diff --git a/notebooks/Diffraction/D1_Diffraction_Rings.ipynb b/notebooks/Diffraction/D1_Diffraction_Rings.ipynb index fea7465a..169e5802 100644 --- a/notebooks/Diffraction/D1_Diffraction_Rings.ipynb +++ b/notebooks/Diffraction/D1_Diffraction_Rings.ipynb @@ -85,7 +85,7 @@ " version = '-1'\n", " return version\n", "\n", - "if test_package('pyTEMlib') < '0.2025.12.0':\n", + "if test_package('pyTEMlib') < '0.2026.06.0':\n", " print('installing pyTEMlib')\n", " !{sys.executable} -m pip install git+https://github.com/pycroscopy/pyTEMlib.git@main -q --upgrade\n", "\n", diff --git a/notebooks/Introduction/1_Installation.ipynb b/notebooks/Introduction/1_Installation.ipynb index 6d1b3324..4247726e 100644 --- a/notebooks/Introduction/1_Installation.ipynb +++ b/notebooks/Introduction/1_Installation.ipynb @@ -73,7 +73,7 @@ " version = '-1'\n", " return version\n", "\n", - "if test_package('pyTEMlib') < '0.2025.12.1':\n", + "if test_package('pyTEMlib') < '0.2026.06.0':\n", " print('installing pyTEMlib')\n", " !{sys.executable} -m pip install --upgrade pyTEMlib -q\n", "\n", diff --git a/notebooks/Introduction/2_Open_Files.ipynb b/notebooks/Introduction/2_Open_Files.ipynb index 295c128f..0febf7f2 100644 --- a/notebooks/Introduction/2_Open_Files.ipynb +++ b/notebooks/Introduction/2_Open_Files.ipynb @@ -70,7 +70,7 @@ " version = '-1'\n", " return version\n", "\n", - "if test_package('pyTEMlib') < '0.2025.12.1':\n", + "if test_package('pyTEMlib') < '0.2026.06.0':\n", " print('installing pyTEMlib')\n", " !{sys.executable} -m pip install --upgrade pyTEMlib -q\n", "\n", diff --git a/notebooks/Spectroscopy/S2-Analyse_CoreLoss_EELS.ipynb b/notebooks/Spectroscopy/S2-Analyse_CoreLoss_EELS.ipynb index 6f19213b..6113c6ab 100644 --- a/notebooks/Spectroscopy/S2-Analyse_CoreLoss_EELS.ipynb +++ b/notebooks/Spectroscopy/S2-Analyse_CoreLoss_EELS.ipynb @@ -47,15 +47,14 @@ }, "source": [ "## Content\n", - "The main feature in a low-loss EELS spectrum is the ``volume plasmon`` peak.\n", + "The main feature in a core-loss EELS spectrum are the ``ionization edges``.\n", "\n", - "This ``volume plasmon`` and all other features in the ``low-loss`` region of an EELS spectrum are described by Dielectric Theory of Electrodynamics.\n", + "We destinguish between the area under the ionization edges that give us the chemical composition and the shape of the edges which contains information on atomic bonding. The information is obscured by a rather high background, originating from higher energy edges and the tail of the plasmon peaks (convoluted by multiple scattering).\n", "\n", - "The simplest theory to interprete this energy range is the Drude theory. \n", + "The shape of an ``ionization edge`` represents the momentum resolved density of states (DOS) and is best described with density functional theory or an excitation theory like GW.\n", "\n", - "Another easy to observe component is the multiple scattering of this plasmon peak, which we can correct for or use for thickness determination.\n", "\n", - ">See [Notebook: Analysing Low-Loss Spectra with Drude Theory](https://raw.githubusercontent.com/gduscher/MSE672-Introduction-to-TEM/main/Spectroscopy/CH4_03-Drude.ipynb) of the MSE672-Introduction-to-TEM Lecture in my Github account.\n", + ">See [Notebook: Analysing Core-Loss Spectra with Drude Theory](https://raw.githubusercontent.com/gduscher/MSE672-Introduction-to-TEM/main/Spectroscopy/CH4_09-Analyse_Core_Loss.ipynb) of the MSE672-Introduction-to-TEM Lecture in my Github account.\n", "\n", "\n", "## Load important packages\n", @@ -65,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2024-09-25T17:42:31.009870Z", @@ -93,7 +92,7 @@ " return version\n", "\n", "# pyTEMlib setup ------------------\n", - "if test_package('pyTEMlib') < '0.2026.3.0':\n", + "if test_package('pyTEMlib') < '0.2026.6.0':\n", " print('installing pyTEMlib')\n", " !{sys.executable} -m pip install --upgrade git+https://github.com/pycroscopy/pyTEMlib.git@main -q --upgrade\n", "# ------------------------------\n", @@ -129,7 +128,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "pyTEM version: 0.2026.5.0\n" + "pyTEM version: 0.2026.6.0\n" ] } ], @@ -145,9 +144,9 @@ "import numpy as np\n", "import matplotlib.pylab as plt\n", "\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "sys.path.insert(0,'../../')\n", + "# %load_ext autoreload\n", + "# %autoreload 2\n", + "# sys.path.insert(0,'../../')\n", "import pyTEMlib\n", "\n", "# For archiving reasons it is a good idea to print the version numbers out at this point\n", @@ -177,12 +176,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "73c3bd17412445c687604b7d0f27210b", + "model_id": "c3a5f96c01534eb59af1fe837c651cac", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "VBox(children=(Dropdown(description='directory:', layout=Layout(width='90%'), options=('c:\\\\Users\\\\gduscher\\\\O…" + "VBox(children=(Dropdown(description='directory:', layout=Layout(width='90%'), options=('C:\\\\Users\\\\gduscher\\\\O…" ] }, "metadata": {}, @@ -191,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4c17d98655514698b92b0fef4cce16e8", + "model_id": "5987b3ac2ab94398a15b1f35b7656875", "version_major": 2, "version_minor": 0 }, @@ -215,55 +214,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "39a08887bfa74c81854d96ae42a529dc", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", 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", 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", 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- "text/html": [ - "\n", - "
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[18.0, 18.0]\n", + "\t\t\t\tPixel Size (um) : [14.0, 14.0]\n", "\t\t\tConfiguration :\n", - "\t\t\t\tLocation : filter\n", "\t\t\t\tTranspose :\n", "\t\t\t\t\tDiagonal Flip : 0\n", - "\t\t\t\t\tHorizontal Flip : 1\n", - "\t\t\t\t\tVertical Flip : 1\n", - "\t\t\tName : EF-CCD\n", - "\t\t\tSource : EF-CCD\n", - "\t\t\tSource ID : Continuum-C20101F0-0055NS\n", - "\t\t\tSource Model : Continuum\n", - "\t\t\tTemperature (C) : 4.9921875\n", + "\t\t\t\t\tHorizontal Flip : 0\n", + "\t\t\t\t\tVertical Flip : 0\n", + "\t\t\tName : QUEFINA 1\n", + "\t\t\tSource : QUEFINA 1\n", "\t\tFrame :\n", "\t\t\tArea :\n", "\t\t\t\tTransform :\n", "\t\t\t\t\tClass Name : cm_acquisitiontransform_list\n", "\t\t\t\t\tTransform List :\n", "\t\t\t\t\t\t0 :\n", - "\t\t\t\t\t\t\tBinning : [1.0, 1.0]\n", + "\t\t\t\t\t\t\tBinning : [1, 1]\n", "\t\t\t\t\t\t\tClass Name : cm_acquisitiontransform\n", "\t\t\t\t\t\t\tSub Area Adjust : [0, 0, 0, 0]\n", "\t\t\t\t\t\t\tTranspose :\n", @@ -376,17 +297,17 @@ "\t\t\t\t\t\t\t\tHorizontal Flip : 0\n", "\t\t\t\t\t\t\t\tVertical Flip : 0\n", "\t\t\tCCD :\n", - "\t\t\t\tPixel Size (um) : [18.0, 18.0]\n", + "\t\t\t\tPixel Size (um) : [14.0, 14.0]\n", "\t\t\tIntensity :\n", "\t\t\t\tTransform :\n", "\t\t\t\t\tClass Name : cm_valuetransform_list\n", "\t\t\t\t\tTransform List :\n", "\t\t\t\t\t\t0 :\n", "\t\t\t\t\t\t\tClass Name : cm_valuetransform_affine\n", - "\t\t\t\t\t\t\tOffset : 0.0\n", + "\t\t\t\t\t\t\tOffset : 250.0\n", "\t\t\t\t\t\t\tScale : 1.0\n", "\t\t\t\t\t\t1 :\n", - "\t\t\t\t\t\t\tADC Max : 4294967295.0\n", + "\t\t\t\t\t\t\tADC Max : 65535.0\n", "\t\t\t\t\t\t\tADC Min : 0.0\n", "\t\t\t\t\t\t\tClass Name : cm_valuetransform_adc\n", "\t\tParameters :\n", @@ -396,51 +317,32 @@ "\t\t\t\tName : default\n", "\t\t\tDetector :\n", "\t\t\t\tcontinuous : 1\n", - "\t\t\t\texposure (s) : 5.000020000000001\n", + "\t\t\t\texposure (s) : 3.01000999977623\n", "\t\t\t\thbin : 1\n", - "\t\t\t\theight : 128\n", + "\t\t\t\theight : 520\n", "\t\t\t\tleft : 0\n", - "\t\t\t\ttop : 928\n", + "\t\t\t\ttop : 0\n", "\t\t\t\tvbin : 1\n", "\t\t\t\twidth : 2048\n", "\t\t\tEnvironment :\n", "\t\t\t\tMode Name : Spectroscopy\n", "\t\t\tHigh Level :\n", - "\t\t\t\tAcquire Stack : 0\n", "\t\t\t\tAcquisition Buffer Size : 0\n", - "\t\t\t\tAlignment Filter : \n", "\t\t\t\tAntiblooming : 0\n", - "\t\t\t\tAsync Processing : 0\n", - "\t\t\t\tAsync Readout : 0\n", - "\t\t\t\tAuto Save : 0\n", "\t\t\t\tBinning : [1, 1]\n", - "\t\t\t\tCapture Duration : 124.84979999999999\n", - "\t\t\t\tCCD Read Area : [928, 0, 1056, 2048]\n", + "\t\t\t\tCCD Read Area : [0, 0, 520, 2048]\n", "\t\t\t\tCCD Read Ports : 1\n", "\t\t\t\tChoose Number Of Frame Shutters Automatically : 1\n", "\t\t\t\tClass Name : cm_camera_highlevelparameters\n", - "\t\t\t\tComplex Exposure :\n", - "\t\t\t\t\tDelay (s) :\n", - "\t\t\t\t\t\t0 :\n", - "\t\t\t\t\t\t\tIndex : 0\n", - "\t\t\t\t\t\t\tValue : 1e-05\n", - "\t\t\t\t\tFrame Count : 0\n", - "\t\t\t\t\tSub-Exposure Count : 2\n", "\t\t\t\tContinuous Readout : 1\n", - "\t\t\t\tCorrections : 913\n", - "\t\t\t\tCorrections Mask : 945\n", - "\t\t\t\tDark Ref Method : 1\n", - "\t\t\t\tExposure (s) : 5.000020000000001\n", - "\t\t\t\tExposure Live Time : inf\n", - "\t\t\t\tExposure Priority : 1\n", - "\t\t\t\tFrame Exposure : 5.000020000000001\n", - "\t\t\t\tHardwareCorrections : 0\n", - "\t\t\t\tLookback Duration : 0.0\n", + "\t\t\t\tCorrections : 305\n", + "\t\t\t\tCorrections Mask : 817\n", + "\t\t\t\tExposure (s) : 3.01000999977623\n", "\t\t\t\tNumber Of Frame Shutters : 1\n", - "\t\t\t\tProcessing : Gain Normalized\n", - "\t\t\t\tQuality Level : 0\n", + "\t\t\t\tProcessing : Dark Subtracted\n", + "\t\t\t\tQuality Level : 1\n", "\t\t\t\tRead Frame Style : 0\n", - "\t\t\t\tRead Mode : 11\n", + "\t\t\t\tRead Mode : 14\n", "\t\t\t\tSecondary Shutter Post Exposure Compensation (s) : 0.0\n", "\t\t\t\tSecondary Shutter Pre Exposure Compensation (s) : 0.0\n", "\t\t\t\tShutter :\n", @@ -452,88 +354,53 @@ "\t\t\t\t\tShutter Index : 0\n", "\t\t\t\tShutter Post Exposure Compensation (s) : 0.0\n", "\t\t\t\tShutter Pre Exposure Compensation (s) : 0.0\n", - "\t\t\t\tStack Format : 0\n", "\t\t\t\tTransform :\n", "\t\t\t\t\tDiagonal Flip : 0\n", - "\t\t\t\t\tHorizontal Flip : 1\n", - "\t\t\t\t\tVertical Flip : 1\n", - "\t\t\t\tVertical Collapse : 1\n", + "\t\t\t\t\tHorizontal Flip : 0\n", + "\t\t\t\t\tVertical Flip : 0\n", "\t\t\tObjects :\n", "\t\t\t\t0 :\n", "\t\t\t\t\tClass Name : cm_imgproc_finalcombine\n", "\t\t\t\t\tFrame Combine Style : Copy\n", "\t\t\t\t\tParameter 1 : 1.0\n", "\t\t\tParameter Set Name : Acquire\n", - "\t\t\tParameter Set Tag Path : Spectroscopy:EELS:Acquire\n", - "\t\t\tVersion : 34144256\n", - "\tDataBar :\n", - "\t\tSignal Name : EELS HL\n", + "\t\t\tParameter Set Tag Path : Spectroscopy:Acquire:Acquire\n", + "\t\t\tVersion : 33947648\n", "\tEELS :\n", "\t\tAcquisition :\n", - "\t\t\tAlign min. correlation coefficient : 0.5\n", - "\t\t\tApply ZLP Lock : 0\n", "\t\t\tContinuous mode : 0\n", - "\t\t\tDate : 6/2/2026\n", + "\t\t\tDate : 12/7/2017\n", "\t\t\tDual acquire enabled : 1\n", "\t\t\tDual acquire sibling :\n", - "\t\t\t\tUnique Image ID : [268788659, 777285021, 476527662, 1560289237]\n", + "\t\t\t\tUID :\n", + "\t\t\t\t\t0 : 890318572\n", + "\t\t\t\t\t1 : 6453622\n", + "\t\t\t\t\t2 : 760373265\n", + "\t\t\t\t\t3 : 467668515\n", "\t\t\tDual energy-loss (eV) : 0.0\n", - "\t\t\tEnable auto-align : 0\n", - "\t\t\tEnable persistence : 0\n", - "\t\t\tEnd time : 2:45:46 PM\n", - "\t\t\tExposure (s) : 4.9950049950049955\n", - "\t\t\tIntegration time (s) : 129.8701298701299\n", + "\t\t\tEnd time : 5:44:01 PM\n", + "\t\t\tExposure (s) : 3.0\n", + "\t\t\tIntegration time (s) : 63.0\n", "\t\t\tIs dual acquire low-loss : 0\n", - "\t\t\tNumber of frames : 26\n", - "\t\t\tPersistence (s) : 0.05\n", - "\t\t\tSaturation fraction : 0.00892220703125\n", - "\t\t\tShutter duty cycle : 1.0\n", - "\t\t\tStart time : 2:43:15 PM\n", + "\t\t\tNumber of frames : 21\n", + "\t\t\tSaturation fraction : 0.0013905914966017008\n", + "\t\t\tStart time : 5:42:48 PM\n", "\t\tExperimental Conditions :\n", - "\t\t\tCollection semi-angle (mrad) : 15.0\n", - "\t\t\tConvergence semi-angle (mrad) : 5.5\n", + "\t\t\tCollection semi-angle (mrad) : 33.0\n", + "\t\t\tConvergence semi-angle (mrad) : 30.0\n", "\tEELS Spectrometer :\n", - "\t\tAperture index : 1\n", - "\t\tAperture label : 5 mm\n", - "\t\tDispersion (eV/Ch) : 0.15000000596046448\n", - "\t\tDispersion index : 2\n", + "\t\tDispersion (eV/ch) : 0.25\n", + "\t\tDispersion index : 4\n", "\t\tDrift tube enabled : 1\n", - "\t\tDrift tube voltage (V) : 830.7000122070312\n", - "\t\tEnergy loss (eV) : 830.7000122070312\n", + "\t\tDrift tube voltage (V) : 150.0\n", + "\t\tEnergy loss (eV) : 150.0\n", "\t\tHT offset (V) : 0.0\n", - "\t\tHT offset enabled : 1.0\n", - "\t\tInstrument ID : 1330\n", - "\t\tInstrument name : GIF Continuum ER\n", + "\t\tHT offset enabled : 0.0\n", + "\t\tInstrument ID : 3977\n", + "\t\tInstrument name : Enfinium ER NION\n", "\t\tMode : 0\n", - "\t\tPrism offset (V) : -0.0\n", + "\t\tPrism offset (V) : 0.0\n", "\t\tPrism offset enabled : 1\n", - "\t\tSlit inserted : 1\n", - "\t\tSlit width (eV) : 360.0\n", - "\tElemental Quantification :\n", - "\t\tState :\n", - "\t\t\tAutoID :\n", - "\t\t\t\tEnabled : 0\n", - "\t\t\t\tOverlay : 1\n", - "\t\t\t\tSensitivity : 1\n", - "\t\t\tElemental Parameters :\n", - "\t\t\t\tActive Edge Group : -1\n", - "\t\t\t\tActive Element : 25\n", - "\t\t\t\tAnalytical Technique : EELS\n", - "\t\t\t\tElement Parameters List :\n", - "\t\t\t\tEnd Energy : 1107.9000244140625\n", - "\t\t\t\tStart Energy : 800.7000122070312\n", - "\t\t\tMark Element :\n", - "\t\t\t\tAtomic number : 25\n", - "\t\t\t\tEnabled : 0\n", - "\t\t\tPrefs :\n", - "\t\t\t\tInclude plural scattering : 1\n", - "\t\t\t\tLabel peaks : Major edge only label\n", - "\t\t\t\tPlural scattering low-loss :\n", - "\t\t\t\t\tUnique Image ID : [268788659, 777285021, 476527662, 1560289237]\n", - "\t\t\t\tShow quant windows : 0\n", - "\tGMS Version :\n", - "\t\tCreated : 3.53.4136.0\n", - "\t\tSaved : 3.53.4136.0\n", "\tMeta Data :\n", "\t\tAcquisition Mode : Parallel dispersive\n", "\t\tFormat : Spectrum\n", @@ -541,25 +408,17 @@ "\tMicroscope Info :\n", "\t\tCs(mm) : 0.0\n", "\t\tEmission Current (A) : 0.0\n", - "\t\tFormatted Indicated Mag : 10Mx\n", - "\t\tFormatted Voltage : 60kV\n", + "\t\tFormatted Indicated Mag : 100x\n", + "\t\tFormatted Voltage : 200kV\n", "\t\tIllumination Mode : STEM\n", - "\t\tIllumination Sub-mode : 0\n", - "\t\tImaging Mode : DIFFRACTION\n", - "\t\tIndicated Magnification : 10000000.0\n", - "\t\tName : FEI Tecnai Remote TCPIP\n", + "\t\tImaging Mode : IMAGING\n", + "\t\tIndicated Magnification : 100.0\n", + "\t\tName : Unknown\n", "\t\tOperation Mode : SCANNING\n", - "\t\tOperation Mode Type : 7\n", "\t\tProbe Current (nA) : 0.0\n", "\t\tProbe Size (nm) : 0.0\n", - "\t\tSTEM Camera Length : 91.0\n", - "\t\tStage Position :\n", - 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"\t\tImageIndex : 0\n", + "\t\tSourceSize_Pixels : [669, 342]\n", + "WindowPosition : [410, 693, 752, 1362]\n" ] } ], "source": [ - "main_dataset.view_original_metadata()#['ImageTags']['EELS'].keys()" + "main_dataset.view_original_metadata()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['Acquisition', 'Experimental Conditions'])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "main_dataset.original_metadata['ImageTags']['EELS'].keys()" ] }, { @@ -614,40 +628,40 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "energy dispersion = 0.301 eV/channel\n", - "onset = 441.5 eV\n" + "energy dispersion = 0.264 eV/channel\n", + "onset = 95.2 eV\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\gduscher\\AppData\\Local\\Temp\\ipykernel_30772\\3737294941.py:7: DeprecationWarning: __array_wrap__ must accept context and return_scalar arguments (positionally) in the future. (Deprecated NumPy 2.0)\n", + "C:\\Users\\gduscher\\AppData\\Local\\Temp\\ipykernel_25244\\2815632154.py:7: DeprecationWarning: __array_wrap__ must accept context and return_scalar arguments (positionally) in the future. (Deprecated NumPy 2.0)\n", " main_dataset.energy_loss/=main_dataset.energy_loss.slope/energy_dispersion\n", - "C:\\Users\\gduscher\\AppData\\Local\\Temp\\ipykernel_30772\\3737294941.py:8: DeprecationWarning: __array_wrap__ must accept context and return_scalar arguments (positionally) in the future. (Deprecated NumPy 2.0)\n", - " main_dataset.energy_loss -= main_dataset.energy_loss[0]-(530-channels[0]*energy_dispersion)\n" + "C:\\Users\\gduscher\\AppData\\Local\\Temp\\ipykernel_25244\\2815632154.py:8: DeprecationWarning: __array_wrap__ must accept context and return_scalar arguments (positionally) in the future. (Deprecated NumPy 2.0)\n", + " main_dataset.energy_loss -= main_dataset.energy_loss[0]-(188-channels[0]*energy_dispersion)\n" ] }, { "data": { "text/plain": [ - "{'single_exposure_time': 5.0,\n", - " 'exposure_time': 5.00039152,\n", - " 'number_of_frames': 5,\n", - " 'convergence_angle': 40.0,\n", - " 'collection_angle': 30.0,\n", + "{'single_exposure_time': 3.0,\n", + " 'exposure_time': 3.01000999977623,\n", + " 'number_of_frames': 21,\n", + " 'convergence_angle': 30.0,\n", + " 'collection_angle': 33.0,\n", " 'microscope': 'Unknown',\n", - " 'acceleration_voltage': 200000.0}" + " 'acceleration_voltage': 60000.0}" ] }, - "execution_count": 116, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -655,21 +669,22 @@ "source": [ "\n", "\n", - "channels = np.searchsorted(main_dataset.energy_loss, [528, 878])\n", - "energy_dispersion= (883-532)/ (channels[1]-channels[0])\n", + "channels = np.searchsorted(main_dataset.energy_loss, [188, 390])\n", + "energy_dispersion= (401-188)/ (channels[1]-channels[0])\n", "\n", "print(f'energy dispersion = {energy_dispersion:.3f} eV/channel')\n", - "print(f'onset = {530-channels[0]*energy_dipsersion:.1f} eV')\n", + "print(f'onset = {188-channels[0]*energy_dispersion:.1f} eV')\n", "\n", "main_dataset.energy_loss/=main_dataset.energy_loss.slope/energy_dispersion\n", - "main_dataset.energy_loss -= main_dataset.energy_loss[0]-(530-channels[0]*energy_dispersion)\n", + "main_dataset.energy_loss -= main_dataset.energy_loss[0]-(188-channels[0]*energy_dispersion)\n", "\n", "np.array(main_dataset.energy_loss.values[0:4])\n", "main_dataset.energy_loss.dimension_type='SPECTRAL'\n", "\n", "\n", - "main_dataset.metadata['experiment']['collection_angle'] =30.0\n", - "main_dataset.metadata['experiment']['convergence_angle'] = 40.0\n", + "main_dataset.metadata['experiment']['collection_angle'] =33.0\n", + "main_dataset.metadata['experiment']['convergence_angle'] = 30.0\n", + "main_dataset.metadata['experiment']['acceleration_voltage'] = 60.0 * 1000\n", "main_dataset.metadata['experiment']" ] }, @@ -685,18 +700,18 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ecfba18219224237b0ea52870a408d80", + "model_id": "2203def89feb40cea82ec7559b9a44e7", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Dropdown(description='select dataset:', options=('Channel_000: EELS LL Spectrum', 'Channel_001: EELS HL Spectr…" + "Dropdown(description='select dataset:', options=('Channel_000: 2EELS Acquire (high-loss)', 'Channel_001: 1EELS…" ] }, "metadata": {}, @@ -709,31 +724,31 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "zero_loss is 0.00eV off\n" + "zero_loss is -20.79eV off\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "bbb83c1451ea4af6a0652bd7d9b7bba0", + "model_id": "b6f50f7fbd854c66b4e8ecf464e55c23", "version_major": 2, "version_minor": 0 }, - "image/png": 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m4sSJioqKcj8SEhLOxVsAAAA4pyxjjLG7CF8aPXq0FixYoK+++kpNmjSp0LaDBw+WZVn6+OOPy1yem5ur3Nxc9/PMzEwlJCToyJEjqlOnTqXqrg4+/zpDPxzI0u29W9hdCgAA5crMzFRUVFSN/vtdLc4BLDFmzBh9/PHHWrZsWYXDnyRdfPHFmj17drnLQ0NDFRoaWpkSq7Xb/7VeknRBYrTOb1rP5moAAEB5qkUANMZozJgx+vDDD7V06VIlJiae1X42btyouLg4H1dX8/xyNPf0KwEAANtUiwA4evRovf322/r3v/+tyMhIZWRkSJKioqIUHh4uSRo3bpz27dunWbNmSZImTZqk5s2bq3379srLy9Ps2bM1b948zZs3z7b3UV1Ur5MKAACofqpFAJwyZYokqU+fPh7tM2bM0LBhwyRJ6enp2rNnj3tZXl6e7r//fu3bt0/h4eFq3769FixYoEGDBlVV2dUYCRAAAH9W7S4CqUqcROqp+cMLJElTbj5fAzsylA4A8E/8/a6G08AAAADg1AiA8Dm6lAEA8G8EQPgcJxUAAODfCIDwOUMfIAAAfo0ACAAAUMMQAOFzDAEDAODfCIDwORcJEAAAv0YABAAAqGEIgAAAADUMARA+xwgwAAD+jQAIn2MaGAAA/BsBED5HDyAAAP6NAAgAAFDDEADhc/QAAgDg3wiA8DnyHwAA/o0ACJ8wpbr9DF2AAAD4NQIgfILMBwBA4CAAwidMOV8DAAD/QwCET3gM+5IAAQDwawRA+IRnDyAJEAAAf0YAhE9wDiAAAIGDAAifKN3rRxgEAMC/EQDhE5wCCABA4CAAwufoAQQAwL8RAOETnj2AJEAAAPwZARA+wTmAAAAEDgIgfI78BwCAfyMAwic8ev3oAgQAwK8RAOET3AoOAIDAQQCET5S+FZxlYx0AAOD0CIDwCXr9AAAIHARA+ASn/QEAEDgIgPANAiAAAAGDAAifYPJnAAACBwEQPsEQMAAAgYMACJ8g/wEAEDgIgPAJQxcgAAABgwAIn2AiaAAAAgcBED5RugOQzkAAAPwbARA+wVXAAAAEDgIgfMOjB5AwCACAPyMAwic4BxAAgMBBAIRPcA4gAACBgwAInyh9DiD5DwAA/0YAhE8YzgEEACBgEAABAABqGAIgfMLjIhA6AAEA8GsEQPhE6WFf5gQEAMC/EQDhE1wFDABA4CAAwufIfwAA+DcCIHyCHkAAAAIHARA+4TkPIAkQAAB/RgCET9ADCABA4CAAwifIfAAABA4CIHzCYxoYugABAPBr1SIATpw4URdccIEiIyMVGxurq6++Wjt27DjtdqmpqerWrZvCwsKUlJSkqVOnVkG11RMTQQMAEDiqRQBMTU3V6NGjtXr1ai1evFgFBQXq37+/srKyyt0mLS1NgwYN0qWXXqqNGzfqkUce0d1336158+ZVYeXVh8c5gPaVAQAAzkCQ3QX4wmeffebxfMaMGYqNjdX69evVq1evMreZOnWqmjZtqkmTJkmS2rZtq3Xr1umFF17Qtddee65LroZKDwHbWAYAADitatEDeLIjR45IkqKjo8tdZ9WqVerfv79H24ABA7Ru3Trl5+ef0/qqI88eQBIgAAD+rFr0AJZmjFFKSoouueQSdejQodz1MjIy1LBhQ4+2hg0bqqCgQAcOHFBcXJzXNrm5ucrNzXU/z8zM9F3hAY5zAAEACBzVrgfwrrvu0ubNm/XOO++cdl3Lsjyel1y9enJ7iYkTJyoqKsr9SEhIqHzB1QTnAAIAEDiqVQAcM2aMPv74Yy1ZskRNmjQ55bqNGjVSRkaGR9v+/fsVFBSk+vXrl7nNuHHjdOTIEfdj7969Pqs90HkM+9IFCACAX6sWQ8DGGI0ZM0Yffvihli5dqsTExNNuk5ycrPnz53u0LVq0SN27d1dwcHCZ24SGhio0NNQnNVc3ZD4AAAJHtegBHD16tGbPnq23335bkZGRysjIUEZGhrKzs93rjBs3Trfccov7+ahRo7R7926lpKRo+/btevPNNzV9+nTdf//9dryFgMcQMAAAgaNaBMApU6boyJEj6tOnj+Li4tyPuXPnutdJT0/Xnj173M8TExO1cOFCLV26VF26dNFTTz2lV155hSlgfIDeQAAA/Fu1GQI+nZkzZ3q19e7dWxs2bDgHFdU8pc8BZBoYAAD8W7XoAYT9DNeAAAAQMAiA8DnyHwAA/o0ACJ+gBxAAgMBBAIRPcA4gAACBgwAIn/Do9SP/AQDg1wiA8AnyHwAAgYMACJ8oPRXPmUzLAwAA7EMAhE949ACS/wAA8GsEQPgEt4IDACBwEADhI6WHgG0sAwAAnBYBED7h2QNIAgQAwJ8RAOETnAMIAEDgIADCJwh9AAAEDgIgfIJpYAAACBwEQPgEE0EDABA4CIDwCY+LQEiAAAD4NQIgfKL0lb9cBQwAgH8jAMLn6AEEAMC/EQDhG9wJBACAgEEAhE8Q+gAACBwEQPgEF4EAABA4CIDwCcNEMAAABAwCIHyCHkAAAAIHARA+wb2AAQAIHARA+ITHreAYAgYAwK8RAOET9AACABA4CIDwDeYBBAAgYBAA4RMet4IjAQIA4NcIgPAJj6uA6QMEAMCvEQDhE4ZpAAEACBgEQPgE+Q8AgMBBAIRPeEwDw0mAAAD4NQIgfIIeQAAAAgcBED7BreAAAAgcBED4iCnjKwAA4I8IgPAJzx5AIiAAAP6MAAifI/4BAODfCIDwCVPuEwAA4G+C7Hzx3NxcrVmzRj/88IOOHz+umJgYde3aVYmJiXaWhbPAnUAAAAgctgTAlStX6u9//7s++ugj5eXlqW7dugoPD9evv/6q3NxcJSUl6c9//rNGjRqlyMhIO0pEBXEvYAAAAkeVDwEPGTJEv//979W4cWN9/vnnOnr0qA4ePKgff/xRx48f186dO/WXv/xF//nPf9S6dWstXry4qkvEWWAaGAAAAkeV9wD2799f7733nkJCQspcnpSUpKSkJA0dOlRff/21fvrppyquEGfDcyJoEiAAAP6sygPg6NGjz3jd9u3bq3379uewGvgKU78AABA4bL0KeO/evfrxxx/dz9esWaOxY8dq2rRpNlaFyiILAgDg32wNgH/4wx+0ZMkSSVJGRoauuOIKrVmzRo888oiefPJJO0tDBXleBQwAAPyZrQFw69atuvDCCyVJ7777rjp06KCVK1fq7bff1syZM+0sDRXEVcAAAAQOWwNgfn6+QkNDJUlffPGFfvvb30qSzjvvPKWnp9tZGirIMBM0AAABw9YA2L59e02dOlXLly/X4sWLdeWVV0qSfvrpJ9WvX9/O0lBBTAMDAEDgsDUAPv/883r99dfVp08f3XTTTercubMk6eOPP3YPDSMwmHK+BgAA/sfWW8H16dNHBw4cUGZmpurVq+du//Of/6yIiAgbK0NFlZ4GhilhAADwb7b2APbt21dHjx71CH+SFB0drRtuuMGmqnA26AEEACBw2BoAly5dqry8PK/2nJwcLV++3IaKcNY4BxAAgIBhyxDw5s2b3V9v27ZNGRkZ7ueFhYX67LPP1LhxYztKw1nymAbGxjoAAMDp2RIAu3TpIsuyZFmW+vbt67U8PDxcf//7322oDGfL8ypgIiAAAP7MlgCYlpYmY4ySkpK0Zs0axcTEuJeFhIQoNjZWTqfTjtJwloh8AAAEDlsCYLNmzSRJLpfLjpfHOUYHIAAA/s3WaWAk6dtvv9XSpUu1f/9+r0D4+OOPn/F+li1bpr/+9a9av3690tPT9eGHH+rqq68ud/2lS5fqsssu82rfvn27zjvvvDN+XRTxvBcwCRAAAH9mawB84403dMcdd6hBgwZq1KiRLMtyL7Msq0IBMCsrS507d9bw4cN17bXXnvF2O3bsUJ06ddzPSw9H48xxL2AAAAKHrQHw6aef1jPPPKOHHnqo0vsaOHCgBg4cWOHtYmNjVbdu3Uq/fk3HreAAAAgcts4DeOjQIV133XV2lqCuXbsqLi5O/fr105IlS2ytJZB5TgRNAgQAwJ/ZGgCvu+46LVq0yJbXjouL07Rp0zRv3jx98MEHatOmjfr166dly5aVu01ubq4yMzM9HihmGAIGACBQ2DoE3LJlSz322GNavXq1OnbsqODgYI/ld9999zl77TZt2qhNmzbu58nJydq7d69eeOEF9erVq8xtJk6cqAkTJpyzmgIZt4IDACBw2BoAp02bptq1ays1NVWpqakeyyzLOqcBsCwXX3yxZs+eXe7ycePGKSUlxf08MzNTCQkJVVGa3zMkQAAAAoatATAtLc3Ol/eyceNGxcXFlbs8NDRUoaGhVVhR4Ch99w/OAQQAwL/ZPg+grxw7dky7du1yP09LS9OmTZsUHR2tpk2baty4cdq3b59mzZolSZo0aZKaN2+u9u3bKy8vT7Nnz9a8efM0b948u95CQPPoACT/AQDg12wNgCNGjDjl8jfffPOM97Vu3TqPiZ1LhmqHDh2qmTNnKj09XXv27HEvz8vL0/333699+/YpPDxc7du314IFCzRo0KAKvgtIhD4AAAKJrQHw0KFDHs/z8/O1detWHT58WH379q3Qvvr06eMxDHmymTNnejx/8MEH9eCDD1boNVA+TgEEACBw2BoAP/zwQ682l8ulO++8U0lJSTZUhLPlcQ4g3YEAAPg1W+cBLIvD4dC9996rl19+2e5ScJaIfwAA+De/C4CS9N1336mgoMDuMlAB3AoOAIDAYesQcOk59aSiocP09HQtWLBAQ4cOtakqnI3SU7+Q/wAA8G+2BsCNGzd6PHc4HIqJidGLL7542iuE4V8M88AAABAwbA2AS5YssfPl4UNcBQwAQODwi4mgf/nlF+3YsUOWZal169aKiYmxuyRUEOcAAgAQOGy9CCQrK0sjRoxQXFycevXqpUsvvVTx8fG69dZbdfz4cTtLQyVwKzgAAPybrQEwJSVFqampmj9/vg4fPqzDhw/r3//+t1JTU3XffffZWRoqyOMiEPIfAAB+zdYh4Hnz5un9999Xnz593G2DBg1SeHi4rr/+ek2ZMsW+4lAhDAEDABA4bO0BPH78uBo2bOjVHhsbyxBwACP/AQDg32wNgMnJyRo/frxycnLcbdnZ2ZowYYKSk5NtrAwVxa3gAAAIHLYOAf/tb3/TlVdeqSZNmqhz586yLEubNm1SWFiYPv/8cztLQwWR+QAACBy2BsAOHTpo586dmj17tr755hsZY3TjjTfq5ptvVnh4uJ2loYKYBxoAgMBh+zyA4eHhGjlypN1loJI8LgLhLEAAAPyarecATpw4UW+++aZX+5tvvqnnn3/ehopwtpgGBgCAwGFrAHz99dd13nnnebW3b99eU6dOtaEinC3PHkAAAODPbA2AGRkZiouL82qPiYlRenq6DRXhbHmeA0gEBADAn9kaABMSErRixQqv9hUrVig+Pt6GinDWSk8DY2MZAADg9Gy9COS2227T2LFjlZ+fr759+0qS/vOf/+jBBx/kVnABxpT7BAAA+BtbA+CDDz6oX3/9VXfeeafy8vIkSWFhYXrooYc0btw4O0tDBXEOIAAAgcPWAGhZlp5//nk99thj2r59u8LDw9WqVSuFhobaWRbOAlO/AAAQOGyfB1CSateurQsuuMDuMlAJHj2AXAQCAIBfq/KLQEaNGqW9e/ee0bpz587VW2+9dY4rgi+Ycr4GAAD+p8p7AGNiYtShQwf16NFDv/3tb9W9e3fFx8crLCxMhw4d0rZt2/TVV19pzpw5aty4saZNm1bVJeIsePYA2lcHAAA4vSoPgE899ZTGjBmj6dOna+rUqdq6davH8sjISF1++eX6xz/+of79+1d1eThLHncCoQ8QAAC/Zss5gLGxsRo3bpzGjRunw4cPa/fu3crOzlaDBg3UokULWZZlR1nwEXoAAQDwb7ZfBFK3bl3VrVvX7jJQWQwBAwAQMGy9EwiqDzIfAACBgwAInyg99QvTwAAA4N8IgPAJ7gQCAEDgIADCJzzmASQBAgDg12wNgE888YR2795tZwnwEc8eQBIgAAD+zNYAOH/+fLVo0UL9+vXT22+/rZycHDvLQSV4zANI/gMAwK/ZGgDXr1+vDRs2qFOnTrr33nsVFxenO+64Q2vXrrWzLJwFzgEEACBw2H4OYKdOnfTyyy9r3759evPNN7Vv3z717NlTHTt21N/+9jcdOXLE7hJRQfQAAgDg32wPgCVcLpfy8vKUm5srY4yio6M1ZcoUJSQkaO7cuXaXh9PwnPqFBAgAgD+zPQCuX79ed911l+Li4nTvvfeqa9eu2r59u1JTU/XNN99o/Pjxuvvuu+0uE6fBVcAAAAQOWwNgp06ddPHFFystLU3Tp0/X3r179dxzz6lly5budW655Rb98ssvNlaJM8E5gAAABA5b7wV83XXXacSIEWrcuHG568TExMjlclVhVTgbnlcBEwEBAPBntvYAGmNUr149r/bs7Gw9+eSTNlSEs0UPIAAAgcPWADhhwgQdO3bMq/348eOaMGGCDRXhbHEOIAAAgcP2HkDLsrza//e//yk6OtqGinC2PHoASYAAAPg1W84BrFevnizLkmVZat26tUcILCws1LFjxzRq1Cg7SsNZM2V8BQAA/JEtAXDSpEkyxmjEiBGaMGGCoqKi3MtCQkLUvHlzJScn21EazhKdfgAABA5bAuDQoUMlSYmJierRo4eCg4PtKAPnCmEQAAC/VuUBMDMzU3Xq1JEkde3aVdnZ2crOzi5z3ZL14P+4ChgAgMBR5QGwXr16Sk9PV2xsrOrWrVvmRSAlF4cUFhZWdXk4S8wDCABA4KjyAPjll1+6r/BdsmRJVb88zhF6AAEACBxVHgB79+5d5tcIbMwDCABA4LB1HsDPPvtMX331lfv5q6++qi5duugPf/iDDh06ZGNlqCjPHkASIAAA/szWAPjAAw8oMzNTkrRlyxalpKRo0KBB+v7775WSkmJnaaggz3MAbSwEAACcli3TwJRIS0tTu3btJEnz5s3T4MGD9eyzz2rDhg0aNGiQnaWhojgHEACAgGFrD2BISIiOHz8uSfriiy/Uv39/SVJ0dLS7ZxCBwZT7BAAA+BtbewAvueQSpaSkqGfPnlqzZo3mzp0rSfr222/VpEkTO0tDBZWe+oVzAAEA8G+29gBOnjxZQUFBev/99zVlyhQ1btxYkvTpp5/qyiuvtLM0VBBXAQMAEDhsDYBNmzbVJ598ov/973+69dZb3e0vv/yyXnnllQrta9myZRo8eLDi4+NlWZY++uij026Tmpqqbt26KSwsTElJSZo6dWpF3wKKMQ8gAACBw9YhYElyuVzatWuX9u/fL5fL5bGsV69eZ7yfrKwsde7cWcOHD9e111572vXT0tI0aNAgjRw5UrNnz9aKFSt05513KiYm5oy2hyfPHkAiIAAA/szWALh69Wr94Q9/0O7du71CQ0VvBTdw4EANHDjwjNefOnWqmjZtqkmTJkmS2rZtq3Xr1umFF14gAJ4Fz3MAAQCAP7M1AI4aNUrdu3fXggULFBcXV+Z9gc+VVatWua86LjFgwABNnz5d+fn5Cg4O9tomNzdXubm57udcqXwC5wACABA4bA2AO3fu1Pvvv6+WLVtW+WtnZGSoYcOGHm0NGzZUQUGBDhw4oLi4OK9tJk6cqAkTJlRViYGF0AcAQMCw9SKQiy66SLt27bLt9U/ucSwZxiyvJ3LcuHE6cuSI+7F3795zXmOgOHnqF84DBADAf9naAzhmzBjdd999ysjIUMeOHb2GXTt16nTOXrtRo0bKyMjwaNu/f7+CgoJUv379MrcJDQ1VaGjoOaspkJ2c94yRqnBEHwAAVICtAbDkYosRI0a42yzLkjGmwheBVFRycrLmz5/v0bZo0SJ17969zPP/cGpeAdCeMgAAwBmw/V7AvnLs2DGP4eS0tDRt2rRJ0dHRatq0qcaNG6d9+/Zp1qxZkoouQJk8ebJSUlI0cuRIrVq1StOnT9c777zjs5pqsqIhYLoAAQDwR7YGwGbNmvlsX+vWrdNll13mfp6SkiJJGjp0qGbOnKn09HTt2bPHvTwxMVELFy7Uvffeq1dffVXx8fF65ZVXmALmLHmdA2hTHQAA4PRsnwj6X//6l6ZOnaq0tDStWrVKzZo106RJk5SYmKghQ4ac8X769OlzygsPZs6c6dXWu3dvbdiw4WzKxknKOgcQAAD4J1uvAp4yZYpSUlI0aNAgHT582H3OX926dd0TNCMwnJz3Tu4RBAAA/sPWAPj3v/9db7zxhh599FE5nU53e/fu3bVlyxYbK0NF0eMHAEDgsDUApqWlqWvXrl7toaGhysrKsqEinL2T5wG0qQwAAHBatgbAxMREbdq0yav9008/Vbt27aq+IJw1Ah8AAIHD1otAHnjgAY0ePVo5OTkyxmjNmjV65513NHHiRP3jH/+wszRUkNc5gARCAAD8lq0BcPjw4SooKNCDDz6o48eP6w9/+IMaN26sv/3tb7rxxhvtLA0VdPIV2FwEAgCA/7J9GpiRI0dq5MiROnDggFwul2JjY+0uCWeBHkAAAAKHrecA9u3bV4cPH5YkNWjQwB3+MjMz1bdvXxsrQ0VxKzgAAAKHrQFw6dKlysvL82rPycnR8uXLbagIZ8u7B5AICACAv7JlCHjz5s3ur7dt26aMjAz388LCQn322Wdq3LixHaXhLHmfAwgAAPyVLQGwS5cusixLlmWVOdQbHh6uv//97zZUBl+hAxAAAP9lSwBMS0uTMUZJSUlas2aNYmJi3MtCQkIUGxvrcWcQ+D+vwEcABADAb9kSAJs1ayZJcrlcdrw8zoGTp31hGhgAAPyX7dPAfPvtt1q6dKn279/vFQgff/xxm6pCRXldBUz+AwDAb9kaAN944w3dcccdatCggRo1aiTLstzLLMsiAAYQpoEBACBw2BoAn376aT3zzDN66KGH7CwDPuA1BEwXIAAAfsvWeQAPHTqk6667zs4ScI4Q/wAA8F+2BsDrrrtOixYtsrME+AjnAAIAEDhsHQJu2bKlHnvsMa1evVodO3ZUcHCwx/K7777bpspQUd6zwJAAAQDwV7YGwGnTpql27dpKTU1VamqqxzLLsgiAgYR5AAEACBi2BsC0tDQ7Xx4+5D0PIAAA8Fe2ngOI6oNzAAEACBxV3gOYkpKip556ShEREUpJSTnlui+99FIVVYXK4hxAAAACR5UHwI0bNyo/P9/9dXlKTwoN/3fyvH/0AAIA4L+qPAAuWbKkzK8R2Mh7AAAEDs4BhE9wKzgAAAIHARA+4XUOIGPAAAD4LQIgfINzAAEACBgEQPgEeQ8AgMBBAIRPMA8gAACBgwAIn/C+EwgJEAAAf0UAhE/QAwgAQOAgAMInmAYGAIDAQQCETzANDAAAgYMACJ/wuhWcTXUAAIDTIwDinKADEAAA/0UAxDlCAgQAwF8RAOETXAUMAEDgIADCJ7znAQQAAP6KAAifoAcQAIDAQQCET3hNA0MfIAAAfosACJ/wmgaG/AcAgN8iAMInvCeCtqUMAABwBgiA8A2vW8GRAAEA8FcEQPiEiyFgAAACBgEQPkHeAwAgcBAA4RP0AAIAEDgIgPAJAh8AAIGDAAif8JoImkFhAAD8FgEQPsE8gAAABA4CIHzC+04gAADAXxEA4RPeF4EQAQEA8FcEQPiE9zmAAADAXxEA4ROukwMgCRAAAL9FAISPcBYgAACBggAIn6AHEACAwFGtAuBrr72mxMREhYWFqVu3blq+fHm56y5dulSWZXk9vvnmmyqsuPrwmgbGpjoAAMDpVZsAOHfuXI0dO1aPPvqoNm7cqEsvvVQDBw7Unj17Trndjh07lJ6e7n60atWqiiquXrwGgEmAAAD4rWoTAF966SXdeuutuu2229S2bVtNmjRJCQkJmjJlyim3i42NVaNGjdwPp9NZRRVXLy4X08AAABAoqkUAzMvL0/r169W/f3+P9v79+2vlypWn3LZr166Ki4tTv379tGTJklOum5ubq8zMTI8HipTEPYfl+RwAAPifahEADxw4oMLCQjVs2NCjvWHDhsrIyChzm7i4OE2bNk3z5s3TBx98oDZt2qhfv35atmxZua8zceJERUVFuR8JCQk+fR+BrKTDz2FZHs8BAID/CbK7AF+yisNHCWOMV1uJNm3aqE2bNu7nycnJ2rt3r1544QX16tWrzG3GjRunlJQU9/PMzExCYLGSIV+Hw5JcRoY+QAAA/Fa16AFs0KCBnE6nV2/f/v37vXoFT+Xiiy/Wzp07y10eGhqqOnXqeDxQxOXuASxuIP8BAOC3qkUADAkJUbdu3bR48WKP9sWLF6tHjx5nvJ+NGzcqLi7O1+XVCCU9fs6SIWA7iwEAAKdUbYaAU1JS9Kc//Undu3dXcnKypk2bpj179mjUqFGSioZv9+3bp1mzZkmSJk2apObNm6t9+/bKy8vT7NmzNW/ePM2bN8/OtxGwOAcQAIDAUW0C4A033KCDBw/qySefVHp6ujp06KCFCxeqWbNmkqT09HSPOQHz8vJ0//33a9++fQoPD1f79u21YMECDRo0yK63ENBKAp/lvgqYBAgAgL+yDBO2nbXMzExFRUXpyJEjNf58wFaPLlR+oVG9WsE6dDxfs0ZcqF6tY+wuCwAAL/z9ribnAMJ+rpOHgG2sBQAAnBoBED7hMQ2MuBMIAAD+jAAInzh5GhjiHwAA/osACJ9ylDPxNgAA8B8EQFRa6eFeh0UXIAAA/o4AiEpzlQp7juJPFNPAAADgvwiAqLTSPYAldwJxueyqBgAAnA4BEJVWugfQWXwVSCFXAQMA4LcIgKi00sO9wc6ij5TLRQAEAMBfEQBRaaU7+4Kc9AACAODvCICoNI8AWHwVSCE9gAAA+C0CICqt9BBwUMk5gARAAAD8FgEQleYqawiYAAgAgN8iAKLSSk8DUzIE7OIcQAAA/BYBEJVWdg+gTcUAAIDTIgCi8jwuAuEqYAAA/B0BEJVWeriXeQABAPB/BEBUWunevqDiAFhAAAQAwG8RAFFpJb19DksqPgWQHkAAAPwYARCVVpL1nA5LDs4BBADA7xEAUWklYc+yLDkt5gEEAMDfEQBRaSXDvU7Lck8DwxAwAAD+iwCISiu5CthhSQ6LIWAAAPwdARCVVjLc63BYcjroAQQAwN8RAFFpHheBFPcAMg0MAAD+iwCISjsxBHyiB5AhYAAA/BcBEJXmHgK2GAIGACAQEABRaSU9gE6HTvQAuuysCAAAnAoBEJXmKg57jlLzALoYAgYAwG8RAFFphaXOAXTfCYQhYAAA/BYBEJV2Ygi41J1A6AEEAMBvEQBRaS7XiYmgncWfqMJCAiAAAP6KAIhKKz0RtINpYAAA8HsEQFRayel+DstSENPAAADg9wiAqDT3OYCWxb2AAQAIAARAVFpZ9wLmKmAAAPwXARCVduJWcCcmgmYeQAAA/BcBEJVWehoY9xAwPYAAAPgtAiAqraCw1DyADAEDAOD3CICotILisBfscBAAAQAIAARAVFp+YdHNgIOcpe8EYmdFAADgVAiAqLSSIeAg54keQOYBBADAfxEAUWkFrqIewODSdwIhAAIA4LcIgKi0PHcPoKUQp1Xc5rKzJAAAcAoEQFRagfscQIfCQ4IkSdl5hXaWBAAAToEAiEorOQcwxOlQeLBTkpSdTwAEAMBfEQBRafnF5wAGOSzVCikOgPQAAgDgtwiAqLTSVwGHFwfA43kFdpYEAABOgQCISis5BzDYWaoHkCFgAAD8FgEQlZbrDoAnzgHMLzTuCaIBAIB/IQCi0krO96sV4nQPAUv0AgIA4K8IgKi048UBMDzEqZBSdwPhQhAAAPwTARCVVtLTVyvYKcuyVCu45EIQAiAAAP6IAIhKOzEEXDQJdBhXAgMA4NcIgKi0rNyioFdy/l+D2qGSpIwjObbVBAAAyletAuBrr72mxMREhYWFqVu3blq+fPkp109NTVW3bt0UFhampKQkTZ06tYoqrV4OZuVJkupHhEiSkmIiJEnf/5JlW00AAKB8QXYX4Ctz587V2LFj9dprr6lnz556/fXXNXDgQG3btk1Nmzb1Wj8tLU2DBg3SyJEjNXv2bK1YsUJ33nmnYmJidO2119rwDgLXgWO5kqQGkUU9fy0aFAXA7RmZXuv+cjRXK787oKzcQh3PK1B4iFOhQU4VFLrUMCpMh4/nqU5YsHsKmdAgpwpcRhEhTsXXDVfDOmEeVxoDAICKs4wxxu4ifOGiiy7S+eefrylTprjb2rZtq6uvvloTJ070Wv+hhx7Sxx9/rO3bt7vbRo0apf/9739atWrVGb1mZmamoqKidOTIEdWpU6fybyIAHTmer85PLpIkbX6iv+qEBWvFrgO6+R//lcOSbrigqWqHOvXjoWzt+PlopXsFHZbUNq6O2sbVUdPoWmpQO1TRESGKiwpTTGSoCl1GMZGhCg1yyLIsX7xFAEA1w9/vatIDmJeXp/Xr1+vhhx/2aO/fv79WrlxZ5jarVq1S//79PdoGDBig6dOnKz8/X8HBwees3tPZkXFU32RkKr/QyGWMwoOdysotUGZOvmIjw2RZksOyFBLkkDFSXqFLJTneGMnIKDffpdwClyJCi77F5UUhp8NSXoFLToelIKf3Wr8czVW9WiEykowxyivumcvNdykrt0DfZByVJDWrX0t1woqOWY8W9fWbTnFasDld76zZU+brNqoTpm7N6+l4boEKXEYHj+Upv9ClurWClZldoLAQpw5l5alWiFNhwU5l5uQr40iOjucV6uufMvX1T969iycLcToUElQ0LU3t0CDtO5wtSTq/aV2FBDkUEuSUMUbHcguUk+9SiNNSaJBTIUEOhQY5tHnfEUWEONWqYaQclhQS5FR+gUu1QpwKDXbI5ZIsq+gYBjks5eS7PJ47HJYsWbKsouNvWZJlWUXfC0vuZdl5hQoLdhZtaxW1lXzPfjycrWCHQ42iwtzLHQ5LLpdRoTEqdBU/jJHDKmo3Krovs9NhybIsfZOeqS5N6yrY4XBvU/r7H+Q48X3PLzTatf+YOjWJkssYuUxRHU6HJWOK9v3DgSzVCQ9W3Voh+ulwtuKiwhTsdHjU7VbcaHk+lVVqrdLbGUn7M3MVHREsp8OhQ8fzVCcsyL1/q+jAKchRdJzyC4yO5hYoPNjpbit0GWVk5qhJvVoen4ec/EIVFLqUlVeohnXCJKn4sy/tPnhccVFhcjgs5ea7VDssSJaK9lVyHA8fz1N0RIgsS3K5ipY5in9+gpyWVPyzd+BYnhrUDtH3B7L046/ZahFbW3XCghQVHqySIx/stHTwWJ5iIkMV7HToWG6B8gpcCnE6lFtQ9HkIdjpUUPy9Kn38SqZZKig88b10OCw5HUVX3q9N+1UN64SpXXzRH7WS3wnFt+zW0Zx81YsIkTFFV/DXCnGqpBvAsoqOyfG8QtWtFVxca1FNJZ93R/Fn1LKKPhMFxb+nQoIcRZ8Z14l9WZZ0KCtfYcFO1QpxylH8OSr5rOUVuNyvk19qQvmSz0Lp+nPyXQoJKjprqdDlUmZ2QfH7KKrBFLfXCgmS02GpwGVU6HKpZD76kp/Bk+svdBV9ziNCnO7fb8ZILmN0JDtfDWqHFv8sGIU4nXKedOJUoUvKKyyU0+FQVvFnseRz6DJF/2h1maLvufu1Zamw+Dg4imtyFP9iKHQZBTstj3lUS7bLKyj6/NYJC9LRnAJFhDrlqMA/dJ3Fv5OMTvwOKPneF7qKfhdGhDrlchUdf8dJx8tR/PsqM6dAkaFBCgtxqtDlUn5B0S+K0CCHghwOHcnOd/8MlfztkKRfs/JUt1aw+3dfWUrqKXAZZecVqGfLBkqKqX3G7xFnrloEwAMHDqiwsFANGzb0aG/YsKEyMjLK3CYjI6PM9QsKCnTgwAHFxcV5bZObm6vc3Fz388zM04eQs7FgS7pe+c/Oc7Lvc+WPFzVzf21Zlibf1FXXdWuiZd8eUJDTUoPaIWoXVxQqfs7M0bXnN5HDcea/uEqkH8nW//Ye1rc/H9O+Q9k6mJWng1m52vtrto5k58mS5f4lnlfocn99JDvfvY8New6f8ev9IumHg8crXKe/WbTt5wqtP2/Dj+eoEgComOeu6agbL/Q+lQuVUy0CYImTh/yMMaccBixr/bLaS0ycOFETJkyoZJWn1yy6lnq0qO/u9cjJL9SmvYcV4nSoXXwdOayifyXmF7pkJIUFOT3+Zbsm7VflFhQFn5jIULWLK7t7u+RfzMFOh1zmxD19S6z87qD7696tY2RZRf86l4r+pVc7NEgRoUHq1qyeBnZo5LGtZVnq0yZWfdrE+u7ASIqLCldcVLiu7FD2cpfLKCuvQLkFLuUVFPWCFrqM9h46rn+u/EGdGkepXXxUUTgscMmSVDssSGHBRechlmy36ruDmrturyQp5YrWqhcRUtxDY+nw8Xxl5xcqIjSoqPehuBcupPj7VfK8sLg3rqQHo/i/op604q+nf5Xmrn14z+ZF7cWfw32Hc/TF9p+Lh9ITinr4intYHI6i73dJT6PDsmSM5HQUHfv8QpdcLqN/rtrt3v/vujaW02HJWerzXdJLUuLH4lDdIiZCocFOOUtes/hnqbDQ6JuMTCVE19LynQfc2w3q6Pn9L/lXvPv/Mic991xe0rJu9yHl5Bfq/Kb1PD5/vVrHKNhRVItRUe+XVNTzFRbkKP4sF9WZlVug/Udz1ax+hLvXp+R1vj9wTD9n5qp9fB3VDg1SaJBTR3PytXP/MbWMqa1aoU7l5Bf1wBlT1NNY4DJK/fYXdy2Xtmogy7L0y9FchQc7FB0R6j6GS3acWK8sfdrESJKWllrvgub1FBEapBCnQ/mFLmXnF7p/loMcDo8eFMuyVFDoci8r6Q0see+rvz+o/OJjc2Hz6KJRApni/VnKL3ApK69AESFBOnQ8T9/+fFSJDSIUFxUuqegfWN+VOk2jR4v6yi90KbT4d0yJks+hZUlBToccxb1TJ/eulf6MdG9Wz/07rajWXyUVXTRWPyLE/bul5Htb9IZP/C8kyKGCQqODWbn69udjHjU6HZbHa3VqEqWIkCAFlep1K+nVM0bFP5tGliyt+v6gx75Keui+2nVif8lJRa+RV/xzVfS9kPt7EuJ0eKzfOaGu6oQFyWFZKnC5VFBY9D0wMh69re6f6eKf+5IRjib1wpVQr5YcjhN1FxQardt9yP0aoUEONY2updg6oSpL6V52qehn0OPYlnoPhS6jtT8ccrdd2irGo/eu5Pt94Fiudu4/cezbxdVRdESI8gpd7pGC0j+3yUn13fss3X5JywbumozxHhkocLncnw9JWvPDrwTAc6BaBMAGDRrI6XR69fbt37/fq5evRKNGjcpcPygoSPXr1y9zm3HjxiklJcX9PDMzUwkJCZWs3tu13Zro2m5NPNpcLuP+xXo63/9yTH1fTJUk/ee+3u6h2Yo6cjxfH2z8Udec30RR4fYNiVeEw2EpMixYkSe1t4ytrcsqEEbzClzuADimb8tzdj7hxj2H3D2S4we391r+46HjigwNVlStszv+S7/9RbsPHle9WsF6+YYulajUW/OHF0gq+iX/2s3dfLLPkkAd7HToT9P/6/6jPmvEhT7Zv3RiWLcifs3K0z+Wf6+hPZq7h4/LsmBzuka/vcH9/IbuCVq3+1d3qJo5vOh9XDlpmb7JOKpRvVvo4YHnncW7KFtmTr56/98S1YsI0Zw/X3zaXvbcgkKFBp24qMrlMkp6ZKEk6b1RybqgeXSl6nlp0Q698uUuSdL7d/RwtxtjdPWrK3TgWJ7+PbqnIivwO8oYo8RxRTX+e3RPdU6oK6no1JkBk5YpKSZCH991yRnvr+RzfM35jfXS9V282l+7+XwN6ug9InSyZxdu17Rl37vrOlsFhS4FnTzOLM/vzRcpvdUiJsJnv5dK73v+XZeoQ+OoMtf79uej6v/yMvfzhfdc6rVOyc9tWLBD7/z5Ynd7yfEcP7idhvdMPGU9hS6jFsX1SFKvVjFn/mZwxqpFAAwJCVG3bt20ePFi/e53v3O3L168WEOGDClzm+TkZM2fP9+jbdGiRerevXu55/+FhoYqNLTsf22daxUZLk1sEKHrujVRdO2Qsw5/khRVK/i0P6jV1YAOjfTS4m/Vo2X9c3oxyXPXdtLD8zbr7n6tylx+8nlsFTXl5m56YdEOpVzRulL7OZXQYN/NJmVZloKLz0X9Tcc4Ld95wD2tkK9UNPxJUnREiB688vRBbVDHRlo1rq/yClz6bGuGhvVsrq37MnX3Oxv1yKC27vWmD7tAy779Rdd39+0/IOuEBevL+/rI6bTO6HdG6fAnFf2eefyqdtp76Li6N6tX6XpuvTRJP2fmakjXeI92y7L0wZ09lV/oUlhwxa7qtyxLr/+pm346nO0Of5LUplGkFt/byz0P6Zn6w0VN9dHGfRrbz/Nn5LWbz9f/fjysK9s3KmdLT7ddmqit+47ohgsq9z0tK/xJRd+bf464UIey8tQy1rfnxDkcll79w/nKyMwpN/xJUuuGkXrumo567N9bdX//NmWu8/TVHfTiom81qncLj/YP7+yhdT8c0tDk5qetx+mwdN8VrfXZ1xm6vnuChnSJP+02qLhqcxXw3Llz9ac//UlTp05VcnKypk2bpjfeeENff/21mjVrpnHjxmnfvn2aNWuWpKJpYDp06KDbb79dI0eO1KpVqzRq1Ci98847ZzwNDFcRVW+u4pP8UbaSf9Ff0a6h3rilu8/3X+gy+vKb/eratG6F/6gDZ6rk4raTwzCqN/5+V5MeQEm64YYbdPDgQT355JNKT09Xhw4dtHDhQjVrVnRxQnp6uvbsOXFFamJiohYuXKh7771Xr776quLj4/XKK68wByDcCH+nFhUerCPZ+bq8rW/P8yzhdFi6ol3Zp3AAvmJZFuEPNVK16QG0A/+CQE2WfiRbG/cUDZERlgEEEv5+V6MeQABVKy4qXHEdw+0uAwBwFqrVvYABAABwegRAAACAGoYACAAAUMMQAAEAAGoYAiAAAEANQwAEAACoYQiAAAAANQwBEAAAoIYhAAIAANQwBEAAAIAahgAIAABQwxAAAQAAahgCIAAAQA0TZHcBgcwYI0nKzMy0uRIAAHCmSv5ul/wdr4kIgJVw9OhRSVJCQoLNlQAAgIo6evSooqKi7C7DFpapyfG3klwul3766SdFRkbKsiy7y7FdZmamEhIStHfvXtWpU8fucvwex6tiOF4Vw/GqGI5XxQT68TLG6OjRo4qPj5fDUTPPhqMHsBIcDoeaNGlidxl+p06dOgH5C8EuHK+K4XhVDMerYjheFRPIx6um9vyVqJmxFwAAoAYjAAIAANQwBED4TGhoqMaPH6/Q0FC7SwkIHK+K4XhVDMerYjheFcPxCnxcBAIAAFDD0AMIAABQwxAAAQAAahgCIAAAQA1DAAQAAKhhCIDwmddee02JiYkKCwtTt27dtHz5crtLqnLLli3T4MGDFR8fL8uy9NFHH3ksN8boiSeeUHx8vMLDw9WnTx99/fXXHuvk5uZqzJgxatCggSIiIvTb3/5WP/74YxW+i6ozceJEXXDBBYqMjFRsbKyuvvpq7dixw2MdjtkJU6ZMUadOndyT7yYnJ+vTTz91L+dYlW/ixImyLEtjx451t3G8PD3xxBOyLMvj0ahRI/dyjlf1QgCET8ydO1djx47Vo48+qo0bN+rSSy/VwIEDtWfPHrtLq1JZWVnq3LmzJk+eXOby//u//9NLL72kyZMna+3atWrUqJGuuOIK932lJWns2LH68MMPNWfOHH311Vc6duyYrrrqKhUWFlbV26gyqampGj16tFavXq3FixeroKBA/fv3V1ZWlnsdjtkJTZo00XPPPad169Zp3bp16tu3r4YMGeL+I8yxKtvatWs1bdo0derUyaOd4+Wtffv2Sk9Pdz+2bNniXsbxqmYM4AMXXnihGTVqlEfbeeedZx5++GGbKrKfJPPhhx+6n7tcLtOoUSPz3HPPudtycnJMVFSUmTp1qjHGmMOHD5vg4GAzZ84c9zr79u0zDofDfPbZZ1VWu132799vJJnU1FRjDMfsTNSrV8/84x//4FiV4+jRo6ZVq1Zm8eLFpnfv3uaee+4xxvDZKsv48eNN586dy1zG8ap+6AFEpeXl5Wn9+vXq37+/R3v//v21cuVKm6ryP2lpacrIyPA4TqGhoerdu7f7OK1fv175+fke68THx6tDhw414lgeOXJEkhQdHS2JY3YqhYWFmjNnjrKyspScnMyxKsfo0aP1m9/8RpdffrlHO8erbDt37lR8fLwSExN144036vvvv5fE8aqOguwuAIHvwIEDKiwsVMOGDT3aGzZsqIyMDJuq8j8lx6Ks47R79273OiEhIapXr57XOtX9WBpjlJKSoksuuUQdOnSQxDEry5YtW5ScnKycnBzVrl1bH374odq1a+f+A8uxOmHOnDnasGGD1q5d67WMz5a3iy66SLNmzVLr1q31888/6+mnn1aPHj309ddfc7yqIQIgfMayLI/nxhivNpzdcaoJx/Kuu+7S5s2b9dVXX3kt45id0KZNG23atEmHDx/WvHnzNHToUKWmprqXc6yK7N27V/fcc48WLVqksLCwctfjeJ0wcOBA99cdO3ZUcnKyWrRooX/+85+6+OKLJXG8qhOGgFFpDRo0kNPp9PoX3v79+73+tViTlVxNd6rj1KhRI+Xl5enQoUPlrlMdjRkzRh9//LGWLFmiJk2auNs5Zt5CQkLUsmVLde/eXRMnTlTnzp31t7/9jWN1kvXr12v//v3q1q2bgoKCFBQUpNTUVL3yyisKCgpyv1+OV/kiIiLUsWNH7dy5k89XNUQARKWFhISoW7duWrx4sUf74sWL1aNHD5uq8j+JiYlq1KiRx3HKy8tTamqq+zh169ZNwcHBHuukp6dr69at1fJYGmN011136YMPPtCXX36pxMREj+Ucs9Mzxig3N5djdZJ+/fppy5Yt2rRpk/vRvXt33Xzzzdq0aZOSkpI4XqeRm5ur7du3Ky4ujs9XdWTHlSeofubMmWOCg4PN9OnTzbZt28zYsWNNRESE+eGHH+wurUodPXrUbNy40WzcuNFIMi+99JLZuHGj2b17tzHGmOeee85ERUWZDz74wGzZssXcdNNNJi4uzmRmZrr3MWrUKNOkSRPzxRdfmA0bNpi+ffuazp07m4KCArve1jlzxx13mKioKLN06VKTnp7ufhw/fty9DsfshHHjxplly5aZtLQ0s3nzZvPII48Yh8NhFi1aZIzhWJ1O6auAjeF4ney+++4zS5cuNd9//71ZvXq1ueqqq0xkZKT79zjHq3ohAMJnXn31VdOsWTMTEhJizj//fPdUHjXJkiVLjCSvx9ChQ40xRVMpjB8/3jRq1MiEhoaaXr16mS1btnjsIzs729x1110mOjrahIeHm6uuusrs2bPHhndz7pV1rCSZGTNmuNfhmJ0wYsQI989YTEyM6devnzv8GcOxOp2TAyDHy9MNN9xg4uLiTHBwsImPjzfXXHON+frrr93LOV7Vi2WMMfb0PQIAAMAOnAMIAABQwxAAAQAAahgCIAAAQA1DAAQAAKhhCIAAAAA1DAEQAACghiEAAgAA1DAEQAA4SZ8+fTR27FjbXv/gwYOKjY3VDz/84NP9btmyRU2aNFFWVpZP9wsg8BAAAcDPTJw4UYMHD1bz5s3PaP3Bgwfr8ssvL3PZqlWrZFmWNmzYoI4dO+rCCy/Uyy+/7MNqAQQiAiCAgJSfn293CedEdna2pk+frttuu+2Mt7n11lv15Zdfavfu3V7L3nzzTXXp0kXnn3++JGn48OGaMmWKCgsLfVYzgMBDAARQKcYY/d///Z+SkpIUHh6uzp076/3333cvX7p0qSzL0n/+8x91795dtWrVUo8ePbRjxw6P/cyfP1/dunVTWFiYkpKSNGHCBBUUFLiXW5alqVOnasiQIYqIiNDTTz8tSXr66acVGxuryMhI3XbbbXr44YfVpUsXSdKyZcsUHBysjIwMj9e677771KtXrzN+j4cOHdItt9yievXqqVatWho4cKB27tzpXr57924NHjxY9erVU0REhNq3b6+FCxe6t7355psVExOj8PBwtWrVSjNmzCj3tT799FMFBQUpOTnZo33btm0aNGiQateurYYNG+pPf/qTDhw4IEm66qqrFBsbq5kzZ3psc/z4cc2dO1e33nqru23AgAE6ePCgUlNTz/j9A6h+CIAAKuUvf/mLZsyYoSlTpujrr7/Wvffeqz/+8Y9eAePRRx/Viy++qHXr1ikoKEgjRoxwL/v888/1xz/+UXfffbe2bdum119/XTNnztQzzzzjsY/x48dryJAh2rJli0aMGKG33npLzzzzjJ5//nmtX79eTZs21ZQpU9zr9+rVS0lJSfrXv/7lbisoKNDs2bM1fPjwM36Pw4YN07p16/Txxx9r1apVMsZo0KBB7l7I0aNHKzc3V8uWLdOWLVv0/PPPq3bt2pKkxx57TNu2bdOnn36q7du3a8qUKWrQoEG5r7Vs2TJ1797doy09PV29e/dWly5dtG7dOn322Wf6+eefdf3110uSgoKCdMstt2jmzJkqfXv39957T3l5ebr55pvdbSEhIercubOWL19+xu8fQDVkAOAsHTt2zISFhZmVK1d6tN96663mpptuMsYYs2TJEiPJfPHFF+7lCxYsMJJMdna2McaYSy+91Dz77LMe+/jXv/5l4uLi3M8lmbFjx3qsc9FFF5nRo0d7tPXs2dN07tzZ/fz55583bdu2dT//6KOPTO3atc2xY8fKfV+9e/c299xzjzHGmG+//dZIMitWrHAvP3DggAkPDzfvvvuuMcaYjh07mieeeKLMfQ0ePNgMHz683Nc62ZAhQ8yIESM82h577DHTv39/j7a9e/caSWbHjh3GGGO2b99uJJkvv/zSvU6vXr3c34fSfve735lhw4adcU0Aqh96AAGctW3btiknJ0dXXHGFateu7X7MmjVL3333nce6nTp1cn8dFxcnSdq/f78kaf369XryySc99jFy5Eilp6fr+PHj7u1O7hnbsWOHLrzwQo+2k58PGzZMu3bt0urVqyUVnRN3/fXXKyIiQsuXL/d4zbfeesvrPW7fvl1BQUG66KKL3G3169dXmzZttH37dknS3Xffraefflo9e/bU+PHjtXnzZve6d9xxh+bMmaMuXbrowQcf1MqVK095TLOzsxUWFubRtn79ei1ZssSj1vPOO0+S3Mf5vPPOU48ePfTmm2+625cvX+7R01oiPDzc47gCqHmC7C4AQOByuVySpAULFqhx48Yey0JDQz2eBwcHu7+2LMtje5fLpQkTJuiaa67xeo3SYSgiIsJrecm+SphSQ6CSFBsbq8GDB2vGjBlKSkrSwoULtXTpUklFgXLTpk3udRs2bOi1/5P3V7q95LVvu+02DRgwQAsWLNCiRYs0ceJEvfjiixozZowGDhyo3bt3a8GCBfriiy/Ur18/jR49Wi+88EKZ+23QoIEOHTrk0eZyuTR48GA9//zzXuuXhGmp6GKQu+66S6+++qpmzJihZs2aqV+/fl7b/Prrr2rRokWZrw+gZqAHEMBZa9eunUJDQ7Vnzx61bNnS45GQkHDG+zn//PO1Y8cOr320bNlSDkf5v6batGmjNWvWeLStW7fOa73bbrtNc+bM0euvv64WLVqoZ8+ekop6wkq/VmRkZJnvsaCgQP/973/dbQcPHtS3336rtm3butsSEhI0atQoffDBB7rvvvv0xhtvuJfFxMRo2LBhmj17tiZNmqRp06aV+566du2qbdu2eR2fr7/+Ws2bN/c6PqVD8fXXXy+n06m3335b//znPzV8+HCvgCxJW7duVdeuXcutAUD1Rw8ggLMWGRmp+++/X/fee69cLpcuueQSZWZmauXKlapdu7aGDh16Rvt5/PHHddVVVykhIUHXXXedHA6HNm/erC1btriv9i3LmDFjNHLkSHXv3l09evTQ3LlztXnzZiUlJXmsN2DAAEVFRenpp5/Wk08+WaH32KpVKw0ZMkQjR47U66+/rsjISD388MNq3LixhgwZIkkaO3asBg4cqNatW+vQoUP68ssv3eHw8ccfV7du3dS+fXvl5ubqk08+8QiOJxswYIDGjRunQ4cOqV69epKKLjJ54403dNNNN+mBBx5QgwYNtGvXLs2ZM0dvvPGGnE6nJKl27dq64YYb9Mgjj+jIkSMaNmyY1/5/+OEH7du3r9x5AwHUDPQAAqiUp556So8//rgmTpyotm3basCAAZo/f74SExPPeB8DBgzQJ598osWLF+uCCy7QxRdfrJdeeknNmjU75XY333yzxo0bp/vvv1/nn3++0tLSNGzYMK9z6BwOh4YNG6bCwkLdcsstFX6PM2bMULdu3XTVVVcpOTlZxhgtXLjQPaxdWFio0aNHq23btrryyivVpk0bvfbaa5KKrrodN26cOnXqpF69esnpdGrOnDnlvlbHjh3VvXt3vfvuu+62+Ph4rVixQoWFhRowYIA6dOige+65R1FRUV49pLfeeqsOHTqkyy+/XE2bNvXa/zvvvKP+/fuf9tgCqN4sU94JLgAQgK644go1atTIY+oXSRo5cqR+/vlnffzxxzZVduYWLlyo+++/X1u3bj3lEHhF5ebmqlWrVnrnnXfcw+AAaiaGgAEErOPHj2vq1KkaMGCAnE6n3nnnHX3xxRdavHixe50jR45o7dq1euutt/Tvf//bxmrP3KBBg7Rz507t27evQudSns7u3bv16KOPEv4A0AMIIHBlZ2dr8ODB2rBhg3Jzc9WmTRv95S9/8biauE+fPlqzZo1uv/127oELAMUIgAAAADUMF4EAAADUMARAAACAGoYACAAAUMMQAAEAAGoYAiAAAEANQwAEAACoYQiAAAAANQwBEAAAoIYhAAIAANQw/w+QjY4E9aGBgAAAAABJRU5ErkJggg==", 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", 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\n", " " ], @@ -764,34 +779,46 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 21, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "(0.0, 0.49931338168017564)" - ] - }, - "execution_count": 119, - "metadata": {}, - "output_type": "execute_result" - }, + "ename": "KeyError", + "evalue": "'intentsity_scale_ppm'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[21]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m main_dataset.metadata[\u001b[33m'experiment'\u001b[39m][\u001b[33m'intentsity_scale_ppm'\u001b[39m]\n\u001b[32m 2\u001b[39m main_dataset.plot()\n", + "\u001b[31mKeyError\u001b[39m: 'intentsity_scale_ppm'" + ] + } + ], + "source": [ + "main_dataset.metadata['experiment']['intentsity_scale_ppm'] \n", + "main_dataset.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "df1724388ed94de794ed9f84589a1dbd", + "model_id": "de81f9bfb7e3410faf8cb33a16797326", "version_major": 2, "version_minor": 0 }, - "image/png": 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", + "image/png": 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\n", " Figure\n", "
\n", - " \n", + " \n", "
\n", " " ], @@ -818,8 +845,37 @@ "spectrum.title = main_dataset.title+ '_calibrated'\n", "spectrum.quantity = 'inelastic scattering probability'\n", "spectrum. units = 'ppm'\n", - "view =spectrum.plot()\n", - "plt.ylim(0,spectrum.max()*1.05)\n" + "view = spectrum.plot()\n", + "# plt.ylim(0,spectrum.max()*1.05)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'single_exposure_time': 0.01,\n", + " 'exposure_time': 3.01000999977623,\n", + " 'number_of_frames': 21,\n", + " 'convergence_angle': 30.0,\n", + " 'collection_angle': 33.0,\n", + " 'microscope': 'Unknown',\n", + " 'acceleration_voltage': 60000.0,\n", + " 'flux': np.float32(5.5095315e+08),\n", + " 'intentsity_scale_ppm': np.float64(0.002412798729033098),\n", + " 'incident_beam_current_counts': np.float32(1.1570016e+08)}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "low_loss_spectrum.metadata['experiment']" ] }, { @@ -833,36 +889,18 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# ------ Input ---------\n", - "elements = ['O', 'Co', 'Ce', 'Cu']\n", + "elements = ['B', 'N']\n", "# ----------------------\n", "main_dataset.metadata.setdefault('core_loss', {})['edges'] ={}\n", "for element in elements:\n", " pyTEMlib.eels_tools.add_element_to_dataset(main_dataset, element)\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Refine excluded fitting windows" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [], - "source": [ - "main_dataset.metadata['core_loss']['edges']['3']['start_exclude'] += 2\n", - "main_dataset.metadata['core_loss']['edges']['2']['end_exclude'] = main_dataset.metadata['core_loss']['edges']['3']['start_exclude']-2\n", - "main_dataset.metadata['core_loss']['edges']['2']['start_exclude'] -=5" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -872,34 +910,34 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 122, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9a308cd34031499587a0423c7a87d4ed", + "model_id": "4543d4ca40314fdf8dfabf50659e4c75", "version_major": 2, "version_minor": 0 }, - "image/png": 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uiIiIiJyBAVCzBACmVkAA0AsiCt+eIWdBRERE5CQMgBoXET0UO4KvBwAMbsjAsd2/ylwRERERORoDIGHcwytRJ+oBAL2+f4CXhyMiInJxDIBa02IhaMB0ebi8WzcCAEJQguzj6c6uioiIiJyIAVCrBMHq7oBhSTjsFgcA0H9xlxwVERERkZMwAJKkPHAYACBSzEPWsX3yFkNEREQOwwBIkuhrFkq3DZ/dLGMlRERE5EguEQAXL16MsWPHws/PDyEhIbj22mtx7NixTp+3detWJCYmwtPTEwMHDsSKFSucUK1yhfaLwb6LlgMA+opncXL/dpkrIiIiIkdwiQC4detWzJkzB2lpadi0aRMaGhowffp0VFVVtfuczMxMzJo1C5MmTUJ6ejqeeeYZPProo1i7dq0TK5dD60kgzY28+BbpdumZI44uhoiIiGRgkLsAe/jpp5+s7n/44YcICQnBnj17MHny5Dafs2LFCkRFRWHZsmUAgGHDhmH37t1YsmQJbrjhBkeXrABCm1t1ej32+k7GmMptqC87i/q6Wri5ezi5NiIiInIkl2gBbKmsrAwAEBQU1O4+qampmD59utW2yy+/HLt370Z9fX2bz6mtrUV5ebnVlyuq840EACQf+wfEV8Ox+4f3ZK6IiIiI7MnlAqAoipg/fz4mTpyIuLi4dvcrKChAaGio1bbQ0FA0NDSgqKjta+IuXrwYAQEB0ldkZKRda1eKXkk3SrfdhUYE7NX22EgiIiJX43IBcO7cudi/fz8+/fTTTvcVWqyFJ5oXSW653WLhwoUoKyuTvnJycnpesLO1sRB0S7FJF6ME/tL9wY0n0NjQ4MiqiIiIyIlcKgA+8sgj+Pbbb7F582b069evw33DwsJQUFBgta2wsBAGgwG9e/du8zkeHh7w9/e3+lKtdkKuxXm99TE4uf93R1ZDRERETuQSAVAURcydOxfr1q3Dr7/+iujo6E6fk5KSgk2bNllt27hxI5KSkuDm5uaoUlWj8Yp/oRBNYyirNy2WsRoiIiKyJ5cIgHPmzMGaNWvwySefwM/PDwUFBSgoKMCFCxekfRYuXIi7775buj979mxkZWVh/vz5OHLkCD744AOsXLkSCxYskONHUJwhY6YgZFEmDl62BgAw+kIaDv7xncxVERERkT24RABcvnw5ysrKMHXqVISHh0tfn3/+ubRPfn4+srOzpfvR0dFYv349tmzZgtGjR+Pll1/Gm2++qZElYGw3IuUK6XZV+joZKyEiIiJ7cYl1AEUbJjasWrWq1bYpU6Zg7969DqhIyTo/Vs0JOh12xi/CuAOL4FWZ3fkTiIiISPFcogWQHMs7NAYA0Ks2T+ZKiIiIyB4YAKlTQX2HAABCjYUwNjbKXA0RERH1FAMgdapP32g0igLchQaUnD0jdzlERETUQwyAWmPDeMmW3Nw9UCz0AgCUFGTauyIiIiJyMgZArepkIeiWzhv6AAAqC087oBgiIiJyJgZAskmVp+m6yXUlKrz8HREREVlhACSb1PlEmG6U5cpbCBEREfUYAyDZxr8vAMC9ikvBEBERqR0DINnEM8y0FEzv6lMyV0JEREQ9xQCoWV2bBNJ/5BTTv8YclBWfdURBRERE5CQMgGSTXn3CkSOYxgGe/nOzzNUQERFRTzAAks3yA0YDAGoO/ShvIURERNQjDIBa042FoC0MQy8HAIwt+ganj+y2V0VERETkZAyAWtXFhaABIGH63cjQD4JOEJH/20coKznngMKIiIjI0RgAyWaCToeyUX8BAKTkrUbAm4OQ8UoSqipK5S2MiIiIuoQBkLpk0IRrUSfqpfuDGzKQ+dY1MlZEREREXcUASF0SGByG9OCrrLbF1e5D9vF98hREREREXcYAqDndnwRiMfSOJUgLvQ3pE97GGSEcAJD/0xs9fl0iIiJyDoPcBZBcuj4JxCIgqA+SH1oBANjbWI9+O+YhvHSPvQojIiIiB2MLIPXIwLEzAQBRxlxeIYSIiEglGACpRwKDw6QrhJzas0nmaoiIiMgWDIBa04OFoNuT1zsZANB3+9+5NiAREZEKMABSj8Xc+CKKEYAQlCDjj3Vyl0NERESdYADUqm5cCaQ9wWFRyAgxXSYudO8yu70uEREROQYDINmFoV8iACBSzMOBrWwFJCIiUjIGQLKLAeNmSbf9tr4AY2OjjNUQERFRRxgANcf+k0AAUzfw6Vt+AQAMMGYj58QBh3wfIiIi6jkGQM2y3xhAiwHDknDYLQ4AkL+T3cBERERKxQBIdlUWMREAMObEW0jf8BFEo1HmioiIiKglBkCyq5E3LkQBguEuNCIh9VHs+f4duUsiIiKiFhgAtcYBC0E35+MXiPyU56X73gfWOPT7ERERUdcxAJLdJVx+D3YMfxYA4N9QJHM1RERE1BIDoFbZcSHotsRefDeMooB+YgEyD+9y6PciIiKirmEAJIcIDA7DAe+xAIDoLy5Fxr7fZK6IiIiILBgAyWEuRE2Tbvt9fR8a6utkrIaIiIgsGAA1x7GTQJobdfUj2OszCQAQhnM4sXeL0743ERERtY8BULMcOwYQALx8/DDmie9xHn4AgKE/3uTw70lERESdYwAkhzvtHS/dLirIkbESIiIiAhgAyQki735Xup3xzf/KWAkREREBDIDkBMFhkdiVsBgAkJK/GmXnuTYgERGRnBgAtcbBVwJpz6gZ90u3A/4Vg7KSc7LUQURERAyA2uXghaBbcvfwlGYEA8DRXz7q1uuIoojK2gaIooglG47hyf/+idqGRnuVSUREpAkMgOQ0Y574Hgc8xgAA+h1+F8bGrge3Rz/bh/hFG/Dy90fw1uYT+GL3Gfzfpgx7l0pEROTSGADJqQJvXAYA6CueRfpPH3bpufWNRnz3Zx5EEfjgj0xp+2e7su1ZIhERkctjANQcecYAWkQOHoU9fqYrhOgOre3Sc08UVra5vbS6HiVVvMoIERGRrRgANcu5YwCb8xh3HwAgoXo7/tz8pc3PO362ot3HTp1rOxwSERFRawyA5HRxk65BWsjNAIBRWx/E/s3/tel5p4uqre4H+3pg0uBgAMCpc1X2LZKIiMiFMQCSLIbf/rp027jrfZuek1VsHfL69/bGwGAfAMBJtgASERHZjAGQZOEf2BsHLl4FABhdnYq8zKOdPudUkXUAHB7uj5gQXwDASbYAEhER2YwBUGtkWgi6LYMSL5VuZ/28vMN9GxqNOFZgGgP4yrVxuHVsJJ6YEYuBwaYAuCerxHGFEhERuRiXCYDbtm3DVVddhYiICAiCgK+//rrD/bds2QJBEFp9HT3aeUuUS3DyQtBt8fLxQ+rARwEAfkX7Otx3X04pLtQ3ItDbDbeNi8LrN4yEv6cbBplbAM9X1+NQXpmjSyYiInIJLhMAq6qqMGrUKLz11ltdet6xY8eQn58vfQ0ePNhBFVJb+oyaAQCIq92H43u3trvfjkxTC99Fg4Kh1zWF17AAT2kc4I5TbAUkIiKyhUHuAuxl5syZmDlzZpefFxISgsDAQPsXRDaJGpqIRlGAXhDR+OPTaBz5G/SG1r+WJ81rAA4P92/12Mz4MLy9+SQnghAREdnIZVoAuyshIQHh4eG45JJLsHnz5g73ra2tRXl5udWX+ihnDCBgukZwxlWmBaGH1R+G/pXeOPj7t632s4S7mD4+rR4b0Nu07XQxJ4IQERHZQrMBMDw8HO+++y7Wrl2LdevWITY2Fpdccgm2bdvW7nMWL16MgIAA6SsyMtKJFdub/GMALYYmXSKtCwgAcT/fZfV4o1FEhrkF0DLmr7locxdwy3UCiYiIqG0u0wXcVbGxsYiNjZXup6SkICcnB0uWLMHkyZPbfM7ChQsxf/586X55ebnKQ6By9J5wN/D1F9L9+rpauLl7AAAyCitQXdcIH3c9ooNbB8AB5gCYV3YBNfWN8HTTO6doIiIildJsC2BbkpOTkZGR0e7jHh4e8Pf3t/oi+xg8ehIOTPtQul+weDQaGxoAAIdyTV3tcX0DrCaAWPT2cYefpwGiCGQWsRuYiIioMwyAzaSnpyM8PFzuMjQrfsr1aBBNv5KRYh7+++KNKKuqQ17pBQCmK3+0RRAExIb6Aej4esFERERk4jJdwJWVlThx4oR0PzMzE/v27UNQUBCioqKwcOFC5ObmYvXq1QCAZcuWYcCAARgxYgTq6uqwZs0arF27FmvXrpXrR3AOBS0E3Zbd4bchueBjAMAt+s346IdvkGcYCgAI8/ds93kRgV5A1nlsOFSAa0b3dUqtREREauUyAXD37t2YNm2adN8yVu+ee+7BqlWrkJ+fj+zsbOnxuro6LFiwALm5ufDy8sKIESPwww8/YNasWU6vXRYKWAi6LbE3/B3pH2YioXo7AGDrvqMo6RsBABjYp/X4P4uiyloAwPoDBdhwqACXjwhzfLFEREQqJYiiwpuEFKy8vBwBAQEoKytTz3jAd6cBeXuB278AhlwudzXtql8xBW4F+3B/3QL8ahwDAPjh0YkYERHQ5v6/ZZzDXSt3SvePvTIDHgZOBiEiotZU+f5tZxwDSIrkpjP9agrmdQv9PAyI6aAFcNLgPlh68yjp/j9+PObYAomIiFSMAVBzVNLga+6inhUfjthQP3x439hOl3e5fkw/zJ4SAwD44I9MFJTVOLxMIiIiNWIAJEW7YUw/bHh8MpIGBNm0//zLhki3v/0z11FlERERqRoDoGYpcxJIK10coupu0OH+i6IBAEfyuSQMERFRWxgASaG6H1BHRZominyVniutIUhERERNGADJ5YyP7i3dXn8gX8ZKiIiIlIkBUGtUt+pP1+sNC/DETYn9AACv/HAEU/65GVztiIiIqAkDoFYpdCFoSQ/ru2fCAOl2VnE1DuWV97AgIiIi18EASC4prm8AnpoxVLp/7dt/sBWQiIjIjAGQlK0Hoe2hqTF44ybT4tANRhEPfrQbNfWN9qqMiIhItRgANUctrWD26aK+ZnQE/DxMl7z+5Wghhv79J6SdKrbLaxMREakVAyC5NINehz9fmI4xUYHStlvfTeNVQoiISNMYADVL4ZNAJD1vsdTpBKy4K9EqBCYv/gXLfj6Osur6Hr8+ERGR2jAAkjLZeZZyiJ8n1j18kdW2ZT9nYNRLG/HR9tN2/V5EJI9Nh8/iiS//5FhfIhswAJKmrHt4Ai4ZGoKLh4bATW8Kma/+cIRXDCFyAX9ZvRtf7jmDGcu2yV0KkeIZ5C6AnExtS6HYud4xUb2w8t6xAIC6BiOGPPcj6hqNmPD6rzjx6kwY9PxMRKR2p4ur5S6BSPH4bqdVih8C6PgC3Q06/O2yIdL9Kf/cgjPn+cZBRESujwGQNO2BSdHS7dzSC5j7SToXjCYiIpfHAEia5u1uwMEXL8e1oyMAAPtySnHyXJXMVRERETkWA6DmqKR1S5oF7Ph6fT0MWHZrAsZFBwEALl26FZ/tzHb49yUix2FLPlHHGACJzKYM6SPdXrLxON9AiFTG3dD0ltZo5N8vUUcYADVL8bNAnO7O8f1xw5h+AICiylocya+QuSIi6ormZ7X6RgZAoo4wAJJCmU/lTmyFC/B2wxs3j8LFQ0MAALPe/A3nq+qc9v2JqGd0zRaQr2s0ylgJkfIxABK1cGNiP+n2l3tyZKyEiLqrngGQqEMMgFrDXpFOzYoPxy1JkQCAz3bmcCwgkUo0NvtbZQAk6hgDoFbZ+Vq7dufEWcBt+ftVw+Hjrsepoiqs3ZsrSw1E1DUNzUJffQM/uBF1hAGQqA2+HgbcmdIfAPDR9tPyFkNEnTIaRTSf+MsxgEQdYwAkasd9E0xXCTmQW4ap/9yM8pp6mSsiovY0tFj2pa7B9gB4MLcMmUVcAJ60hQFQc9TSLeL8WcAthQV4IqaPDwDTxeXvWrkTlbUNstVDRO1rue6frWMAz5bX4Mr/9zumLdnC8b6kKQyARB349x2JSB5oukLInzmlWLLhmMwVEVFbGozWgc/WALj/TJl0u6C8xq41ESkZA6BmKXwSiELEhvnhs7+m4NFLBgMAVm0/jYc/3sOWAiKFaWix8LOtYwBLq5vW+jxbXmvXmoiUjAGQlEnmWcAtzb9sCB65eBAAYP2BAuzJOi9zRUTUXMsxgLZeCeRCfaN0u5pDPEhDGACJbDT/siGIDfUDANy4IpWtgOQwxZW12HT4LIy8nq3NWnUB2zgJpLquKQBWNbtN5OoYALWGoaXbBEHAK9fFSfeHPf8TMs7yesFkf3M/ScdfVu/GB39kyl2KarTsArZ1DKBVAGQLIGkIAyApm8IC69gBQbh8RCgAoKbeiNvf38ErDpDdpZ4qBgCs2HpS5krUo9UyMDb+XV6oawp9VXUMgKQdDIBapfQrgSjY8jsScfv4KADAuYpaHMkvl7kiclUtQw21r7HVLGDbjl1NfdPzqmvZBUzawQBI1EU6nYDXrovH1Ng+AET8kX4I1ZVlnT6PqKsU1gCuaC0Dn60LQTffj+t8kpYwAJIyqaCFckw/f6R6PIKH9syC95IoHNi6Tu6SiDSruwtBN9+vml3ApCEMgJrDJgV7uTxaj3ChRLpvTFsuYzVE2tZ6GRgbWwCb7cdZwKQlDICapfwWNqWLDfW3uj/qwk6krf67TNWQK2rgBCObtTxWtk4Cad4FzFnApCUMgKRQ8l8LuHOtaxt78v+hqqLU+aWQS6rnJBCbtWoBbLDt2Fl3AbMFkLSDAZDIDqoXZKMIgdALInze6I8961fKXRK5gJbj2qh93V0HsPnkkQsMgKQhDIBao+gWNZWxHEtBD2/fAJwcPld6KHHnfKR9/KJMhZGaNb/CDAOg7VpdCaQbXcCcBEJawgBIyqSwawF3yFxrxJgZVpuTM5Yi99QROSoiFWPo656WLYA2jwFkFzBpFAOgVqlgmRW18fINbLWt7+pknD+X7/xiSLW4+HP3dHcWcPP9LtQzAJJ2MAASdZv1G46PX6B0e0fva6XbZ9+9Hif+/MNJNZHatWwB5Exg27TqAuYkEKIOMQCSQqlhFrCFqVZPLx9pS+xt/0DaoMcBAEPrDyNy3TXIPr5PjuJIZVq2ZNXaeEULrWsZnLuzDAwngZCWMABqjhoClUq0CKeCToecO7bh5PU/IjA4DPHXzMMRt+EAAA+hHnWf34/ammo5KiUVaRlkGABt090xgM1nAVfXNVhNwiFyZQyAmsUxgHbTbDxl5OBRiBk5AYCpS3jowj9w+pZfUCfqMajxJDxeD8eeH96HaOSbOrWtZVdmbQNbpWzRugu465NAjCIDN2mHywTAbdu24aqrrkJERAQEQcDXX3/d6XO2bt2KxMREeHp6YuDAgVixYoXjCyXbqGIWcOe1CTodBgxLwp6Bs6Vtibv+htoXQ5CXedSRxZFKtWoBrGcgsUW3LwXXIvCxG5i0wmUCYFVVFUaNGoW33nrLpv0zMzMxa9YsTJo0Cenp6XjmmWfw6KOPYu3atQ6ulFxP562pY29fhHTvi6T7nkI9cn74X0cWRSrVsiuTLVK2ab0QdNcngQBANWcCk0YY5C7AXmbOnImZM2favP+KFSsQFRWFZcuWAQCGDRuG3bt3Y8mSJbjhhhscVKUCcHyLLAxu7kh4cj2O7tgI488vYnj9QcQU/YLammp4eHrLXR4pSOsxgAwktrC0ALrpBdQ3il0YA9iyBZCLQZM2uEwLYFelpqZi+vTpVtsuv/xy7N69G/X19TJVRU1UMAu4G7UNHT8dgxb8gkIEIRil2Lv6aY4HJCucBdw9luVyPN30AGzrAhZFUWopdNeb3g65FAxphWYDYEFBAUJDQ622hYaGoqGhAUVFRW0+p7a2FuXl5VZfqsWFoO2ni8fS3cMTpwbdAwBIyfsIwku9UFpU4IjKSIWMLT5Y1LBL0iaW4OztbnsAbN5K6O/lBoABkLRDswEQAIQWb9yW6f8tt1ssXrwYAQEB0ldkZKTDayQl637r5OjrFyBb11e6H/hWLHat+5c9iiKVazUGkJNAbGI5bl6WFkAbFoJuPk4w0NsUADkJhLRCswEwLCwMBQXWrS6FhYUwGAzo3bt3m89ZuHAhysrKpK+cnBxnlKpNqpgFbNH11lRPb18Y7v4KJ/Qx0rax+59HXW2NPQsjFeI6gN3TaB5K4eVuGtpuyxjA5kvFBLAFkDRGswEwJSUFmzZtstq2ceNGJCUlwc3Nrc3neHh4wN/f3+pLfdQQqFSih+MTIwbEYtDf92JvStPM9dP/nISG+rqeVkYqxnUAu8fSBezlZnpba7m8S1ssIVEnAL4epuBYzUkgpBEuEwArKyuxb98+7Nu3D4BpmZd9+/YhOzsbgKn17u6775b2nz17NrKysjB//nwcOXIEH3zwAVauXIkFCxbIUb4MOAbQbno4nnLM5XdhZ/wiAMCQhuMwvNoHO774px0KIzViC2D3NI0BNAU5m8YAmo+tm14njR28wDGXpBEuEwB3796NhIQEJCQkAADmz5+PhIQEPP/88wCA/Px8KQwCQHR0NNavX48tW7Zg9OjRePnll/Hmm2+69hIwqqKCWcB2NPa6x3DUMEy6P/7wK9j55p04sO0rzhLWmFazgBlIbGIZA9iVWcCWfdwNOniZAyC7gEkrXGYdwKlTp3Z4DcdVq1a12jZlyhTs3bvXgVWRa7NfOBV0Ogx9Lg0nD6QhZu3lAIBxJd8Bv36HnXtnYdy8T+32vUjZ2ALYPZau86ZZwJ3/fVq6gN2btQAyAJJWuEwLINlIIy1qzmW/7vSY+GRk3brZatu40vXYt+kTu30PUjauA9g9LZeBsW0SiHkNQINO6jrmQtCkFQyApExqmAXsoDDdf+gYYFEZyh47KW0b/cdDqK4sc8j3I2VpbNHlz3UAbdPWQtAd9QoBTSHRTa+Tlo9hCyBpBQOgVnEhaPtx0LEM6BWMXaNfbdrwz8HIOsohC66uZcMVWwBt07IFUBRbd6e31DQJRGiaBMIASBrBAEjUbY5vnUy6+mEcM8QCALyFWlR89bjDvyfJq2ULIJeBsU3LhaCBzscBNk0C0XMMIGkOAyAplLZmAbdH0OkweGEqdo1+DQAQV7sPaW8/iLNnTnbyTFKr1rOA2QJoC0trn2U2L9D5WoBSANQL0gLS1exyJ41gANQcbQcqx3Bsd7pOr8fYa+dgZ+AsAEDyuS8R+v4YnDlx0KHfl+TBWcDdY5kF7NGsBbCziSBtrgPISSCkEQyAmsUxgD3m5NbJsY9+jHSfidJ9zzVXoDA306k1kOO1uhYwu4BtYjlubjoB7nrTW1tnawHWcR1A0jAGQFImNcwCtnDShBpBp0P8vK+w2/9SAEAwShHy3mgc+uMHp3x/cg62AHaPpevcoNfBTW/6m+wsAFrGCLrpdfB24yQQ0hYGQCIVMbi5Y/C9K5AjREjbRmy6HXvWfyhjVWRPLccAMpDYxtIFbNAJcDPY2AJo1QVsGgNYxS5g0ggGQK3R+KQKx3Bud3pAUB9EvnAEaWF3SNsSd87DgW3fOLUOcgzLLGBPN9PpmYHENpYuYIO+qQu4rsG2WcAe7AImDWIAJIVSwSxgmWtzj06xuh//693AogCkffKyTBWRPVhaAP093QAA1bUMJLaQuoB1AtxsHANY38h1AEm7GAC1igtBq178tJux13cKTusirbYnH1+Co7t+lqkq6inLGMAAL1MArKxlC6AtmgKgDu62dgE3tp4F3GAUO10+hsgVMAAS9ZRMWdrN3QNjFnyLAc8fRM6dv+OEPkZ6bOgPN+DEn3/IUxj1iCUA+psDYBUDoE0sl4LT6wVpEoity8A0nwUMsBWQtIEBkJRJFbOAlVNb5KB4DPr7Xhya/qm0rf77J2SsiLqroUULYFVdI4ydXNKMmoKzm04ndQHbuhC0m14Hd70Oep3pvFNdz9BNro8BUHP4RmJ/yulOHzFhFlKj/goAGFZ/CNnH98lbEHVZyy5gALjAq1N0yhKc9VZjADubBGJ63N2ggyAI0lIwnAhCWsAASNRdCp2gknL/P3HIPR4AEPXJFKT/70wc2LpO5qrIVpYg4+Ohh7lBit3ANrB0ATefBWzrMjCW/b1UPhGktLoOxZW1cpdBKsEAqFnKabXqkEJDlhUFTqgJvPUdVIheAICE6u2I33wfUt95BKKRg9uVrlFaz04HH/PadJwI0jmrWcAG2xaCbj4JBIA0EURtLYCiKKKiph5XvPk7El/5GXmlF+QuiVSAAZAUSnmhqjXlhtO+A0cg94r/oBBB0raU/NU4+9IQNNTXyVgZdaZ5kPHxMC9OzKVgOiWtA9iVMYDNJoEAgJc5cFerZO1FURSx6NtDiF64HvGLNiLXHPwmvP4rUk8WQ2z2AbqytsHqfltSVz2NqhdCsXfJ1fywqAEGuQsgJ1NDixrZxdBxlwHjTNcKTl35N6TkvI8wnANe7YOC+3cjLGqwzBVSWxrNQUavF+DjYWqR4mLQnWu6FFzzLuCOz3d1zdYBBKDYtQAbGo144r/74edpwItXj4Bg7nXYePgsVm0/3eZzbnsvDQAwLbYPfssoQoNRxJ3JUXjl2nir/USjEQe2fYULB79HStE6QADGVG7F/v+9DH5XvYboEeMd+rORfBgASeHUEFiV31qZfN8/ceLVzRjUeBIAcO7jv8Bw338Q1CcCOr2+k2eTM0mTGQQBvlILIANgZ7pzKTjL45YWQKV2Ae/ILMFX6bkAgCAfdwR6uWFmfDj+5z97pH1evS4OekFAXN8AXP3W77BMHN987Jy0z5q0bPi4GzD7on7IO7gN1TtXY2zZTxjZxvccWbMb+HI6Tho3ICY+2ZE/HsmEAVCrFDhuzYrS6wNU1Zoq6HQI/Ms3yHz3CkQbsxBfmw6siMOO4Osxfi6vI6wkjc26gL05BtBmli5gy5IugC2TQMyzgC2TQCyzgBU263rr8aYQt+znDADAou8OS9vevn0MrhgZLt0//NIMvLHxGN77LbPVa72z7RSuT7sRI3RnWj1WJxrgLlj/rsWsvRxV//XEseTXUXd6B0R3H6Q88EaPfyaSH8cAEvWUGsIqgOCwSEQ/vx9pMY9J28YXrQMWBWDnsttkrIyaaxQty5nobB4D2FnQ0QKpO9egs30h6HYmgVxQWJf7qXNVHT4e7Otudd/TTY9nrxiO46/MxHdzJyJz8Swcf2UmZsWHAQBiW4S/nYGzUHD/bri/WCxtK4OPdNtHqMGYHfOQfPZTpOS8j4z0bT39kUgBGABJ2RTdyqbk2tqXfNdLOH71NzgPf2nbuNL1yMn4U8aqyMIyBtCgF+DvZQqA5TX17e5/orASIxdtxOOf73NGeYokiqLVdX1tnQRSY27p8zS3/DVNAlFWC2B2iSkA3j4+qs3H/Tzd2tzubtAhvl8ABEGAu0GHf9+RiEMvXi49Xj93H7CoDOPmfSqNCT5+9TfYEXQ1xLl7kTbo8TZfd/A3VyF11dOorixDdWVZD34ykhMDoOaoM7SQfQ0ZMxWBz2dh56hXpG2RH09G4wuBOJz6o4yVkWUMoE4QEORtatk5X9X+zO1Nh8/iQn0jvkrP1WxXcaNRlD4ruut1zRaC7jgA1koBsGULoP0DYHVdA17/8Sh2nS7p0vNEUUR2STUA4OLYEGn70ptHSbe93W0fx+vjYQD0pt8rNzf3Vo8PGTMV4x/9DwKDw5B85yJgURkyb9qIkzdsQFqfm6T9Uk4vh/eSKHgvicLupTfgcNpP2Ln2/1BVUdqln4/kwwBI1GPq6AJuSdDpMO66R7ArYLq0TS+IGL7hVuxeeiM/2cvEMpnBTS+gl485AFa3HwALyprWfDtd1HFXoatqPtvXTa+TJnV0Ngu4pt50rC0tgI6cBPLNvjys2HoSN61I7VJQL6yoRU29EXqdgEEhvtL2ED9P6XZXAiCALvesRI8Yj5j4ZCTPeR9poa2HiySV/4zhP92CcQcWweeN/kj/5xVcRkYFGAA1S52hRVEU3T1tu5AZT7fallS+CUeX3wFjo7K6wrSg+SXNAr1NXXvnq9vvAq5oFiayiqsdW5xCNR/r56ZvNgawsy7gBusWQC8HBsCj+eXS7Z8OFqC2oVGa8NMRy/9pRKAnwgKaQp/ldwNoqtt25u/bjfHLyQ+tQM1TeUgbNK/dfRKqfofwUi/UvRCEvT+t6vL3IOdgACRlkk5MKghZKpkE0p7+wxKBRWXAojLsGv2qtH1M1W/QvRyE9I1rZKxOe5rGAOps6gKuqGkKgKeLtdoC2DwACjZ3AVvGAHoYzC2A5pbAC/X270ovrGi6RNvL3x/GyEUbcckbWzptDcwy/5/2D/KBp5seO5+9BLufuxR6XdN5xzJbvOu6d+7y9PJB8p0vAovKcOGJM0gLuRl1oh6H3eKs9nMXGjEm7THseeM6HPrjh27WSI7CAEjUbSoIp1009tq5EJ8/j93+l0nbErbPwY4v/iljVdrSfD27QO/Ou4Armk0Q0W4XcFO3uSB0JQBauoAtYwAdNwmkuLLp/7DsQj1qG4w4XVyNu1buQPJrv+BIsxbC5nLM4/+iensDMHX9Bvt6YEioHxKiAjF9eKhVGLSJHXsvvHz8MH72O2h4IhPDn/0D5+ccbbVPYsWviN14J46+kozDr01E+sY1EI1G1NfVovjsGez+dgWqKkphbGxEZfl5lJcWt/GdyN64DqDWuEi3pbKouwWwJUGnQ9L8/yLz8C5Ef3EpAGD84VeARa/ggEcChjy+Hh6e3jJX6bqaXwqul4+pm6+0gy7g5i1IWm0BbL4GIAB42DwG0LoF0JFdwCXthPj07FIAwM3vpGL/C9Olq3xYZJkDYP8g6785vU7AuocmtNrfNt3vAm6LoNPB2zcAANCrT3ib+xgEI4Y2HDHd2T4H2D4HbgB6m7+w9ykAgGWUY9Ytv5p6J8hh2AKoVYrvtjTXp+TAquTa7CB6+FjUPJlrtS2+Nh3C4n7Y/cN7MlXl+hqbXdIsqFkLoLGd8WLWXcDaHgNoCYDSMjAdtACKoojahrYngThiFnBlTcddvRU1DfjL6j2ttlvGAEYFtf7Q1b3wZ/UKPXx+51IjH8S5v/6JatGjS8/r//nFwKIAHFw8hTOLHYQBkIja5enti73jl+G0rmn9MXehEUm7FgCLApD6wRMyVueaLN2Wep1O6gI2iu2vBdg8WJyrqNXkUjD17QTA+g4mgdQ2e6z1JBD7H0PL/8s/bxyJ8dFBePeuRGkix8A+pkWXfz5yFou+PSQ9RxRFZJq79S1dwHbh4A+vu0a/ir0+k3DhiTNIeeAN9IkYgDPXfIm0IQtQ/NAhnLjuB+z2vxSH3eKw33MsdgRfDwA45D4KaWF3WL1WXO0+HHnvQYfWq1XsAibqKcW3pvbMmJn3ATPvQ2NDA/a8dRfGla6XHkvJfhepqz0x6voFUhcQ9YylBdBNZ1q8t5e3G85X1yOvtEYKhM1VtGhZyi6uxvAI/1b7ubJ66ZJupr9FW64EUtPscm9NLYCmt0R7twAajaIUAKcNDcFNSZEAgISoXqhtaES/Xt644/00/HGiGKu2n8aNif0Q1zcA208Wo+xCPbzc9Bgc4mfXmgA47Nw19tq5AOZabRsyZgowZgoAoHdoP2DUxBbP+hAjzLfysx6DuOoqRIhnAQDeExgAHYEtgJqjkm5LVYQqlRxLO9EbDBg371MY/15itQREyqk3Ubh0InZ/9w7ys47JV6CLaL4MDAD0721qHcpqY3xfTX2jFHKig0375ZzXXjdw88vAAWi2DmBHAdDS0to0acTH3AJYZecAWNWsRdHXo6ndpY+fB/r1MrXsvXptvLT9zpU7sPt0CVZsPQkAuCmpn/Qz2Yeyz13h/WMR8cJxVP0tC/XPFGJ48gy5S3JJDIBEPaaGsGo/Or0eyXe+iPNzjqLUPGR7gDEbSXuehMeHlzIE9lBDs0vBAU3BLrONANi8u3domKmFyDJrVEva7wJuP+hIl4FrFqwsC2+XXahHgx2vr2z5fzLoBGmCSksDgn2w9qEJAEyTfm5ckYrfMoogCMBfJg20Wy3WlH3u8vELhJt718YOku0YADVL2X/4quDik0A606tPOBof2oHUAQ9J24JQjtz/LkRJYW4Hz6SONM0CNp2e+5vHfmUVtQ52lu5fH3e91FLIAGjbJJALLa4DDAC9vN2lzoeOFt/uKss4TV9PQ4cTNxL798J7dydZbbsuoS8i25gAYheq6GkhR2EAJIVSwSxgCw2fRHuH9kPKva/j4KX/QWrkXwAASRW/IOjfw7F76Q04eSBN5grVp7HZOoBAJy2A5mDh5+kmzRLN1nAAtIwBtLSy1XYwCcQSnv29mq6oodcJ6GUeZ1lcVdvm87rDcrWW5t2/7blseCiW3zEGMX18cO+EAfjnjaM6fU6XqOGcSk7BSSBE3cYTqUXcxKuBiVfj0Gs7MaLuTwCm64Ni7c/Y+9NkxNz/PgJ6h8pcpTo0NLsSCNDxGEDLItB+ngappfDkOe2tBVjXYL0OoI9H57N5yy+Yjp2/p/XbYJCPO0qq6lBS2f7i210ltQDaEAABYGZ8OGbGt72eXo9ZBUDtfngltgBqDzMLOVDfv36BetH6uqRjqrbh6GfPIOfEAV5b2AYtJ4FEmwPg2fLaVoFGalnyNGCEeeZvdkk1yuzYfakGLbuALbN5q2rb/32zLKvTvAUQAHqbxwEWdXD5va6yjAH082SbCykHA6BWKf2Dn5quBaz4g+k8gcFhqH7sGHaNfg0Z136Pgx6jAQDjz/0XkWsmYt//XStrfWrQ2OxKIAAQ4O0mrRd3usU4wIpmXcCB3u5SN/D+3FInVasM9S1mAftIAdCWFsAWAdDXFABLKu3XBdzVFkDHanZO1fDwFWIAJOo+jqVpU0BQH4y9dg4Gj56EgY98ixqx6Q12TOU2HFg8DbmnDnXwCtpmCTOWWcAAMKCdbuDmXcAAMLKfaS3G/WfKHF6nktQ1WI8BtHQBX6hvlAJ1S+XSGMDWXcAAUGLHFsCmllq3TvYkch4GQKKe4qfodnn7BuDYlH9jj980aVt87V70XT0BB7Z9JWNlytXYYhYw0DQR5PjZSqt9pUkg5pal+L6mAHgwV1sB0LJun6Xr16dZS9uF+ra7gdttAfQxLTtyzo4tgFVdmATicPzgSmYMgJqjlj9+NcwCVnJtyjHq4puR+LevceGJMziti5S2x/96L7AoAAcWT8WZEwflK1BhWo4BBEzLgwDAHyeKrPataDG2zBIA9+WUtnvtYFdUbT4OlpY/D4MOlsPXXjdwe2MALUuuWC7BZg/KGgPILmAyYQAk6jGeRG3h5eOHguAJrbbH16ZD9/F1yDq2Dw319ut2U6sGaUJD0+/VpMHBAID0nPOobWhq0aqQxpaZQszIyED4ehiQX1aD7/bnOatk2bVsARQEQWoFbDcAXmi9DAzQ1I2+I7MEB+zUlV6hqDGARCYMgJrF0NJjim6dVKaQyQ8AANK9J2DXyJdwSjcAABAhFqL/p1NgeLUPUlf+TcYK5ddWC2BUkDcCvNxQ3yjieEFTN3DLMYC+HgbcMT4KAPDYZ/vwxa4cZ5UtK8vsaMul3Ey3DebH2ukCrml7GZghoX64ZnQERBF4/acjdqmvUrFdwHwf0DIGQFImVc0CJlsNjBuPor/uR9y8rzH2+scw8Pk/kTbocaulY1Jy3gcWBQCLAlBUoI0A05xlDKBlSRPA1KJlaZk60Gx83/lqU4upZZYwADw8bRBiQ02XhXty7X6MeXlTh+vhuQLLci/ezQKWt7k7uLKLXcAA8MTlsRAE4I8Txcgu7vnC2pXm7+WriC7gZtgFrGkMgEQ9xZNolwRH9Le6vmfynYtQ/0Qmdox4vtW+pe9dg5yMP51ZnqxEUWyzBRAA4vq2DoCF5aaJCqH+ntK2AC83fPfIRKnbuKSqDv/6JcOhdcutShoD2BSwLK1t7YVfqQu4jZm5/Xp546IY0/HbeLigx/WVt5isIy9+qCYTBkCtYbelHfFY2ou3bwDG3/Q3VC/Ixl6fydL2QY0nEbFmCupfCMKOt+5DbY1rX+as+ZIlhhYBsPkED4uz5TUAgFB/D6t93Q06/N8to6XuzXe2nsKlS7fiHz8dbXdZFDWraqML2Nt8u73FoC0tgAFebYcyy8SbYwUVPa7PMuM4oI3WRqdjFzCZMQBqleJbrdQwC9hC6cdSPbx9AzDmie8gPn8eR2f9FwCgF0S4CY0YX7QOHq+HY/cb1+P8uXyZK3WMhuYBUG99eh47IAiCABzJL8c3+3Jxoa5RalkKadYCaBHs64E/X5iOUeau4xOFlVi+5SRinlmPN3/JwLKfj6Okqk5aQ0/NqltMAgE6XgxaFMV2l4GxGBBsmg2cW3qhx/WVXWi/u1lWin8fIEdiACTqLjVkU5USdDoMHXcZDl76n1aPJVX8gvPLZ7jkZeUaOmgB7OPngQcnRgMAnvhyP7afNC0J4+Wmb7drURAEhAW0DodLNx3Hsp8zMOblTRjy3I8Y/Ox6HC0ot9eP4XRVLZaBMd02HRNLS5/V/nWNsBzq9kJZmL8XAODMefsFQEW0APLERWYMgESkWEPHz8BpXSTy0Qepfe+Ttg80nobu5SCkrXoGeaePyVihfTU2Nr05txwDCABPzxyGQSG+qGs04rX1phmqMSE+EDpoyRlgXkS6I/WNImYs+w1zP9mLJRuOYf+ZUgBAWXU99mSVdPGncL7S6tateZZLuu06fb7V/ufNV/lwN+jgYWj7bXB4uD/c9AKyS6p7FI5r6htRa25lVUQLILuAycylAuC///1vREdHw9PTE4mJifjtt9/a3XfLli0QBKHV19GjR51YsRxU8ulPFbOAzbXxHOowBjd3hC1IQ68n9yHlL8uARWVIG/S49Hjy6bcR+OEkpH74FFI/fAqFuZnyFWsH9cam7tiWLYCAKRTeNs60zMvJc6aFiicP7tPha86ZNgg3jOmHl6+Ns9p++YhQ/O8NI622fb8/H29tPoGr3/oDA57+AaNe2ogblqciftEGHC0oh2hUXnex0SiiuMo0GaaPX9NYyMuGhwIA9madh9hiKEmxOQAG+7i3G54DvN0wZUgIAOCXI4Xdrs/SAikISpkEQmTiMgHw888/x7x58/Dss88iPT0dkyZNwsyZM5Gdnd3h844dO4b8/Hzpa/DgwU6qmIhs4entC09vX+n++NufR2rkg9J9b6EWKVkrkJK1AiHvjcbeDa27jdXC0lLkbtC1G0wuGtRbut030At/nTyww9f093TDGzePwtUjI6RtR16agXfuSsJNSf3wf7eMwvt3J2FWfFi7rxFcm4OI5UMgvNQLWBSAtI9fQnVlGXb+dynKzhe1+zxnKLtQj3pzy6ml1Q8wTeJwN+hQXFXX6qoexebLvPX2tZ4809LkIaaZwC2vwNIVzcca6toI9c7HK4GQict8HFm6dCkeeOABPPig6Y1h2bJl2LBhA5YvX47Fixe3+7yQkBAEBgY6qUol4R++/fBYOpOg0yHlgTdwaPslEMVGVB74EWPzP4FeML2xjUmdi/Q/P4H/zBcQPXwsdHp9J6+oHJYJGe11SwLA0DB/PHrJYGScrcD/uy2h1WSR9gR4u+Ht28fA000HL/MMWUEQcF1CPwDApeYWM1EUseHQWTy9bj9Kq+sxfXgorsl8B/5i01i45Iw3gCVvYBwAHHwRqf3ux9BrnoSPfy+4e7Qec+hIReYwF+DlBg9D0/+1h0GP0f0CsfN0CXafPo+BfZo+RBRXmloAmwfGtkwwLwWz/WQxDueVY3iEf5frO1dh+l5BPh1/L3nw3KVlLhEA6+rqsGfPHjz99NNW26dPn47t27d3+NyEhATU1NRg+PDheO655zBt2rQO9ydnUcEsYCXXpgEjJswy3bjoKgD/RtbRvahcNw8j6v5EQvV2YO3l+POHsajx6QuvqhzEP/kzBJ2yOz0sl3nrKAACwPzLhnTr9a8YGd7pPoIgYEZcGGbENbUIVn81EPhza7vPSTnzAfD2BwCAfV7JGPDARwgMbr9F0Z7OVZgCYHAbYS5pQC/sPF2CXadLcPPYputQW7qAOwtlMX18cOmwEPx8pBDPf3MQX85O6XC8ZVtyzpuWLurXy6tLz3MYnrfITNlnQxsVFRWhsbERoaGhVttDQ0NRUND2Ip7h4eF49913sXbtWqxbtw6xsbG45JJLsG3btna/T21tLcrLy62+iNiNogz9h47BiGe2IW3IE9K2UTW7ML74a4ys2QPhpV4oWBSDA1vXoaa6soNXkk9tvaUFUFmtlt69zGEu6X5UL8jGroDpSAu5GWmhtyFHiECV2NTqN/pCGgLfikXZoghgUQB2vnknykrOOay2w/mm83D/3q0nu4yNDgIA/HSowGo5mKbQ2HEXsCAIePnaOHi56bE76zwuX7YN05Zswe7Ttk+MSc8uBQAMCvHteEenYRcwmbhEC6BFy09moii2+2ktNjYWsbGx0v2UlBTk5ORgyZIlmDx5cpvPWbx4MV588UX7FSwHfvqzIx5LJUq+/Tkc3Z2C0owdSD72D6vHwlCEsM33AZuB/Z6JGPr4eqd3WXak1oYuYFlI5w0B3r4BGPv4l1YP11RXIu2jBRAaqjG++BsAQABM4+7GlXwHvPkd0r0nwOviBegdEYM+EQPsVtqeLNMs36QBvVo9NnFQMPoGeiG39AK+2ZeH283XSc4zr+0X0cYSOS2FB3hh/mVD8Or6Izh+1vTB4cYVqfjXraNx5ciINmdrW5worMRX6WcAANNiQ7r2gxE5mEsEwODgYOj1+latfYWFha1aBTuSnJyMNWvWtPv4woULMX/+fOl+eXk5IiMj292fekAVs4BJqYYmXQIkXYLd3wahIXcf+hVuRj/R+vwwsmYPsDgUaaG3QRc2HAPGXYWQvtEyVWxS12wSiLJYZry3HXY8vX2R/NAKAEDap6/B/Uwq6vwikVzwsbRPQvV24PvrAQA7gq9H4v+8A4Nbz8bFiaKI3ZYA2D+o1eNueh2uH9MX/+/XE3j5+8OYNDgYkUHeyCszBcC+vbxt+j4PToqGr6cBa9KycCjP1OL42Gf78Nhn+/D69fG4NqEv3PQ6HDh+AmW7v0RWWT3ePzcc2TWm1x8R4S9dmk92XAaGzFwiALq7uyMxMRGbNm3CddddJ23ftGkTrrnmGptfJz09HeHh7Y+R8fDwgIdHx10GqsGm/55r1ipCypR09Wyr++fP5SPn4B8YufUBaVvy2U+BswD+/DsOuY+ExxWL4RPYB2GRg50+ZtDWMYBO14Xf9eTbnpFunz5yPyqL81CX9h6GVaTBSzCNvRtftA6Fr25Bpc4fjVe/hZj4Cd2arJNdUo1zFbVw0wsYab7iSUsPTY3BL0cKcTi/HCt/z8Siq0cg17y4c0Sgba2/gmBafue2cVGormvAmJc3ocbcXf/0ugN4et0BAMArhpW40/ALACC0MQn/A1ODwfzLhnR57KDjsAuYTFwiAALA/PnzcddddyEpKQkpKSl49913kZ2djdmzTW8ACxcuRG5uLlavXg3ANEt4wIABGDFiBOrq6rBmzRqsXbsWa9eulfPHICIH6tUnHL2m3QhMuxFHdmyA+8anENPYtHbgiLr9wFdXAABO6geiesoixE+2/UNkT1kuaebhpqwxgJ21ALZnwLAk042JV6Oy/DzOFOYi78BmJO77O0KEEoQYS4CvrwS+Bnb7X4bEeV+goaEe5efPoXdov05ff7d5kee4vgHwbOeYebsbMGfaIMz5ZC/2Zp9H2YV6aRJIPxtbAFu+3mXDw/Ddn3mtHgsSmq4bHCaYxgkuu2U0Lhlme08UkbO4TAC85ZZbUFxcjJdeegn5+fmIi4vD+vXr0b9/fwBAfn6+1ZqAdXV1WLBgAXJzc+Hl5YURI0bghx9+wKxZs+T6EZxELV2qKpgFbMFP0ao0bPzlwPjLkb5xDYwNtRCP/ICkil+kx2MaTwG/3o3j24ZAuHIpBgwfBzd3x/YAVNeZJir4Km3BYDu0dvv694Kvfy/0GxSH/DHTUfzJXxFXu096PKl8E/BSL7gB6A1gb/K/ED4sBT4BwfAP7N3ma+7ILAZguk5yRyzLtxwrqMDB3DIApjUUu3tptkmDg60C4FMzhsLDoEPikUAg17RtZF9/ZP51loJa/szYBUxmCjvL9MzDDz+Mhx9+uM3HVq1aZXX/ySefxJNPPumEqsh1qSCcUqcSpt9pujHrAdRcqMK+dW9gYMaHCIGpBWdIw3GplWrPuKVInPVA+y/WQ1W1phZAH6UFwG62ALYnvH8swhduRUN9HTIP7UDx9v8gufBzq33GpD0GpJlu5wqh8PifnxEcFiU9/v5vp/DFbtMEi6lDOr4aSlSQN3w9DKisbcAd7+8AYBqX113XJ/SFt7seh/LK4WnQ46GpMaYHzjSNaRQgKv/DodLrI4dS2lmGnIZ/+PbDY+kqPL18kHzH8wCeh2g0Yv8/Z2DUhR3S44k75wM752PH8GcRe/Hddl/rzrJUiY+7wrqAHTTe1eDmjsGjJ2HQyItQVPAsstI3oS7vAFJyV1nt11c8C6yIBwBs878K7/g+jD9Ombp/R0cGIiWm7RZCC71OwMPTYvC/PzVdN/reCQO6X7dehytHRuDKZldXaUWxvRdKrYucTWEjjYnM1DALWLEneLIHQafDqKc2ovG5Yuweu8TqsfGHX0XgW7HIeCUJmYd2oKG+zi7fs9LcBeztrrTP5o79XRd0OgRH9EfiFQ8i5S//Qmq/+6XHdox43mrfyeXfQX96CwBgXHQQPv+fZJu6WR+eOghTY/sg0NsNX/xPCiYMcsCsXKtzgkLPD+wCJjOlnWWIiBRFbzAg6Yq/oGDkNGR98yrGF62THhvckAF8OR0AkBpxNwZf/SQy077BoItuQK8+nV91o6WyatN1Y3t5d29smsM5qctw7D3/wJ5NIzF4wtUYH9gbJ4Yko+znJUis+BUA4I1aPH/lcNw/sWvL9qy6b1yH68P2nNjmTSIlYgug1rDVyo7sOy6KlC0schDGz/0Qjc8VI7X/bJxFb+QJTYv7puStRvCKOIzd9yyMb4/HkR0buvw9SsyzU3sp7bqxTl7yyODmjsRZ90mTPwaNugiJf/sKiEoBALx12+guhz8Lh07KUEMLYHM8d2kaWwBJoVQ0C5g0RW8wIOW+fwAwXWXk+N6tGPLt1Vb79EYZev94M/ZvHgthwhz0iY6HXm9Arz4RHS5+fK7SdImyzq5R63xK+bBj+v5KWyaxSfMWQIWeu9gFTGaK/TMiB5P9RO5KeCy1bMiYKUgb3HSFoFM3bpSujTuyZhfif70XYSsT0efdUdj/rxvbfR2jUcQJ86XGBvZpfV1bWSklzAgK/2CothZA0jS2ABJ1l1LfhMjpRl4zD3veSQeGXYPEuPFoiM1B2kdPI/nMSqv9xlRuBRYFIE8Iga9YiVNeIxF665sIixqCY2crUFHbAG93PWL6+Mr0k7RHIS2AgqXNQql/eypoAeSVQMiMAVBzlHpSakENs4CJzLx9A5D4t6+l+wY3dyQ/uBTAUuSeOoKz656ER12p6UojACLEQgDA6AtpwIfj8JuQhDmiaV3SpAFBcNMrrHNGaZc9VGq4YgsgqQgDIFG3KaRVhBSt78Bh6LvgOwDAgW3fwHfL84g2nrbaZ5TxEMrNawDendzf2SXaTu7fdaV3AauhBVBkCyCZMABqFv/wiZwtfvI1wORrIBqNqKurgb44A4b3JgM6N/h5GPDGzaNw6XAFXjdWMS2ACu8ZUEULoFLrImdjACSFUvon/ebkflMktRF0Onh4egMG03p//l7u2DP/MrgrdnqrQlq72QJIZDdKPdsQKR9P8NRTzca6Kjf8gS2AtlJDC6Bi/i9Jbgo+45BDMLTYn9ytIqRellmtolHeOjrFFkDbsAWQ1IMBkJRJep9R8klUybWROig90Jgppj62APacQsI8yY4BUKv4x08kP9Usd6SQbkO2APYcu4DJjAGQqLt4IqUeswQaeavolKiQViOlLwStihZAIhMGQFIopX/SJ7IDqUVLJWMAZf+wo6LzgmJrVEiYJ9kxAGqOUk9KKsYTKXWXarqAzeT+XVf68VJDCyB7LsiMAVCz+Mffcwo9wZOKqKRFSzGhQektpmKbN4mUiAGQlEnpn/StyP2mSKqlmt9zhXQbKn0SiBpaAJXyf0myYwAk6i6lvgmReqhlHUCltQAqPVwBPD+Q4jEAag1PSkQKovAWLYlCWo3YAthzignzJDcGQFIoNSyPoZA3RVIvtXQBKy40KPV4qaEFkOctMmEA1Cr+8RMpgMJbtCSW0CBvFU1d5go9XmpoASQyYwAk6jG53xVJtTgGsGsU/8FVBS2ASvm/JNkxAJIyqaFrTKkneFIPNfyeA1BOfQpvMVVFC6BS6yJnYwDUHP7x2x0/SFO3KTzQWEiNRkppAVTq8VJBC6CF3P+XJDsGQKJuU/gJnpRP8YHGQindhgoPzGpoAWQXMJkxAGqW0v/4FX6iJ7IHodkpWMm/65ba5G41Uvy1k1XUAkiaxwBI1F3S+V3pYZqUq9nvjqIDg1JajRTeYqqGFkAuA0NmDIBao+g3GSKNsXoTVvDfpuJaAJV6rFTQAsguYDJjACRlkvuNpivUVCspl1IDAwC2ANpIFS2ARCYMgFqlmtCi5JOokmsjVbAaA6jUcW3NyH3eUPpC0GpoAbSQ+/+SZMcASEQkF7V1AcvdAqj0WdNiu3eIFIcBkBRKBZ9OlfKmSCqmskkgsrcaqWkMoHxVdIjnLTJjANQcpZ6V2qHYEz2RHbAFsGsU3wKohjGACrmuM8mOAZCo25TSKkKqpZoxgEr5XVdTC6BSayQyYQDULLlP5J2Q/Y2GyBlU0gWsmBZAyw2FHis1tAAq5f+SZMcASAqn0JOoFZ5IqZvU0gWsmNp4JZCeU2pd5GwMgFqj2JOSCvFYUo+prAVQ7pZ5aSFoectolxpaAC3k/r8k2TEAkkLx5EQaoLYxgHL/XUrHS6nhSgUtgOwCJjMGQK1Sy6c/pZ5EAShnYDypllq6gJXSAqj0SSBqagEkzWMAJCKSjUq6gJUSZpS+DIwaWgD5wZXMGABJmdRwcmJXCvWUGn7PAbYA2koNLYA8b5EZA6DmKPSk1C611UvUBaoZA2gh9xhAtgAS2QsDIFFPyd4qQqolqKQLmC2AtlFDCyC7gMmMAVCzlP7Hr/T6AOWe4EmdlPz7pJBuQ7YA9pxS6yKnYwAkZePJilye0hc3bkb2ViOFHytVtABayP1/SXJjANQapZ+T1ISDqckeLOMAlfxhRym/64LCu4DV0ALILmAyYwAkZeLJibRC8d2agGJCg9IXghbbvUOkOC4VAP/9738jOjoanp6eSExMxG+//dbh/lu3bkViYiI8PT0xcOBArFixwkmVKoDcJ3KbqeAkqppjScqk9FYtKKg2pR8rpdbVjFJac0l2LhMAP//8c8ybNw/PPvss0tPTMWnSJMycORPZ2dlt7p+ZmYlZs2Zh0qRJSE9PxzPPPINHH30Ua9eudXLlpF4qONmT8gkKH9cGgJNAbNQymCoyqCqxJpKDywTApUuX4oEHHsCDDz6IYcOGYdmyZYiMjMTy5cvb3H/FihWIiorCsmXLMGzYMDz44IO4//77sWTJEidXTm3jp1PSCKV3awIKWgbGTJHBCmj1f6jYOqGc/0uSjUHuAuyhrq4Oe/bswdNPP221ffr06di+fXubz0lNTcX06dOttl1++eVYuXIl6uvr4ebm1uo5tbW1qK2tle6Xl5fbofo2HP4WOPKdY167vsoxr+soR9cDpTlyV9G2sjPmGzyRUk+Yf39+fBpw95G3lPYUHDDfUEgL4PENQEWBvLW0pbrE+v5Xf4Xsx6ylC+fNNxRWFzmdSwTAoqIiNDY2IjQ01Gp7aGgoCgraPkkUFBS0uX9DQwOKiooQHh7e6jmLFy/Giy++aL/C21N4GDjwheNeX9ABHv6Oe3178A4y/XvuiOlLySy1EnWHd2+g/Axw7Ae5K+mcd29lfP+iY6YvpTvwpdwVtI/nLc1ziQBoIbRo0hZFsdW2zvZva7vFwoULMX/+fOl+eXk5IiMju1tu+2IuBjz87P+6FiHDAd8+jnt9exg/G/ANAeoU3mKpMwBDr5S7ClKz2z8HMrfKXUXnfEOBQZfKW8PYvwBeQUBdpbx1dCR4iOn8nbtH7ko6IMj/f0myc4kAGBwcDL1e36q1r7CwsFUrn0VYWFib+xsMBvTu3fanXA8PD3h4eNin6I5EjjN9aZmnP5B4r9xVEDleWJzpizrn4Qsk3iN3FbaJSpa7AqIOucQkEHd3dyQmJmLTpk1W2zdt2oQJEya0+ZyUlJRW+2/cuBFJSUltjv8jIiIichUuEQABYP78+Xj//ffxwQcf4MiRI3j88ceRnZ2N2bNnAzB13959993S/rNnz0ZWVhbmz5+PI0eO4IMPPsDKlSuxYMECuX4EIiIiIqdwiS5gALjllltQXFyMl156Cfn5+YiLi8P69evRv39/AEB+fr7VmoDR0dFYv349Hn/8cbz99tuIiIjAm2++iRtuuEGuH4GIiIjIKQRRVPJCRcpWXl6OgIAAlJWVwd9f4bNqiYiICADfvwEX6gImIiIiItswABIRERFpDAMgERERkcYwABIRERFpDAMgERERkcYwABIRERFpDAMgERERkcYwABIRERFpDAMgERERkca4zKXg5GC5iEp5ebnMlRAREZGtLO/bWr4YGgNgD1RUVAAAIiMjZa6EiIiIuqqiogIBAQFylyELXgu4B4xGI/Ly8uDn5wdBEOQuR1bl5eWIjIxETk6OZq+r2BU8XrbjseoaHq+u4fGynSsdK1EUUVFRgYiICOh02hwNxxbAHtDpdOjXr5/cZSiKv7+/6k8MzsTjZTseq67h8eoaHi/bucqx0mrLn4U2Yy8RERGRhjEAEhEREWkMAyDZhYeHB1544QV4eHjIXYoq8HjZjseqa3i8uobHy3Y8Vq6Fk0CIiIiINIYtgEREREQawwBIREREpDEMgEREREQawwBIREREpDEMgNSuRYsWQRAEq6+wsDDpcVEUsWjRIkRERMDLywtTp07FoUOHrF6jtrYWjzzyCIKDg+Hj44Orr74aZ86ccfaP4hS5ubm488470bt3b3h7e2P06NHYs2eP9DiPV5MBAwa0+t0SBAFz5swBwGPVUkNDA5577jlER0fDy8sLAwcOxEsvvQSj0Sjtw2PWpKKiAvPmzUP//v3h5eWFCRMmYNeuXdLjWj5W27Ztw1VXXYWIiAgIgoCvv/7a6nF7HZvz58/jrrvuQkBAAAICAnDXXXehtLTUwT8ddYlI1I4XXnhBHDFihJifny99FRYWSo+//vrrop+fn7h27VrxwIED4i233CKGh4eL5eXl0j6zZ88W+/btK27atEncu3evOG3aNHHUqFFiQ0ODHD+Sw5SUlIj9+/cX7733XnHHjh1iZmam+PPPP4snTpyQ9uHxalJYWGj1e7Vp0yYRgLh582ZRFHmsWnrllVfE3r17i99//72YmZkpfvnll6Kvr6+4bNkyaR8esyY333yzOHz4cHHr1q1iRkaG+MILL4j+/v7imTNnRFHU9rFav369+Oyzz4pr164VAYhfffWV1eP2OjYzZswQ4+LixO3bt4vbt28X4+LixCuvvNJZPybZgAGQ2vXCCy+Io0aNavMxo9EohoWFia+//rq0raamRgwICBBXrFghiqIolpaWim5ubuJnn30m7ZObmyvqdDrxp59+cmjtzvbUU0+JEydObPdxHq+OPfbYY2JMTIxoNBp5rNpwxRVXiPfff7/Vtuuvv1688847RVHk71dz1dXVol6vF7///nur7aNGjRKfffZZHqtmWgZAex2bw4cPiwDEtLQ0aZ/U1FQRgHj06FEH/1RkK3YBU4cyMjIQERGB6Oho3HrrrTh16hQAIDMzEwUFBZg+fbq0r4eHB6ZMmYLt27cDAPbs2YP6+nqrfSIiIhAXFyft4yq+/fZbJCUl4aabbkJISAgSEhLw3nvvSY/zeLWvrq4Oa9aswf333w9BEHis2jBx4kT88ssvOH78OADgzz//xO+//45Zs2YB4O9Xcw0NDWhsbISnp6fVdi8vL/z+++88Vh2w17FJTU1FQEAAxo8fL+2TnJyMgIAAlz5+asMASO0aP348Vq9ejQ0bNuC9995DQUEBJkyYgOLiYhQUFAAAQkNDrZ4TGhoqPVZQUAB3d3f06tWr3X1cxalTp7B8+XIMHjwYGzZswOzZs/Hoo49i9erVAMDj1YGvv/4apaWluPfeewHwWLXlqaeewm233YahQ4fCzc0NCQkJmDdvHm677TYAPGbN+fn5ISUlBS+//DLy8vLQ2NiINWvWYMeOHcjPz+ex6oC9jk1BQQFCQkJavX5ISIhLHz+1MchdACnXzJkzpdvx8fFISUlBTEwMPvroIyQnJwMABEGweo4oiq22tWTLPmpjNBqRlJSE1157DQCQkJCAQ4cOYfny5bj77rul/Xi8Wlu5ciVmzpyJiIgIq+08Vk0+//xzrFmzBp988glGjBiBffv2Yd68eYiIiMA999wj7cdjZvKf//wH999/P/r27Qu9Xo8xY8bg9ttvx969e6V9eKzaZ49j09b+Wjl+asEWQLKZj48P4uPjkZGRIc0GbvlprrCwUPr0GBYWhrq6Opw/f77dfVxFeHg4hg8fbrVt2LBhyM7OBgAer3ZkZWXh559/xoMPPiht47Fq7YknnsDTTz+NW2+9FfHx8bjrrrvw+OOPY/HixQB4zFqKiYnB1q1bUVlZiZycHOzcuRP19fWIjo7mseqAvY5NWFgYzp492+r1z50759LHT20YAMlmtbW1OHLkCMLDw6UT6aZNm6TH6+rqsHXrVkyYMAEAkJiYCDc3N6t98vPzcfDgQWkfV3HRRRfh2LFjVtuOHz+O/v37AwCPVzs+/PBDhISE4IorrpC28Vi1Vl1dDZ3O+nSt1+ulZWB4zNrm4+OD8PBwnD9/Hhs2bMA111zDY9UBex2blJQUlJWVYefOndI+O3bsQFlZmUsfP9WRZeoJqcLf/vY3ccuWLeKpU6fEtLQ08corrxT9/PzE06dPi6JoWi4gICBAXLdunXjgwAHxtttua3O5gH79+ok///yzuHfvXvHiiy92iaUUWtq5c6doMBjEV199VczIyBA//vhj0dvbW1yzZo20D4+XtcbGRjEqKkp86qmnWj3GY2XtnnvuEfv27SstA7Nu3ToxODhYfPLJJ6V9eMya/PTTT+KPP/4onjp1Sty4caM4atQocdy4cWJdXZ0oito+VhUVFWJ6erqYnp4uAhCXLl0qpqeni1lZWaIo2u/YzJgxQxw5cqSYmpoqpqamivHx8VwGRmEYAKldlvWf3NzcxIiICPH6668XDx06JD1uNBrFF154QQwLCxM9PDzEyZMniwcOHLB6jQsXLohz584Vg4KCRC8vL/HKK68Us7Oznf2jOMV3330nxsXFiR4eHuLQoUPFd9991+pxHi9rGzZsEAGIx44da/UYj5W18vJy8bHHHhOjoqJET09PceDAgeKzzz4r1tbWSvvwmDX5/PPPxYEDB4ru7u5iWFiYOGfOHLG0tFR6XMvHavPmzSKAVl/33HOPKIr2OzbFxcXiHXfcIfr5+Yl+fn7iHXfcIZ4/f95JPyXZQhBFUZSxAZKIiIiInIxjAImIiIg0hgGQiIiISGMYAImIiIg0hgGQiIiISGMYAImIiIg0hgGQiIiISGMYAImIiIg0hgGQiIiISGMYAImIiIg0hgGQiIiISGMYAImIiIg0hgGQiIiISGMYAImIiIg0hgGQiIiISGMYAImIiIg0hgGQiIiISGMYAImIiIg0hgGQiIiISGMYAImIiIg0hgGQiIiISGMYAImIiIg0hgGQiIiISGMYAImIiIg0hgGQiIiISGMYAImIiIg0hgGQiIiISGMYAImIiIg0hgGQiIiISGMYAImIiIg0hgGQiIiISGMYAImIiIg05v8DWNm7yl9vCtIAAAAASUVORK5CYII=", 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\n", " " ], @@ -916,7 +954,8 @@ "mask = pyTEMlib.eels_tools.core_loss_tools.get_mask(main_dataset.energy_loss.values, main_dataset.metadata['core_loss'])\n", "plt.figure()\n", "plt.plot(main_dataset.energy_loss.values, main_dataset)\n", - "plt.plot(main_dataset.energy_loss.values, main_dataset*mask)\n" + "plt.plot(main_dataset.energy_loss\n", + " .values, main_dataset*mask)\n" ] }, { @@ -928,7 +967,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -937,33 +976,34 @@ "text": [ "\n", "Relative composition: \n", - "O: 23.378% Co: 21.626% Ce: 5.155% Cu: 49.841% \n", - "931.1\n" + "B: 46.559% N: 53.441% \n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\gduscher\\OneDrive - University of Tennessee\\GitHub\\pyTEMlib\\notebooks\\Spectroscopy\\../..\\pyTEMlib\\eels_tools\\core_loss_tools.py:570: RuntimeWarning: Number of calls to function has reached maxfev = 10000.\n", - " [p, _] = scipy.optimize.leastsq(residuals, pin, args=(energy_scale, blurred), maxfev=10000)\n" - ] + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2f9b004ad91646088a5bc17657378811", + "model_id": "0d592c8eec3b4a14bf0c393385b8fcb3", "version_major": 2, "version_minor": 0 }, - "image/png": 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"text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", - " \n", + " \n", "
\n", " " ], @@ -981,7 +1021,7 @@ "plt.plot(main_dataset.energy_loss.values, main_dataset.metadata['core_loss']['model']['spectrum'])\n", "plt.plot(main_dataset.energy_loss.values, main_dataset*mask)\n", "plt.plot(main_dataset.energy_loss.values, main_dataset)\n", - "print(main_dataset.metadata['core_loss']['edges']['3']['onset'])\n" + "# print(main_dataset.metadata['core_loss']['edges']['3']['onset'])\n" ] }, { @@ -993,18 +1033,16 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Relative chemical composition of EELS HL Spectrum\n", - "O: 23.378 % areal_density: 36.18atoms/nm^2\n", - "Co: 21.626 % areal_density: 33.47atoms/nm^2\n", - "Ce: 5.155 % areal_density: 7.98atoms/nm^2\n", - "Cu: 49.841 % areal_density: 77.13atoms/nm^2\n" + "Relative chemical composition of 2EELS Acquire (high-loss)\n", + "B: 46.559 % areal_density: 15.08atoms/nm^2\n", + "N: 53.441 % areal_density: 17.31atoms/nm^2\n" ] } ], @@ -1029,30 +1067,56 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 28, "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "'flux_ppm'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[125]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m B_areal_density = edges[\u001b[33m'\u001b[39m\u001b[33m0\u001b[39m\u001b[33m'\u001b[39m][\u001b[33m'\u001b[39m\u001b[33mareal_density\u001b[39m\u001b[33m'\u001b[39m] /\u001b[43mmain_dataset\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mexperiment\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mflux_ppm\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m*\u001b[32m1e-6\u001b[39m\n\u001b[32m 2\u001b[39m N_areal_density = edges[\u001b[33m'\u001b[39m\u001b[33m1\u001b[39m\u001b[33m'\u001b[39m][\u001b[33m'\u001b[39m\u001b[33mareal_density\u001b[39m\u001b[33m'\u001b[39m] /main_dataset.metadata[\u001b[33m'\u001b[39m\u001b[33mexperiment\u001b[39m\u001b[33m'\u001b[39m][\u001b[33m'\u001b[39m\u001b[33mflux_ppm\u001b[39m\u001b[33m'\u001b[39m]*\u001b[32m1e-6\u001b[39m\n\u001b[32m 4\u001b[39m \u001b[38;5;66;03m#the B atom areal density of a single layer of h-BN (18.2 nm−2) \u001b[39;00m\n", - "\u001b[31mKeyError\u001b[39m: 'flux_ppm'" + "name": "stdout", + "output_type": "stream", + "text": [ + " B areal density is 15 atoms per square nm, which equates 0.8 atomic layers\n", + " N areal density is 17 atoms per square nm, which equates 1.0 atomic layers\n" ] } ], "source": [ - "B_areal_density = edges['0']['areal_density'] /main_dataset.metadata['experiment']['flux_ppm']*1e-6\n", - "N_areal_density = edges['1']['areal_density'] /main_dataset.metadata['experiment']['flux_ppm']*1e-6\n", + "B_areal_density = edges['0']['areal_density'] *main_dataset.metadata['experiment']['intentsity_scale_ppm']*1e-6\n", + "N_areal_density = edges['1']['areal_density'] *main_dataset.metadata['experiment']['intentsity_scale_ppm']*1e-6\n", "\n", "#the B atom areal density of a single layer of h-BN (18.2 nm−2) \n", "print(f\" B areal density is {B_areal_density:.0f} atoms per square nm, which equates {abs(B_areal_density)/18.2:.1f} atomic layers\")\n", "print(f\" N areal density is {N_areal_density:.0f} atoms per square nm, which equates {abs(N_areal_density)/18.2:.1f} atomic layers\")" ] }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'single_exposure_time': 3.0,\n", + " 'exposure_time': 3.01000999977623,\n", + " 'number_of_frames': 21,\n", + " 'convergence_angle': 30.0,\n", + " 'collection_angle': 33.0,\n", + " 'microscope': 'Unknown',\n", + " 'acceleration_voltage': 60000.0,\n", + " 'flux': np.float32(5.5095315e+08),\n", + " 'intentsity_scale_ppm': np.float64(7.594741720534484e-06),\n", + " 'incident_beam_current_counts': np.float32(3.471005e+10)}" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "main_dataset.metadata['experiment']" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1068,24 +1132,24 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\gduscher\\OneDrive - University of Tennessee\\GitHub\\pyTEMlib\\notebooks\\Spectroscopy\\../..\\pyTEMlib\\eels_tools\\peak_fit_tools.py:186: RuntimeWarning: Number of calls to function has reached maxfev = 10000.\n", + "C:\\Users\\gduscher\\OneDrive - University of Tennessee\\GitHub\\pyTEMlib\\pyTEMlib\\eels_tools\\peak_fit_tools.py:186: RuntimeWarning: Number of calls to function has reached maxfev = 10000.\n", " [p, _] = scipy.optimize.leastsq(residuals3, pin, args=(x, y),maxfev = 10000)\n" ] }, { "data": { "text/plain": [ - "np.int64(7)" + "np.int64(4)" ] }, - "execution_count": 126, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1103,7 +1167,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1111,36 +1175,33 @@ "output_type": "stream", "text": [ "dask.array\n", - "Fitting 16 peaks to spectrum\n", - "[ 9.01279572e+02 8.07291950e+05 6.14106507e+00 8.84152016e+02\n", - " 8.78259563e+05 3.68157886e+00 9.54894907e+02 -1.07701319e+05\n", - " 1.98484936e+01 7.81147724e+02 6.29889825e+05 3.38830932e+00\n", - " 5.80952894e+05 1.79570088e+10 1.15389939e+05 8.98872586e+02\n", - " -3.38931671e+05 4.48465347e+00 9.32704210e+02 -3.13027019e+05\n", - " 3.12508692e+00 7.96395947e+02 1.52971646e+05 3.02503573e+00\n", - " 5.49947019e+02 -3.15487443e+04 1.06085669e+01 9.76956664e+02\n", - " -3.10992738e+04 1.19807888e+01 5.39262044e+02 7.35869329e+04\n", - " 3.71928722e+00 5.32230263e+02 5.38938719e+04 3.89868577e+00\n", - " 4.92523209e+02 -7.01713503e+04 4.77977540e+00 4.88984535e+02\n", - " 3.63842077e+04 1.02348949e+01 9.38003644e+02 -5.69692017e+04\n", - " 2.70009052e+00 9.41584893e+02 -3.39847362e+04 2.06565725e+00]\n" + "Fitting 11 peaks to spectrum\n", + "[ 4.88670657e+06 4.92704091e+10 1.52038816e+06 2.04375734e+02\n", + " 1.52966080e+04 1.81937510e+01 1.93645429e+02 -2.65104409e+04\n", + " 7.45293028e+00 1.96767781e+02 5.21172346e+04 3.03737188e+00\n", + " 2.71837354e+03 3.38829112e+06 1.05783172e+03 4.10834165e+02\n", + " 5.69513376e+03 1.06373380e+01 2.97268051e+02 1.36981888e+03\n", + " 2.94057856e+01 4.00292920e+02 3.45914633e+03 2.29184494e+00\n", + " 1.58621255e+02 -3.53262113e+03 1.08165776e+00 4.88325046e+02\n", + " -1.22384753e+03 1.77681613e+00 5.03200970e+02 1.91644601e+03\n", + " 4.82039527e-01]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9eee841dc62e4aa3a19f09fd4b4f5b73", + "model_id": "1df07a8cbd7544b1b69423dafc85bd69", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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"text/html": [ "\n", "
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\n", " Figure\n", "
\n", - " \n", + " \n", "
\n", " " ], @@ -1160,7 +1221,6 @@ "numer_of_peaks = pyTEMlib.eels_tools.peak_fit_tools.find_relevant_peaks(main_dataset, maximum_number_of_peaks )\n", "print(f\"Fitting {numer_of_peaks} peaks to spectrum\")\n", "pyTEMlib.eels_tools.peak_fit_tools.fit_peaks(main_dataset)\n", - "plt.close('all')\n", "plt.figure()\n", "plt.plot(main_dataset.energy_loss.values, main_dataset, label='spectrum')\n", "plt.plot(main_dataset.energy_loss.values, main_dataset.metadata['peak_fit']['start_model'], label='composition_model')\n", @@ -1186,54 +1246,62 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 32, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Co-L3/L2, sum: 1.99%, ratio: 4.61\n", - "Ce-M5/M4, sum: 12.84%, ratio: 0.00\n", - "Cu-L3/L2, sum: 0.00%, ratio: 0.00\n" - ] - } - ], + "outputs": [], "source": [ "pyTEMlib.eels_tools.find_associated_edges(main_dataset)\n", "out = pyTEMlib.eels_tools.find_white_lines(main_dataset)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "The ioniation edges in a ``Core-Loss spectrum`` reveal the chemical composition and the bonding characteristics of the atoms. The signal is obscured by a stron background and thin samples are required for a good signal background ratio. \n", + "\n", + "The cross sections are calculated from a database.\n", + "\n", + "The energy-loss near-edge structure (ELNES) is often only compared through fingerprinting with the literature.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Appendinx" + ] + }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0 O\n", - "1 Co\n", - "2 Ce\n", - "3 Cu\n" + "0 B\n", + "1 N\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ed2501374a424341aa101ba24243a8cd", + "model_id": "63f9e882f82f406dae7801c8b578dae4", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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", 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\n", " " ], @@ -1391,2782 +1459,6 @@ "regions.set_regions('fit region',bgd_start, energy_scale[-1]-bgd_start)" ] }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-0.23899302] sidpy.Dataset of type SPECTRUM with:\n", - " dask.array\n", - " data contains: intensity (counts)\n", - " and Dimensions: \n", - "energy_loss: energy-loss (eV) of size (2048,)\n", - " with metadata: ['experiment']\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8d8734dfe6e644a59a242674b8af641e", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", 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\n", - " \n", - "
\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "low_loss = eels_tools.align_zero_loss(infoWidget.selected_dataset)\n", - "zero_loss = eels_tools.get_resolution_functions(low_loss, -.5, .5)\n", - "plt.close('all')\n", - "plt.figure()\n", - "plt.plot(low_loss.energy_loss, zero_loss, label='resolution funcion')\n", - "plt.plot(low_loss.energy_loss, low_loss, label = 'spectrum')\n", - "plt.plot(low_loss.energy_loss, low_loss-zero_loss, label = 'difference')\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'experiment': {'acceleration_voltage': 200000.0,\n", - " 'beam_current': 0,\n", - " 'collection_angle': 50.0,\n", - " 'convergence_angle': 30.0,\n", - " 'count_conversion': 1,\n", - " 'exposure_time': 10.0,\n", - " 'flux_ppm': 0.0,\n", - " 'number_of_frames': 10000,\n", - " 'single_exposure_time': 0.001},\n", - " 'peak_fit': {'edge_model': array([0., 0., 0., ..., 0., 0., 0.]),\n", - " 'fit_end': 30.0,\n", - " 'fit_start': 1.0,\n", - " 'peak_model': array([0., 0., 0., ..., 0., 0., 0.]),\n", - " 'peak_out_list': array([[1.14401631e+01, 2.92439760e+06, 2.45501896e+01],\n", - " [7.97104419e-01, 2.89095444e+07, 1.71055819e+00]]),\n", - " 'peaks': {'0': {'amplitude': 20986284.461096264,\n", - " 'associated_edge': '',\n", - " 'position': 0.8433632583641564,\n", - " 'type': 'Gauss',\n", - " 'width': 1.6347046209768021,\n", - " 'asymmetry': 0.0},\n", - " '1': {'amplitude': 60297550.94024267,\n", - " 'associated_edge': '',\n", - " 'asymmetry': 0.0,\n", - " 'position': 0.3943234078051374,\n", - " 'type': 'Gauss',\n", - " 'width': 0.8462042318305517},\n", - " '2': {'amplitude': 5149807.779796465,\n", - " 'associated_edge': '',\n", - " 'position': 4.681561552802686,\n", - " 'type': 'Gauss',\n", - " 'width': 3.805046085043105,\n", - " 'asymmetry': 0.0},\n", - " '3': {'amplitude': 1768787.351821872,\n", - " 'associated_edge': '',\n", - " 'position': 15.717359457313327,\n", - " 'type': 'Gauss',\n", - " 'width': 5.034726824660322,\n", - " 'asymmetry': 0.0},\n", - " '4': {'amplitude': 1860806.817558061,\n", - " 'associated_edge': '',\n", - " 'asymmetry': 0.0,\n", - " 'position': 18.651735459709663,\n", - " 'type': 'Gauss',\n", - " 'width': 9.474450049577818},\n", - " '5': {'amplitude': 871061.5868498409,\n", - " 'associated_edge': '',\n", - " 'position': 27.65565717924107,\n", - " 'type': 'Gauss',\n", - " 'width': 11.173122920182822,\n", - " 'asymmetry': 0.0},\n", - " '6': {'amplitude': 747444.1958128264,\n", - " 'associated_edge': '',\n", - " 'asymmetry': 0.0,\n", - " 'position': 15.508181435473912,\n", - " 'type': 'Gauss',\n", - " 'width': 1.0964231231036516},\n", - " '7': {'amplitude': 582735.7941706154,\n", - " 'associated_edge': '',\n", - " 'position': 8.793443515617403,\n", - " 'type': 'Gauss',\n", - " 'width': 3.2503281905987924,\n", - " 'asymmetry': 0.0},\n", - " '8': {'amplitude': 1490223.5949046034,\n", - " 'associated_edge': '',\n", - " 'asymmetry': 0.0,\n", - " 'position': 5.072798469661356,\n", - " 'type': 'Gauss',\n", - " 'width': 0.8350422095351262},\n", - " '9': {'amplitude': 4264163.850603235,\n", - " 'associated_edge': '',\n", - " 'asymmetry': 0.0,\n", - " 'position': 2.27260716852653,\n", - " 'type': 'Gauss',\n", - " 'width': 1.7783602300920873}}},\n", - " 'zero_loss': {'endFitEnergy': 0.5,\n", - " 'fit_parameter': array([ 1.55362639e-01, 1.46387345e+05, 4.26253898e-01, -7.36465028e-02,\n", - " 2.54673199e+05, 2.91093028e-01]),\n", - " 'original_low_loss': 'EELS_0290_new',\n", - " 'shifted': array([-0.2791726]),\n", - " 'startFitEnergy': -0.5}}" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "infoWidget.dataset.metadata" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## peakfit" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'peak_dialog' from 'pyTEMlib' (C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyTEMlib\\__init__.py)", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpyTEMlib\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m peak_dialog\n\u001b[32m 3\u001b[39m peakFitWidget = peak_dialog.PeakFitWidget({\u001b[33m'\u001b[39m\u001b[33mlow_loss\u001b[39m\u001b[33m'\u001b[39m:infoWidget.dataset})\n", - "\u001b[31mImportError\u001b[39m: cannot import name 'peak_dialog' from 'pyTEMlib' (C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pyTEMlib\\__init__.py)" - ] - } - ], - "source": [ - "from pyTEMlib import peak_dialog\n", - " \n", - "peakFitWidget = peak_dialog.PeakFitWidget({'low_loss':infoWidget.dataset})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###### from pyTEMlib import peak_dialog" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "peakFitWidget = peak_dialog.PeakFitWidget({'core_loss': spectrum})" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'experiment': {'single_exposure_time': 5.0,\n", - " 'exposure_time': 100.0,\n", - " 'number_of_frames': 20,\n", - " 'collection_angle': 50.0,\n", - " 'convergence_angle': 5.4,\n", - " 'acceleration_voltage': 60000.0,\n", - " 'flux_ppm': 1470027.5390625,\n", - " 'count_conversion': 1,\n", - " 'beam_current': 0}}" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fi.metadata" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e145189778264685b2b323832802cce2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "AppLayout(children=(GridspecLayout(children=(Button(description='Fit Area', layout=Layout(grid_area='widget001…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "datasets = {'core_loss': spectrum, 'dataset': core_loss}\n", - "low_loss.metadata = infoWidget.selected_dataset.metadata\n", - "peakFitWidget = peak_dialog.PeakFitWidget(datasets)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\py11\\Lib\\site-packages\\pyNSID\\io\\hdf_utils.py:381: FutureWarning: validate_h5_dimension may be removed in a future version\n", - " warn('validate_h5_dimension may be removed in a future version',\n", - "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\py11\\Lib\\site-packages\\pyNSID\\io\\hdf_utils.py:381: FutureWarning: validate_h5_dimension may be removed in a future version\n", - " warn('validate_h5_dimension may be removed in a future version',\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "datasets = {'core_loss': spectrum, 'dataset': core_loss}\n", - "pyTEMlib.file_tools.save_dataset(datasets)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function gauss in module pyTEMlib.eels_tools:\n", - "\n", - "gauss(x, p)\n", - " Gaussian Function\n", - " \n", - " p[0]==mean, p[1]= amplitude p[2]==fwhm\n", - " area = np.sqrt(2* np.pi)* p[1] * np.abs(p[2] / 2.3548)\n", - " FWHM = 2 * np.sqrt(2 np.log(2)) * sigma = 2.3548 * sigma\n", - " sigma = FWHM/3548\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a9a368e4f6894cfca5c2a8583574166a", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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TLMF169YpGAyGlCktLVV5ebldZsWKFfJ6vcrMzLTLrF69OmRpmBUrVigQCOi8884L+9cOoJnms4B7NfuPVP8vWMcjBEAAiKSwBsCDBw+qpKREJSUlkqStW7eqpKRE27dv17Fjx/SNb3xDb7/9tp555hnV19eroqJCFRUVdgjz+XyaOnWq5syZo9dee00bN27UTTfdpGHDhunaa6+VJA0ZMkQTJkxQXl6eioqKVFRUpLy8PGVnZ2vw4MGSpKysLA0dOlS5ubnauHGjXnvtNc2dO1d5eXl2q11OTo68Xq9uueUWlZaWatmyZVqwYIFmz57dbhcwgDCxt4KLDW0BHHC5daw9KNUddb5eAOASceF8s7fffltf/vKX7fuzZ8+WJN18882aP3++XnrpJUnSF77whZDXvfHGG7rqqqskSQ8++KDi4uI0ZcoUHTlyRNdcc42WLl2q2NhYu/wzzzyjmTNn2rOFJ0+eHLL2YGxsrF5++WVNmzZNY8aMUWJionJycrRo0SK7jM/n08qVK3XHHXdo+PDhSklJ0ezZs+06A4gg06wFsHkAPHu4NS7Q1FutgPGBtl8PADglHmPY+qKrqqur5fP5FAwGGQ8IdMafb5E2L5Ouu1/yD5N+P8F6fPrb0u+vkw7tkW4vtJ4DgDDj73eYWwABoEPsWcAe6ezLpIu/LZ15jpQ6SOrdzwqATAQBgIghAAJwXshWcHHSDY83Pdf7+OSww3udrxcAuERUdwIB4FKNI09iYls/17uvdSQAAkDEEAABOK/5VnAt9TrTOh4NOlYdAHAbAiAA5zWfBdxS484gNey1DQCRQgAE4LzmW8G11LgwdM0B5+oDAC5DAATgvIYTtQAeXxeQAAgAEUMABOC85juBtNTYBXyULmAAiBQCIADnNV8HsCVaAAEg4giAAJx3olnAdgCkBRAAIoUACMB5J5wE4rOOBEAAiBgCIADndagFkC5gAIgUAiAA53VkFvDR6qYdQwAAYUUABOC8jswCNvVS3RHn6gQALkIABOC8E+0EkpDU9DjdwAAQEQRAAM470RhAj4eZwAAQYQRAAM5rHNvXVgCU2A8YACKMAAjAeSeaBCKFTgQBAIQdARCA8040CUSiBRAAIowACMB5JxoDKEneM6xjzUFn6gMALkMABOC8E80Clpq6gGsJgAAQCQRAAM470VZwkpTQ2ALIMjAAEAkEQADOazhZF3DjGEACIABEAgEQgPPsSSAnGQNIFzAARAQBEIDzTjoJpHEhaFoAASASCIAAnHeySSAJzAIGgEgiAAJw3skmgbAVHABEFAEQgPM62gXMGEAAiAgCIADnnWwrOLqAASCiCIAAnGeMdWx3KzgmgQBAJBEAATjPngTiaft5loEBgIgiAAJw3snGACY0GwPYuGg0ACBsCIAAnNfRWcASrYAAEAEEQADOO9kkkDivFBNnnRMAASDswhoAV69erUmTJikQCMjj8ejFF18Med4Yo/nz5ysQCCgxMVFXXXWVNm/eHFKmpqZGM2bMUGpqqpKSkjR58mTt3LkzpExVVZVyc3Pl8/nk8/mUm5ur/fv3h5TZvn27Jk2apKSkJKWmpmrmzJmqra0NKbNp0yaNHTtWiYmJOuuss3TPPffINA5OBxA59lZw7bQAejxMBAGACAprADx06JAuueQSLVmypM3n77vvPi1evFhLlizRhg0b5Pf7NW7cOB040PQLftasWVq2bJny8/NVWFiogwcPKjs7W/X19XaZnJwclZSUqKCgQAUFBSopKVFubq79fH19vSZOnKhDhw6psLBQ+fn5ev755zVnzhy7THV1tcaNG6dAIKANGzbo4Ycf1qJFi7R48eJwfksAtOVkYwClpnGALAUDAOFnIkSSWbZsmX2/oaHB+P1+c++999qPHT161Ph8PvPYY48ZY4zZv3+/iY+PN/n5+XaZsrIyExMTYwoKCowxxmzZssVIMkVFRXaZtWvXGknm/fffN8YYs3z5chMTE2PKysrsMs8++6zxer0mGAwaY4x55JFHjM/nM0ePHrXLLFy40AQCAdPQ0NChrzEYDBpJ9nsC6ICGBmPu7mPdDu5pv9xvR1llPn7duboBcAX+fhvj2BjArVu3qqKiQllZWfZjXq9XY8eO1Zo1ayRJxcXFqqurCykTCASUkZFhl1m7dq18Pp9GjBhhlxk5cqR8Pl9ImYyMDAUCAbvM+PHjVVNTo+LiYrvM2LFj5fV6Q8rs2rVLn376aZtfQ01Njaqrq0NuADrJNJvVe6IWQJaCAYCIcSwAVlRUSJLS09NDHk9PT7efq6ioUEJCglJSUk5YJi0trdX7p6WlhZRpeZ2UlBQlJCScsEzj/cYyLS1cuNAed+jz+TRgwICTf+EAQjU0Dec4cRdw424gjAEEgHBzfBawp8XCr8aYVo+11LJMW+XDUcYcnwDSXn3mzZunYDBo33bs2HHCegNoQ4dbABkDCACR4lgA9Pv9klq3rlVWVtotb36/X7W1taqqqjphmd27d7d6/z179oSUaXmdqqoq1dXVnbBMZWWlpNatlI28Xq/69OkTcgPQSc0DYHuzgKVmXcC0AAJAuDkWAAcOHCi/36+VK1faj9XW1mrVqlUaPXq0JCkzM1Px8fEhZcrLy1VaWmqXGTVqlILBoNavX2+XWbdunYLBYEiZ0tJSlZeX22VWrFghr9erzMxMu8zq1atDloZZsWKFAoGAzjvvvPB/AwBYTAe7gL3H/4NFFzAAhF1YA+DBgwdVUlKikpISSdbEj5KSEm3fvl0ej0ezZs3SggULtGzZMpWWluqWW25R7969lZOTI0ny+XyaOnWq5syZo9dee00bN27UTTfdpGHDhunaa6+VJA0ZMkQTJkxQXl6eioqKVFRUpLy8PGVnZ2vw4MGSpKysLA0dOlS5ubnauHGjXnvtNc2dO1d5eXl2q11OTo68Xq9uueUWlZaWatmyZVqwYIFmz5590i5pAKcgpAv4BC2A9hhAuoABIOzCOaX4jTfeMJJa3W6++WZjjLUUzN133238fr/xer3mS1/6ktm0aVPIexw5csRMnz7d9O3b1yQmJprs7Gyzffv2kDJ79+41N954o0lOTjbJycnmxhtvNFVVVSFltm3bZiZOnGgSExNN3759zfTp00OWfDHGmHfffddceeWVxuv1Gr/fb+bPn9/hJWCMYRo50CWH9zUtA3Osrv1yhb+2yjx/q3N1A+AK/P02xmMMW190VXV1tXw+n4LBIOMBgY46tFe6//PW+c+qpJh2OiLe/l/pbz+ULsyWvv2Mc/UDcNrj7zd7AQNwWsgkkI7sBMJ6mwAQbgRAAM5qnARyogkgEsvAAEAEEQABOKsj+wBL7AQCABFEAATgLDsAnmAGsNSsBZBlYAAg3OKiXQEALtPQwS7gri4DY4y08Wlpy4tSYl9p1DQpcGmnqwkApzMCIABnNbYAnmgXEKmpBbD2gNTQcOIJI80VzJPWPdp0f/My6Zu/l4ZM6nxdAeA0RRcwAGd1eAxgctN53aGOvff7y4+HP4905Rxp8FekhjrphVul3Zu7VF0AOB0RAAE4yw6AJ9lxJ65X0zjBjowDbKiXXvu5dT56hnTNz6RvPS2df7VUd1h6aUZT9zMAuBwBEICzOjoJxOPp3FIwHyyX9rwv9fJZrX+S1c18/aPWmoJlxdI7f+h6vQHgNEIABOCsjk4CkULHAZ7MO3+0jpm3SIlnNj2e7Je+/J/W+apfSXVHO1pTS/0xafcW6fC+zr0OALoxAiAAZ3V0DKDU8aVgDlZKH79qnV+a2/r5y6ZKfc6WDpRLxUs7XFVVvic9MlJ6dJT0wIXW9nQAcBogAAJwVkdnAUsdXwrmwwJrh5HApVLqoNbPx3mlL821zt96QKo9fPJrH6yUnv6GtPcj6359jbU38QcFJ38tAHRzBEAAzuroVnBS024gJ2sB/PAV63jBde2XufQm6cxzpUOVHWvJW/4jqXqn1G+Q9KNPpMvymh6vO3Ly1wNAN0YABOCsjk4CkZqNATxBC+CxGumTN63zC7LaLxcbL33pR9Z54YNS7QmWlvnXG9ZC0p4Yaw3BpH7SuHukPmdJwe10BQPo8QiAAJxljHU82TIwkjV7V5Jqqtsvs2OdFRCT0iT/JSd+v0u+LaUMlA5/Jm34n7bL1B+TCu6yzi/Lk/zDjteld1OALHrMKgcAPRQBEICzujIL+ERdwDvWW8fzrjj5biGx8dLYO63zf/y67bGFxb+3lpPp3a9p9nCjS75tbS8X3C598PLJ6w8A3RQBEICzOjMJpHE5lyP72y9T9o51PHt4x64/bIrU93zp8F5pzcOhzx3ZL7250Dq/al7ocjKSFJ8oDf++dd5eCyIA9AAEQADO6swkkMS+1vFIO2vwGSOVvW2dn5XZsevHxklX/5d1/tYD1hp/jQrmWcEwdbCU+b22X595i1X3raulzz7u2DUBoJshAAJwVmfWAex9PAC2twhz9S7p4G5rQon/4o7X4aKvWTOGG+qk/O9Y6/0VPij980+SPNKkX1tBsS1nDpAGHZ9sUvz7jl8TALoRAiAAZ3VmFnBiinU8UtX282XF1jF9qDVJo6M8x0NeynlS1afWYs+vzreeu/on0rmjTvz6xm7gkmc6v7MIAHQDBEAAzurMJJCTtQA2BsCOdv82l5wu3fw36fyrrfuJKdKEe6Ur5578tf92reQbYAXTLX/p/LUBIMra6eMAgAhpXAbmZDN2pZOPATyVAChZ3bm5y6xWvNj4jk1MkaxyX7xZeuMX1pqAl3yra9cHgCihBRCAszozBrCxC/jY0dbbtzXUS7tKrPOuBsBG8b06Hv4afTHX6sbeUSTt3nxq1wcAhxEAATirU1vBJUsxxzsqWrYCfvaRVHtAik+SPndheOvYEcl+6cKJ1vnbTAYB0LMQAAE4qzOTQDyepm7gluMAG7t/A1/ofOtduDROBnn3uRNvLQcA3QwBEICzOjMJRGqaCNKyBdBe/++L4alXVwwcK/X9vLVV3T/zo1cPAOgkAiAAZ3VmDKB08hbAUx3/dypiYqTLb7POV/3qxFvWAUA3QgAE4KzObAUnNVsKZm/TY3VHmiZeRDMAStLw70kpA60Fqd9YGN26AEAHEQABOMtuAfR0rHyy3zoeqGh6rGKT1HBMSvqctR5fNMV5pet+ZZ0X/VZ6/+Xo1gcAOoAACMBZnZkEIknJ/a3jgfKmx5p3/3Y0SEbSBeOlEbdb53/+nvTe36JbHwA4CQIgAGd1dhJIn7OsY3VZ02PdYfxfS+P+W7owW6qvkZ67UXppZvtb2AFAlBEAATircR3Ajo4B7HO8BbC6rRbAKM4AbikuQfrmUmnkHdb9d56SHs6U3vmj1NAQ1aoBQEsEQADOslsAOxoAG1sAd1nHw/ukfZ9Y54FuFAAlazu5CQukW162Fqc+vFd6abr0v1lNu5YAQDfgaAA8duyY/uu//ksDBw5UYmKiPv/5z+uee+5RQ7P/HRtjNH/+fAUCASUmJuqqq67S5s2h2yzV1NRoxowZSk1NVVJSkiZPnqydO3eGlKmqqlJubq58Pp98Pp9yc3O1f//+kDLbt2/XpEmTlJSUpNTUVM2cOVO1tbUR+/oBqPOzgBvHANYekI4GpR3rrPupFzTNEO5uzrtCur1QyvqllHCGtHOD9OSXpdf+u2kvZACIIkcD4K9+9Ss99thjWrJkid577z3dd999uv/++/Xwww/bZe677z4tXrxYS5Ys0YYNG+T3+zVu3DgdONC0vtasWbO0bNky5efnq7CwUAcPHlR2drbq6+vtMjk5OSopKVFBQYEKCgpUUlKi3Nxc+/n6+npNnDhRhw4dUmFhofLz8/X8889rzpw5znwzALeyt4Lr4OQN7xnSGcdnAu/5UNpeZJ0PGBH+uoVTbLw0ero0/W0p4xtW8H1rkfSXO+gSBhB9xkETJ0403//+90Meu+GGG8xNN91kjDGmoaHB+P1+c++999rPHz161Ph8PvPYY48ZY4zZv3+/iY+PN/n5+XaZsrIyExMTYwoKCowxxmzZssVIMkVFRXaZtWvXGknm/fffN8YYs3z5chMTE2PKysrsMs8++6zxer0mGAx26OsJBoNGUofLAzDGFD1uzN19jHnuux1/zVNftV7z9lJjfjfeOn/n6YhVMSLeedqY+SlW3V+ea0xDQ7RrBLgWf7+NcbQF8IorrtBrr72mDz/8UJL0z3/+U4WFhfrKV74iSdq6dasqKiqUlZVlv8br9Wrs2LFas2aNJKm4uFh1dXUhZQKBgDIyMuwya9eulc/n04gRTS0EI0eOlM/nCymTkZGhQCBglxk/frxqampUXFwcoe8AgE53AUtS+kXWcXuRtPP4FnDnjAxvvSLt0hulrz8pySOtf0L6x0PRrhEAF4tz8mI//vGPFQwGdeGFFyo2Nlb19fX65S9/qe985zuSpIoKa6HX9PT0kNelp6dr27ZtdpmEhASlpKS0KtP4+oqKCqWlpbW6flpaWkiZltdJSUlRQkKCXaalmpoa1dTU2Perq6s7/LUDOM50chkYqSkA/vNP1jF1sNTv/PDWywkZX5cO7pEKfiy9do/U/xLp/KujXSsALuRoC+Bzzz2np59+Wn/605/0zjvv6KmnntKiRYv01FNPhZTztBgbZIxp9VhLLcu0Vb4rZZpbuHChPanE5/NpwIAo70AA9ESdnQUsSZ+/KvT+0Mlhq47jRtwmXXqT1RL6f1Ol/dujXSMALuRoAPzRj36ku+66S9/+9rc1bNgw5ebm6oc//KEWLrT2z/T7rYHeLVvgKisr7dY6v9+v2tpaVVVVnbDM7t27W11/z549IWVaXqeqqkp1dXWtWgYbzZs3T8Fg0L7t2LGjs98CAF3pAu4TkIZ+1Tr3+qTLbw1/vZzi8UhfeUAKXCod2Sf93/el+rpo1wqAyzgaAA8fPqyYmNBLxsbG2svADBw4UH6/XytXrrSfr62t1apVqzR69GhJUmZmpuLj40PKlJeXq7S01C4zatQoBYNBrV+/3i6zbt06BYPBkDKlpaUqL29aXHbFihXyer3KzGx7dwGv16s+ffqE3AB0UmdnATe6/jFp0q+lvNekM1oP8ehR4ntJU/5ghdmdG6RVv4p2jQC4jKNjACdNmqRf/vKXOuecc3TRRRdp48aNWrx4sb7//e9LsrpkZ82apQULFmjQoEEaNGiQFixYoN69eysnJ0eS5PP5NHXqVM2ZM0f9+vVT3759NXfuXA0bNkzXXnutJGnIkCGaMGGC8vLy9Pjjj0uSbr31VmVnZ2vw4MGSpKysLA0dOlS5ubm6//77tW/fPs2dO1d5eXkEOyCSGjq5F3CjhN5S5i1hr07UnHmONOkh6f++J61eZHVzn3dFtGsFwCUcDYAPP/ywfvrTn2ratGmqrKxUIBDQbbfdpp/97Gd2mTvvvFNHjhzRtGnTVFVVpREjRmjFihVKTk62yzz44IOKi4vTlClTdOTIEV1zzTVaunSpYmOb/qA888wzmjlzpj1bePLkyVqyZIn9fGxsrF5++WVNmzZNY8aMUWJionJycrRo0SIHvhOAi3V2K7jTWcYN0sevSSVPSy/cai0e3V0XtwZwWvEYw7L0XVVdXS2fz6dgMEirIdBRbyywujwv+3dp4gPRrk301RyUnhgr7f1YGjJJmvLHznePA+gU/n6zFzAAp3VlFvDpzHuG9PX/kWLipff+Km38Y7RrBMAFCIAAnEUXcGuBS6Vrfmqdv/Jf0oG21yIFgHAhAAJwVuMyMJ1ZCNoNRk2XAl+UaoLS8h9FuzYATnP8BgbgrIYu7ATiBjGx0uTfWF3j770kfbTy5K8BgC7iNzAAZ3VlIWi38A+TRv7AOn/t501L5gBAmBEAATjLdHEdQLe4co6UkCxVbJK2LIt2bQCcpgiAAJxFF/CJ9e4rjZ5unRc+JLFSF4AI4DcwAGcxC/jkLr9ViuslVbwr7VgX7doAOA0RAAE4iy7gk+vdV7p4inW+7vHo1gXAaYkACMBZdhcwu12c0PCp1vGD5dLR6ujWBcBphwAIwFnMAu6Y/pdIqRdIx45K7/8t2rUBcJohAAJwFlvBdYzHIw37pnW+6c/RrQuA0w4BEICz2Amk4zK+bh23rqYbGEBY8RsYgLOYBdxx/c6X+v2b1HBM+tfr0a4NgNMIARCAs+gC7pwLJljHj1ZEtx4ATisEQADOsieB8OunQwZlWcePVrA1HICw4TcwAGcxBrBzzhllbQ13aI9U8c9o1wbAaYLfwACcRRdw58QlSOeNsc4/LYxuXQCcNgiAAJzFOoCdd94V1nHrW9GtB4DTBgEQgLMaZwHTBdxx511pHbevleqPRbcuAE4L/AYG4Cy6gDvPP0zy+qSaaqni3WjXBsBpgAAIwFmsA9h5MbHSuaOtc8YBAggDAiAAZxljHT2e6Najp2kcB0gABBAGBEAAzqILuGvOGWUdd65vCtEA0EUEQADOogu4a/zDpLhe0pEqae/H0a4NgB6OAAjAWfZC0ATATolLkPp/wTrfsT6qVQHQ8xEAATirgWVgumzA5dZxJwEQwKnhNzAAZ9EF3HWNAZAWQACniAAIwFnsBdx1Zx8PgJXvSUeD0a0LgB6N38AAnNVAAOyy5HTpzHMkGamsONq1AdCD8RsYgLPoAj41A0ZYxx0bolsPAD0aARCAs5gFfGoau4F3rItuPQD0aARAAJZ/Pic9/Q0pWBbZ6zAL+NQMuMw67ny7qTsdADqJ38AALCt/Kn28UnrlPyN7HbqAT016hhTfW6oJSp99EO3aAOihHA+AZWVluummm9SvXz/17t1bX/jCF1Rc3DSY2Rij+fPnKxAIKDExUVdddZU2b94c8h41NTWaMWOGUlNTlZSUpMmTJ2vnzp0hZaqqqpSbmyufzyefz6fc3Fzt378/pMz27ds1adIkJSUlKTU1VTNnzlRtbW3Evnag26o/Jh3cbZ3v+ySy12qgC/iUxMZLZ2Va53QDA+giRwNgVVWVxowZo/j4eP3973/Xli1b9MADD+jMM8+0y9x3331avHixlixZog0bNsjv92vcuHE6cOCAXWbWrFlatmyZ8vPzVVhYqIMHDyo7O1v19fV2mZycHJWUlKigoEAFBQUqKSlRbm6u/Xx9fb0mTpyoQ4cOqbCwUPn5+Xr++ec1Z84cR74XQLdypKrpPNL7zNpjAD2Rvc7pjPUAAZwq46Af//jH5oorrmj3+YaGBuP3+829995rP3b06FHj8/nMY489ZowxZv/+/SY+Pt7k5+fbZcrKykxMTIwpKCgwxhizZcsWI8kUFRXZZdauXWskmffff98YY8zy5ctNTEyMKSsrs8s8++yzxuv1mmAw2KGvJxgMGkkdLg90W5UfGHN3H+v24LDIXmvRYOs6u0oie53T2QcF1vfwN1+Mdk2AHom/38Y42gL40ksvafjw4frmN7+ptLQ0XXrppXryySft57du3aqKigplZWXZj3m9Xo0dO1Zr1qyRJBUXF6uuri6kTCAQUEZGhl1m7dq18vl8GjFihF1m5MiR8vl8IWUyMjIUCATsMuPHj1dNTU1IlzTgCs1bAI/uj+y17EkgdAF32dnHJ4Ls/Vg6tDe6dQHQIzkaAD/55BM9+uijGjRokF555RXdfvvtmjlzpv7whz9IkioqKiRJ6enpIa9LT0+3n6uoqFBCQoJSUlJOWCYtLa3V9dPS0kLKtLxOSkqKEhIS7DIt1dTUqLq6OuQGnBaO7Gs6PxpsCmmR0NgFzCSQruvdV0odbJ2zLzCALnA0ADY0NOiLX/yiFixYoEsvvVS33Xab8vLy9Oijj4aU87QYG2SMafVYSy3LtFW+K2WaW7hwoT2pxOfzacCAASesE9BjNG8BlCK7zZhhGZiwaBwHuL0ouvUA0CM5+hu4f//+Gjp0aMhjQ4YM0fbt2yVJfr9fklq1wFVWVtqtdX6/X7W1taqqqjphmd27d7e6/p49e0LKtLxOVVWV6urqWrUMNpo3b56CwaB927FjR4e+bqDbO7K/xf2qNouFBbOAw8PeEYQWQACd52gAHDNmjD74IHTdqg8//FDnnnuuJGngwIHy+/1auXKl/Xxtba1WrVql0aNHS5IyMzMVHx8fUqa8vFylpaV2mVGjRikYDGr9+qZfjOvWrVMwGAwpU1paqvLycrvMihUr5PV6lZmZ2Wb9vV6v+vTpE3IDTgu1h0LvR7QFsLELmBbAU3LOSOu46x3pGMtXAeicOCcv9sMf/lCjR4/WggULNGXKFK1fv15PPPGEnnjiCUlWl+ysWbO0YMECDRo0SIMGDdKCBQvUu3dv5eTkSJJ8Pp+mTp2qOXPmqF+/furbt6/mzp2rYcOG6dprr5VktSpOmDBBeXl5evzxxyVJt956q7KzszV4sDVuJisrS0OHDlVubq7uv/9+7du3T3PnzlVeXh7BDu5T1yIAHjsauWvRBRwe/f5NSkyxWmsrNklnt/0fVwBoi6MB8LLLLtOyZcs0b9483XPPPRo4cKAeeugh3XjjjXaZO++8U0eOHNG0adNUVVWlESNGaMWKFUpOTrbLPPjgg4qLi9OUKVN05MgRXXPNNVq6dKliY5u6lJ555hnNnDnTni08efJkLVmyxH4+NjZWL7/8sqZNm6YxY8YoMTFROTk5WrRokQPfCaCbqTvS4v7hyF2LWcDh4fFY3cAfFkg7igiAADrFY0ykV309fVVXV8vn8ykYDNJqiJ7tL9OljX9suj/lj9LQyZG51j2pUkOdNPs9qU/g5OXRvrcWS6/9XBr6VWnKH6JdG6DH4O83ewEDkNpoATzSdrlwoAs4fJpPBOH/8gA6gd/AAFp3+UaqC9iYZlvB0QV8ygKXSjFx0oFyaf/2aNcGQA9CAATQehZwpFoAG8OfxELQ4ZDQW+r/Bet82z+iWhUAPQsBEEBT4Evse/x+hFoAG441nRMAw2PgldZx61vRrQeAHoUACKAp8PXud/x+hFoAQwKgo4sQnL4Gfsk6bl3NOEAAHUYABNAUAJM+F3o/3EICYHxkruE2A0Za38vqnVLV1mjXBkAPQQAE0NTi1zvSXcD1Tee0AIZHQm/p7Mus862ro1sXAD0GARCAdKzGOiaeaR0j3gXsYSu4cLLHARIAAXQMv4EBSPXH95LtdaZ1jHQXMK1/4WWPA3yLcYAAOoQACMD5FkACYHidfZkU10s6VCnt+SDatQHQAxAAAbdraLC2ZpOkxBTrSADsWeK8TbuCbF0V3boA6BEIgIDb1dc0nTd2AbdcGDpcGieBsAZg+J1/tXX8aGV06wGgRyAAAm53rFkApAWw5xqUZR0/fSuyezkDOC0QAAG3a5wAIknePtbxGAGwx0kbIvU5Wzp2VPq0MNq1AdDNEQABt2tsAYz1SvG9rPO6o5G5FgEwcjweadA46/yjFdGtC4BujwAIuF1jAIzzSnGJoY+FG2MAI6t5AGQ5GAAnQAAE3K6+WQBsbAGkC7hnGjjW2hau6lNp77+iXRsA3RgBEHC75l3AjS2A9bWh27aFCwEwsrxnSOeNsc7pBgZwAgRAwO0aJ4HEJVitgI0i0Q1MAIy8xtnAHyyPbj0AdGsEQMDtQiaBJDZ7PAITQewAyBjAiBn8Feu4bY10aG906wKg2yIAAm5nTwJJsIJZTLx1PxJrydmTQGgBjJi+AyX/MMnUSx/+Pdq1AdBNEQABt7MngfQKPUa0BZAAGFFDJlvH9/4a3XoA6LYIgIDb2V3ACdYxngDY412YbR3/9bpUcyC6dQHQLREAAbezJ4EcnwDSOBM4EotBMwbQGWlDpL7nW58tewMDaAMBEHC75pNApKYgGJEWQMYAOsLjkYZMss7pBgbQBgIg4HbNJ4FIkV0Mmi5g5zQGwI9WRGZCD4AejQAIuF2rSSBOdAETACMu8EXJN0CqPSh9+Eq0awOgmyEAAm537PgYwMZJIBHtAmYMoGNiYqSMr1vnm/4c3boA6HYIgIDbNd8LWGpaDJoxgD3fxVOs40crpCNV0a0LgG6FAAi4XWPQs1sAj3cFR2QhaLqAHZV+kZR2kTUbeMtfol0bAN0IARBwu2Mtl4GJ4DqA9XXWkQDonIu/aR03/V906wGgWyEAAm7XchJIRBeCPh4AY+PD/95oW8Y3rOOnhVKwLLp1AdBtEAABt2s1CSSCs4AbWwAbr4XIO3OAdM5oSUba9P+iXRsA3QQBEHC7VpNAItkF3CJswhlf+I51fOePkjHRrQuAbiGqAXDhwoXyeDyaNWuW/ZgxRvPnz1cgEFBiYqKuuuoqbd68OeR1NTU1mjFjhlJTU5WUlKTJkydr586dIWWqqqqUm5srn88nn8+n3Nxc7d+/P6TM9u3bNWnSJCUlJSk1NVUzZ85UbW1tpL5coHtquRdwRMcANgZAuoAdddENUsIZ0r5/WV3BAFwvagFww4YNeuKJJ3TxxReHPH7fffdp8eLFWrJkiTZs2CC/369x48bpwIGmDc1nzZqlZcuWKT8/X4WFhTp48KCys7NVX19vl8nJyVFJSYkKCgpUUFCgkpIS5ebm2s/X19dr4sSJOnTokAoLC5Wfn6/nn39ec+bMifwXD3Qnx1ouBN04C5gu4NOG9wxp2PGxgO88Fd26dAfGSIc+k3ZtlLaulj56VfpwhbS9SKp8T6o5GO0aAhEXlal4Bw8e1I033qgnn3xSv/jFL+zHjTF66KGH9JOf/EQ33HCDJOmpp55Senq6/vSnP+m2225TMBjU7373O/3xj3/UtddeK0l6+umnNWDAAL366qsaP3683nvvPRUUFKioqEgjRoyQJD355JMaNWqUPvjgAw0ePFgrVqzQli1btGPHDgUCAUnSAw88oFtuuUW//OUv1adPH4e/K0CUtLsOYASWgaELOHq+eLNUvFTa8pJ03T6pd99o18g5R6qkf70u7dgglb0t7d4s1R0+8WuSA1L6UOmcUdJ5V0hnDZdimb2O00dUWgDvuOMOTZw40Q5wjbZu3aqKigplZWXZj3m9Xo0dO1Zr1qyRJBUXF6uuri6kTCAQUEZGhl1m7dq18vl8dviTpJEjR8rn84WUycjIsMOfJI0fP141NTUqLi4O/xcNdFft7gRSE/5r0QUcPYFLpfRhVuB/1wWTQY4GpQ3/Iy3Nlu47X/q/70vrHpV2bmgKf2ekS5+7UPJfbN1SBkq9fNZzB3ZJH78qvf7f0v+Olx4YLP31P6wudMZR4jTg+H9n8vPz9c4772jDhg2tnquoqJAkpaenhzyenp6ubdu22WUSEhKUkpLSqkzj6ysqKpSWltbq/dPS0kLKtLxOSkqKEhIS7DIt1dTUqKam6Y9idXX1Cb9WoEdo2QJozwKORAsgXcBR4/FImTdLy+dKG56ULr/V2i7udFP+rlT0qLR5WWgr9ueGSAO/JJ093ArDZ57T9G++pcP7pL0fW13E2/5hdRMf/sxqQS1ear3X5f8uXZIjJfR24qsCws7RALhjxw79x3/8h1asWKFevXq1W87j8YTcN8a0eqyllmXaKt+VMs0tXLhQP//5z09YD6DHaTkJJJKzgFteC8665DvSa/9thZuPVkiDJ0S7RuGzY720epH00StNj33uQunSm6QLJ0p9P9/x9+rdV+p9uTTgcmnEbdZ/XD4tlDa/YC2ovec96eU50pu/kq6cLWV+r+nnBughHP3vX3FxsSorK5WZmam4uDjFxcVp1apV+s1vfqO4uDi7Ra5lC1xlZaX9nN/vV21traqqqk5YZvfu3a2uv2fPnpAyLa9TVVWlurq6Vi2DjebNm6dgMGjfduzY0YXvAtDNtDcJJJI7gdAFHB3eM6TM71rnRb+Nbl3C5bOPpWe/I/1unBX+PDFSxtelqSulaUXS6BmdC39tiY2Xzv+yNPlhafZ70oR7rRbEQ5VSwV3Sby61giFdw+hBHA2A11xzjTZt2qSSkhL7Nnz4cN14440qKSnR5z//efn9fq1cudJ+TW1trVatWqXRo0dLkjIzMxUfHx9Spry8XKWlpXaZUaNGKRgMav369XaZdevWKRgMhpQpLS1VeXm5XWbFihXyer3KzMxss/5er1d9+vQJuQE9XuO4vLiWewGzDuBp6fLbJE+s1a1Z/m60a9N1R/ZLf79LemSE9MFy62u6NFea/rb0jf+1Wu9O0nPUJYlnSiN/IE0vlrIfkvqcbY0XfH6q9NQkqfL98F8TiABHu4CTk5OVkZER8lhSUpL69etnPz5r1iwtWLBAgwYN0qBBg7RgwQL17t1bOTk5kiSfz6epU6dqzpw56tevn/r27au5c+dq2LBh9qSSIUOGaMKECcrLy9Pjjz8uSbr11luVnZ2twYMHS5KysrI0dOhQ5ebm6v7779e+ffs0d+5c5eXlEezgLna3LLOAXeHMAdLQr1rdmUWPSF97LNo16rz3/mZ1wR483oszKEvK+oX0ucHO1SEuQRr+Patbfc1vpLcekD59S3rsCunqn0ijZ0oxsc7VB+ikbjcC+M4779SsWbM0bdo0DR8+XGVlZVqxYoWSk5PtMg8++KCuv/56TZkyRWPGjFHv3r3117/+VbGxTT9szzzzjIYNG6asrCxlZWXp4osv1h//+Ef7+djYWL388svq1auXxowZoylTpuj666/XokWLHP16gahrNQkkkrOA6QLuFkZNt46b/izt2xrdunTGwUrp/90sPXejFf76ni/d9IJ045+dDX/NxfeSxt4p3bFOumCCtd/1q/OlpRN71vcWruMxhkELXVVdXS2fz6dgMEirIXquX6Rb4/3+410p5VyrC+uREVJiX+nHYf4D9uQ11jps335WuvAr4X1vdM4fv2atjfeFm6Tre8B4wPf+Jr003VrTzxMrjZkpjf1xU4t1d2CMVPKM9PcfS7UHrd1Xvvpb6aLro10ztMDf727YAgjAQca0ngTCXsDucNV/Wsd/Pivt/Vd063IidUel5T+yWv2OVFnr9d36hnTt/O4V/iRrzOGlN0k/+Id0zmgrBP75ZumVn0j1x6JdOyAEARBws4Zjko53ArScBHLsaPhnNdIF3H0MuEz6t3GSqZdW3x/t2rTts4+k/7lWWv+EdX/Mf0h5r0v9L4luvU4m5Tzp5r9a4wAlae0S6Q9flQ7uiWq1gOYIgICbNR/n1zgJpDEAmoamwBYutAB2L1+eZx3/mW8tetydlPxJenystHuT1DtVuvF5adw9Pec/D7FxUtZ/S1P+YHUFbyuU/ucaac+H0a4ZIIkACLhb8wDYci9gKfwzgdkJpHs5K1MaNkWSsZZU6Q5DwmsOSC/cJr34A6nukLV7xw/+IQ269uSv7Y6GflXKe8NqFdy/Tfrdtdai0kCUEQABN2ucAeyJbVqyIjZB0vH108I9E9iecUwA7DaunS/F95Z2FFmzgqNpV4nV6vduvvVv8uqfSrkvSsn+6NbrVH3uAunfX5POvszao/gP17tjP2Z0awRAwM1aTgCRrIHs9mLQYW4BbFxcOq6bDd53M99Z0hWzrfOCu6IzTs0YqegxazePff+yFlf+3nLpS3NPn7X0klKtcYFDv2otFfNCnrTu8WjXCi5GAATcrOUuII0iNRO4sUuZfVO7lzH/IaVnSIf3Si/PdrYr+NBeayu3gh9b/x4vzJZuf0s6Z6RzdXBKfKL0jaXSiB9Y9/9+p7V/cXfoeofrEAABN2u5C0ijxha6cAbAhvpmgZMWwG4lLkG6/hEpJk567yVrLTsnfLJKemyM9OHfraEHX1kkfetpqXdfZ64fDTEx0oSF0ti7rPuv/7f06t2EQDiOAAi42bF2xuQ1TggJ537AzcMkLYDdT/9LpKuOh5K/zZbK3oncterrpNfusZZGOVAupV5gLe9yeV5k9u/tbjweawZ21i+s+//4tbXWISEQDiIAAm5W38YYQCky+wE3D5O0AHZPV8yRLrjO+nfx3E1ScGf4r1H5nvS7LGvvXBnpizdLt74p+YeF/1rd3egZUvZDkjzShietMZiEQDiEAAi4WbtdwL1Cnw+HusPHr5VgdYOh+4mJkW54XOo3SKouO95Ctzs8711fJ626T3rsSmnXO5LXJ31zqTT5N1JCUniu0RMN/540+WHrfN1j0or/IgTCEfwWBtzM7gJuJwCGcxbwMWYA9wi9fFLuMsk3QNr7sfS/Wae+ePHHr0mPf0l645fWDNgLrpPuWCdd9LXw1Lmn+2Lu8ZZAWbuGMCYQDiAAAm5W304AjMQs4DpmAPcYZw6QvvsX6cxzpapPre3Y3v1/nQ8lO9+Wnv6G9PQNUuUWKbGv9PXfSd95VurTPyJV77GGf0+a+IB1/o9fW5NDCIGIIAIg4GbttgBGYBaw3QJIAOwR+p1vLV48YIRUE7TWrfvDV6VP/3HiYFJzUHr3z9LvJ1pbn3280ppdPPIOaUaxNOwb7pjo0RWX/bt03X3W+VsPSIUPRrc+OK3FRbsCAKKo3TGAEZgFbLcA0gXcY5zxOemWl6V/PCS9+Stp6yrrdua50vlfllIHS736WJ9tcKc1tm/HhqbJQzHx0rBvWgs69zs/ql9KjzHiNuvncuVPpdd+LiWmWK2DQJgRAAE3a68FMBKzgGkB7Jli46Uv/cjaM7hwsdUVvH+bVLy0/df0/byU8XUp83vWTiPonDEzpSNV1vf7bz+UEs9kvCTCjgAIuFl7YwAjMguYFsAeLeVcadKvpfELpX+9Ju3aKO39l1R7yBrXeUa6tZvIgBFS2hC6eU/VNT+zQmDx76Xn8yRvH+nfrol2rXAaIQACbma3yjkwC7j2oHVMOCN87wnnJfSWhkyybogcj8eaFHJ0v7R5mbUu43dfkgZcFu2a4TTBJBDAzY4d35qt5RjASMwCrjkeAL0EQKBDYmKlrz0hnX+1tY7mM9+wFtIGwoAACLhZu13AEZgFXHvAOtICCHRcXIK1P/LZl1utgU9/XQqWRbtWOA0QAAE3a3cZmAjMAq45HgC9fcL3noAbJCRJOc9Zs66ry6yWwCP7o10r9HAEQMDN2lsGJhKzgOkCBrqud1/ppv+TzvBbi2rn3xje/6DBdQiAgJudbCu4cM4CZhIIcGrOPMcKgd4+0rZCadltUkNDtGuFHooACLhZu1vBHW8BDOcsYLsLmAAIdJl/mDUmMCZe2vKi9Mp/smUcuoQACLjZycYAhnUWMGMAgbD4/Fjpa49Z5+seldYuiW590CMRAAE3a3cruAjMAj4atI7e5PC9J+BWw74hZf3COl/xX1Lp89GtD3ocAiDgZu0tBN3YBVx7OHzXapy1mNg3fO8JuNmo6dKI263z1YuiWxf0OARAwM3qjy8E3TIANrbSNXbbhsORKuvYmwAIhIXHIw37pnXeOMkK6CACIOBm7XUBhzsA1tc1LQSdmBKe9wQg6fiey8wDQScRAAE3a28SSC+fdaw7JNUfO/Xr2IvWepreG8Cp8zSekADROQRAwM3aWwam+Vp9tWFoBTyyzzr28ln7mwIIk8YWQAIgOocACLhZu8vAJDQtBh2ObuBDn1lHxv8B4eVpbAIkAKJzCICAm7U3BlBqWq/vaPWpX+dghXVMDpz6ewFohhZAdA0BEHCz9loApfBOBDnQGAD9p/5eAJrQAogucjQALly4UJdddpmSk5OVlpam66+/Xh988EFIGWOM5s+fr0AgoMTERF111VXavHlzSJmamhrNmDFDqampSkpK0uTJk7Vz586QMlVVVcrNzZXP55PP51Nubq72798fUmb79u2aNGmSkpKSlJqaqpkzZ6q2tjYiXzvQLbU3BlAKcwAst44EQCDMaAFE1zgaAFetWqU77rhDRUVFWrlypY4dO6asrCwdOnTILnPfffdp8eLFWrJkiTZs2CC/369x48bpwIGmP0KzZs3SsmXLlJ+fr8LCQh08eFDZ2dmqr6+3y+Tk5KikpEQFBQUqKChQSUmJcnNz7efr6+s1ceJEHTp0SIWFhcrPz9fzzz+vOXPmOPPNAKKtoaFpHcC2uoB7He8CrglDF7DdAtj/1N8LQBNaANFVJooqKyuNJLNq1SpjjDENDQ3G7/ebe++91y5z9OhR4/P5zGOPPWaMMWb//v0mPj7e5Ofn22XKyspMTEyMKSgoMMYYs2XLFiPJFBUV2WXWrl1rJJn333/fGGPM8uXLTUxMjCkrK7PLPPvss8br9ZpgMNih+geDQSOpw+WBbqX2iDF397FuR9r4N/xsjvXcht+d+rV+P9F6r3f/fOrvBaBJ+SbrZ+u+f4t2TXoU/n4bE9UxgMGgtTdo377WzMCtW7eqoqJCWVlZdhmv16uxY8dqzZo1kqTi4mLV1dWFlAkEAsrIyLDLrF27Vj6fTyNGjLDLjBw5Uj6fL6RMRkaGAoGmQenjx49XTU2NiouLI/QVA91IY/evdOIu4HBMAqneZR3pAgbCixZAdFFctC5sjNHs2bN1xRVXKCMjQ5JUUWF1E6Wnp4eUTU9P17Zt2+wyCQkJSklJaVWm8fUVFRVKS0trdc20tLSQMi2vk5KSooSEBLtMSzU1NaqpafqjWV0dhj+MQLQcaxYAYxNaP9/rTOvYuIVbV9Ufk/ZbP79KOe/U3gtAC4wBRNdErQVw+vTpevfdd/Xss8+2es5j/4/GYoxp9VhLLcu0Vb4rZZpbuHChPanE5/NpwIABJ6wT0K3VHbaO8b2btSI0c8bnrGPjGn5dtX+b1HBMiktkGRgg3GgBRBdFJQDOmDFDL730kt544w2dffbZ9uN+v9U91LIFrrKy0m6t8/v9qq2tVVVV1QnL7N69u9V19+zZE1Km5XWqqqpUV1fXqmWw0bx58xQMBu3bjh07OvNlA91L3RHrGJ/Y9vNJjQGw8tSus/dj69j381IMK08BEUELIDrJ0d/GxhhNnz5dL7zwgl5//XUNHDgw5PmBAwfK7/dr5cqV9mO1tbVatWqVRo8eLUnKzMxUfHx8SJny8nKVlpbaZUaNGqVgMKj169fbZdatW6dgMBhSprS0VOXl5XaZFStWyOv1KjMzs836e71e9enTJ+QG9FiNATDuZAFwT8ff80CF9OavpDVLpNrjs/t3lVhHf0aXqgngRGgBRNc4Ogbwjjvu0J/+9Cf95S9/UXJyst0C5/P5lJiYKI/Ho1mzZmnBggUaNGiQBg0apAULFqh3797Kycmxy06dOlVz5sxRv3791LdvX82dO1fDhg3TtddeK0kaMmSIJkyYoLy8PD3++OOSpFtvvVXZ2dkaPHiwJCkrK0tDhw5Vbm6u7r//fu3bt09z585VXl4ewQ7ucNIWwOPjaA92MAAe2is9ebVUXWbd37xMuuVv0s7j/xE7q+3/WAE4BR7GAKJrHA2Ajz76qCTpqquuCnn897//vW655RZJ0p133qkjR45o2rRpqqqq0ogRI7RixQolJyfb5R988EHFxcVpypQpOnLkiK655hotXbpUsbFNm8w/88wzmjlzpj1bePLkyVqyZIn9fGxsrF5++WVNmzZNY8aMUWJionJycrRo0aIIffVAN3OyANg4Y/dghTWRI/YEvy6Mkf460wp/yQGp7pBU9rb0p29J2/5hlTnvyvDVHcBxtACiazzG8N+GrqqurpbP51MwGKTVED3Plpek/5crDRgpTX2l9fMNDdKC/tKxo9LMEmsGb9Gj0vonpF4+afQMKePrVgtE8VNWAIyJl/JetxaP/uPXmhaaTrtI+sE/2p5sAqDrPvtIWjJc8vqkedujXZseg7/fUVwGBkCUnawFMCZGShko7XlP2veJ1aX72s+bnn9+qlT6gjR0svT3O63Hrvmp1P9i6/yGJ6UXp1kth9kPEv6AiKAFEF1DAATc6thJAqAkpQ6yAuDb/yt98HfrsavmWcfVi6QPXrZukjRovDRqRtNrL7peunCiJM+Ju48BdB1jANFF/FYG3OpkLYCSNW7vvZek9/9m3c/4ujT2x9YfnQsnSgXzrGVeBn9FyvpF62VeYuMjU3cALRAA0TkEQMCt7IWgTxAAL/yK9OrdVtk+Z0kTH2hqcfAPs2b5AogeWgDRRQRAwK3sFsDe7ZfxnS199y/S+y9Lw78nJaa0XxZAFDAGEF1DAATcyl4IuteJyw243LoB6H5oAUQXsS8T4FYdaQEE0M3RAoiuIQACbtWRSSAAujdaANFFBEDArexJILQAAj0XLYDoGgIg4FZ2C+BJxgAC6L5oAUQXEQABt6IFEDgN0AKIriEAAm5Ve9A6JpwR3XoA6DpaANFFBEDArWoOWEdvcnTrAeAU0AKIriEAAm7VGAB79YluPQB0HS2A6CICIOBWR6utIy2AQA/mOXkRoA0EQMCN6uukY8dnAXtpAQR6LA9dwOgaAiDgRo3dvxKTQIAejRZAdA0BEHCjxgAY10uKS4huXQB0nadZAGQcIDqBAAi4ETOAgdMEARBdQwAE3IgACJwemrcAMg4QnUAABNyopnEGMBNAgNMGLYDoBAIg4Ea0AAKnB1oA0UUEQMCNjgatIy2AQA/HGEB0DQEQcKPD+6xj75To1gPAqaEFEF1EAATc6PBn1jHpc9GtB4BTRAsguoYACLjRoeMBsHdqdOsB4NTQAoguIgACbmS3ABIAgZ6NFkB0DQEQcKNDe60jLYBAz0YLILqIAAi40YFd1jE5Pbr1AHCKaAFE1xAAAbepPSwdPt4C6BsQ3boAODW0AKKLCICA2wR3WseEZKmXL7p1AXCKaAFE1xAAAbfZv906+s5u0XoAoMehBRBdRAAE3GbPe9bxcxdEtx4AwoAWQHQNARBwm92brWN6RnTrAeDU0QKILnJ9AHzkkUc0cOBA9erVS5mZmXrrrbeiXSUgcoyRtv3DOu9/SXTrAiAMaAFE17g6AD733HOaNWuWfvKTn2jjxo268sordd1112n79u3RrhoQGbtLrTGAMfHSuWOiXRsAp4pxvOgiVwfAxYsXa+rUqfr3f/93DRkyRA899JAGDBigRx99NNpVA8KvepdUMM86v3Ci5D0juvUBEAa0AKJr4qJdgWipra1VcXGx7rrrrpDHs7KytGbNmjZfU1NTo5qaGvt+dXV1ROq28ZWnVL/5pZDHPG2M7fC0+8PeRtkOl2vrPdu6dsfer733bPs6HXttW7/kOvp+natPB7+P7XwOp/SeHaxje/8GYlSvhIaj1s0clbfhiBIbDkmSaj1ePXDka9qdv7HN1wLoQYzRQ8dPSx67Rcc8CdGsTUTEXjRZl46/OdrVOO24NgB+9tlnqq+vV3p66E4I6enpqqioaPM1Cxcu1M9//vOI1+1oWalGVb8a8evAXRqMRyXmfP2i7ia98168pF3RrhKAMLjbe4ZSPAf1hQOro12ViFhb9vloV+G05NoA2MjTYvyEMabVY43mzZun2bNn2/erq6s1YED4d1JIGTZBa7192nimnbEebTxs2izbXltTG493YlxJp67V5vt24lodfH3bdWrnWh3+/rVT+BS/1vbbLjv2ubRVV+OJ0bHYRNXFJOpYbKKOxfbSoV5+HYtN1FckfaXdawLoad6sflz9978T7WpETMoFo6NdhdOSawNgamqqYmNjW7X2VVZWtmoVbOT1euX1eiNetwsvu0a67JqIXwcAcDr4vKTsaFcCPYxrJ4EkJCQoMzNTK1euDHl85cqVGj2a/20AAIDTl2tbACVp9uzZys3N1fDhwzVq1Cg98cQT2r59u26//fZoVw0AACBiXB0Av/Wtb2nv3r265557VF5eroyMDC1fvlznnntutKsGAAAQMR5jWDioq6qrq+Xz+RQMBtWnT1uTNgAAQHfD328XjwEEAABwKwIgAACAyxAAAQAAXIYACAAA4DIEQAAAAJchAAIAALgMARAAAMBlCIAAAAAuQwAEAABwGVdvBXeqGjdRqa6ujnJNAABARzX+3XbzZmgEwFNw4MABSdKAAQOiXBMAANBZBw4ckM/ni3Y1ooK9gE9BQ0ODdu3apeTkZHk8nrC+d3V1tQYMGKAdO3a4dp/CnojPrWfic+uZ+Nx6pu7wuRljdODAAQUCAcXEuHM0HC2ApyAmJkZnn312RK/Rp08ffrH1QHxuPROfW8/E59YzRftzc2vLXyN3xl4AAAAXIwACAAC4DAGwm/J6vbr77rvl9XqjXRV0Ap9bz8Tn1jPxufVMfG7dA5NAAAAAXIYWQAAAAJchAAIAALgMARAAAMBlCIAAAAAuQwDshh555BENHDhQvXr1UmZmpt56661oV8m15s+fL4/HE3Lz+/3288YYzZ8/X4FAQImJibrqqqu0efPmkPeoqanRjBkzlJqaqqSkJE2ePFk7d+50+ks57a1evVqTJk1SIBCQx+PRiy++GPJ8uD6rqqoq5ebmyufzyefzKTc3V/v374/wV3f6Otnndsstt7T6GRw5cmRIGT43Zy1cuFCXXXaZkpOTlZaWpuuvv14ffPBBSBl+3ro/AmA389xzz2nWrFn6yU9+oo0bN+rKK6/Uddddp+3bt0e7aq510UUXqby83L5t2rTJfu6+++7T4sWLtWTJEm3YsEF+v1/jxo2z94mWpFmzZmnZsmXKz89XYWGhDh48qOzsbNXX10fjyzltHTp0SJdccomWLFnS5vPh+qxycnJUUlKigoICFRQUqKSkRLm5uRH/+k5XJ/vcJGnChAkhP4PLly8PeZ7PzVmrVq3SHXfcoaKiIq1cuVLHjh1TVlaWDh06ZJfh560HMOhWLr/8cnP77beHPHbhhReau+66K0o1cre7777bXHLJJW0+19DQYPx+v7n33nvtx44ePWp8Pp957LHHjDHG7N+/38THx5v8/Hy7TFlZmYmJiTEFBQURrbubSTLLli2z74frs9qyZYuRZIqKiuwya9euNZLM+++/H+Gv6vTX8nMzxpibb77ZfPWrX233NXxu0VdZWWkkmVWrVhlj+HnrKWgB7EZqa2tVXFysrKyskMezsrK0Zs2aKNUKH330kQKBgAYOHKhvf/vb+uSTTyRJW7duVUVFRcjn5fV6NXbsWPvzKi4uVl1dXUiZQCCgjIwMPlMHheuzWrt2rXw+n0aMGGGXGTlypHw+H59nBL355ptKS0vTBRdcoLy8PFVWVtrP8blFXzAYlCT17dtXEj9vPQUBsBv57LPPVF9fr/T09JDH09PTVVFREaVauduIESP0hz/8Qa+88oqefPJJVVRUaPTo0dq7d6/9mZzo86qoqFBCQoJSUlLaLYPIC9dnVVFRobS0tFbvn5aWxucZIdddd52eeeYZvf7663rggQe0YcMGXX311aqpqZHE5xZtxhjNnj1bV1xxhTIyMiTx89ZTxEW7AmjN4/GE3DfGtHoMzrjuuuvs82HDhmnUqFE6//zz9dRTT9kD0bvyefGZRkc4Pqu2yvN5Rs63vvUt+zwjI0PDhw/Xueeeq5dfflk33HBDu6/jc3PG9OnT9e6776qwsLDVc/y8dW+0AHYjqampio2NbfU/m8rKylb/k0J0JCUladiwYfroo4/s2cAn+rz8fr9qa2tVVVXVbhlEXrg+K7/fr927d7d6/z179vB5OqR///4699xz9dFHH0nic4umGTNm6KWXXtIbb7yhs88+236cn7eegQDYjSQkJCgzM1MrV64MeXzlypUaPXp0lGqF5mpqavTee++pf//+GjhwoPx+f8jnVVtbq1WrVtmfV2ZmpuLj40PKlJeXq7S0lM/UQeH6rEaNGqVgMKj169fbZdatW6dgMMjn6ZC9e/dqx44d6t+/vyQ+t2gwxmj69Ol64YUX9Prrr2vgwIEhz/Pz1kNEZeoJ2pWfn2/i4+PN7373O7NlyxYza9Ysk5SUZD799NNoV82V5syZY958803zySefmKKiIpOdnW2Sk5Ptz+Pee+81Pp/PvPDCC2bTpk3mO9/5junfv7+prq623+P22283Z599tnn11VfNO++8Y66++mpzySWXmGPHjkXryzotHThwwGzcuNFs3LjRSDKLFy82GzduNNu2bTPGhO+zmjBhgrn44ovN2rVrzdq1a82wYcNMdna241/v6eJEn9uBAwfMnDlzzJo1a8zWrVvNG2+8YUaNGmXOOussPrco+sEPfmB8Pp958803TXl5uX07fPiwXYaft+6PANgN/fa3vzXnnnuuSUhIMF/84hftqfVw3re+9S3Tv39/Ex8fbwKBgLnhhhvM5s2b7ecbGhrM3Xffbfx+v/F6veZLX/qS2bRpU8h7HDlyxEyfPt307dvXJCYmmuzsbLN9+3anv5TT3htvvGEktbrdfPPNxpjwfVZ79+41N954o0lOTjbJycnmxhtvNFVVVQ59laefE31uhw8fNllZWeZzn/uciY+PN+ecc465+eabW30mfG7OauvzkmR+//vf22X4eev+PMYY43SrIwAAAKKHMYAAAAAuQwAEAABwGQIgAACAyxAAAQAAXIYACAAA4DIEQAAAAJchAAIAALgMARAAAMBlCIAAAAAuQwAEAABwGQIgAACAyxAAAQAAXIYACAAA4DIEQAAAAJchAAIAALgMARAAAMBlCIAAAAAuQwAEAABwGQIgAACAyxAAAQAAXIYACAAA4DIEQAAAAJchAAIAALgMARAAAMBlCIAAAAAuQwAEAABwGQIgAACAyxAAAQAAXIYACAAA4DIEQAAAAJchAAIAALjM/wdq3rK8dHL4iAAAAABJRU5ErkJggg==", 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\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "model = spectrum.metadata['peak_fit']['peak_model'].copy()\n", - "spectrum.energy_loss -= 250\n", - "spectrum.energy_loss *= 0.05\n", - "spectrum.energy_loss += 250 \n", - "\n", - "import scipy\n", - "help(eels_tools.gauss)\n", - "gauss = eels_tools.gauss(spectrum.energy_loss, [spectrum.energy_loss[1024], 1, .2])\n", - "gauss2 = eels_tools.gauss(spectrum.energy_loss, [spectrum.energy_loss[1024], 1, .1])\n", - "plt.figure()\n", - "plt.plot(gauss)\n", - "plt.plot(model)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([12.5 , 12.55, 12.6 , 12.65])" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "spectrum.energy_loss.values[:4]" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "64136d3e706f4d3ba20fce25a69a5f5d", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "spectrum = infoWidget.dataset.copy()\n", - "model = spectrum.metadata['peak_fit']['peak_model']\n", - "zero_loss = np.array(np.roll(zero_loss, -np.argmax(np.array(zero_loss))))\n", - "j = np.fft.fft(model/model.sum())\n", - "z = np.fft.fft(zero_loss/zero_loss.sum()+1e-12)\n", - "#z2 = np.fft.fft(gauss2/gauss2.sum()*2)\n", - "\n", - "red =(np.fft.ifft(j/z).real)#\n", - "plt.figure()\n", - "plt.plot(spectrum.energy_loss, red/red.sum(), label='deconvoluted', linewidth=3)\n", - "#plt.plot(zero_loss/zero_loss.sum())\n", - "plt.plot(spectrum.energy_loss, model/model.sum(), label='Gaussian mixing')\n", - "# plt.plot(red-model)\n", - "plt.ylim(0,.005)\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "285.8, 1.15, 58843\n", - "304.8, 13.34, 77560\n", - "293.0, 2.01, 80868\n", - "292.1, 0.64, 74606\n", - "296.5, 6.59, 64373\n", - "301.8, 2.73, 12192\n", - "308.9, 2.47, 3753\n", - "287.2, 2.06, 13455\n", - "290.2, 0.99, 6659\n", - "329.1, 26.32, 40827\n", - "289.0, 1.82, 11961\n" - ] - } - ], - "source": [ - "for peak in spectrum.metadata['peak_fit']['peaks'].values():\n", - " print (f\"{peak['position']:.1f}, {peak['width']:.2f}, {peak['amplitude']:.0f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'peakFitWidget' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[29], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mpeakFitWidget\u001b[49m\u001b[38;5;241m.\u001b[39mfit_peaks()\n", - "\u001b[1;31mNameError\u001b[0m: name 'peakFitWidget' is not defined" - ] - } - ], - "source": [ - "peakFitWidget.fit_peaks()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "67101ddd2de9426b8e6a99bb6314cb2d", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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\n", - " \n", - "
\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.close('all')\n", - "plt.figure()\n", - "plt.plot(peakFitWidget.energy_scale,peakFitWidget.peak_model, label='model')\n", - "plt.plot(peakFitWidget.energy_scale,peakFitWidget.dataset, label='spectrum')\n", - "plt.plot(peakFitWidget.energy_scale, peakFitWidget.dataset-peakFitWidget.peak_model-resolution_functions, label='dif')\n", - "plt.ylim(-4e7,1e8)\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 215, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "too many values to unpack (expected 2)", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[215], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m dataset \u001b[38;5;241m=\u001b[39m peakFitWidget\u001b[38;5;241m.\u001b[39mdataset\n\u001b[1;32m----> 2\u001b[0m peak_model, peak_out_list \u001b[38;5;241m=\u001b[39m find_peaks(dataset\u001b[38;5;241m-\u001b[39mresolution_functions, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m30\u001b[39m)\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(peak_out_list\u001b[38;5;241m.\u001b[39mreshape([\u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mlen\u001b[39m(peak_out_list)\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m3\u001b[39m),\u001b[38;5;241m3\u001b[39m]))\n\u001b[0;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure()\n", - "\u001b[1;31mValueError\u001b[0m: too many values to unpack (expected 2)" - ] - } - ], - "source": [ - "dataset = peakFitWidget.dataset\n", - "peak_model, peak_out_list = find_peaks(dataset-resolution_functions, -1, 30)\n", - "print(peak_out_list.reshape([int(len(peak_out_list)/3),3]))\n", - "plt.figure()\n", - "plt.plot(peak_model)\n", - "plt.plot(dataset-resolution_functions)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'experiment': {'single_exposure_time': 5.0,\n", - " 'exposure_time': 100.0,\n", - " 'number_of_frames': 20,\n", - " 'collection_angle': 50.0,\n", - " 'convergence_angle': 5.42,\n", - " 'acceleration_voltage': 60000.0,\n", - " 'flux_ppm': 1477088.59375,\n", - " 'count_conversion': 1,\n", - " 'beam_current': 0},\n", - " 'zero_loss': {'shifted': array([-0.2791726])},\n", - " 'peak_fit': {'fit_start': 270.05000372603536,\n", - " 'fit_end': 350.3000052496791,\n", - " 'peaks': {'0': {'position': 285.5328133398505,\n", - " 'amplitude': 56813.851980084386,\n", - " 'width': 1.1447315680844505,\n", - " 'type': 'Gauss',\n", - " 'associated_edge': '',\n", - " 'asymmetry': 0.0},\n", - " '1': {'position': 291.8502789181616,\n", - " 'amplitude': 80137.36537116366,\n", - " 'width': 0.631699853337263,\n", - " 'type': 'Gauss',\n", - " 'associated_edge': '',\n", - " 'asymmetry': 0.0},\n", - " '2': {'position': 292.68460898549887,\n", - " 'amplitude': 67856.46722391486,\n", - " 'width': 1.6722959462172458,\n", - " 'type': 'Gauss',\n", - " 'associated_edge': '',\n", - " 'asymmetry': 0.0},\n", - " '3': {'position': 288.33077096412796,\n", - " 'amplitude': 15200.2171172901,\n", - " 'width': 4.03592603054892,\n", - " 'type': 'Gauss',\n", - " 'associated_edge': '',\n", - " 'asymmetry': 0.0},\n", - " '4': {'position': 300.97498872546737,\n", - " 'amplitude': 29204.484305388254,\n", - " 'width': 2.9452493043214063,\n", - " 'type': 'Gauss',\n", - " 'associated_edge': '',\n", - " 'asymmetry': 0.0},\n", - " '5': {'position': 431.4963301648659,\n", - " 'amplitude': 62303.24268512067,\n", - " 'width': 160.13636902692892,\n", - " 'type': 'Gauss',\n", - " 'associated_edge': '',\n", - " 'asymmetry': 0.0},\n", - " '6': {'position': 297.57744196974863,\n", - " 'amplitude': 82438.00185689674,\n", - " 'width': 4.570549606775482,\n", - " 'type': 'Gauss',\n", - " 'associated_edge': '',\n", - " 'asymmetry': 0.0},\n", - " '7': {'position': 306.55294236999856,\n", - " 'amplitude': 64324.25014690288,\n", - " 'width': 8.408828675311156,\n", - " 'type': 'Gauss',\n", - " 'associated_edge': '',\n", - " 'asymmetry': 0.0},\n", - " '8': {'position': 327.23866847395425,\n", - " 'amplitude': 24332.78308524786,\n", - " 'width': 16.520135069307326,\n", - " 'type': 'Gauss',\n", - " 'associated_edge': '',\n", - " 'asymmetry': 0.0},\n", - " '9': {'position': 302.73254089477655,\n", - " 'amplitude': 30379.325543758885,\n", - " 'width': 4.215055093093726,\n", - " 'type': 'Gauss',\n", - " 'associated_edge': '',\n", - " 'asymmetry': 0.0},\n", - " '10': {'position': 313.39578279938394,\n", - " 'amplitude': 13507.040368003669,\n", - " 'width': 9.086317779116715,\n", - " 'type': 'Gauss',\n", - " 'associated_edge': '',\n", - " 'asymmetry': 0.0},\n", - " '11': {'position': 293.7938526566573,\n", - " 'amplitude': 56893.252049088034,\n", - " 'width': 3.7967168653233117,\n", - " 'type': 'Gauss',\n", - " 'associated_edge': '',\n", - " 'asymmetry': 0.0}},\n", - " 'edge_model': array([0., 0., 0., ..., 0., 0., 0.]),\n", - " 'peak_model': array([0., 0., 0., ..., 0., 0., 0.])}}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "infoWidget.datasets['Channel_000'].metadata" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'resolution_functions' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[28], line 73\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m p_out\n\u001b[0;32m 72\u001b[0m fit_end \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m40\u001b[39m\n\u001b[1;32m---> 73\u001b[0m peak_out_list \u001b[38;5;241m=\u001b[39m find_peaks(infoWidget\u001b[38;5;241m.\u001b[39mselected_dataset\u001b[38;5;241m-\u001b[39m\u001b[43mresolution_functions\u001b[49m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, fit_end)\n\u001b[0;32m 74\u001b[0m p \u001b[38;5;241m=\u001b[39m fit_peaks(dataset\u001b[38;5;241m-\u001b[39mresolution_functions,peak_out_list,\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, fit_end)\n\u001b[0;32m 75\u001b[0m model \u001b[38;5;241m=\u001b[39m model_ll(energy_scale,np\u001b[38;5;241m.\u001b[39marray(p), \u001b[38;5;28;01mFalse\u001b[39;00m)\n", - "\u001b[1;31mNameError\u001b[0m: name 'resolution_functions' is not defined" - ] - } - ], - "source": [ - "import scipy\n", - "def residuals_ll(p, x, y, only_positive_intensity):\n", - " \"\"\"part of fit\"\"\"\n", - "\n", - " err = (y - model_ll(x, p, only_positive_intensity)) / np.sqrt(np.abs(y))\n", - " return err\n", - "\n", - "def model_ll(x, p, only_positive_intensity):\n", - " \"\"\"part of fit\"\"\"\n", - "\n", - " y = np.zeros(len(x))\n", - "\n", - " number_of_peaks = int(len(p) / 3)\n", - " for i in range(number_of_peaks):\n", - " if only_positive_intensity:\n", - " p[i * 3 + 1] = abs(p[i * 3 + 1])\n", - " p[i * 3 + 2] = abs(p[i * 3 + 2])\n", - " if p[i * 3 + 2] > abs(p[i * 3]) * 4.29193 / 2.0:\n", - " p[i * 3 + 2] = abs(p[i * 3]) * 4.29193 / 2. # ## width cannot extend beyond zero, maximum is FWTM/2\n", - "\n", - " y = y + gauss(x, p[i * 3:])\n", - "\n", - " return y\n", - "\n", - "def gauss(x, p): # p[0]==mean, p[1]= amplitude p[2]==fwhm,\n", - " \"\"\"Gaussian Function\n", - "\n", - " p[0]==mean, p[1]= amplitude p[2]==fwhm\n", - " area = np.sqrt(2* np.pi)* p[1] * np.abs(p[2] / 2.3548)\n", - " FWHM = 2 * np.sqrt(2 np.log(2)) * sigma = 2.3548 * sigma\n", - " sigma = FWHM/3548\n", - " \"\"\"\n", - " if p[2] == 0:\n", - " return x * 0.\n", - " else:\n", - " return p[1] * np.exp(-(x - p[0]) ** 2 / (2.0 * (p[2] / 2.3548) ** 2))\n", - "\n", - "def find_peaks(dataset, fit_start, fit_end):\n", - " energy_scale = dataset.get_spectral_dims(return_axis=True)[0].values\n", - "\n", - " start_channel = np.searchsorted(energy_scale, fit_start)\n", - " end_channel = np.searchsorted(energy_scale, fit_end)\n", - " spectrum = np.abs(np.array(dataset)[start_channel:end_channel])\n", - " i_pk = scipy.signal.find_peaks_cwt(spectrum, widths=range(3, len(energy_scale) // 30)) # \n", - " \n", - " p_in = np.ravel([[energy_scale[i]-fit_start, spectrum[i], .7] for i in i_pk])\n", - " \n", - " return p_in\n", - " \n", - "def fit_peaks(spectrum, pin, start_fit, end_fit, only_positive_intensity=False):\n", - "\n", - " energy_scale = spectrum.get_spectral_dims(return_axis=True)[0]\n", - " start_fit = np.searchsorted(energy_scale, start_fit)\n", - " end_fit = np.searchsorted(energy_scale, end_fit)\n", - " \n", - " fit_energy = energy_scale[start_fit:end_fit]\n", - " spectrum = np.array(spectrum)\n", - " fit_spectrum = spectrum[start_fit:end_fit]\n", - "\n", - " #pin_flat = [item for sublist in pin for item in sublist]\n", - " [p_out, _] = leastsq(residuals_ll, np.array(pin), args=(fit_energy, fit_spectrum,\n", - " only_positive_intensity))\n", - " #p_out, pcov = curve_fit(residuals_ll, fit_energy, fit_spectrum, p0=np.array(pin))\n", - " p = []\n", - " for i in range(int(len(pin)/3)):\n", - " if only_positive_intensity:\n", - " p_out[i * 3 + 1] = abs(p_out[i * 3 + 1])\n", - " p.append([p_out[i * 3], p_out[i * 3 + 1], abs(p_out[i * 3 + 2])])\n", - " return p_out\n", - "\n", - "\n", - "fit_end = 40\n", - "peak_out_list = find_peaks(infoWidget.selected_dataset-resolution_functions, -1, fit_end)\n", - "p = fit_peaks(dataset-resolution_functions,peak_out_list,-1, fit_end)\n", - "model = model_ll(energy_scale,np.array(p), False)\n", - "\n", - "print(len(peak_out_list)/3)\n", - "\n", - "print(fit_end)\n", - "print(p.reshape([int(len(p)/3),3]))\n", - "print(len(p)/3)\n", - "plt.figure()\n", - "plt.plot(energy_scale, model)\n", - "plt.plot(energy_scale, dataset-resolution_functions)\n", - "plt.plot(energy_scale, dataset-resolution_functions-model)\n", - "plt.ylim(0,1e8)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 254, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "23.0\n", - "1877529801.672969\n", - "23.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\py11\\Lib\\site-packages\\scipy\\optimize\\_minpack_py.py:494: RuntimeWarning: Number of calls to function has reached maxfev = 14000.\n", - " warnings.warn(errors[info][0], RuntimeWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 254, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "fa9bc54f5e194e18a5e19cefcf40c501", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", 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label='difference')\n", - "plt.ylim(0,1e8)\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": 237, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-1198221.9850117 2628210.59194557 4581554.52393588 ...\n", - " -2260492.70274396 -2410574.75171163 -2582093.5075449 ]\n", - "(2048,)\n", - "11.0\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 237, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3ff30cdd8d574d8597a71059766a4316", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", 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\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model[:130] = 0.\n", - "model2_f = scipy.fft.fft(np.array(dataset)) #model+np.array(resolution_functions))\n", - "\n", - "res_f =scipy.fft.fft(np.array(resolution_functions))\n", - "smear = gauss(energy_scale, [0,1, .2])\n", - "smear *= resolution_functions.sum()/smear.sum()\n", - "gaus_f = scipy.fft.fft(smear) # p[0]==mean, p[1]= amplitude p[2]==fwhm,\n", - "model2 = scipy.fft.ifft(model2_f/res_f*gaus_f).real\n", - "print(model2)\n", - "#model2 = model+np.array(resolution_functions)\n", - "print(model2.shape)\n", - "print(len(p)/3)\n", - "plt.figure()\n", - "plt.plot(energy_scale, model+resolution_functions, label='model')\n", - "plt.plot(energy_scale, model2, label='model1')\n", - "plt.plot(energy_scale, dataset, label='spectrum')\n", - "plt.plot(energy_scale, dataset-resolution_functions-model, label='difference')\n", - "plt.ylim(0,1e8)\n", - "plt.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 193, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 193, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a189faa4ebf64464ab15201f031af421", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "#plt.plot(energy_scale, model+resolution_functions)\n", - "plt.plot(energy_scale, model2)" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2, 3, 4, 5, 6])" - ] - }, - "execution_count": 86, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.append([1,2,3], [4,5,6])" - ] - }, - { - "cell_type": "code", - "execution_count": 224, - "metadata": {}, - "outputs": [], - "source": [ - "plt.close('all')" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([0., 0., 0., ..., 0., 0., 0.]),\n", - " array([2.03888490e+01, 6.60088441e+05, 9.51555853e+00]),\n", - " 7)" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "\n", - "\n", - "\n", - "peak_model, peak_out_list = eels_tools.find_peaks(infoWidget.selected_dataset, 1,40)\n", - "\n", - "new_list = np.reshape(peak_out_list, [len(peak_out_list) // 3, 3])\n", - "area = np.sqrt(2 * np.pi) * np.abs(new_list[:, 1]) * np.abs(new_list[:, 2] / np.sqrt(2 * np.log(2)))\n", - "arg_list = np.argsort(area)[::-1]\n", - "area = area[arg_list]\n", - "peak_out_list = new_list[arg_list]\n", - "\n", - "number_of_peaks = np.searchsorted(area * -1, -np.average(area))\n", - "\n", - "peak_model, peak_out_list[0], number_of_peaks" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(19, 3)" - ] - }, - "execution_count": 94, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "peak_model, peak_out_list[0], number_of_peaks" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [], - "source": [ - "peakmodel, peak_out_list, number_of_peaks = smooth(infoWidget.selected_dataset, 1, False)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "def smooth(dataset, iterations, advanced_present):\n", - " \"\"\"Gaussian mixture model (non-Bayesian)\n", - "\n", - " Fit lots of Gaussian to spectrum and let the program sort it out\n", - " We sort the peaks by area under the Gaussians, assuming that small areas mean noise.\n", - "\n", - " \"\"\"\n", - "\n", - " # TODO: add sensitivity to dialog and the two functions below\n", - " \n", - " # peaks = dataset.metadata['peak_fit']\n", - "\n", - " peaks ={'fit_start':1,\n", - " 'fit_end': 40}\n", - "\n", - " peak_model, peak_out_list = eels_tools.find_peaks(dataset, peaks['fit_start'], peaks['fit_end'])\n", - " peak_out_list = [peak_out_list]\n", - "\n", - " flat_list = [item for sublist in peak_out_list for item in sublist]\n", - " new_list = np.reshape(flat_list, [len(flat_list) // 3, 3])\n", - " area = np.sqrt(2 * np.pi) * np.abs(new_list[:, 1]) * np.abs(new_list[:, 2] / np.sqrt(2 * np.log(2)))\n", - " arg_list = np.argsort(area)[::-1]\n", - " area = area[arg_list]\n", - " peak_out_list = new_list[arg_list]\n", - "\n", - " number_of_peaks = np.searchsorted(area * -1, -np.average(area))\n", - "\n", - " return peak_model, peak_out_list, number_of_peaks\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "6" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(peakFitWidget.peak_out_list)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "peakFitWidget.sidebar[7,0].value = 2" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(7,\n", - " [('Peak 1', 0),\n", - " ('ll', -2),\n", - " ('Peak 3', 2),\n", - " ('Peak 4', 3),\n", - " ('Peak 5', 4),\n", - " ('Peak 6', 5),\n", - " ('add peak', -1)])" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "options = list(peakFitWidget.sidebar[7,0].options)\n", - "options.insert(-1, (f'Peak {len(options)}', len(options)-1))\n", - "len(options), options" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "67a009afa3b84bae87ca9c3395503b13", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.plot(resolution_functions)\n", - "plt.plot(eels_dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " \n", - "view = resolution_functions.plot()\n", - "view.gca().plot(eels_dataset.energy_loss, eels_dataset)\n", - "eels_dataset.metadata\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'tuple' object has no attribute 'metadata'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\EELS\\Analyse_Low_Loss.ipynb Cell 11\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> 1\u001b[0m energy_shift \u001b[39m=\u001b[39m resolution_functions\u001b[39m.\u001b[39;49mmetadata[\u001b[39m'\u001b[39m\u001b[39mlow_loss\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m'\u001b[39m\u001b[39mshifts\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[0;32m 2\u001b[0m fwhm \u001b[39m=\u001b[39m resolution_functions\u001b[39m.\u001b[39mmetadata[\u001b[39m'\u001b[39m\u001b[39mlow_loss\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m'\u001b[39m\u001b[39mwidths\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[0;32m 4\u001b[0m t_mfp \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mlog(eels_dataset\u001b[39m.\u001b[39msum(axis\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m)\u001b[39m/\u001b[39mresolution_functions\u001b[39m.\u001b[39msum(axis\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m))\n", - "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'metadata'" - ] - } - ], - "source": [ - "energy_shift = resolution_functions.metadata['low_loss']['shifts']\n", - "fwhm = resolution_functions.metadata['low_loss']['widths']\n", - "\n", - "t_mfp = np.log(eels_dataset.sum(axis=2)/resolution_functions.sum(axis=2))\n", - "\n", - "plt.figure()\n", - "ax1 = plt.subplot(131)\n", - "plt.imshow(energy_shift)\n", - "plt.colorbar()\n", - "plt.title(f' energy shift - mean: {np.mean(energy_shift):.2f}, std {np.std(energy_shift):.3f}')\n", - "ax2 = plt.subplot(132)\n", - "plt.imshow(fwhm)\n", - "plt.colorbar()\n", - "plt.title(f' peak widths - mean: {np.mean(fwhm):.2f}, std {np.std(fwhm):.3f}')\n", - "ax3 = plt.subplot(133)\n", - "plt.imshow(t_mfp)\n", - "plt.colorbar()\n", - "plt.title(f' thickness - mean: {np.mean(np.array(t_mfp)):.2f}, std {np.std(np.array(t_mfp)):.3f}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Shift energy scale" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c34d26fa707f4790989755df5871d0d4", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", 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", 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\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "shifted_resolution_functions = eels_tools.get_resolution_functions(shifted_dataset, zero_loss_fit_width=.9)\n", - "dif = shifted_dataset - shifted_resolution_functions\n", - "view = dif.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\jupyter_client\\session.py:719: UserWarning: Message serialization failed with:\n", - "Out of range float values are not JSON compliant\n", - "Supporting this message is deprecated in jupyter-client 7, please make sure your message is JSON-compliant\n", - " content = self.pack(content)\n" - ] - }, - { - "ename": "ValueError", - "evalue": "array of sample points is empty", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[8], line 5\u001b[0m\n\u001b[0;32m 1\u001b[0m datasets \u001b[39m=\u001b[39m fileWidget\u001b[39m.\u001b[39mdatasets\n\u001b[0;32m 2\u001b[0m \u001b[39m#datasets['energy_corrected'] = shifted_dataset\u001b[39;00m\n\u001b[0;32m 3\u001b[0m \u001b[39m#datasets['energy_corrected_resolution_function'] = shifted_resolution_functions\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m infoWidget\u001b[39m=\u001b[39m interactive_eels\u001b[39m.\u001b[39;49mInfoWidget(datasets)\n", - "File \u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\EELS\\../..\\pyTEMlib\\info_dialog.py:428\u001b[0m, in \u001b[0;36mInfoWidget.__init__\u001b[1;34m(self, datasets)\u001b[0m\n\u001b[0;32m 423\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure\u001b[39m.\u001b[39mcanvas\u001b[39m.\u001b[39mtoolbar_visible \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m 426\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxis \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m--> 428\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mset_dataset()\n\u001b[0;32m 429\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_action()\n\u001b[0;32m 431\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstart_cursor \u001b[39m=\u001b[39m ipywidgets\u001b[39m.\u001b[39mFloatText(value\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, description\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mStart:\u001b[39m\u001b[39m'\u001b[39m, disabled\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, color\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mblack\u001b[39m\u001b[39m'\u001b[39m, layout\u001b[39m=\u001b[39mipywidgets\u001b[39m.\u001b[39mLayout(width\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m200px\u001b[39m\u001b[39m'\u001b[39m))\n", - "File \u001b[1;32mc:\\Users\\gduscher\\Documents\\Github\\pyTEMlib\\notebooks\\EELS\\../..\\pyTEMlib\\info_dialog.py:527\u001b[0m, in \u001b[0;36mInfoWidget.set_dataset\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m 525\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msidebar[\u001b[39m13\u001b[39m,\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mvalue \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdatasets[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkey]\u001b[39m.\u001b[39mmetadata[\u001b[39m'\u001b[39m\u001b[39mexperiment\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m'\u001b[39m\u001b[39mbeam_current\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[0;32m 526\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfigure\u001b[39m.\u001b[39mclear()\n\u001b[1;32m--> 527\u001b[0m view \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdataset\u001b[39m.\u001b[39;49mplot(figure\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfigure)\n\u001b[0;32m 528\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39mview, \u001b[39m'\u001b[39m\u001b[39maxes\u001b[39m\u001b[39m'\u001b[39m):\n\u001b[0;32m 529\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxis \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39mview\u001b[39m.\u001b[39maxes[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]\n", - "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\sidpy\\sid\\dataset.py:556\u001b[0m, in \u001b[0;36mDataset.plot\u001b[1;34m(self, verbose, figure, **kwargs)\u001b[0m\n\u001b[0;32m 554\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mview \u001b[39m=\u001b[39m ComplexSpectralImageVisualizer(\u001b[39mself\u001b[39m, figure\u001b[39m=\u001b[39mfigure, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 555\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 556\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mview \u001b[39m=\u001b[39m SpectralImageVisualizer(\u001b[39mself\u001b[39;49m, figure\u001b[39m=\u001b[39;49mfigure, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[0;32m 557\u001b[0m \u001b[39m# plt.show()\u001b[39;00m\n\u001b[0;32m 558\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 559\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mNotImplementedError\u001b[39;00m(\u001b[39m'\u001b[39m\u001b[39mDatasets with data_type \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m cannot be plotted, yet.\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdata_type))\n", - "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\sidpy\\viz\\dataset_viz.py:593\u001b[0m, in \u001b[0;36mSpectralImageVisualizer.__init__\u001b[1;34m(self, dset, figure, horizontal, **kwargs)\u001b[0m\n\u001b[0;32m 591\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39menergy_scale)\u001b[39m!=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mspectrum\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m]:\n\u001b[0;32m 592\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mspectrum \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mspectrum\u001b[39m.\u001b[39mT\n\u001b[1;32m--> 593\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49maxes[\u001b[39m1\u001b[39;49m]\u001b[39m.\u001b[39;49mplot(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49menergy_scale, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mspectrum\u001b[39m.\u001b[39;49mcompute())\n\u001b[0;32m 594\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes[\u001b[39m1\u001b[39m]\u001b[39m.\u001b[39mset_title(\u001b[39m'\u001b[39m\u001b[39mspectrum \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m, \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mx, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39my))\n\u001b[0;32m 595\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mxlabel \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdset\u001b[39m.\u001b[39mlabels[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mspec_dim]\n", - "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1690\u001b[0m, in \u001b[0;36mAxes.plot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1688\u001b[0m lines \u001b[39m=\u001b[39m [\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_lines(\u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39mdata, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)]\n\u001b[0;32m 1689\u001b[0m \u001b[39mfor\u001b[39;00m line \u001b[39min\u001b[39;00m lines:\n\u001b[1;32m-> 1690\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49madd_line(line)\n\u001b[0;32m 1691\u001b[0m \u001b[39mif\u001b[39;00m scalex:\n\u001b[0;32m 1692\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_request_autoscale_view(\u001b[39m\"\u001b[39m\u001b[39mx\u001b[39m\u001b[39m\"\u001b[39m)\n", - "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2304\u001b[0m, in \u001b[0;36m_AxesBase.add_line\u001b[1;34m(self, line)\u001b[0m\n\u001b[0;32m 2301\u001b[0m \u001b[39mif\u001b[39;00m line\u001b[39m.\u001b[39mget_clip_path() \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 2302\u001b[0m line\u001b[39m.\u001b[39mset_clip_path(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpatch)\n\u001b[1;32m-> 2304\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_update_line_limits(line)\n\u001b[0;32m 2305\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m line\u001b[39m.\u001b[39mget_label():\n\u001b[0;32m 2306\u001b[0m line\u001b[39m.\u001b[39mset_label(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m_child\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_children)\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n", - "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2327\u001b[0m, in \u001b[0;36m_AxesBase._update_line_limits\u001b[1;34m(self, line)\u001b[0m\n\u001b[0;32m 2323\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_update_line_limits\u001b[39m(\u001b[39mself\u001b[39m, line):\n\u001b[0;32m 2324\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 2325\u001b[0m \u001b[39m Figures out the data limit of the given line, updating self.dataLim.\u001b[39;00m\n\u001b[0;32m 2326\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 2327\u001b[0m path \u001b[39m=\u001b[39m line\u001b[39m.\u001b[39;49mget_path()\n\u001b[0;32m 2328\u001b[0m \u001b[39mif\u001b[39;00m path\u001b[39m.\u001b[39mvertices\u001b[39m.\u001b[39msize \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m 2329\u001b[0m \u001b[39mreturn\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\matplotlib\\lines.py:1029\u001b[0m, in \u001b[0;36mLine2D.get_path\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1027\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Return the `~matplotlib.path.Path` associated with this line.\"\"\"\u001b[39;00m\n\u001b[0;32m 1028\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_invalidy \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_invalidx:\n\u001b[1;32m-> 1029\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mrecache()\n\u001b[0;32m 1030\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_path\n", - "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\matplotlib\\lines.py:681\u001b[0m, in \u001b[0;36mLine2D.recache\u001b[1;34m(self, always)\u001b[0m\n\u001b[0;32m 679\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_x_filled \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_x\u001b[39m.\u001b[39mcopy()\n\u001b[0;32m 680\u001b[0m indices \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marange(\u001b[39mlen\u001b[39m(x))\n\u001b[1;32m--> 681\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_x_filled[nanmask] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49minterp(\n\u001b[0;32m 682\u001b[0m indices[nanmask], indices[\u001b[39m~\u001b[39;49mnanmask], \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_x[\u001b[39m~\u001b[39;49mnanmask])\n\u001b[0;32m 683\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 684\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_x_filled \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_x\n", - "File \u001b[1;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36minterp\u001b[1;34m(*args, **kwargs)\u001b[0m\n", - "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\numpy\\lib\\function_base.py:1595\u001b[0m, in \u001b[0;36minterp\u001b[1;34m(x, xp, fp, left, right, period)\u001b[0m\n\u001b[0;32m 1592\u001b[0m xp \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mconcatenate((xp[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m:]\u001b[39m-\u001b[39mperiod, xp, xp[\u001b[39m0\u001b[39m:\u001b[39m1\u001b[39m]\u001b[39m+\u001b[39mperiod))\n\u001b[0;32m 1593\u001b[0m fp \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mconcatenate((fp[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m:], fp, fp[\u001b[39m0\u001b[39m:\u001b[39m1\u001b[39m]))\n\u001b[1;32m-> 1595\u001b[0m \u001b[39mreturn\u001b[39;00m interp_func(x, xp, fp, left, right)\n", - "\u001b[1;31mValueError\u001b[0m: array of sample points is empty" - ] - } - ], - "source": [ - "datasets = fileWidget.datasets\n", - "#datasets['energy_corrected'] = shifted_dataset\n", - "#datasets['energy_corrected_resolution_function'] = shifted_resolution_functions\n", - "\n", - "infoWidget= interactive_eels.InfoWidget(datasets)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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"File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:54\u001b[0m, in \u001b[0;36m_wrapfunc\u001b[1;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[0;32m 52\u001b[0m bound \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(obj, method, \u001b[39mNone\u001b[39;00m)\n\u001b[0;32m 53\u001b[0m \u001b[39mif\u001b[39;00m bound \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m---> 54\u001b[0m \u001b[39mreturn\u001b[39;00m _wrapit(obj, method, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[0;32m 56\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m 57\u001b[0m \u001b[39mreturn\u001b[39;00m bound(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)\n", - "File \u001b[1;32mc:\\Users\\gduscher\\AppData\\Local\\anaconda3\\envs\\pyTEMlib\\Lib\\site-packages\\numpy\\core\\fromnumeric.py:43\u001b[0m, in \u001b[0;36m_wrapit\u001b[1;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[0;32m 41\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m:\n\u001b[0;32m 42\u001b[0m wrap \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m---> 43\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39;49m(asarray(obj), method)(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[0;32m 44\u001b[0m \u001b[39mif\u001b[39;00m wrap:\n\u001b[0;32m 45\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(result, mu\u001b[39m.\u001b[39mndarray):\n", - "\u001b[1;31mValueError\u001b[0m: object of too small depth for desired array" - ] - } - ], - "source": [ - "FWHM, energy_shift = eels_tools.fix_energy_scale(eels_dataset)\n", - "\n", - "print(f'Zero Loss with energy resolution of {FWHM:.2f} eV at position {energy_shift:.3f} eV')\n", - "eels_dataset.energy_loss -= energy_shift\n", - "\n", - "zero_loss, _ = eels_tools.resolution_function(eels_dataset.energy_loss, eels_dataset, .4)\n", - "print(zero_loss)\n", - "plt.figure()\n", - "plt.plot(eels_dataset.energy_loss, eels_dataset, label='spectrum')\n", - "plt.plot(eels_dataset.energy_loss, zero_loss, label = 'zero-loss')\n", - "plt.plot(eels_dataset.energy_loss, np.array(eels_dataset)-zero_loss , label = 'difference')\n", - "\n", - "plt.title ('Lorentzian Product Fit of Zero-Loss Peak')\n", - "#plt.xlim(-5,30)\n", - "plt.legend();\n", - "Izl = zero_loss.sum()\n", - "Itotal = np.array(eels_dataset).sum()\n", - "tmfp = np.log(Itotal/Izl)\n", - "print(f'Sum of Zero-Loss: {Izl:.3f} %')\n", - "print(f'Sum of Spectrum: {Itotal:.3f} %')\n", - "print (f'thickness [IMFP]: {tmfp:.5f}')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Fitting a Drude Function to Plasmon\n", - "\n", - "The position and the width are important materials parameters and we can derive them by fitting the Drude function to the volume plasmon region.\n", - "### Drude Function\n", - "\n", - "Most of the inelastically scattered electron arise from interaction with outer shell electrons. These interactions, therefore, have a high intensity and are easy to obtain. \n", - "\n", - "The energy-loss function $F_{el}$ on the other hand is determined by the dielectric function $\\varepsilon$ through:\n", - "\n", - "$$\n", - "F_{el} = \\Im \\left[\\frac{-1}{\\varepsilon(\\omega)} \\right]\n", - "$$\n", - "\n", - "The dielectric function in the Drude theory is given by two input parameters the position of the plasmon energy $E_p$\n", - "and the width of the plasmon $\\Gamma$\n", - "\n", - "$$ ε(ω) = ε1 + iε2 = 1 + χ = 1 − \\frac{\\omega_p^2}{\\omega^2+\\Gamma^2} + \\frac{i\\Gamma \\omega_p^2}{\\omega(\\omega^2+\\Gamma^2)}$$\n", - "Here $\\omega$ is the angular frequency (rad/s) of forced oscillation and $\\omega_p$ is the natural or resonance frequency for plasma oscillation, given by\n", - "$$ ω_p = \\sqrt{\\frac{ne^2}{(ε_0m_0)}} $$\n", - "A transmitted electron represents a sudden impulse of applied electric field, containing\n", - "all angular frequencies (Fourier components). Setting up a plasma oscillation of the loosely bound outer-shell electrons in a solid is equivalent to creating a pseudoparticle of energy $E_p = \\hbar \\omega_p$, known as a plasmon (Pines, 1963)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "hideCode": false, - "hidePrompt": false, - "tags": [] - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'spec' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[5], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01moptimize\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m leastsq, curve_fit\n\u001b[1;32m----> 2\u001b[0m eels_dataset \u001b[38;5;241m=\u001b[39m spec\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mDrude\u001b[39m(E,Ep,Ew, gamma\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[0;32m 4\u001b[0m eps \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m Ep\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m/\u001b[39m(E\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m+\u001b[39mEw\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m) \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39mj\u001b[38;5;241m*\u001b[39m Ew\u001b[38;5;241m*\u001b[39m Ep\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m/\u001b[39mE\u001b[38;5;241m/\u001b[39m(E\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m+\u001b[39mEw\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\n", - "\u001b[1;31mNameError\u001b[0m: name 'spec' is not defined" - ] - } - ], - "source": [ - "from scipy.optimize import leastsq, curve_fit\n", - "eels_dataset = spec\n", - "def Drude(E,Ep,Ew, gamma=1):\n", - " eps = 1 - Ep**2/(E**2+Ew**2) +1j* Ew* Ep**2/E/(E**2+Ew**2)\n", - " eps = 1 - (Ep**2 - Ew * E * 1j) / (E**2 + 2 * E * gamma * 1j) # Mod drude ter\n", - " elf = (-1/eps).imag\n", - " return eps,elf\n", - "\n", - "def errfDrude(p, y, x):\n", - " eps,elf = Drude(x,p[0],p[1])\n", - " err = y - p[2]*elf\n", - " #print (p,sum(np.abs(err)))\n", - " return np.abs(err)#/np.sqrt(y)\n", - "\n", - "\n", - "pin2 = np.array([9,1,.7, 1.11])\n", - "E = energy_scale = eels_dataset.energy_loss\n", - "startFit =np.argmin(abs(energy_scale-6))\n", - "endFit = np.argmin(abs(energy_scale-15))\n", - " \n", - "p2, lsq = leastsq(errfDrude, pin2, args=(eels_dataset[startFit:endFit], energy_scale[startFit:endFit]), maxfev=2000)\n", - "\n", - "eps, elf =Drude(energy_scale,p2[0],p2[1],p2[3])\n", - "drudePSD = p2[2]* elf\n", - "plt.figure()\n", - "\n", - "plt.plot(energy_scale,eels_dataset)\n", - "plt.plot(energy_scale,drudePSD)\n", - "plt.plot(energy_scale,eels_dataset-drudePSD)\n", - "plt.axhline(0, color='black')\n", - "\n", - "#plt.gca().set_xlim(0,40)\n", - "#plt.gca().set_ylim(-0.01,0.2)\n", - "print(f\"Drude Theory with Plamson Energy: {p2[0]:2f} eV and plasmon Width {p2[1]:.2f} eV\") \n", - "print(f\"Max of Plasmon at {energy_scale[drudePSD.argmax(0)]:.2f} eV\")\n", - "print(f\"Amplitude of {p2[2]:.2f} was deteremined by fit \")\n", - "p2\n" - ] - }, - { - "cell_type": "code", - "execution_count": 157, - "metadata": { - "hideCode": false, - "hideOutput": true, - "hidePrompt": false, - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:3: SyntaxWarning: invalid escape sequence '\\e'\n", - "<>:4: SyntaxWarning: invalid escape sequence '\\e'\n", - "<>:3: SyntaxWarning: invalid escape sequence '\\e'\n", - "<>:4: SyntaxWarning: invalid escape sequence '\\e'\n", - "C:\\Users\\gduscher\\AppData\\Local\\Temp\\ipykernel_17524\\467939617.py:3: SyntaxWarning: invalid escape sequence '\\e'\n", - " plt.plot(energy_scale,eps.real,label = 'Re($\\epsilon)$')\n", - "C:\\Users\\gduscher\\AppData\\Local\\Temp\\ipykernel_17524\\467939617.py:4: SyntaxWarning: invalid escape sequence '\\e'\n", - " plt.plot(energy_scale,eps.imag,label = 'Im($\\epsilon)$')\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9d236541265e430fa5408488c5cfbf4b", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", 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27, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0cc20b277e93457c962dd8f5cd789562", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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nZ+fgfYlBkjsGkAAIAAD8rKgApjiOf8SbMSbnWIrrunIcR48//rguvPBCffnLX9bSpUv16KOP9lsFXLJkiaLRaPpRU1Mz6N/heOVUAOkCBgAAWawIgBUVFQqHwznVvra2tpyqYMq4ceN02mmnKRqNpo9NnjxZxhjt3r27z2sWLVqkjo6O9GPXrl2D9yUGSW4FEAAAwM+KAFhcXKy6ujo1NTX5jjc1NWnGjBl9XjNz5kzt2bNHBw4cSB/74x//qFAopPHjx/d5TUlJicrLy32PQuPmjAGkAggAAPysCICStHDhQj3yyCNatWqVtm/frjvvvFPNzc1qaGiQlKjezZ07N33+DTfcoDFjxuhb3/qWtm3bpldeeUXf+c539Fd/9VcaMWJEvr7G8cuZBcwYQAAA4GfFJBBJmj17ttrb27V48WK1tLRoypQpamxs1MSJEyVJLS0tam5uTp8/atQoNTU16fbbb9e0adM0ZswYXX/99fre976Xr68wKHKXgSEAAgAAP8cY1gk5Vp2dnYpGo+ro6CiY7uCd215X7f/9i/TrNo3W2Pt35rFFAAAUlkL8+3uoWdMFjITsnUBCzAIGAABZCICWMW78k08CAACBRgC0THaPPhVAAACQjQBomdxJIAAAAH4EQMtkjwFkJxAAAJCNAGid7C5gJnkDAAA/AqBl3JxJIARAAADgRwC0TfYkEJZ5BAAAWQiAtkmOAYybxPQPdgIBAADZCICWcZOzgOPJW0sABAAA2QiAtkl2+brJW8s6gAAAIBsB0DKpZWDiCktiHUAAAJCLAGgZYxKzgDNdwFQAAQCAHwHQNqkuYCfVBcwYQAAA4EcAtExqKziXSSAAAKAfBEDbZE0CCTsEQAAA4EcAtEz2JBDvMQAAAIkAaJ3sLuDEMaqAAAAggwBonWQAdLwBkAogAADIIABaxriJap+3C9ilCxgAAHgQAC1j3MQ6gMZTAXSTxwAAACQCoLVc7yQQxgACAAAPAqBtkjuB+MYA0gUMAAA8CICWSVX7jG8WMF3AAAAggwBomdQkENfxTgKhCxgAAGQQAG2T6gJmFjAAAOgHAdAyJrn3r2EdQAAA0A8CoG3c1ELQ4ZxjAAAAEgHQPslqn38dQAIgAADIIABapu9ZwARAAACQQQC0DRVAAADwCQiAlslUAB3FjZM8SAAEAAAZBEDLOKmw54TkJm8vW8EBAAAvAqBlUuP9jBylYp/rshMIAADIIADaxmTWAUxPBGEMIAAA8CAAWiYz49dTAWQMIAAA8CAA2iY9BtDJjAFkL2AAAOBBALSNZx1AIyd5iAAIAAAyCIC2SVcAlQmATAIBAAAeBEDLeHcCcZ1UBZAxgAAAIIMAaBvPOoCZLmACIAAAyCAA2sa3DmCqC5gACAAAMgiA1klO+HCczDqATAIBAAAeBEDbpCqAToh1AAEAQJ8IgLZxMwtBu+kKIAEQAABkEAAtk+7s9UwCcVkIGgAAeBAAbWMSa/75J4GwDiAAAMggAFrGSRX7nEwApAsYAAB4EQAtY5IVQP86gHQBAwCADAKgbdI7gTgsBA0AAPpEALROah3AkIyTuL0sBA0AALwIgLbxbQWXPEQFEAAAeBAAbeMJe4Z1AAEAQB8IgLbxVABdJoEAAIA+EABtk94KLiw5qXUAqQACAIAMAqBlHNe7DExyEggVQAAA4EEAtE2fXcDsBAIAADKsCoDLly9XbW2tSktLVVdXp7Vr1x7Vda+99poikYg+97nPndgGDonkOoBOWEoGQIcuYAAA4GFNAFy9erUWLFige++9V5s3b9all16qWbNmqbm5ecDrOjo6NHfuXH3xi18copaeYKku4FBmJxCXHmAAAOBhTQBcunSp5s+fr5tuukmTJ0/WsmXLVFNToxUrVgx43c0336wbbrhB06dPH6KWnliOdx1AJ7UXMF3AAAAgw4oA2NPTo02bNqm+vt53vL6+XuvWrev3up/97Gd69913dd999x3Vz+nu7lZnZ6fvUXDSewGHPZNA6AIGAAAZVgTAffv2KR6Pq7Ky0ne8srJSra2tfV7zzjvv6J577tHjjz+uSCRyVD9nyZIlikaj6UdNTc1xt33webaCYx1AAADQBysCYIqT6vJMMsbkHJOkeDyuG264Qd/97nc1adKko/78RYsWqaOjI/3YtWvXcbd5sDmeMYCiCxgAAPTh6EpfBa6iokLhcDin2tfW1pZTFZSkrq4ubdy4UZs3b9Ztt90mSXJdV8YYRSIRvfDCC7riiityrispKVFJScmJ+RKDxbcXcKoCmMf2AACAgmNFBbC4uFh1dXVqamryHW9qatKMGTNyzi8vL9fWrVu1ZcuW9KOhoUHnnHOOtmzZoosuumiomj7oUpNAHCcsN3V7WQYGAAB4WFEBlKSFCxdqzpw5mjZtmqZPn66f/vSnam5uVkNDg6RE9+0HH3ygxx57TKFQSFOmTPFdP3bsWJWWluYcH36SYS8UktIVQAIgAADIsCYAzp49W+3t7Vq8eLFaWlo0ZcoUNTY2auLEiZKklpaWT1wT0AaZZWDC6WVgmAQCAAC8HEM6OGadnZ2KRqPq6OhQeXl5vpsjSdr0o/+luq4XtWHSdxTd2ajJvW/pjYt/rPO/dGO+mwYAQEEoxL+/h5oVYwCR4SRn/DqhsIyTvL1kfAAA4EEAtI3pax1AxgACAIAMAqBlUhVAhcJKTQIRARAAAHgQAK2TqAA6nr2AGeYJAAC8CICWCXnHALIOIAAA6AMB0DbJ7l7jqQCm1wYEAAAQAdA6TjLseSuAxqULGAAAZBAALZPZCi5za5kFDAAAvAiAlgmlwl44LKVDIBVAAACQQQC0TqYCyCQQAADQFwKgZVIVwMROIKwDCAAAchEALeOkunudzELQrAMIAAC8CICWSe0EEvLtBUwFEAAAZBAALZOuAIZCSm8FxzqAAADAgwBoGd9OIOkxgHQBAwCADAKgZRzPXsDpCiBdwAAAwIMAaJn0QtChSHodQCaBAAAALwKgZULpreAczzqA8Ty2CAAAFBoCoGXSXcChiEyIWcAAACAXAdAymUkgIRknIkkyVAABAIAHAdAyqQpgKJTZC9hxY/lsEgAAKDAEQMukxgAqFJIJUQEEAAC5CICWSc0CDoUi6Z1AqAACAAAvAqBlMrOAQ8n9gCUZKoAAACCDAGgZ7xjAVBcwy8AAAAAvAqBlMhXAcHI3EFEBBAAAPgRAy6QCYChMBRAAAPSNAGiZdAXQyYwBdKgAAgAADwKgZUKpMYDhsEQFEAAA9IEAaJmQSVUAw1JyKzjHsAwMAADIIABaxjsJRKFkF7DLXsAAACCDAGiZcDIAhouKMl3AVAABAIAHAdAyYSXG+0XCRYluYGV2BwEAAJAIgFYxrquIk1wGJhJJVwCZBQwAALwIgBaJxzNdveFIcWYMIAEQAAB4EAAtEov1pp+HIxE5VAABAEAfCIAWiXsCYCRSJIWpAAIAgFwEQIvEYt4u4KLEUjAiAAIAAD8CoEVcbxdwOCLHSXQBhwiAAADAgwBokXg8EQDjxlEoHJYTZhkYAACQiwBokdQYwLgSwY9lYAAAQF8IgBaJJ8cAxpIBMJSsANIFDAAAvAiAFnHTFcDkbU1VAEUXMAAAyCAAWiS1EHQ8uQVcKMQkEAAAkIsAaBE37h8D6NAFDAAA+kAAtIibNQkktRNISARAAACQQQC0iJvsAnZzJoEwBhAAAGQQAC2SXgfQSdxWJ1yU+JNJIAAAwIMAaBGTXQFMbgUXZgwgAADwIABaxI35ZwE74dQYQCqAAAAggwBokZwxgMlJIGETy1ubAABA4SEAWsQkxwC6qXUAi4olSRFmAQMAAA8CoEVcN1kBTAbASDIAhkUFEAAAZBAALZKeBOIku36LSiRJESaBAAAADwKgRbK7gCORxDIwRVQAAQCABwHQIrldwMkKIAEQAAB4WBUAly9frtraWpWWlqqurk5r167t99ynnnpKV155pU499VSVl5dr+vTpev7554ewtSdA3B8Aw6kxgI5JLxEDAABgTQBcvXq1FixYoHvvvVebN2/WpZdeqlmzZqm5ubnP81955RVdeeWVamxs1KZNm3T55Zfrmmuu0ebNm4e45YMntQyMSVUAi0vS7/XGuvPSJgAAUHisCYBLly7V/PnzddNNN2ny5MlatmyZampqtGLFij7PX7Zsmf72b/9WF1xwgc4++2z98z//s84++2z96le/GuKWDyLXHwCLijIBMNbTk5cmAQCAwmNFAOzp6dGmTZtUX1/vO15fX69169Yd1We4rquuri6NHj2633O6u7vV2dnpexQSE0uEPDeUmPyRWgZGkuK9BEAAAJBgRQDct2+f4vG4KisrfccrKyvV2tp6VJ/xox/9SAcPHtT111/f7zlLlixRNBpNP2pqao6r3YPNxFMBMLkAdCQi1ziSpJ5euoABAECCFQEwxXEc32tjTM6xvjzxxBO6//77tXr1ao0dO7bf8xYtWqSOjo70Y9euXcfd5kGVrACaZAXQcRz1KrEmYJwACAAAkiL5bsBgqKioUDgczqn2tbW15VQFs61evVrz58/XL37xC/3FX/zFgOeWlJSopKRkwHPyKVMBLEofiymsEvUq3tubr2YBAIACY0UFsLi4WHV1dWpqavIdb2pq0owZM/q97oknntCNN96on//85/rKV75yopt54iUDoDwBsDe5K0gsxhhAAACQYEUFUJIWLlyoOXPmaNq0aZo+fbp++tOfqrm5WQ0NDZIS3bcffPCBHnvsMUmJ8Dd37lz9+Mc/1sUXX5yuHo4YMULRaDRv3+O4JAOgCWcmf8SSt9ilCxgAACRZEwBnz56t9vZ2LV68WC0tLZoyZYoaGxs1ceJESVJLS4tvTcCHH35YsVhMt956q2699db08Xnz5unRRx8d6uYPCie5FZw3AMaVWBImxixgAACQZE0AlKRbbrlFt9xyS5/vZYe6l19++cQ3aIg58WSVz1sBdCKSkeIsBA0AAJKsGAOIBMdNVAAdXwUw2QUcYxIIAABIIABaJBUATSSrAijJpQsYAAAkEQAtEuqrApgKgHEqgAAAIIEAaJGQm6jyOZ4KoOukFoKmAggAABIIgBZJVwAj3gpgYk1AwyQQAACQRAC0SCoAhryzgEOJnUvc3iN5aRMAACg8BECLhE1MkhQqymxXF0+GwXjP4by0CQAAFB4CoEXCqQpgxBsASyVRAQQAABkEQIuETSoAZrqATbILWL1UAAEAQAIB0CKRVAAs8gTAZDXQxKgAAgCABAKgRYpMYqZvpHhE+pgbTgVAZgEDAIAEAqBFik1irb9I6cjMwUhiDKBDBRAAACQRAC1SokQALC49KXMwFQDjVAABAEACAdAipcku4GJPBdApogIIAAD8CICWiMdiKnYS6wB6K4CpABiiAggAAJIIgJboPnIw/bz0pFHp56FIKgCyFzAAAEggAFqi+7AnAI7wBMDkjOCwSwUQAAAkEAAtkQqAPSaiUDicPh4qTlQAIwRAAACQRAC0RE+yC/iIU+I7Hi5JTAgpdpkEAgAAEgiAluhNBsBuFfuOh0vKJEml5tCQtwkAABQmAqAleo8kAl6P4w+AkRGJADjCZS9gAACQQAC0RKw7EQB7s7qAIyPKJUkjRAAEAAAJBEBL9B7ukiR1h0b4jhclA+BJ5ohkzJC3CwAAFB4CoCXiRxIBsCc80ne8ZFQiAIYcI9NzYMjbBQAACg8B0BLxw52SpN6IPwCOGlWuuHEkSYcPdAx5uwAAQOEhAFrC7U5U92KRUb7jI4ojOqTEWoAHuvYPdbMAAEABIgBawulOVABNsb8C6DiODjqJvYGpAAIAAIkAaA0nOb7PLR6V894RJzEx5EgXARAAABAArRHqTe4FXFKe8153OFEB7D5EAAQAAARAa4R7ExXAUEluBbA3GQB7DnUOaZsAAEBhIgBaojiWCIDhEbkVwNTM4NhhAiAAACAAWqM0lgh3RWUVOe/1FkUTTw59dPQfeHCftH/XYDQNAAAUGAKgJUbFEwGwtDw3APaUjpEkhY+0H92HHemQ/rUu8di7bdDaCAAACgMB0BLlJhEAR0ZPzXkvPiIRAIuPNgC++5J0ZL8U75a2/2qwmggAAAoEAdAC3UcO6SSnW5JUNroy94SRiVBY0n2UXcDN6zPPW353vM0DAAAFhgBogc6P2iRJceOoLDo65/2S8rGSpNLej4/uA9v/lHneuvW42wcAAAoLAdACne2tkqQOp0yhcDjn/ZGnVEmSyuL7j+4DvQGwY5fUe+R4mwgAAAoIAdACBz5sliR9HB7T5/vRU6sTf7odkusO/GGxHml/s+eAkT7eORjNBAAABYIAaIHuj3ZLkrqKx/b5/uixiQAYcVwd7Phw4A/b/75kXKlopDTuc4lj3oogAAAY9giAFoh37JEkdY/oYwKIpFEnnaQPzcmSpP0tOwb+sPZ3E3+OOUOqODt5jAAIAIBNCIAWiBxIBEB31Lh+z2kNJ8YBHmr9hDD3UTIAjj5TGnNW4nkqFAIAACsQAC1QcjgxCzh88mn9nrO/JBEOj3x4tBXAMxMh0HsMAABYgQBogfKevZKk0tHj+z2np2yCJMn96BMmdKQrgGckQqD3GAAAsAIBcJiLx2Kqjie6gCsmfrbf88JjaiVJpV3vD/yBHyUrhKPPzATAA3ulI53H3VYAAFAYCIDDXOv7f1CxE9MRU6TKmrP6Pa+0eookqerwnyRj+j4p1i11JGYUa8yZUmk0vYsIVUAAAOxBABzmPnzvTUnSB+HxCkci/Z536pmfU68JK2o65e7f1fdJbdsTS8CUnpwJfkwEAQDAOgTAYe7IB29Jkj4eefqA502sHKM/KTFGcO/bv+n7pJYtiT+rPyc5TuI5E0EAALAOAXCYK967WZIUG3vegOdFwiHtOikxRvDg2y/3fdKeLYk/UwtAS5lxgKwFCACANQiAw5hxXU04uFWSdPKkSz7x/M7TLpUkRfes6fuE5g2JP087P3OMAAgAgHUIgMPY7nffVIX2q8eEdfp5Mz/x/DHnXqUeE9ap3btkWn7vf3N/s/ThdskJS7WfzxyvTEwe0d43pd7Dg9h6AACQLwTAYeyD3zwlSXq79DyVjhj5iedfPPl0/Y+5QJK0f+3D/jffeibxZ82F0ohTMsdHnyGVjZPiPdLu1wej2QAAIM8IgMPYKe81SpIO1V51VOePKA5r62mzJUnl256Q9m5LvNF7WPrtTxPPp37Df5HjSKcnu5ffffG42wwAAPKPADhMvfv7dTon9rZ6TVhnXvbNo77uwsuuVlP8fIUVl/vz2dLb/y09c4vUsUsaVSWdNzv3onNmJf5885f9ryEIAACGDQLgMNX5/D9Jkn5X/nlVVE046us+f/apWlH2N3rfHatQR7P0xDekt56SnJD0tQekohG5F02aJRWPSowT/NP/DNZXAAAAeUIAHIa2rnlKf37wVcWNo4ov/8OnujYUcnTrNdN1bc9iPRm/QkdGTZAmTJf+91PS2Vf2fVHxSdL58xLP/+d+KdZzfF8AAADkFQFwmNn9pzd12kt/I0naeOp1On1y3af+jC9OrtTnP/cZ3dN7ky7o+qHWff4/pDMvH/iiSxYkdghp3So9dw9dwQAADGNWBcDly5ertrZWpaWlqqur09q1awc8f82aNaqrq1NpaanOOOMMPfTQQ0PU0mPzxzdeVvF/XKPR6tS74TM09a9+csyf9f3rztOFtaPV1R3TN1f+Rvf9vzfV2nGk/wtGjU10EUvSxpXSz6+X2v7Q97nGSB0fSB/tkOKxY24jAAA4MRxj7CjlrF69WnPmzNHy5cs1c+ZMPfzww3rkkUe0bds2TZiQO0Zu586dmjJlir797W/r5ptv1muvvaZbbrlFTzzxhL7+9a8f1c/s7OxUNBpVR0eHysvLB/srpXW079X2X3xXdS1PqsiJ673QBI26+b9VUTn+uD73SG9c//DMm/rFpt2SpEjI0RcmnarLPzNWF9WOVm3FSEXCWf9G2PJz6Vd/k1gWRpIqz5Wqp0onVUjxXqn9HWnXb6Uj+xPvjxgtffbaxOSSmosyW8wBAJAnQ/X3dyGzJgBedNFFOv/887VixYr0scmTJ+vaa6/VkiVLcs7/u7/7Oz377LPavn17+lhDQ4N+97vfaf369Uf1M0/0f0A73vyNPnz5Yf3Zh40qcxKLMG8adZnOmr9S0VMqBu3nvPanffrxr9/Rb9/7yHe8JBLSmaeOUvXJI1R9cqnGRUfolJOKNK63WZ/dvkxjPnhRjon3/aFOWAoXSTFPVbF8fGKdwbF/JkVPk8qqpJJyqXhk4lE0UgpHpFBEChUl/7SqSA0AKAAEQCmS7wYMhp6eHm3atEn33HOP73h9fb3WrVvX5zXr169XfX2979hVV12llStXqre3V0VFRTnXdHd3q7u7O/26s7NzEFqfa1Pjz1T2xnJNiv1RZ0iSI+0Ina7OmYt0/uXXyxnkUDTzrArNPKtCb7d26dfb92rNHz/Umx906FBPXNtaOrWtpa/v+S1V6DpNC72tSc5unewcUI+KtMeM1mb3bO0In64iN6zp4W36ilmrK8wGndS5W3prd2LW8VFyFVJcYcWdkOKKKO6E5SqcPBZWXBG5Tup5WG7yfSNJjiOjVMXRSRxLvU6/l3htnMx5ktLvpa93Escynyf/9XLS56ReH0+t0/tzj19+qq5mMH7ucVeMj78N2d/j0zZpUP53AAIs/Nmv6s+vmpfvZljHigC4b98+xeNxVVZW+o5XVlaqtbW1z2taW1v7PD8Wi2nfvn0aN25czjVLlizRd7/73cFreD9692zVpNgf1WvC2lo2U0UXfEufveRrCoXDJ/TnnlNVpnOqynTr5WfJdY2aPzqkdz88oD0dR9Sy/7BaO4+o83CvOg/H1HmkV52HS/XakTF6Me6qJ+b6PywmKWb035qs/9ZkjdBcnR96R1OdHZro7NU4p11jnf0aqSM6yTmikTqiUqc3p00huQrJVZGRpG7Jino1AOBorf/gjHw3wUpWBMAUJ+uf5saYnGOfdH5fx1MWLVqkhQsXpl93dnaqpqbmWJvbr4lX3qwNr4zS2fX/R+cf5zi/YxUKOTq9YqROr/jkLeYkyXWNeuKueuKuuntTf8bVGzeKu0auMXLNF9PP4670sWv0kTGKm+Q58ZjceFxyeyU3lphAYmIKuTHJjcsxvXLSz2Ny4r1yTDzxSD/vlVw3OUvZJPKiMenXkiSTDKvG875M+rUjk/hvJ502jWTke+3IeGZCJ2s8ntfHk1SdVDOP+RMybT32AR6mn+fHcPkxy3zIsXwPJ6sRx9KknM/41B/Cv1iA43XKpBn5boKVrAiAFRUVCofDOdW+tra2nCpfSlVVVZ/nRyIRjRkzps9rSkpKVFJSMjiNHsC4iedo3JzFJ/znDKZQyFFpKKzSorBUmu/WAACAgVgxwr64uFh1dXVqamryHW9qatKMGX3/y2H69Ok557/wwguaNm1an+P/AAAAbGFFAJSkhQsX6pFHHtGqVau0fft23XnnnWpublZDQ4OkRPft3Llz0+c3NDTo/fff18KFC7V9+3atWrVKK1eu1N13352vrwAAADAkrOgClqTZs2ervb1dixcvVktLi6ZMmaLGxkZNnDhRktTS0qLm5ub0+bW1tWpsbNSdd96pBx98UNXV1frJT35y1GsAAgAADFfWrAOYD6wjBADA8MPf3xZ1AQMAAODoEAABAAAChgAIAAAQMARAAACAgCEAAgAABAwBEAAAIGAIgAAAAAFDAAQAAAgYAiAAAEDAWLMVXD6kNlHp7OzMc0sAAMDRSv29HeTN0AiAx6Grq0uSVFNTk+eWAACAT6urq0vRaDTfzcgL9gI+Dq7ras+ePSorK5PjOIP62Z2dnaqpqdGuXbsCu0/hcMR9G564b8MT9214KoT7ZoxRV1eXqqurFQoFczQcFcDjEAqFNH78+BP6M8rLy/k/tmGI+zY8cd+GJ+7b8JTv+xbUyl9KMGMvAABAgBEAAQAAAoYAWKBKSkp03333qaSkJN9NwafAfRueuG/DE/dteOK+FQYmgQAAAAQMFUAAAICAIQACAAAEDAEQAAAgYAiAAAAAAUMALEDLly9XbW2tSktLVVdXp7Vr1+a7SYF1//33y3Ec36Oqqir9vjFG999/v6qrqzVixAhddtlleuutt3yf0d3drdtvv10VFRUaOXKkvvrVr2r37t1D/VWs98orr+iaa65RdXW1HMfRM88843t/sO7Vxx9/rDlz5igajSoajWrOnDnav3//Cf529vqk+3bjjTfm/A5efPHFvnO4b0NvyZIluuCCC1RWVqaxY8fq2muv1dtvv+07h9+5wkYALDCrV6/WggULdO+992rz5s269NJLNWvWLDU3N+e7aYH12c9+Vi0tLenH1q1b0+/94Ac/0NKlS/XAAw/o9ddfV1VVla688sr0PtGStGDBAj399NN68skn9eqrr+rAgQO6+uqrFY/H8/F1rHXw4EFNnTpVDzzwQJ/vD9a9uuGGG7RlyxY999xzeu6557RlyxbNmTPnhH8/W33SfZOkL33pS77fwcbGRt/73Leht2bNGt16663asGGDmpqaFIvFVF9fr4MHD6bP4XeuwBkUlAsvvNA0NDT4jn3mM58x99xzT55aFGz33XefmTp1ap/vua5rqqqqzPe///30sSNHjphoNGoeeughY4wx+/fvN0VFRebJJ59Mn/PBBx+YUChknnvuuRPa9iCTZJ5++un068G6V9u2bTOSzIYNG9LnrF+/3kgyf/jDH07wt7Jf9n0zxph58+aZr33ta/1ew30rDG1tbUaSWbNmjTGG37nhgApgAenp6dGmTZtUX1/vO15fX69169blqVV45513VF1drdraWn3jG9/Qjh07JEk7d+5Ua2ur736VlJToC1/4Qvp+bdq0Sb29vb5zqqurNWXKFO7pEBqse7V+/XpFo1FddNFF6XMuvvhiRaNR7ucJ9PLLL2vs2LGaNGmSvv3tb6utrS39HvetMHR0dEiSRo8eLYnfueGAAFhA9u3bp3g8rsrKSt/xyspKtba25qlVwXbRRRfpscce0/PPP69/+7d/U2trq2bMmKH29vb0PRnofrW2tqq4uFinnHJKv+fgxBuse9Xa2qqxY8fmfP7YsWO5nyfIrFmz9Pjjj+vFF1/Uj370I73++uu64oor1N3dLYn7VgiMMVq4cKEuueQSTZkyRRK/c8NBJN8NQC7HcXyvjTE5xzA0Zs2alX5+7rnnavr06TrzzDP17//+7+mB6Mdyv7in+TEY96qv87mfJ87s2bPTz6dMmaJp06Zp4sSJ+q//+i9dd911/V7HfRs6t912m37/+9/r1VdfzXmP37nCRQWwgFRUVCgcDuf8q6atrS3nX1HIj5EjR+rcc8/VO++8k54NPND9qqqqUk9Pjz7++ON+z8GJN1j3qqqqSnv37s35/A8//JD7OUTGjRuniRMn6p133pHEfcu322+/Xc8++6xeeukljR8/Pn2c37nCRwAsIMXFxaqrq1NTU5PveFNTk2bMmJGnVsGru7tb27dv17hx41RbW6uqqirf/erp6dGaNWvS96uurk5FRUW+c1paWvTmm29yT4fQYN2r6dOnq6OjQ7/97W/T5/zmN79RR0cH93OItLe3a9euXRo3bpwk7lu+GGN022236amnntKLL76o2tpa3/v8zg0DeZl6gn49+eSTpqioyKxcudJs27bNLFiwwIwcOdK89957+W5aIN11113m5ZdfNjt27DAbNmwwV199tSkrK0vfj+9///smGo2ap556ymzdutX85V/+pRk3bpzp7OxMf0ZDQ4MZP368+fWvf23eeOMNc8UVV5ipU6eaWCyWr69lpa6uLrN582azefNmI8ksXbrUbN682bz//vvGmMG7V1/60pfMeeedZ9avX2/Wr19vzj33XHP11VcP+fe1xUD3raury9x1111m3bp1ZufOneall14y06dPN6eddhr3Lc/++q//2kSjUfPyyy+blpaW9OPQoUPpc/idK2wEwAL04IMPmokTJ5ri4mJz/vnnp6fVY+jNnj3bjBs3zhQVFZnq6mpz3XXXmbfeeiv9vuu65r777jNVVVWmpKTEfP7znzdbt271fcbhw4fNbbfdZkaPHm1GjBhhrr76atPc3DzUX8V6L730kpGU85g3b54xZvDuVXt7u/nmN79pysrKTFlZmfnmN79pPv744yH6lvYZ6L4dOnTI1NfXm1NPPdUUFRWZCRMmmHnz5uXcE+7b0OvrnkkyP/vZz9Ln8DtX2BxjjBnqqiMAAADyhzGAAAAAAUMABAAACBgCIAAAQMAQAAEAAAKGAAgAABAwBEAAAICAIQACAAAEDAEQAAAgYAiAAAAAAUMABAAACBgCIAAAQMAQAAEAAAKGAAgAABAwBEAAAICAIQACAAAEDAEQAAAgYAiAAAAAAUMABAAACBgCIAAAQMAQAAEAAAKGAAgAABAwBEAAAICAIQACAAAEDAEQAAAgYAiAAAAAAUMABAAACBgCIAAAQMAQAAEAAAKGAAgAABAwBEAAAICAIQACAAAEzP8HbYbfJrTDOD4AAAAASUVORK5CYII=", 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GERD)\n", - " mult = sum(SSD)/sum(SSD2)\n", - "\n", - " SSD2 *= mult.real/np.exp(-tmfp)\n", - " EP = np.array(SSD2).argmax(0)\n", - " PPSD = SSD2/FAC*np.power(tmfp,(order))*np.exp(-tmfp)*1e12\n", - " # Add this order t0 final spectrum\n", - " PSD += PPSD\n", - " # Get next order factor\n", - " FAC=FAC*(order+2.)\n", - "\n", - " # convolute next order\n", - " ssd = ssd * ssd2\n", - "\n", - "\n", - "\n", - "\n", - " \n", - "plt.figure()\n", - "plt.plot(SSD/SSD.max())\n", - "plt.plot(PSD/PSD.max())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "229957.93374337783 45464262.67521657\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 168, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f35ed77d2a7a4b7ebb0bc8705a557d02", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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GERD)\n", - " mult = SSD_sum/sum(SSD2)\n", - "\n", - " SSD2 *= mult.real/np.exp(-tmfp)\n", - " EP = np.array(SSD2).argmax(0)\n", - " PPSD = SSD2/FAC*np.power(tmfp,(order))*np.exp(-tmfp)\n", - " # Add this order t0 final spectrum\n", - " PSD += PPSD\n", - " \n", - " # convolute next order\n", - " dispersion = dataset.energy_loss.slope\n", - " \n", - " factorZL = Eh/dispersion*2 #, #zero_loss.sum()*(np.exp(tmfp)-1)/PSD.sum()*Eh/dispersion/4\n", - " #print(factorZL, Eh)\n", - " \n", - " BGDcoef = splrep(energy_scale,PSD,s=0)\n", - " \n", - " cts =splev( dataset.energy_loss, BGDcoef)*factorZL #*p[1]\n", - " \n", - " #cts += zero_loss\n", - " \n", - " return cts\n", - " \n", - " \n", - "\n", - "#zero_loss, _ = eels_tools.resolution_function(eels_dataset.energy_loss, eels_dataset, .4)\n", - "#Izl = zero_loss.sum()\n", - "#Itotal = np.array(eels_dataset).sum()\n", - "#tmfp = np.log(Itotal/Izl)\n", - "tmfp = 0.3\n", - "zero_loss = infoWidget.datasets['resolution_function']\n", - "\n", - "LL = MakeDrudeVL(spec, tmfp, zero_loss, p0[0]-5,.5, p0[2])\n", - "print(LL.max(), LL.sum())\n", - "plt.figure()\n", - "plt.plot(LL, label='Multi')\n", - "plt.plot(eels_dataset, label='spec')\n", - "plt.plot(eels_dataset-LL, label='dif')\n", - "plt.legend()\n", - "#plt.ylim(-.1,.2)" - ] - }, - { - "cell_type": "code", - "execution_count": 162, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\gduscher\\AppData\\Local\\Temp\\ipykernel_17524\\2622750204.py:19: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", - " plt.legend()\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 162, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8b294f37f8f54034a83eb1d33455d968", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "def errfDrude(p, y):\n", - " LL = MakeDrudeVL(y, p[3], zero_loss, p[0],p[1],p[2])\n", - " err = y - LL\n", - " #print (p,sum(np.abs(err)))\n", - " return np.abs(err)#/np.sqrt(y)\n", - "\n", - "\n", - "pin2 = np.array([15,1,.7, 0.3])\n", - "E = energy_scale = eels_dataset.energy_loss\n", - "startFit =np.argmin(abs(energy_scale-13))\n", - "endFit = np.argmin(abs(energy_scale-18))\n", - " \n", - "p2, lsq = leastsq(errfDrude, pin2, args=(eels_dataset), maxfev=2000)\n", - "\n", - "LL = MakeDrudeVL(eels_dataset, p2[3], zero_loss, p2[0],p2[1],p2[2])\n", - "plt.figure()\n", - "plt.plot(LL)\n", - "plt.plot(eels_dataset)\n", - "plt.legend()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8326588466281821" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "p2[3]" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.7408182206817179" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.power(0.3,(0))*np.exp(-.3)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array(1.68808473)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "zero_loss.sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def newDrudeBgd(x, p):\n", - " tmfp = 500 #p[3]\n", - " startB = x[0]\n", - " endB = x[-1]\n", - " p = np.abs(p)\n", - "\n", - "\n", - " LLene = np.linspace(1, 2047,2048)\n", - " eps = pyTEMlib.eels_tools.drude(LLene,p[0], p[1], p[2])\n", - " SSD = (-1/eps).imag\n", - " ssd = np.fft.fft(SSD)\n", - "\n", - " ssd2 = ssd.copy()\n", - " SSD2 = SSD.copy()\n", - "\n", - " ### sum contribution from each order of scattering:\n", - " PSD = np.zeros(len(LLene))\n", - " for order in range(1):\n", - " # This order convoluted spectum \n", - " PPSD = np.zeros(len(LLene))\n", - " # convoluted SSD is SSD2\n", - " SSD2 = np.fft.ifft(ssd).real\n", - "\n", - " # scale right (could be done better? GERD) \n", - " print( sum(SSD)/sum(SSD2))\n", - " mult = sum(SSD)/sum(SSD2)\n", - " SSD2 *= abs(mult)\n", - "\n", - " PPSD = SSD2/scipy.special.factorial(order+1)*np.power(tmfp,(order+1))*np.exp(-tmfp) #using equation 4.1 of egerton ed2\n", - " # Add this order to final spectrum\n", - " PSD += PPSD\n", - "\n", - " # next order convolution\n", - " ssd = ssd * ssd2\n", - "\n", - "\n", - " cts = np.zeros(len(x))\n", - "\n", - " if startB < 0:\n", - " startB = 0\n", - " BGDcoef = scipy.interpolate.splrep(LLene[int(startB):int(endB)],PSD[int(startB):int(endB)],s=0)\n", - "\n", - "\n", - " lin = np.zeros(len(x))\n", - "\n", - " cts = scipy.interpolate.splev( x, BGDcoef)*p[1]\n", - " return cts" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'p0' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[7], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m LLene \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2047\u001b[39m,\u001b[38;5;241m2048\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m p \u001b[38;5;241m=\u001b[39m p0\n\u001b[0;32m 3\u001b[0m eps \u001b[38;5;241m=\u001b[39m pyTEMlib\u001b[38;5;241m.\u001b[39meels_tools\u001b[38;5;241m.\u001b[39mdrude(LLene,p[\u001b[38;5;241m0\u001b[39m], p[\u001b[38;5;241m1\u001b[39m], p[\u001b[38;5;241m2\u001b[39m])\n\u001b[0;32m 4\u001b[0m SSD \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m/\u001b[39meps)\u001b[38;5;241m.\u001b[39mimag\n", - "\u001b[1;31mNameError\u001b[0m: name 'p0' is not defined" - ] - } - ], - "source": [ - "LLene = np.linspace(1, 2047,2048)\n", - "p = p0\n", - "eps = pyTEMlib.eels_tools.drude(LLene,p[0], p[1], p[2])\n", - "SSD = (-1/eps).imag\n", - "\n", - "plt.figure()\n", - "plt.plot(SSD)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "tags": [] - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'spec' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[8], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m newDrudeBgd( np\u001b[38;5;241m.\u001b[39marray(spec\u001b[38;5;241m.\u001b[39menergy_loss[\u001b[38;5;241m200\u001b[39m:]), p0)\n", - "\u001b[1;31mNameError\u001b[0m: name 'spec' is not defined" - ] - } - ], - "source": [ - "newDrudeBgd( np.array(spec.energy_loss[200:]), p0)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def DrudeBgd(y, x, imfp, p):\n", - "\n", - "\n", - "\n", - " # Fit function is the spectrum - new LL bgd devided by poinson noise\n", - " def newLL(p, y, x):\n", - " \n", - " err = (y - newDrudeBgd( x,p))\n", - " #print(p, sum( err))\n", - " return err\n", - "\n", - " # Least square fit\n", - " pDLLBgd, lsq = scipy.optimize.leastsq(newLL, p0, args=(y, x), maxfev=2000)\n", - " #print(sum(newLL(pZL, y, x)))\n", - " # cts is the result of the fit\n", - " cts=newDrudeBgd(x, abs(pDLLBgd))\n", - " #print(\"new LLL background \", pZL)\n", - " #tags['DrudeLLBgd'] = pDLLBgd\n", - " print(pDLLBgd)\n", - " \n", - " \n", - " return cts" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'experiment': {'single_exposure_time': 0.1,\n", - " 'exposure_time': 10.0,\n", - " 'number_of_frames': 100,\n", - " 'collection_angle': 100.0,\n", - " 'convergence_angle': 0.0,\n", - " 'microscope': 'Libra 200 MC',\n", - " 'acceleration_voltage': 199990.28125,\n", - " 'flux_ppm': 4875.3037109375,\n", - " 'count_conversion': 1,\n", - " 'beam_current': 0},\n", - " 'zero_loss': {'shifted': array([-0.14012023]),\n", - " 'startFitEnergy': -0.5,\n", - " 'endFitEnergy': 0.5,\n", - " 'fit_parameter': array([-1.63207010e-02, 2.06835590e+04, 1.92327897e-01, 3.38795374e-02,\n", - " 2.25742582e+04, 2.93662067e-01]),\n", - " 'original_low_loss': 'EELS90muOAonaxis3_new_new'},\n", - " 'plasmon': {'parameter': array([1.50312722e+01, 7.45905381e-01, 3.48811157e+05]),\n", - " 'epsilon': array([ 0. +0.j , 0. +0.j ,\n", - " 0. +0.j , ...,\n", - " 293283.07757369+9900.8221703j , 293342.39439935+9892.35528781j,\n", - " 293401.61631948+9883.90252999j])}}" - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "infoWidget.datasets['plasmon'].metadata" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0000000000000007\n", - "1.0000000000000007\n", - "1.0000000000000007\n", - "0.9999999999999997\n", - "1.0\n", - "0.9999999999999997\n", - "1.0000000000000007\n", - "1.0000000000000007\n", - "[1.50312722e+01 7.45905381e-01 3.48811157e+05 1.70000000e+01]\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "99ba61bedc474e729cc9147018d5c40a", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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Calculate plasmon mean free paths using a free-electron formula Eq.(3.58)\n", - " # with m = m0 and assuming small width of the plasmon peak.\n", - " # Equally good for calculating total-inelastic MFP using a value of\n", - " # Em in Eq.(5.38) or a more approximate value using Eq.(5.2).\n", - " # Probe convergence alpha incorporated using Scheinfein & Isaacson formula.\n", - " # Above values assume dipole conditions (beta* < Bethe-ridge angle).\n", - " # The program also estimates a total-inelastic MFP by using dipole formula\n", - " # with effective collection angle bstar = Bethe-ridge angle.\n", - " # To obtain this value, enter alarge value (~ 100 mrad) for alpha or beta.\n", - " E0 = 200000 #Incident-electron energy E0 (keV): ');\n", - " Ep = energy_scale[0]+ssdLL.argmax(0)*(energy_scale[1]-energy_scale[0]) #'Plasmon energy of mean energy loss (eV): ');\n", - " print(Ep, ssdLL.argmax(0))\n", - " alpha = 10 #'Convergence semiangle (mrad) [can be 0]: ');\n", - " beta = 30 #'Collection semiangle (mrad): ');\n", - " \n", - " F = (1.0+E0/1022.0)/(1.0+E0/511.0)**2;\n", - " Fg = (1.0+E0/1022.0)/(1.0+E0/511.0);\n", - " T = E0*F; #keV\n", - " tgt = 2.0*Fg*E0;\n", - " a0 = 0.0529; #nm\n", - " #print('2.gamma.T = ',tgt);\n", - "\n", - " # calculation of convergence correction\n", - " #tgt=2.*E0.*(1.+E0./1022.)./(1.+E0./511.); % keV\n", - " thetae=(Ep+1e-6)/tgt; # in mrad, avoid NaN for e=0\n", - " a2=alpha*alpha*1e-6 + 1e-10; #radians^2, avoiding inf for alpha=0\n", - " b2=beta*beta*1e-6; #radians^2\n", - " t2=thetae*thetae*1e-6; #radians^2\n", - " eta1=np.sqrt((a2+b2+t2)**2-4*a2*b2)-a2-b2-t2;\n", - " eta2=2*b2*np.log(0.5/t2*(np.sqrt((a2+t2-b2)**2+4*b2*t2)+a2+t2-b2));\n", - " eta3=2*a2*np.log(0.5/t2*(np.sqrt((b2+t2-a2)**2+4*a2*t2)+b2+t2-a2));\n", - " eta=(eta1+eta2+eta3)/a2/np.log(4/t2);\n", - " f1=(eta1+eta2+eta3)/2/a2/np.log(1+b2/t2);\n", - " f2=f1;\n", - " if(alpha/beta>1):\n", - " f2=f1*a2/b2;\n", - "\n", - " bstar=thetae*np.sqrt(np.exp(f2*np.log(1+b2/t2))-1); #% mrad\n", - " #print('effective semiangle beta* = %g mrad\\n',bstar);\n", - " bstar = 40\n", - " \n", - " thetabr = 1000 * (Ep/E0/1000.0)**0.5;\n", - " print('Bethe Ridge Angle', thetabr)\n", - " #print('Bethe-ridge angle(mrad) = ',tags['Bethe Ridge Angle'],'nm\\n')\n", - "\n", - " pmfp = 0.0\n", - " imfp = 0.0\n", - " if (bstar < thetabr):\n", - " pmfp = 4000*a0*T/Ep/np.log(1+bstar**2/thetae**2);\n", - " imfp = 106*F*E0/Ep/np.log(2.0*bstar*E0/Ep);\n", - " #print('Free-electron MFP(nm) = %g nm\\n',pmfp);\n", - " #print('Using Eq.(5.2), MFP(nm) = %g nm\\n',imfp);\n", - " \n", - " else:\n", - " #print('Dipole range is exceeded\\n');\n", - " imfp = 4000*a0*T/Ep/np.log(1+thetabr**2/thetae**2);\n", - " #print('total-inelastic MFP(nm) = %g nm\\n',imfp);\n", - " \n", - "\n", - " return pmfp, imfp" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(15.040794864773837, 920)" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Ep = np.array(eels_dataset.energy_loss)[0]+np.array(ssdLL).argmax(0)*(np.array(eels_dataset.energy_loss)[1]-np.array(eels_dataset.energy_loss)[0])\n", - "Ep, np.array(ssdLL).argmax(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "15.040794864773837 920\n", - "Bethe Ridge Angle 0.27423343035426806\n" - ] - }, - { - "data": { - "text/plain": [ - "(0.0, 219.06514501302627)" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "PMFP(np.array(ssdLL), np.array(eels_dataset.energy_loss))" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.17584541453708571\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "aa46026986f74f8fb3f7f5c1e7abfa79", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Use resolution Function as ZL if existing\n", - "# print len(LLSpec)\n", - "LLSpec = eels_dataset\n", - "zero_loss, _ = eels_tools.resolution_function(eels_dataset.energy_loss, eels_dataset, .4)\n", - "\n", - "j = np.fft.fft(eels_dataset)\n", - "z = np.fft.fft(zero_loss)\n", - "j1 = z*np.log(j/z)\n", - "ssdLL =np.fft.ifft(j1).real#,'fourier-log deconvolution')\n", - "\n", - "#parent.text2.append('\\n Single Scattering Deconvolution, Done')\n", - "if np.array(eels_dataset).sum() > 0.0: \n", - " tmfp = np.log(np.array(eels_dataset).sum()/zero_loss.sum())\n", - "else:\n", - " tmfp = 0.0\n", - "Ep = np.array(eels_dataset.energy_loss)[0]+np.array(ssdLL).argmax(0)*(np.array(eels_dataset.energy_loss)[1]-np.array(eels_dataset.energy_loss)[0])\n", - "\n", - "print(tmfp)\n", - "plt.figure()\n", - "plt.plot(ssdLL)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [], - "source": [ - "def drude(dataset, ep, ew, tnm, eb, verbose=False):\n", - " \n", - " \n", - " e0 = 200\n", - " beta = 30\n", - " e = dataset.energy_loss\n", - " epc = e[1]-e[0]\n", - "\n", - " b = beta/1000.0 # %rad\n", - " T = 1000.0*e0*(1.+e0/1022.12)/(1.0+e0/511.06)**2;# %eV # equ.5.2a or Appendix E p 427 \n", - " tgt = 1000*e0*(1022.12 + e0)/(511.06 + e0);# %eV Appendix E p 427 \n", - " rk0 = 2590*(1.0+e0/511.06)*np.sqrt(2.0*T/511060);\n", - " os = e[0]\n", - " ewMod = eb\n", - " eps = 1 - (ep**2-ewMod*e*1j)/(e**2+2*e*ew*1j) #Mod Drude term\n", - " eps[np.nonzero(eps==0.0)]= 1e-19\n", - " elf = np.imag(-1/eps)\n", - "\n", - " the = e/tgt; #% varies with energy loss! # Appendix E p 427 \n", - " srfelf=np.imag(-4./(1.0+eps))-elf; #% for 2 surfaces\n", - " angdep = np.arctan(b/the)/the - b/(b*b+the*the);\n", - " srfint = angdep*srfelf/(3.1416*0.05292*rk0*T); #% probability per eV\n", - " anglog = np.log(1.0+ b*b/the/the);\n", - " I0 = eels_dataset.sum() *1 \n", - " volint = abs(tnm/(np.pi*0.05292*T*2)*elf*anglog); #S equ 4.26% probability per eV\n", - " volint = (volint+srfint) *I0 *epc #S probability per channel\n", - " ssd = volint #+ srfint;\n", - " if os <-1.0:\n", - " xs = int(abs(-os/epc))\n", - "\n", - " ssd[0:xs]=0.0\n", - " volint[0:xs]=0.0\n", - " srfint[0:xs]=0.0\n", - " \n", - " Ps = np.trapz(e,srfint); #% 2 surfaces but includes negative begrenzungs contribn.\n", - " Pv = abs(np.trapz(e,abs(volint/np.array(eels_dataset)))); #% integrated volume probability\n", - " Pv = (volint/I0).sum() ## our data have he same epc and the trapz formula does not include \n", - " lam = tnm/Pv; #% does NOT depend on free-electron approximation (no damping). \n", - " lamfe = 4.0*0.05292*T/ep/np.log(1+(b* tgt / ep) **2); #% Eq.(3.44) approximation\n", - " if verbose:\n", - " print('Ps(2surfaces+begrenzung terms) =', Ps, 'Pv=t/lambda(beta)= ',Pv,'\\n');\n", - " print('Volume-plasmon MFP(nm) = ', lam,' Free-electron MFP(nm) = ',lamfe,'\\n');\n", - " print('--------------------------------\\n');\n", - "\n", - " \n", - " return ssd#/np.pi" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "027f84e640224d78831fb5680c8f6b54", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", 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\n", - " Figure\n", - "
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\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ssd = drude(eels_dataset, 15, .5, 3, 1)\n", - "plt.figure()\n", - "plt.plot(eels_dataset.energy_loss, ssd)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def doSSD(LLSpec):\n", - " \n", - " # Use resolution Function as ZL if existing\n", - " # print len(LLSpec)\n", - " extract_zero_loss(LLSpec)\n", - " \n", - " j = np.fft.fft(LLSpec)\n", - " z = np.fft.fft(tags['zero_loss'])\n", - " z2 = z ## Could be a zl extracted from Spectrum\n", - " j1 = z2*np.log(j/z)\n", - " ssdLL =np.fft.ifft(j1).real#,'fourier-log deconvolution')\n", - " tags['ssdLL']=ssdLL.copy()\n", - " \n", - " \n", - " #parent.text2.append('\\n Single Scattering Deconvolution, Done')\n", - " if np.array(LLSpec).sum() > 0.0: \n", - " tmfp = np.log(np.array(LLSpec).sum()/tags['zero_loss'].sum())\n", - " else:\n", - " tmfp = 0.0\n", - "\n", - " # Use resolution function if available, use ZL otherwise\n", - " zl2 = tags['zero_loss']\n", - " \n", - " \n", - " #####################\n", - " ####### for SSD convoluted Spectra\n", - " #####################\n", - " startE = (6.0-tags['offset'])/tags['dispersion']\n", - " SSD = ssdLL.copy()\n", - " SSD2 = SSD.copy()\n", - " SSD2[0:startE]=0.0\n", - " EP = np.array(SSD2).argmax(0) # plasmon peak position\n", - " ZP = np.array(zl2).argmax(0) # zl peak position \n", - " #print ('\\n EP: ',EP,startE, tags['offset']+EP*tags['dispersion'])\n", - "\n", - "\n", - " guess = [tags['offset']+EP*tags['dispersion'], 10000.0, 6.0, 0.98]\n", - " pin = np.array(guess)\n", - "\n", - " def errfct(p, y, x):\n", - " err = (y - Lorentzian(x,p))\n", - " return err\n", - "\n", - " def Lorentzian(x,p):\n", - " y = ((0.5 * p[1]* p[2]/3.14)/((x- p[0])**2+(( p[2]/2)**2)))\n", - " return y\n", - "\n", - " p, lsq = leastsq(errfct, pin, args=(SSD, tags['ene']), maxfev=2000)\n", - " tags['PLpos'] = p[0]\n", - " tags['PLwidth'] = p[2]\n", - " tags['PLarea'] = p[1]\n", - " #parent.text2.insertPlainText('\\n Position 1 Amplitude 1, Width 1, \\n')\n", - " #parent.text2.insertPlainText(str(p[0:3]))\n", - " PL1 = Lorentzian(tags['ene'],p)\n", - "\n", - " pmfp, imfp = PMFP()\n", - " startxE = tags['Drude Fit Start']\n", - " endxE = tags['Drude Fit End']\n", - " startx = (startxE-tags['offset'])/tags['dispersion']\n", - " endx = (endxE-tags['offset'])/tags['dispersion']\n", - "\n", - " if p[0] < startxE:\n", - " p[0] = startxE\n", - " if p[0] > endxE:\n", - " p[0] = endxE\n", - " if p[2] > (endxE-startxE)/2.0:\n", - " p[2] = (endxE-startxE)/2.0\n", - " \n", - " \n", - " guess = [p[0],p[2],tmfp*imfp,0.1,1.0]\n", - " guess = [22,10,50,0.1,1.0]\n", - " pin2 = np.array(guess)\n", - "\n", - " \n", - " def errfDrude(p, y, x):\n", - " p = abs(p)\n", - " if p[0] < startxE:\n", - " p[0] = startxE\n", - " if p[0] > endxE:\n", - " p[0] = endxE\n", - " if p[1] > endxE-startxE/3.0:\n", - " p[1] = endxE-startxE/3.0\n", - " if p[2] > 200:\n", - " p[2] = 200\n", - " if p[2]<0:\n", - " p[2] =0\n", - " if p[3] > 10:\n", - " p[3] = 10\n", - " if p[3]<0:\n", - " p[3] =0\n", - " if not tags['Drude Fit Asymm'] :\n", - " p[3] = 0\n", - " \n", - " err = (y - drude(x,p[0],p[1],p[2],abs(p[3])))\n", - "\n", - " y[np.nonzero(y<=0)] = 1e-12\n", - " return np.abs(err)/np.sqrt(y)\n", - " \n", - " \n", - "\n", - " \n", - " p2, lsq = leastsq(errfDrude, pin2, args=(tags['spec'][startx:endx], tags['ene'][startx:endx]), maxfev=2000)\n", - " p2[3] = abs(p2[3])\n", - " drudePSD = drude(tags['ene'],p2[0],p2[1],p2[2],abs(p2[3]))\n", - " tags['Drude SSD'] = drudePSD\n", - " \n", - " tags['Drude P Pos'] = p2[0]\n", - " tags['Drude P Width'] = p2[1]\n", - " tags['Drude P thick'] = p2[2]\n", - " tags['Drude P Assym'] = abs(p2[3])\n", - " Pv = drudePSD.sum()/tags['spec'].sum()\n", - " tags['Drude P Probab'] = Pv\n", - " tags['Drude P IMFP'] = p2[2]/Pv #(Wave vs. intensity)\n", - " #tags['Drude P/LL IMFP',p2[2]/tmfp,'nm')\n", - " tags['LLthick'] = tmfp\n", - "\n", - " e = 1.60217646E-19; #% electron charge in Coulomb\n", - " eps0 = 8.854187817*1e-12 # vacuum permittivity\n", - " mel = 9.109e-31; #% REST electron mass in kg\n", - " h = 4.135667516*1e-15; #% Planck's constant\n", - " hbar = h/2.0/np.pi;\n", - "\n", - " tags['Drude e- density']= np.sqrt( (p2[0]/hbar)**2/e**2*eps0*mel)*1e-7 #gerd true? /nm^2\n", - "\n", - " tags['Drude VL'] = MakeDrudeVL()\n", - " \n", - " \n", - " return tmfp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Surface Plasmon\n", - "\n", - "Spectra from thin specimen show the excitations of the surface plasmons on each side of the specimen. For any normal specimen these surface plasmons do interact, but this is not true for extremely thick specimen ($>> 10$nm).\n", - "The surface plasmon frequency $\\omega_S$ for thin specimen is related to the bulk plasmon frequency $\\omega_P$ by Ritchie [Ritchie-PR1957]: \n", - "$$\n", - "\\omega_S=\\omega_P\\left[ \\frac{1\\pm \\exp(-q_st) }{1+\\varepsilon} \\right]^{1/2}\n", - "$$\n", - "\n", - "\n", - "The symmetric mode, where like charges face one another, corresponds to the higher angular frequency $q_s$. Please note, that this relationship does only apply for large $q_s$\n", - "\n", - "The differential probability for surface excitation at both surfaces of a sample with thickness $t$ can be expressed (normal incident, no retardation effects) by:\n", - "$$\n", - "\\frac{d^2 P_s}{d\\Omega d E}=\\frac{2\\hbar}{\\pi^2 \\gamma a_0 m_0^2 \\mu^3}\\frac{\\theta}{(\\theta^2+\\theta^2_E)^2} \\Im\\left[ \\frac{(\\varepsilon_a - \\varepsilon_b)^2 } {\\varepsilon_a^2 \\varepsilon_b}\\right]\n", - "$$\n", - "with \n", - "$$\n", - "R_c = \\frac{\\varepsilon_a \\sin^2(tE/2\\hbar\\mu)}{\\varepsilon_b + \\varepsilon_z }\\tanh (q_s t/2) \n", - "+ \\frac{\\varepsilon_a \\cos^2(tE/2\\hbar\\mu)}{\\varepsilon_b + \\varepsilon_a} \\coth (q_s t/2) \n", - "$$\n", - "and $\\varepsilon_a$ and $\\varepsilon_b$ are the permitivities of the two surfaces.\n", - "\n", - "\n", - "A secondary effect of the surface excitation is the reduced intensity of the bulk plasmon peak. The effect is usually smaller than 1\\%, but can be larger for spectra with small collection angle, because the preferred scattering of surfuce losses into small angles.\n", - "The correction for surface plasmon will be discussed in the Kramers--Kronig Analysis.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "The beauty of ``Low--Loss spectroscopy`` is its derivation of the dielectric function to high energies without prior knowledge of the composition. The signal is strong and the acquisition time is mostly restricted by the dynamic range of the spectrum.\n", - "\n", - "\n", - "**Think of low-loss spectroscopy as Electrodynamics**\n", - "\n", - "The advantages of EELS is the derivation of these values spatially resolved.\n", - "And from a linescan across an Si/SiO$_2$ interface the dielectric function per pixel can be obtained. From that we can calculate the dielectric polarizability $\\alpha_e (E)$, which may be a measure of the dielectric strength.\n", - "\n", - "\n", - "We obtain more or less easily:\n", - "- relative thickness\n", - "- absolute thickness \n", - "- inelastic mean free path\n", - "- plasmon frequency\n", - "- plasmon width\n", - "- band gap\n", - "- dielectric function\n", - "- reflectivity \n", - "- absorption\n", - "- effective number of electrons per atoms \n", - " \n", - "\n", - "\n", - "The analysis of the optical data requires the exact knowledge of the zero-loss peak. Because of the weighting in the Fourier Analysis, the low energy part contributes heavily to the dielectric function. Therefore, energy resolution is critical for an exact determination of all the optical values from EELS. The new monochromated TEMs are now able to achieve an energy resolution of 10 meV (one is at the oak Ridge National Laboratory), which allows for a sharper zero-loss peak. Such a sharp zero-loss peak will enable us to extract this low energy data more accurately. The dielectric function and the parameters derived from it, can be more precisely determined from such EELS spectra.\n" - ] - }, { "cell_type": "markdown", "metadata": { @@ -4192,7 +1484,7 @@ "metadata": { "hide_code_all_hidden": false, "kernelspec": { - "display_name": "pytemlib (3.14.5)", + "display_name": "pytemlib (3.14.5.final.0)", "language": "python", "name": "python3" }, diff --git a/pyTEMlib/utilities.py b/pyTEMlib/utilities.py index edb56460..d0d7d89b 100644 --- a/pyTEMlib/utilities.py +++ b/pyTEMlib/utilities.py @@ -179,7 +179,7 @@ def effective_collection_angle(energy_scale: np.ndarray, # """ z1 = beam_ev # eV - z2 = energy_scale[0] + z2 = np.max([energy_scale[0], 1.0]) z3 = energy_scale[-1] z4 = 100.0 z5 = alpha*0.001 # rad @@ -191,11 +191,11 @@ def effective_collection_angle(energy_scale: np.ndarray, for zx in range(int(z2),int(z3),int(z4)): #! zx = current energy loss eta=0.0; x0=float(zx)*(z1+511060.)/(z1*(z1+1022120.)); # x0 = relativistic theta-e - x1 = np.pi/(2.*x0); - x2=x0*x0+z5*z5; - x3=z5/x0*z5/x0; - x4=0.1*np.sqrt(x2); - dtheta=(z6-x4)/z7; + x1 = np.pi/(2.*x0) + x2=x0*x0+z5*z5 + x3=z5/x0*z5/x0 + x4=0.1*np.sqrt(x2) + dtheta=(z6-x4)/z7 # # calculation of the analytical expression # @@ -225,10 +225,9 @@ def effective_collection_angle(energy_scale: np.ndarray, # # calculation of beta * # - x6=np.power((1.+x1*x1),eta); - x7=x0*np.sqrt(x6-1.); - y=x7*1000.; - + x6=np.power((1.+x1*x1),eta) + x7=x0*np.sqrt(x6-1.) + y=x7*1000. return y