwgTfeeIPJkye7y8k9bp3Zs2fz4Ycf8vHHHzNgwADWrFnDrbfeSnx8PFOmTHGXk/vbftrjXjZU3tv3W7qNmhAdHY1er6+XUebk5NTLYEXjbrrpJr777jt+//13EhIS3Mfj4uIADnl/4+LisFqtFBQUNFrmSLRy5UpycnIYNmwYBoMBg8HAwoULeeWVVzAYDO57I/e29bp27UpqaqrHsf79+7Nnzx5A/n7b6s477+See+7h4osvZuDAgVxxxRXcdtttPPnkk4Dc3/bUXvcyLi6O7OzsevUfOHDAq/dbkpcmmEwmhg0bxty5cz2Oz507l7Fjx/ooKv+hlOLGG2/kq6++4rfffqNHjx4e53v06EFcXJzH/bVarSxcuNB9f4cNG4bRaPQok5mZyfr164/o38GJJ57IunXrWLNmjftr+PDhXHbZZaxZs4aePXvKvW2jo48+ut7U/i1bttC9e3dA/n7bqry8HJ3O821Ir9e7p0rL/W0/7XUvx4wZQ1FREcuWLXOXWbp0KUVFRd69314bGuzHaqZKv/vuuyo9PV3deuutKigoSO3atcvXoXV4//rXv1RYWJhasGCByszMdH+Vl5e7yzz11FMqLCxMffXVV2rdunXqkksuaXD6XkJCgpo3b55atWqVOuGEE47IqZBNqTvbSCm5t221bNkyZTAY1OOPP662bt2qPvroIxUYGKg+/PBDdxm5x603ZcoU1a1bN/dU6a+++kpFR0eru+66y11G7m/zlZSUqNWrV6vVq1crQL3wwgtq9erV7mU92utennLKKeqoo45SixcvVosXL1YDBw6UqdId1cyZM1X37t2VyWRSQ4cOdU/1FYcGNPg1a9Ysdxmn06keeughFRcXp8xmszruuOPUunXrPOqpqKhQN954o4qMjFQBAQHqjDPOUHv27PHys+n4Dk5e5N623ffff6/S0tKU2WxWKSkp6u233/Y4L/e49YqLi9Utt9yikpKSlMViUT179lT333+/qqqqcpeR+9t8v//+e4Ovt1OmTFFKtd+9zMvLU5dddpkKCQlRISEh6rLLLlMFBQVeepYumlJKea+dRwghhBCibWTMixBCCCH8iiQvQgghhPArkrwIIYQQwq9I8iKEEEIIvyLJixBCCCH8iiQvQgghhPArkrwIIYQQwq9I8iKEEEIIvyLJixBCCCH8iiQvQgghhPArkrwIIYQQwq/8P3HqLIJpu1iOAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Beamline input block\n",
"energy = 8000.0 # Energy in eV\n",
"energy_keV = energy/1000.0 # Energy in keV\n",
"wl = 1239.84 / (energy * 10**9)\n",
"sigmaH = (sigmaH_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaV = (sigmaV_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaHp = (sigmaHp_e**2 + wl/L_und/2)**0.5\n",
"sigmaVp = (sigmaVp_e**2 + wl/L_und/2)**0.5\n",
"\n",
"flag_HE = True\n",
"slit1_H = 500.0e-6 # H slit size before CRL 1\n",
"slit1_V = 300.0e-6 # V slit size before CRL 1\n",
"slit2_H = 500.0e-6 # H slit size before CRL 2\n",
"slit2_V = 300.0e-6 # V slit size before CRL 2\n",
"\n",
"epics.caput(\"100idPyCRL:testSSH1.VAL\", slit1_H)\n",
"epics.caput(\"100idPyCRL:testSSV1.VAL\", slit1_V)\n",
"epics.caput(\"100idPyCRL:testSSH2.VAL\", slit2_H)\n",
"epics.caput(\"100idPyCRL:testSSV2.VAL\", slit2_V)\n",
"epics.caput(\"100idPyCRL:CRL:thickerr_flag\", flag_HE)\n",
"epics.caput(\"100idPyCRL:CRL:EnergySelect\",0)\n",
"epics.caput(\"100idPyCRL:testMonoE.VAL\",float(energy_keV))\n",
"\n",
"lookup_table, L1_inF_list_sort_indices, index1to2 = Zoom_CRL2D_lookup()\n",
"\n",
"time.sleep(1)\n",
"ioc_lookup=epics.caget(\"100idPyCRL:CRL:fSizes\")\n",
"\n",
"plt.plot(np.linspace(0,1023,1024), lookup_table, label='XS lookup', ls='--')\n",
"plt.plot(np.linspace(0,1023,1024), ioc_lookup, label='IOC lookup', ls='-.')\n",
"plt.title(str(energy_keV)+' keV lookup table')\n",
"plt.yscale('log')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "f69fbadd-3ef6-4f09-8453-5cdd08ddbfe5",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(np.linspace(0,1023,1024), ioc_lookup/lookup_table)\n",
"plt.title(str(energy_keV)+' keV lookup table ratio')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "1f0e7d9e-8b38-400c-b5f0-903d0d33751c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Beamline input block\n",
"energy = 12000.0 # Energy in eV\n",
"energy_keV = energy/1000.0 # Energy in keV\n",
"wl = 1239.84 / (energy * 10**9)\n",
"sigmaH = (sigmaH_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaV = (sigmaV_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaHp = (sigmaHp_e**2 + wl/L_und/2)**0.5\n",
"sigmaVp = (sigmaVp_e**2 + wl/L_und/2)**0.5\n",
"\n",
"flag_HE = True\n",
"slit1_H = 500.0e-6 # H slit size before CRL 1\n",
"slit1_V = 300.0e-6 # V slit size before CRL 1\n",
"slit2_H = 500.0e-6 # H slit size before CRL 2\n",
"slit2_V = 300.0e-6 # V slit size before CRL 2\n",
"\n",
"epics.caput(\"100idPyCRL:testSSH1.VAL\", slit1_H)\n",
"epics.caput(\"100idPyCRL:testSSV1.VAL\", slit1_V)\n",
"epics.caput(\"100idPyCRL:testSSH2.VAL\", slit2_H)\n",
"epics.caput(\"100idPyCRL:testSSV2.VAL\", slit2_V)\n",
"epics.caput(\"100idPyCRL:CRL:thickerr_flag\", flag_HE)\n",
"epics.caput(\"100idPyCRL:CRL:EnergySelect\",0)\n",
"epics.caput(\"100idPyCRL:testMonoE.VAL\",float(energy_keV))\n",
"\n",
"lookup_table, L1_inF_list_sort_indices, index1to2 = Zoom_CRL2D_lookup()\n",
"\n",
"time.sleep(1)\n",
"ioc_lookup=epics.caget(\"100idPyCRL:CRL:fSizes\")\n",
"\n",
"plt.plot(np.linspace(0,1023,1024), lookup_table, label='XS lookup', ls='--')\n",
"plt.plot(np.linspace(0,1023,1024), ioc_lookup, label='IOC lookup', ls='-.')\n",
"plt.title(str(energy_keV)+' keV lookup table')\n",
"plt.yscale('log')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "24de222a-39de-4302-a14c-969383245771",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(np.linspace(0,1023,1024), ioc_lookup/lookup_table)\n",
"plt.title(str(energy_keV)+' keV lookup table ratio')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "e5d0d2d6-b594-4421-9b54-42909312b50e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Beamline input block\n",
"energy = 16000.0 # Energy in eV\n",
"energy_keV = energy/1000.0 # Energy in keV\n",
"wl = 1239.84 / (energy * 10**9)\n",
"sigmaH = (sigmaH_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaV = (sigmaV_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaHp = (sigmaHp_e**2 + wl/L_und/2)**0.5\n",
"sigmaVp = (sigmaVp_e**2 + wl/L_und/2)**0.5\n",
"\n",
"flag_HE = True\n",
"slit1_H = 500.0e-6 # H slit size before CRL 1\n",
"slit1_V = 300.0e-6 # V slit size before CRL 1\n",
"slit2_H = 500.0e-6 # H slit size before CRL 2\n",
"slit2_V = 300.0e-6 # V slit size before CRL 2\n",
"\n",
"epics.caput(\"100idPyCRL:testSSH1.VAL\", slit1_H)\n",
"epics.caput(\"100idPyCRL:testSSV1.VAL\", slit1_V)\n",
"epics.caput(\"100idPyCRL:testSSH2.VAL\", slit2_H)\n",
"epics.caput(\"100idPyCRL:testSSV2.VAL\", slit2_V)\n",
"epics.caput(\"100idPyCRL:CRL:thickerr_flag\", flag_HE)\n",
"epics.caput(\"100idPyCRL:CRL:EnergySelect\",0)\n",
"epics.caput(\"100idPyCRL:testMonoE.VAL\",float(energy_keV))\n",
"\n",
"lookup_table, L1_inF_list_sort_indices, index1to2 = Zoom_CRL2D_lookup()\n",
"\n",
"time.sleep(1)\n",
"ioc_lookup=epics.caget(\"100idPyCRL:CRL:fSizes\")\n",
"\n",
"plt.plot(np.linspace(0,1023,1024), lookup_table, label='XS lookup', ls='--')\n",
"plt.plot(np.linspace(0,1023,1024), ioc_lookup, label='IOC lookup', ls='-.')\n",
"plt.title(str(energy_keV)+' keV lookup table')\n",
"plt.yscale('log')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "e31f4fab-2070-4405-ad44-2b63899ad7d1",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(np.linspace(0,1023,1024), ioc_lookup/lookup_table)\n",
"plt.title(str(energy_keV)+' keV lookup table ratio')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "ae1dc155-aada-4464-ba47-a59ebbc93da1",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Beamline input block\n",
"energy = 20000.0 # Energy in eV\n",
"energy_keV = energy/1000.0 # Energy in keV\n",
"wl = 1239.84 / (energy * 10**9)\n",
"sigmaH = (sigmaH_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaV = (sigmaV_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaHp = (sigmaHp_e**2 + wl/L_und/2)**0.5\n",
"sigmaVp = (sigmaVp_e**2 + wl/L_und/2)**0.5\n",
"\n",
"flag_HE = True\n",
"slit1_H = 500.0e-6 # H slit size before CRL 1\n",
"slit1_V = 300.0e-6 # V slit size before CRL 1\n",
"slit2_H = 500.0e-6 # H slit size before CRL 2\n",
"slit2_V = 300.0e-6 # V slit size before CRL 2\n",
"\n",
"epics.caput(\"100idPyCRL:testSSH1.VAL\", slit1_H)\n",
"epics.caput(\"100idPyCRL:testSSV1.VAL\", slit1_V)\n",
"epics.caput(\"100idPyCRL:testSSH2.VAL\", slit2_H)\n",
"epics.caput(\"100idPyCRL:testSSV2.VAL\", slit2_V)\n",
"epics.caput(\"100idPyCRL:CRL:thickerr_flag\", flag_HE)\n",
"epics.caput(\"100idPyCRL:CRL:EnergySelect\",0)\n",
"epics.caput(\"100idPyCRL:testMonoE.VAL\",float(energy_keV))\n",
"\n",
"lookup_table, L1_inF_list_sort_indices, index1to2 = Zoom_CRL2D_lookup()\n",
"\n",
"time.sleep(1)\n",
"ioc_lookup=epics.caget(\"100idPyCRL:CRL:fSizes\")\n",
"\n",
"plt.plot(np.linspace(0,1023,1024), lookup_table, label='XS lookup', ls='--')\n",
"plt.plot(np.linspace(0,1023,1024), ioc_lookup, label='IOC lookup', ls='-.')\n",
"plt.title(str(energy_keV)+' keV lookup table')\n",
"plt.yscale('log')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "f28d5ddf-8e56-4129-ad28-468a951e57ad",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(np.linspace(0,1023,1024), ioc_lookup/lookup_table)\n",
"plt.title(str(energy_keV)+' keV lookup table ratio')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "eb475749-4777-44d0-bda8-7430a2f0918e",
"metadata": {},
"source": [
"# IOC to XS calc comparison\n",
"Testing that indexing is consistent"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "518d09f8-dbb5-4a0c-b6ae-f2dc1b8688de",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"IOC actual fSize 1.549050969632956e-06\n",
"CRL configs: 386 and 450\n",
"XS code fSize: 2.973983449945341e-06\n"
]
}
],
"source": [
"energy = 16000.0 # Energy in eV\n",
"energy_keV = energy/1000.0 # Energy in keV\n",
"wl = 1239.84 / (energy * 10**9)\n",
"sigmaH = (sigmaH_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaV = (sigmaV_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaHp = (sigmaHp_e**2 + wl/L_und/2)**0.5\n",
"sigmaVp = (sigmaVp_e**2 + wl/L_und/2)**0.5\n",
"\n",
"flag_HE = True\n",
"slit1_H = 500.0e-6 # H slit size before CRL 1\n",
"slit1_V = 300.0e-6 # V slit size before CRL 1\n",
"slit2_H = 500.0e-6 # H slit size before CRL 2\n",
"slit2_V = 300.0e-6 # V slit size before CRL 2\n",
"\n",
"epics.caput(\"100idPyCRL:testSSH1.VAL\", slit1_H)\n",
"epics.caput(\"100idPyCRL:testSSV1.VAL\", slit1_V)\n",
"epics.caput(\"100idPyCRL:testSSH2.VAL\", slit2_H)\n",
"epics.caput(\"100idPyCRL:testSSV2.VAL\", slit2_V)\n",
"epics.caput(\"100idPyCRL:CRL:thickerr_flag\", flag_HE)\n",
"epics.caput(\"100idPyCRL:CRL:EnergySelect\",0)\n",
"epics.caput(\"100idPyCRL:testMonoE.VAL\",float(energy_keV))\n",
"\n",
"lookup_table, L1_inF_list_sort_indices, index1to2 = Zoom_CRL2D_lookup()\n",
"\n",
"time.sleep(0.5)\n",
"epics.caput(\"100idPyCRL:CRL:focalSize\",'0.0000015')\n",
"time.sleep(0.5)\n",
"print(f'IOC actual fSize {epics.caget(\"100idPyCRL:CRL:fSize_actual\")}')\n",
"crl1 = epics.caget(\"100idPyCRL:CRL:1:lenses\")\n",
"crl2 = epics.caget(\"100idPyCRL:CRL:2:lenses\")\n",
"print(f'CRL configs: {crl1} and {crl2}')\n",
"print(f'XS code fSize: {Zoom_CRL2D_focuscal(crl1, crl2)}')"
]
},
{
"cell_type": "markdown",
"id": "58d9d25b-13a6-4b0f-a712-14142befcb7f",
"metadata": {},
"source": [
"Looks like things aren't lining up..."
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "9fa7faed-ccf0-4c94-9e94-61eb4927ab0e",
"metadata": {},
"outputs": [],
"source": [
"# Beamline input block\n",
"energy = 16000.0 # Energy in eV\n",
"energy_keV = energy/1000.0 # Energy in keV\n",
"wl = 1239.84 / (energy * 10**9)\n",
"sigmaH = (sigmaH_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaV = (sigmaV_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaHp = (sigmaHp_e**2 + wl/L_und/2)**0.5\n",
"sigmaVp = (sigmaVp_e**2 + wl/L_und/2)**0.5\n",
"\n",
"flag_HE = True\n",
"slit1_H = 500.0e-6 # H slit size before CRL 1\n",
"slit1_V = 300.0e-6 # V slit size before CRL 1\n",
"slit2_H = 500.0e-6 # H slit size before CRL 2\n",
"slit2_V = 300.0e-6 # V slit size before CRL 2\n",
"\n",
"lookup_table, L1_inF_list_sort_indices, index1to2 = Zoom_CRL2D_lookup()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "c7e81db4-7d0e-49c8-8e6f-d8c83b5a27b9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.549050969632956e-06\n",
"386\n",
"450\n"
]
}
],
"source": [
"ind = 385\n",
"print(f'{lookup_table[ind]}')\n",
"print(f'{L1_inF_list_sort_indices[ind]}')\n",
"print(f'{index1to2[ind]}')"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "c92ce294-f916-4c95-8a0f-13aa6048fe9b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"38"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.nanargmin(np.abs(np.asarray(lookup_table)-Zoom_CRL2D_focuscal(L1_inF_list_sort_indices[ind], index1to2[ind])))"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "303d93de-18a1-4f1a-87e0-08a6a1316ce7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"38"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.nanargmin(np.abs(np.asarray(lookup_table)-Zoom_CRL2D_focuscal(386,450)))"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "bfdf5fd8-af3c-4edb-b22c-c5caf571082d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.973753657172478e-06"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lookup_table[38]"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "c5895d0f-473f-4f17-bf48-a21b7058f785",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"======== Find size at focus ========================================\n",
"Energy: 16.0 keV\n",
"CRL1 configuration index in sorted list is 377\n",
"CRL1 configuration index is 377 or [1, 0, 0, 1, 1, 1, 1, 0, 1, 0]\n",
"CRL1 f is 3.08 m, focus at q1 = 3.27 m (-11.03 m from sample)\n",
"CRL2 configuration index in sorted list is 452\n",
"CRL2 configuration index is 452 or [0, 0, 1, 0, 0, 0, 1, 1, 1, 0]\n",
"CRL2 f is 2.58 m\n",
"Focal size is 1.76 μm x 1.29 μm at the focal point (7.9 mm from sample)\n",
"======== Find size at sample =======================================\n",
"CRL1 configuration index in sorted list is 385\n",
"CRL1 configuration index is 386 or [0, 1, 0, 0, 0, 0, 0, 1, 1, 0]\n",
"CRL1 f is 3.01 m, focus at q1 = 3.20 m (-11.10 m from sample)\n",
"CRL2 configuration index in sorted list is 450\n",
"CRL2 configuration index is 451 or [1, 1, 0, 0, 0, 0, 1, 1, 1, 0]\n",
"CRL2 f is 2.59 m\n",
"Beam size is 1.81 μm x 1.32 μm at the sample position)\n"
]
}
],
"source": [
"Zoom_CRL2D_control(0.0000015)"
]
},
{
"cell_type": "markdown",
"id": "80692d2c-f449-4248-87c3-82d2948df21b",
"metadata": {},
"source": [
"Matching \"control\" but not focuscal?"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "8fe407df-ebab-448c-bd02-f046cae679f5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.549050969632956e-06"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Zoom_CRL2D_focuscal(386, 451)"
]
},
{
"cell_type": "markdown",
"id": "21e15f8d-e8f0-4a4e-99fe-07cf766af5a5",
"metadata": {},
"source": [
"Not quite --> looks like IOC is reporting 10-bit config for CRL1 but sorted index for CRL2 --> correct needed to index sorted_invF_index['2'] with index1to2_sorted[indexSorted] to get lens configuration"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "a4193f6d-6312-4388-94b5-f7b35539c61a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"IOC actual fSize 8.016234618405476e-06\n",
"CRL configs: 178 and 698\n",
"XS code fSize: 8.016234618405506e-06\n"
]
}
],
"source": [
"energy = 16000.0 # Energy in eV\n",
"energy_keV = energy/1000.0 # Energy in keV\n",
"wl = 1239.84 / (energy * 10**9)\n",
"sigmaH = (sigmaH_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaV = (sigmaV_e**2 + wl*L_und/2/np.pi/np.pi)**0.5\n",
"sigmaHp = (sigmaHp_e**2 + wl/L_und/2)**0.5\n",
"sigmaVp = (sigmaVp_e**2 + wl/L_und/2)**0.5\n",
"\n",
"flag_HE = True\n",
"slit1_H = 500.0e-6 # H slit size before CRL 1\n",
"slit1_V = 300.0e-6 # V slit size before CRL 1\n",
"slit2_H = 500.0e-6 # H slit size before CRL 2\n",
"slit2_V = 300.0e-6 # V slit size before CRL 2\n",
"\n",
"epics.caput(\"100idPyCRL:testSSH1.VAL\", slit1_H)\n",
"epics.caput(\"100idPyCRL:testSSV1.VAL\", slit1_V)\n",
"epics.caput(\"100idPyCRL:testSSH2.VAL\", slit2_H)\n",
"epics.caput(\"100idPyCRL:testSSV2.VAL\", slit2_V)\n",
"epics.caput(\"100idPyCRL:CRL:thickerr_flag\", flag_HE)\n",
"epics.caput(\"100idPyCRL:CRL:EnergySelect\",0)\n",
"epics.caput(\"100idPyCRL:testMonoE.VAL\",float(energy_keV))\n",
"\n",
"lookup_table, L1_inF_list_sort_indices, index1to2 = Zoom_CRL2D_lookup()\n",
"\n",
"time.sleep(0.5)\n",
"epics.caput(\"100idPyCRL:CRL:focalSize\",'0.000008')\n",
"time.sleep(0.5)\n",
"print(f'IOC actual fSize {epics.caget(\"100idPyCRL:CRL:fSize_actual\")}')\n",
"crl1 = epics.caget(\"100idPyCRL:CRL:1:lenses\")\n",
"crl2 = epics.caget(\"100idPyCRL:CRL:2:lenses\")\n",
"print(f'CRL configs: {crl1} and {crl2}')\n",
"print(f'XS code fSize: {Zoom_CRL2D_focuscal(crl1, crl2)}')"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "eeb78b22-0a25-47e3-a046-2f4c53a7c1ec",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"======== Find size at focus ========================================\n",
"Energy: 16.0 keV\n",
"CRL1 configuration index in sorted list is 175\n",
"CRL1 configuration index is 173 or [1, 0, 1, 1, 0, 1, 0, 1, 0, 0]\n",
"CRL1 f is 6.66 m, focus at q1 = 7.65 m (-6.65 m from sample)\n",
"CRL2 configuration index in sorted list is 704\n",
"CRL2 configuration index is 706 or [0, 1, 0, 0, 0, 0, 1, 1, 0, 1]\n",
"CRL2 f is 1.57 m\n",
"Focal size is 10.80 μm x 6.02 μm at the focal point (10.6 mm from sample)\n",
"======== Find size at sample =======================================\n",
"CRL1 configuration index in sorted list is 176\n",
"CRL1 configuration index is 178 or [0, 1, 0, 0, 1, 1, 0, 1, 0, 0]\n",
"CRL1 f is 6.65 m, focus at q1 = 7.62 m (-6.68 m from sample)\n",
"CRL2 configuration index in sorted list is 698\n",
"CRL2 configuration index is 698 or [0, 1, 0, 1, 1, 1, 0, 1, 0, 1]\n",
"CRL2 f is 1.59 m\n",
"Beam size is 10.72 μm x 5.99 μm at the sample position)\n"
]
}
],
"source": [
"Zoom_CRL2D_control(0.000008)"
]
},
{
"cell_type": "markdown",
"id": "d5d40ba4-b755-4360-aa79-2c581fe3233b",
"metadata": {},
"source": [
"much better...\n",
"\n",
"# What next?\n",
"\n",
"Check that off-lookup-table moves match up"
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "acd0ca79-ad6f-4664-8584-56de1a59d5e6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For configs: 178 and 704, IOC yeilds 7.839122984830116e-06 focal size\n",
"XS code fSize: 7.839122984830116e-06\n"
]
}
],
"source": [
"crl1 = epics.caget(\"100idPyCRL:CRL:1:lenses\")\n",
"crl2 = epics.caget(\"100idPyCRL:CRL:2:lenses\")\n",
"fSize = epics.caget(\"100idPyCRL:CRL:fSize_actual\")\n",
"print(f'For configs: {crl1} and {crl2}, IOC yeilds {fSize} focal size')\n",
"print(f'XS code fSize: {Zoom_CRL2D_focuscal(crl1, crl2)}')"
]
},
{
"cell_type": "code",
"execution_count": 72,
"id": "d618af46-42f9-44fc-8b7f-5ed541f662fa",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For configs: 222 and 518, IOC yeilds 8.177871564000929e-06 focal size\n",
"XS code fSize: 8.177871564000929e-06\n"
]
}
],
"source": [
"crl1 = epics.caget(\"100idPyCRL:CRL:1:lenses\")\n",
"crl2 = epics.caget(\"100idPyCRL:CRL:2:lenses\")\n",
"fSize = epics.caget(\"100idPyCRL:CRL:fSize_actual\")\n",
"print(f'For configs: {crl1} and {crl2}, IOC yeilds {fSize} focal size')\n",
"print(f'XS code fSize: {Zoom_CRL2D_focuscal(crl1, crl2)}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "db3e48f8-4dc6-4e9e-a8d7-1a2ac8a4592a",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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",
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