diff --git a/example.ipynb b/example.ipynb new file mode 100644 index 0000000..573b15b --- /dev/null +++ b/example.ipynb @@ -0,0 +1,554 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import udkm1Dsim as ud\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import time\n", + "\n", + "import example_materials.sap as sap_gen\n", + "import example_materials.Nb as Nb_gen\n", + "import example_materials.TbFe2_110 as TbFe2_110\n", + "import example_materials.Al as Al_gen\n", + "u=ud.u #import pint units" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# import 1TM data for various materials (constants, unit cell, ...)\n", + "sap = sap_gen.sap_1TM()\n", + "Nb = Nb_gen.Nb_1TM()\n", + "TbFe2 = TbFe2_110.TbFe2_110_1TM()\n", + "Al = Al_gen.Al_1TM()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Structure properties:\n", + "\n", + "Name : TbFe30 Sample\n", + "Thickness : 192.0000 nanometer\n", + "Roughness : 0.0000 nanometer\n", + "----\n", + "20 times Al: 2.0000 nanometer\n", + "200 times TbFe2: 20.0000 nanometer\n", + "500 times Nb: 50.0000 nanometer\n", + "1200 times sap: 120.0000 nanometer\n", + "----\n", + "no substrate\n", + "\n" + ] + } + ], + "source": [ + "nAl = 20 # in 0.1nm steps\n", + "nTbFe2 = 200 # in 0.1nm steps \n", + "nNb = 500 # in 0.1nm steps \n", + "nSap = 1200# in 0.1nm steps\n", + "num_unit_cell = [nAl, nTbFe2, nNb, nSap]\n", + "\n", + "\n", + "fluenz_sim = [6]*u.mJ/u.cm**2 # Pump Puls Parameter\n", + "puls_width = [0.15]*u.ps\n", + "pump_delay = [0]*u.ps\n", + "multi_abs = True # absorption: lambert-beer vs. multilayer (better model)\n", + "init_temp = 300\n", + "heat_diff = True\n", + "delays = np.r_[-2:20:0.1]*u.ps # untersuchte Zeitskala\n", + "\n", + "sample_name = 'TbFe30 Sample'\n", + "layers = ['Al', 'TbFe2', 'Nb', 'sap'] # define layers material\n", + "sample_dic = {'Al': Al, 'TbFe2': TbFe2, 'Nb': Nb, 'sap': sap}\n", + "properties = {'Al': {'C': Al.prop['heat_capacity']},\n", + " 'TbFe2': {'C': TbFe2.prop['heat_capacity']},\n", + " 'Nb': {'C': Nb.prop['heat_capacity']},\n", + " 'sap': {'C': sap.prop['heat_capacity']}}\n", + "\n", + "\n", + "for l in range(len(layers)): # add parameters dictionary for the unit cell of each layer\n", + " prop_aml = {}\n", + " prop_aml['a_axis'] = sample_dic[layers[l]].prop['a_axis']\n", + " prop_aml['b_axis'] = sample_dic[layers[l]].prop['b_axis']\n", + " prop_aml['sound_vel'] = sample_dic[layers[l]].prop['sound_vel']\n", + " prop_aml['lin_therm_exp'] = sample_dic[layers[l]].prop['lin_therm_exp']\n", + " prop_aml['heat_capacity'] = sample_dic[layers[l]].prop['heat_capacity']\n", + " prop_aml['therm_cond'] = sample_dic[layers[l]].prop['therm_cond']\n", + " prop_aml['opt_pen_depth'] = sample_dic[layers[l]].prop['opt_pen_depth']\n", + " prop_aml['opt_ref_index'] = sample_dic[layers[l]].prop['opt_ref_index']\n", + " prop_aml['phonon_damping'] = sample_dic[layers[l]].prop['phonon_damping']\n", + " properties[layers[l]]['amorphous_layer'] = ud.AmorphousLayer(\n", + " layers[l], layers[l], 0.1*u.nm, sample_dic[layers[l]].prop['density'], **prop_aml)\n", + "\n", + "S = ud.Structure(sample_name) # create empty sample (structure)\n", + "for l in range(len(layers)): # add all previously specified layers to the structure\n", + " S.add_sub_structure(properties[layers[l]]['amorphous_layer'], num_unit_cell[l])\n", + "S.visualize() # plot the structure diagram\n", + "print(S) # print structure information\n", + "\n", + "_, _, distances = S.get_distances_of_layers()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Absorption profile is calculated by Lambert-Beer's law.\n", + "Absorption profile is calculated by multilayer formalism.\n", + "Total reflectivity of 70.6 % and transmission of 1.1 %.\n" + ] + } + ], + "source": [ + "''' Determine the optical absorption from the excitation conditions '''\n", + "\n", + "h = ud.Heat(S, True) # wärme initialisieren, dann anregung simulieren\n", + "h.excitation = {'fluence': fluenz_sim, 'delay_pump': pump_delay, 'pulse_width': puls_width, 'backside': False,\n", + " 'multilayer_absorption': multi_abs, 'wavelength': 800*u.nm, 'theta': 90*u.deg}\n", + "\n", + "dAdzLB = h.get_Lambert_Beer_absorption_profile()\n", + "dAdz, _, _, _ = h.get_multilayers_absorption_profile()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\cwalz\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\matplotlib\\cbook.py:1345: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", + " return np.asarray(x, float)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "'''Plot the absorption profile'''\n", + "\n", + "plt.figure()\n", + "plt.plot(distances.to('nm'), dAdz*1e-9*1e2, label=r'Multilayer')\n", + "plt.plot(distances.to('nm'), dAdzLB*1e-9*1e2, label=r'Lambert-Beer')\n", + "plt.xlim(0, 100)\n", + "plt.legend()\n", + "plt.xlabel('Distance (nm)')\n", + "plt.ylabel('Differnetial Absorption (%)')\n", + "plt.title('Laser Absorption Profile')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Heat simulation properties:\n", + "\n", + "This is the current structure for the simulations:\n", + "\n", + "Structure properties:\n", + "\n", + "Name : TbFe30 Sample\n", + "Thickness : 192.0000 nanometer\n", + "Roughness : 0.0000 nanometer\n", + "----\n", + "20 times Al: 2.0000 nanometer\n", + "200 times TbFe2: 20.0000 nanometer\n", + "500 times Nb: 50.0000 nanometer\n", + "1200 times sap: 120.0000 nanometer\n", + "----\n", + "no substrate\n", + "\n", + "\n", + "Display properties:\n", + "\n", + "================================ =======================================================\n", + " parameter value\n", + "================================ =======================================================\n", + " force recalc True\n", + " cache directory ./\n", + " display messages True\n", + " save data False\n", + " progress bar True\n", + " excitation fluence [6.0] mJ/cm²\n", + " excitation delay [0.0] ps\n", + " excitation pulse length [0.15] ps\n", + " excitation wavelength 800.0 nm\n", + " excitation theta 90.0 deg\n", + "excitation multilayer absorption True\n", + " excitation backside False\n", + " heat diffusion True\n", + " interpolate at interfaces 11\n", + " backend scipy\n", + " distances no distance mesh is set for heat diffusion calculations\n", + " top boundary type isolator\n", + " bottom boundary type isolator\n", + "================================ =======================================================\n", + "Surface incidence fluence scaled by factor 1.0000 due to incidence angle theta=90.00 deg\n", + "Calculating _heat_diffusion_ for excitation 1:1 ...\n", + "Absorption profile is calculated by multilayer formalism.\n", + "Total reflectivity of 70.6 % and transmission of 1.1 %.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "43108d2e935a45c89f8610bb63e4241f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time for _heat_diffusion_ with 1 excitation(s): 35.412479 s\n", + "Calculating _heat_diffusion_ without excitation...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "47473caffee94089b545cd7d54eac9f7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time for _heat_diffusion_: 33.637347 s\n", + "Elapsed time for _temp_map_: 69.449830 s\n" + ] + } + ], + "source": [ + "''' Get Temperature Map from the absorption profile including heat diffusion '''\n", + "\n", + "\n", + "h.save_data = False\n", + "h.disp_messages = True\n", + "# h.ode_options = {'max_step':1e-11}\n", + "# h.progress_bar = True\n", + "h.heat_diffusion = heat_diff\n", + "h.boundary_conditions = {'top_type': 'isolator', 'bottom_type': 'isolator'}\n", + "\n", + "Init_temp = init_temp\n", + "\n", + "print(h)\n", + "\n", + "temp_map, delta_temp_map = h.get_temp_map(delays, Init_temp)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# %% temp maps plotten für e- und ph\n", + "plt.figure(figsize=[6, 5])\n", + "plt.subplot(1, 1, 1)\n", + "plt.pcolormesh(distances.to('nm').magnitude, delays.to('ps').magnitude, temp_map[:, :],\n", + " shading='auto', cmap='RdBu_r', vmin=np.min(temp_map[:, :]), vmax=np.max(temp_map[:, :]))\n", + "plt.colorbar()\n", + "plt.xlim(0, 100)\n", + "plt.xlabel('Distance (nm)')\n", + "plt.ylabel('Delay (ps)')\n", + "plt.title('Phonon')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating linear thermal expansion ...\n", + "Calculating coherent dynamics with ODE solver ...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "488d48cf4ab744cf8a27323575280342", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time for _strain_map_: 1.738810 s\n" + ] + } + ], + "source": [ + "''' Get the picosecond strain dynamics from the Temperature Map '''\n", + "\n", + "pnum = ud.PhononNum(S, True)\n", + "pnum.disp_messages = True\n", + "# pnum.progress_bar = True\n", + "pnum.save_data = False\n", + "strain_map = pnum.get_strain_map(delays, temp_map, delta_temp_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# %% und plotten\n", + "plt.figure(figsize=[6, 5])\n", + "plt.pcolormesh(distances.to('nm').magnitude, delays.to('ps').magnitude, 1e3*strain_map, shading='auto',\n", + " cmap='seismic', vmin=-1*np.max(1e3*strain_map), vmax=1*np.max(1e3*strain_map))\n", + "plt.colorbar()\n", + "plt.xlim(0, 100)\n", + "# plt.ylim(-2, 0)\n", + "plt.xlabel('Distance (nm)')\n", + "plt.ylabel('Delay (ps)')\n", + "plt.title(r'Strain Map ($10^{-3}$)')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\cwalz\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\h5py\\_hl\\base.py:118: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", + " data = np.asarray(data, order=\"C\", dtype=as_dtype)\n" + ] + } + ], + "source": [ + "ud.helpers.save_to_h5('example_data.h5', delays, distances, S, abs_profile=dAdz, temp_map=temp_map, strain_map=strain_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([-2.00000000e-12, -1.90000000e-12, -1.80000000e-12, -1.70000000e-12,\n", + " -1.60000000e-12, -1.50000000e-12, -1.40000000e-12, -1.30000000e-12,\n", + " -1.20000000e-12, -1.10000000e-12, -1.00000000e-12, -9.00000000e-13,\n", + " -8.00000000e-13, -7.00000000e-13, -6.00000000e-13, -5.00000000e-13,\n", + " -4.00000000e-13, -3.00000000e-13, -2.00000000e-13, -1.00000000e-13,\n", + " 1.77635684e-27, 1.00000000e-13, 2.00000000e-13, 3.00000000e-13,\n", + " 4.00000000e-13, 5.00000000e-13, 6.00000000e-13, 7.00000000e-13,\n", + " 8.00000000e-13, 9.00000000e-13, 1.00000000e-12, 1.10000000e-12,\n", + " 1.20000000e-12, 1.30000000e-12, 1.40000000e-12, 1.50000000e-12,\n", + " 1.60000000e-12, 1.70000000e-12, 1.80000000e-12, 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300.00000213, 300.00000213]]),\n", + " array([[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", + " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", + " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n", + " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n", + " ...,\n", + " [ 5.19970259e-03, 5.19802747e-03, 5.19635591e-03, ...,\n", + " -9.83806837e-07, -6.56131258e-07, -3.28148846e-07],\n", + " [ 5.19667867e-03, 5.19482722e-03, 5.19259189e-03, ...,\n", + " -8.58432582e-07, -5.72691144e-07, -2.86473302e-07],\n", + " [ 5.19519024e-03, 5.19409016e-03, 5.19174773e-03, ...,\n", + " -7.03942211e-07, -4.69585438e-07, -2.34885671e-07]]),\n", + " Empty(dtype=dtype('`. + + Notes: + - Dimension scales are attached to datasets (delays and distances). + - If any optional dataset is missing, a placeholder empty dataset is written. + - Layer names and thicknesses are stored as file attributes. + """ + with h5.File(filename, "w") as h5file: + h5file.create_dataset('distances', data=distances[:cut], + compression='gzip', compression_opts=compression) + h5file.create_dataset('delays', data=delays*1e-12, #save delay in seconds + compression='gzip', compression_opts=compression) + h5file['distances'].make_scale('distances') + h5file['delays'].make_scale('delays') + + try: + h5file.create_dataset('abs_profile', data=abs_profile[:cut], + compression='gzip', compression_opts=compression) + h5file['abs_profile'].dims[0].attach_scale(h5file['distances']) + except: h5file.create_dataset('abs_profile', data=h5.Empty("f")) + try: + h5file.create_dataset('temp_map', data=temp_map[:, :cut], + compression='gzip', compression_opts=compression) + h5file['temp_map'].dims[0].attach_scale(h5file['delays']) + h5file['temp_map'].dims[1].attach_scale(h5file['distances']) + except: h5file.create_dataset('temp_map', data=h5.Empty("f")) + try: + h5file.create_dataset('strain_map', data=strain_map[:, :cut], + compression='gzip', compression_opts=compression) + h5file['strain_map'].dims[0].attach_scale(h5file['delays']) + h5file['strain_map'].dims[1].attach_scale(h5file['distances']) + except: h5file.create_dataset('strain_map', data=h5.Empty("f")) + try: + h5file.create_dataset('magnetization_map', data=magnetization_map[:, :cut], + compression='gzip', compression_opts=compression) + h5file['magnetization_map'].dims[0].attach_scale(h5file['delays']) + h5file['magnetization_map'].dims[1].attach_scale(h5file['distances']) + except: h5file.create_dataset('magnetization_map', data=h5.Empty("f")) + + structure_info = structure.get_all_positions_per_unique_layer() + layers_list = list(structure_info.keys()) + thickness_list = [len(value) for value in structure_info.values()] + h5file.attrs['layer_names'] = np.array(layers_list,dtype='S') + h5file.attrs['layer_thicknesses'] = np.array(thickness_list) + + + +def read_from_h5(filename): + r"""Read simulation data from an HDF5 file. + + Args: + filename (str): Path to the HDF5 file. + + Returns: + tuple: + delays (ndarray[float]): 1D array of delay times in seconds. + distances (ndarray[float]): 1D array of spatial positions in meters. + abs_profile (ndarray[float]): 1D absorption profile over distance. + temp_map (ndarray[float]): 3D temperature map (delay x distance x subsystem). + strain_map (ndarray[float]): 2D strain map (delay x distance). + magnetization_map (ndarray[float]): 2D magnetization map (delay x distance). + """ + with h5.File(filename, "r") as h5file: + delays = h5file['delays'][()] + distances = h5file['distances'][()] + abs_profile = h5file['abs_profile'][()] + temp_map = h5file['temp_map'][()] + strain_map = h5file['strain_map'][()] + magnetization_map = h5file['magnetization_map'][()] + return delays, distances, abs_profile, temp_map, strain_map, magnetization_map \ No newline at end of file