From c076b36fe340ebe9603f988d4f6e50ccaf6de72b Mon Sep 17 00:00:00 2001 From: jonwzheng Date: Mon, 30 Jun 2025 11:03:38 -0400 Subject: [PATCH 01/17] update temperature-dependent solvation notebook --- ...ture_dependent_solvation_free_energy.ipynb | 41 +++++++++++++------ rmgpy/data/solvation.py | 6 ++- 2 files changed, 33 insertions(+), 14 deletions(-) diff --git a/ipython/temperature_dependent_solvation_free_energy.ipynb b/ipython/temperature_dependent_solvation_free_energy.ipynb index 37a92b7e12..bfc8cb4384 100644 --- a/ipython/temperature_dependent_solvation_free_energy.ipynb +++ b/ipython/temperature_dependent_solvation_free_energy.ipynb @@ -94,6 +94,15 @@ "## Run solvation calculations" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "solvationDatabase.get_T_dep_solvation_energy_from_LSER_298(solute_data, solvent_data, T)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -105,10 +114,10 @@ "solute_data = solvationDatabase.get_solute_data(solute)\n", "solvent_data = solvationDatabase.get_solvent_data(solvent_name)\n", "\n", - "print('Results are given in K-factor (VLE solute mole fraction ratio, y2/x2) and solvation free energy:\\n')\n", + "print('Results are given in K-factor (VLE solute mole fraction ratio, y2/x2), solvation free energy, and Henry\\'s law constant:\\n')\n", "for T in temperature:\n", - " dGsolv, Kfactor = solvationDatabase.get_T_dep_solvation_energy_from_LSER_298(solute_data, solvent_data, T) # dGsolv is J/mol\n", - " print(' At {0} K, K-factor = {1:.3f}, solvation free energy = {2:.2f} kJ/mol'.format(T, Kfactor, dGsolv/1000))" + " dGsolv, Kfactor, kH = solvationDatabase.get_T_dep_solvation_energy_from_LSER_298(solute_data, solvent_data, T) # dGsolv is J/mol\n", + " print(f' At {T} K, K-factor = {Kfactor:.3f}, solvation free energy = {dGsolv/1000:.2f} kJ/mol, kH = {kH:.2f}')" ] }, { @@ -164,11 +173,11 @@ "solvent_data = solvationDatabase.get_solvent_data(solvent_name)\n", "solvent_name_in_CoolProp = solvent_data.name_in_coolprop\n", "\n", - "print('Results are given in K-factor (VLE solute mole fraction ratio, y2/x2) and solvation free energy:\\n')\n", + "print('Results are given in K-factor (VLE solute mole fraction ratio, y2/x2), solvation free energy, and Henry\\'s law constant:\\n')\n", "for T in temperature:\n", - " dGsolv, Kfactor = solvationDatabase.get_T_dep_solvation_energy_from_input_298(\n", + " dGsolv, Kfactor, kH = solvationDatabase.get_T_dep_solvation_energy_from_input_298(\n", " dGsolv298, dHsolv298, dSsolv298, solvent_name_in_CoolProp, T) # dGsolv is J/mol\n", - " print(' At {0} K, K-factor = {1:.3f}, solvation free energy = {2:.2f} kJ/mol'.format(T, Kfactor, dGsolv/1000))" + " print(f' At {T} K, K-factor = {Kfactor:.3f}, solvation free energy = {dGsolv/1000:.2f} kJ/mol, kH = {kH:.2f}')" ] }, { @@ -198,14 +207,16 @@ "log_Kfactor_list = []\n", "log_KfactorPsat_list = []\n", "dGsolv_list = []\n", + "kH_list = []\n", "\n", "for T in temp_list:\n", - " dGsolv, Kfactor = solvationDatabase.get_T_dep_solvation_energy_from_LSER_298(solute_data, solvent_data, T)\n", + " dGsolv, Kfactor, kH = solvationDatabase.get_T_dep_solvation_energy_from_LSER_298(solute_data, solvent_data, T)\n", " dGsolv = dGsolv / 1000 # convert to kJ/mol\n", " Psat = PropsSI('P', 'T', T, 'Q', 0, solvent_data.name_in_coolprop) # saturation pressure of the solvent, in Pa\n", " log_Kfactor_list.append(math.log(Kfactor))\n", " log_KfactorPsat_list.append(math.log(Kfactor*Psat*1e-6)) # in ln(1/MPa)\n", - " dGsolv_list.append(dGsolv)" + " dGsolv_list.append(dGsolv)\n", + " kH_list.append(kH)" ] }, { @@ -231,13 +242,19 @@ "plt.plot(temp_list, dGsolv_list)\n", "plt.title('Solvation Gibbs Free Energy', fontsize=20)\n", "ax.set_xlabel('Temperature (K)', fontsize = 16)\n", - "ax.set_ylabel('Solvation Energy (kJ/mol)', fontsize = 16)\n" + "ax.set_ylabel('Solvation Energy (kJ/mol)', fontsize = 16)\n", + "\n", + "ax = fig.add_subplot(2, 2, 4)\n", + "plt.plot(temp_list, kH_list)\n", + "plt.title('Henry\\'s Law Constant', fontsize=20)\n", + "ax.set_xlabel('Temperature (K)', fontsize = 16)\n", + "ax.set_ylabel('$k_H$', fontsize = 16)\n" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -251,9 +268,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.10" + "version": "3.9.23" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/rmgpy/data/solvation.py b/rmgpy/data/solvation.py index b95f708f02..bfa1fcb966 100644 --- a/rmgpy/data/solvation.py +++ b/rmgpy/data/solvation.py @@ -2285,8 +2285,10 @@ def get_T_dep_solvation_energy_from_LSER_298(self, solute_data, solvent_data, T) Returns: delG (float): solvation free energy at the input temperature in J/mol. Kfactor (float): K-factor at the input temperature. K-factor is defined as a ratio of the mole fraction - of a solute in a gas-phase to the mole fraction of a solute in a liquid-phase at equilibrium. - + of a solute in a gas-phase to the mole fraction of a solute in a liquid-phase at equilibrium + kH (float): the Henry's law constant at the input temperature. kH is defined as the ratio of the pressure + of a solute in the gas-phase to the concentration of a solute in the liquid-phase at equilibrium. + Raises: DatabaseError: if `solute_data.name_in_coolprop` is None or `solute_data` has any missing Abarham or Mintz solvent parameters. From 9bb35f57eba5fb71c5ed151732415d6eec6a7334 Mon Sep 17 00:00:00 2001 From: jonwzheng Date: Mon, 30 Jun 2025 11:31:00 -0400 Subject: [PATCH 02/17] initial changes to regression test example --- ipython/regression_test.ipynb | 17 +++++------------ rmgpy/tools/observablesregression.py | 16 ++++++++-------- 2 files changed, 13 insertions(+), 20 deletions(-) diff --git a/ipython/regression_test.ipynb b/ipython/regression_test.ipynb index b04c798c07..89490d75ad 100644 --- a/ipython/regression_test.ipynb +++ b/ipython/regression_test.ipynb @@ -20,7 +20,7 @@ "metadata": {}, "outputs": [], "source": [ - "from rmgpy.tools.observablesRegression import ObservablesTestCase\n", + "from rmgpy.tools.observablesregression import ObservablesTestCase\n", "from IPython.display import display, Image\n", "from rmgpy.species import Species" ] @@ -93,20 +93,13 @@ " print('Plotting condition {0} comparison for species {1}'.format(condition_index, species_label))\n", " display(Image(filename=\"condition_{0}_species_{1}.png\".format(condition_index, species_label)))" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:rmg_env]", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "conda-env-rmg_env-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -118,9 +111,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.9.23" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/rmgpy/tools/observablesregression.py b/rmgpy/tools/observablesregression.py index 17819bb14e..af85ee8e07 100644 --- a/rmgpy/tools/observablesregression.py +++ b/rmgpy/tools/observablesregression.py @@ -173,14 +173,14 @@ def __init__(self, title='', old_dir='', new_dir='', observables=None, expt_data self.old_sim.load_model() self.new_sim.load_model() else: - self.old_sim.load_chemkin_model(old_chemkin_path, - transport_file=old_transport_path, - surface_file=old_surface_chemkin_path, - quiet=True) - self.new_sim.load_chemkin_model(new_chemkin_path, - transport_file=new_transport_path, - surface_file=new_surface_chemkin_path, - quiet=True) + surface_args_old = {} + surface_args_new = {} + if surface: + surface_args_old["surface_file"] = old_surface_chemkin_path + surface_args_new["surface_file"] = new_surface_chemkin_path + + self.old_sim.load_chemkin_model(old_chemkin_path, old_transport_path, quiet=True, **common_args_old) + self.new_sim.load_chemkin_model(new_chemkin_path, new_transport_path, quiet=True, **common_args_new) def __str__(self): """ From 3a57e5528d5c94576f04883947de09c042f8aa2b Mon Sep 17 00:00:00 2001 From: jonwzheng Date: Mon, 30 Jun 2025 11:58:35 -0400 Subject: [PATCH 03/17] update kinetic mechanism analyzer --- ipython/mechanism_analyzer.ipynb | 19 ++++++------------- 1 file changed, 6 insertions(+), 13 deletions(-) diff --git a/ipython/mechanism_analyzer.ipynb b/ipython/mechanism_analyzer.ipynb index f59f25aca6..1f37cad6d0 100644 --- a/ipython/mechanism_analyzer.ipynb +++ b/ipython/mechanism_analyzer.ipynb @@ -62,7 +62,7 @@ "ckcsv_path= os.path.join(mech_path, 'CKSoln.ckcsv')\n", "\n", "model = CoreEdgeReactionModel()\n", - "model.core.species, model.core.reactions = load_chemkin_file(chemkin_path, dictionary_path)" + "model.core.species, model.core.reactions = load_chemkin_file(chemkin_path, dictionary_path, use_chemkin_names = True)" ] }, { @@ -218,7 +218,7 @@ "source": [ "## [user input] Functionality 1: find most dominant species at the investigated time point\n", "\n", - "Pleae specify\n", + "Please specify\n", "\n", "- dominant_list: If you want to visualize the first top 10 dominant species, specify the list as [0, 9]. If you want to visualize from the 5th most dominant species to the 10th most dominant species, specify the list as [4, 9].\n" ] @@ -820,20 +820,13 @@ "else:\n", " print(\"TOTAL flux from h_abs and disp is {0:.3E} mole/cm3/s.\".format(total_flux))\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:rmg_env]", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "conda-env-rmg_env-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -845,9 +838,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.9.23" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } From b2712d60c036d66d2fc10aa18b80cb7605e80b26 Mon Sep 17 00:00:00 2001 From: jonwzheng Date: Mon, 30 Jun 2025 12:01:41 -0400 Subject: [PATCH 04/17] fix typo in observablesregression fix --- rmgpy/tools/observablesregression.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rmgpy/tools/observablesregression.py b/rmgpy/tools/observablesregression.py index af85ee8e07..45e05b5460 100644 --- a/rmgpy/tools/observablesregression.py +++ b/rmgpy/tools/observablesregression.py @@ -179,8 +179,8 @@ def __init__(self, title='', old_dir='', new_dir='', observables=None, expt_data surface_args_old["surface_file"] = old_surface_chemkin_path surface_args_new["surface_file"] = new_surface_chemkin_path - self.old_sim.load_chemkin_model(old_chemkin_path, old_transport_path, quiet=True, **common_args_old) - self.new_sim.load_chemkin_model(new_chemkin_path, new_transport_path, quiet=True, **common_args_new) + self.old_sim.load_chemkin_model(old_chemkin_path, old_transport_path, quiet=True, **surface_args_old) + self.new_sim.load_chemkin_model(new_chemkin_path, new_transport_path, quiet=True, **surface_args_new) def __str__(self): """ From 0bcb19fd778b902aee08ff4644bb6aa10217f81f Mon Sep 17 00:00:00 2001 From: jonwzheng Date: Mon, 30 Jun 2025 13:25:57 -0400 Subject: [PATCH 05/17] update cantera sensitivity comparison --- ipython/cantera_sensitivity_comparison.ipynb | 247 ++----------------- 1 file changed, 18 insertions(+), 229 deletions(-) diff --git a/ipython/cantera_sensitivity_comparison.ipynb b/ipython/cantera_sensitivity_comparison.ipynb index c89b386983..4cb8cabe6c 100644 --- a/ipython/cantera_sensitivity_comparison.ipynb +++ b/ipython/cantera_sensitivity_comparison.ipynb @@ -13,17 +13,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.4.0\n" - ] - } - ], + "outputs": [], "source": [ "import cantera\n", "print(cantera.__version__) # Check Cantera version" @@ -31,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -55,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -73,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -95,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -111,67 +103,14 @@ "#job.load_chemkin_model('data/ethane_model/chem_annotated.inp',transport_file='data/ethane_model/tran.dat')\n", "\n", "# Generate the conditions based on the settings we declared earlier\n", - "job.generate_conditions(reactor_type_list, reaction_time_list, mol_frac_list, Tlist, Plist)" + "job.generate_conditions(reactor_type_list, reaction_time_list, mol_frac_list, Tlist=Tlist, Plist=Plist)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cantera simulation is: \n", - "Cantera Simulation: Condition 1 Species Mole Fractions\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cantera Simulation: Condition 1 Ethane Reaction Sensitivity\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cantera Simulation: Condition 1 Ethane Thermo Sensitivity\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Simulate and plot\n", "alldata = job.simulate()\n", @@ -192,84 +131,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading input file \"/Users/kevin/Documents/Green_Lab/RMG/RMG-Py/ipython/temp/input.py\"...\n", - "# Data sources\n", - "database(\n", - " thermoLibraries = ['DFT_QCI_thermo', 'primaryThermoLibrary'],\n", - " reactionLibraries = [],\n", - " seedMechanisms = [],\n", - " kineticsDepositories = ['training'],\n", - " kineticsFamilies = 'default',\n", - " kineticsEstimator = 'rate rules',\n", - ")\n", - "\n", - "generatedSpeciesConstraints(\n", - " allowed=['input species','seed mechanisms','reaction libraries'],\n", - " maximumRadicalElectrons = 2,\n", - " maximumCarbonAtoms = 10,\n", - ")\n", - "\n", - "# List of species\n", - "species(\n", - " label='ethane',\n", - " reactive=True,\n", - " structure=SMILES(\"CC\"),\n", - ")\n", - "\n", - "species(\n", - " label='methane',\n", - " reactive=True,\n", - " structure=SMILES(\"C\"),\n", - ")\n", - "\n", - "# Reaction systems\n", - "simpleReactor(\n", - " temperature=(1300,'K'),\n", - " pressure=(1.0,'bar'),\n", - " initialMoleFractions={\n", - " \"ethane\": 1.0,\n", - " },\n", - " terminationTime=(0.5,'ms'),\n", - " sensitivity=['ethane','methane']\n", - ")\n", - "\n", - "simulator(\n", - " atol=1e-16,\n", - " rtol=1e-8,\n", - ")\n", - "\n", - "model(\n", - " toleranceMoveToCore=0.01,\n", - " filterReactions=True,\n", - ")\n", - "\n", - "options(\n", - " units='si',\n", - " generateOutputHTML=False,\n", - " generatePlots=False,\n", - " saveEdgeSpecies=True,\n", - " saveSimulationProfiles=True,\n", - ")\n", - "\n", - "\n", - "Warning: Edge species saving was turned on. This will slow down model generation for large simulations.\n", - "\n", - "Thermo file has default temperature range 300.0 to 1000.0 and 1000.0 to 5000.0\n", - "Conducting simulation and sensitivity analysis of reaction system 1...\n", - "At time 5.0000e-04 s, reached target termination time.\n", - "Simulation took 1.9629549980163574 seconds\n" - ] - } - ], + "outputs": [], "source": [ "# Copy example input file to temp folder\n", "shutil.copy('./data/ethane_model/input.py', './temp')\n", @@ -284,44 +150,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMG Native Simulation: Species Mole Fractions\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMG Native Simulation: Ethane Reaction Sensitivity\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "print('RMG Native Simulation: Species Mole Fractions')\n", "display(Image(filename=\"./temp/solver/simulation_1_27.png\"))\n", @@ -332,44 +163,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CHEMKIN Simulation: Species Mole Fractions\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CHEMKIN Simulation: Ethane Reaction Sensitivity\n" - ] - }, - { - "data": { - "image/png": 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tAQDp6enw9PQUtQpX1pW1IgcHB9F45pfrTJ1OnTqhTZs22LlzJ/T09ITlZWVlGD16NCwtLZGQkABDQ0Ns2rQJX3zxBf744w/Ur1+/0jx79OiBHj16VLlvHx8frFu3TrTs3LlzGDBggPD56dOnMDAwUGop79u3r/BvV1dXtG7dGvb29jhw4ICoa7ShoaHoPERGRoqCOLlcDolEgiVLlgjLNmzYgMDAQFy8eBFlZWVwcnIS7Vsul8Pc3LzK46t4LSsC8orn5bfffsPy5ctx/fp1FBQUoKSkBHXq1BHloe7canKv6eJaLi0thVwuh6GhIQwNDdGkSRON0iqCR216OeTn58Pf3x8uLi4qW5d37Nih1IobGBgo+qzJtfH06VNRnbx48QL9+/dHSEiI0vl+2cv3d1ZWFlxcXIT1JSUleP78uSiYHjBggGgSxGbNmikNyblz506lY71TU1PRvXt3BAcHi873y8ehMGvWLDx+/Bi///47LCwssGfPHvTu3RsJCQnC943iWCr7npoxYwYmTpwofM7Pz4etra3KbYmIiOjdx2C7mqpqMVFHX19f9FkikaidnVix7sCBA8I4ZAV145w12d+tW7fg5+eHUaNGYe7cuTAzM8PJkycxbNgw0VhXVXko6kCR18aNG/HRRx+JtlMEm+bm5pBIJNWeOEjRzb5evXpqt6vYhRyo/nnS9hxV5O/vj+joaKSlpYl+hMfFxSEmJgaPHz8WAsC1a9fi6NGj2LJli0bd26tibGysFLj9/fffos8WFhYoKipCcXGx0OquSv369WFvb49r166Jlufk5IjOQ0BAgOi8T5s2DTY2Nhg3bpywzMrKCkD5taKnp4fExETRgwgAGk0Kp+68nD17Fv369UNISAh8fX1hamqKqKgopbkU1OWhyb1mYWEBoLw7eVXXoyo5OTkwMjISAsyEhAR07dpVbZoffvgBP/zwA5o2bQqJRIL09HSNXnn35MkTdOnSBVKpFLt371Y6dqB8BvyXrxlVEw9WpOrasLCwEN3fT548wYULF3Dp0iWMHTsWQHn9lpWVoWbNmjhy5Ag6dOgAQPn+btCggai1f9euXYiOjhZNovbyQ5RatWopHUdlrelpaWno0KEDRowYIeoNoeo4gPKHq6tXr0ZKSgpatGgBAHB3d0dCQgLWrFkjCvpfvj8qMjAw0Og7m4iIiN4PDLarqUmTJtDX18fZs2dhZ2cHoPzH99WrV9G+fXud7svFxQUGBgbIysqqNG9F0PRyl+GqXLhwASUlJQgLCxO6Te7cuVOrPKysrGBjY4MbN24otYhVLJ+LiwvS0tK0fs82AKSkpKBhw4ZCoKNKWVkZ9u/fj61btwrLXFxclF7BpJhE6nVZsGABpFIpOnbsiPj4eKGFTtHa9XL31Bo1arzyq6C0oegenJaWpvZd3v/9739x+/ZtpRb3lJQUUY8KExMTUUuxiYkJzMzMVLbWymQyvHjxAg8ePBDNSl9RrVq1tL6OgfJXS9nb22PmzJnCslu3bmmVhyb3WuPGjVGnTh2kpaVV2WKrSkpKClq1aiV81qYbuZmZGXx9fbFmzRqMGzdOadx2bm6uMCQhPz8fvr6+MDAwwL59+6rdEq+KqmtDJpMhLS1N+FynTh0kJyeL0q1duxZxcXH47bff0KhRI2H5y/d3zZo1RdePpaWlVj0A1ElNTUWHDh0wePBglcNCZDIZ7t27h8ePH6Nu3boAKr939fT0lO7dlJQUncycTkRERO8+TpBWTVKpFMOGDcOUKVMQGxuLlJQUDBkyROX7ZV+ViYkJJk+ejAkTJmDLli3IyMjApUuXsGbNGmECMnt7e0gkEsTExODhw4ei2cTVady4MUpKSrBq1SrcuHED27Ztq9b7qefMmYP58+djxYoVuHr1KpKTkxEeHo6lS5cK2/j6+uLkyZOidMXFxUhKSkJSUhKKi4tx584dJCUl4fr166LtEhISqgzSExMTUVhYiHbt2gnLRo0ahYyMDEycOBFXrlzBr7/+qtX7y6tryZIlCAwMRIcOHYQJvD7++GPUrVsXgwcPxp9//omrV69iypQpuHnzJvz9/dXmt3v3bq3H6VamXr16aNWqlehcFBQUYPLkyThz5gwyMzMRHx+Pbt26wcLCQqn7uibnojJOTk4IDAzEoEGDsGvXLty8eRN//PEHFi5cKLxKysHBAQUFBYiNjcWjR4+qHDqg0KRJE2RlZSEqKgoZGRlYuXKl1rNCa3Kv1ahRA506dVK6lgsKCoRrGQBu3ryJpKQkZGVlibZ7uf4UQaS6v4oTeK1duxYvXrxA27ZtER0djWvXriE9PR0rV64Uhkg8efIEnTt3RmFhITZv3oz8/Hzcu3cP9+7d0/pBhqbXxsv3d40aNeDq6ir6s7S0RO3ateHq6ip6UPAq15Q2UlNT4ePjg88//xwTJ04U6uThw4fCNjKZDPXq1cOpU6eEZc2bN0eTJk3wzTff4Pz588jIyEBYWBiOHj0q6mFQVFSExMTE/8mxEBER0duPwfYrWLx4Mdq1a4eAgAB06tQJn376aZWzSlfX3LlzERwcjPnz58PZ2Rm+vr7Yv3+/0DpkY2ODkJAQTJ8+HVZWVkK3zap4eHhg6dKlWLhwIVxdXREZGYn58+drXb7hw4dj06ZNiIiIQMuWLdG+fXtERESIWq9GjBiBgwcPIi8vT1h29+5dyGQyyGQyZGdnY8mSJZDJZBg+fLiwzbNnz7B7927ReHBV9u7dC39/f1HXUTs7O0RHR2P//v1wd3fH+vXrhZmdX7dly5ahT58+6NChA65evQoLCwscOnQIBQUF6NChA1q3bo2TJ09i7969cHd3V5tXXl4erly5orOyjRw5UtQlV09PD8nJyejevTucnJwwePBgODk54cyZM6JW6zNnziAvLw+9evWq9r7Dw8MxaNAgTJo0Cc2aNUNAQADOnTsnjF318vLCqFGj0LdvX9SrVw+LFi3SKN/u3btjwoQJGDt2LDw8PHD69GnMnj1b6/JVda8B5fUXFRUlatW8cOGCcC0DwMSJEyGTyYTJ+oDyMcSnT59GUFCQ1uVSaNSoES5evAgfHx9MmjQJrq6u+PzzzxEbGyuM109MTMS5c+eQnJyMJk2aoH79+sLf7du3tdqfptfGgAEDkJaWpvV1qun9rQv//ve/8fDhQ0RGRorqpE2bNsI2enp6GDp0qOj+0NfXx8GDB1GvXj1069YNbm5u2Lp1K7Zs2QI/Pz9hu71798LOzq7SXhtERET0zyIpe5XBx6Rz8fHx8PHxwePHj6ucofpd1KdPH8hkMsyYMUPjNGvWrMHevXtx5MgRpXVDhgxBbm4u9uzZAzc3N8yaNQt9+vTRZZFV8vb2hoeHB5YvX/5a8p8zZw727NlTZffi6nr27BmaNWuGqKgojSaMU+jduzdkMhl++OGH11Kud0VZWRk8PT0xfvx44c0CmpgyZQry8vKU3g/+vpg6dSry8vKwYcMGjdOou7/flPv376NFixZITEzUanb0tm3bYvz48ejfv79G2+fn58PU1FR4LZuuLTt6Ved5EhHRq5nwufZD0Ojtos3/32zZfks1bNhQqx/x74rFixdrNBFWRfr6+li1apVomeLdxIrWp+LiYnz11VdVTjSlS2vXroVUKlUal/oqsrKyRO9Vfl1q166NrVu34tGjRxqnkcvlcHd3x4QJE15jyd4NEokEv/zyC0pKSrRKZ2lpiblz576mUr15M2fOhL29vVZd1VXd32+alZUVNm/erDQEQJ0HDx6gV69e7+X3NhEREVUPW7bfMk+fPsWdO3cAlI8LV/WuYXrz9XTnzh3hFVB2dnZqZ/XWRklJifAOawMDA74WiOg9xpZtIqJ/HrZsv/u0+f+bs5G/ZXQ14+777k3X08uvhdKVl2dhJiIiIiKidxO7kRMRERERERHpGINtIiIiIiIiIh1jsE1ERERERESkYwy2iYiIiIiIiHSME6QRERG9hzjjLRER0ZvFlm0iIiIiIiIiHWOwTURERERERKRjDLaJiIiIiIiIdIzBNhEREREREZGOMdgmIiIiIiIi0jEG20REREREREQ6xld/ERERERG9A5Ydvfqmi0CviK9l/GdhyzYRERERERGRjjHYJiIiIiIiItIxBttEREREREREOsZgm4iIiIiIiEjHGGwTERERERER6RiDbSIiIiIiIiIdY7BNREREREREpGMMtv+BvL29MX78eOGzg4MDli9frnH6+Ph4SCQSSCQSfPnllzovz5u2efNmdO7cWas0vXr1wtKlS0XLXqWeFGlzc3MBABEREfjggw+0ysPb21vYf1JSksp8qyMzM1PI18PDo9r5/C9duXIF1tbWePLkicZpJk+ejHHjxr3GUr0+xcXFaNKkCU6dOqVxmpiYGMhkMpSWlr7Gkqmmi+tSl6pTf8nJyWjYsCEKCwtfY8mIiIjoXcJgm6rtypUriIiI0Hj7t+0HtSpyuRzBwcGYPXu2sCw1NRVfffUVHBwcIJFIVD6YCA4Oxrx585Cfny8s8/LyQnZ2Nvr06aNyX97e3li/fr3uD6KCESNGIDs7G66urhqnmTdvHry8vGBkZKQywLe1tUV2djYmTZpUrTLdu3cP3333HRwdHWFgYABbW1t069YNsbGxwjaVPQCaM2eOKMDftWsXWrdujQ8++ADGxsbw8PDAtm3blNLNnDkTY8aMgYmJCYDya9fHxwdWVlaoXbs2HB0dMWvWLDx//lxIM3XqVISHh+PmzZvVOk6F4uJiLFq0CO7u7jAyMoKFhQU++eQThIeHC/sbMmSIygcyL98zmpQbAH755RfY29vjk08+EZYFBATAzs4OtWvXRv369TFw4EDcvXtXWP/FF19AIpHg119/faXjrcrb9nBNFVX1V9V90bJlS7Rt2xbLli37XxaViIiI3mIMtqnaLC0ttW5tfdtFR0dDKpXis88+E5YVFRXB0dERCxYsgLW1tcp0bm5ucHBwQGRkpLCsVq1asLa2hqGhodL2OTk5OH36NLp166b7g6jAyMgI1tbWqFmzpsZpiouL0bt3b3z77bcq1+vp6cHa2hpSqVTr8mRmZuLDDz9EXFwcFi1ahOTkZBw6dAg+Pj4YM2aM1vmZmZlh5syZOHPmDC5fvoygoCAEBQXh8OHDwjZ///039u3bh6CgIGGZvr4+Bg0ahCNHjuDKlStYvnw5Nm7ciB9//FHYxtLSEp07d1b7QCQ+Ph4ODg6Vri8uLoavry8WLFiAkSNH4vTp0zh//jzGjBmDVatWITU1Vavj1aTcALBq1SoMHz5ctMzHxwc7d+7ElStXEB0djYyMDPTq1Uu0TVBQEFatWqVVmd5HquqvqvsCKK+/devW4cWLF6+7iERERPQOYLD9nissLMSgQYMglUpRv359hIWFVZlGIpFg06ZN6NGjB4yMjNC0aVPs27evynRlZWVYtGgRHB0dYWhoCHd3d/z2228AyoMsHx8fAEDdunUhkUgwZMgQIW1paSmmTp0KMzMzWFtbY86cOaK8ly5dipYtW8LY2Bi2trYYPXo0CgoKhPWKbtaHDx+Gs7MzpFIpunTpguzsbFE+4eHhcHZ2Ru3atdG8eXOsXbtWtD4qKgoBAQGiZW3atMHixYvRr18/GBgYVHr8AQEB2L59e5X1BAAHDhyAu7s7bGxsAAAHDx6Ek5MTDA0N4ePjg8zMTLXpFS2827Ztg4ODA0xNTdGvXz+tukkDwNOnT+Hv7w9PT0/k5OQAAEJCQjBhwgS0bNlSq7w0MXr0aEgkEpw/fx69evWCk5MTWrRogYkTJ+Ls2bNa5+ft7Y0ePXrA2dkZjRs3xvfffw83NzecPHlS2Gbnzp1wd3dHw4YNhWWOjo4ICgqCu7s77O3tERAQgMDAQCQkJIjy1+acqrJ8+XKcOHECsbGxGDNmDDw8PODo6Ij+/fvj3LlzaNq0qVb5aVLuixcv4vr16/D39xelnTBhAjw9PWFvbw8vLy9Mnz4dZ8+eFbWKBwQE4Pz587hx40a1jzktLQ1+fn6QSqWwsrLCwIED8ejRIwDlLfjHjx/HihUrhKEIFa/1xMREtG7dGkZGRvDy8sKVK1eEdRkZGejevTusrKwglUrRpk0b/P7776J9Ozg4IDQ0FCDHmicAACAASURBVEOHDoWJiQns7Ozwyy+/iLa5c+cO+vbti7p168Lc3Bzdu3cXlaGy+tPkvvD19cV///tfHD9+XNtqIyIiovcQg+333JQpU3Ds2DHs3r0bR44cQXx8PBITE6tMFxISgj59+uDy5cvw8/NDYGCgEIxVZtasWQgPD8e6deuQmpqKCRMmYMCAATh+/DhsbW0RHR0NoLwrbHZ2NlasWCGk3bJlC4yNjXHu3DksWrQIP/30E44ePSqsr1GjBlauXImUlBRs2bIFcXFxmDp1qmj/RUVFWLJkCbZt24YTJ04gKysLkydPFtZv3LgRM2fOxLx585Ceno7Q0FDMnj0bW7ZsEbZJSEhA69atq6wfVdq2bYvz589DLpdXue2+ffvQvXt3AMDt27fRs2dP+Pn5ISkpCcOHD8f06dOrzCMjIwN79uxBTEwMYmJicPz4cSxYsEDj8ubl5aFz584oLi5GbGwszMzMNE6rSkREBCQSSaXrc3JycOjQIYwZMwbGxsZK61+1l0RZWRliY2Nx5coVtGvXTlh+4sSJKs/p9evXcejQIbRv3160vG3btrh9+zZu3bpVrTJFRkaiU6dOkMlkSuv09fVV1oM2VJX7xIkTcHJyQp06dSpNl5OTg8jISHh5eUFfX19Ybm9vD0tLS1Hw3rVrV0ilUrV/CtnZ2Wjfvj08PDxw4cIFHDp0CPfv3xeGUqxYsQIff/yxMLwhOzsbtra2QvqZM2ciLCwMFy5cQM2aNTF06FBhXUFBAfz8/PD777/j0qVL8PX1Rbdu3ZCVlSU6trCwMLRu3RqXLl3C6NGj8e233+Kvv/4CUP4d4ePjA6lUihMnTuDkyZPCg7ni4mKN668ytWrVgru7u9JDGyIiIvpn0rxvKb1zCgoKsHnzZmzduhWff/45gPKgtmILX2WGDBmCr7/+GgAQGhqKVatW4fz58+jSpYvK7QsLC7F06VLExcXh448/BlDeCnfy5Els2LAB7du3F4I5Vd3P3dzchK6wTZs2xerVqxEbGyuUu+IYz0aNGmHu3Ln49ttvRS3Tz58/x/r169G4cWMAwNixY/HTTz8J6+fOnYuwsDD07NlTyCctLQ0bNmzA4MGDkZubi9zcXDRo0KDK+lHFxsYGcrkc9+7dg729faXbyeVyHD58GMHBwQCAdevWwdHREcuWLYNEIkGzZs2QnJyMhQsXqt1faWkpIiIihHHIAwcORGxsLObNm1dlWe/fv4++ffuicePG2L59O2rVqqXFkapmamqKZs2aVbr++vXrKCsrQ/PmzTXKb9q0aZg1a5ZoWXFxMVxcXETL8vLyhLrX09PD2rVrhesG+L+u66p4eXnh4sWLkMvlGDlypOh6ASD0PMjMzFR7Titz7do1eHt7a7RtTEyMUtf8yrojqyt3ZmZmpdfwtGnTsHr1ahQVFcHT0xMxMTFK29jY2Ihaejdt2oSnT59qdAzr1q1Dq1atEBoaKiz7f//v/8HW1hZXr16Fk5MTatWqJQxveNm8efOEBwfTp0+Hv78/nj17htq1a8Pd3R3u7u7Ctj///DN2796Nffv2YezYscJyPz8/jB49WjjeZcuWIT4+Hs2bN0dUVBRq1KiBTZs2CQ+GwsPD8cEHHyA+Ph6dO3dWW3+aeLn+KpLL5aKHcRXneCAiIqL3D4Pt91hGRgaKi4uF4BcoH+OqLiBScHNzE/5tbGwMExMTPHjwoNLt09LS8OzZM1GQA5QHR6pa9dTtDwDq168v2t+xY8cQGhqKtLQ05Ofno6SkBM+ePUNhYaHQOmhkZCQE2i/n8fDhQ9y+fRvDhg3DiBEjhG1KSkpgamoKAEJAUbt27SrLq4pibHZRUZHa7eLi4mBubi50R01PT4enp6eoVbjiOauMg4ODEGgDynWmTqdOndCmTRvs3LkTenp6GqWpSo8ePdCjR49K15eVlQGA2tbviqZMmSIaagAAK1euxIkTJ0TLTExMkJSUhIKCAsTGxmLixIlwdHQUgtynT59Wek537NiBJ0+e4M8//8SUKVOwZMkSUY8JVee0YkD84sULyOVy0bLPPvsM//nPf4Rj1vR4fXx8sG7dOtGyc+fOYcCAAVqVW93xTpkyBcOGDcOtW7cQEhKCQYMGISYmRlRGQ0ND0fEqHjhoIjExEceOHVM5nj8jIwNOTk5q01f8Hqhfvz4A4MGDB7Czs0NhYSFCQkIQExODu3fvoqSkBE+fPlVq2a6Yh0QigbW1tXBfJCYm4vr166L7BgCePXuGjIwMAOrrTxMv119F8+fPR0hISLXzJiIioncLg+33mCK4qY6KXUuB8h+t6l4JpFh34MABpR/n6sY5a7K/W7duwc/PD6NGjcLcuXNhZmaGkydPYtiwYaLxpqryUNSBIq+NGzfio48+Em2nCDbNzc0hkUjw+PHjKsuriqKbfb169dRuV7ELOVD986TtOarI398f0dHRSEtLey1js1Vp2rQpJBIJ0tPTNXoVmoWFBZo0aSJapqqre40aNYTtPDw8kJ6ejvnz5wvBtoWFRaXnVNGF2cXFBS9evMDIkSMxadIk4ZpQdU4Vr1EDyoPhadOmIT4+XlhWcUI8JycnpKenV3msQPlDrZeP9++//9a63BYWFkhOTlaZzsLCAhYWFnBycoKzszNsbW1x9uxZ0cOdnJwc0fF27dq1ym7RivkTSktL0a1bN5W9MhTBszoVr2nFAwDFNT1lyhQcPnwYS5YsQZMmTWBoaIhevXoJ3b9V5aHIR5FHaWkpPvzwQ9FEhgqKY1ZXf5rIyckRPfSraMaMGZg4caLwOT8/X9SNnoiIiN4vDLbfY02aNIG+vj7Onj0LOzs7AMDjx49x9epVpbGpr8rFxQUGBgbIysqqNG9FV2VtZ+q9cOECSkpKEBYWhho1yqcZ2Llzp1Z5WFlZwcbGBjdu3EBgYGCl5XNxcUFaWprW79kGgJSUFDRs2BAWFhaVblNWVob9+/dj69atwjIXFxfs2bNHtF11JgvTxoIFCyCVStGxY0fEx8crdc1+HczMzODr64s1a9Zg3LhxSuOVc3NzdTK7fVlZmairrkwmQ1pamkbpnj9/Lnr4kZKSAn19fbRo0UJYVjEg/vvvv1GzZk2lIFmhf//++OGHH3Dp0iWlHh4lJSWQy+WvPG775XLLZDKsW7euylZ1xfYV60rRwluxrNp0I2/VqhWio6Ph4OBQ6Qz4tWrVqtZs3QkJCRgyZIjQe6KgoKDKiQRVlW/Hjh2wtLSsdEy2pvVXmZSUFKVZ3hUMDAw0evhIRERE7wdOkPYek0qlGDZsGKZMmYLY2FikpKRgyJAhQsCqSyYmJpg8eTImTJiALVu2ICMjA5cuXcKaNWuECcjs7e0hkUgQExODhw8fimYTV6dx48YoKSnBqlWrcOPGDWzbtq1a76eeM2cO5s+fjxUrVuDq1atITk5GeHg4li5dKmzj6+srmskaKO8Kn5SUhKSkJBQXF+POnTtISkrC9evXRdslJCRUGaQnJiaisLBQNIHXqFGjkJGRgYkTJ+LKlSv49ddftXp/eXUtWbIEgYGB6NChgzCBFABkZWUhKSkJWVlZePHihXDsVZ2v3bt3Vzkee+3atXjx4gXatm2L6OhoXLt2Denp6Vi5cqVGXedfNn/+fBw9ehQ3btzAX3/9haVLl2Lr1q2irte+vr44c+aMKMCLjIzEzp07kZ6ejhs3buDf//43ZsyYgb59+4qCxISEBHz22WcqX9+mifHjx+OTTz5Bx44dsWbNGvz555+4ceMGdu7ciY8++gjXrl3TKj9Nyu3j44PCwkLRa8XOnz+P1atXIykpCbdu3cKxY8fQv39/NG7cWFTvZ8+ehYGBgWiZjY0NmjRpovZPYcyYMcjJycHXX38tzGp+5MgRDB06VKh/BwcHnDt3DpmZmXj06JHGvTGaNGmCXbt2ISkpCX/++Sf69++vcVqFwMBAWFhYoHv37khISMDNmzdx/PhxfP/990IvAlX1B2h2X2RmZuLOnTvo1KmTVuUiIiKi9xOD7ffc4sWL0a5dOwQEBKBTp0749NNPK50s6lXNnTsXwcHBmD9/PpydneHr64v9+/ejUaNGAMp/tIeEhGD69OmwsrISTWqkjoeHB5YuXYqFCxfC1dUVkZGRmD9/vtblGz58ODZt2oSIiAi0bNkS7du3R0REhFA+ABgxYgQOHjyIvLw8Ydndu3chk8kgk8mQnZ2NJUuWQCaTid7D++zZM+zevVs0HlyVvXv3wt/fXxTQ2dnZITo6Gvv374e7uzvWr18vmmDqdVq2bBn69OmDDh064OrVqwCA4OBgyGQy/PjjjygoKBCO/cKFC2rzysvLE72qSZVGjRrh4sWL8PHxwaRJk+Dq6orPP/8csbGxSuOVNVFYWIjRo0ejRYsW8PLywm+//YZ//etfonPj5+cHfX190WuiatasiYULF6Jt27Zwc3PDnDlzMGbMGGzatEmU//bt26s8p+oYGBjg6NGjmDp1KjZs2ABPT0+0adMGK1euxLhx4+Dq6qpVfpqU29zcHD179hR1lTY0NMSuXbvQsWNHNGvWDEOHDoWrqyuOHz8uamndvn07AgMDYWRkVK3jbdCgAU6dOoUXL17A19cXrq6u+P7772Fqaio85Js8eTL09PTg4uKCevXqKY25rsyyZctQt25deHl5oVu3bvD19UWrVq20Kp+RkRFOnDgBOzs79OzZE87Ozhg6dCiePn0qtHSrqj9As/ti+/bt6Ny5c7Um0yMiIqL3j6TsVQb20j9SfHw8fHx88PjxY510+33b9OnTBzKZDDNmzNA4zZo1a7B3714cOXJEad2QIUOQm5uLPXv2wM3NDbNmzRJehfQ6eXt7w8PDA8uXL38t+c+ZMwd79uwRjWF+W61duxZ79+7F4cOHNU5z4MABTJkyBZcvX660S/TbKjk5GZ06dVI5GVhlHj58iObNm+PChQuiB1D/RNWpP7lcjqZNm2L79u345JNPNEqTn58PU1NT5OXlVetVY0T0z7Ps6NU3XQR6RRM+Vz9ZKL39tPn/my3bVG0NGzYUXg/2Plm8eLHK2ZTV0dfXx6pVq0TLEhISIJVKhRay4uJifPXVV+jatavOylqVtWvXQiqVvtKETy/LysqCVCr9n7W+68LIkSPRrl07PHnyROM0hYWFCA8Pf+cCbQBo2bIlFi1apNWY5ps3b2Lt2rX/+EAbqF793bp1CzNnztQ40CYiIqL3H1u2SWtPnz7FnTt3AJSPC1f1vlx68/V0584dYWIrOzs7nbxLGyif2EsRhBgYGHA2ZaJqYss2EWmLLdvvPrZsv/u0+f/73WuyoTfO0NCw0tmX6f+86XrS5v3I2lA3+zYREREREZVjN3IiIiIiIiIiHWOwTURERERERKRjDLaJiIiIiIiIdIzBNhEREREREZGOcYI0IiIiIqJ3AGeyJnq3sGWbiIiIiIiISMcYbBMRERERERHpGINtIiIiIiIiIh1jsE1ERERERESkYwy2iYiIiIiIiHSMwTYRERERERGRjvHVX0RERERERP8Dy45efdNF+Ed506/LY8s2ERERERERkY4x2CYiIiIiIiLSMQbbRERERERERDrGYJuIiIiIiIhIxxhsExEREREREekYg20iIiIiIiIiHWOwTURERERERKRjDLaJiIiIiIiIdIzBNqnk7e2N8ePHC58dHBywfPlyjdPHx8dDIpFAIpHgyy+/1Hl53rTNmzejc+fOWqXp1asXli5dKlr2KvWkSJubmwsAiIiIwAcffKBVHt7e3sL+k5KSVOZbHbo+/+q0a9cOv/76q8bbP3jwAPXq1cOdO3deY6nerNmzZ2PkyJEaby+Xy2FnZ4fExMTXWKrKafv98rppW38A0KZNG+zates1lYiIiIjeRQy26bW6cuUKIiIiNN5eF4He6yaXyxEcHIzZs2cLy1JTU/HVV1/BwcEBEolEZeAQHByMefPmIT8/X1jm5eWF7Oxs9OnTR+W+vL29sX79et0fRAUjRoxAdnY2XF1dNU4TEBAAOzs71K5dG/Xr18fAgQNx9+5dYX1Vx6VOZQ9WVD1MiImJwb1799CvXz9RekWgr/iruN7S0hIDBw7Ejz/+qHXZXnbp0iX07t0bVlZWqF27NpycnDBixAhcvXoVAPDnn3/i66+/hq2tLQwNDeHs7IwVK1ZUeVwKEokEe/bsET4rrq+Kf9OnTxeluX//PlasWIEffvhBZZ7z58+HRCIR1bGBgQEmT56MadOmVaseNFWdB0L/a6rq78SJE+jWrRsaNGigdE4UZs+ejenTp6O0tPR/WVwiIiJ6izHYptfK0tLyrf9xra3o6GhIpVJ89tlnwrKioiI4OjpiwYIFsLa2VpnOzc0NDg4OiIyMFJbVqlUL1tbWMDQ0VNo+JycHp0+fRrdu3XR/EBUYGRnB2toaNWvW1DiNj48Pdu7ciStXriA6OhoZGRno1auXsF7dcenSypUrERQUhBo1xF9ligcIir8NGzaI1gcFBSEyMhKPHz+uNG9vb2+1D4piYmLg6ekJuVyOyMhIpKenY9u2bTA1NRUexCQmJqJevXr417/+hdTUVMycORMzZszA6tWrq33MP/30k+jYZs2aJVq/efNmfPzxx3BwcFBK+8cff+CXX36Bm5ub0rrAwEAkJCQgPT292mV7H6iqv8LCQri7u6s9b/7+/sjLy8Phw4f/B6UkIiKidwGDbUJhYSEGDRoEqVSK+vXrIywsrMo0EokEmzZtQo8ePWBkZISmTZti3759VaYrKyvDokWL4OjoCENDQ7i7u+O3334DAGRmZsLHxwcAULduXUgkEgwZMkRIW1paiqlTp8LMzAzW1taYM2eOKO+lS5eiZcuWMDY2hq2tLUaPHo2CggJhvaJV7fDhw3B2doZUKkWXLl2QnZ0tyic8PBzOzs6oXbs2mjdvjrVr14rWR0VFISAgQLSsTZs2WLx4Mfr16wcDA4NKjz8gIADbt2+vsp4A4MCBA3B3d4eNjQ0A4ODBg3BycoKhoSF8fHyQmZmpNv2cOXPg4eGBbdu2wcHBAaampujXrx+ePHmi0f4Vnj59Cn9/f3h6eiInJwcAMGHCBHh6esLe3h5eXl6YPn06zp49i+fPn2uV96t49OgRfv/9d6VzAfzfAwTFn6mpqWh9y5YtYW1tjd27d1dr30VFRQgKCoKfnx/27duHTp06oVGjRvjoo4+wZMkSIbgfOnQoVq5cifbt28PR0REDBgxAUFDQK3U3NjExER2bVCoVrVd1fQJAQUEBAgMDsXHjRtStW1dpvbm5Oby8vDS+PlUpLi7G1KlTYWNjA2NjY3z00UeIj48HUN5rJSgoCHl5eUKrfMV7uKioCEOHDoWJiQns7Ozwyy+/iPKeNm0anJycYGRkBEdHR8yePVt0vWlyvav7/lFQVX9du3bFzz//jJ49e1Z67Hp6evDz83ul+iMiIqL3C4NtwpQpU3Ds2DHs3r0bR44cQXx8vEZjN0NCQtCnTx9cvnwZfn5+CAwMFIKxysyaNQvh4eFYt24dUlNTMWHCBAwYMADHjx+Hra0toqOjAZR3P8/OzhZ1ud2yZQuMjY1x7tw5LFq0CD/99BOOHj0qrK9RowZWrlyJlJQUbNmyBXFxcZg6dapo/0VFRViyZAm2bduGEydOICsrC5MnTxbWb9y4ETNnzsS8efOQnp6O0NBQzJ49G1u2bBG2SUhIQOvWrausH1Xatm2L8+fPQy6XV7ntvn370L17dwDA7du30bNnT/j5+SEpKQnDhw9X6j6sSkZGBvbs2YOYmBjExMTg+PHjWLBggcblzcvLQ+fOnVFcXIzY2FiYmZkpbZOTk4PIyEh4eXlBX19fbX5z5sxR2eJaHSdPnoSRkRGcnZ2V1kVGRsLCwgItWrTA5MmTVT5gaNu2LRISEqq178OHD+PRo0dK15eCut4ceXl5KutRUwsXLoS5uTk8PDwwb948FBcXC+seP36MlJQUldfnmDFj4O/vj06dOlWa98t1EhkZCalUqvavYk+NoKAgnDp1ClFRUbh8+TJ69+6NLl264Nq1a/Dy8sLy5ctRp04doVW+4r0XFhaG1q1b49KlSxg9ejS+/fZb/PXXX8J6ExMTREREIC0tDStWrMDGjRuxbNkyUfmrut7Vff9UVX+aqOqaksvlyM/PF/0RERHR+0vzfqP0XiooKMDmzZuxdetWfP755wDKg9qGDRtWmXbIkCH4+uuvAQChoaFYtWoVzp8/jy5duqjcvrCwEEuXLkVcXBw+/vhjAICjoyNOnjyJDRs2oH379kIQoqr7uZubmzDOtmnTpli9ejViY2OFclccg9qoUSPMnTsX3377rahl+vnz51i/fj0aN24MABg7dix++uknYf3cuXMRFhYmtGA1atQIaWlp2LBhAwYPHozc3Fzk5uaiQYMGVdaPKjY2NpDL5bh37x7s7e0r3U4ul+Pw4cMIDg4GAKxbtw6Ojo5YtmwZJBIJmjVrhuTkZCxcuFDt/kpLSxEREQETExMAwMCBAxEbG4t58+ZVWdb79++jb9++aNy4MbZv345atWqJ1k+bNg2rV69GUVERPD09ERMTU2WeFhYWQt2rs3btWmzatEm0rKSkBLVr1xY+Z2ZmwsrKSqkLeWBgIBo1agRra2ukpKRgxowZ+PPPP0UPZoDyc3Hp0qUqy6LKtWvXAADNmzfXKt2ZM2ewc+dOHDhwQLQ8Ly9PqYVale+//x6tWrVC3bp1cf78ecyYMQM3b94U6urWrVsoKytTuj6joqJw8eJF/PHHH2rzt7GxEfWYCAgIwEcffaQ2jZWVFYDyQHf79u34+++/hf1PnjwZhw4dQnh4OEJDQ2FqagqJRKJyqIWfnx9Gjx4NoPzaWrZsGeLj44U6rthd3sHBAZMmTcKOHTtEDzzUXe+afP9UVn+asrGxQVZWFkpLS5WuS6B8vHxISEi18iYiIqJ3D4Ptf7iMjAwUFxcLPz4BwMzMDM2aNasybcVxn8bGxjAxMcGDBw8q3T4tLQ3Pnj0TgmOF4uJiyGQyrfYHAPXr1xft79ixYwgNDUVaWhry8/NRUlKCZ8+eobCwEMbGxgDKuxdXDPYq5vHw4UPcvn0bw4YNw4gRI4RtSkpKhG7IT58+BQBR0KcNxRjmoqIitdvFxcXB3NwcLVu2BACkp6fD09MTEolE2KbiOauMg4ODEHgAynWmTqdOndCmTRvs3LkTenp6SuunTJmCYcOG4datWwgJCcGgQYMQExMjKuPLxo4di7Fjx1a578DAQMycOVO0bNeuXQgNDRU+P336VOV5qHjuXF1d0bRpU7Ru3RoXL15Eq1athHWGhoai8xAaGqqU/9mzZ0Xl/c9//oPPPvsMZWVlVR7Dy1JTU9G9e3cEBwcr3QMmJia4ePGiUpqmTZuKPk+YMEH4t5ubG+rWrYtevXoJrd2qrs/bt2/j+++/x5EjR6q8bl+uExMTE9H1o87FixdRVlYGJycn0XK5XA5zc/Mq01e8vxUBecVr9bfffsPy5ctx/fp1FBQUoKSkBHXq1BHloe561+T7Rxf3d2lpKeRyucr5CmbMmIGJEycKn/Pz82Fra1utfREREdHbj8H2P1x1ggaFl7sMSyQStTPxKtYdOHBAGIesoG6csyb7u3XrFvz8/DBq1CjMnTsXZmZmOHnyJIYNGyYa16kqD0UdKPLauHGjUmueItg0NzeHRCJRO7GWOopu9vXq1VO7XcUu5ED1z5O256gif39/REdHIy0tTQj6K7KwsICFhQWcnJzg7OwMW1tbnD17VqOHAFUxNTVFkyZNRMssLS2V9q/JeWjVqhX09fVx7do1UbCdk5MjOg+jRo0SzZ4eGBiIr776SjROV3HdKgLKv/76S6PjTUtLQ4cOHTBixAilCc2A8iEQLx+vJjw9PQEA169fh7m5OSwsLACUd4dWHFtiYiIePHiADz/8UEj34sULnDhxAqtXr4ZcLheu75frJDIyEt98843aMmzYsAGBgYEoLS2Fnp4eEhMTlR7OaNJqr+5aPXv2LPr164eQkBD4+vrC1NQUUVFRSvNLqMtDk+8fVfWnjZycHBgZGVU6MaCBgYFG33VERET0fmCw/Q/XpEkT6Ovr4+zZs7CzswNQ/kPz6tWraN++vU735eLiAgMDA2RlZVWat6Kr8osXL7TK+8KFCygpKUFYWJjQfXPnzp1a5WFlZQUbGxvcuHEDgYGBlZbPxcUFaWlpWr9nGwBSUlLQsGFD4Ue9KmVlZdi/fz+2bt0qLHNxcVF63dDZs2e13r82FixYAKlUio4dOyI+Ph4uLi6Vbqt4GKDJWHRdkclkuHfvHh4/fqxywi+F1NRUPH/+HPXr1xctT0lJgbe3t/DZzMxMNJba0NAQlpaWKoPgzp07w8LCAosWLVI5yVpubq4wDCI1NRUdOnTA4MGDNeq+rw1FN3jFsTVu3Bh16tRBWlqa8ECgY8eOSE5OFqULCgpC8+bNMW3aNFFgnJKSIuplok03cplMhhcvXuDBgweimforqlWrltb3NgCcOnUK9vb2ot4Ot27d0ioPTb5/VNWfNlJSUkQPdIiIiOifjcH2P5xUKsWwYcMwZcoUmJubw8rKCjNnzlQ53vBVmZiYYPLkyZgwYQJKS0vx6aefIj8/H6dPn4ZUKsXgwYNhb28PiUSCmJgY+Pn5wdDQUKNWscaNG6OkpASrVq1Ct27dcOrUqWq9n3rOnDkYN24c6tSpg65du0Iul+PChQt4/Pix0P3T19cXJ0+eFI0RLy4uRlpamvDvO3fuICkpCVKpVBSsJSQkVBmkJyYmorCwEO3atROWjRo1CmFhYZg4cSK++eYbJCYmavX+8upasmQJXrx4gQ4dOgjjZ8+fP4/z58/j008/Rd26dXHjxg0EBwejcePGVbbyrl69Grt370ZsbOwrl00mk6FevXo4deoUvvjiCwDlwyIiIyPh5+cHCwsLpKWlYdKkM9d0iAAAIABJREFUSZDJZPjkk0+EtEVFRUhMTBR1G9eGsbExNm3ahN69eyMgIADjxo1DkyZN8OjRI+zcuRNZWVmIiopCamoqfHx80LlzZ0ycOBH37t0DUN5TQtuW0zNnzuDs2bPw8fGBqakp/vjjD0yYMEF45zlQ3kLeqVMnnDx5El9++SWA8vvu5XeoGxsbw9zcXGl5QkIC5s6dK3zWphu5k5MTAgMDMWjQIISFhUEmk+HRo0eIi4tDy5Yt4efnBwcHBxQUFCA2Nhbu7u4wMjKCkZFRlXk3adJEqNM2bdrgwIEDWs8kr8n3j6r6A8rntrh+/brw+ebNm0hKSoKZmZlQ94Bm9zcRERH9c3A2csLixYvRrl07BAQEoFOnTvj0009FXU51ae7cuQgODsb8+fPh7OwMX19f7N+/H40aNQJQ3k03JCQE06dPh5WVlUbjewHAw8MDS5cuxcKFC+Hq6orIyEjMnz9f6/INHz4cmzZtQkREBFq2bIn27dsjIiJCKB9QPib44MGDyMvLE5bdvXsXMpkMMpkM2dnZWLJkCWQyGYYPHy5s8+zZM+zevVs0pliVvXv3wt/fX/Teazs7O0RHR2P//v1wd3fH+vXrqx0oamvZsmXo06cPOnTogKtXr8LQ0BC7du1Cx44d0axZMwwdOhSurq44fvx4lV1kHz16hIyMDJ2US09PD0OHDlV6b3lsbCx8fX3RrFkzjBs3Dp07d8bvv/8uasHdu3cv7OzsKm2B1UT37t1x+vRp6Ovro3///mjevDm+/vpr5OXl4eeffwYA/Pvf/8bDhw8RGRmJ+vXrC39t2rTRen8GBgbYsWMHvL294eLiguDgYIwYMULpVVMjR45EVFSUxsMFFM6cOYO8vDzR+9K1FR4ejkGDBmHSpElo1qwZAgICcO7cOWFcspeXF0aNGoW+ffuiXr16WLRokUb5du/eHRMmTMDYsWPh4eGB06dPC+8y10ZV3z+A6vq7cOGCcH8DwMSJEyGTyYQJDAHgzp07OH36NIKCgrQuFxEREb2fJGWvMmiXqBLx8fHw8fHB48eP1b4G6V3Vp08fyGQyzJgxQ+M0a9aswd69e3HkyBGldUOGDEFubi727NkDNzc3zJo1SzR++HXx9vaGh4cHli9f/lryr3hcr8P9+/fRokULJCYmqp3d/WVt27bF+PHj0b9//9dSrjeprKwMnp6eGD9+vPC2AE307t0bMpkMP/zww2ss3duvuvU3ZcoU5OXlKb0fXJ38/HyYmpoiLy9PabI3IiJ6Py07evVNF+EfZcLn2g8Lq4o2/3+zZZteq4YNG2r1g/VdsXjxYo26t1ekr6+PVatWiZYlJCSI3lVcXFyMr776Cl27dtVZWauydu1aSKVSpXG9r+Ll43pdrKyssHnzZmRlZWmc5sGDB+jVq9d7eV0C5ZOC/fLLLygpKdE4jVwuh7u7u2i283+q6tQfUD6BX8Uu+ERERERs2abX4unTp7hz5w6A8nHhqt6rS2++nu7cuSO87sju/7N352FNXO3fwL9xAYEgCsgiWwRkUwQULGqr4AIWCrTWDXHBtVbRRy3igloUEaosKipi6wNordSniAtal4JstaigVCBWFEWsInUDCkoQyfsHb+bHkAAJxrr0/lxXroucmTlzZs4k5J6zjKGh2LO0O+pNHxch7wJq2SaEkH8fatn+Z73plm2aII28FkpKSh16lNG/zZs+Ty0fgSQvb/q4CCGEEEIIedOoGzkhhBBCCCGEECJnFGwTQgghhBBCCCFyRsE2IYQQQgghhBAiZxRsE0IIIYQQQgghckYTpBFCCCGEEELIP+B1zI5N3l7Usk0IIYQQQgghhMgZBduEEEIIIYQQQoicUbBNCCGEEEIIIYTIGQXbhBBCCCGEEEKInFGwTQghhBBCCCGEyBkF24QQQgghhBBCiJzRo78IIYQQQggh5B8Qdbb4TRfhX+VNP2qNWrYJIYQQQgghhBA5o2CbEEIIIYQQQgiRMwq2CSGEEEIIIYQQOaNgmxBCCCGEEEIIkTMKtgkhhBBCCCGEEDmjYJsQQgghhBBCCJEzCrYJIYQQQgghhBA5o2CbAACcnJywZMkS5j2Px8PWrVul3j49PR0cDgccDgeffvqp3Mvzpu3duxcuLi4ybTN+/HhERkay0l7lPIm2raysBADEx8ejR48eMuXh5OTE7D8/P19ivh0h7/r/J6SlpcHCwgKNjY1SbyOpTl+3tWvXYt68eTJt4+DggMOHD7+mEsmfPK5Beaqvr4epqSl+/fVXqbdJSUmBnZ2dTNcTIYQQQt5vFGwTubp+/Tri4+OlXv9t+5EtiUAgwLp167B27VomraioCJ9//jl4PB44HI7EGxPr1q1DSEgIqqurmbShQ4eivLwcEydOlLgvJycn7N69W/4H0czcuXNRXl6O/v37y7ytQCCAra0tK1gH2j+u9ty8eRMzZ86Evr4+FBUV0adPH3h7eyM3N5dZh8Ph4MiRI2Lb+vr6sgL8mJgYDBgwAN27d0f37t0xZMgQ/Pzzz2LbBQQEIDAwEJ06NX0NZmdnY9iwYdDQ0ICSkhIsLCwQFRXF2kZSncoqKCgItra2YumlpaVi57WiogLbtm3D6tWrWduLbmyIXjo6Oqy81q5di5UrV76Vgd/bdiNNkj179sDIyAjDhg0D0FQ3s2fPRp8+faCkpAQTExN8/fXXqK+vZ7b55JNPwOFw8MMPP7ypYhNCCCHkLUPBNpErLS0tmVtb33ZJSUngcrn46KOPmLRnz57B2NgYYWFhYoGOyIABA8Dj8XDgwAEmTUFBATo6OlBSUhJb/8mTJzh//jw8PDzkfxDNKCsrQ0dHB126dJF524CAAPTu3Vssva3jak9ubi4GDRqE4uJixMbGgs/nIzk5GRYWFvjqq69kzk9fXx9hYWHIzc1Fbm4uRo4cCS8vLxQVFTHrnD9/Hjdu3MCECROYNBUVFfj5+SEzMxPXrl3DmjVrsGbNGuzZs4dZR1KdthQfHw8nJyeZyy3J3r17MWTIEPB4PFZ6v379UF5ezrwKCgpYy93d3VFVVYXTp0/LpRz/NtHR0ZgzZw7z/o8//kBjYyNiY2NRVFSEqKgo7N69m3UTBABmzpyJ6Ojof7q4hBBCCHlLUbD9L1RbW4vp06eDy+VCV1cXERER7W7D4XDw3Xff4bPPPoOysjL69u2LY8eOtbudUCjE5s2bYWxsDCUlJdjY2OCnn34C0NRa5OzsDADo2bMnOBwOfH19mW0bGxsREBAAdXV16OjoICgoiJV3ZGQkrK2toaKiAgMDAyxYsAA1NTXMclE369OnT8PS0hJcLhdjx45FeXk5K5+4uDhYWlqiW7dusLCwwK5du1jLExMT4enpyUpzcHDAli1bMHnyZCgqKrZ6/J6enjh48GC75wkATpw4ARsbG+jp6QEATp48CTMzMygpKcHZ2RmlpaVtbi9qMd2/fz94PB7U1NQwefJk/P3331LtX+T58+dwd3eHo6Mjnjx5wqT//PPPOHPmDMLDw2XKry1CoRC+vr7o27cvsrKy4O7uDhMTE9ja2uLrr7/G0aNHZc7Tw8MDbm5uMDMzg5mZGUJCQsDlcpGTk8Osk5iYCBcXF3Tr1o1Js7Ozg7e3N/r16wcej4epU6fC1dUVWVlZrPxlqdNXJenaA4AuXbpAR0eHefXq1Yu1vHPnznBzc3ulcjo5OWHRokVYsmQJevbsCW1tbezZswe1tbWYOXMmVFVVYWJiItZrgM/nw83NDVwuF9ra2pg2bRoePXoEoKkXQkZGBrZt28a0yje/rvPy8mBvbw9lZWUMHToU169fZ5aVlJTAy8sL2tra4HK5cHBwwC+//MLaN4/Hw6ZNmzBr1iyoqqrC0NCQdbMEAO7du4dJkyahZ8+e0NDQgJeXF6sMly9fxs2bN+Hu7s6kjR07FnFxcXBxcYGxsTE8PT3h7+8v1lXf09MTFy9exK1btzp0zgkhhBDyfqFg+19o+fLlOHfuHJKTk3HmzBmkp6cjLy+v3e3Wr1+PiRMn4urVq3Bzc4OPjw8rGJNkzZo1iIuLQ0xMDIqKirB06VJMnToVGRkZMDAwQFJSEoCm7ufl5eXYtm0bs21CQgJUVFRw4cIFbN68GRs2bMDZs2eZ5Z06dcL27dtRWFiIhIQEpKWlISAggLX/Z8+eITw8HPv370dmZibKysrg7+/PLP/2228RGBiIkJAQXLt2DZs2bcLatWuRkJDArJOVlQV7e/t2z48kgwcPxsWLFyEQCNpd99ixY/Dy8gIA3L17F+PGjYObmxvy8/MxZ84crFy5st08SkpKcOTIEaSkpCAlJQUZGRkICwuTurxVVVVwcXFBfX09UlNToa6uDqCpO/PcuXOxf/9+KCsrS51fUFCQWKtsc/n5+SgqKsJXX33FdOdu7lV7Sbx8+RKJiYmora3FkCFDmPTMzMx26/TKlSs4f/48RowYwUqXpU5fxdOnT1FYWCixnDdu3EDv3r3Rp08fTJ48WWJwN3jwYNaNgqysLHC53DZfmzZtYuWRkJAATU1NXLx4EYsWLcKXX36JCRMmYOjQobh8+TJcXV0xbdo0PHv2DABQXl6OESNGwNbWFrm5uTh16hQqKiqY4QXbtm3DkCFDmKEM5eXlMDAwYPYXGBiIiIgI5ObmokuXLpg1axazrKamBm5ubvjll19w5coVuLq6wsPDA2VlZawyR0REwN7eHleuXMGCBQvw5Zdf4o8//gDQ9H3g7OwMLpeLzMxMZGdnMzfhRF3CMzMzYWZmhu7du7dZP1VVVcznQ8TIyAhaWlpiN2hEBAIBqqurWS9CCCGEvL9k70dK3mk1NTXYu3cv9u3bhzFjxgBo+kGtr6/f7ra+vr7w9vYGAGzatAnR0dG4ePEixo4dK3H92tpaREZGIi0tjQl0jI2NkZ2djdjYWIwYMYL5sSqp+/mAAQPw9ddfAwD69u2LHTt2IDU1lSl383Gfffr0QXBwML788ktWy/SLFy+we/dumJiYAAD8/PywYcMGZnlwcDAiIiIwbtw4Jh8+n4/Y2FjMmDEDlZWVqKyslNh1Whp6enoQCAR48OABjIyMWl1PIBDg9OnTWLduHYCmccfGxsaIiooCh8OBubk5CgoK8M0337S5v8bGRsTHx0NVVRUAMG3aNKSmpiIkJKTdslZUVGDSpEkwMTHBwYMHoaCgAOD/Wp/nz58Pe3v7dlvYm9PU1GTOvSQ3btwAAFhYWEiVn7e3Nzp37sxKEwgErFZIACgoKMCQIUNQV1cHLpeL5ORkWFlZMctLS0tbrVN9fX08fPgQDQ0NCAoKYnUnBqSv07YUFBSAy+Wy0oRCIev9nTt3IBQKxcr5wQcfYN++fTAzM0NFRQU2btyIoUOHoqioCBoaGqxylpWVobGxEZ06dYK9vT1rPLgkLYNHGxsbrFmzBgCwatUqhIWFQVNTE3PnzgXQNIY9JiYGV69ehaOjI2JiYjBw4EBW0P7f//4XBgYGKC4uhpmZGRQUFJihDC2FhIQwNzdWrlwJd3d31NXVoVu3brCxsYGNjQ2z7saNG5GcnIxjx47Bz8+PSXdzc8OCBQsAACtWrEBUVBTS09NhYWGBxMREdOrUCd999x04HA6App4tPXr0QHp6OlxcXNq8NkRKSkoQHR0tsVeQnp5eq5+R0NBQrF+/vs28CSGEEPL+oGD7X6akpAT19fWsVj51dXWYm5u3u+2AAQOYv1VUVKCqqoq//vqr1fX5fD7q6uqY4Fikvr4ednZ2Mu0PAHR1dVn7O3fuHDZt2gQ+n4/q6mo0NDSgrq4OtbW1UFFRAdA0Prl5sNc8j4cPH+Lu3buYPXs2EzwAQENDA9TU1AA0dakGwOpuLAvRGGZRy19r0tLSoKGhAWtrawDAtWvX4OjoyAQEAFh11hoej8cE2oD4OWvL6NGj4eDggEOHDrEC2ujoaFRXV2PVqlVS5dOcn58fKxBqSRRgNj/OtkRFRWH06NGstBUrVuDly5esNHNzc+Tn56OyshJJSUmYMWMGMjIymID7+fPnrdZpVlYWampqkJOTg5UrV8LU1JS5yQSI12lZWRkrkG9oaMCLFy9YwfTUqVNZE9+Zm5uLDcO4d+8ea6x3a9fexx9/zPxtbW2NIUOGwMTEBAkJCVi2bBmrnI2NjRAIBFBSUoKSkhJMTU0lHnNrmn8GO3fuzLpGAUBbWxsAmGssLy8P586dE7uRADR995iZmUm9P11dXSZvQ0ND1NbWYv369UhJScH9+/fR0NCA58+fi7VsN89DNHlc8/LdvHmT9RkBgLq6OpSUlABo+9oAgPv372Ps2LGYMGGC2I0YoOm8t/Z5X7VqFauOqqurWS37hBBCCHm/ULD9L9Oy9UwWXbt2Zb3ncDhtznYsWnbixAlmHLJIW+OcpdnfnTt34Obmhvnz5yM4OBjq6urIzs7G7Nmz8eLFizbzEJ0DUV7ffvstPvjgA9Z6omBTQ0MDHA4HT58+bbe8koi62bccU9tS8y7kQMfrSdY6as7d3R1JSUng8/msgCotLQ05OTlidWZvbw8fHx9Wl3tZiYKva9euSZyhuyUdHR2xgFFVVVVsNnsFBQVmPXt7e1y6dAnbtm1DbGwsgKYW99bqtE+fPgCaAtmKigoEBQWxgu2Wddq7d29Wi/Hhw4eRlJTEmkStZZfk5uUTaTlhnaamJoCm7uRtXT8qKiqwtrZmegk0L6eysjJzcyArK4sVqEuyevVq1qRfkq6n5mmimySia6yxsREeHh4Se2CIgue2tJX38uXLcfr0aYSHh8PU1BRKSkoYP348a0bw1srcvHyDBg2SOMGd6BxramqKTTgncv/+fTg7O2PIkCFiY8FFnjx50mp9KSoqSvXdRwghhJD3AwXb/zKmpqbo2rUrcnJyYGhoCKDpx3xxcbHY2NRXZWVlBUVFRZSVlbWat6ircsuWyfbk5uaioaEBERERzFjfQ4cOyZSHtrY29PT0cOvWLfj4+LRaPisrK/D5fJmfsw0AhYWF0NfXZwInSYRCIY4fP459+/YxaVZWVmKPuWo+wdfrEBYWBi6Xi1GjRiE9PZ1prd2+fTs2btzIrHf//n24urrixx9/FLtJIStbW1tYWVkhIiICkyZNEhu3XVlZKZfZ7YVCIWuMtZ2dHfh8vszbAeJ12qVLF1bgrKWl1aFW5JZMTEzQvXt38Pn8NluEBQIBrl27xpotX1TOgQMHMu870o1cVgMHDkRSUhJ4PF6rs90rKCjI/HkHmm4W+Pr64rPPPgPQNCRGliENovL9+OOP0NLSanVMtp2dHWJiYiAUClk9Lu7duwdnZ2cMGjQIcXFxEucYELWQS9NzhxBCCCHvP5og7V+Gy+Vi9uzZWL58OVJTU1FYWAhfX1+JPxxflaqqKvz9/bF06VIkJCSgpKQEV65cwc6dO5nWUCMjI3A4HKSkpODhw4es2cTbYmJigoaGBkRHR+PWrVvYv39/h55PHRQUhNDQUGzbtg3FxcUoKChAXFwcIiMjmXVcXV2RnZ3N2q6+vh75+fnIz89HfX097t27h/z8fNy8eZO1XlZWVrtBel5eHmprazF8+HAmbf78+SgpKcGyZctw/fp1/PDDDzI9v7yjwsPD4ePjg5EjRzKTShkaGqJ///7MSxT4mZiYtDvWf8eOHRg1alSryzkcDuLi4lBcXIzhw4fj5MmTuHXrFq5evYqQkBBWa7+0Vq9ejaysLJSWlqKgoACBgYFIT09n3VCRVKc7d+7E8ePHcePGDdy4cQNxcXEIDw/H1KlTWetJU6fy0KlTJ4wePVqsnP7+/sjIyMDt27dx4cIFjB8/HtXV1ZgxY0ab5RTdAGjr9arB9sKFC/HkyRN4e3szs3KfOXMGs2bNYgJsHo+HCxcuoLS0FI8ePZK654WpqSkOHz6M/Px8/P7775gyZYrMzxH38fGBpqYmvLy8kJWVhdu3byMjIwP/+c9/8OeffwIAnJ2dUVtby3pU3P379+Hk5AQDAwOEh4fj4cOHePDgAR48eMDKX9QDRJohH4QQQgh5/1Gw/S+0ZcsWDB8+HJ6enhg9ejQ+/PBDDBo06LXsKzg4GOvWrUNoaCgsLS3h6uqK48ePM1119fT0sH79eqxcuRLa2tptju9tztbWFpGRkfjmm2/Qv39/HDhwAKGhoTKXb86cOfjuu+8QHx8Pa2trjBgxAvHx8Uz5AGDu3Lk4efIkqqqqmLT79+/Dzs4OdnZ2KC8vR3h4OOzs7FhjOOvq6pCcnMwaDy7J0aNH4e7uzmoJNDQ0RFJSEo4fPw4bGxvs3r1bbKbo1yUqKgoTJ07EyJEjUVxc/Ep5PXr0iBkL25rBgwcjNzcXJiYmmDt3LiwtLeHp6YmioiJs3bpV5n1WVFRg2rRpMDc3x6hRo3DhwgWcOnWKNXfA1KlTwefzWY+WamxsxKpVq2Brawt7e3tER0cjLCyMNaGetHUqL/PmzUNiYiIrqPzzzz/h7e0Nc3NzjBs3DgoKCsjJyWFN1nbv3j2cP38eM2fO/EfKKdK7d2/8+uuvePnyJVxdXdG/f3/85z//gZqaGnNDz9/fH507d4aVlRV69eolNua6NVFRUejZsyeGDh0KDw8PuLq6slrupaGsrIzMzEwYGhpi3LhxsLS0xKxZs/D8+XOmpVtDQwPjxo1jdTU/c+YMbt68ibS0NOjr60NXV5d5NXfw4EH4+PjINGM/IYQQQt5fHOGrDOIl5P9LT0+Hs7Mznj59Kpduv2+biRMnws7OTqZJwnbu3ImjR4/izJkzYst8fX1RWVmJI0eOYMCAAVizZg3zeKTXycnJCba2th0KYqXR/LjedgEBAaiqqmLGcUujrTp9HYRCIRwdHbFkyRLWuPH2LF++HFVVVa2OKyZtKygowOjRoyVOptaahw8fwsLCArm5uaybdW2prq6Gmpoaqqqq2n3UGCGEkPdD1NlXa8ggslk6pu3JWTtClv/f1LJN5EpfX1+moOBdsWXLFokzLLela9euiI6OZqWJnnUsajWrr6/H559/3u7EVfK0a9cucLncVieB6oiWx/UuCAwMhJGRkUzjhyXV6evE4XCwZ88eNDQ0yLSdlpYWgoODX1Op3n/W1tbYvHmzTGPCb9++jV27dkkdaBNCCCHk/Uct20Qunj9/jnv37gFoGhcu6Rm65M2fp3v37jGPlDI0NGQmqHtVb/q4CHkXUcs2IYT8+1DL9j/rTbds02zkRC7kMfvyv8GbPk8tH8EmL2/6uAghhBBCCHnbUDdyQgghhBBCCCFEzijYJoQQQgghhBBC5IyCbUIIIYQQQgghRM4o2CaEEEIIIYQQQuSMJkgjhBBCCCGEkH/A65gdm7y9qGWbEEIIIYQQQgiRMwq2CSGEEEIIIYQQOaNgmxBCCCGEEEIIkTMKtgkhhBBCCCGEEDmjYJsQQgghhBBCCJEzCrYJIYQQQgghhBA5o0d/EUIIIYQQQuQi6mzxmy7CW40e/fXvQi3bhBBCCCGEEEKInFGwTQghhBBCCCGEyBkF24QQQgghhBBCiJxRsE0IIYQQQgghhMgZBduEEEIIIYQQQoicUbBNCCGEEEIIIYTIGQXbhBBCCCGEEEKInL3WYNvJyQlLlixh3vN4PGzdulXq7dPT08HhcMDhcPDpp5/KvTxv2t69e+Hi4iLTNuPHj0dkZCQr7VXOk2jbyspKAEB8fDx69OghUx5OTk7M/vPz8yXm2xHyrv9/QlpaGiwsLNDY2Cj1NpLq9J/i6+v7Vp3bjpw/f39/LF68+DWW6s16/PgxtLS0UFpaKvU2O3bsgKen5+sr1Gsg6/+H123t2rWYN2+e1OsLBAIYGhoiLy/vNZaKEEIIIe+Sd6Jl+/r164iPj5d6fXkEeq+bQCDAunXrsHbtWiatqKgIn3/+OXg8HjgcjsQfnuvWrUNISAiqq6uZtKFDh6K8vBwTJ06UuC8nJyfs3r1b/gfRzNy5c1FeXo7+/ftLtX5paSlmz56NPn36QElJCSYmJvj6669RX1/PrNPecbXn5s2bmDlzJvT19aGoqIg+ffrA29sbubm5zDocDgdHjhwR27ZlEBoTE4MBAwage/fu6N69O4YMGYKff/5ZbLuAgAAEBgaiU6emj1Z2djaGDRsGDQ0NKCkpwcLCAlFRUaxtJNWpvJWWlrJuhrytWp6/8vJyTJkyBebm5ujUqZPEm2UBAQGIi4vD7du3X2nf9fX12Lx5M2xsbKCsrAxNTU0MGzYMcXFxePHiBQAgNDQUDg4OUFVVhZaWFj799FNcv36dlU9rQWNQUBBsbW2Z9/Hx8czNpOavuro61nahoaHw8PAAj8cTy/Px48fQ19cX+76bO3cuLl26hOzs7Fc5Ja9FR27o/dMqKiqwbds2rF69mklrr+4VFRXh7++PFStWvIkiE0IIIeQt9E4E21paWm/9jzNZJSUlgcvl4qOPPmLSnj17BmNjY4SFhUFHR0fidgMGDACPx8OBAweYNAUFBejo6EBJSUls/SdPnuD8+fPw8PCQ/0E0o6ysDB0dHXTp0kWq9f/44w80NjYiNjYWRUVFiIqKwu7du1k/bts6rvbk5uZi0KBBKC4uRmxsLPh8PpKTk2FhYYGvvvpK5vz09fURFhaG3Nxc5ObmYuTIkfDy8kJRURGzzvnz53Hjxg1MmDCBSVNRUYGfnx8yMzNx7do1rFmzBmvWrMGePXuYdSTVaUvx8fFwcnKSudzvEknnTyAQoFevXggMDISNjY3E7bS0tODi4vJKN5Tq6+vh6uqKsLAwzJs3D+fPn8fFixexcOFCREdHM/WckZGBhQsXIicnB2fPnkVDQwNcXFxQW1vbof12794d5eXlrFe3bt2Y5c+fP8fevXsxZ84cidvPnj0bAwYMEEtXVFTElClTEB0d3eq+RTdgiLi9e/diyJAhrBsc0tS9j48PsrKycO3atTdQakIIIYS8beQWbNfW1mL69OngcrnQ1dVT8I/uAAAgAElEQVRFREREu9twOBx89913+Oyzz6CsrIy+ffvi2LFj7W4nFAqxefNmGBsbQ0lJCTY2Nvjpp58ANP2AdHZ2BgD07NkTHA4Hvr6+zLaNjY0ICAiAuro6dHR0EBQUxMo7MjIS1tbWUFFRgYGBARYsWICamhpmuahV5vTp07C0tASXy8XYsWNRXl7OyicuLg6Wlpbo1q0bLCwssGvXLtbyxMREsW6eDg4O2LJlCyZPngxFRcVWj9/T0xMHDx5s9zwBwIkTJ2BjYwM9PT0AwMmTJ2FmZgYlJSU4Ozu32zVV1Bq3f/9+8Hg8qKmpYfLkyfj777+l2r/I8+fP4e7uDkdHRzx58gRjx45FXFwcXFxcYGxsDE9PT/j7++Pw4cMy5SuJUCiEr68v+vbti6ysLLi7u8PExAS2trb4+uuvcfToUZnz9PDwgJubG8zMzGBmZoaQkBBwuVzk5OQw6yQmJsLFxYUVLNnZ2cHb2xv9+vUDj8fD1KlT4erqiqysLFb+stRpa9q65vr06cOUh8PhiAXu4eHh0NXVhYaGBhYuXMi05ALA999/D3t7e6iqqkJHRwdTpkzBX3/9xSwX9SRJTU2Fvb09lJWVMXToULEW3+PHj2PQoEHo1q0bjI2NsX79ejQ0NDDLJZ0/Ho+Hbdu2Yfr06VBTU2v12F/1/G3duhWZmZlITU3FwoULYWtrC2NjY0yZMgUXLlxA3759AQCnTp2Cr68v+vXrBxsbG8TFxaGsrKzDXYc5HA50dHRYr+Z+/vlndOnSBUOGDBHbNiYmBpWVlfD395eYt6enJ44cOYLnz593qGyi77qUlBSYm5tDWVkZ48ePR21tLRISEsDj8dCzZ08sWrQIL1++ZLarr69HQEAA9PT0oKKigg8++ADp6ekAmq6VmTNnoqqqimnJb/4d/OzZM8yaNQuqqqowNDRk3ZQCgBUrVsDMzAzKysowNjbG2rVrWdeqNN9Xbf3/EJH0/SxN3WtoaGDo0KGv/FkmhBBCyPtBbsH28uXLce7cOSQnJ+PMmTNIT0+X6gfo+vXrMXHiRFy9ehVubm7w8fHBkydP2txmzZo1iIuLQ0xMDIqKirB06VJMnToVGRkZMDAwQFJSEoCm7ufl5eXYtm0bs21CQgJUVFRw4cIFbN68GRs2bMDZs2eZ5Z06dcL27dtRWFiIhIQEpKWlISAggLX/Z8+eITw8HPv370dmZibKyspYP3i//fZbBAYGIiQkBNeuXcOmTZuwdu1aJCQkMOtkZWXB3t6+3fMjyeDBg3Hx4kUIBIJ21z127Bi8vLwAAHfv3sW4cePg5uaG/Px8zJkzBytXrmw3j5KSEhw5cgQpKSlISUlBRkYGwsLCpC5vVVUVXFxcUF9fj9TUVKirq7e6XmvLmgsKCpLYpVYkPz8fRUVF+Oqrr5juyM29ai+Jly9fIjExEbW1tawgKDMzs906vXLlCs6fP48RI0aw0mWpU0nau+YuXrwIAPjll19QXl7Ouqlx7tw5lJSU4Ny5c0hISEB8fDxr2EZ9fT2Cg4Px+++/48iRI7h9+zbrBpZIYGAgIiIikJubiy5dumDWrFnMstOnT2Pq1KlYvHgx+Hw+YmNjER8fj5CQEGYdac5fawYPHoy7d+/izp07TBqXy23z9fHHHzPrHjhwAKNHj4adnZ1Y3l27doWKiorE/VZVVQGAVNetJDU1NTAyMoK+vj4++eQTXLlyhbW8tXPC5/OxYcMG7Nu3T+I1DgD29vZ48eIFU/cd8ezZM2zfvh2JiYk4deoU0tPTMW7cOJw8eRInT57E/v37sWfPHlawOnPmTPz6669ITEzE1atXMWHCBIwdOxY3btzA0KFDsXXrVlaLfvPvzoiICNjb2+PKlStYsGABvvzyS/zxxx/MclVVVcTHx4PP52Pbtm349ttvxYZltPd91db/DwB4+vQpCgsL270WW6v7wYMHi91MI4QQQsi/k3R9fttRU1ODvXv3Yt++fRgzZgyApqBWX1+/3W19fX3h7e0NANi0aROio6Nx8eJFjB07VuL6tbW1iIyMRFpaGhPoGBsbIzs7G7GxsRgxYgTz40dS9/MBAwbg66+/BgD07dsXO3bsQGpqKlPu5mNC+/Tpg+DgYHz55ZesVsIXL15g9+7dMDExAQD4+flhw4YNzPLg4GBERERg3LhxTD6iAGPGjBmorKxEZWUlevfu3e75kURPTw8CgQAPHjyAkZFRq+sJBAKcPn0a69atA9DUEmZsbIyoqChwOByYm5ujoKAA33zzTZv7a2xsRHx8PFRVVQEA06ZNQ2pqKitQak1FRQUmTZoEExMTHDx4EAoKChLXKykpQXR0tFQ9IjQ1NZlzL8mNGzcAABYWFu3mBQDe3t7o3LkzK00gEMDd3Z2VVlBQgCFDhqCurg5cLhfJycmwsrJilpeWlrZap/r6+nj48CEaGhoQFBQk1i1Y2jptTXvXXK9evQA0tby1bD3t2bMnduzYgc6dO8PCwgLu7u5ITU3F3LlzAYAVNBsbG2P79u0YPHgwampqwOVymWUhISHMTYSVK1fC3d0ddXV16NatG0JCQrBy5UrMmDGDySc4OBgBAQHM57Gt89ceUc+N0tJS5vy1Nz69+fCEGzduyNxNXygUYtmyZfjwww/F5ipYsWIF1qxZw0qrr69nXS8WFhaIj4+HtbU1qqursW3bNgwbNgy///4705Iu6ZwIBAJ4e3tjy5YtMDQ0xK1btySWT0VFBT169EBpaanYzR1pvXjxAjExMcznbfz48di/fz8qKirA5XJhZWUFZ2dnnDt3DpMmTUJJSQkOHjyIP//8kym3v78/Tp06hbi4OGzatAlqampMi35Lbm5uWLBgAXMOo6KikJ6eznyWm59THo+Hr776Cj/++CPrhmhb31fS/P+4c+cOhEJhm9diW3Wvp6fXao8hgUDAuqH2OudpIIQQQsibJ5dgu6SkBPX19axWPnV1dZibm7e7bfPxhioqKlBVVWV1UW2Jz+ejrq6OCY5F6uvrJbZKtbU/ANDV1WXt79y5c9i0aRP4fD6qq6vR0NCAuro61NbWMq1bysrKrGCveR4PHz7E3bt3MXv2bCZYAYCGhgamG6yoW2fz7rKyEAUJz549a3O9tLQ0aGhowNraGgBw7do1ODo6ssZpSuqe2hKPx2N+uALi56wto0ePhoODAw4dOiQW0Ircv38fY8eOxYQJE1odm9qcn58f/Pz8Wl0uFAoBQOrxqFFRURg9ejQrbcWKFayusQBgbm6O/Px8VFZWIikpCTNmzEBGRgYTQD1//rzVOs3KykJNTQ1ycnKwcuVKmJqaMjeZAPE6LSsrYwVmDQ0NePHiBSu4nTp1Knbv3i3VNdeWfv36sepGV1cXBQUFzPsrV64gKCgI+fn5ePLkCTNTeMsyNv9s6erqAgD++usvZobmS5cusW7QvHz5EnV1dXj27BmUlZXbPH/tkfSZMDU1lXp7oVAo8/hlPz8/XL16VeIkZMuXLxdr/d++fTsyMzOZ946OjnB0dGTeDxs2DAMHDkR0dDS2b98OQPI1tWrVKlhaWmLq1KntllFJSYl1Tvr168e0/os+J82vKSMjI9Y8BC2/67S1tcHj8VjbaGtrM98Hly9fhlAohJmZGascAoEAGhoa7Za3+TUkCsibf9f89NNP2Lp1K27evImamho0NDSge/furDza+r6S5v+HNN/PbdV9y3PeXGhoKNavX99qvoQQQgh5v8gl2Bb9aOuIrl27st5zOJw2H/sjWnbixAmmNUukrXHO0uzvzp07cHNzw/z58xEcHAx1dXVkZ2dj9uzZrHGBkvIQnQNRXt9++y0++OAD1nqigEZDQwMcDgdPnz5tt7ySiLrZi1orW9O8CznQ8XqStY6ac3d3R1JSEvh8PhP0N3f//n04OztjyJAhYuMzO0r0Q//atWus2Z9bo6OjIxaYqaqqis1mr6CgwKxnb2+PS5cuYdu2bYiNjQXQ1OLeWp2KxkxbW1ujoqICQUFBrGC7ZZ327t2b1TJ7+PBhJCUlsSZREwUZ0lxzbWmrfmtra+Hi4gIXFxd8//336NWrF8rKyuDq6sqaOb5lPqLAVZRPY2Mj1q9fz7S8NycKato6f+2R9JloHhBK8tFHHzEzypuZmck0qdWiRYtw7NgxZGZmSuzBo6mpKXZNtdfVvFOnTnBwcGB6ZojyaXlO0tLSUFBQwHTdFn2uNTU1ERgYyArmnjx5wjonJ0+eZL7L7t27BycnJ9Z11vJakHRttHW9NDY2onPnzsjLyxO79tqrj9b2J8o7JycHkydPxvr16+Hq6go1NTUkJiaK9YZpr3xA2/8/NDU1ATR1J5f0Hdte3bc8582tWrUKy5YtY95XV1fDwMBA4rqEEEIIeffJJdg2NTVF165dkZOTA0NDQwBNP1SKi4s73H2xNVZWVlBUVERZWVmreYu6KrdsmWxPbm4uGhoaEBERwYyDPHTokEx5aGtrQ09PD7du3YKPj0+r5bOysgKfz5f5OdsAUFhYCH19feZHoSRCoRDHjx/Hvn37mDQrKyuxx1w1n+DrdQgLCwOXy8WoUaOQnp7Oagm9d+8enJ2dMWjQIMTFxbU69lRWtra2sLKyQkREBCZNmiSWb2VlpVxmtxcKhawuoXZ2duDz+TJvB4jXaZcuXVjBmpaWFpSUlCS21kp7zQGyfyb++OMPPHr0CGFhYUxQ0PzRadIaOHAgrl+/3mZrs7TnT5LCwkJ07doV/fr1Y9Jk6UY+ZcoUrF69GleuXBHrIdPQ0ACBQAAVFRUIhUIsWrQIycnJSE9PZ26iyINQKER+fj7rppSdnR2+//571npJSUmsSc8uXbqEWbNmISsri9UKXVJSgrq6OtbxNB+iIHpygCw9ANpjZ2eHly9f4q+//mI9aaE5BQUFma9DAPj1119hZGSEwMBAJq35GH1pSPP/w8TEBN27dwefz2e10Etb94WFha32slJUVJTqpjAhhBBC3g9yCba5XC5mz56N5cuXQ0NDA9ra2qxn5cqTqqoq/P39sXTpUjQ2NuLDDz9EdXU1zp8/Dy6XixkzZsDIyAgcDgcpKSlwc3ODkpKSVK0qJiYmaGhoQHR0NDw8PPDrr7926HFCQUFBWLx4Mbp3746PP/4YAoEAubm5ePr0KdOq4erqiuzsbNYY8fr6eibYqK+vx71795Cfnw8ul8v6QZyVldVukJ6Xl4fa2loMHz6cSZs/fz4iIiKwbNkyfPHFF8jLy5Pp+eUdFR4ejpcvX2LkyJHM+Mv79+/DyckJhoaGCA8Px8OHD5n1W3vsmciOHTuQnJyM1NRUics5HA7i4uIwevRoDB8+HKtXr4aFhQVqampw/PhxnDlzhpkMSVqrV6/Gxx9/DAMDA/z9999ITExEeno6Tp06xazj6urKmgQPAHbu3AlDQ0NmzGl2djbCw8OxaNEi1nrS1Glb2rvmRMH6qVOnoK+vj27duknVxdzQ0BAKCgqIjo7G/PnzUVhYiODgYJnLt27dOnzyyScwMDDAhAkT0KlTJ1y9ehUFBQXYuHEjAMnnD/i/oLmmpgYPHz5Efn4+c8NKJCsrCx999BErgJYliFyyZAlOnDiBUaNGITg4GB9++CFUVVWRm5uLb775Bnv37oWtrS0WLlyIH374AUePHoWqqioePHgAAFBTU5P5EXXr16+Ho6Mj+vbti+rqamzfvh35+fnYuXMns46rqytWrVqFp0+fomfPngAgNl/Bo0ePAACWlpasm0hZWVkwNjZuc34DeTMzM4OPjw+mT5+OiIgI2NnZ4dGjR0hLS4O1tTXc3NzA4/FQU1OD1NRU5pnmysrK7eZtamqKsrIyJCYmwsHBASdOnEBycrJM5ZPm/0enTp0wevRoZGdn49NPP2W2lbbus7KyOvQZIYQQQsj7R27R8JYtWzB8+HB4enpi9OjR+PDDDzFo0CB5Zc8SHByMdevWITQ0FJaWlnB1dcXx48eZlgY9PT2sX78eK1euhLa2dpvje5uztbVFZGQkvvnmG/Tv3x8HDhxAaGiozOWbM2cOvvvuO2byoxEjRiA+Pp7VEjJ37lycPHmSmdEWaOpSbWdnBzs7O5SXlyM8PBx2dnasccx1dXVITk5mjc2V5OjRo3B3d2c999rQ0BBJSUk4fvw4bGxssHv3bmzatEnm4+uIqKgoTJw4ESNHjkRxcTHOnDmDmzdvIi0tDfr6+tDV1WVe7Xn06BFKSkraXGfw4MHIzc2FiYkJ5s6dC0tLS3h6eqKoqAhbt26VufwVFRWYNm0azM3NMWrUKFy4cAGnTp1ijf2cOnUq+Hw+65FXjY2NWLVqFWxtbWFvb4/o6GiEhYWxJtSTtk7b0t4116VLF2zfvh2xsbHo3bs3a3hBW3r16oX4+Hj873//g5WVFcLCwhAeHi5z+VxdXZGSkoKzZ8/CwcEBjo6OiIyMZLW0Sjp/AJjPRF5eHn744QfY2dnBzc2Ntc7Bgwdf6fwpKiri7NmzCAgIQGxsLBwdHeHg4IDt27dj8eLFzCRYMTExqKqqgpOTE+ua/fHHH2XeZ2VlJebNmwdLS0u4uLjg3r17yMzMxODBg5l1rK2tYW9vL3MPG+DVz0lHxcXFYfr06fjqq69gbm4OT09PXLhwgekZMXToUMyfPx+TJk1Cr169sHnzZqny9fLywtKlS+Hn5wdbW1ucP38ea9eulbl87f3/AIB58+YhMTGRNVxGmrr/7bffUFVVhfHjx8tcLkIIIYS8fzjCVxlw/Zqlp6fD2dkZT58+lUu337fNxIkTYWdnh1WrVkm9zc6dO3H06FGcOXNGbJmvry8qKytx5MgRDBgwAGvWrMHEiRPlWWSJnJycYGtr26EgVhrNj+ttFxAQgKqqKmYctzTaqtN/m46cvxMnTmD58uW4evUq6+bS++LkyZPw9/dHYWGh1L2FCgsLMWrUKBQXF0vVg4GwCYVCODo6YsmSJay5FdozYcIE2NnZYfXq1VKtX11dDTU1NVRVVYlN9EYIIe+qqLPFb7oIb7WlY8zaX4m81WT5/y3/ft6vgb6+vkw/eN4VW7Zskap7e3Ndu3ZFdHQ0Ky0rKwtcLpeZPKu+vh6ff/456znCr9uuXbvA5XJZs1i/qpbH9S4IDAyEkZGRTGNSJdXpv1VHzl9tbS3i4uLey0AbaHoc1hdffIF79+5Jvc39+/exb98+CrQ7iMPhYM+ePWhoaJB6G4FAABsbGyxduvQ1lowQQggh75K3umX7+fPnzA9MLpfb7ljef6s3fZ7u3bvHTNgkGuMrD2/6uAgh5HWilm1CyPuIWrbbRi3b7z5Z/n+/1U1Brc2+TNje9Hlq+QgdeXnTx0UIIYQQQgghHfVOdCMnhBBCCCGEEELeJRRsE0IIIYQQQgghckbBNiGEEEIIIYQQImcUbBNCCCGEEEIIIXL2Vk+QRgghhBBCCHl30GzbhPwfatkmhBBCCCGEEELkjIJtQgghhBBCCCFEzijYJoQQQgghhBBC5IyCbUIIIYQQQgghRM4o2CaEEEIIIYQQQuSMgm1CCCGEEEIIIUTO6NFfhBBCCCGEkPdO1NniN10EMfRotH8XatkmhBBCCCGEEELkjIJtQgghhBBCCCFEzijYJoQQQgghhBBC5IyCbUIIIYQQQgghRM4o2CaEEEIIIYQQQuSMgm1CCCGEEEIIIUTOKNgmhBBCCCGEEELkjIJtQgghhBBCCCFEzijYfgVOTk5YsmQJ857H42Hr1q1Sb5+eng4OhwMOh4NPP/1U7uV50/bu3QsXFxeZthk/fjwiIyNZaa9ynkTbVlZWAgDi4+PRo0cPmfJwcnJi9p+fny8x344oLS1l8rW1te1wPtKYNm0aNm3aJPX6AoEAhoaGyMvLe42lkq+O1O3r9PjxY2hpaaG0tFTqbXbs2AFPT8/XV6jXbPjw4fjhhx+kXr+goAD6+vqora19jaWSXVpaGiwsLNDY2Cj1Nv7+/li8ePFrLBUhhBBC3jUUbL8Frl+/jvj4eKnXl0eg97oJBAKsW7cOa9euZdKKiorw+eefg8fjgcPhSLwxsW7dOoSEhKC6uppJGzp0KMrLyzFx4kSJ+3JycsLu3bvlfxDNzJ07F+Xl5ejfv7/U21y+fBljxoxBjx49oKGhgXnz5qGmpoZZbmBggPLycnz11Vcyl8fX11fijQdJ18bVq1dx4sQJLFq0iLW9KNAXvRwdHZnlioqK8Pf3x4oVK2Qu2z9B1htbb0JoaCg8PDzA4/EAAL///ju8vb1hYGAAJSUlWFpaYtu2baxt5s6di0uXLiE7O/uV9i0UCrFnzx588MEH4HK56NGjB+zt7bF161Y8e/YMABAUFCTxJo/oJpDoxtLjx48xduxY9O7dG4qKijAwMICfnx/rMwoAKSkpePDgASZPnsykffHFFzAxMYGSkhJ69eoFLy8v/PHHH8xya2trDB48GFFRUa90vBwOB0eOHBFLb/k5CQ0NhYODA1RVVaGlpYVPP/0U169fF9suICAAgYGB6NTp//5FHjhwADY2NlBWVoauri5mzpyJx48fs7aJi4vD7du3X+lYCCGEEPL+oGD7LaClpfVWtcjJQ1JSErhcLj766CMm7dmzZzA2NkZYWBh0dHQkbjdgwADweDwcOHCASVNQUICOjg6UlJTE1n/y5AnOnz8PDw8P+R9EM8rKytDR0UGXLl2kWv/+/fsYPXo0TE1NceHCBZw6dQpFRUXw9fVl1uncuTN0dHTA5XJfU6mb7NixAxMmTICqqiorfezYsSgvL2deJ0+eZC338fFBVlYWrl271mrevr6+CAoKeh3Ffqc9f/4ce/fuxZw5c5i0vLw89OrVC99//z2KiooQGBiIVatWYceOHcw6ioqKmDJlCqKjo19p/9OmTcOSJUvg5eWFc+fOIT8/H2vXrsXRo0dx5swZmfLq1KkTvLy8cOzYMRQXFyM+Ph6//PIL5s+fz1pv+/btmDlzJitAHTRoEOLi4nDt2jWcPn0aQqEQLi4uePnyJbPOzJkzERMTw0pricfjIT09XaZyS5KRkYGFCxciJycHZ8+eRUNDA1xcXFgt6+fPn8eNGzcwYcIEJi07OxvTp0/H7NmzUVRUhP/973+4dOkSq361tLTg4uLy2m/8EUIIIeTdQcG2lGprazF9+nRwuVzo6uoiIiKi3W04HA6+++47fPbZZ1BWVkbfvn1x7NixdrcTCoXYvHkzjI2NoaSkBBsbG/z0008AmlqdnJ2dAQA9e/YEh8NhBXCNjY0ICAiAuro6dHR0xAKhyMhIWFtbQ0VFBQYGBliwYAGrtVXUFff06dOwtLQEl8tlgrLm4uLiYGlpiW7dusHCwgK7du1iLU9MTBTrDuvg4IAtW7Zg8uTJUFRUbPX4PT09cfDgwXbPEwCcOHECNjY20NPTAwCcPHkSZmZmUFJSgrOzc7tdeEWte/v37wePx4OamhomT56Mv//+W6r9izx//hzu7u5wdHTEkydPkJKSgq5du2Lnzp0wNzeHg4MDdu7ciaSkJNy8eVOmvF9FY2Mj/ve//0nsmqyoqAgdHR3mpa6uzlquoaGBoUOHSl0XkvB4PGzcuJH57BgZGeHo0aN4+PAhvLy8wOVyYW1tjdzcXNZ258+fx/Dhw6GkpAQDAwMsXryYCYicnJxw584dLF26lGmVb66ta/fSpUsYM2YMNDU1oaamhhEjRuDy5cus7aX53PL5fLi5uYHL5UJbWxvTpk3Do0ePmOU///wzunTpgiFDhjBps2bNwvbt2zFixAgYGxtj6tSpmDlzJg4fPszK29PTE0eOHMHz5887cMaBQ4cO4cCBAzh48CBWr14NBwcH8Hg8eHl5IS0tjfn+kFbPnj3x5Zdfwt7eHkZGRhg1ahQWLFiArKwsZp1Hjx7hl19+EbvO5s2bh+HDh4PH42HgwIHYuHEj7t69y/pcurq64vHjx8jIyOjQ8cri1KlT8PX1Rb9+/WBjY4O4uDiUlZWxhkskJibCxcUF3bp1Y9JycnLA4/GwePFi9OnTBx9++CG++OILsetWlu8uQgghhLz/KNiW0vLly3Hu3DkkJyfjzJkzSE9Pl2o86/r16zFx4kRcvXoVbm5u8PHxwZMnT9rcZs2aNYiLi0NMTAyKioqwdOlSTJ06FRkZGTAwMEBSUhKApu7n5eXlrK6oCQkJUFFRwYULF7B582Zs2LABZ8+eZZZ36tQJ27dvR2FhIRISEpCWloaAgADW/p89e4bw8HDs378fmZmZKCsrg7+/P7P822+/RWBgIEJCQnDt2jVs2rQJa9euRUJCArNOVlYW7O3t2z0/kgwePBgXL16EQCBod91jx47By8sLAHD37l2MGzcObm5uyM/Px5w5c7By5cp28ygpKcGRI0eQkpKClJQUZGRkICwsTOryVlVVwcXFBfX19UhNTYW6ujoEAgEUFBRYrXyilvn2ugjHx8eLBZAddfXqVVRWVkqsi/T0dGhpacHMzAxz587FX3/9JbbO4MGDWUFVR0RFRWHYsGG4cuUK3N3dMW3aNEyfPh1Tp07F5cuXYWpqiunTp0MoFAJoGsfr6uqKcePG4erVq/jxxx+RnZ0NPz8/AMDhw4ehr6+PDRs2MK3yIu1du3///TdmzJiBrKws5OTkoG/fvnBzcxO7udLW57a8vBwjRoyAra0tcnNzcerUKVRUVLCGOWRmZkp1/VdVVYnd5LC3t8eLFy9w8eJFJq1fv37gcrmtvvr168ese+DAAZibmzOfi+Y4HA7U1NTaLVdb7t+/j8OHD2PEiBFMWnZ2NpSVlWFpadnqdrW1tYiLi0OfPn1gYGDApCsoKMDGxuaVr7OOqKqqAgBWHUiqu6FDh+LPP//EyZMnIRQKUVFRgZ9++gnu7u6s9QYPHoy7d+/izp07EvcnEAhQXV3NehFCCCHk/SVdn9h/uZqaGuzduxf79u3DmDFjADQFtfr6+u1u6+vrC29vbwDApk2bEAz1EjUAACAASURBVB0djYsXL2Ls2LES16+trUVkZCTS0tKYVjFjY2NkZ2cjNjYWI0aMYH4YSup+PmDAAHz99dcAgL59+2LHjh1ITU1lyt18ArU+ffogODgYX375Jatl+sWLF9i9ezdMTEwAAH5+ftiwYQOzPDg4GBERERg3bhyTD5/PR2xsLGbMmIHKykpUVlaid+/e7Z4fSfT09CAQCPDgwQMYGRm1up5AIMDp06exbt06AEBMTAyMjY0RFRUFDocDc3NzFBQU4Jtvvmlzf42NjYiPj2e6WU+bNg2pqakICQlpt6wVFRWYNGkSTExMcPDgQSgoKAAARo4ciWXLlmHLli34z3/+g9raWqxevRoAxHoJtKSmpgZzc/N2952SkiLWBb1lV9zS0lJ07twZWlparPSPP/4YEyZMgJGREW7fvo21a9di5MiRyMvLY/U60NPTk2mCL0nc3NzwxRdfAGgakx8TEwMHBwemm+6KFSswZMgQVFRUQEdHB1u2bMGUKVOYa7Vv375Mi3BMTAzU1dXRuXNnqKqqig1HaO/aHTlyJGv92NhY9OzZExkZGfjkk0+Y9LY+tzExMRg4cCBrwrn//ve/MDAwQHFxMczMzFBaWtru9f/bb7/h0KFDOHHiBCtdRUUFPXr0QGlpKRPQnjx5Ei9evGg1r65duzJ/37hxQ6rrB2i6sdHyGhLd9GjJ29sbR48exfPnz+Hh4YHvvvuOWVZaWgptbW3WzSWRXbt2ISAgALW1tbCwsMDZs2eZz4mIPK4zb29vdO7cmZUmEAjEAmIRoVCIZcuW4cMPP2TNxSCp7oYOHYoDBw5g0qRJqKurQ0NDAzw9PcW6+4t62JSWlkr87goNDcX69es7dHyEEEIIefdQy7YUSkpKUF9fz+oSqq6uLtUP2gEDBjB/q6ioQFVVVWILogifz0ddXR3GjBnDarnat28fSkpKZNofAOjq6rL2d+7cOYwZMwZ6enpQVVXF9OnT8fjxY9aYRWVlZSZYaZnHw4cPcffuXcyePZtVvo0bNzLlE3V/bd4NUxaiFmDRRE6tSUtLg4aGBqytrQEA165dg6OjI6tVuHmdtYbH47HGM7c8Z20ZPXo0jI2NcejQIVYA0a9fPyQkJCAiIoIZ721sbAxtbW2xgKClzz77jDWJVGucnZ2Rn5/PejUPgICmulBUVBRrKZ80aRLc3d3Rv39/eHh44Oeff0ZxcbFY4KekpMSqhwMHDrDq/cCBA9i0aZNYWnPNr0ltbW0AYOqseZronOfl5SE+Pp6Vp6urKxobG9udfKqta1e0j/nz58PMzAxqampQU1NDTU0NysrKWi1zy89tXl4ezp07xyqfhYUFALA+A21d/0VFRfDy8sK6deuYG2HNtTzvRkZGMDU1bfXVPLATCoVS94wwNzcXu4Zajt0XiYqKwuXLl3HkyBGUlJRg2bJlzLK2jtfHxwdXrlxBRkYG+vbti4kTJ6Kurq7N450/fz7r/JaVleHjjz8WS2tZvpbH0tbM7n5+frh69apYt29Jx8Ln87F48WKsW7cOeXl5OHXqFG7fvi02br29765Vq1ahqqqKed29e7fV8hFCCCHk3Uct21JoraVHGs1bnICmbpxtPU5GtOzEiRNMK4lIW+OcpdnfnTt34Obmhvnz5yM4OBjq6urIzs7G7NmzWa1mkvIQnQNRXt9++y0++OAD1nqiIFJDQwMcDgdPnz5tt7ySiLrr9urVq831mnchBzpeT7LWUXPu7u5ISkoCn89nBZAAMGXKFEyZMgUVFRVQUVEBh8NBZGQk+vTp06FytqSiogJTU1NW2p9//sl6r6mpiWfPnqG+vl6sNbE5XV1dGBkZ4caNG6z0J0+esOrB09OTVe8rVqyAnp4e65FHouBZpPn5FQWBktJE57yxsRFffPGFxMcoGRoatnoMLfMV5d38uvD19cXDhw+xdetWGBkZQVFREUOGDEF9fX27+TQvn4eHh8QeE7q6ugCazntr1z+fz8fIkSMxd+5crFmzRuI6Lc97v379Wu2aDDQF40VFRQAAMzOzNie1a05BQUHsGmptEkDR2H4LCwtoaGjgo48+wtq1a6Grq9vm8YpuavTt2xeOjo7o2bMnkpOTmZ4DouNtfpNkw4YNrO7/Tk5O+Oabb1jXXsvWZx0dHbFjUVVVlfjUhkWLFuHYsWPIzMwU66Ek6VhCQ0MxbNgwLF++HEDTzRgVFRV89NFH2LhxI1Pv7X13KSoqSvU9TgghhJD3AwXbUjA1NUXXrl2Rk5PD/Nh/+vQpiouLWeMW5cHKygqKioooKytrNW9R0NTW7L2S5ObmoqGhAREREUx3z0OHDsmUh7a2NvT09HDr1i34+Pi0Wj4rKyvw+XyZn7MNAIWFhdDX14empmar6wiFQhw/fhz79u1j0qysrMQe/5OTkyPz/mURFhYGLpeLUaNGIT09HVZWVmLriILP//73v+jWrZvElszXRfRoJz6f3+azvB8/foy7d+8yQYNIYWEh7OzsmPeqqqqsXgCqqqpQV1cXC3JexcCBA1FUVNRmngoKCjJf/0DTXAK7du2Cm5sbgKZx/s0nNpO2fElJSeDxeK0GpnZ2dvj+++/F0ouKijBy5EjMmDGj1WEKJSUlqKurY513WbqRT5kyBZMnT8bRo0fFxm0LhUJUV1e/8rht0Q0M0bwKdnZ2ePDgAZ4+fYqePXu2u23L+RgKCwsxfvx45r2WlhZr6EOXLl2gp6f3yteZUCjEokWLkJycjPT0dIk3vuzs7MDn81lpz549E6tr0c3F5jdzCgsL0bVrV9YYekIIIYT8e1E3cilwuVzMnj0by5cvR2pqKgoLC+Hr6ytxfOKrUlVVhb+/P5YuXYqEhASUlJTgypUr2LlzJzMBmZGRETgcDlJSUvDw4UPWbOJtMTExQUNDA6Kjo3Hr1i3s37+/Q4+pCQoKQmhoKLZt24bi4mIUFBQgLi4OkZGRzDqurq5iE4HV19cz3Tvr6+tx79495Ofni83OnZWV1W6QnpeXh9raWgwfPpxJmz9/PtO99fr16/jhhx9ken55R4WHh8PHxwcjR45kdf/esWMHLl++jOLiYuzcuRN+fn4IDQ1t9zFvycnJTLfkV9WrVy8MHDiQVRc1NTXw9/fHb7/9htLSUqSnp8PDwwOampr47LPPWNtLUxfytmLFCvz2229YuHAh8vPzceP/sXf/cTXe///AH4eoUyc/ipR+F/1aUsRofhQpamVGtfxso9mGNpZEorT8LjZN8mOFebO9l1B8lJWITITp10TTssqG6pTSSXV9/+h2rm9X51TnJG9mz/vtdm63zvW6Xq/rdb2uc53O83q9rtd19y5OnTrFeU64gYEBLl68iNLSUrmC5SFDhuDw4cMoKCjA1atXMWfOHKmPlOvIkiVLUFFRAW9vb2RlZeH3339HSkoKPvroI/YCgLOzM/Ly8jg9pHl5eXBwcMCUKVOwYsUKPHz4EA8fPsSjR4845WdkZMDIyIjT0yvPMHJPT094eXnB29sbmzZtwvXr1/HHH38gKSkJjo6OOH/+vFz7e+bMGcTGxiI3NxfFxcU4c+YMPv30U7zzzjvsM8RtbGwwcOBAXL58mc33+++/Y9OmTcjOzkZJSQmuXLkCT09P8Pl89mIH0HJ/c2lpKRwdHeWqV1csWbIE33//Pf7zn/9AVVWVPQatZ36X9t3l5uaG48ePIzo6Gr///jsuX74MPz8/jB49mtPDnpGRgfHjx8v9mSKEEELIm4mCbRlt27YNEyZMgLu7OxwdHTFu3DiMHDnypWwrLCwM69atw6ZNm2Bubg5nZ2ckJiayvTDa2toIDQ1FYGAgBg0axM7S3Blra2tERkZiy5YtsLS0xJEjR7Bp0ya567do0SLs378fcXFxGDZsGCZOnIi4uDhOL5Gvry/OnDnDzvYLtMxibGNjAxsbG5SXl2P79u2wsbHhPKu2vr4eCQkJ8PX17bAOJ0+ehKurK6e3SU9PD/Hx8UhMTMTw4cOxZ88eziRWL9OOHTvg6emJSZMmobCwEACQlZWFKVOmYNiwYdi7dy9iYmKkDo1uSygU4s6dO91Wt48//phzH3XPnj2Rk5OD6dOnw8TEBAsWLICJiQmuXLnC6bW+cuUKhEIhp8fxf8HKygoXLlzA3bt3MX78eNjY2LDDlcU2bNiA4uJiGBsbd3q7QWvfffcdKisrYWNjg3nz5sHPz09i8rjODB48GJcvX0ZTUxOcnZ1haWmJzz//HH379mUvwA0bNgy2trackSP//e9/8ejRIxw5cgRaWlrsa9SoUZzyjx492unnvyM8Hg//+c9/EBkZiYSEBEycOBFWVlYICQnB9OnT4ezsLFd5fD4f+/btw7hx42Bubo4vvvgC7777LpKSkth1evbsiY8++ojzOVNSUkJGRgZcXFwwZMgQeHp6QkVFBZmZmZw2P3r0KJycnDqcDLG7REdHQygUwt7ennMMfvjhB3aduXPnIj8/n3MO+vj4IDIyElFRUbC0tISHhwdMTU0lHtv2oseOEEIIIW8WHvMiNySTF5Keng4HBwdUVlZ22tv5T+Tp6QkbGxusXr1a5jzffvstTp48iZSUFIk0Hx8fVFVV4cSJE7CyssLatWs5j1t6Wezt7WFtbY2dO3e+lPJDQkJw4sQJ3Lp166WUX19fD1NTUxw7dkymCePEPDw8YGNjw86iTuRz5swZ+Pv7Izc3V+ZRMLm5uZg8eTIKCwtfeKj3/9pff/2Ft956C9nZ2TIHziKRCEOHDsXRo0fxzjvvvOQayi4gIABCoRAxMTEy5zl9+jRWrlyJ27dvt3t7QVviIf1CoRB9+vTpanUJIYS0Y8e5wlddBQnLp5i86iqQFyTP/2/q2X4N6OjocCYLelNs27ZN4rFCnenVq5fE43QyMjI4s1w3NDRg5syZmDZtWrfVtTO7d++GQCBATk5Ot5VZUlICgUDw0nvflZSUcOjQIbmGW4tEIgwfPhzLly9/iTV7s4kfeVZaWipznrKyMhw6dOgfF2gDLXMTHDhwQGKW8I788ccfCAoKeq0CbQAICgqCvr6+XPMCiJ8jLmugTQghhJA3H/Vsv0LPnj1jf4gLBAKJZwaTFq+6nUpLS9l7OvX09Dqc1VsejY2N7LOFFRUVoaur2y3lEkL+GahnmxBCXi7q2SYvgzz/v+kS/CvE5/O7dRbnN9Wrbqe2j2DrLgoKCnT8CSGEEEIIeUPRMHJCCCGEEEIIIaSbUbBNCCGEEEIIIYR0Mwq2CSGEEEIIIYSQbkbBNiGEEEIIIYQQ0s1ogjRCCCGEEELIG4dm/iavGvVsE0IIIYQQQggh3YyCbUIIIYQQQgghpJtRsE0IIYQQQgghhHQzCrYJIYQQQgghhJBuRsE2IYQQQgghhBDSzWg2ckIIIYQQQsi/xo5zha9s2zRD+r8L9WwTQgghhBBCCCHdjIJtQgghhBBCCCGkm1GwTQghhBBCCCGEdDMKtgkhhBBCCCGEkG5GwTYhhBBCCCGEENLNKNgmhBBCCCGEEEK6GQXbhBBCCCGEEEJIN6NgmxBCCCGEEEII6WYUbJMXZm9vjy+++IJ9b2BggJ07d8qcPz09HTweDzweD++991631+dVO3DgAJycnOTKM2vWLERGRnKWvUg7ifNWVVUBAOLi4tCvXz+5yrC3t2e3f+vWLanldkVxcTFbrrW1dZfLkcW8efOwceNGmdcXiUTQ09NDdnb2S6zVqxUcHIyPP/5YrjyjRo3C8ePHX1KNXr20tDSYmZmhublZ5jzSzllCCCGE/LtRsE1eG3fu3EFcXJzM63dHoPeyiUQirFu3DsHBweyyvLw8zJw5EwYGBuDxeFIvTKxbtw7h4eGorq5ml9nZ2aG8vByenp5St2Vvb489e/Z0/0604uvri/LyclhaWsqcR7yfrV+BgYFsuq6uLsrLy/Hll1/KXR8fHx+pFx6kfTZu376N06dPY9myZZz8bes2ZswYNl1RURH+/v5YtWqV3HVr6969e/jwww+ho6MDRUVFGBoawtvbG9evXwfQctFh4cKFMDQ0BJ/Ph7GxMdavX4+GhoYO90usvYtcDMNg2rRp4PF4OHHiBCftr7/+wtdff401a9awy2pqavDFF19AX18ffD4fdnZ2uHbtGidfcHAwAgMD5QpGpXn48CGWLVsGIyMjKCoqQldXF25ubkhNTQUAVFRUYNmyZTA1NYWysjL09PTg5+cHoVDIliG+WCO+ANRa2wtvnR1vsYCAAAQFBaFHD8l/kZcvX4aCgoLEhSFp5ywhhBBC/t0o2CavDQ0NDbl7W1938fHxEAgEGD9+PLusrq4ORkZG2Lx5MzQ1NaXms7KygoGBAY4cOcIu6927NzQ1NcHn8yXWr6ioQGZmJtzc3Lp/J1pRVlaGpqYmFBQU5Mq3YcMGlJeXs6+1a9eyaT179oSmpiYEAkF3V5cjKioKHh4eUFVV5SyfOnUqp25nzpzhpM+ZMwcZGRkoKChot2wfHx+EhIS0m379+nWMHDkShYWFiImJQX5+PhISEmBmZsZeZPjtt9/Q3NyMmJgY5OXlYceOHdizZw8nEO6KnTt3gsfjSU07cOAAxo4dCwMDA3bZokWLcO7cORw+fBg5OTlwcnKCo6MjSktL2XVcXV0hFAqRnJzc5XoVFxdj5MiRSEtLw9atW5GTk4OzZ8/CwcEBS5YsAQCUlZWhrKwM27dvR05ODuLi4nD27FksXLiwy9vt7HhnZmbi7t278PDwkMgrFAoxf/58TJ48WSJN2jlLCCGEkH83CraJXGprazF//nwIBAJoaWkhIiKi0zw8Hg/79+/HjBkzoKysjKFDh+LUqVOd5mMYBlu3boWRkRH4fD6GDx+On376CUDLD3UHBwcAQP/+/cHj8eDj48PmbW5uRkBAANTU1KCpqSkRCEVGRmLYsGFQUVGBrq4uPvvsMzx9+pRNFw+zTk5Ohrm5OQQCAfsjvbXY2FiYm5tDSUkJZmZm2L17Nyf92LFjcHd35ywbNWoUtm3bhg8++ACKiort7r+7uzuOHj3aaTsBwOnTpzF8+HBoa2sDAM6cOQMTExPw+Xw4ODiguLi4w/whISGwtrbG4cOHYWBggL59++KDDz5ATU2NTNsXe/bsGVxdXTFmzBhUVFSwy1VVVaGpqcm+XnZg3VZzczP++9//ShwLoKX3unXd1NTUOOnq6uqws7OT+Vi0xTAMfHx8MHToUGRkZMDV1RXGxsawtrbG+vXrcfLkSQAtQWBsbCycnJxgZGQEd3d3+Pv7v9Bw7V9//RWRkZH47rvvpKa3/Xw+e/YM8fHx2Lp1KyZMmIAhQ4YgJCQEhoaGiI6OZtfr2bMnXFxcutwmAPDZZ5+Bx+MhKysLs2bNgomJCd566y2sWLECv/zyCwDA0tIS8fHxcHNzg7GxMSZNmoTw8HAkJiaisbGxS9vt7HgfO3YMTk5OUFJSksi7ePFizJ49G2PHjpVatjznLCGEEELefBRsE7msXLkS58+fR0JCAlJSUpCeni7T/ayhoaHw9PTE7du34eLigjlz5nCCMWnWrl2L2NhYREdHIy8vD8uXL8fcuXNx4cIF6OrqIj4+HkDL8PPy8nJ8/fXXbN6DBw9CRUUFV69exdatW7FhwwacO3eOTe/Rowe++eYb5Obm4uDBg0hLS0NAQABn+3V1ddi+fTsOHz6MixcvoqSkBP7+/mz6vn37EBQUhPDwcBQUFGDjxo0IDg7GwYMH2XUyMjJga2vbaftIM3r0aGRlZUEkEnW67qlTpzB9+nQAwIMHD/D+++/DxcUFt27dwqJFizjDtttTVFSEEydOICkpCUlJSbhw4QI2b94sc32FQiGcnJzQ0NCA1NRUThCzZcsWqKurw9raGuHh4Zyh0e2Ji4trt0dWXrdv30ZVVZXUY5Geng4NDQ2YmJjA19cXf//9t8Q6o0ePRkZGRpe2fevWLeTl5eHLL7+UOiy5o9EcQqFQIhiUVV1dHby9vREVFSV1BEVlZSVyc3M5bdLY2IimpiaJQJPP5+PSpUucZW3bJCMjAwKBoMOX+H75iooKnD17FkuWLIGKiopE3Tprkz59+sg9ukKss+N98eJFqZ+T2NhYFBUVYf369e2W3dk5KxKJUF1dzXkRQggh5M3VtV8r5F/p6dOnOHDgAA4dOoQpU6YAaAlqdXR0Os3r4+MDb29vAMDGjRuxa9cuZGVlYerUqVLXr62tRWRkJNLS0theJCMjI1y6dAkxMTGYOHEiG4RIG35uZWXF/igeOnQooqKikJqayta79X2choaGCAsLw6effsrpmX7+/Dn27NkDY2NjAMDSpUuxYcMGNj0sLAwRERF4//332XLy8/MRExODBQsWoKqqClVVVRg8eHCn7SONtrY2RCIRHj58CH19/XbXE4lESE5Oxrp16wAA0dHRMDIywo4dO8Dj8WBqaoqcnBxs2bKlw+01NzcjLi6OHWY9b948pKamIjw8vNO6/vXXX/Dy8oKxsTGOHj2K3r17s2mff/45RowYgf79+yMrKwurV6/G/fv3sX///g7L7Nu3L0xNTTvddlJSkkRPeVNTE+d9cXExevbsCQ0NDc7yadOmwcPDA/r6+rh//z6Cg4MxadIkZGdnc0YdaGtrdzo6oD13794FAJiZmcmVr6ioCLt27ZI6ekTaOVdXV8d5v3z5ctjZ2bEXYdr6448/wDAM5/OpqqqKsWPHIiwsDObm5hg0aBCOHj2Kq1evYujQoZz82traKCkpQXNzM3r06AFbW1up9023Jj5n7927B4Zh5G6TJ0+eICwsDIsXL5ZIs7Ozk7iY8ezZM8691bIc7+LiYolz9u7duwgMDERGRkaHQX5n5+ymTZsQGhoq1z4TQggh5J+Lgm0is6KiIjQ0NHCGUKqpqckUEFlZWbF/q6ioQFVVVWoPolh+fj7q6+vZ4FisoaEBNjY2cm0PALS0tDjbO3/+PDZu3Ij8/HxUV1ejsbER9fX1qK2tZXvalJWV2UC7bRmPHj3CgwcPsHDhQvj6+rLrNDY2om/fvgBafugDkDocVRbie7PbBlFtpaWlQV1dHcOGDQMAFBQUYMyYMZxe4faGvbZmYGDAuZ+5bZt1xNHREaNGjcKPP/6Inj17ctKWL1/O/m1lZYX+/ftj1qxZbG93e2bMmIEZM2Z0um0HBwfOEGcAuHr1KubOncu+f/bsGRQVFSV6yr28vNi/LS0tYWtrC319fZw+fZq9iAK0HIvWx+HIkSOcgE8kEoHH42H79u3sspiYGMyZMwcMwwCAXL30ZWVlmDp1Kjw8PLBo0SKJ9IyMDIl7z+3t7dm/T506hbS0NNy8ebPdbbT3+Tx8+DA++ugjaGtro2fPnhgxYgRmz56NGzducNbj8/lobm6GSCQCn88Hn8/HkCFDZNq/rrRJdXU1XF1dYWFhIbV3+YcffoC5uTln2Zw5czjvZTnez54947RJU1MTZs+ejdDQUJiYmHRYx87O2dWrV2PFihWcfdLV1e2wTEIIIYT8c1GwTWQm/oHcFb169eK85/F4Hc5kLE47ffo0ex+yWEf3OcuyvT/++AMuLi745JNPEBYWBjU1NVy6dAkLFy7E8+fPOyxD3Abisvbt24e3336bs5442FRXVwePx0NlZWWn9ZVGPMx+4MCBHa7Xegg50PXjJO8xas3V1RXx8fHIz89ng/72iGd/vnfvXofBtqxUVFQkgrw///yT837AgAGoq6tDQ0MDp9e9LS0tLejr67O90WIVFRWc4+Du7s457qtWrYK2tjb8/PzYZYMGDQIANkArKCiQ6dFmZWVlcHBwwNixY7F3716p6xgaGkqM5mjd45qWloaioiKJdWbOnInx48cjPT0dAwYMANAynLz1vhkbG+PChQuora1FdXU1tLS04OXlBUNDQ4k2UVZWZgPMjIwMTJs2rcN9W7NmDdasWYOhQ4eCx+OhoKBApsfY1dTUYOrUqRAIBEhISJD4rAIts9q3/RxIm0ywNWnHe8CAAZxztqamBtevX8fNmzexdOlSAC3nP8MwUFBQQEpKCiZNmgSg83NWUVFRpu8vQgghhLwZKNgmMhsyZAh69eqFX375BXp6egBafqgXFhZi4sSJ3botCwsLKCoqoqSkpN2yxUFT2yHDnbl+/ToaGxsRERHBDjv98ccf5Spj0KBB0NbWxu+//y7Re9a6fhYWFsjPz5f7OdsAkJubCx0dHTYokoZhGCQmJuLQoUPsMgsLC4lHPIknnHpZNm/eDIFAgMmTJyM9PR0WFhbtrivubdXS0nqpdWpNHOTm5+d3GPA+efIEDx48kKhbbm4uZ0SFqqoqp2dZVVUVampqUnt2ra2tYWFhgYiICHh5eUkMda6qqmKD4tLSUjg4OGDkyJGIjY2Veo+3LAIDAyV6xIcNG4YdO3awM9YbGxujT58+yM/Pl9pjq6KiAhUVFVRWViI5ORlbt27lpOfm5mLEiBHse3mGkaupqcHZ2Rnffvst/Pz8JO7bbt0m1dXVcHZ2hqKiIk6dOtXlkSLSSDveNjY2yM/PZ9/36dMHOTk5nHy7d+9GWloafvrpJ85FCFnOWUIIIYT8e1CwTWQmEAiwcOFCrFy5Eurq6hg0aFC7z6J9UaqqqvD398fy5cvR3NyMcePGobq6GpmZmRAIBFiwYAH09fXB4/GQlJQEFxcX8Pl8mWa5NjY2RmNjI3bt2gU3Nzdcvny5S8+nDgkJgZ+fH/r06YNp06ZBJBLh+vXrqKysZIeKOjs749KlS5x7xBsaGtgf8w0NDSgtLcWtW7cgEAg4wVpGRkanQXp2djZqa2sxYcIEdtknn3yCiIgIrFixAosXL0Z2drZczy/vqu3bt6OpqQmTJk1Ceno6zMzMcOXKFfzyyy9wcHBA3759ce3aNSxfvhzu7u7sBZv2JCQkYPXq1fjtt99euG4DBw7EiBEjcOnSJTbYfvr0KUJCQjBz5kxoaWmhuLgYa9aswYABClzasAAAIABJREFUAySGr2dkZCAsLKxL2+bxeIiNjYWjoyMmTJiANWvWwMzMDE+fPkViYiJSUlJw4cIFlJWVwd7eHnp6eti+fTsePXrEltHeI+LaI55puy09PT02OOzRowccHR1x6dIlTu9ycnIyGIaBqakp7t27h5UrV8LU1BQffvghp6y2n095hpEDLQGrnZ0dRo8ejQ0bNsDKygqNjY04d+4coqOjUVBQgJqaGjg5OaGurg7ff/89Z1KxgQMHStyy0BFZj7ezszNnksMePXpIPFdeQ0MDSkpKEstlOWcJIYQQ8u9Bs5ETuWzbtg0TJkyAu7s7HB0dMW7cOIwcOfKlbCssLAzr1q3Dpk2bYG5uDmdnZyQmJrLBgra2NkJDQxEYGIhBgwaxQzw7Y21tjcjISGzZsgWWlpY4cuQINm3aJHf9Fi1ahP379yMuLg7Dhg3DxIkTERcXx+np8vX1xZkzZyAUCtllZWVlsLGxgY2NDcrLy7F9+3bY2NhweiLr6+uRkJDAuR9cmpMnT8LV1ZUzhFhPTw/x8fFITEzE8OHDsWfPHnYW6Jdtx44d8PT0xKRJk1BYWAhFRUX88MMPsLe3h4WFBdatWwdfX1+ZHo8kFApx586dbqvbxx9/zHkGcs+ePZGTk4Pp06fDxMQECxYsgImJCa5cucLptb5y5QqEQiFmzZrV5W2PHj0a169fh7GxMXx9fWFubg53d3fk5eVh586dAICUlBTcu3cPaWlp0NHRgZaWFvt6WT7++GMcO3aMc7uAUCjEkiVLYGZmhvnz52PcuHFISUnhDN0uLS1FZmamRAAuD0NDQ9y4cQMODg748ssvYWlpiSlTpiA1NZW9Bz87OxtXr15FTk4OhgwZwmmTBw8eyLU9WY/33LlzkZ+fL/dnT9ZzlhBCCCH/HjzmRW7EJaQbpKenw8HBAZWVlR0+8uefytPTEzY2Nli9erXMeb799lucPHkSKSkpEmk+Pj6oqqrCiRMnYGVlhbVr18LT07M7qyyVvb09rK2t2eCwu4WEhODEiROdDkXuqvr6epiamuLYsWMyTRgn5uHhARsbG6xZs+al1OtVYhgGY8aMwRdffME+LUAWK1euhFAobPee8n+6gIAACIVCxMTEyJyno3O2PdXV1ejbty/7ODNCCCH/GzvOFb6ybS+f0vFkm+T1J8//b+rZJq8NHR0duX7w/1Ns27ZNpuHtrfXq1Qu7du3iLBM/x1jcO9vQ0ICZM2d2OilVd9q9ezcEAoHEPawvoqSkhPMM5pdFSUkJhw4dwuPHj2XOIxKJMHz4cM6M6m8SHo+HvXv3orGxUa58GhoaXR5W/08QFBQEfX19ueaDkHbOEkIIIeTfjXq2ySv37NkzlJaWAmi5L1ze+1P/LV51O5WWlrKPi9LT0+twVm95NDY2ss+wVlRUpEchkX8N6tkmhJBXg3q2yYuQ5/83TZBGXjl5J1b6t3rV7dT2EWzdRUFBgY4/IYQQQgh549AwckIIIYQQQgghpJtRsE0IIYQQQgghhHQzCrYJIYQQQgghhJBuRsE2IYQQQgghhBDSzWiCNEIIIYQQQsi/Bs0ITv5XqGebEEIIIYQQQgjpZhRsE0IIIYQQQggh3YyCbUIIIYQQQgghpJtRsE0IIYQQQgghhHQzCrYJIYQQQgghhJBuRsE2IYQQQgghhBDSzejRX4QQQgghhBDyP7DjXOFLKZceZ/Z6op5tQgghhBBCCCGkm1GwTQghhBBCCCGEdDMKtgkhhBBCCCGEkG5GwTYhhBBCCCGEENLNKNgmhBBCCCGEEEK6GQXbhBBCCCGEEEJIN6NgmxBCCCGEEEII6WavbbBtb2+PL774gn1vYGCAnTt3ypw/PT0dPB4PPB4P7733XrfX51U7cOAAnJyc5Moza9YsREZGcpa9SDuJ81ZVVQEA4uLi0K9fP7nKsLe3Z7d/69YtqeV2RXcf/9dFV477qFGjcPz48ZdUo1cvLS0NZmZmaG5uljmPtHPhTdKVNvH394efn99LrFX7QkJCYG1t/Uq2Lc2dO3egqamJmpoamfNERUXB3d39JdaKEEIIIf80r22w3V3u3LmDuLg4mdfvjkDvZROJRFi3bh2Cg4PZZXl5eZg5cyYMDAzA4/GkXphYt24dwsPDUV1dzS6zs7NDeXk5PD09pW7L3t4ee/bs6f6daMXX1xfl5eWwtLSUaf3WgXTb17Vr1wB0vl+duXfvHj788EPo6OhAUVERhoaG8Pb2xvXr1wEAxcXFWLhwIQwNDcHn82FsbIz169ejoaFBop7SPkttLx61vuggfn3wwQecPNKOe2vHjh2TenEhODgYgYGBcgVe0jx8+BDLli2DkZERFBUVoaurCzc3N6SmpgIAKioqsGzZMpiamkJZWRl6enrw8/ODUChkyyguLuZcWGmt7QUtHx8fiTYZM2aMRL6AgAAEBQWhRw/Jr7PLly9DQUFBIpCTdi50RXV1NYKCgmBmZgYlJSVoamrC0dERx48fB8MweP78OVatWoVhw4ZBRUUFgwcPxvz581FWVsYph8fj4cSJExLl+/j4tHuxaNOmTeDxeFIvAkprk2+//Rbm5ubg8/kwNTXFoUOHJPLExsbi/v37XWkKmbW3r6+ToKAgLFmyBKqqqgCA+vp6+Pj4YNiwYVBQUJB6THx9fXHt2jVcunTpf11dQgghhLym3vhgW0NDQ+7e1tddfHw8BAIBxo8fzy6rq6uDkZERNm/eDE1NTan5rKysYGBggCNHjrDLevfuDU1NTfD5fIn1KyoqkJmZCTc3t+7fiVaUlZWhqakJBQUFmdYXB9KtX4sWLYKBgQFsbW0BdLxfnbl+/TpGjhyJwsJCxMTEID8/HwkJCTAzM8OXX34JAPjtt9/Q3NyMmJgY5OXlYceOHdizZw/WrFkj9/bExBcdxK+YmBhOurTjLvbHH3/A399fapqrqyuEQiGSk5Pb3XZISAh8fHzaTS8uLsbIkSORlpaGrVu3IicnB2fPnoWDgwOWLFkCACgrK0NZWRm2b9+OnJwcxMXF4ezZs1i4cKGMLSBp6tSpnDY5c+YMJz0zMxN3796Fh4eHRF6hUIj58+dj8uTJEmnSzgV5VVVVwc7ODocOHcLq1atx48YNXLx4EV5eXggICIBQKERdXR1u3LiB4OBg3LhxA8ePH0dhYeEL94Beu3YNe/fuhZWVlUSatDaJjo7G6tWrERISgry8PISGhmLJkiVITExk19HQ0ICTk1OHF9fS09NhYGDwQnV/3f355584deoUPvzwQ3ZZU1MT+Hw+/Pz84OjoKDWfoqIiZs+ejV27dv2vqkoIIYSQ19xrEWzX1tZi/vz5EAgE0NLSQkRERKd5eDwe9u/fjxkzZkBZWRlDhw7FqVOnOs3HMAy2bt0KIyMj8Pl8DB8+HD/99BOAloDCwcEBANC/f3/weDxOANLc3IyAgACoqalBU1MTISEhnLIjIyPZHixdXV189tlnePr0KZsuHmadnJwMc3NzCAQCNphoLTY2Fubm5lBSUoKZmRl2797NST927JjEj/VRo0Zh27Zt+OCDD6CoqNju/ru7u+Po0aOdthMAnD59GsOHD4e2tjYA4MyZMzAxMQGfz4eDgwOKi4s7zC8eGnr48GEYGBigb9+++OCDD+QamgkAz549g6urK8aMGYOKigo2kBa/1NXVcerUKXz00Ufg8Xhyld0WwzDw8fHB0KFDkZGRAVdXVxgbG8Pa2hrr16/HyZMnAbQEgbGxsXBycoKRkRHc3d3h7+//QsO1xRcdxK++ffty0qUdd6AlEJgzZw5CQ0NhZGQkkd6zZ0+4uLjIfNyl+eyzz8Dj8ZCVlYVZs2bBxMQEb731FlasWIFffvkFAGBpaYn4+Hi4ubnB2NgYkyZNQnh4OBITE9HY2Nil7SoqKnLaRE1NjZN+7NgxODk5QUlJSSLv4sWLMXv2bIwdO1Zq2fKcC9KsWbMGxcXFuHr1KhYsWAALCwuYmJjA19cXt27dgkAgQN++fXHu3Dl4enrC1NQUY8aMwa5du5CdnY2SkpIubffp06eYM2cO9u3bh/79+0ukS2uTw4cPY/HixfDy8oKRkRE++OADLFy4EFu2bOHkfdE2AYDExESMHDkSSkpKMDIyQmhoKHv8xYH6jBkzwOPxJAL3jr4rzp49i3HjxqFfv35QV1fHu+++i6KiIjZdPGri+PHjcHBwgLKyMoYPH44rV65wtpGZmYkJEyaAz+dDV1cXfn5+qK2tZdN//PFHDB8+HDo6OuwyFRUVREdHw9fXt92LmUBL+504cQLPnj2Tu90IIYQQ8uZ5LYLtlStX4vz580hISEBKSgrS09ORnZ3dab7Q0FB4enri9u3bcHFxwZw5c1BRUdFhnrVr1yI2NhbR0dHIy8vD8uXLMXfuXFy4cAG6urqIj48H0DL8vLy8HF9//TWb9+DBg1BRUcHVq1exdetWbNiwAefOnWPTe/TogW+++Qa5ubk4ePAg0tLSEBAQwNl+XV0dtm/fjsOHD+PixYsoKSmBv78/m75v3z4EBQUhPDwcBQUF2LhxI4KDg3Hw4EF2nYyMDLYHV16jR49GVlYWRCJRp+ueOnUK06dPBwA8ePAA77//PlxcXHDr1i0sWrQIgYGBnZZRVFSEEydOICkpCUlJSbhw4QI2b94sc32FQiGcnJzQ0NCA1NRUiWBLXM/Hjx932DMrFhIS0mHP3K1bt5CXl4cvv/xS6rDkjkZJCIVCqfWT1ZEjRzBgwAC89dZb8Pf3l7go0d5x37BhAwYOHNhhD/Lo0aORkZHRpXpVVFTg7NmzWLJkCVRUVCTSO2uTPn36yDxqoa309HRoaGiwQezff//NSb948aLUNomNjUVRURHWr1/fbtltz4WSkhIIBIIOX5988gmAlgtvx44dw5w5czB48GCJsgUCQbv7LBQKwePxujziZsmSJXB1dW23h1Vam4hEIokLEnw+H1lZWXj+/Dm7bPTo0Xjw4AH++OOPLtUtOTkZc+fOhZ+fH/Lz8xETE4O4uDiEh4cDAHubR2xsLMrLy9n3QOffFbW1tVixYgWuXbuG1NRU9OjRAzNmzJC4PSIoKAj+/v64desWTExM4O3tzQb7OTk5cHZ2xvvvv4/bt2/jhx9+wKVLl7B06dIO209Wtra2eP78ObKysqSmi0QiVFdXc16EEEIIeXN17RdwN3r69CkOHDiAQ4cOYcqUKQBagtrWvQrt8fHxgbe3NwBg48aN2LVrF7KysjB16lSp69fW1iIyMhJpaWlsb5eRkREuXbqEmJgYTJw4kQ2WpA0/t7KyYn+8Dx06FFFRUUhNTWXr3freSUNDQ4SFheHTTz/l9Ew/f/4ce/bsgbGxMQBg6dKl2LBhA5seFhaGiIgIvP/++2w54h+tCxYsQFVVFaqqqqT+wJeFtrY2RCIRHj58CH19/XbXE4lESE5Oxrp16wC0DEM1MjLCjh07wOPxYGpqipycHImesbaam5sRFxfH3vs4b948pKamsj++O/LXX3/By8sLxsbGOHr0KHr37i11vQMHDsDZ2Rm6urqdljlgwAC27aW5e/cuAMDMzKzTslorKirCrl27pI7KkPZZrqur47yfM2cODA0NoampidzcXKxevRq//vorezGnveN++fJlHDhwQOo90K1pa2ujpKQEzc3NUi8idOTevXtgGEbuNnny5AnCwsKwePFiiTQ7OzuJejx79oxzb/W0adPg4eEBfX193L9/H8HBwZg0aRKys7PZ0RvFxcUSbXL37l0EBgYiIyOjwyC/7bkwePDgTtuxT58+AIDHjx+jsrJS7japr69HYGAgZs+ezZYl5u3tjZ49e3KWiUQiuLq6su+PHTuGGzducILUtqS1ibOzM/bv34/33nsPI0aMQHZ2Nr777js8f/4cjx8/hpaWFgCwo1iKi4s7/H5oT3h4OAIDA7FgwQIALd+vYWFhCAgIwPr16zFw4EAALRdo2vYQd/ZdMXPmTM76Bw4cgIaGBvLz8znzPfj7+7NtFhoairfeegv37t2DmZkZtm3bhtmzZ7Pf1UOHDsU333yDiRMnIjo6GkpKSuwtE12hoqKCfv36obi4GBMnTpRI37RpE0JDQ7tUNiGEEEL+eV55sF1UVISGhgbOUE81NTWYmpp2mrf1/YoqKipQVVWV6PlqLT8/H/X19WxwLNbQ0AAbGxu5tgcAWlpanO2dP38eGzduRH5+Pqqrq9HY2Ij6+nrU1tayPYLKysqcYK91GY8ePcKDBw+wcOFC+Pr6sus0NjayQ4rFwxOlDZuVhfge5rbBXltpaWlQV1fHsGHDAAAFBQUYM2YMZ5h2e8NzWzMwMGB/PAOSbdYRR0dHjBo1Cj/++KNEECL2559/Ijk5GT/++KNMZS5dupTTi9UWwzAAINdw9LKyMkydOhUeHh5YtGiRRHpGRganDYCWycBaa328LS0tMXToUNja2uLGjRsYMWKE1ONeU1ODuXPnYt++fRgwYECHdeTz+WhuboZIJAKfz0dGRgamTZvGpjc0NIBhGPaWCqBlmPSaNWu61CbV1dVwdXWFhYWF1N7lH374Aebm5pxlc+bM4bz38vJi/7a0tIStrS309fVx+vRp9mLUs2fPOG3S1NSE2bNnIzQ0FCYmJh3Wse25oKCggCFDhsi0f11pk+fPn+ODDz5Ac3OzxK0hALBjxw6J3upVq1ahqakJQMvoks8//xwpKSkdnv9t2wRomSTv4cOHGDNmDBiGwaBBg+Dj44OtW7dyzi1p3w8CgYD9u6mpCSKRiLNs/Pjx+L//+z8AQHZ2Nq5du8a5mNbU1IT6+nrU1dVBWVm53Xp39l1RVFSE4OBg/PLLL3j8+DHbo11SUsIJtlt/T4svIvz9998wMzNDdnY27t27x7lXn2EYNDc34/79+zA3N5fafvLg8/ntfr+uXr0aK1asYN9XV1fLdJGQEEIIIf9MrzzYFv9o7YpevXpx3vN4vA5nXBannT59mu3BEevoPmdZtvfHH3/AxcUFn3zyCcLCwqCmpoZLly5h4cKFnGGa0soQt4G4rH379uHtt9/mrCf+Qayurg4ej4fKyspO6yuNeJi9uIepPa2HkANdP07yHqPWXF1dER8fj/z8fDbobys2Nhbq6urd9sgdcYBWUFAg06OIysrK4ODggLFjx2Lv3r1S1zE0NJQYJdHZsOoRI0agV69euHv3LkaMGCH1uBcVFaG4uJgzgZ24bRUUFHDnzh32wk5FRQWUlZXZYMrW1pbTi/vNN9+gtLSUM1JBPMpj6NCh4PF4KCgokOkxajU1NZg6dSoEAgESEhIkPgMAoKurKxHYdjaZnZaWFvT19dnRB0DLSIXWbVJTU4Pr16/j5s2b7EWV5uZmMAwDBQUFpKSkYNKkSWybAP//XCgpKYGFhUWHdZg7dy727NmDgQMHon///igoKOhwfbHnz5/D09MT9+/fR1pamkSvNgBoampKtImqqio7m312djb+/vtvTq9rU1MTLl68iKioKIhEIvTs2VOiTYCWtv3uu+8QExODv/76C1paWti7dy9UVVU5F2qkfT+0/pxcvXoVq1atQnp6OqdssebmZoSGhrIXQ1rrLIDt7LvCzc0Nurq62LdvHwYPHozm5mZYWlpyngDQthzxxRBxOc3NzVi8eLHUR5zp6ekBkPxMyauioqLd71dFRUWZ/tcQQggh5M3wyoPtIUOGoFevXvjll1/YHzuVlZUoLCyUOgzvRVhYWEBRURElJSXtli0eqizuTZLV9evX0djYiIiICHZ4rKy9rWKDBg2CtrY2fv/9d4levtb1s7CwQH5+vtzPWwaA3Nxc6OjodNgTyjAMEhMTOY8GsrCwkHhcj3hirJdl8+bNEAgEmDx5MtLT0yUCIYZhEBsbi/nz50sN6LrC2toaFhYWiIiIgJeXl8RQ56qqKjZwLi0thYODA0aOHInY2Fi5h2d3JC8vD8+fP2d75qQddzMzM+Tk5HDyrV27FjU1Nfj66685PWa5ubkYMWIE+57P53MCOzU1NVRXV0vt2VVTU4OzszO+/fZb+Pn5Sdy33bpNqqur4ezsDEVFRZw6deqFegjbevLkCR48eMC2CQDY2NggPz+ffd+nTx+JNtm9ezfS0tLw008/wdDQkF3e9lyQZxh5jx494OXlhcOHD2P9+vUSw7Zra2uhqKgIBQUFNtC+e/cuzp8/D3V19S7t/+TJkyX27cMPP4SZmRlWrVrFXpBr2yat9erVi72t4dixY3j33Xc5n9vc3Fz06tULb731Frus9Wfizz//7HAEwIgRI3Dnzp0ORwj06tVL7u/XJ0+eoKCgADExMeyM+115xNaIESOQl5fXYf06ar/OFBUVob6+XqaRUoQQQgh5873yYFsgEGDhwoVYuXIl1NXVMWjQoHafmfuiVFVV4e/vj+XLl6O5uRnjxo1DdXU1MjMzIRAIsGDBAujr64PH4yEpKQkuLi7g8/mcIZPtMTY2RmNjI3bt2gU3Nzdcvny5S8+nDgkJgZ+fH/r06YNp06ZBJBLh+vXrqKysZIcfOjs749KlS5x7xBsaGtgfiA0NDSgtLWVnRG79wzIjI6PTID07Oxu1tbWYMGECu+yTTz5BREQEVqxYgcWLFyM7O1uu55d31fbt29HU1IRJkyYhPT2dc49sWloa7t+/L9ejpaKiopCQkMA+G7otHo+H2NhYODo6YsKECVizZg3MzMzw9OlTJCYmIiUlBRcuXEBZWRns7e2hp6eH7du349GjR2wZHc1WLE1RURGOHDkCFxcXDBgwAPn5+fjyyy9hY2ODd955h12v7XFXUlKSeDa5OOhtu1yW496R3bt3w87ODqNHj8aGDRtgZWWFxsZGnDt3DtHR0SgoKEBNTQ2cnJxQV1eH77//njMB1MCBA9u9FUCap0+fIiQkBDNnzoSWlhaKi4uxZs0aDBgwADNmzGDXc3Z25kwe2KNHD4l919DQkNpWbdtEnmHkQMs8Eenp6Xj77bcRHh4OW1tb9OrVCxkZGdi0aROuXbsGgUCAWbNm4caNG0hKSkJTUxMePnwIoOUiRnvzEEijqqoqsQ8qKipQV1fnLG/bJgBQWFiIrKwsvP3226isrERkZCQ7kWPbNhk/fnyXHpkHtDy//N1334Wuri48PDzQo0cP3L59Gzk5Ofjqq68AtAwXT01NxTvvvANFRUWpM6q31b9/f6irq2Pv3r3Q0tJCSUmJTBM0trVq1SqMGTMGS5Ysga+vL1RUVFBQUIBz586xj+xydnbGokWL0NTUxPnM5ufno6GhARUVFaipqWEvzLQeAZORkQEjI6MO54UghBBCyL/HazEb+bZt2zBhwgS4u7vD0dER48aN6/IENZ0JCwvDunXrsGnTJpibm8PZ2RmJiYlsj5e2tjZCQ0MRGBiIQYMGdXh/b2vW1taIjIzEli1bYGlpiSNHjmDTpk1y12/RokXYv38/4uLiMGzYMEycOBFxcXGcHjlfX1+cOXMGQqGQXVZWVgYbGxvY2NigvLwc27dvh42NDece4vr6eiQkJHDuD5bm5MmTcHV15Qx11tPTQ3x8PBITEzF8+HDs2bMHGzdulHv/umLHjh3w9PTEpEmTUFhYyC4/cOAA7OzsJO797cjjx485jwuSZvTo0bh+/TqMjY3h6+sLc3NzuLu7Iy8vDzt37gQApKSk4N69e0hLS4OOjg60tLTYl7x69+6N1NRUODs7w9TUFH5+fnBycsLPP//M+bEv7bjLorS0FJmZmZznBsvL0NAQN27cgIODA7788ktYWlpiypQpSE1NRXR0NICWizRXr15FTk4OhgwZwmmTBw8eyLW9nj17IicnB9OnT4eJiQkWLFgAExMTXLlyhXNf79y5c5Gfn487d+7IVb6s50JH+vfvj19++QVz587FV199BRsbG4wfPx5Hjx7Ftm3b0LdvX/aZzX/++Sesra05bZKZmdnlbXdEWps0NTUhIiICw4cPx5QpU1BfX4/MzEyJmfmPHj36Qm3i7OyMpKQknDt3DqNGjcKYMWMQGRnJmWwtIiIC586dg66ursw9wD169MCxY8eQnZ0NS0tLLF++HNu2bZO7flZWVrhw4QLu3r2L8ePHw8bGBsHBwZzz1sXFBb169cLPP//Myevi4gIbGxskJiYiPT2d/b5t7UXbjxBCCCFvFh7zIjdNv8bS09Ph4OCAysrKLj9i53Xm6ekJGxsbrF69WuY83377LU6ePImUlBSJNB8fH1RVVeHEiROwsrLC2rVr4enp2Z1Vlsre3h7W1tZsENvdWu/Xm6Arx33lypUQCoXt3lP+TxcQEAChUIiYmBiZ83R0LrwJutImp0+fxsqVK3H79u0uP6rtTbF7926cPHkSycnJMufJzc3F5MmTUVhYyE5o2Znq6mr07duXfUQeIYSQN9+Oc4Wdr9QFy6d0PDEs6T7y/P9+LXq2XyYdHR328WBvkm3btsk0vL21Xr16sUMlxTIyMiAQCNjZeRsaGjBz5kzOTNUv2+7duyEQCCTuR30RbffrTdGV466hoYGwsLCXVKNXLygoCPr6+nLdByztXHiTdKVNamtrERsb+68PtAHg448/xoQJEySedd+RsrIyHDp0SOZAmxBCCCFvvje2Z/vZs2coLS0F0HJfuLz30f5bvOp2Ki0tZR9rpaenJ9c9rB151ftFCCGdoZ5tQgj596Ge7X8+ef5/v7FdGG1nWybSvep2avsItu7yqveLEEIIIYQQ8u/2xg8jJ4QQQgghhBBC/tco2CaEEEIIIYQQQroZBduEEEIIIYQQQkg3o2CbEEIIIYQQQgjpZm/sBGmEEEIIIYQQ8jqhWcP/XahnmxBCCCGEEEII6WYUbBNCCCGEEEIIId2Mgm1CCCGEEEIIIaSbUbBNCCGEEEIIIYR0Mwq2CSGEEEIIIYSQbkbBNiGEEEIIIYQQ0s3o0V+EEEIIIYQQ0oEd5wq7pRx69Ne/C/VsE0IIIYQp2CWeAAAgAElEQVQQQggh3YyCbUIIIYQQQgghpJtRsE0IIYQQQgghhHQzCrYJIYQQQgghhJBuRsE2IYQQQgghhBDSzSjYJoQQQgghhBBCuhkF24QQQgghhBBCSDd74WDb3t4eX3zxBfvewMAAO3fulDl/eno6eDweeDwe3nvvvRetjkR9XrUDBw7AyclJrjyzZs1CZGQkZ9mLtJM4b1VVFQAgLi4O/fr1k6sMe3t7dvu3bt2SWu7LEhcXx277dTq2L2revHnYuHFjt5aZk5MDHR0d1NbWdmu5L5OPj0+3nPvdJS0tDWZmZmhubpY5j7Rz9p/iyZMn0NDQQHFxscx5oqKi4O7u/vIq9Yp1pU2SkpJgY2Mj1+eGEEIIIW+216Zn+86dO4iLi5N5/f9VoPciRCIR1q1bh+DgYHZZXl4eZs6cCQMDA/B4PKkXJtatW4fw8HBUV1ezy+zs7FBeXg5PT0+p27K3t8eePXu6fyda8fX1RXl5OSwtLWXOEx4eDjs7OygrK0sN8J88eYKpU6di8ODBUFRUhK6uLpYuXcrZdy8vL5SXl2Ps2LFdqvfNmzfh4eGBQYMGQUlJCSYmJvD19UVhYSEA4Ndff4W3tzd0dXXB5/Nhbm6Or7/+mlNGRxcoeDweTpw4wb53d3eHnp4elJSUoKWlhXnz5qGsrIyT5/bt2zh9+jSWLVvGLjt+/DicnZ0xYMAAzkUNaRiGwbRp0yS2PWzYMIwePRo7duyQvYH+R4qLizvdr9dBQEAAgoKC0KNHy9fj8ePHMWXKFAwcOBB9+vTB2LFjkZyczMkj7ZztiurqagQFBcHMzAxKSkrQ1NSEo6Mjjh8/DoZhALR/QbHtZ/TSpUt45513oK6uDj6fDzMzM6mfi02bNsHNzQ0GBgYAZDsnfX19ce3aNVy6dOmF9pdhGOzduxdvv/02BAIB+vXrB1tbW+zcuRN1dXUAgH379mH8+PHo378/+vfvD0dHR2RlZXHKkbVNWrt8+TIUFBRgbW0tkda2TQAgNTUVdnZ2UFVVhZaWFlatWoXGxkY2/d133wWPx8N//vOfrjQFIYQQQt5Ar02wraGhIXdv6+suPj4eAoEA48ePZ5fV1dXByMgImzdvhqamptR8VlZWMDAwwJEjR9hlvXv3hqamJvh8vsT6FRUVyMzMhJubW/fvRCvKysrQ1NSEgoKCzHkaGhrg4eGBTz/9VGp6jx49MH36dJw6dQqFhYWIi4vDzz//jE8++YRdh8/nQ1NTE71795a7zklJSRgzZgxEIhGOHDmCgoICHD58GH379mUvgmRnZ2PgwIH4/vvvkZeXh6CgIKxevRpRUVFybw8AHBwc8OOPP+LOnTuIj49HUVERZs2axVknKioKHh4eUFVVZZfV1tbinXfewebNmzvdxs6dO8Hj8aSmffjhh4iOjkZTU1O7+Q0MDJCeni7bDv2LZGZm4u7du/Dw8GCXXbx4EVOmTMGZM2eQnZ0NBwcHuLm54ebNm+w60s5ZeVVVVcHOzg6HDh3C6tWrcePGDVy8eBFeXl4ICAiAUCiUqzwVFRUsXboUFy9eREFBAdauXYu1a9di79697DrPnj3DgQMHsGjRInaZLOekoqIiZs+ejV27drW7ffHFlY7MmzcPX3zxBaZPn47z58/j1q1bCA4OxsmTJ5GSkgKg5cKqt7c3zp8/jytXrkBPTw9OTk4oLS2Vqz1aEwqFmD9/PiZPniyRJq1Nbt++DRcXF0ydOhU3b97EsWPHcOrUKQQGBnLyfvjhhx22CSGEEEL+ZRg5PH36lJk3bx6joqLCaGpqMtu3b2cmTpzIfP755+w6+vr6zI4dO9j3AJh9+/Yx7733HsPn85khQ4YwJ0+eZNPPnz/PAGAqKys522pubma2bNnCGBoaMkpKSoyVlRXz3//+l2EYhrl//z4DgPNasGABwzAMM3HiRGbZsmXMypUrmf79+zODBg1i1q9fzyk7IiKCsbS0ZJSVlRkdHR3m008/ZWpqatj02NhYpm/fvszZs2cZMzMzRkVFhXF2dmbKyso45Xz33XeMmZkZo6ioyJiamjLffvstJ93NzY3x9/dvtz3btlVrISEhzPjx4yWWL1iwgJk+fTpn2aFDhxhbW1v2/enTp5mhQ4cySkpKjL29PRMbG8tpY/H+ia1fv54ZPnw4c+jQIUZfX5/p06cP4+XlxVRXV7PrtD3ODCN57Orq6hgXFxfm7bffZp48ecJZt+02O/L1118zOjo6Esul1aEjtbW1zIABA5j33ntPanrbz1xrn332GePg4MC+76j+AJiEhIR2yzp58iTD4/GYhoYGhmEYpqmpienXrx+TlJQkdX3x5/vmzZtS02/dusXo6Ogw5eXlUrctEokYRUVFJjU1td066evrM+fPn+9w+z/88AMzbtw4RklJibG1tWXu3LnDZGVlMSNHjmTPib///puTt6Nzou05O3HiRIZh/v9netu2bYympiajpqbGfPbZZ2x7MQzDHD58mBk5ciQjEAiYQYMGMd7e3sxff/3Fpos/iz///DMzcuRIhs/nM2PHjmV+++03Tv1OnTrFjBgxglFUVGQMDQ2ZkJAQ5vnz52z6smXLmFmzZrXbbmIWFhZMaGgoZ1l756ysPv30U0ZFRYUpLS2VSKupqWHr2d55IMs5NmPGDGbu3Lns+/j4eGbAgAGd1k3aOZmens707t2bqaurk5pH/Dlqzw8//MAAYE6cOCGR1tzczFRVVUnN19jYyKiqqjIHDx5kl8nbJl5eXszatWvZ777WpLXJ6tWrOd+xDMMwCQkJjJKSEud7sri4mAHAFBUVSa17W0KhkAHACIVCmdYnhBDy6kSm3OmWF/nnk+f/t1w92ytXrsT58+eRkJCAlJQUpKenIzs7u9N8oaGh8PT0ZHsH5syZg4qKig7zrF27FrGxsYiOjkZeXh6WL1+OuXPn4sKFC9DV1UV8fDyAluHn5eXlnGG/Bw8ehIqKCq5evYqtW7diw4YNOHfuHJveo0cPfPPNN8jNzcXBgweRlpaGgIAAzvbr6uqwfft2HD58GBcvXkRJSQn8/f3Z9H379iEoKAjh4eEoKCjAxo0bERwcjIMHD7LrZGRkwNbWttP2kWb06NHIysqCSCTqdN1Tp05h+vTpAIAHDx7g/fffh4uLC27duoVFixZJ9L5IU1RUhBMnTiApKQlJSUm4cOGCTD2sYkKhEE5OTmhoaEBqairU1NRkzttaWVkZjh8/jokTJ3a6ro+PD+zt7dtNT05OxuPHjyWOrVhHIymEQmGX96G1iooKHDlyBHZ2dujVqxeAll6yqqqqLn026urq4O3tjaioqHZHRvTu3RvDhw9HRkbGC9V9/fr1WLt2LW7cuAEFBQV4e3sjICAAX3/9NTIyMlBUVIR169ax63d2ToiH/v78888oLy/H8ePH2bznz59HUVERzp8/j4MHDyIuLo5zW0lDQwPCwsLw66+/4sSJE7h//z58fHwk6hwUFISIiAhcv34dCgoK+Oijj9i05ORkzJ07F35+fsjPz0dMTAzi4uIQHh7OrnPx4sVOj0tzczNqamokPh9tz9mSkhIIBIIOX+Le4ubmZhw7dgxz5szB4MGDJbYpEAjkGlEizc2bN5GZmck5t2TZ3/bOSVtbWzx//lxiSLesjhw5AlNTU/a7qzUej4e+fftKzVdXV4fnz593+fyMjY1FUVER1q9fLzVdWpuIRCIoKSlxlvH5fNTX13P+B+rr60NDQ+OFzz1CCCGEvBlk/vX29OlTHDhwAIcOHcKUKVMAtAS1Ojo6neb18fGBt7c3AGDjxo3YtWsXsrKyMHXqVKnr19bWIjIyEmlpaex9ukZGRrh06RJiYmIwceJE9oeWtOHnVlZW7A+poUOHIioqCqmpqWy9W9/bZ2hoiLCwMHz66afYvXs3u/z58+fYs2cPjI2NAQBLly7Fhg0b2PSwsDBERETg/fffZ8sR/4BfsGABqqqqUFVVJfWHsyy0tbUhEonw8OFD6Ovrt7ueSCRCcnIyG/RER0fDyMgIO3bsAI/Hg6mpKXJycrBly5YOt9fc3Iy4uDh2WPO8efOQmprKCUTa89dff8HLywvGxsY4evRol4Z7e3t74+TJk3j27Bnc3Nywf//+TvNoaWl1OBnR3bt3AQBmZmZy1eXKlSv48ccfcfr0ac5yoVAIgUAgUxmrVq1CVFQU6urqMGbMGCQlJbFpxcXF6NmzJzQ0NOSqFwAsX74cdnZ2UgOU1rS1teWa3Ekaf39/ODs7AwA+//xzeHt7IzU1Fe+88w4AYOHChZyAuLNzYuDAgQAAdXV1iQsF/fv3R1RUFHr27AkzMzO4uroiNTUVvr6+AMAJmo2MjPDNN99g9OjRePr0KeeYhIeHs0FhYGAgXF1dUV9fDyUlJYSHhyMwMBALFixgywkLC0NAQAD7fVFcXNzpORsREYHa2lqJ+RPanrODBw/u9P70Pn36AAAeP36MyspKmT+ru3fvljhHGhsbJQJCANDR0cGjR4/Q2NiIkJAQzvDojva3s3NSRUUF/fr1Q3FxsUwXx9q6e/cuTE1N5c4XGBgIbW1tODo6cpbL0iZ3795FYGAgMjIy2r14Ia1NnJ2dsXPnThw9ehSenp54+PAhvvrqKwBAeXk5Z92Ozj2RSMS5gPqi9/gTQggh5PUmc892UVERGhoaOJNUqampyfRjycrKiv1bRUUFqqqq+Pvvv9tdPz8/H/X19ZgyZQqnF+jQoUMoKiqSa3tAS1DWenvnz5/HlClToK2tDVVVVcyfPx9PnjzhzOCsrKzMBtpty3j06BEePHiAhQsXcur31VdfsfV79uwZAEj98SsL8b3Z4kmC2pP2/9q787ioqv9/4K9BkG1AUQRMttzBBEdxDRKXwBUriwSjNNdPpqmpyUdR1BIUUss0S/1oGqKmIpjmkkjiHiYpYpoUUohZgYALIHJ+f/ib++XKADMwoODr+XjM4+Gcu537vveOvO8599z4eDRt2hQdO3YEAFy6dAk9evSQPSupzcBizs7OsueHH41ZRfr374+WLVti+/btVUq0AWD58uX46aefsHv3bqSlpWH69OmVLhMWFoZNmzaVO138/wGldHHx4kUMGzYM8+bNk27OqFlYWCA5ObnMR5OZM2fi3LlzOHjwIBo0aIA333xTqs+9e/dgbGxc6fOsj4qLi0N8fLxWo/2bmprKzp2JEyfKztWMjAwMHDiwTFlppa8jW1tbAJDOM3WZLtdERTp06IAGDRpI3x89/86dO4dhw4bByckJFhYWUo+GiurcvHlzAJDWc/bsWSxcuFBWP/Wgf+pY3bt3r8JrNjo6GqGhodi2bVuZmyWPXrOGhoZo3bp1hR/1OtTnhrbnxMiRI8uch6VvBpaWmJiIpKQkrFmzRkoY1SraX22uyUfPsw4dOkix7dChAwDI4q0uU++zrtfA0qVLER0djV27dpWpd2UxefDgAQIDA7FgwQK0bdu23G1oiomPjw8iIiIwceJEGBsbo23bthg8eDAAyM5bTTEpLSwsDI0aNZI+Dg4OOu0/ERER1S1at2xXJXFRU3efVVMoFBW2SKqn7d27Fy1atJBNMzY2rtb2rl27hkGDBmHixIlYtGgRmjRpgmPHjmHMmDG4f/9+hetQx0C9rrVr16J79+6y+dR/eDVt2hQKhQI5OTmV1lcTdTd7dWtgeUp3IQeqfpx0PUalDR48GDt37kRqaqosGdOFnZ0d7Ozs0L59ezRt2hReXl4ICQmREqaqUP9B/csvv2h1wyE1NRV9+/bFuHHjMHfu3DLTDQwM0Lp1a622bW1tDWtra7Rt2xYuLi5wcHDAqVOn0LNnT1hbW+Pu3bsoKirS6eZEfHw80tLSyvTkGD58OLy8vGQDnmVnZ8tuFi1cuFD2GIS3tzeWLFkiO38fbc0rfU6ok6JHy9TniDbXREUqOv/u3LkDHx8f+Pj44Ouvv0azZs2QkZEBX19fFBUVVVrn0nVcsGCB1PJemjq5sra2Lvea3bZtG8aMGYNvvvmmTKsqUPaazcjIgKura4X7/cYbb2DNmjVo1qwZrKyscOnSpQrnV2vUqFGZc7G8nhLPPvssgIc3Sv766y+EhoZKPY0q2l9trsns7GzZb9S+ffuk39HMzEx4e3vLbkiVPj5t27bVen8BIDIyEosXL8b3339f5oYqUHlM8vPzkZSUhHPnzuHdd98F8PCcEELA0NAQBw8eRN++fcuNyfTp0zFt2jRkZWXBysoK6enpCA4OluJbXkxKCw4Olt20yMvLY8JNRERUj2mdbLdu3RpGRkY4deoUHB0dAQA5OTm4cuVKlboQVsTV1RXGxsbIyMgod93qJKWiEZc1SUpKQnFxMT7++GPp1T7bt2/XaR22trZo0aIFfvvtN4wcObLc+rm6uiI1NVXn92wDQEpKCuzt7WFtbV3uPEII7NmzR9a66+rqKnsVFACcOnVK5+3rIjw8HEqlEv369UNCQkKlCUZl1DcMtHlevSI+Pj6wtrbG0qVLERMTU2b6rVu3pMT14sWL6Nu3L9566y2tus7r4tH9Ub9qKDU1VeNrh8oze/ZsWRdg4GECtXz58jIj0aekpMhGQLexsZElHoaGhmjRooXWNw8qo+01Aeh+zf7yyy/4559/EB4eLiUmSUlJOtexc+fOuHz5coX7rFKpkJqaWqY8Ojoab7/9NqKjo6UWzUc9es3q0o3cwMAAr7/+OjZv3oz58+eXufFx584dGBsbV/u5bSGE7LpSqVT4+uuvtVoOkF+TaWlpKCgogEqlkspKP/Kirmt58Q4MDMSIESMQGxtb5rEIIQTy8vKk57YjIiLw4Ycf4sCBA1UeB8PS0hIXLlyQla1evRrx8fHYsWOHlDRXFBOFQiEdm+joaDg4OKBz587S9IKCAqSlpcliUpqxsbFWN4yJiIioftD6LzelUokxY8Zg5syZaNq0KWxtbWXvotUnCwsLzJgxA9OmTUNJSQk8PT2Rl5eHEydOQKlU4q233oKTkxMUCgW+/fZbDBo0CKamplo9T9uqVSsUFxdj5cqVGDp0KI4fP16l91OHhoZiypQpsLS0xMCBA1FYWIikpCTk5ORILRe+vr44duyY7BnxoqIi6Y/5oqIiZGZmIjk5GUqlUvZHaWJiYqVJ+tmzZ3Hnzh288MILUtnEiRPx8ccfY/r06ZgwYQLOnj2r0/vLqyoyMhIPHjxA3759kZCQID17mpGRgezsbGRkZODBgwdS8tG6dWsolUrs27cPf/31F7p27QqlUonU1FTMmjULzz//vOwdt5oEBwcjMzOz3K7k5ubmWLduHV577TX4+flhypQpaN26Nf755x9s374dGRkZ2Lp1Ky5evIg+ffrAx8cH06dPx40bNwA8bJGtrGfBo86cOYMzZ87A09MTVlZW+O233zBv3jy0atVKal1v1qwZOnfujGPHjsmSbXWc1O/kvnz5MoD/a2FUfx7l6Ogoa11LT09HZmamxtbXmlTZNWFjYwNTU1Ps378f9vb2MDExKXcQrNIcHR3RsGFDrFy5EhMnTkRKSgoWLVqkc/3mzZuHIUOGwMHBAa+99hoMDAxw/vx5XLhwQXr+1tfXVzbIIfAwqXrzzTfxySefoEePHtL5YWpqKqv/o9esuhu5thYvXoyEhAR0794dH330ETw8PGBkZITExESEhYXhxx9/1On1iKtWrYKjo6N0LR47dgyRkZGyd7v7+voiODgYOTk5sLKyAgCtr8nExES0bNlS1oNCF/7+/oiJiUFAQABCQkKkd5lfuHABy5cvx+TJk/HSSy9h6dKlCAkJwZYtW+Ds7CzFX901XVsGBgZ47rnnZGU2NjYwMTGRlWuKCfAw4R8wYAAMDAywa9cuhIeHY/v27bKeG6dOnYKxsbFWPWmIiIio/tMpU46IiMALL7wAPz8/9O/fH56enujSpUuNVGzRokWYN28ewsLC4OLiAl9fX+zZs0dKKlq0aIEFCxZg9uzZsLW1lboFVqZTp05YtmwZlixZgueeew5RUVEICwvTuX5jx47FunXrsHHjRnTs2BG9e/fGxo0bZUnPuHHjsG/fPtn7ca9fvw6VSgWVSoWsrCxERkZCpVLJWiwLCgoQExMjDQ5VntjYWAwePFjW2uXo6IidO3diz549cHd3x5o1a7B48WKd968qli9fDn9/f/Tt2xdXrlwB8DDBUalUmD9/Pm7fvi3tu7pl0tTUFGvXroWnpydcXFwwdepUDBkyRDagWHmysrLKPLP7qGHDhuHEiRMwMjJCYGAg2rdvj4CAAOTm5koJ1jfffIO///4bUVFRaN68ufTp2rWrzjEwNTXFrl270K9fP7Rr1w5vv/02nnvuOfzwww+yFq3x48eXeSdzXFwcVCqV1HI6YsQIqFQqnW8GRUdHw8fHp8KB9WpCZdeEoaEhPv30U3zxxRd45plnKh3kTa1Zs2bYuHEjvvnmG7i6uiI8PByRkZE618/X1xfffvstDh06hK5du6JHjx5YtmyZLE5vvPEGUlNTpRsdAPDFF1+guLgYkyZNkp0f7733njSPttdsRaysrHDq1Cm88cYb+PDDD6FSqeDl5YXo6GhERERodWOitJKSEgQHB6NTp07w8PDAypUrER4eLnuOuWPHjvDw8JD17tH2moyOjq7W/ioUCmzZsgXLli1DTEwMevfuDTc3N4SGhmLYsGHS4HyrV69GUVERXn31VVn8q3IOaENTTADgu+++g5eXFzw8PLB3717ExsbipZdeks0THR2NkSNHwszMrEbqRkRERHWLQlTnYWw9SEhIQJ8+fZCTk6NTq01d4e/vD5VKheDgYK2XWbVqFWJjY3Hw4MEy00aNGoVbt25h9+7dcHNzw9y5c8uMilwTvL290alTJ60G56rPddCXgoICtGvXDlu3btVrK1hhYSHatGmD6OhoadRw0s2sWbOQm5uLL774QutlKrpmn3T79u3DjBkzkJKSonVPpZSUFPTr1w9XrlzR+SZAXVCVmPz9999o3749kpKSyjzHXR51V/nc3FzpkQIiInoyLT90RS/rmfZi+YN0Ut2gy//f+u8DXkX29vbSoD31SUREhE5dHYGHgwitXLlSVpaYmAilUim1hhYVFWH48OEYOHCg3upamdWrV0OpVJZ57rGmRUVFQalU1qt315qYmGDTpk34559/9Lrea9euYc6cOUy0q2HOnDlwcnLS6dlyTddsXTFo0CBMmDABmZmZWi9z/fp1bNq0qV4m2kDVYvL7779j9erVWifaREREVP899pbte/fuSX/QKJVKjc+k0uOPU2ZmpvQ6M/UztLUlPz8ff/31FwCgcePGFQ4aR0RUV7Blm4io7mDLNqnp8v939Ya21QNTU1O9jYhcnz3uOD36CrbaZGFhIXsHOBERERER0ZPuielGTkRERERERFRfMNkmIiIiIiIi0jMm20RERERERER6xmSbiIiIiIiISM8e+wBpRERERERETzKOIk5VwZZtIiIiIiIiIj1jsk1ERERERESkZ0y2iYiIiIiIiPSMyTYRERERERGRnjHZJiIiIiIiItIzJttEREREREREesZXfxEREREREdWC5YeuaD0vXzdW97Flm4iIiIiIiEjPmGwTERERERER6RmTbSIiIiIiIiI9Y7JNREREREREpGdMtomIiIiIiIj0jMk2ERERERERkZ4x2SYiIiIiIiLSMybbRERERERERHr2RCTb3t7emDp1qvTd2dkZK1as0Hr5hIQEKBQKKBQKvPTSS3qvz+O2fv16+Pj46LTMq6++imXLlsnKqhMn9bK3bt0CAGzcuBGNGzfWaR3e3t7S9pOTkzWutyrS09Ol9Xbq1KnK66lNly9fhp2dHfLz87VeZsaMGZgyZUoN1urx+vfff2FjY4P09HStl/nss8/g5+dXc5V6zKoSk2+//RYqlQolJSU1VzE9qw+/uV27dsWuXbtqqEZERERUFz0Ryba+XL58GRs3btR6fn0kejWtsLAQ8+bNQ0hIiFR28eJFDB8+HM7OzlAoFBpvTMybNw8fffQR8vLypLJevXohKysL/v7+Grfl7e2NNWvW6H8nShk3bhyysrLw3HPP6bTc3r170b17d5iamsLa2hqvvPKKNM3BwQFZWVl4//33q1SnGzduYPLkyWjZsiWMjY3h4OCAoUOH4vDhw9I85d0ACg0NlSX4u3btgoeHBxo3bgxzc3N06tQJmzdvLrPcnDlzMGnSJFhYWAB4eO726dMHtra2MDExQcuWLTF37lzcv39fWmbWrFnYsGEDfv/99yrtp1pRURGWLl0Kd3d3mJmZwdraGs8//zw2bNggbS8sLAxdu3aFhYUFbGxs8NJLL+Hy5cuy9Wgbk40bN0o3Q0p/CgoKZMuFhYVh6NChcHZ2LrPOf//9F/b29mWu13HjxuHHH3/EsWPHqhMSCCHw5Zdfonv37lAqlWjcuDE8PDywYsUK3L17FwCwdu1aeHl5wcrKClZWVujfvz/OnDkjW095SWNFN6eOHz8OQ0NDjTeKNMXk8OHD6NWrFywsLNC8eXN88MEHKC4ulqYPGTIECoUCW7ZsqUooalRd/c3V5tiHhIRg9uzZdeomBxEREdWsepVs29jY6Nza+qTbuXMnlEolvLy8pLK7d++iZcuWCA8Ph52dncbl3Nzc4OzsjKioKKmsYcOGsLOzg6mpaZn5s7OzceLECQwdOlT/O1GKmZkZ7OzsYGhoqPUyO3fuRFBQEEaPHo2ff/4Zx48fR2BgoDS9QYMGsLOzg1Kp1Lk+6enp6NKlC+Lj47F06VJcuHAB+/fvR58+fTBp0iSd19ekSRPMmTMHJ0+exPnz5zF69GiMHj0aBw4ckOb5888/ERcXh9GjR0tlRkZGePPNN3Hw4EFcvnwZK1aswNq1azF//nxpHhsbG/j4+FR4QyQhIUFjsqpWVFQEX19fhIeHY/z48Thx4gTOnDmDSZMmYeXKlbh48V6n+EQAACAASURBVCIA4IcffsCkSZNw6tQpHDp0CMXFxfDx8cGdO3d0jgkAWFpaIisrS/YxMTGRpt+7dw/r16/H2LFjNS4/ZswYuLm5lSk3NjZGYGAgVq5cWaV6qQUFBWHq1KkYNmwYjhw5guTkZISEhCA2NhYHDx4E8DC2AQEBOHLkCE6ePAlHR0f4+PggMzOzytvNzc3Fm2++iX79+pWZpikm58+fx6BBgzBgwACcO3cOW7duRVxcHGbPni1bdvTo0ZXGRKFQ6NRi/rTQ9JurzbEfPHgwcnNzZdc6ERERPd1qPdm+c+cO3nzzTSiVSjRv3hwff/xxpcsoFAqsW7cOL7/8MszMzNCmTRvExcVVupwQAkuXLkXLli1hamoKd3d37NixA8DDJKtPnz4AACsrKygUCowaNUpatqSkBLNmzUKTJk1gZ2eH0NBQ2bqXLVuGjh07wtzcHA4ODnjnnXdw+/Ztabq6JevAgQNwcXGBUqnEgAEDkJWVJVvPhg0b4OLiAhMTE7Rv3x6rV6+WTd+6dWuZbrJdu3ZFREQERowYAWNj43L338/PD9HR0ZXGCXjYcuzu7o4WLVoAAPbt24e2bdvC1NQUffr0qfSPcnVr5ubNm+Hs7IxGjRphxIgROnWTBh4mGIMHD0aPHj2QnZ2N4uJivPfee4iIiMDEiRPRtm1btGvXDq+++qpO6y3PO++8A4VCgTNnzuDVV19F27Zt0aFDB0yfPh2nTp3SeX3e3t54+eWX4eLiglatWuG9996Dm5ubrOV1+/btcHd3h729vVTWsmVLjB49Gu7u7nBycoKfnx9GjhyJxMRE2fp1OaaarFixAkePHsXhw4cxadIkdOrUCS1btkRgYCBOnz6NNm3aAAD279+PUaNGoUOHDnB3d8eGDRuQkZGBs2fPVmm7CoUCdnZ2sk9p3333HQwNDdGzZ88yy37++ee4desWZsyYoXHdfn5+2L17N+7du1elum3fvh1RUVGIjo7Gf//7X3Tt2hXOzs4YNmwY4uPjpd+JqKgovPPOO+jUqRPat2+PtWvXoqSkRNYDQlcTJkxAYGCgxv3WFJOtW7fCzc0N8+bNQ+vWrdG7d2+EhYVh1apVsmvNz88PZ86cwW+//ValeqlboA8cOACVSgVTU1P07dsXN2/exHfffQcXFxdYWloiICBAavkH6u9vrjbHvkGDBhg0aFC1rk8iIiKqX2o92Z45cyaOHDmCmJgYHDx4EAkJCVr9Ab9gwQL4+/tLLTsjR45EdnZ2hcvMnTsXGzZswOeff46LFy9i2rRpeOONN/DDDz/AwcEBO3fuBPCwC29WVhY++eQTadmvvvoK5ubmOH36NJYuXYqFCxfi0KFD0nQDAwN8+umnSElJwVdffYX4+HjMmjVLtv27d+8iMjISmzdvxtGjR5GRkSFLGNauXYs5c+bgo48+wqVLl7B48WKEhITgq6++kuZJTEyEh4dHpfHRpFu3bjhz5gwKCwsrnTcuLg7Dhg0DAPzxxx945ZVXMGjQICQnJ2Ps2LFlWs40SUtLw+7du/Htt9/i22+/xQ8//IDw8HCt65ubmwsfHx8UFRXh8OHDaNKkCX766SdkZmbCwMAAKpUKzZs3x8CBA6UW2Iqouy+XJzs7G/v378ekSZNgbm5eZnp1e0kIIXD48GFcvnwZL7zwglR+9OjRSo/p1atXsX//fvTu3VtW3q1bN/zxxx+4du1aleoUFRWF/v37Q6VSlZlmZGSkMQ7Aw2MDPGy5r4rbt2/DyckJ9vb2GDJkCM6dOyebXl5MUlNTsXDhQmzatAkGBpp/rjw8PHD//n1Zt94OHTpAqVSW++nQoYM0b1RUFNq1ayed/6UpFAo0atRI43bv3r2L+/fvVzkmGzZsQFpamqz3QmmaYlJYWCjrEQAApqamKCgokP2OOjk5wcbGpszNGl2Fhobis88+w4kTJ/DHH3/A398fK1aswJYtW7B3714cOnRI1oL+tPzmlnfsu3XrVmHMCwsLkZeXJ/sQERFRPSZqUX5+vmjYsKHYunWrVPbvv/8KU1NT8d5770llTk5OYvny5dJ3AGLu3LnS99u3bwuFQiG+++47IYQQR44cEQBETk6ObB4TExNx4sQJWR3GjBkjAgICyl1OCCF69+4tPD09ZWVdu3YVH3zwQbn7tn37dtG0aVPp+4YNGwQAcfXqVals1apVwtbWVvru4OAgtmzZIlvPokWLRM+ePYUQQuTk5AgA4ujRo+Vu99FYlfbzzz8LACI9PV1W/tZbb4lhw4ZJ3wsKCoSFhYU4f/68EEKI4OBg4eLiIkpKSqR5PvjgA1msNmzYIBo1aiRNnz9/vjAzMxN5eXlS2cyZM0X37t2l771795YdZyH+7xj88ssvwt3dXbzyyiuisLBQmh4dHS0ACEdHR7Fjxw6RlJQkAgICRNOmTcW///4rW9f8+fOFu7u79H3Xrl2iXbt25UROiNOnTwsAYteuXeXOo+bk5CQaNmwozM3NZR8jIyPZNoUQ4tatW8Lc3FwYGhoKY2NjsX79etl0d3d3sXDhQo3b6dmzpzA2NhYAxPjx48WDBw9k03NzcwUAkZCQoHH5I0eOCCcnp3L3w9TUVEyZMqXS/S2tpKREDB06tMw1oW1MTp48KTZv3iySk5PF0aNHxfDhw4Wpqam4cuWKNM+wYcPE22+/LVt/QUGBcHNzE5s3b5b2TdP1KoQQVlZWYuPGjdL39PR08euvv5b7KX1NuLi4CD8/P51iIoQQ77zzjmjVqpW4d++eVNa7d29hZGRUJibGxsay6+XKlSvCxsZGXL58WQhR9twtLyYHDhwQBgYGYsuWLaK4uFj8+eefwtPTUwAo81uiUqlEaGhoufUHIH7//XeN09Sx/v7776WysLAwAUCkpaVJZRMmTBC+vr5CiKfnN1cIzcdeCCFiY2OFgYFBmetWbf78+QJAmU9ubm6F2yMiovpj2cHLWn/oyaT+e1yb/7+1f3BWD9LS0lBUVCTrFtmkSRO0a9eu0mVLP69pbm4OCwsL3Lx5s9z5U1NTUVBQgBdffFFWXlRUpLFVr6LtAUDz5s1l2zty5AgWL16M1NRU5OXlobi4GAUFBbhz547UOmhmZoZWrVppXMfff/+NP/74A2PGjMG4ceOkeYqLi6WWNHW32EdbsrSlfja7dDdPTeLj49G0aVN07NgRAHDp0iX06NFD1iqsqZvro5ydnaUBv4CyMatI//790bVrV2zfvh0NGjSQytWDDc2ZMwfDhw8H8LBF0N7eHt988w0mTJhQ7jpffvllvPzyy+VOF0IAQIWt36XNnDlT1u0VAD799FMcPXpUVmZhYYHk5GTcvn0bhw8fxvTp09GyZUt4e3sDeHhcyzum27ZtQ35+Pn7++WfMnDkTkZGRstY7Tce09LPqDx48QGFhoazMy8sL3333nbTP2u6v2rvvvovz589rHIRMm5j06NEDPXr0kL4///zz6Ny5M1auXIlPP/0UgOaYBAcHw8XFBW+88UaldTQ1NZXFxMnJSat9A6oWk6VLlyI6OhoJCQll6j1y5EjMmTNHVrZr1y4sXrwYwMNjFBgYiAULFqBt27blbkNTTHx8fKRHKoKCgmBsbIyQkBAcO3ZMdt0AZWMycODAMq2uHTp0kO176W7ZgPx30NbWFmZmZmjZsqWsTN2j4Gn5za3o2JuamqKkpASFhYUax8YIDg7G9OnTpe95eXlwcHCoODBERERUZ9Vqsq1ObqrCyMhI9l2hUFQ46qt62t69e6XnkNUqes5Zm+1du3YNgwYNwsSJE7Fo0SI0adIEx44dw5gxY2SjR2tahzoG6nWtXbsW3bt3l82n/qO5adOmUCgUyMnJqbS+mqi72Tdr1qzC+Up3IQeqfpx0PUalDR48GDt37kRqaqqU9AMP/1gGAFdXV6nM2NgYLVu2REZGRpXqqdamTRsoFApcunRJq1ehWVtbo3Xr1rIyTV2IDQwMpPk6deqES5cuISwsTEq2ra2tyz2m6j+8XV1d8eDBA4wfPx7vv/++dE5oOqbq16gBwOnTp/HBBx8gISFBKiv9R3/btm1x6dKlSvdVbfLkyYiLi8PRo0dlz5iraRuT0gwMDNC1a1f8+uuvsvU8GpP4+HhcuHBBeuZXfV5aW1tjzpw5WLBggTRvdna2LCYdOnSosKu9k5OT9CiCrjGJjIzE4sWL8f3332sctK1Ro0ZlYmJjYyP9Oz8/H0lJSTh37hzeffddAA9/D4QQMDQ0xMGDB9G3b99yz5Pp06dj2rRpyMrKgpWVFdLT0xEcHIxnn31WNt+jMVm3bp3sufY2bdpg3759ZX4fSyt9TSsUigqv8afhN7eyY5+dnQ0zMzONiTbwMA7axIKIiIjqh1pNtlu3bg0jIyOcOnUKjo6OAICcnBxcuXKlzLOp1eXq6gpjY2NkZGSUu+6GDRsCeNjSpIukpCQUFxfj448/lp4j3b59u07rsLW1RYsWLfDbb79h5MiR5dbP1dUVqampOr/zFQBSUlJgb28Pa2vrcucRQmDPnj3YtGmTVObq6ordu3fL5qvKYGG6CA8Ph1KpRL9+/ZCQkCAl1126dIGxsTEuX74MT09PAMD9+/eRnp6uU+ulJk2aNIGvry9WrVqFKVOmlHle+datW3oZ3V4IIXtuXqVSITU1Vavl7t+/L7v5kZKSAiMjI9kzx6UTuz///BOGhoZlkj21wMBA/Pe//8W5c+fKtDYWFxejsLAQ5ubmEEJg8uTJiImJQUJCQplErjqEEEhOTpbdVFGpVPj6669l8+3cuVOWHP744494++23kZiYKGu9TEtLQ0FBgWx/9u3bJ0vCHlU6KQsMDMSIESMQGxtb5rltIQTy8vKkls+IiAh8+OGHOHDgQJXHUrC0tMSFCxdkZatXr0Z8fDx27NghxVpTTNQUCgWeeeYZAEB0dDQcHBzQuXNnaXpBQQHS0tJkMdGUVDs5OVU4er0u6vtvrjbHPiUlRXYciIiI6OlWq8m2UqnEmDFjMHPmTDRt2hS2traYM2dOuQMfVYeFhQVmzJiBadOmoaSkBJ6ensjLy8OJEyegVCrx1ltvwcnJCQqFAt9++y0GDRoEU1NTrV4f1apVKxQXF2PlypUYOnQojh8/XqX3U4eGhmLKlCmwtLTEwIEDUVhYiKSkJOTk5EhdDX19fXHs2DHZu3uLioqkZK2oqAiZmZlITk6GUqmUJVmJiYmVJulnz57FnTt3ZAN4TZw4ER9//DGmT5+OCRMm4OzZszq9v7yqIiMj8eDBA/Tt2xcJCQlo3749LC0tMXHiRMyfPx8ODg5wcnJCREQEAOC1116rcH0xMTEIDg7GL7/8Uu48q1evRq9evdCtWzcsXLgQbm5uKC4uxqFDh/D555/r1OIJPHwvsoeHB1q1aoWioiLs27cPmzZtwueffy7N4+vri7Fjx+LBgwdSi1pUVBSMjIzQsWNHGBsb4+zZswgODsbrr78ue01aYmIivLy8ym05q8zUqVOxd+9e9OvXD4sWLYKnpycsLCyQlJSEJUuWYP369ejUqRMmTZqELVu2IDY2FhYWFrhx4waAh622um57wYIF6NGjB9q0aYO8vDx8+umnSE5OxqpVq2QxCQ4ORk5ODqysrABAllADwD///AMAcHFxkd0ESUxMRMuWLWXz63Ijxt/fHzExMQgICEBISAhefPFFNGvWDBcuXMDy5csxefJkvPTSS1i6dClCQkKwZcsWODs7SzFRD7qmLQMDgzLvmbexsYGJiYmsXFNMgIdJ34ABA2BgYIBdu3YhPDy8zOMXp06dgrGxsVaPf+hLff7N1fbYa/ObS0RERE+PWh+NPCIiAi+88AL8/PzQv39/eHp6okuXLjWyrUWLFmHevHkICwuDi4sLfH19sWfPHqnlqEWLFliwYAFmz54NW1tbqUtnZTp16oRly5ZhyZIleO655xAVFYWwsDCd6zd27FisW7cOGzduRMeOHdG7d29s3LhR1oo4btw47Nu3TxoNGgCuX78OlUoFlUqFrKwsREZGQqVSyd7HW1BQgJiYGNmziZrExsZi8ODBsoTO0dERO3fuxJ49e+Du7o41a9ZIz5vWtOXLl8Pf3x99+/bFlStXAEB6zVlQUBC6du2Ka9euIT4+XpaAaJKbm4vLly9XOM+zzz6Ln376CX369MH777+P5557Di+++CIOHz4sS5C1defOHbzzzjvo0KEDevXqhR07duDrr7+WHZtBgwbByMgI33//vVRmaGiIJUuWoFu3bnBzc0NoaCgmTZqEdevWydYfHR1d6TGtiLGxMQ4dOoRZs2bhiy++QI8ePdC1a1d8+umnmDJlipTsff7558jNzYW3tzeaN28ufbZt26bzNm/duoXx48fDxcVFejfx0aNH0a1bN2mejh07wsPDQ+fWSqD6MVEoFNiyZQuWLVuGmJgY9O7dWzoGw4YNg6+vL4CHN2aKiorw6quvymISGRlZ5W1XpLyYfPfdd/Dy8oKHhwf27t2L2NjYMo9BREdHY+TIkTAzM6uRupWnvv7manPsMzMzceLECYwePVrnehEREVH9pBDVeZD6CZGQkIA+ffogJydHL91+nzT+/v5QqVQIDg7WeplVq1YhNjYWBw8eLDNt1KhRuHXrFnbv3g03NzfMnTsX/v7++qyyRt7e3ujUqRNWrFhRI+sPDQ3F7t27Zc8wP6lWr16N2NhYHDhwQOtl9u7di5kzZ+L8+fOymyP1xb59+zBjxgykpKRo3dslJSUF/fr1w5UrV8p9RVddVpWY/P3332jfvj2SkpL02v3/aVKV39yZM2ciNzcXX375pdbLqB9RyM3NhaWlZVWqSkREdczyQ1e0nnfai+UPpEqPjy7/f9d6y3ZNsre3R0BAwOOuht5FRETo1E0VePhMaun33wIPuzgqlUpERUUBeNgFffjw4Rg4cKDe6lqZ1atXQ6lUlnlmtToyMjKgVCprrfVdH8aPH48XXngB+fn5Wi9z584dbNiwoV4m2sDDFv8JEyYgMzNT62WuX7+OTZs21ctEG6haTH7//XesXr2aiXY1VOU318bGBosWLaqhGhEREVFdVC9atu/duyf9MapUKmFnZ/eYa/RketxxyszMlAa8cnR0lAZLqq7i4mKkp6cDeNhNmq/SIaK6gC3bRERPH7Zs1326/P9dL5rITE1Nyx19mf7P445TRa8Yqo6KRt8mIiIiIiJ6HOpVN3IiIiIiIiKiJwGTbSIiIiIiIiI9Y7JNREREREREpGdMtomIiIiIiIj0rF4MkEZERERERPSk4wjjTxe2bBMRERERERHpGZNtIiIiIiIiIj1jsk1ERERERESkZ0y2iYiIiIiIiPSMyTYRERERERGRnjHZJiIiIiIiItIzJttEREREREREesZkm4iIiIiIiEjPmGwTERERERER6RmTbSIiIiIiIiI9Y7JNREREREREpGdMtomIiIiIiIj0jMk2ERERERERkZ4x2SYiIiIiIiLSMybbRERERERERHpm+LgrQERE9DQSQgAA8vLyHnNNiIiISFvq/7fV/49XhMk2ERHRY5Cfnw8AcHBweMw1ISIiIl3l5+ejUaNGFc6jENqk5ERERKRXJSUluH79OiwsLKBQKGp0W3l5eXBwcMAff/wBS0vLGt3W04oxrlmMb81jjGseY1yzaiu+Qgjk5+fjmWeegYFBxU9ls2WbiIjoMTAwMIC9vX2tbtPS0pJ/4NUwxrhmMb41jzGueYxxzaqN+FbWoq3GAdKIiIiIiIiI9IzJNhEREREREZGeNQgNDQ193JUgIiKimtWgQQN4e3vD0JBPkNUUxrhmMb41jzGueYxxzXrS4ssB0oiIiIiIiIj0jN3IiYiIiIiIiPSMyTYRERERERGRnjHZJiIiIiIiItIzJttERET1UE5ODoKCgtCoUSM0atQIQUFBuHXrVoXL3LhxA0FBQbCzs4O5uTk6d+6MHTt21FKN65aqxBcATp48ib59+8Lc3ByNGzeGt7c37t27Vws1rnuqGmMAEEJg4MCBUCgU2L17dw3XtO7SNcbZ2dmYPHky2rVrBzMzMzg6OmLKlCnIzc2txVo/2VavXo1nn30WJiYm6NKlCxITEyucf+fOnXB1dYWxsTFcXV0RExNTSzWtm3SJ79q1a+Hl5QUrKytYWVmhf//+OHPmTC3Wlsk2ERFRvRQYGIjk5GTs378f+/fvR3JyMoKCgipcJigoCJcvX0ZcXBwuXLiAV155Ba+//jrOnTtXS7WuO6oS35MnT2LAgAHw8fHBmTNn8OOPP+Ldd9+FgQH/HNOkKjFWW7FiBRQKRQ3XsO7TNcbXr1/H9evXERkZiQsXLmDjxo3Yv38/xowZU4u1fnJt27YNU6dOxZw5c3Du3Dl4eXlh4MCByMjI0Dj/yZMn8frrryMoKAg///wzgoKC4O/vj9OnT9dyzesGXeObkJCAgIAAHDlyBCdPnoSjoyN8fHyQmZlZe5UWREREVK+kpqYKAOLUqVNS2cmTJwUA8csvv5S7nLm5udi0aZOsrEmTJmLdunU1Vte6qKrx7d69u5g7d25tVLHOq2qMhRAiOTlZ2Nvbi6ysLAFAxMTE1HR166TqxLi07du3i4YNG4r79+/XRDXrlG7duomJEyfKytq3by9mz56tcX5/f38xYMAAWZmvr68YMWJEjdWxLtM1vo8qLi4WFhYW4quvvqqJ6mnEW6lERET1zMmTJ9GoUSN0795dKuvRowcaNWqEEydOlLucp6cntm3bhuzsbJSUlGDr1q0oLCyEt7d3LdS67qhKfG/evInTp0/DxsYGvXr1gq2tLXr37o1jx47VVrXrlKqew3fv3kVAQAA+++wz2NnZ1UZV66yqxvhRubm5sLS0fGLea/y4FBUV4ezZs/Dx8ZGV+/j4lBvPkydPlpnf19dXp/g/LaoS30fdvXsX9+/fR5MmTWqiihox2SYiIqpnbty4ARsbmzLlNjY2uHHjRrnLbdu2DcXFxWjatCmMjY0xYcIExMTEoFWrVjVZ3TqnKvH97bffAAChoaEYN24c9u/fj86dO6Nfv3749ddfa7S+dVFVz+Fp06ahV69eGDZsWE1Wr16oaoxL+/fff7Fo0SJMmDBB39Wrc/755x88ePAAtra2snJbW9ty43njxg2d5n+aVSW+j5o9ezZatGiB/v3710QVNWKyTUREVEeEhoZCoVBU+ElKSgIAjc+rCiEqfI517ty5yMnJwffff4+kpCRMnz4dr732Gi5cuFBj+/Qkqcn4lpSUAAAmTJiA0aNHQ6VSYfny5WjXrh3+97//1dxOPWFqMsZxcXGIj4/HihUranQfnnQ1/TuhlpeXh8GDB8PV1RXz58/X+37UVY/GrrJ46jr/066q8Vq6dCmio6Oxa9cumJiY1FT1yni6+3sQERHVIe+++y5GjBhR4TzOzs44f/48/vrrrzLT/v777zKtAmppaWn47LPPkJKSgg4dOgAA3N3dkZiYiFWrVmHNmjXV34EnXE3Gt3nz5gAAV1dXWbmLi0u5g/vURzUZ4/j4eKSlpaFx48ay8uHDh8PLywsJCQlVrnddUpMxVsvPz8eAAQOgVCoRExMDIyOjatW5PrC2tkaDBg3KtLLevHmz3Hja2dnpNP/TrCrxVYuMjMTixYvx/fffw83NrSarWQaTbSIiojrC2toa1tbWlc7Xs2dP5Obm4syZM+jWrRsA4PTp08jNzUWvXr00LnP37l0AKDMydoMGDaRW2fquJuPr7OyMZ555BpcvX5aVX7lyBQMHDqx+5euImozx7NmzMXbsWFlZx44dsXz5cgwdOrT6la8jajLGwMMWbV9fXxgbGyMuLq5WWwmfZA0bNkSXLl1w6NAhvPzyy1L5oUOHyn2soWfPnjh06BCmTZsmlR08eLDC+D+tqhJfAIiIiMCHH36IAwcOwMPDozaqKldrQ7ERERFRrRkwYIBwc3MTJ0+eFCdPnhQdO3YUQ4YMkab/+eefol27duL06dNCCCGKiopE69athZeXlzh9+rS4evWqiIyMFAqFQuzdu/dx7cYTS9f4CiHE8uXLhaWlpfjmm2/Er7/+KubOnStMTEzE1atXH8cuPPGqEuNHgaORV0jXGOfl5Ynu3buLjh07iqtXr4qsrCzpU1xc/Lh244mxdetWYWRkJNavXy9SU1PF1KlThbm5uUhPTxdCCBEUFCQbOfv48eOiQYMGIjw8XFy6dEmEh4cLQ0ND2Qjx9H90je+SJUtEw4YNxY4dO2Tnan5+fq3Vmck2ERFRPfTvv/+KkSNHCgsLC2FhYSFGjhwpcnJypOm///67ACCOHDkilV25ckW88sorwsbGRpiZmQk3N7cyrwKjh6oSXyGECAsLE/b29sLMzEz07NlTJCYm1nLN646qxrg0JtsV0zXGR44cEQA0fn7//ffHsxNPmFWrVgknJyfRsGFD0blzZ/HDDz9I03r37i3eeust2fzffPONaNeunTAyMhLt27cXO3furOUa1y26xNfJyUnjuTp//vxaq69CCCFqvTmdiIiIiIiIqB7jaOREREREREREesZkm4iIiIiIiEjPmGwTERERERER6RmTbSIiIiIiIiI9Y7JNREREREREpGdMtomIiIiIiIj0jMk2ERERERERkZ4x2SYiIiIiIiLSMybbRERERFSneHt7Y+rUqQAAZ2dnrFixQud1hISEYPz48VrNe/PmTTRr1gyZmZk6byc9PR0KhQLJyckAgISEBCgUCty6dUun9RQVFaF169Y4fvy4xvXqSqFQYPfu3QCqt39EVD4m20RERET0VPnrr7/wySef4L///a9W89vY2CAoKAjz58/XOH3UqFGYPXu2PqtYxpdffgknJyc8//zzellfVlYWBg4cCKDy/SOiqmGyTURERERPlfXr16Nnz55wdnbWepnRo0cjKioKOTk5svKSkhLs3bsXw4YN03Mt5VauXImxY8fqbX12dnYwNjaWvpe3ReaabQAABg9JREFUf0RUdUy2iYiIiOiJdefOHbz55ptQKpVo3rw5Pv744wrnVygUWLduHV5++WWYmZmhTZs2iIuLk82zdetW+Pn5ycpKSkqwZMkStG7dGsbGxnB0dMRHH30kTe/YsSPs7OwQExMjW+748eMwMDBA9+7dAQBnzpyBSqWCiYkJPDw8cO7cuQrru3HjRjRu3BgHDhyAi4sLlEolBgwYgKysLGmen376CVevXsXgwYPLXU9JSQnGjRuHtm3b4tq1axVuE5B3I69o/4io6phsExEREdETa+bMmThy5AhiYmJw8OBBJCQk4OzZsxUus2DBAvj7++P8+fMYNGgQRo4ciezsbABATk4OUlJS4OHhIVsmODgYS5YsQUhICFJTU7FlyxbY2trK5unWrRsSExNlZXFxcRg6dCgMDAxw584dDBkyBO3atcPZs2cRGhqKGTNmVLqPd+/eRWRkJDZv3oyjR48iIyNDttzRo0fRtm1bWFpaaly+qKgI/v7+SEpKwrFjx+Dk5FTpNjXRtH9EVHWGj7sCRERERESa3L59G+vXr8emTZvw4osvAgC++uor2NvbV7jcqFGjEBAQAABYvHgxVq5ciTNnzmDAgAG4du0ahBB45plnpPnz8/PxySef4LPPPsNbb70FAGjVqhU8PT1l623RokWZluq4uDhERkYCAKKiovDgwQP873//g5mZGTp06IA///wT//nPfyqs7/3797FmzRq0atUKAPDuu+9i4cKF0vT09HRZfR+N0eDBg3Hv3j0kJCSgUaNGFW6rIpr2j4iqji3bRERERPRESktLQ1FREXr27CmVNWnSBO3atatwOTc3N+nf5ubmsLCwwM2bNwEA9+7dAwCYmJhI81y6dAmFhYXo169fhes1NTXF3bt3Zcv9+eef6N+/v/Td3d0dZmZm0jyl614eMzMzKdEGgObNm0v1Vde5dH1LCwgIwO3bt3Hw4MFqJdpA2f0jouphsk1ERERETyQhRJWWMzIykn1XKBQoKSkBAFhbWwOAbCAwU1NTrdabnZ2NZs2aSd/j4uLw4osvSsvrs76l12VtbV3uwGWDBg3C+fPncerUqSptu7RH94+IqofJNhERERE9kVq3bg0jIyNZIpmTk4MrV65UeZ2tWrWCpaUlUlNTpbI2bdrA1NQUhw8frnDZlJQUqFQq6XtsbKxsoDVXV1f8/PPPUus5AL0kwSqVCr/88ovGZP4///kPwsPD4efnhx9++KFa23l0/4ioephsExEREdETSalUYsyYMZg5cyYOHz6MlJQUjBo1CgYGVf8T1sDAAP3798exY8ekMhMTE3zwwQeYNWsWNm3ahLS0NJw6dQrr16+X5rl79y7Onj0LHx8fAMDNmzfx448/YsiQIdI8gYGBMDAwwJgxY5Camop9+/ZJz3NXR58+fXDnzh1cvHhR4/TJkyfjww8/xJAhQ2T7pYtH94+Iqo/JNhERERE9sSIiIvDCCy/Az88P/fv3h6enJ7p06VKtdY4fPx5bt26VupYDQEhICN5//33MmzcPLi4ueP3112XPTcfGxsLR0RFeXl4AgD179qB79+6wsbGR5lEqldizZw9SU1OhUqkwZ84cLFmypFp1BYCmTZvilVdeQVRUVLnzTJ06FQsWLMCgQYNw4sQJnbfx6P4RUfUpRFUfLiEiIiIiqoOEEOjRowemTp0qjVpemW7dumHq1KkIDAwEAPj5+cHT0xOzZs2qyapKLly4gP79++Pq1auwsLDQ+/of3T8iqr4GoaGhoY+7EkREREREtUWhUKBbt27IysqCu7t7pfPfvHkTRUVFmDBhAhQKBQDgjz/+QEBAQLVHANeWra0tbGxsYGpqWub939Wlaf+IqPrYsk1EREREVI9ERUVhwoQJGqc5OTmV++w3EekXk20iIiIionokPz8ff/31l8ZpRkZGcHJyquUaET2dmGwTERERERER6RlHIyciIiIiIiLSMybbRERERERERHrGZJuIiIiIiIhIz5hsExEREREREekZk20iIiIiIiIiPWOyTURERERERKRnTLaJiIiIiIiI9IzJNhEREREREZGe/T9226dnFZBlVAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Let's also compare against the same simulation and sensitivity analysis that was conducted in CHEMKIN\n", "# and saved as a .csv file\n", @@ -383,18 +179,11 @@ "print('CHEMKIN Simulation: Ethane Reaction Sensitivity')\n", "display(Image(filename=\"./temp/chemkin_sensitivity_ethane.png\"))" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -408,9 +197,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.9.23" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } From 2fb3b78ed2795f852bdb2fa478d1775952ca65c6 Mon Sep 17 00:00:00 2001 From: jonwzheng Date: Mon, 30 Jun 2025 13:27:21 -0400 Subject: [PATCH 06/17] fix keywords --- ipython/cantera_sensitivity_comparison.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ipython/cantera_sensitivity_comparison.ipynb b/ipython/cantera_sensitivity_comparison.ipynb index 4cb8cabe6c..26aea69782 100644 --- a/ipython/cantera_sensitivity_comparison.ipynb +++ b/ipython/cantera_sensitivity_comparison.ipynb @@ -103,7 +103,8 @@ "#job.load_chemkin_model('data/ethane_model/chem_annotated.inp',transport_file='data/ethane_model/tran.dat')\n", "\n", "# Generate the conditions based on the settings we declared earlier\n", - "job.generate_conditions(reactor_type_list, reaction_time_list, mol_frac_list, Tlist=Tlist, Plist=Plist)" + "job.generate_conditions(reactor_type_list=reactor_type_list, reaction_time_list=reaction_time_list, \n", + " mol_frac_list=mol_frac_list, Tlist=Tlist, Plist=Plist)" ] }, { From 82d49413c27de3d12f59a884a6fc3f34b6394280 Mon Sep 17 00:00:00 2001 From: jonwzheng Date: Mon, 30 Jun 2025 13:59:29 -0400 Subject: [PATCH 07/17] update cantera notebooks --- ipython/cantera_simulation.ipynb | 18 +++---- rmgpy/tools/canteramodel.py | 88 ++++++++++++++++++-------------- 2 files changed, 56 insertions(+), 50 deletions(-) diff --git a/ipython/cantera_simulation.ipynb b/ipython/cantera_simulation.ipynb index 188879bbff..446eb39740 100644 --- a/ipython/cantera_simulation.ipynb +++ b/ipython/cantera_simulation.ipynb @@ -80,7 +80,8 @@ "#job.load_chemkin_model('data/ethane_model/chem_annotated.inp',transport_file='data/ethane_model/tran.dat')\n", "\n", "# Generate the conditions based on the settings we declared earlier\n", - "job.generate_conditions(reactor_type_list, reaction_time_list, mol_frac_list, Tlist, Plist)" + "job.generate_conditions(reactor_type_list=reactor_type_list, reaction_time_list=reaction_time_list, \n", + " mol_frac_list=mol_frac_list, Tlist=Tlist, Plist=Plist)" ] }, { @@ -170,20 +171,13 @@ " print('Condition {0}'.format(i+1))\n", " display(Image(filename=\"temp/{0}_mole_fractions.png\".format(i+1)))" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:rmg_env]", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "conda-env-rmg_env-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -195,9 +189,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.9.23" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/rmgpy/tools/canteramodel.py b/rmgpy/tools/canteramodel.py index 15d26c2947..e3698aad71 100644 --- a/rmgpy/tools/canteramodel.py +++ b/rmgpy/tools/canteramodel.py @@ -175,54 +175,66 @@ def generate_cantera_conditions(reactor_type_list, reaction_time_list, mol_frac_ This saves all the reaction conditions into the Cantera class. """ - # Create individual ScalarQuantity objects for Tlist, Plist, Vlist, and reaction_time_list - if Tlist: - Tlist = Quantity(Tlist) # Be able to create a Quantity object from it first - Tlist = [(Tlist.value[i], Tlist.units) for i in range(len(Tlist.value))] - if Plist: - Plist = Quantity(Plist) - Plist = [(Plist.value[i], Plist.units) for i in range(len(Plist.value))] - if Vlist: - Vlist = Quantity(Vlist) - Vlist = [(Vlist.value[i], Vlist.units) for i in range(len(Vlist.value))] - if reaction_time_list: - reaction_time_list = Quantity(reaction_time_list) - reaction_time_list = [(reaction_time_list.value[i], reaction_time_list.units) - for i in range(len(reaction_time_list.value))] + def convert_to_quantity_list(input_list): + """ + Convert an input list to a list of (value, unit) tuples using Quantity. + """ + if not input_list: + return None + quantity = Quantity(input_list) + return [(quantity.value[i], quantity.units) for i in range(len(quantity.value))] + + # Convert input lists to quantity lists + Tlist = convert_to_quantity_list(Tlist) + Plist = convert_to_quantity_list(Plist) + Vlist = convert_to_quantity_list(Vlist) + reaction_time_list = convert_to_quantity_list(reaction_time_list) conditions = [] if surface_mol_frac_list is None: - surface_mol_frac_list = [] # initialize here to avoid mutable default argument - - if Tlist is None: - for reactor_type in reactor_type_list: - for reaction_time in reaction_time_list: - for mol_frac in mol_frac_list: - for surface_mol_frac in surface_mol_frac_list: + surface_mol_frac_list = [None] + + for reactor_type in reactor_type_list: + for reaction_time in reaction_time_list: + for mol_frac in mol_frac_list: + for surface_mol_frac in surface_mol_frac_list: + # Handle the three possible cases where one of T,P,V must be None + if Tlist is None: for P in Plist: for V in Vlist: - conditions.append(CanteraCondition(reactor_type, reaction_time, mol_frac, surface_mol_frac=surface_mol_frac, P0=P, V0=V)) - - elif Plist is None: - for reactor_type in reactor_type_list: - for reaction_time in reaction_time_list: - for mol_frac in mol_frac_list: - for surface_mol_frac in surface_mol_frac_list: + conditions.append(CanteraCondition( + reactor_type=reactor_type, + reaction_time=reaction_time, + mol_frac=mol_frac, + surface_mol_frac=surface_mol_frac, + P0=P, + V0=V + )) + elif Plist is None: for T in Tlist: for V in Vlist: - conditions.append(CanteraCondition(reactor_type, reaction_time, mol_frac, surface_mol_frac=surface_mol_frac, T0=T, V0=V)) - - elif Vlist is None: - for reactor_type in reactor_type_list: - for reaction_time in reaction_time_list: - for mol_frac in mol_frac_list: - for surface_mol_frac in surface_mol_frac_list: + conditions.append(CanteraCondition( + reactor_type=reactor_type, + reaction_time=reaction_time, + mol_frac=mol_frac, + surface_mol_frac=surface_mol_frac, + T0=T, + V0=V + )) + elif Vlist is None: for T in Tlist: for P in Plist: - conditions.append(CanteraCondition(reactor_type, reaction_time, mol_frac, surface_mol_frac=surface_mol_frac, T0=T, P0=P)) + conditions.append(CanteraCondition( + reactor_type=reactor_type, + reaction_time=reaction_time, + mol_frac=mol_frac, + surface_mol_frac=surface_mol_frac, + T0=T, + P0=P + )) - else: - raise Exception("Cantera conditions must leave one of T0, P0, and V0 state variables unspecified") + else: + raise Exception("Cantera conditions must leave one of T0, P0, and V0 state variables unspecified") return conditions From ed910a6094461dbcbc19fc9c9f45362552e36d71 Mon Sep 17 00:00:00 2001 From: jonwzheng Date: Mon, 30 Jun 2025 13:59:51 -0400 Subject: [PATCH 08/17] update np.float from cksv parser --- rmgpy/tools/ckcsvparser.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rmgpy/tools/ckcsvparser.py b/rmgpy/tools/ckcsvparser.py index 181f1812b0..ea4b24f5ab 100644 --- a/rmgpy/tools/ckcsvparser.py +++ b/rmgpy/tools/ckcsvparser.py @@ -150,7 +150,7 @@ def get_concentration_dict_from_ckcsv(ckcsv_file): units = row[1].strip()[1:-1].lower() header = tokens[0] + '_(' + units + ')' - content_col = np.array([float(r) for r in row[2:]], np.float) + content_col = np.array([float(r) for r in row[2:]], np.float64) content_col *= {'cm3': 1.0, 'm3': 1.00e+6}[units] first_col_dict[header] = content_col continue From 09b90c543194c0240d5ef66bde85c39d11973fe6 Mon Sep 17 00:00:00 2001 From: jonwzheng Date: Mon, 30 Jun 2025 14:18:27 -0400 Subject: [PATCH 09/17] update combustion demo --- ...ustion_model_and_ignition_delay_demo.ipynb | 36 +++++++------------ 1 file changed, 13 insertions(+), 23 deletions(-) diff --git a/ipython/combustion_model_and_ignition_delay_demo.ipynb b/ipython/combustion_model_and_ignition_delay_demo.ipynb index 2b3ff36d16..96e64a7f79 100644 --- a/ipython/combustion_model_and_ignition_delay_demo.ipynb +++ b/ipython/combustion_model_and_ignition_delay_demo.ipynb @@ -185,7 +185,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "import time\n", @@ -245,12 +247,13 @@ "mol_frac_list=[{species_dict[fuel_species]: fuel_stoich,\n", " species_dict[O2_species]: 1,\n", " species_dict[N2_species]: 3.76}]\n", - "T_list = ([temperature],'K')\n", - "P_list = ([pressure],'atm')\n", + "Tlist = ([temperature],'K')\n", + "Plist = ([pressure],'atm')\n", "\n", "job = Cantera(species_list=species_list, reaction_list=reaction_list, output_directory=directory)\n", "job.load_chemkin_model(os.path.join(chem_path, 'chem_annotated.inp'), os.path.join(chem_path, 'tran.dat'))\n", - "job.generate_conditions(reactor_type_list, reaction_time_list, mol_frac_list, T_list, P_list)\n", + "job.generate_conditions(reactor_type_list=reactor_type_list, reaction_time_list=reaction_time_list, \n", + " mol_frac_list=mol_frac_list, Tlist=Tlist, Plist=Plist)\n", "\n", "alldata = job.simulate()\n", "print(\"Simulation Completed\")" @@ -266,9 +269,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "############### Settings ###############\n", @@ -280,7 +281,6 @@ "import pandas as pd\n", "from rmgpy.tools import plot as rmg_plot\n", "from operator import itemgetter\n", - "%matplotlib notebook\n", "\n", "times = alldata[0][0].data\n", "temperatures = alldata[0][1][0].data\n", @@ -353,9 +353,6 @@ "plt.rcParams['ytick.labelsize'] = 12\n", "plt.rcParams['figure.autolayout'] = True\n", "\n", - "plt.style.use('ggplot')\n", - "plt.style.use('seaborn-pastel')\n", - "\n", "fig = plt.figure(figsize=fsize)\n", "\n", "plt.subplot(1,2,1)\n", @@ -418,7 +415,7 @@ "outputs": [], "source": [ "import cantera as ct\n", - "gas = ct.Solution(os.path.join(directory, 'cantera', 'chem.cti'))\n", + "gas = ct.Solution(os.path.join(directory, 'cantera', 'chem.yaml'))\n", "comp = str(species_dict[fuel_species])+\":\"+str(fuel_stoich)+\",\"+str(species_dict[O2_species])+\":1,\"+str(species_dict[N2_species])+\":3.76\"\n", "gas.TPX = temperature, pressure, comp\n", "reactor = ct.IdealGasConstPressureReactor(gas)\n", @@ -439,21 +436,14 @@ "fullpath = os.getcwd() + '/' + img_file\n", "display(Image(fullpath))" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [conda env:rmg_env]", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "conda-env-rmg_env-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -465,9 +455,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.9.23" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 924f5049f8452e72e760ee120dad627828d83c49 Mon Sep 17 00:00:00 2001 From: jonwzheng Date: Mon, 30 Jun 2025 14:25:14 -0400 Subject: [PATCH 10/17] initial fix to parameter uncertainty assignment --- ...ter_sources_and_assign_uncertainties.ipynb | 29 ++++++------------- 1 file changed, 9 insertions(+), 20 deletions(-) diff --git a/ipython/find_parameter_sources_and_assign_uncertainties.ipynb b/ipython/find_parameter_sources_and_assign_uncertainties.ipynb index ba841a86b9..3bac96c3e8 100644 --- a/ipython/find_parameter_sources_and_assign_uncertainties.ipynb +++ b/ipython/find_parameter_sources_and_assign_uncertainties.ipynb @@ -38,7 +38,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "scrolled": false + "scrolled": true }, "outputs": [], "source": [ @@ -58,8 +58,8 @@ "metadata": {}, "outputs": [], "source": [ - "chem_file = './data/parse_source/chem_annotated.inp'\n", - "dict_file = './data/parse_source/species_dictionary.txt'" + "chem_file = './data/parse_source/chemkin/chem_annotated.inp'\n", + "dict_file = './data/parse_source/chemkin/species_dictionary.txt'" ] }, { @@ -108,9 +108,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "print('All Kinetic Sources')\n", @@ -349,28 +347,19 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "# See the kinetics correlated parameter partial uncertainties\n", "uncertainty.kinetic_input_uncertainties" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:rmg_env]", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "conda-env-rmg_env-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -382,9 +371,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.9.23" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } From fb13da2270893e20c64c08f1e410e58551449361 Mon Sep 17 00:00:00 2001 From: jonwzheng Date: Mon, 30 Jun 2025 14:35:22 -0400 Subject: [PATCH 11/17] clear local uncertainty --- ipython/local_uncertainty.ipynb | 25 ++++++++----------------- 1 file changed, 8 insertions(+), 17 deletions(-) diff --git a/ipython/local_uncertainty.ipynb b/ipython/local_uncertainty.ipynb index 659773e4c5..3d655f1358 100644 --- a/ipython/local_uncertainty.ipynb +++ b/ipython/local_uncertainty.ipynb @@ -41,12 +41,12 @@ "# This is a small ethane pyrolysis model\n", "\n", "# Must use annotated chemkin file\n", - "chemkin_file = 'data/parse_source/chem_annotated.inp'\n", - "dict_file = 'data/parse_source/species_dictionary.txt'\n", + "chemkin_file = 'data/parse_source/chemkin/chem_annotated.inp'\n", + "dict_file = 'data/parse_source/chemkin/species_dictionary.txt'\n", "\n", "# Initialize the Uncertainty class instance and load the model\n", "uncertainty = Uncertainty(output_directory='./temp/uncertainty')\n", - "uncertainty.load_model(chemkin_file, dict_file)\n", + "uncertainty.load_model(chemkin_file, dict_file, use_chemkin_names=True)\n", "\n", "# Map the species to the objects within the Uncertainty class\n", "ethane = Species().from_smiles('CC')\n", @@ -83,9 +83,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "# Show the sensitivity plots\n", @@ -258,20 +256,13 @@ " print('{}: Reaction Uncertainty Contributions'.format(species))\n", " display(Image(filename=os.path.join(uncertainty.output_directory, 'correlated', 'kineticsLocalUncertainty_{}.png'.format(species.to_chemkin()))))" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:rmg_env]", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "conda-env-rmg_env-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -283,9 +274,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.9.23" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } From ebb63a9694908b19024e5ec9829b8f008212024e Mon Sep 17 00:00:00 2001 From: jonwzheng Date: Mon, 30 Jun 2025 15:00:26 -0400 Subject: [PATCH 12/17] update regression test --- rmgpy/tools/observablesregression.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/rmgpy/tools/observablesregression.py b/rmgpy/tools/observablesregression.py index 45e05b5460..b8e333301e 100644 --- a/rmgpy/tools/observablesregression.py +++ b/rmgpy/tools/observablesregression.py @@ -131,12 +131,14 @@ def __init__(self, title='', old_dir='', new_dir='', observables=None, expt_data old_species_list, old_reaction_list = load_chemkin_file( old_chemkin_path, old_species_dict_path, - old_transport_path + old_transport_path, + use_chemkin_names = True ) new_species_list, new_reaction_list = load_chemkin_file( new_chemkin_path, new_species_dict_path, - new_transport_path + new_transport_path, + use_chemkin_names = True ) old_surface_species_list = None From 3422f7fcb0b3f59c97f5c0d45e9bf2b4a2599a3b Mon Sep 17 00:00:00 2001 From: jonwzheng Date: Thu, 3 Jul 2025 15:28:36 -0400 Subject: [PATCH 13/17] fix circular import error when running generateFluxDiagram --- rmgpy/tools/fluxdiagram.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rmgpy/tools/fluxdiagram.py b/rmgpy/tools/fluxdiagram.py index 81ecf44f89..e3549a3f78 100644 --- a/rmgpy/tools/fluxdiagram.py +++ b/rmgpy/tools/fluxdiagram.py @@ -40,7 +40,6 @@ import numpy as np import pydot -from rmgpy.kinetics.diffusionLimited import diffusion_limiter from rmgpy.rmg.settings import SimulatorSettings from rmgpy.solver.base import TerminationConversion, TerminationTime from rmgpy.solver.liquid import LiquidReactor @@ -541,6 +540,7 @@ def create_flux_diagram(input_file, chemkin_file, species_dict, save_path=None, Generates the flux diagram based on a condition 'input_file', chemkin.inp chemkin_file, a species_dict txt file, plus an optional chemkin_output file. """ + from rmgpy.kinetics.diffusionLimited import diffusion_limiter if species_path is None: species_path = os.path.join(os.path.dirname(input_file), 'species') generate_images = True From e5a032bf887218b84d2457faf9772c302887ed59 Mon Sep 17 00:00:00 2001 From: jonwzheng Date: Wed, 9 Jul 2025 16:58:57 -0400 Subject: [PATCH 14/17] fix local uncertainty notebook Also add the two small simulation outputs so that the user doesn't have to rerun the job. --- ipython/data/parse_source/chem_annotated.inp | 791 ++++++++++++++++++ .../data/parse_source/species_dictionary.txt | 238 ++++++ ipython/local_uncertainty.ipynb | 6 +- 3 files changed, 1032 insertions(+), 3 deletions(-) create mode 100644 ipython/data/parse_source/chem_annotated.inp create mode 100644 ipython/data/parse_source/species_dictionary.txt diff --git a/ipython/data/parse_source/chem_annotated.inp b/ipython/data/parse_source/chem_annotated.inp new file mode 100644 index 0000000000..6f23b5fff3 --- /dev/null +++ b/ipython/data/parse_source/chem_annotated.inp @@ -0,0 +1,791 @@ +ELEMENTS + H + D /2.014/ + T /3.016/ + C + CI /13.003/ + O + OI /17.999/ + N + Ne + Ar + He + Si + S + F + Cl + Br + I + X /195.083/ +END + +SPECIES + Ar ! Ar + He ! He + Ne ! Ne + N2 ! N2 + ethane(1) ! ethane(1) + C(3) ! C(3) + [CH3](4) ! [CH3](4) + C[CH2](5) ! C[CH2](5) + [H](6) ! [H](6) + C2H4(9) ! C=C(9) + [H][H](13) ! [H][H](13) + C2H3(14) ! [CH]=C(14) + [CH2]CC(16) ! [CH2]CC(16) + C#C(20) ! C#C(20) + C4H7(24) ! [CH2]CC=C(24) + C4H6(25) ! C=CC=C(25) + C2H2(27) ! [C]=C(27) + C3H5(28) ! [CH]=CC(28) + C4H7(29) ! [CH]=CCC(29) + C4H5(31) ! [CH]=CC=C(31) + [CH2]C1CC1(46) ! [CH2]C1CC1(46) + C4H6(47) ! [CH2]C=C[CH2](47) + C4H7(49) ! [CH2]C=CC(49) + CC1[CH]C1(86) ! CC1[CH]C1(86) + C4H6(115) ! C=C=CC(115) + C4H6(200) ! C1=CCC1(200) +END + + + +THERM ALL + 300.000 1000.000 5000.000 + +! Thermo library: primaryThermoLibrary +Ar Ar 1 G 200.000 6000.000 1000.00 1 + 2.50000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 2 +-7.45375000E+02 4.37967000E+00 2.50000000E+00 0.00000000E+00 0.00000000E+00 3 + 0.00000000E+00 0.00000000E+00-7.45375000E+02 4.37967000E+00 4 + +! Thermo library: primaryThermoLibrary +He He 1 G 200.000 6000.000 1000.00 1 + 2.50000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 2 +-7.45375000E+02 9.28724000E-01 2.50000000E+00 0.00000000E+00 0.00000000E+00 3 + 0.00000000E+00 0.00000000E+00-7.45375000E+02 9.28724000E-01 4 + +! Thermo library: primaryThermoLibrary +Ne Ne 1 G 200.000 6000.000 1000.00 1 + 2.50000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 0.00000000E+00 2 +-7.45375000E+02 3.35532000E+00 2.50000000E+00 0.00000000E+00 0.00000000E+00 3 + 0.00000000E+00 0.00000000E+00-7.45375000E+02 3.35532000E+00 4 + +! Thermo library: primaryThermoLibrary +N2 N 2 G 200.000 6000.000 1000.00 1 + 2.95258000E+00 1.39690000E-03-4.92632000E-07 7.86010000E-11-4.60755000E-15 2 +-9.23949000E+02 5.87189000E+00 3.53101000E+00-1.23661000E-04-5.02999000E-07 3 + 2.43531000E-09-1.40881000E-12-1.04698000E+03 2.96747000E+00 4 + +! Thermo group additivity estimation: group(Cs-CsHHH) + group(Cs-CsHHH) +ethane(1) C 2H 6 G 100.000 5000.000 954.51 1 + 4.58983307E+00 1.41507715E-02-4.75962003E-06 8.60293917E-10-6.21716348E-14 2 +-1.27217663E+04-3.61740116E+00 3.78033462E+00-3.24262480E-03 5.52380397E-05 3 +-6.38580942E-08 2.28636966E-11-1.16203409E+04 5.21033695E+00 4 + +! Thermo library: primaryThermoLibrary +C(3) C 1H 4 G 100.000 5000.000 1084.12 1 + 9.08259430E-01 1.14540962E-02-4.57174412E-06 8.29193029E-10-5.66316007E-14 2 +-9.71997168E+03 1.39931301E+01 4.20541633E+00-5.35558620E-03 2.51123688E-05 3 +-2.13763364E-08 5.97526027E-12-1.01619434E+04-9.21283218E-01 4 + +! Thermo library: primaryThermoLibrary + radical(CH3) +[CH3](4) C 1H 3 G 100.000 5000.000 1337.63 1 + 3.54145742E+00 4.76786844E-03-1.82148431E-06 3.28876598E-10-2.22545603E-14 2 + 1.62239579E+04 1.66035007E+00 3.91546761E+00 1.84154318E-03 3.48741774E-06 3 +-3.32747622E-09 8.49956934E-13 1.62856394E+04 3.51741472E-01 4 + +! Thermo group additivity estimation: group(Cs-CsHHH) + group(Cs-CsHHH) + radical(CCJ) +C[CH2](5) C 2H 5 G 100.000 5000.000 900.31 1 + 5.15617570E+00 9.43128370E-03-1.81949426E-06 2.21204013E-10-1.43488224E-14 2 + 1.20640959E+04-2.91080147E+00 3.82184856E+00-3.43376314E-03 5.09263340E-05 3 +-6.20220235E-08 2.37077381E-11 1.30660124E+04 7.61638915E+00 4 + +! Thermo library: primaryThermoLibrary +[H](6) H 1 G 100.000 5000.000 4879.80 1 + 4.28461071E+00-1.45494649E-03 4.44804306E-07-6.04359642E-11 3.07921551E-15 2 + 2.37230923E+04-1.18931307E+01 2.50000000E+00-3.01680531E-12 3.74582141E-15 3 +-1.50856878E-18 1.86626471E-22 2.54742178E+04-4.44972899E-01 4 + +! Thermo group additivity estimation: group(Cds-CdsHH) + group(Cds-CdsHH) +C2H4(9) C 2H 4 G 100.000 5000.000 940.45 1 + 5.20303125E+00 7.82435984E-03-2.12679614E-06 3.79681495E-10-2.94663143E-14 2 + 3.93626600E+03-6.62431874E+00 3.97973264E+00-7.57545379E-03 5.52967870E-05 3 +-6.36214316E-08 2.31763871E-11 5.07746136E+03 4.04626943E+00 4 + +! Thermo library: primaryThermoLibrary +[H][H](13) H 2 G 100.000 5000.000 1959.07 1 + 2.78818509E+00 5.87615921E-04 1.59022130E-07-5.52762536E-11 4.34328120E-15 2 +-5.96155632E+02 1.12618494E-01 3.43536393E+00 2.12711953E-04-2.78628671E-07 3 + 3.40270013E-10-7.76039045E-14-1.03135983E+03-3.90841661E+00 4 + +! Thermo group additivity estimation: group(Cds-CdsHH) + group(Cds-CdsHH) + radical(Cds_P) +C2H3(14) C 2H 3 G 100.000 5000.000 931.96 1 + 5.44796766E+00 4.98355762E-03-1.08820555E-06 1.79836782E-10-1.45095844E-14 2 + 3.38297738E+04-4.87809437E+00 3.90670476E+00-4.06240187E-03 3.86779713E-05 3 +-4.62975954E-08 1.72900180E-11 3.47971783E+04 6.09789219E+00 4 + +! Thermo group additivity estimation: group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cs-CsHHH) + radical(RCCJ) +[CH2]CC(16) C 3H 7 G 100.000 5000.000 995.41 1 + 5.69429543E+00 1.96033646E-02-7.42051054E-06 1.35883288E-09-9.56217643E-14 2 + 8.87585429E+03-4.32881807E+00 3.09191659E+00 1.32172046E-02 2.75849094E-05 3 +-3.90850849E-08 1.43314166E-11 1.02284116E+04 1.24057745E+01 4 + +! Thermo group additivity estimation: group(Ct-CtH) + group(Ct-CtH) +C#C(20) C 2H 2 G 100.000 5000.000 888.63 1 + 5.76206627E+00 2.37155039E-03-1.49560503E-07-2.19208164E-11 2.21824257E-15 2 + 2.50944416E+04-9.82620311E+00 3.03573895E+00 7.71249669E-03 2.53452132E-06 3 +-1.08127265E-08 5.50729325E-12 2.58526447E+04 4.54464265E+00 4 + +! Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + radical(RCCJ) +C4H7(24) C 4H 7 G 100.000 5000.000 1000.94 1 + 7.59467430E+00 2.06426717E-02-7.89794754E-06 1.45967155E-09-1.03415744E-13 2 + 2.08073587E+04-1.19152615E+01 2.68062512E+00 2.10824413E-02 2.02127906E-05 3 +-3.64249680E-08 1.41447652E-11 2.27528007E+04 1.66008169E+01 4 + +! Thermo group additivity estimation: group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + group(Cds-CdsHH) +C4H6(25) C 4H 6 G 100.000 5000.000 940.96 1 + 1.10824653E+01 1.17733243E-02-3.11402426E-06 5.37717081E-10-4.10599112E-14 2 + 8.42123083E+03-3.51702703E+01 2.68201492E+00 1.69327425E-02 3.73631471E-05 3 +-6.26456621E-08 2.59135984E-11 1.13546034E+04 1.20325528E+01 4 + +! Thermo group additivity estimation: group(Cds-CdsHH) + group(CdJ2_singlet-Cds) +C2H2(27) C 2H 2 G 100.000 5000.000 1423.27 1 + 4.43042444E+00 4.87756740E-03-1.79374105E-06 3.04084635E-10-1.96651556E-14 2 + 4.78744081E+04-1.67101717E-01 3.69251626E+00 6.06584960E-03-2.11278263E-06 3 + 1.63599807E-11 1.07663446E-13 4.81741497E+04 3.96842441E+00 4 + +! Thermo group additivity estimation: group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + radical(Cds_P) +C3H5(28) C 3H 5 G 100.000 5000.000 997.88 1 + 5.66470838E+00 1.44326145E-02-5.46738219E-06 1.00157959E-09-7.04860271E-14 2 + 2.93870866E+04-4.48510726E+00 3.23408227E+00 1.18208205E-02 1.70306145E-05 3 +-2.64366956E-08 9.91221259E-12 3.04873065E+04 1.03182757E+01 4 + +! Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + radical(Cds_P) +C4H7(29) C 4H 7 G 100.000 5000.000 999.92 1 + 8.06561312E+00 2.00624551E-02-7.60828718E-06 1.39783386E-09-9.86863849E-14 2 + 2.57831831E+04-1.54390019E+01 2.56531795E+00 2.39246087E-02 1.38111964E-05 3 +-3.10264137E-08 1.25442192E-11 2.77900544E+04 1.56311519E+01 4 + +! Thermo group additivity estimation: group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + group(Cds-CdsHH) + radical(Cds_P) +C4H5(31) C 4H 5 G 100.000 5000.000 935.57 1 + 1.13227232E+01 8.94057323E-03-2.08011664E-06 3.38985538E-10-2.61959472E-14 2 + 3.83166741E+04-3.40909155E+01 2.61017827E+00 2.04313104E-02 2.07968037E-05 3 +-4.53919901E-08 2.00578838E-11 4.10742695E+04 1.33867867E+01 4 + +! Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + ring(Cyclopropane) + radical(Isobutyl) +[CH2]C1CC1(46) C 4H 7 G 100.000 5000.000 926.05 1 + 1.02343998E+01 1.41135874E-02-2.99950204E-06 4.56679945E-10-3.49849430E-14 2 + 2.27934484E+04-2.92335596E+01 3.04744695E+00 5.45470301E-03 7.53345975E-05 3 +-1.02231846E-07 4.01852003E-11 2.58269353E+04 1.40788186E+01 4 + +! Thermo group additivity estimation: group(Cs-(Cds-Cds)HHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + radical(Allyl_P) + +! radical(Allyl_P) +C4H6(47) C 4H 6 G 100.000 5000.000 961.82 1 + 9.92836169E+00 1.51090416E-02-5.13573052E-06 9.43600337E-10-6.92759988E-14 2 + 3.02830457E+04-2.74898954E+01 2.65641760E+00 1.89333016E-02 3.01003591E-05 3 +-5.20366396E-08 2.11241927E-11 3.29038783E+04 1.36623384E+01 4 + +! Thermo group additivity estimation: group(Cs-(Cds-Cds)HHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + radical(Allyl_P) +C4H7(49) C 4H 7 G 100.000 5000.000 998.56 1 + 7.82810550E+00 2.08399209E-02-7.96744898E-06 1.47335591E-09-1.04523771E-13 2 + 1.26472001E+04-1.58825850E+01 2.64211980E+00 2.15955473E-02 2.09678537E-05 3 +-3.79203098E-08 1.47841987E-11 1.46809437E+04 1.41266987E+01 4 + +! Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + ring(Cyclopropane) + +! radical(cyclopropane) +CC1[CH]C1(86) C 4H 7 G 100.000 5000.000 963.98 1 + 7.79150450E+00 1.93089313E-02-6.71839525E-06 1.25740716E-09-9.31100316E-14 2 + 2.58575046E+04-1.60924149E+01 3.19574786E+00 4.34960008E-03 6.95104879E-05 3 +-8.80809345E-08 3.25732031E-11 2.83246377E+04 1.41117218E+01 4 + +! Thermo group additivity estimation: group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cd-(Cd)CddHH) + group(Cdd-CdsCds) +C4H6(115) C 4H 6 G 100.000 5000.000 1038.23 1 + 6.96170373E+00 1.77754624E-02-7.11726623E-06 1.33754803E-09-9.49358139E-14 2 + 1.67162686E+04-2.17802726E+01 2.84853532E+00 1.92563603E-02 1.14985894E-05 3 +-2.39434432E-08 9.20174537E-12 1.83446214E+04 1.94649608E+00 4 + +! Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(Cyclobutene) +C4H6(200) C 4H 6 G 100.000 5000.000 950.24 1 + 9.42747389E+00 1.39643334E-02-4.06886448E-06 7.75020190E-10-6.20410141E-14 2 + 1.43342935E+04-2.81802850E+01 3.39619667E+00-3.43704755E-03 9.09457500E-05 3 +-1.13273491E-07 4.24106127E-11 1.74123953E+04 1.07744587E+01 4 + +END + + + +REACTIONS KCAL/MOLE MOLES + +! Reaction index: Chemkin #1; RMG #2 +! Template reaction: R_Recombination +! Flux pairs: [CH3](4), ethane(1); [CH3](4), ethane(1); +! Matched reaction 9 CH3 + CH3 <=> C2H6 in R_Recombination/training +! This reaction matched rate rule [Root_N-1R->H_N-1CNOS->N_N-1COS->O_1CS->C_N-1C-inRing] +! family: R_Recombination +[CH3](4)+[CH3](4)<=>ethane(1) 9.450000e+14 -0.538 0.135 + +! Reaction index: Chemkin #2; RMG #5 +! Template reaction: H_Abstraction +! Flux pairs: [CH3](4), C(3); ethane(1), C[CH2](5); +! Matched reaction 215 C2H6 + CH3_r3 <=> C2H5b + CH4 in H_Abstraction/training +! This reaction matched rate rule [C/H3/Cs\H3;C_methyl] +! family: H_Abstraction +[CH3](4)+ethane(1)<=>C(3)+C[CH2](5) 3.500000e+01 3.440 10.384 + +! Reaction index: Chemkin #3; RMG #3 +! Template reaction: R_Recombination +! Flux pairs: C[CH2](5), ethane(1); [H](6), ethane(1); +! Matched reaction 58 H + C2H5 <=> C2H6-2 in R_Recombination/training +! This reaction matched rate rule [Root_1R->H_N-2R->S_N-2CHNO->H_N-2CNO-inRing_Ext-2CNO-R_N-Sp-3R!H=2CCNNOO_N-2CNO->O_3R!H->C_Sp-3C-2CN] +! family: R_Recombination +[H](6)+C[CH2](5)<=>ethane(1) 1.000000e+14 0.000 0.000 + +! Reaction index: Chemkin #4; RMG #19 +! Template reaction: R_Recombination +! Flux pairs: [CH3](4), C(3); [H](6), C(3); +! Matched reaction 57 H + CH3 <=> CH4 in R_Recombination/training +! This reaction matched rate rule [Root_1R->H_N-2R->S_N-2CHNO->H_N-2CNO-inRing_N-2CNO->O] +! family: R_Recombination +[H](6)+[CH3](4)<=>C(3) 1.930000e+14 0.000 0.270 + +! Reaction index: Chemkin #5; RMG #9 +! Template reaction: R_Addition_MultipleBond +! Flux pairs: C2H4(9), C[CH2](5); [H](6), C[CH2](5); +! Matched reaction 2541 H + C2H4 <=> C2H5-2 in R_Addition_MultipleBond/training +! This reaction matched rate rule [Cds-HH_Cds-HH;HJ] +! family: R_Addition_MultipleBond +[H](6)+C2H4(9)<=>C[CH2](5) 4.620000e+08 1.640 1.010 + +! Reaction index: Chemkin #6; RMG #11 +! Template reaction: Disproportionation +! Flux pairs: C[CH2](5), C2H4(9); [CH3](4), C(3); +! Matched reaction 5 CH3_r1 + C2H5 <=> CH4 + C2H4 in Disproportionation/training +! This reaction matched rate rule [Root_N-4R->H_4CNOS-u1_N-1R!H->O_N-4CNOS->O_4CNS->C_1CNS->C_Sp-2R!H-1C_2R!H->C] +! family: Disproportionation +[CH3](4)+C[CH2](5)<=>C(3)+C2H4(9) 6.570000e+14 -0.680 0.000 + +! Reaction index: Chemkin #7; RMG #15 +! Template reaction: Disproportionation +! Flux pairs: C[CH2](5), C2H4(9); C[CH2](5), ethane(1); +! Matched reaction 6 C2H5 + C2H5-2 <=> C2H6 + C2H4 in Disproportionation/training +! This reaction matched rate rule [Root_N-4R->H_4CNOS-u1_N-1R!H->O_N-4CNOS->O_Ext-4CNS-R_N-Sp-5R!H#4CCCNNNSSS_N-2R!H->S_N-5R!H->O_Sp-5CS-4CCNSS_1CNS->C] +! family: Disproportionation +C[CH2](5)+C[CH2](5)<=>C2H4(9)+ethane(1) 6.900000e+13 -0.350 0.000 + +! Reaction index: Chemkin #8; RMG #20 +! Template reaction: H_Abstraction +! Flux pairs: [H](6), [H][H](13); ethane(1), C[CH2](5); +! Matched reaction 210 C2H6 + H <=> C2H5b + H2_p in H_Abstraction/training +! This reaction matched rate rule [C/H3/Cs\H3;H_rad] +! family: H_Abstraction +[H](6)+ethane(1)<=>[H][H](13)+C[CH2](5) 1.150000e+08 1.900 7.530 + +! Reaction index: Chemkin #9; RMG #22 +! Template reaction: Disproportionation +! Flux pairs: C[CH2](5), C2H4(9); [H](6), [H][H](13); +! Matched reaction 4 H + C2H5 <=> H2 + C2H4 in Disproportionation/training +! This reaction matched rate rule [Root_4R->H_Sp-2R!H-1R!H_2R!H-u1_N-1R!H->O_1CN->C_2R!H->C] +! family: Disproportionation +[H](6)+C[CH2](5)<=>[H][H](13)+C2H4(9) 1.083000e+13 0.000 0.000 + +! Reaction index: Chemkin #10; RMG #24 +! Template reaction: H_Abstraction +! Flux pairs: [H](6), [H][H](13); C(3), [CH3](4); +! Matched reaction 186 CH4b + H <=> CH3_p1 + H2_p in H_Abstraction/training +! This reaction matched rate rule [C_methane;H_rad] +! family: H_Abstraction +[H](6)+C(3)<=>[H][H](13)+[CH3](4) 4.100000e+03 3.156 8.755 + +! Reaction index: Chemkin #11; RMG #25 +! Template reaction: R_Recombination +! Flux pairs: [H](6), [H][H](13); [H](6), [H][H](13); +! Matched reaction 56 H + H <=> H2 in R_Recombination/training +! This reaction matched rate rule [Root_1R->H_N-2R->S_2CHNO->H] +! family: R_Recombination +[H](6)+[H](6)<=>[H][H](13) 5.450000e+10 0.000 1.500 + +! Reaction index: Chemkin #12; RMG #26 +! Template reaction: R_Recombination +! Flux pairs: C2H3(14), C2H4(9); [H](6), C2H4(9); +! Matched reaction 60 H + C2H3 <=> C2H4 in R_Recombination/training +! This reaction matched rate rule [Root_1R->H_N-2R->S_N-2CHNO->H_N-2CNO-inRing_Ext-2CNO-R_Sp-3R!H=2CCNNOO_N-3R!H->O] +! family: R_Recombination +[H](6)+C2H3(14)<=>C2H4(9) 1.210000e+14 0.000 0.000 + +! Reaction index: Chemkin #13; RMG #29 +! Template reaction: H_Abstraction +! Flux pairs: C(3), [CH3](4); C2H3(14), C2H4(9); +! Matched reaction 842 C2H3 + CH4b <=> C2H4 + CH3_p23 in H_Abstraction/training +! This reaction matched rate rule [Cd/H2/NonDeC;C_methyl] +! family: H_Abstraction +C(3)+C2H3(14)<=>[CH3](4)+C2H4(9) 2.236000e-02 4.340 5.700 + +! Reaction index: Chemkin #14; RMG #33 +! Template reaction: H_Abstraction +! Flux pairs: ethane(1), C[CH2](5); C2H3(14), C2H4(9); +! Matched reaction 774 C2H3 + C2H6 <=> C2H4 + C2H5 in H_Abstraction/training +! This reaction matched rate rule [Cd/H2/NonDeC;C_rad/H2/Cs\H3] +! family: H_Abstraction +C2H3(14)+ethane(1)<=>C2H4(9)+C[CH2](5) 1.080000e-03 4.550 3.500 + +! Reaction index: Chemkin #15; RMG #35 +! Template reaction: H_Abstraction +! Flux pairs: [H](6), [H][H](13); C2H4(9), C2H3(14); +! Matched reaction 217 C2H4 + H <=> C2H3_p + H2_p in H_Abstraction/training +! This reaction matched rate rule [Cd/H2/NonDeC;H_rad] +! family: H_Abstraction +[H](6)+C2H4(9)<=>[H][H](13)+C2H3(14) 2.400000e+02 3.620 11.266 + +! Reaction index: Chemkin #16; RMG #37 +! Template reaction: Disproportionation +! Flux pairs: C2H3(14), C2H4(9); C[CH2](5), C2H4(9); +! Matched reaction 11 C2H3-2 + C2H5 <=> C2H4-2 + C2H4 in Disproportionation/training +! This reaction matched rate rule [Root_N-4R->H_4CNOS-u1_N-1R!H->O_N-4CNOS->O_Ext-4CNS-R_N-Sp-5R!H#4CCCNNNSSS_N-2R!H->S_N-5R!H->O_N-Sp-5CS-4CCNSS] +! family: Disproportionation +C2H3(14)+C[CH2](5)<=>C2H4(9)+C2H4(9) 4.560000e+14 -0.700 0.000 + +! Reaction index: Chemkin #17; RMG #39 +! Template reaction: R_Addition_MultipleBond +! Flux pairs: C#C(20), C2H3(14); [H](6), C2H3(14); +! Matched reaction 2697 H + C2H2 <=> C2H3-2 in R_Addition_MultipleBond/training +! This reaction matched rate rule [Ct-H_Ct-H;HJ] +! family: R_Addition_MultipleBond +[H](6)+C#C(20)<=>C2H3(14) 1.030000e+09 1.640 2.110 + +! Reaction index: Chemkin #18; RMG #42 +! Template reaction: Disproportionation +! Flux pairs: C2H3(14), C#C(20); [CH3](4), C(3); +! Estimated from node Root_N-4R->H_4CNOS-u1_N-1R!H->O_N-4CNOS->O_4CNS->C_1CNS->C_N-Sp-2R!H-1C +! Multiplied by reaction path degeneracy 2.0 +[CH3](4)+C2H3(14)<=>C(3)+C#C(20) 1.620000e+06 1.870 0.000 + +! Reaction index: Chemkin #19; RMG #47 +! Template reaction: Disproportionation +! Flux pairs: C2H3(14), C#C(20); C[CH2](5), ethane(1); +! Estimated from node Root_N-4R->H_4CNOS-u1_N-1R!H->O_N-4CNOS->O_Ext-4CNS-R_N-Sp-5R!H#4CCCNNNSSS_N-2R!H->S_N-5R!H->O_Sp-5CS-4CCNSS +! Multiplied by reaction path degeneracy 2.0 +C2H3(14)+C[CH2](5)<=>C#C(20)+ethane(1) 2.106380e+13 -0.251 0.000 + +! Reaction index: Chemkin #20; RMG #54 +! Template reaction: Disproportionation +! Flux pairs: C2H3(14), C#C(20); [H](6), [H][H](13); +! Estimated from node Root_4R->H_N-Sp-2R!H-1R!H_1R!H->C +! Multiplied by reaction path degeneracy 2.0 +[H](6)+C2H3(14)<=>[H][H](13)+C#C(20) 4.800000e+08 1.500 0.000 + +! Reaction index: Chemkin #21; RMG #61 +! Template reaction: Disproportionation +! Flux pairs: C2H3(14), C#C(20); C2H3(14), C2H4(9); +! Estimated from node Root_N-4R->H_4CNOS-u1_N-1R!H->O_N-4CNOS->O_Ext-4CNS-R_N-Sp-5R!H#4CCCNNNSSS_N-2R!H->S_N-5R!H->O +! Multiplied by reaction path degeneracy 2.0 +C2H3(14)+C2H3(14)<=>C#C(20)+C2H4(9) 2.959700e+13 -0.312 0.000 + +! Reaction index: Chemkin #22; RMG #67 +! Template reaction: Singlet_Carbene_Intra_Disproportionation +! Flux pairs: C2H2(27), C#C(20); +! Estimated from node Root +! Multiplied by reaction path degeneracy 2.0 +C2H2(27)<=>C#C(20) 7.903180e+18 -1.971 9.786 + +! Reaction index: Chemkin #23; RMG #30 +! Template reaction: R_Addition_MultipleBond +! Flux pairs: [CH3](4), [CH2]CC(16); C2H4(9), [CH2]CC(16); +! Matched reaction 2902 CH3 + C2H4 <=> C3H7 in R_Addition_MultipleBond/training +! This reaction matched rate rule [Cds-HH_Cds-HH;CsJ-HHH] +! family: R_Addition_MultipleBond +[CH3](4)+C2H4(9)<=>[CH2]CC(16) 8.610000e+02 2.988 7.238 + +! Reaction index: Chemkin #24; RMG #59 +! Template reaction: R_Addition_MultipleBond +! Flux pairs: C2H3(14), C4H7(24); C2H4(9), C4H7(24); +! Matched reaction 234 C2H3 + C2H4 <=> C4H7-3 in R_Addition_MultipleBond/training +! This reaction matched rate rule [Cds-HH_Cds-HH;CdsJ-H] +! family: R_Addition_MultipleBond +C2H3(14)+C2H4(9)<=>C4H7(24) 2.860000e+04 2.410 1.800 + +! Reaction index: Chemkin #25; RMG #138 +! Template reaction: Intra_R_Add_Exocyclic +! Flux pairs: C4H7(24), [CH2]C1CC1(46); +! Matched reaction 335 C4H7 <=> C4H7-2 in Intra_R_Add_Exocyclic/training +! This reaction matched rate rule [Backbone1_Sp-4R!H=1R!H] +! family: Intra_R_Add_Exocyclic +C4H7(24)<=>[CH2]C1CC1(46) 6.320000e+08 0.970 8.900 + +! Reaction index: Chemkin #26; RMG #74 +! Template reaction: R_Addition_MultipleBond +! Flux pairs: C[CH2](5), C4H7(29); C#C(20), C4H7(29); +! Matched reaction 2254 C2H2 + C2H5 <=> C4H7-6 in R_Addition_MultipleBond/training +! This reaction matched rate rule [Ct-H_Ct-H;CsJ-CsHH] +! family: R_Addition_MultipleBond +C#C(20)+C[CH2](5)<=>C4H7(29) 1.360000e+04 2.410 6.200 + +! Reaction index: Chemkin #27; RMG #146 +! Template reaction: intra_H_migration +! Flux pairs: C4H7(29), C4H7(24); +! Estimated using template [R4H_DSS;Cd_rad_out_singleH;Cs_H_out] for rate rule [R4H_DSS;Cd_rad_out_singleH;Cs_H_out_2H] +! Euclidian distance = 1.0 +! Multiplied by reaction path degeneracy 3.0 +! family: intra_H_migration +C4H7(29)<=>C4H7(24) 1.113000e+05 2.230 10.590 + +! Reaction index: Chemkin #28; RMG #144 +! Template reaction: intra_H_migration +! Flux pairs: C4H7(24), C4H7(49); +! Matched reaction 84 C:CC[CH2] <=> C:C[CH]C in intra_H_migration/training +! This reaction matched rate rule [R2H_S;C_rad_out_2H;Cs_H_out_H/Cd] +! family: intra_H_migration +C4H7(24)<=>C4H7(49) 1.720000e+06 1.990 27.200 + +! Reaction index: Chemkin #29; RMG #346 +! Template reaction: intra_H_migration +! Flux pairs: C4H7(29), C4H7(49); +! Matched reaction 194 C4H7-4 <=> C4H7-5 in intra_H_migration/training +! This reaction matched rate rule [R3H_DS;Cd_rad_out_singleH;Cs_H_out_H/NonDeC] +! family: intra_H_migration +C4H7(29)<=>C4H7(49) 1.846000e+10 0.740 34.700 + +! Reaction index: Chemkin #30; RMG #65 +! Template reaction: R_Recombination +! Flux pairs: C2H3(14), C4H6(25); C2H3(14), C4H6(25); +! Matched reaction 89 C2H3 + C2H3 <=> C4H6-4 in R_Recombination/training +! This reaction matched rate rule [Root_N-1R->H_N-1CNOS->N_N-1COS->O_1CS->C_N-1C-inRing_Ext-2R-R_N-Sp-3R!H-2R_N-3R!H->O_N-Sp-3CCSS#2R_Ext-1C-R] +! family: R_Recombination +C2H3(14)+C2H3(14)<=>C4H6(25) 3.615000e+13 0.000 0.000 + +! Reaction index: Chemkin #31; RMG #139 +! Template reaction: R_Addition_MultipleBond +! Flux pairs: C4H6(25), C4H7(24); [H](6), C4H7(24); +! Matched reaction 2580 H + C4H6-2 <=> C4H7-11 in R_Addition_MultipleBond/training +! This reaction matched rate rule [Cds-CdH_Cds-HH;HJ] +! family: R_Addition_MultipleBond +[H](6)+C4H6(25)<=>C4H7(24) 3.240000e+08 1.640 2.400 + +! Reaction index: Chemkin #32; RMG #150 +! Template reaction: Disproportionation +! Flux pairs: C4H7(24), C4H6(25); [CH3](4), C(3); +! Estimated from node Root_Ext-1R!H-R_N-4R->O_N-Sp-5R!H=1R!H_4CHNS->C_4C-u1 +! Multiplied by reaction path degeneracy 2.0 +[CH3](4)+C4H7(24)<=>C(3)+C4H6(25) 2.300000e+13 -0.320 0.000 + +! Reaction index: Chemkin #33; RMG #160 +! Template reaction: Disproportionation +! Flux pairs: C4H7(24), C4H6(25); C[CH2](5), ethane(1); +! Estimated from node Root_Ext-1R!H-R_N-4R->O_N-Sp-5R!H=1R!H_Ext-4CHNS-R_N-6R!H->S_4CHNS->C_N- +! Sp-6BrBrBrCCCClClClFFFIIINNNOOOPPPSiSiSi#4C_6BrCClFINOPSi->C_N-1R!H-inRing_Sp-6C-4C +! Multiplied by reaction path degeneracy 2.0 +C[CH2](5)+C4H7(24)<=>ethane(1)+C4H6(25) 3.941700e+12 -0.039 0.000 + +! Reaction index: Chemkin #34; RMG #176 +! Template reaction: Disproportionation +! Flux pairs: C4H7(24), C4H6(25); [H](6), [H][H](13); +! Estimated from node Root_Ext-1R!H-R_N-4R->O_N-Sp-5R!H=1R!H_N-4CHNS->C +! Multiplied by reaction path degeneracy 2.0 +[H](6)+C4H7(24)<=>[H][H](13)+C4H6(25) 3.620000e+12 0.000 0.000 + +! Reaction index: Chemkin #35; RMG #197 +! Template reaction: Disproportionation +! Flux pairs: C4H7(24), C4H6(25); C2H3(14), C2H4(9); +! Estimated from node Root_Ext-1R!H-R_N-4R->O_N-Sp-5R!H=1R!H_Ext-4CHNS-R_N-6R!H->S_4CHNS->C_N- +! Sp-6BrBrBrCCCClClClFFFIIINNNOOOPPPSiSiSi#4C_6BrCClFINOPSi->C_N-1R!H-inRing_N-Sp-6C-4C +! Multiplied by reaction path degeneracy 2.0 +C2H3(14)+C4H7(24)<=>C2H4(9)+C4H6(25) 2.420000e+12 0.000 0.000 + +! Reaction index: Chemkin #36; RMG #468 +! Template reaction: R_Addition_MultipleBond +! Flux pairs: C4H6(25), C4H7(49); [H](6), C4H7(49); +! Matched reaction 2544 H + C4H6 <=> C4H7-9 in R_Addition_MultipleBond/training +! This reaction matched rate rule [Cds-HH_Cds-CdH;HJ] +! family: R_Addition_MultipleBond +[H](6)+C4H6(25)<=>C4H7(49) 4.620000e+08 1.640 -0.470 + +! Reaction index: Chemkin #37; RMG #484 +! Template reaction: Disproportionation +! Flux pairs: C4H7(49), C4H6(25); [CH3](4), C(3); +! Estimated from node Root_Ext-2R!H-R_2R!H->C_4R->C +! Multiplied by reaction path degeneracy 3.0 +[CH3](4)+C4H7(49)<=>C(3)+C4H6(25) 1.500000e+11 0.000 0.000 + +! Reaction index: Chemkin #38; RMG #501 +! Template reaction: Disproportionation +! Flux pairs: C4H7(49), C4H6(25); C[CH2](5), ethane(1); +! Estimated from node Root_Ext-2R!H-R_2R!H->C_4R->C +! Multiplied by reaction path degeneracy 3.0 +C[CH2](5)+C4H7(49)<=>ethane(1)+C4H6(25) 1.500000e+11 0.000 0.000 + +! Reaction index: Chemkin #39; RMG #519 +! Template reaction: Disproportionation +! Flux pairs: C4H7(49), C4H6(25); [H](6), [H][H](13); +! Estimated from node Root_Ext-2R!H-R_2R!H->C_N-4R->C +! Multiplied by reaction path degeneracy 3.0 +[H](6)+C4H7(49)<=>[H][H](13)+C4H6(25) 2.169000e+13 0.000 3.986 + +! Reaction index: Chemkin #40; RMG #558 +! Template reaction: Disproportionation +! Flux pairs: C4H7(49), C4H6(25); C2H3(14), C2H4(9); +! Estimated from node Root_Ext-2R!H-R_2R!H->C_4R->C +! Multiplied by reaction path degeneracy 3.0 +C2H3(14)+C4H7(49)<=>C2H4(9)+C4H6(25) 1.500000e+11 0.000 0.000 + +! Reaction index: Chemkin #41; RMG #752 +! Template reaction: Intra_2+2_cycloaddition_Cd +! Flux pairs: C4H6(25), C4H6(200); +! Matched reaction 1 C4H6_BD <=> C4H6_CB in Intra_2+2_cycloaddition_Cd/training +! This reaction matched rate rule [1,3-butadiene_backbone;CdH2_1;CdH2_2] +! family: Intra_2+2_cycloaddition_Cd +C4H6(25)<=>C4H6(200) 6.706520e+13 -0.361 44.856 + +! Reaction index: Chemkin #42; RMG #141 +! Template reaction: R_Recombination +! Flux pairs: C4H6(47), C4H7(24); [H](6), C4H7(24); +! Estimated from node Root_1R->H_N-2R->S_N-2CHNO->H_N-2CNO-inRing_Ext-2CNO-R_N-Sp-3R!H=2CCNNOO_N-2CNO->O_Ext-2CN-R +! Multiplied by reaction path degeneracy 2.0 +[H](6)+C4H6(47)<=>C4H7(24) 3.532740e+13 0.153 0.000 + +! Reaction index: Chemkin #43; RMG #187 +! Template reaction: Disproportionation +! Flux pairs: C4H6(47), C4H7(24); C[CH2](5), C2H4(9); +! Estimated from node Root_N-4R->H_4CNOS-u1_N-1R!H->O_N-4CNOS->O_Ext-4CNS-R_N-Sp-5R!H#4CCCNNNSSS_N-2R!H->S_N-5R!H->O_Sp-5CS-4CCNSS_Ext-4CNS-R +! Multiplied by reaction path degeneracy 6.0 +C[CH2](5)+C4H6(47)<=>C2H4(9)+C4H7(24) 4.660962e+15 -0.900 1.445 + +! Reaction index: Chemkin #44; RMG #218 +! Template reaction: Disproportionation +! Flux pairs: C4H6(47), C4H7(24); C2H3(14), C#C(20); +! Estimated from node Root_N-4R->H_4CNOS-u1_N-1R!H->O_N-4CNOS->O_Ext-4CNS-R_N-Sp-5R!H#4CCCNNNSSS_N-2R!H->S_N-5R!H->O_Sp-5CS-4CCNSS +! Multiplied by reaction path degeneracy 4.0 +C2H3(14)+C4H6(47)<=>C#C(20)+C4H7(24) 4.212760e+13 -0.251 0.000 + +! Reaction index: Chemkin #45; RMG #462 +! Template reaction: R_Recombination +! Flux pairs: C4H6(47), C4H7(49); [H](6), C4H7(49); +! Estimated from node Root_1R->H_N-2R->S_N-2CHNO->H_N-2CNO-inRing_Ext-2CNO-R_N- +! Sp-3R!H=2CCNNOO_N-2CNO->O_3R!H->C_Sp-3C-2CN_Ext-3C-R_Sp-4R!H=3C_N-3C-inRing +! Multiplied by reaction path degeneracy 2.0 +[H](6)+C4H6(47)<=>C4H7(49) 3.251960e+13 0.255 0.000 + +! Reaction index: Chemkin #46; RMG #526 +! Template reaction: Disproportionation +! Flux pairs: C4H6(47), C4H7(49); C[CH2](5), C2H4(9); +! Estimated from node Root_N-4R->H_4CNOS-u1_N-1R!H->O_N-4CNOS->O_Ext-4CNS-R_N-Sp-5R!H#4CCCNNNSSS_N-2R!H->S_N-5R!H->O_Sp-5CS-4CCNSS_1CNS->C_Ext-5CS-R +! Multiplied by reaction path degeneracy 6.0 +C[CH2](5)+C4H6(47)<=>C2H4(9)+C4H7(49) 1.374000e+14 -0.350 0.000 + +! Reaction index: Chemkin #47; RMG #569 +! Template reaction: Disproportionation +! Flux pairs: C4H6(47), C4H7(49); C2H3(14), C#C(20); +! Estimated from node Root_N-4R->H_4CNOS-u1_N-1R!H->O_N-4CNOS->O_Ext-4CNS-R_N-Sp-5R!H#4CCCNNNSSS_N-2R!H->S_N-5R!H->O_Sp-5CS-4CCNSS +! Multiplied by reaction path degeneracy 4.0 +C2H3(14)+C4H6(47)<=>C#C(20)+C4H7(49) 4.212760e+13 -0.251 0.000 + +! Reaction index: Chemkin #48; RMG #817 +! Template reaction: Disproportionation +! Flux pairs: C4H6(47), C4H6(25); C4H7(24), C4H7(24); +! Estimated from node Root_Ext-1R!H-R_N-4R->O_N-Sp-5R!H=1R!H_Ext-4CHNS-R_N-6R!H->S_4CHNS->C_N- +! Sp-6BrBrBrCCCClClClFFFIIINNNOOOPPPSiSiSi#4C_6BrCClFINOPSi->C_N-1R!H-inRing_Ext-4C-R_2R!H->C +! Multiplied by reaction path degeneracy 4.0 +C4H6(47)+C4H7(24)<=>C4H6(25)+C4H7(24) 4.210600e+14 -0.550 0.640 + +! Reaction index: Chemkin #49; RMG #818 +! Template reaction: Disproportionation +! Flux pairs: C4H7(49), C4H7(24); C4H6(47), C4H6(25); +! Estimated from node Root_Ext-2R!H-R_2R!H->C_4R->C +! Multiplied by reaction path degeneracy 6.0 +C4H6(47)+C4H7(49)<=>C4H6(25)+C4H7(24) 3.000000e+11 0.000 0.000 + +! Reaction index: Chemkin #50; RMG #866 +! Template reaction: Disproportionation +! Flux pairs: C4H6(47), C4H6(25); C4H7(24), C4H7(49); +! Estimated from node Root_Ext-1R!H-R_N-4R->O_N-Sp-5R!H=1R!H_Ext-4CHNS-R_N-6R!H->S_4CHNS->C_N- +! Sp-6BrBrBrCCCClClClFFFIIINNNOOOPPPSiSiSi#4C_6BrCClFINOPSi->C_N-1R!H-inRing_Sp-6C-4C_Ext-6C-R +! Multiplied by reaction path degeneracy 4.0 +C4H6(47)+C4H7(24)<=>C4H6(25)+C4H7(49) 5.800000e+12 0.000 0.000 + +! Reaction index: Chemkin #51; RMG #867 +! Template reaction: Disproportionation +! Flux pairs: C4H7(49), C4H7(49); C4H6(47), C4H6(25); +! Estimated from node Root_Ext-2R!H-R_2R!H->C_4R->C +! Multiplied by reaction path degeneracy 6.0 +C4H6(47)+C4H7(49)<=>C4H6(25)+C4H7(49) 3.000000e+11 0.000 0.000 + +! Reaction index: Chemkin #52; RMG #908 +! Template reaction: Birad_recombination +! Flux pairs: C4H6(47), C4H6(200); +! Estimated from node Root_1R!H->C +C4H6(47)<=>C4H6(200) 8.690880e+06 1.216 0.000 + +! Reaction index: Chemkin #53; RMG #1034 +! Template reaction: 1,2-Birad_to_alkene +! Flux pairs: C4H6(47), C4H6(25); +! Matched reaction 5 C4H6 => C4H6-2 in 1,2-Birad_to_alkene/training +! This reaction matched rate rule [Y_12_01] +! family: 1,2-Birad_to_alkene +C4H6(47)=>C4H6(25) 5.010000e+07 0.000 0.000 + +! Reaction index: Chemkin #54; RMG #271 +! Template reaction: intra_H_migration +! Flux pairs: CC1[CH]C1(86), [CH2]C1CC1(46); +! Estimated using template [R3H_SS;C_rad_out_H/NonDeC;Cs_H_out_2H] for rate rule [R3H_SS_12cy3;C_rad_out_H/NonDeC;Cs_H_out_2H] +! Euclidian distance = 1.0 +! Multiplied by reaction path degeneracy 3.0 +! family: intra_H_migration +CC1[CH]C1(86)<=>[CH2]C1CC1(46) 7.772027e+08 1.395 45.950 + +! Reaction index: Chemkin #55; RMG #459 +! Template reaction: Intra_R_Add_Endocyclic +! Flux pairs: C4H7(49), CC1[CH]C1(86); +! Estimated from node Backbone0_N-2R!H-inRing_N-1R!H-inRing_Sp-2R!H-1R!H_Ext-3R!H-R_Sp-3R!H=1R!H +C4H7(49)<=>CC1[CH]C1(86) 3.683930e+12 -0.105 35.881 + +! Reaction index: Chemkin #56; RMG #460 +! Template reaction: R_Addition_MultipleBond +! Flux pairs: C4H6(115), C4H7(49); [H](6), C4H7(49); +! Matched reaction 2714 H + C4H6-4 <=> C4H7-13 in R_Addition_MultipleBond/training +! This reaction matched rate rule [Ca_Cds-HH;HJ] +! family: R_Addition_MultipleBond +[H](6)+C4H6(115)<=>C4H7(49) 5.460000e+08 1.640 3.780 + +! Reaction index: Chemkin #57; RMG #475 +! Template reaction: Disproportionation +! Flux pairs: C4H7(49), C4H6(115); [CH3](4), C(3); +! Estimated from node Root_Ext-1R!H-R_N-4R->O_Sp-5R!H=1R!H +[CH3](4)+C4H7(49)<=>C(3)+C4H6(115) 4.339290e+05 1.968 2.212 + +! Reaction index: Chemkin #58; RMG #489 +! Template reaction: Disproportionation +! Flux pairs: C4H7(49), C4H6(115); C[CH2](5), ethane(1); +! Estimated from node Root_Ext-1R!H-R_N-4R->O_Sp-5R!H=1R!H_Ext-4CHNS-R_Sp-6R!H-4CHNS +C[CH2](5)+C4H7(49)<=>ethane(1)+C4H6(115) 9.640000e+11 0.000 6.652 + +! Reaction index: Chemkin #59; RMG #513 +! Template reaction: Disproportionation +! Flux pairs: C4H7(49), C4H6(115); [H](6), [H][H](13); +! Estimated from node Root_Ext-1R!H-R_N-4R->O +[H](6)+C4H7(49)<=>[H][H](13)+C4H6(115) 1.356420e+10 0.470 0.000 + +! Reaction index: Chemkin #60; RMG #542 +! Template reaction: Disproportionation +! Flux pairs: C4H7(49), C4H6(115); C2H3(14), C2H4(9); +! Estimated from node Root_Ext-1R!H-R_N-4R->O_Sp-5R!H=1R!H_Ext-4CHNS-R_N-Sp-6R!H-4CHNS +C2H3(14)+C4H7(49)<=>C2H4(9)+C4H6(115) 2.410000e+12 0.000 0.000 + +! Reaction index: Chemkin #61; RMG #1028 +! Template reaction: Intra_Disproportionation +! Flux pairs: C4H6(47), C4H6(115); +! Estimated using an average for rate rule [R3radExo;Y_rad;XH_Rrad] +! Euclidian distance = 0 +! Multiplied by reaction path degeneracy 2.0 +! family: Intra_Disproportionation +C4H6(47)<=>C4H6(115) 1.487400e+09 1.045 15.153 + +! Reaction index: Chemkin #62; RMG #1173 +! Template reaction: Disproportionation +! Flux pairs: C4H7(49), C4H6(115); C4H6(47), C4H7(49); +! Estimated from node Root_Ext-1R!H-R_N-4R->O_Sp-5R!H=1R!H_Ext-4CHNS-R_Ext-6R!H-R +! Multiplied by reaction path degeneracy 2.0 +C4H6(47)+C4H7(49)<=>C4H6(115)+C4H7(49) 1.686000e+11 0.000 6.982 + +! Reaction index: Chemkin #63; RMG #1190 +! Template reaction: Disproportionation +! Flux pairs: C4H7(49), C4H6(115); C4H6(47), C4H7(24); +! Estimated from node Root_Ext-1R!H-R_N-4R->O_Sp-5R!H=1R!H_Ext-4CHNS-R_Ext-6R!H-R +! Multiplied by reaction path degeneracy 2.0 +C4H6(47)+C4H7(49)<=>C4H6(115)+C4H7(24) 1.686000e+11 0.000 12.469 + +! Reaction index: Chemkin #64; RMG #82 +! Template reaction: R_Addition_MultipleBond +! Flux pairs: C2H3(14), C4H5(31); C#C(20), C4H5(31); +! Matched reaction 196 C2H2 + C2H3 <=> C4H5-8 in R_Addition_MultipleBond/training +! This reaction matched rate rule [Ct-H_Ct-H;CdsJ-H] +! family: R_Addition_MultipleBond +C#C(20)+C2H3(14)<=>C4H5(31) 1.168000e+07 1.997 5.452 + +! Reaction index: Chemkin #65; RMG #757 +! Template reaction: R_Recombination +! Flux pairs: C4H5(31), C4H6(25); [H](6), C4H6(25); +! Estimated from node Root_1R->H_N-2R->S_N-2CHNO->H_N-2CNO-inRing_Ext-2CNO-R_Sp-3R!H=2CCNNOO_N-3R!H->O +[H](6)+C4H5(31)<=>C4H6(25) 8.156660e+18 -1.493 0.000 + +! Reaction index: Chemkin #66; RMG #763 +! Template reaction: H_Abstraction +! Flux pairs: [CH3](4), C(3); C4H6(25), C4H5(31); +! From training reaction 1566 used for Cd/H2/NonDeC;C_methyl +! Exact match found for rate rule [Cd/H2/NonDeC;C_methyl] +! Euclidian distance = 0 +! Multiplied by reaction path degeneracy 4.0 +! family: H_Abstraction +[CH3](4)+C4H6(25)<=>C(3)+C4H5(31) 3.432000e-02 4.340 20.710 + +! Reaction index: Chemkin #67; RMG #771 +! Template reaction: H_Abstraction +! Flux pairs: C[CH2](5), ethane(1); C4H6(25), C4H5(31); +! From training reaction 343 used for Cd/H2/NonDeC;C_rad/H2/Cs\H3 +! Exact match found for rate rule [Cd/H2/NonDeC;C_rad/H2/Cs\H3] +! Euclidian distance = 0 +! Multiplied by reaction path degeneracy 4.0 +! family: H_Abstraction +C[CH2](5)+C4H6(25)<=>ethane(1)+C4H5(31) 6.320000e+02 3.130 18.000 + +! Reaction index: Chemkin #68; RMG #775 +! Template reaction: H_Abstraction +! Flux pairs: [H](6), [H][H](13); C4H6(25), C4H5(31); +! From training reaction 217 used for Cd/H2/NonDeC;H_rad +! Exact match found for rate rule [Cd/H2/NonDeC;H_rad] +! Euclidian distance = 0 +! Multiplied by reaction path degeneracy 4.0 +! family: H_Abstraction +[H](6)+C4H6(25)<=>[H][H](13)+C4H5(31) 2.400000e+02 3.620 11.266 + +! Reaction index: Chemkin #69; RMG #780 +! Template reaction: Disproportionation +! Flux pairs: C4H5(31), C4H6(25); C[CH2](5), C2H4(9); +! Estimated from node Root_N-4R->H_4CNOS-u1_N-1R!H->O_N-4CNOS->O_Ext-4CNS-R_N-Sp-5R!H#4CCCNNNSSS_N-2R!H->S_N-5R!H->O_N-Sp-5CS-4CCNSS +! Multiplied by reaction path degeneracy 3.0 +C[CH2](5)+C4H5(31)<=>C2H4(9)+C4H6(25) 4.560000e+14 -0.700 0.000 + +! Reaction index: Chemkin #70; RMG #789 +! Template reaction: H_Abstraction +! Flux pairs: C2H3(14), C2H4(9); C4H6(25), C4H5(31); +! Matched reaction 177 C4H6-3 + C2H3 <=> C2H4 + C4H5 in H_Abstraction/training +! This reaction matched rate rule [Cd/H2/NonDeC;Cd_Cd\H2_pri_rad] +! family: H_Abstraction +C2H3(14)+C4H6(25)<=>C2H4(9)+C4H5(31) 3.437000e-04 4.732 6.579 + +! Reaction index: Chemkin #71; RMG #798 +! Template reaction: Disproportionation +! Flux pairs: C4H5(31), C4H6(25); C2H3(14), C#C(20); +! Estimated from node Root_N-4R->H_4CNOS-u1_N-1R!H->O_N-4CNOS->O_Ext-4CNS-R_N-Sp-5R!H#4CCCNNNSSS_N-2R!H->S_N-5R!H->O +! Multiplied by reaction path degeneracy 2.0 +C2H3(14)+C4H5(31)<=>C#C(20)+C4H6(25) 2.959700e+13 -0.312 0.000 + +! Reaction index: Chemkin #72; RMG #904 +! Template reaction: Disproportionation +! Flux pairs: C4H7(24), C4H6(25); C4H5(31), C4H6(25); +! Estimated from node Root_Ext-1R!H-R_N-4R->O_N-Sp-5R!H=1R!H_Ext-4CHNS-R_N-6R!H->S_4CHNS->C_N- +! Sp-6BrBrBrCCCClClClFFFIIINNNOOOPPPSiSiSi#4C_6BrCClFINOPSi->C_N-1R!H-inRing_N-Sp-6C-4C +! Multiplied by reaction path degeneracy 2.0 +C4H5(31)+C4H7(24)<=>C4H6(25)+C4H6(25) 2.420000e+12 0.000 0.000 + +! Reaction index: Chemkin #73; RMG #905 +! Template reaction: Disproportionation +! Flux pairs: C4H7(49), C4H6(25); C4H5(31), C4H6(25); +! Estimated from node Root_Ext-2R!H-R_2R!H->C_4R->C +! Multiplied by reaction path degeneracy 3.0 +C4H5(31)+C4H7(49)<=>C4H6(25)+C4H6(25) 1.500000e+11 0.000 0.000 + +! Reaction index: Chemkin #74; RMG #1681 +! Template reaction: Disproportionation +! Flux pairs: C4H7(49), C4H6(25); C4H5(31), C4H6(115); +! Estimated from node Root_Ext-1R!H-R_N-4R->O_Sp-5R!H=1R!H_Ext-4CHNS-R_Ext-6R!H-R +C4H5(31)+C4H7(49)<=>C4H6(115)+C4H6(25) 8.430000e+10 0.000 2.130 + +! Reaction index: Chemkin #75; RMG #70 +! Template reaction: R_Addition_MultipleBond +! Flux pairs: [CH3](4), C3H5(28); C#C(20), C3H5(28); +! Matched reaction 2253 C2H2 + CH3 <=> C3H5-2 in R_Addition_MultipleBond/training +! This reaction matched rate rule [Ct-H_Ct-H;CsJ-HHH] +! family: R_Addition_MultipleBond +[CH3](4)+C#C(20)<=>C3H5(28) 1.338000e+05 2.410 6.770 + +END + diff --git a/ipython/data/parse_source/species_dictionary.txt b/ipython/data/parse_source/species_dictionary.txt new file mode 100644 index 0000000000..19abe31c11 --- /dev/null +++ b/ipython/data/parse_source/species_dictionary.txt @@ -0,0 +1,238 @@ +Ar +1 Ar u0 p4 c0 + +He +1 He u0 p1 c0 + +Ne +1 Ne u0 p4 c0 + +N2 +1 N u0 p1 c0 {2,T} +2 N u0 p1 c0 {1,T} + +ethane(1) +1 C u0 p0 c0 {2,S} {3,S} {4,S} {5,S} +2 C u0 p0 c0 {1,S} {6,S} {7,S} {8,S} +3 H u0 p0 c0 {1,S} +4 H u0 p0 c0 {1,S} +5 H u0 p0 c0 {1,S} +6 H u0 p0 c0 {2,S} +7 H u0 p0 c0 {2,S} +8 H u0 p0 c0 {2,S} + +[CH3](4) +multiplicity 2 +1 C u1 p0 c0 {2,S} {3,S} {4,S} +2 H u0 p0 c0 {1,S} +3 H u0 p0 c0 {1,S} +4 H u0 p0 c0 {1,S} + +C[CH2](5) +multiplicity 2 +1 C u0 p0 c0 {2,S} {3,S} {4,S} {5,S} +2 C u1 p0 c0 {1,S} {6,S} {7,S} +3 H u0 p0 c0 {1,S} +4 H u0 p0 c0 {1,S} +5 H u0 p0 c0 {1,S} +6 H u0 p0 c0 {2,S} +7 H u0 p0 c0 {2,S} + +C(3) +1 C u0 p0 c0 {2,S} {3,S} {4,S} {5,S} +2 H u0 p0 c0 {1,S} +3 H u0 p0 c0 {1,S} +4 H u0 p0 c0 {1,S} +5 H u0 p0 c0 {1,S} + +[H](6) +multiplicity 2 +1 H u1 p0 c0 + +C2H4(9) +1 C u0 p0 c0 {2,D} {3,S} {4,S} +2 C u0 p0 c0 {1,D} {5,S} {6,S} +3 H u0 p0 c0 {1,S} +4 H u0 p0 c0 {1,S} +5 H u0 p0 c0 {2,S} +6 H u0 p0 c0 {2,S} + +[H][H](13) +1 H u0 p0 c0 {2,S} +2 H u0 p0 c0 {1,S} + +C2H3(14) +multiplicity 2 +1 C u0 p0 c0 {2,D} {3,S} {4,S} +2 C u1 p0 c0 {1,D} {5,S} +3 H u0 p0 c0 {1,S} +4 H u0 p0 c0 {1,S} +5 H u0 p0 c0 {2,S} + +C#C(20) +1 C u0 p0 c0 {2,T} {3,S} +2 C u0 p0 c0 {1,T} {4,S} +3 H u0 p0 c0 {1,S} +4 H u0 p0 c0 {2,S} + +C2H2(27) +1 C u0 p0 c0 {2,D} {3,S} {4,S} +2 C u0 p1 c0 {1,D} +3 H u0 p0 c0 {1,S} +4 H u0 p0 c0 {1,S} + +[CH2]CC(16) +multiplicity 2 +1 C u0 p0 c0 {2,S} {3,S} {4,S} {5,S} +2 C u0 p0 c0 {1,S} {6,S} {7,S} {8,S} +3 C u1 p0 c0 {1,S} {9,S} {10,S} +4 H u0 p0 c0 {1,S} +5 H u0 p0 c0 {1,S} +6 H u0 p0 c0 {2,S} +7 H u0 p0 c0 {2,S} +8 H u0 p0 c0 {2,S} +9 H u0 p0 c0 {3,S} +10 H u0 p0 c0 {3,S} + +C4H7(24) +multiplicity 2 +1 C u0 p0 c0 {2,S} {3,S} {5,S} {6,S} +2 C u0 p0 c0 {1,S} {4,D} {7,S} +3 C u1 p0 c0 {1,S} {8,S} {9,S} +4 C u0 p0 c0 {2,D} {10,S} {11,S} +5 H u0 p0 c0 {1,S} +6 H u0 p0 c0 {1,S} +7 H u0 p0 c0 {2,S} +8 H u0 p0 c0 {3,S} +9 H u0 p0 c0 {3,S} +10 H u0 p0 c0 {4,S} +11 H u0 p0 c0 {4,S} + +[CH2]C1CC1(46) +multiplicity 2 +1 C u0 p0 c0 {2,S} {3,S} {4,S} {5,S} +2 C u0 p0 c0 {1,S} {3,S} {6,S} {7,S} +3 C u0 p0 c0 {1,S} {2,S} {8,S} {9,S} +4 C u1 p0 c0 {1,S} {10,S} {11,S} +5 H u0 p0 c0 {1,S} +6 H u0 p0 c0 {2,S} +7 H u0 p0 c0 {2,S} +8 H u0 p0 c0 {3,S} +9 H u0 p0 c0 {3,S} +10 H u0 p0 c0 {4,S} +11 H u0 p0 c0 {4,S} + +C4H7(29) +multiplicity 2 +1 C u0 p0 c0 {2,S} {3,S} {5,S} {6,S} +2 C u0 p0 c0 {1,S} {7,S} {8,S} {9,S} +3 C u0 p0 c0 {1,S} {4,D} {10,S} +4 C u1 p0 c0 {3,D} {11,S} +5 H u0 p0 c0 {1,S} +6 H u0 p0 c0 {1,S} +7 H u0 p0 c0 {2,S} +8 H u0 p0 c0 {2,S} +9 H u0 p0 c0 {2,S} +10 H u0 p0 c0 {3,S} +11 H u0 p0 c0 {4,S} + +C4H7(49) +multiplicity 2 +1 C u0 p0 c0 {2,S} {5,S} {6,S} {7,S} +2 C u0 p0 c0 {1,S} {3,D} {8,S} +3 C u0 p0 c0 {2,D} {4,S} {9,S} +4 C u1 p0 c0 {3,S} {10,S} {11,S} +5 H u0 p0 c0 {1,S} +6 H u0 p0 c0 {1,S} +7 H u0 p0 c0 {1,S} +8 H u0 p0 c0 {2,S} +9 H u0 p0 c0 {3,S} +10 H u0 p0 c0 {4,S} +11 H u0 p0 c0 {4,S} + +C4H6(25) +1 C u0 p0 c0 {2,S} {3,D} {5,S} +2 C u0 p0 c0 {1,S} {4,D} {6,S} +3 C u0 p0 c0 {1,D} {7,S} {8,S} +4 C u0 p0 c0 {2,D} {9,S} {10,S} +5 H u0 p0 c0 {1,S} +6 H u0 p0 c0 {2,S} +7 H u0 p0 c0 {3,S} +8 H u0 p0 c0 {3,S} +9 H u0 p0 c0 {4,S} +10 H u0 p0 c0 {4,S} + +C4H6(200) +1 C u0 p0 c0 {2,S} {3,S} {5,S} {6,S} +2 C u0 p0 c0 {1,S} {4,S} {7,S} {8,S} +3 C u0 p0 c0 {1,S} {4,D} {9,S} +4 C u0 p0 c0 {2,S} {3,D} {10,S} +5 H u0 p0 c0 {1,S} +6 H u0 p0 c0 {1,S} +7 H u0 p0 c0 {2,S} +8 H u0 p0 c0 {2,S} +9 H u0 p0 c0 {3,S} +10 H u0 p0 c0 {4,S} + +C4H6(47) +multiplicity 3 +1 C u0 p0 c0 {2,D} {3,S} {5,S} +2 C u0 p0 c0 {1,D} {4,S} {6,S} +3 C u1 p0 c0 {1,S} {7,S} {8,S} +4 C u1 p0 c0 {2,S} {9,S} {10,S} +5 H u0 p0 c0 {1,S} +6 H u0 p0 c0 {2,S} +7 H u0 p0 c0 {3,S} +8 H u0 p0 c0 {3,S} +9 H u0 p0 c0 {4,S} +10 H u0 p0 c0 {4,S} + +CC1[CH]C1(86) +multiplicity 2 +1 C u0 p0 c0 {2,S} {3,S} {4,S} {5,S} +2 C u0 p0 c0 {1,S} {4,S} {6,S} {7,S} +3 C u0 p0 c0 {1,S} {8,S} {9,S} {10,S} +4 C u1 p0 c0 {1,S} {2,S} {11,S} +5 H u0 p0 c0 {1,S} +6 H u0 p0 c0 {2,S} +7 H u0 p0 c0 {2,S} +8 H u0 p0 c0 {3,S} +9 H u0 p0 c0 {3,S} +10 H u0 p0 c0 {3,S} +11 H u0 p0 c0 {4,S} + +C4H6(115) +1 C u0 p0 c0 {2,S} {5,S} {6,S} {7,S} +2 C u0 p0 c0 {1,S} {4,D} {8,S} +3 C u0 p0 c0 {4,D} {9,S} {10,S} +4 C u0 p0 c0 {2,D} {3,D} +5 H u0 p0 c0 {1,S} +6 H u0 p0 c0 {1,S} +7 H u0 p0 c0 {1,S} +8 H u0 p0 c0 {2,S} +9 H u0 p0 c0 {3,S} +10 H u0 p0 c0 {3,S} + +C4H5(31) +multiplicity 2 +1 C u0 p0 c0 {2,S} {3,D} {5,S} +2 C u0 p0 c0 {1,S} {4,D} {6,S} +3 C u0 p0 c0 {1,D} {7,S} {8,S} +4 C u1 p0 c0 {2,D} {9,S} +5 H u0 p0 c0 {1,S} +6 H u0 p0 c0 {2,S} +7 H u0 p0 c0 {3,S} +8 H u0 p0 c0 {3,S} +9 H u0 p0 c0 {4,S} + +C3H5(28) +multiplicity 2 +1 C u0 p0 c0 {2,S} {4,S} {5,S} {6,S} +2 C u0 p0 c0 {1,S} {3,D} {7,S} +3 C u1 p0 c0 {2,D} {8,S} +4 H u0 p0 c0 {1,S} +5 H u0 p0 c0 {1,S} +6 H u0 p0 c0 {1,S} +7 H u0 p0 c0 {2,S} +8 H u0 p0 c0 {3,S} + diff --git a/ipython/local_uncertainty.ipynb b/ipython/local_uncertainty.ipynb index 3d655f1358..9afc08d09f 100644 --- a/ipython/local_uncertainty.ipynb +++ b/ipython/local_uncertainty.ipynb @@ -41,12 +41,12 @@ "# This is a small ethane pyrolysis model\n", "\n", "# Must use annotated chemkin file\n", - "chemkin_file = 'data/parse_source/chemkin/chem_annotated.inp'\n", - "dict_file = 'data/parse_source/chemkin/species_dictionary.txt'\n", + "chemkin_file = 'data/parse_source/chem_annotated.inp'\n", + "dict_file = 'data/parse_source/species_dictionary.txt'\n", "\n", "# Initialize the Uncertainty class instance and load the model\n", "uncertainty = Uncertainty(output_directory='./temp/uncertainty')\n", - "uncertainty.load_model(chemkin_file, dict_file, use_chemkin_names=True)\n", + "uncertainty.load_model(chemkin_file, dict_file)\n", "\n", "# Map the species to the objects within the Uncertainty class\n", "ethane = Species().from_smiles('CC')\n", From 2bfbd6d42294ccc1d62500832d36dd2c4709d688 Mon Sep 17 00:00:00 2001 From: jonwzheng Date: Wed, 9 Jul 2025 17:02:38 -0400 Subject: [PATCH 15/17] fix find_parameter_sources notebook --- ...ter_sources_and_assign_uncertainties.ipynb | 31 ++++++------------- 1 file changed, 9 insertions(+), 22 deletions(-) diff --git a/ipython/find_parameter_sources_and_assign_uncertainties.ipynb b/ipython/find_parameter_sources_and_assign_uncertainties.ipynb index 3bac96c3e8..c6314eae74 100644 --- a/ipython/find_parameter_sources_and_assign_uncertainties.ipynb +++ b/ipython/find_parameter_sources_and_assign_uncertainties.ipynb @@ -25,26 +25,6 @@ "__Note__: The RMG-database version must match the version used to generate the model. RMG will attempt to recreate the kinetics estimate for each reaction and may fail if the database is different." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run RMG job\n", - "This cell is used only when the user is interested in learning this module with the provided toy model, ./data/parse_source/input.py.\n", - "The user can speed up the model generation by commenting out the uncertainty block in ./data/parse_source/input.py." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "!python ../rmg.py ./data/parse_source/input.py" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -58,8 +38,8 @@ "metadata": {}, "outputs": [], "source": [ - "chem_file = './data/parse_source/chemkin/chem_annotated.inp'\n", - "dict_file = './data/parse_source/chemkin/species_dictionary.txt'" + "chem_file = './data/parse_source/chem_annotated.inp'\n", + "dict_file = './data/parse_source/species_dictionary.txt'" ] }, { @@ -353,6 +333,13 @@ "# See the kinetics correlated parameter partial uncertainties\n", "uncertainty.kinetic_input_uncertainties" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From cca4ad0fbe36a5233ac08cf65883968e47c44919 Mon Sep 17 00:00:00 2001 From: jonwzheng Date: Wed, 9 Jul 2025 18:14:24 -0400 Subject: [PATCH 16/17] Fix fragment notebook & utils The previous implementation of the fragment notebook/utils don't work correctly. The utils didn't work independently outside of the notebook. This commit moves the FragmentList definition to the utils code, and just imports everything from the notebook. It also fixes some other errors in the codebase. --- ipython/fragment_reattachment_example.ipynb | 948 +------------------- rmgpy/molecule/fragment_utils.py | 422 ++++++++- 2 files changed, 445 insertions(+), 925 deletions(-) diff --git a/ipython/fragment_reattachment_example.ipynb b/ipython/fragment_reattachment_example.ipynb index 9c45b4cc7a..6267985844 100644 --- a/ipython/fragment_reattachment_example.ipynb +++ b/ipython/fragment_reattachment_example.ipynb @@ -14,453 +14,17 @@ "execution_count": 1, "id": "285ac939-646d-46e6-a5b9-286661e1ad18", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:No normalization for BCUT2D_MWHI\n", - "WARNING:root:No normalization for BCUT2D_MWLOW\n", - "WARNING:root:No normalization for BCUT2D_CHGHI\n", - "WARNING:root:No normalization for BCUT2D_CHGLO\n", - "WARNING:root:No normalization for BCUT2D_LOGPHI\n", - "WARNING:root:No normalization for BCUT2D_LOGPLOW\n", - "WARNING:root:No normalization for BCUT2D_MRHI\n", - "WARNING:root:No normalization for BCUT2D_MRLOW\n", - "WARNING:root:No normalization for AvgIpc\n" - ] - } - ], + "outputs": [], "source": [ - "import sys\n", - "rmgpy_loc = \"/home/gridsan/adoner/RMG-Py/\"\n", - "sys.path.append(rmgpy_loc)\n", "from rmgpy.molecule.fragment import Fragment\n", "from rmgpy.tools.canteramodel import Cantera\n", "from rmgpy.chemkin import load_chemkin_file\n", - "from rmgpy.molecule.fragment_utils import match_sequences, match_concentrations_with_same_sums, match_concentrations_with_different_sums, shuffle, flatten, merge_frag_to_frag, merge_frag_list\n", - "import re\n", + "from rmgpy.molecule import fragment_utils as frag\n", "import os\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, - { - "cell_type": "markdown", - "id": "045d6ebe-c055-42bc-ae55-643599631ea3", - "metadata": {}, - "source": [ - "## Defining the class FragList\n", - "The `FragList` object stores molecule and fragment concentrations from a single time step and provides a series of functions that groups fragments to form molecules. The procedure is described later on in this notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9802cd48-4a7b-4244-acf8-cba0fc1aab69", - "metadata": {}, - "outputs": [], - "source": [ - "class FragList():\n", - " '''\n", - " to instantiate a FragList:\n", - " fl = Fraglist(frag_list)\n", - " where frag_list is a list of tuples of fragments and their amounts\n", - " '''\n", - "\n", - " def __init__(self, frag_list):\n", - " self.raw_fragment_output = frag_list\n", - "\n", - " def sort(self):\n", - " '''\n", - " sort a FragList into\n", - " general_R_list - 2R fragments\n", - " general_L_list - 2L fragments\n", - " rr_ll_list - 2R or 2L fragments\n", - " r_l_moles - 1R and 1L fragments\n", - " multi_label_frag_3 - fragments with 3 cutting labels\n", - " multi_label_frag_4 - fragments with 4 cutting labels\n", - "\n", - " note: in our experience fragments with more than 4 cutting labels has never happened, but a warning will be printed if it does happen\n", - " '''\n", - " moles_remain = []\n", - " one_R_dict = {}\n", - " one_L_dict = {}\n", - " general_R_list = []\n", - " general_L_list = []\n", - " rr_ll_list = []\n", - " r_l_moles = []\n", - " multi_label_frag_3 = []\n", - " multi_label_frag_4 = []\n", - "\n", - " for i, item in enumerate(self.raw_fragment_output):\n", - " frag, amt = item\n", - " if amt > 1e-6 and '[' not in frag:\n", - " count_of_L_labels = len(re.findall(r'L', frag))\n", - " count_of_R_labels = len(re.findall(r'R', frag))\n", - " count_of_cutting_labels = count_of_L_labels + count_of_R_labels\n", - " if count_of_R_labels == 0 and count_of_L_labels == 0:\n", - " moles_remain.append((frag, amt))\n", - " elif count_of_R_labels == 1 and count_of_L_labels == 0:\n", - " one_R_dict[frag] = amt\n", - " elif count_of_R_labels == 2 and count_of_L_labels == 0:\n", - " general_R_list.append((frag, amt * 2))\n", - " rr_ll_list.append(frag)\n", - " elif count_of_R_labels == 0 and count_of_L_labels == 1:\n", - " one_L_dict[frag] = amt\n", - " elif count_of_R_labels == 0 and count_of_L_labels == 2:\n", - " general_L_list.append((frag, amt * 2))\n", - " rr_ll_list.append(frag)\n", - " elif count_of_R_labels == 1 and count_of_L_labels == 1:\n", - " r_l_moles.append((frag, amt))\n", - " else:\n", - " if count_of_cutting_labels == 3:\n", - " multi_label_frag_3.append(\n", - " (frag, amt)) # 2R1L, 1R2L, 3R, 3L\n", - " elif count_of_cutting_labels == 4:\n", - " multi_label_frag_4.append((frag, amt))\n", - " else:\n", - " print(\n", - " f\"Warning! {count_of_cutting_labels} cutting labels in {frag}\")\n", - " self.R1dict=one_R_dict\n", - " self.L1dict=one_L_dict\n", - " self.Rlist=general_R_list\n", - " self.Llist=general_L_list\n", - " self.RRLLlist=rr_ll_list\n", - " self.RLlist=r_l_moles\n", - " self.CL3=multi_label_frag_3\n", - " self.CL4=multi_label_frag_4\n", - " self.molesremain=moles_remain\n", - "\n", - " def random_pick_frag(target_dict):\n", - " '''\n", - " argument(s): target_dict - dictionary where key = species smiles (fragment or molecule), value = moles\n", - " returns: tuple of randomly picked (fragment with 1 cutting label, moles)\n", - "\n", - " choice is weighted by mole fraction of each fragment\n", - " '''\n", - " import random\n", - " import re\n", - " frag_dict_list=[x for x in target_dict.items() if len(\n", - " re.findall(r'[LR]', x[0])) == 1]\n", - " sum_dict=sum([x[1] for x in frag_dict_list])\n", - " frag_dict_prob=[x[1] / sum_dict for x in frag_dict_list]\n", - " item=np.random.choice(frag_dict_list, 1, p = frag_dict_prob)\n", - "\n", - " return item\n", - "\n", - " def pair_frag(amount, target_dict):\n", - " '''\n", - " argument(s): amount - maximum amount of fragments in one pair\n", - " target_dict - dictionary of species smiles and moles\n", - " returns: the target_dict with 1 randomly chosen 1-cutting label fragment fully paired with other randomly chosen 1-cutting label fragments\n", - " '''\n", - " additional_frag_list=[]\n", - " frag1=FragList.random_pick_frag(target_dict)\n", - "\n", - " if target_dict[frag1] >= amount:\n", - " target_dict[frag1] -= amount\n", - " additional_frag_list.append((frag1, amount))\n", - "\n", - " else:\n", - " remain=amount - target_dict[frag1]\n", - " additional_frag_list.append((frag1, amount))\n", - " target_dict[frag1]=0\n", - "\n", - " while remain > 0:\n", - " frag1=FragList.random_pick_frag(target_dict)\n", - "\n", - " if target_dict[frag1] >= remain:\n", - " target_dict[frag1] -= remain\n", - " additional_frag_list.append((frag1, remain))\n", - " remain=0\n", - "\n", - " else:\n", - " frag_amt=target_dict[frag1]\n", - " target_dict[frag1]=0\n", - " additional_frag_list.append((frag1, frag_amt))\n", - " remain=remain - frag_amt\n", - " return additional_frag_list\n", - "\n", - " def pair_CL4s(self):\n", - " '''\n", - " pairs all 4-cutting label fragments with other randomly picked 1-cutting label fragments, creating 3-cutting label fragments\n", - " '''\n", - "\n", - " for species, amount in self.CL4: # 4R, 3R1L, 2R2L, 1R3L, 4L\n", - " ount_of_R_labels=len(re.findall(r'R', species))\n", - " count_of_L_labels=len(re.findall(r'L', species))\n", - " if count_of_R_labels == 4 and count_of_L_labels == 0:\n", - " paired_frag_list=FragList.pair_frag(amount, self.L1dict)\n", - " for frag_amt in paired_frag_list:\n", - " frag=frag_amt[0]\n", - " amt=frag_amt[1]\n", - " frag1=frag # 1L\n", - " frag2=species # 4R\n", - " frag_new=FragList.merge_frag_to_frag(frag1, frag2, 'R') # L,R,R -> 3\n", - " self.CL3.append((frag_new, amt))\n", - "\n", - " elif count_of_R_labels == 0 and count_of_L_labels == 4:\n", - " paired_frag_list = FragList.pair_frag(amount, self.R1dict)\n", - " for frag_amt in paired_frag_list:\n", - " frag = frag_amt[0]\n", - " amt = frag_amt[1]\n", - " frag1 = frag # 1R\n", - " frag2 = species # 4L\n", - " frag_new = FragList.merge_frag_to_frag(frag2, frag1, 'R') # L,R,R -> 3L\n", - " self.CL3.append((frag_new, amt))\n", - "\n", - " elif count_of_R_labels == 2 and count_of_L_labels == 2:\n", - " paired_frag_list = FragList.pair_frag(amount, self.L1dict)\n", - " for frag_amt in paired_frag_list:\n", - " frag = frag_amt[0]\n", - " amt = frag_amt[1]\n", - " frag1 = frag # 1L\n", - " frag2 = species # 2R2L\n", - " frag_new = FragList.merge_frag_to_frag(frag1, frag2, 'R') # L,R,R -> 1R2L\n", - " self.CL3.append((frag_new, amt))\n", - "\n", - " elif count_of_R_labels == 3 and count_of_L_labels == 1:\n", - " paired_frag_list = FragList.pair_frag(amount, self.R1dict)\n", - " for frag_amt in paired_frag_list:\n", - " frag = frag_amt[0]\n", - " amt = frag_amt[1]\n", - " frag1 = frag # 1R\n", - " frag2 = species # 3R1L\n", - " frag_new = FragList.merge_frag_to_frag(frag2, frag1, 'R') # L,R,R -> 3R\n", - " self.CL3.append((frag_new, amt))\n", - "\n", - " elif count_of_R_labels == 1 and count_of_L_labels == 3:\n", - " paired_frag_list = FragList.pair_frag(amount, self.L1dict)\n", - " for frag_amt in paired_frag_list:\n", - " frag = frag_amt[0]\n", - " amt = frag_amt[1]\n", - " frag1 = frag # 1L\n", - " frag2 = species # 1R3L\n", - " frag_new = FragList.merge_frag_to_frag(frag1, frag2, 'R') # L,R,R -> 3L\n", - " self.CL3.append((frag_new, amt))\n", - "\n", - " def pair_CL3s(self):\n", - " '''\n", - " pairs all 3-cutting label fragments with other randomly picked 1-cutting label fragments, creating 2-cutting label fragments\n", - " '''\n", - "\n", - " for species, amount in self.CL3:\n", - " count_of_R_labels = len(re.findall(r'R', species))\n", - " count_of_L_labels = len(re.findall(r'L', species))\n", - " if count_of_R_labels == 2 and count_of_L_labels == 1:\n", - " paired_frag_list = FragList.pair_frag(amount, self.R1dict)\n", - " for frag_amt in paired_frag_list:\n", - " frag = frag_amt[0]\n", - " amt = frag_amt[1]\n", - " frag1 = frag # 1R\n", - " frag2 = species # 2R1L\n", - " frag_new = FragList.merge_frag_to_frag(frag2, frag1, 'R') # L,R,R\n", - " self.Rlist.append((frag_new, amt * 2))\n", - " self.RRLLlist.append(frag_new)\n", - "\n", - " elif count_of_R_labels == 1 and count_of_L_labels == 2:\n", - " paired_frag_list = FragList.pair_frag(amount, self.L1dict)\n", - " for frag_amt in paired_frag_list:\n", - " frag = frag_amt[0]\n", - " amt = frag_amt[1]\n", - " frag1 = frag # 1L\n", - " frag2 = species # 1R2L\n", - " frag_new = FragList.merge_frag_to_frag(frag1, frag2, 'R') # L,R,R\n", - " self.Llist.append((frag_new, amt * 2))\n", - " self.RRLLlist.append(frag_new)\n", - "\n", - " elif count_of_R_labels == 3 and count_of_L_labels == 0:\n", - " paired_frag_list = FragList.pair_frag(amount, self.L1dict)\n", - " for frag_amt in paired_frag_list:\n", - " frag = frag_amt[0]\n", - " amt = frag_amt[1]\n", - " frag1 = frag # 1L\n", - " frag2 = species # 3R\n", - " frag_new = FragList.merge_frag_to_frag(frag1, frag2, 'R') # L,R,R\n", - " self.Rlist.append((frag_new, amt * 2))\n", - " self.RRLLlist.append(frag_new)\n", - "\n", - " # 3L\n", - " elif count_of_R_labels == 0 and count_of_L_labels == 3:\n", - " paired_frag_list = FragList.pair_frag(amount, self.R1dict)\n", - " for frag_amt in paired_frag_list:\n", - " frag = frag_amt[0]\n", - " amt = frag_amt[1]\n", - " frag1 = frag # 1R\n", - " frag2 = species # 3L\n", - " frag_new = FragList.merge_frag_to_frag(frag2, frag1, 'R') # L,R,R\n", - " self.Llist.append((frag_new, amt * 2))\n", - " self.RRLLlist.append(frag_new)\n", - "\n", - " def update_lists(self):\n", - " '''\n", - " adds the 1-cuttinglabel fragments remaining after pairing 4- and 3-cutting label fragments to their corresponding list\n", - " '''\n", - " for one_R_frag, amt in self.R1dict.items():\n", - " self.Rlist.append((one_R_frag, amt))\n", - " for one_L_frag, amt in self.L1dict.items():\n", - " self.Llist.append((one_L_frag, amt))\n", - "\n", - " def grind(conc, size):\n", - " '''\n", - " Split fragment concentrations into several repeating concentration units with specified size\n", - " '''\n", - " grinded_conc = []\n", - " for label, c in conc:\n", - " times = int(c / size)\n", - " grinded_conc.extend([(label, size)] * times)\n", - "\n", - " if c - size * times > 0:\n", - " grinded_conc.append((label, c - size * times))\n", - "\n", - " return grinded_conc\n", - "\n", - "\n", - " def grind_endcaps(self, grindsize=1, repeats=1):\n", - " '''\n", - " splits each fragment concentration in to many subconcentrations of a maximum of grindsize\n", - " '''\n", - " print('grinding endcaps to {}'.format(grindsize))\n", - " self.repeats = repeats\n", - " if self.repeats == 1:\n", - " self.grindsize = grindsize\n", - " seed = np.random.randint(0, 100)\n", - " grmoles = FragList.grind(self.Rlist, self.grindsize)\n", - " glmoles = FragList.grind(self.Llist, self.grindsize)\n", - " self.glmoles = FragList.shuffle(glmoles, seed)\n", - " self.grmoles = FragList.shuffle(grmoles, seed+1)\n", - " else:\n", - " print('repeats > 1 not supported')\n", - "\n", - " def pair_endcaps(self):\n", - " '''\n", - " randomly pairs subconcentrations of endcaps and assigns the correct amount for each pair\n", - " 2L and 2R fragments will be paired with 2 1-cuttinglabel fragments\n", - " the remaining fragment subconcentrations are middle pieces\n", - " '''\n", - " matches = FragList.match_concentrations_with_same_sums(self.glmoles,\n", - " self.grmoles,\n", - " rtol=1e-3)\n", - " self.endcaps = []\n", - " self.middles = []\n", - " for match in matches:\n", - " pair = match[0]\n", - " value = match[1]\n", - " if value > 0.0:\n", - " lfrag, rfrag = pair\n", - " if lfrag not in self.RRLLlist:\n", - " if rfrag not in self.RRLLlist:\n", - " self.endcaps.append((pair, value))\n", - " elif rfrag in self.RRLLlist:\n", - " self.endcaps.append(((lfrag, rfrag, lfrag), value/2))\n", - " else:\n", - " raise Error\n", - " elif lfrag in self.RRLLlist:\n", - " if rfrag not in self.RRLLlist:\n", - " self.endcaps.append(((rfrag, lfrag, rfrag), value/2))\n", - " elif rfrag in self.RRLLlist:\n", - " self.middles.append((pair, value/2))\n", - " else:\n", - " raise Error\n", - " else:\n", - " raise Error\n", - "\n", - " def grind_middles(self):\n", - " '''\n", - " grinds the middle 1L1R fragments into subconcentrations\n", - " the same grindsize is used as for the endcaps\n", - " '''\n", - " print('grinding middle pieces to {}'.format(self.grindsize))\n", - " if self.repeats == 1:\n", - " grinded_r_l_moles = FragList.grind(self.RLlist, self.grindsize)\n", - " seed = np.random.randint(0, 100)\n", - " self.middles = self.middles + \\\n", - " FragList.shuffle(grinded_r_l_moles, seed)\n", - " else:\n", - " print('repeats > 1 not supported')\n", - "\n", - " def distribute_middles(self):\n", - " '''\n", - " loop through the middle pieces\n", - " randomly pick an endcap pair to add to\n", - " repeat until no more middle pieces \n", - " '''\n", - " import random\n", - " sys.setrecursionlimit(15000)\n", - " r_l_frag_distri_amt = [0 for i in range(len(self.endcaps))]\n", - " r_l_frag_distri_dict = [{} for i in range(len(self.endcaps))]\n", - " for i, r_l_tup in enumerate(self.middles):\n", - " r_l_frag = r_l_tup[0]\n", - " r_l_frag_amount = r_l_tup[1]\n", - " rand_idx = random.randrange(len(self.endcaps))\n", - " threshold = 1e-15\n", - " while self.endcaps[rand_idx][1] <= threshold:\n", - " rand_idx = random.randrange(len(self.endcaps))\n", - " r_l_frag_distri_amt[rand_idx] += r_l_frag_amount\n", - " r_l_dict = r_l_frag_distri_dict[rand_idx]\n", - " if r_l_frag in r_l_dict.keys():\n", - " r_l_dict[r_l_frag] += r_l_frag_amount\n", - " else:\n", - " r_l_dict[r_l_frag] = r_l_frag_amount\n", - " matches_random = []\n", - " for i in range(len(self.endcaps)):\n", - " pair = self.endcaps[i]\n", - " if r_l_frag_distri_dict[i].keys():\n", - " r_l_matched = []\n", - " for r_l_frag, amt in r_l_frag_distri_dict[i].items():\n", - " r_l_matched.append((r_l_frag, amt))\n", - " match_list = FragList.match_concentrations_with_different_sums([\n", - " pair], r_l_matched)\n", - " for tups in match_list:\n", - " matches_random.append(tups)\n", - " else: \n", - " matches_random.append(pair)\n", - " flattened_matches_random = [\n", - " (tuple(FragList.flatten(m[0])), m[1]) for m in matches_random]\n", - "\n", - " self.grouped = []\n", - "\n", - " for non_cut_mole, val in self.molesremain:\n", - " self.grouped.append(((non_cut_mole, ), val))\n", - "\n", - " self.grouped.extend(flattened_matches_random)\n", - "\n", - " def get_mwd(self, bins=10, fname='mwd.png'):\n", - " '''\n", - " loop through molecules i.e. grouped fragments\n", - " calculate molecular weight\n", - " create histogram of molecular weights, weighted by molar amount\n", - " store histogram data in histdata\n", - " '''\n", - " self.mwd_amts = [x[1] for x in self.grouped]\n", - " self.mwd_mws = []\n", - " for fraglist, amt in self.grouped:\n", - " mw = sum([Fragment().from_smiles_like_string(\n", - " frag).get_molecular_weight()*1000 for frag in fraglist])\n", - " self.mwd_mws.append(mw)\n", - "\n", - " self.histdata = plt.hist(\n", - " self.mwd_mws, bins=bins, weights=self.mwd_amts)\n", - " plt.xlabel(\"Molecular Weight (g/mol)\")\n", - " plt.ylabel(\"Moles\")\n", - "\n", - " def reattach(self, grindsize = 1):\n", - " '''\n", - " this parent function combines the steps of reattachment into one\n", - " '''\n", - " self.sort() # sort the fragments by number and type of cutting labels\n", - " self.pair_CL4s() # reattach 4-cutting label fragments to make 3-cutting label fragments\n", - " self.pair_CL3s() # reattach 3-cutting label fragments to make 2-cutting label fragments\n", - " self.update_lists() # add 1-cutting label fragments to either Rlist or Llist\n", - " self.grind_endcaps(grindsize=grindsize) # grind the concentrations of fragments into smaller sub-concentrations\n", - " self.pair_endcaps() # pair together 1R and 1L fragments to make endcap pairs\n", - " self.grind_middles() # grind the concentrations of middle LR fragments into smaller sub-concentrations of same size as above\n", - " self.distribute_middles() # randomly select an endcap pair for each middle LR fragment\n", - " return self\n", - "\n" - ] - }, { "cell_type": "markdown", "id": "b310e4f2-7770-45d8-8b67-1dceb2ab85af", @@ -472,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "9d5bd8fb-2a97-487b-8cc2-e1349f5612e1", "metadata": {}, "outputs": [], @@ -500,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "2f9f50fc-eba0-4156-8d87-1ed68294560d", "metadata": {}, "outputs": [], @@ -528,16 +92,24 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "6d69f465-2eff-4549-b9af-9531d77de360", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:Initial mole fractions do not sum to one; normalizing.\n" + ] + } + ], "source": [ "job = Cantera(species_list=species_list,\n", " reaction_list=reaction_list, output_directory='temp_detailed')\n", "job.load_model()\n", - "job.generate_conditions(reactorTypeList, reactionTimeList,\n", - " molFracList, Tlist, Plist)\n", + "job.generate_conditions(reactor_type_list=reactorTypeList, reaction_time_list=reactionTimeList,\n", + " mol_frac_list=molFracList, Tlist=Tlist, Plist=Plist)\n", "all_data = job.simulate()" ] }, @@ -551,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "100d1e81-0c4a-499d-bb45-63ed17fc605c", "metadata": {}, "outputs": [], @@ -578,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "2082ed31-adaf-4346-bcd2-9ff09666faed", "metadata": {}, "outputs": [], @@ -617,12 +189,12 @@ "\n", " 10. the `FragList.get_mwd()` function plots the histogram of molecular weights of the reattached molecules generated in the previous steps and saves its data under the property `FragList.histdata`.\n", " \n", - "- Random reattachment will be repeated 10 times with a new random seed each time, which will allow us to examine the repeatability of reattachment.\n" + "- Random reattachment will be repeated 3 times with a new random seed each time, which will allow us to examine the repeatability of reattachment.\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "id": "ebe147a1-ad99-4320-8813-45b2141353f8", "metadata": { "tags": [] @@ -632,17 +204,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "grinding endcaps to 0.001\n", - "grinding middle pieces to 0.001\n", - "grinding endcaps to 0.001\n", - "grinding middle pieces to 0.001\n", - "grinding endcaps to 0.001\n", - "grinding middle pieces to 0.001\n" + "grinding endcaps to 0.0009\n", + "grinding middle pieces to 0.0009\n", + "grinding endcaps to 0.0009\n", + "grinding middle pieces to 0.0009\n", + "grinding endcaps to 0.0009\n", + "grinding middle pieces to 0.0009\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -656,8 +228,8 @@ "hdl = []\n", "hdl = []\n", "for i in range(3): \n", - " fl = FragList(frag_list)\n", - " fl.reattach(grindsize=0.001)\n", + " fl = frag.FragList(frag_list)\n", + " fl.reattach(grindsize=0.0009) # more stable if lower; may need to re-run to do successfully. \n", " fl.get_mwd(bins=range(0, 501, 15))\n", " hdl.append(fl.histdata) # save histogram data for repeatability analysis\n", "\n" @@ -676,13 +248,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "id": "83a0e056-66dd-4e79-b4b0-f6b74c518a58", "metadata": {}, "outputs": [ { "data": { - "image/png": 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wHQsAgFDp6Od3QKdpmpubVVFRoaysLL/2rKwsHThwoM0xO3fuVHp6un7729/qiiuu0IgRI/TII4/o888/b/c4TU1N8nq9fhsAAOieAjpN09DQoJaWFsXHx/u1x8fHq66urs0xx44d0xtvvKHY2Fi9/PLLamho0OzZs/XJJ5+0e91IYWGhli1bFkhpAAAgQgV1AavD4fDbN8a0arvg/Pnzcjgc2rx5s2666SbdcccdWrFihTZu3Nju6khBQYE8Ho9vq62tDaZMAAAQAQJaGRk0aJCioqJarYLU19e3Wi25IDExUVdccYVcLpevbeTIkTLG6KOPPtK1117baozT6ZTT6QykNAAAEKECWhmJiYlRWlqaSktL/dpLS0uVmZnZ5phx48bp1KlTOnPmjK/tX//6l3r16qWhQ4cGUTIAAOhOAj5Nk5+fr7Vr12r9+vU6cuSIFi5cqJqaGuXl5Un66hRLbm6ur/+MGTM0cOBA3XfffTp8+LDKysr06KOP6v7771efPn1C90oAAEBECvh7RqZPn67Tp09r+fLlcrvdSk1NVUlJiYYPHy5Jcrvdqqmp8fW/7LLLVFpaqoceekjp6ekaOHCgpk2bpieeeCJ0rwIAAESsgL9nxAa+ZyR8xwIAIFQ65XtGAAAAQo0wAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKuCCiNFRUVKSUlRbGys0tLStG/fvg6N279/v6Kjo3XjjTcGc1gAANANBRxGiouLtWDBAi1evFiVlZUaP368cnJyVFNTc9FxHo9Hubm5mjhxYtDFAgCA7ifgMLJixQrNmjVLDzzwgEaOHKmVK1cqOTlZq1evvui4Bx98UDNmzFBGRkbQxQIAgO4noDDS3NysiooKZWVl+bVnZWXpwIED7Y7bsGGDPvzwQy1ZsiS4KgEAQLcVHUjnhoYGtbS0KD4+3q89Pj5edXV1bY45evSoFi1apH379ik6umOHa2pqUlNTk2/f6/UGUiYAAIggQV3A6nA4/PaNMa3aJKmlpUUzZszQsmXLNGLEiA4/f2FhoVwul29LTk4OpkwAABABAgojgwYNUlRUVKtVkPr6+larJZLU2Nio8vJyzZ07V9HR0YqOjtby5cv1zjvvKDo6Wnv27GnzOAUFBfJ4PL6ttrY2kDIBAEAECeg0TUxMjNLS0lRaWqof//jHvvbS0lL96Ec/atU/Li5O1dXVfm1FRUXas2ePXnrpJaWkpLR5HKfTKafTGUhpAAAgQgUURiQpPz9f99xzj9LT05WRkaHnn39eNTU1ysvLk/TVqsbJkye1adMm9erVS6mpqX7jhwwZotjY2FbtAACgZwo4jEyfPl2nT5/W8uXL5Xa7lZqaqpKSEg0fPlyS5Ha7L/mdIwAAABc4jDHGdhGX4vV65XK55PF4FBcXZ7scSdKuXeE7VnZ2+I4FAECodPTzm9+mAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGBVtO0C0PUcPBj4mDFjQl8HAKBnIIx0Y6dO2a4AAIBLI4x0Y3V1wY37z39CWwcAABfDNSMAAMAqVka6MVY4AACRgJURAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVUGFkaKiIqWkpCg2NlZpaWnat29fu323b9+uSZMmafDgwYqLi1NGRoZ27doVdMEAAKB7CfhXe4uLi7VgwQIVFRVp3LhxWrNmjXJycnT48GENGzasVf+ysjJNmjRJv/71r9W/f39t2LBBU6ZM0T/+8Q+NHj06JC8CkevUqeDGJSWFtg4AgD0OY4wJZMDYsWM1ZswYrV692tc2cuRITZ06VYWFhR16juuuu07Tp0/X448/3qH+Xq9XLpdLHo9HcXFxgZTbacK5uJOdHdy4SKiRMAIA3VdHP78DOk3T3NysiooKZWVl+bVnZWXpwIEDHXqO8+fPq7GxUQMGDGi3T1NTk7xer98GAAC6p4BO0zQ0NKilpUXx8fF+7fHx8aqrq+vQczz11FM6e/aspk2b1m6fwsJCLVu2LJDSEKGqq4Mbx8oIAHQfQV3A6nA4/PaNMa3a2rJlyxYtXbpUxcXFGjJkSLv9CgoK5PF4fFttbW0wZQIAgAgQ0MrIoEGDFBUV1WoVpL6+vtVqydcVFxdr1qxZevHFF3X77bdftK/T6ZTT6QykNAAAEKECWhmJiYlRWlqaSktL/dpLS0uVmZnZ7rgtW7bo3nvv1QsvvKDJkycHVykAAOiWAr61Nz8/X/fcc4/S09OVkZGh559/XjU1NcrLy5P01SmWkydPatOmTZK+CiK5ubl6+umndfPNN/tWVfr06SOXyxXClwIAACJRwGFk+vTpOn36tJYvXy63263U1FSVlJRo+PDhkiS3262amhpf/zVr1ujcuXOaM2eO5syZ42ufOXOmNm7c+M1fAQAAiGgBhxFJmj17tmbPnt3mY18PGK+//nowhwAAAD0Ev00DAACsIowAAACrCCMAAMAqwggAALAqqAtYAQDfzMGDwY0bMya0dQBdAWEEACz4z39sVwB0HZymAQAAVhFGAACAVYQRAABgFdeMAN3AqVPBjUtKCm0dABAMwgjQDfzv708GjDACoCsgjCAiBbMSwAcvuoNwroJx+zHChTCCiFRdHfiY7hxGuE205wjmvS8F9/7nfYVw4QJWAABgFSsjAALC0j2AUGNlBAAAWEUYAQAAVnGaBkBAuKgRQKgRRoAeLNjbRAEglAgjQA8W7G2iABBKhBHgEviqdQDoXIQRoAvhtAm6g+58+3c4/z/ak/6gIYwAlxDs774Ecwpk8ODgjgUgPMJ5apMwAsAnnHePcKcKgG8qEn+7izACAP+L64NCg1BtVyT+dhdhBEBYRMJ1BOH8EToA/4cwgpAI9oMGPQd/LQNoD2EkAkTCHRZ80AAAgkUYiQDB3s0BAGhbJPyR15MQRiIAqw4AeoJw3gXCH3ldC2EEANAlROJdIAgNwggA9ACclvDHinPXQhgB0O3s2mW7gq6H0xLoyggjAPANRcKqAysB6MoII+gx+C4UoPuJhCCISwsqjBQVFel3v/ud3G63rrvuOq1cuVLjx49vt//evXuVn5+vQ4cOKSkpSb/85S+Vl5cXdNFAMPjLMDJFQogM54+nwV93nvtIeO+HSsBhpLi4WAsWLFBRUZHGjRunNWvWKCcnR4cPH9awYcNa9T9+/LjuuOMO/fznP9ef//xn7d+/X7Nnz9bgwYN11113heRFAOi+CJHoqXrSe99hjDGBDBg7dqzGjBmj1atX+9pGjhypqVOnqrCwsFX/X/3qV9q5c6eOHDnia8vLy9M777yjN998s0PH9Hq9crlc8ng8iouLC6TcTsMFcgCA7iI7u3Oet6Of3wGtjDQ3N6uiokKLFi3ya8/KytKBAwfaHPPmm28qKyvLry07O1vr1q3Tl19+qd69e7ca09TUpKamJt++x+OR9NWL6irOnrVdAQAAodFZH68XPrcvte4RUBhpaGhQS0uL4uPj/drj4+NV1859Y3V1dW32P3funBoaGpSYmNhqTGFhoZYtW9aqPTk5OZByAQBAF9DY2CiXy9Xu40FdwOpwOPz2jTGt2i7Vv632CwoKCpSfn+/bP3/+vD755BMNHDjwoscJlNfrVXJysmpra7vM6Z/uirkOL+Y7fJjr8GGuwydUc22MUWNjo5Iu8VW5AYWRQYMGKSoqqtUqSH19favVjwsSEhLa7B8dHa2BAwe2OcbpdMrpdPq19e/fP5BSAxIXF8cbO0yY6/BivsOHuQ4f5jp8QjHXF1sRuaBXIE8YExOjtLQ0lZaW+rWXlpYqMzOzzTEZGRmt+u/evVvp6eltXi8CAAB6loDCiCTl5+dr7dq1Wr9+vY4cOaKFCxeqpqbG970hBQUFys3N9fXPy8vTiRMnlJ+fryNHjmj9+vVat26dHnnkkdC9CgAAELECvmZk+vTpOn36tJYvXy63263U1FSVlJRo+PDhkiS3262amhpf/5SUFJWUlGjhwoV69tlnlZSUpGeeeaZLfMeI0+nUkiVLWp0SQugx1+HFfIcPcx0+zHX4hHuuA/6eEQAAgFAK+DQNAABAKBFGAACAVYQRAABgFWEEAABY1aPDSFFRkVJSUhQbG6u0tDTt27fPdkkRp6ysTFOmTFFSUpIcDod27Njh97gxRkuXLlVSUpL69OmjH/zgBzp06JBfn6amJj300EMaNGiQ+vbtqx/+8If66KOPwvgqur7CwkJ973vfU79+/TRkyBBNnTpV77//vl8f5jo0Vq9erRtuuMH3ZU8ZGRn629/+5nucee48hYWFcjgcWrBgga+N+Q6dpUuXyuFw+G0JCQm+x63Otemhtm7danr37m3++Mc/msOHD5v58+ebvn37mhMnTtguLaKUlJSYxYsXm23bthlJ5uWXX/Z7/MknnzT9+vUz27ZtM9XV1Wb69OkmMTHReL1eX5+8vDxzxRVXmNLSUnPw4EEzYcIEM2rUKHPu3Lkwv5quKzs722zYsMG89957pqqqykyePNkMGzbMnDlzxteHuQ6NnTt3mr/+9a/m/fffN++//7557LHHTO/evc17771njGGeO8s///lPc+WVV5obbrjBzJ8/39fOfIfOkiVLzHXXXWfcbrdvq6+v9z1uc657bBi56aabTF5enl/bd77zHbNo0SJLFUW+r4eR8+fPm4SEBPPkk0/62r744gvjcrnMc889Z4wx5tNPPzW9e/c2W7du9fU5efKk6dWrl3n11VfDVnukqa+vN5LM3r17jTHMdWe7/PLLzdq1a5nnTtLY2GiuvfZaU1paam699VZfGGG+Q2vJkiVm1KhRbT5me6575Gma5uZmVVRUKCsry689KytLBw4csFRV93P8+HHV1dX5zbPT6dStt97qm+eKigp9+eWXfn2SkpKUmprKv8VFeDweSdKAAQMkMdedpaWlRVu3btXZs2eVkZHBPHeSOXPmaPLkybr99tv92pnv0Dt69KiSkpKUkpKiu+++W8eOHZNkf66D+tXeSNfQ0KCWlpZWP+4XHx/f6kf9ELwLc9nWPJ84ccLXJyYmRpdffnmrPvxbtM0Yo/z8fN1yyy1KTU2VxFyHWnV1tTIyMvTFF1/osssu08svv6zvfve7vv/gMs+hs3XrVh08eFBvv/12q8d4X4fW2LFjtWnTJo0YMUIff/yxnnjiCWVmZurQoUPW57pHhpELHA6H374xplUbvrlg5pl/i/bNnTtX7777rt54441WjzHXofHtb39bVVVV+vTTT7Vt2zbNnDlTe/fu9T3OPIdGbW2t5s+fr927dys2Nrbdfsx3aOTk5Pj+9/XXX6+MjAxdffXV+tOf/qSbb75Zkr257pGnaQYNGqSoqKhWSa6+vr5VKkTwLlylfbF5TkhIUHNzs/773/+22wf/56GHHtLOnTv12muvaejQob525jq0YmJidM011yg9PV2FhYUaNWqUnn76aeY5xCoqKlRfX6+0tDRFR0crOjpae/fu1TPPPKPo6GjffDHfnaNv3766/vrrdfToUevv7R4ZRmJiYpSWlqbS0lK/9tLSUmVmZlqqqvtJSUlRQkKC3zw3Nzdr7969vnlOS0tT7969/fq43W699957/Fv8P8YYzZ07V9u3b9eePXuUkpLi9zhz3bmMMWpqamKeQ2zixImqrq5WVVWVb0tPT9fPfvYzVVVV6aqrrmK+O1FTU5OOHDmixMRE++/tb3T5awS7cGvvunXrzOHDh82CBQtM3759zb///W/bpUWUxsZGU1lZaSorK40ks2LFClNZWem7RfrJJ580LpfLbN++3VRXV5uf/vSnbd4qNnToUPP3v//dHDx40Nx2223clvc1v/jFL4zL5TKvv/663215n332ma8Pcx0aBQUFpqyszBw/fty8++675rHHHjO9evUyu3fvNsYwz53t/99NYwzzHUoPP/ywef31182xY8fMW2+9Ze68807Tr18/3+eezbnusWHEGGOeffZZM3z4cBMTE2PGjBnju00SHffaa68ZSa22mTNnGmO+ul1syZIlJiEhwTidTvP973/fVFdX+z3H559/bubOnWsGDBhg+vTpY+68805TU1Nj4dV0XW3NsSSzYcMGXx/mOjTuv/9+338XBg8ebCZOnOgLIsYwz53t62GE+Q6dC98b0rt3b5OUlGR+8pOfmEOHDvketznXDmOM+WZrKwAAAMHrkdeMAACAroMwAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwKr/AccZN27BrqREAAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -708,458 +280,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "c2b46571-b217-4962-9578-631740a17466", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "species mole percent\n", - "C.......................................................................................................24.35%\n", - "C=CC....................................................................................................20.65%\n", - "miscillaneous large molecules............................................................................9.93%\n", - "CCC......................................................................................................1.39%\n", - "C=CC=CC..................................................................................................1.29%\n", - "C=CCCC...................................................................................................1.09%\n", - "C=CCC(C)C=CC.............................................................................................1.06%\n", - "C=C(C)C=CC...............................................................................................0.84%\n", - "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC.....................................................................0.77%\n", - "CC=CCC...................................................................................................0.77%\n", - "C=C(C)CCC................................................................................................0.76%\n", - "CC=CC(C)CCC..............................................................................................0.74%\n", - "C=CCC(C)CC(C)CC(C)CCC....................................................................................0.71%\n", - "C=CCC(=C)C...............................................................................................0.67%\n", - "C=CCC=C..................................................................................................0.66%\n", - "C=C(C)C...................................................................................................0.6%\n", - "CC=CC(C)C=CC.............................................................................................0.59%\n", - "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC...............................................................0.58%\n", - "CC1=CCCC1................................................................................................0.57%\n", - "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC..................................................................0.55%\n", - "CCCCC....................................................................................................0.52%\n", - "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC.................................................................0.5%\n", - "C=C(C)CC(C)C=CC..........................................................................................0.49%\n", - "CCCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C........................................................................0.45%\n", - "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC............................................................0.45%\n", - "C=CCC(C)CC(C)CC(C)CC(C)C=CC..............................................................................0.44%\n", - "CC=CC(C)C................................................................................................0.44%\n", - "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC....................................................................0.42%\n", - "C=C(C)CC(C)CC(C)CC(C)CCC.................................................................................0.42%\n", - "C=CCC(C)CCC..............................................................................................0.41%\n", - "C=CCC(C)CC(=C)C..........................................................................................0.41%\n", - "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC......................................................................0.4%\n", - "CC=CC(C)CC(C)CC(C)CC(C)CCC................................................................................0.4%\n", - "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC.......................................................0.4%\n", - "CCCC(C)CC(C)CC(C)C.......................................................................................0.39%\n", - "CC1=CC=CC1...............................................................................................0.39%\n", - "C=CCC(C)CC(C)CC(C)C......................................................................................0.38%\n", - "C=C(C)CC(C)CCC...........................................................................................0.38%\n", - "CC=CC(C)CC(C)CC(C)CCC....................................................................................0.38%\n", - "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C.......................................................................0.37%\n", - "CCCC(C)CC(C)CC(C)CCC.....................................................................................0.37%\n", - "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(=C)C.................................................................0.36%\n", - "CC=CC(C)CC(C)CC(C)CC(C)C.................................................................................0.35%\n", - "CCCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC......................................................................0.35%\n", - "CC=CC(C)CC(C)C=CC........................................................................................0.34%\n", - "C=CCC(C)CC(C)CC(C)C=CC...................................................................................0.34%\n", - "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC.................................................................0.33%\n", - "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC................................................0.33%\n", - "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC...................................................0.32%\n", - "C=C(C)CC(C)CC(C)CC(C)CC(C)C=CC...........................................................................0.32%\n", - "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC.....................................................0.31%\n", - "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC=C....................................................................0.29%\n", - "C=CCC(C)CC(C)CC(C)CC(C)CC(=C)C...........................................................................0.28%\n", - "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C..................................................................0.28%\n", - "CCCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C.........................................................0.28%\n", - "CC1=CCC=C1...............................................................................................0.28%\n", - "C=C(C)CC(C)CC(C)CC(C)C=CC................................................................................0.28%\n", - "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC.................................................0.25%\n", - "C=CCC(C)CC(C)CC(C)CC=C...................................................................................0.25%\n", - "C=CCC(C)CC(C)C=CC........................................................................................0.25%\n", - "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(=C)C............................................................0.24%\n", - "C=CCC(C)CC(C)CC(C)CC(=C)C................................................................................0.24%\n", - "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC...............................................................0.24%\n", - "C=C(C)CC(C)CC(C)C=CC.....................................................................................0.24%\n", - "CC=CC(C)CC(C)CC(C)CC(C)CC(C)C=CC.........................................................................0.24%\n", - "C=C(C)CC(C)C.............................................................................................0.22%\n", - "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C....................................................................0.22%\n", - "C=CCC(C)C................................................................................................0.22%\n", - "C=C(C)CC(C)CC(C)CC(C)C...................................................................................0.21%\n", - "C=CCC(C)CC(C)CC(C)CC(C)C.................................................................................0.21%\n", - "C=CCC(C)CC=C.............................................................................................0.21%\n", - "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC......................................................0.21%\n", - "C=C(C)CC(=C)C............................................................................................0.21%\n", - "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C.........................................................0.2%\n", - "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC...........................................................0.2%\n", - "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC.............................................0.19%\n", - "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC.............................................................0.19%\n", - "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC................................................................0.19%\n", - 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"C=CC=CCCC=C...............................................................................................0.0%\n", - "C=CCCC1=CC=CC1............................................................................................0.0%\n" - ] - } - ], + "outputs": [], "source": [ "results_dictionary = {}\n", "for fraglist, amt in fl.grouped:\n", @@ -1167,7 +291,7 @@ " if len(fraglist) == 1:\n", " new = fraglist[0].smiles\n", " elif len(fraglist) < 6:\n", - " new = FragList.merge_frag_list(fraglist)[0].smiles\n", + " new = frag.merge_frag_list(fraglist)[0].smiles\n", " else:\n", " new = 'miscillaneous large molecules'\n", " if new in results_dictionary.keys():\n", @@ -1185,7 +309,7 @@ ], "metadata": { "kernelspec": { - "display_name": "rmg_noj", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1199,7 +323,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.9.23" } }, "nbformat": 4, diff --git a/rmgpy/molecule/fragment_utils.py b/rmgpy/molecule/fragment_utils.py index 25fe5e5cb5..da8638c6cd 100644 --- a/rmgpy/molecule/fragment_utils.py +++ b/rmgpy/molecule/fragment_utils.py @@ -1,11 +1,411 @@ -from rmgpy.molecule.fragment import Fragment -from rmgpy.tools.canteramodel import Cantera -from rmgpy.chemkin import load_chemkin_file +import random import re import os import numpy as np +import sys import matplotlib.pyplot as plt +from rmgpy.molecule import Bond +from rmgpy.molecule.fragment import Fragment, CuttingLabel +from rmgpy.tools.canteramodel import Cantera +from rmgpy.chemkin import load_chemkin_file + +sys.setrecursionlimit(20000) + +class FragList(): + ''' + to instantiate a FragList: + fl = Fraglist(frag_list) + where frag_list is a list of tuples of fragments and their amounts + ''' + + def __init__(self, frag_list): + self.raw_fragment_output = frag_list + + def sort(self): + ''' + sort a FragList into + general_R_list - 2R fragments + general_L_list - 2L fragments + rr_ll_list - 2R or 2L fragments + r_l_moles - 1R and 1L fragments + multi_label_frag_3 - fragments with 3 cutting labels + multi_label_frag_4 - fragments with 4 cutting labels + + note: in our experience fragments with more than 4 cutting labels has never happened, but a warning will be printed if it does happen + ''' + moles_remain = [] + one_R_dict = {} + one_L_dict = {} + general_R_list = [] + general_L_list = [] + rr_ll_list = [] + r_l_moles = [] + multi_label_frag_3 = [] + multi_label_frag_4 = [] + + for i, item in enumerate(self.raw_fragment_output): + frag, amt = item + if amt > 1e-6 and '[' not in frag: + count_of_L_labels = len(re.findall(r'L', frag)) + count_of_R_labels = len(re.findall(r'R', frag)) + count_of_cutting_labels = count_of_L_labels + count_of_R_labels + if count_of_R_labels == 0 and count_of_L_labels == 0: + moles_remain.append((frag, amt)) + elif count_of_R_labels == 1 and count_of_L_labels == 0: + one_R_dict[frag] = amt + elif count_of_R_labels == 2 and count_of_L_labels == 0: + general_R_list.append((frag, amt * 2)) + rr_ll_list.append(frag) + elif count_of_R_labels == 0 and count_of_L_labels == 1: + one_L_dict[frag] = amt + elif count_of_R_labels == 0 and count_of_L_labels == 2: + general_L_list.append((frag, amt * 2)) + rr_ll_list.append(frag) + elif count_of_R_labels == 1 and count_of_L_labels == 1: + r_l_moles.append((frag, amt)) + else: + if count_of_cutting_labels == 3: + multi_label_frag_3.append( + (frag, amt)) # 2R1L, 1R2L, 3R, 3L + elif count_of_cutting_labels == 4: + multi_label_frag_4.append((frag, amt)) + else: + print( + f"Warning! {count_of_cutting_labels} cutting labels in {frag}") + self.R1dict=one_R_dict + self.L1dict=one_L_dict + self.Rlist=general_R_list + self.Llist=general_L_list + self.RRLLlist=rr_ll_list + self.RLlist=r_l_moles + self.CL3=multi_label_frag_3 + self.CL4=multi_label_frag_4 + self.molesremain=moles_remain + + def random_pick_frag(target_dict): + ''' + argument(s): target_dict - dictionary where key = species smiles (fragment or molecule), value = moles + returns: tuple of randomly picked (fragment with 1 cutting label, moles) + + choice is weighted by mole fraction of each fragment + ''' + frag_dict_list=[x for x in target_dict.items() if len( + re.findall(r'[LR]', x[0])) == 1] + sum_dict=sum([x[1] for x in frag_dict_list]) + frag_dict_prob=[x[1] / sum_dict for x in frag_dict_list] + item=np.random.choice(frag_dict_list, 1, p = frag_dict_prob) + + return item + + def pair_frag(amount, target_dict): + ''' + argument(s): amount - maximum amount of fragments in one pair + target_dict - dictionary of species smiles and moles + returns: the target_dict with 1 randomly chosen 1-cutting label fragment fully paired with other randomly chosen 1-cutting label fragments + ''' + additional_frag_list=[] + frag1=FragList.random_pick_frag(target_dict) + + if target_dict[frag1] >= amount: + target_dict[frag1] -= amount + additional_frag_list.append((frag1, amount)) + + else: + remain=amount - target_dict[frag1] + additional_frag_list.append((frag1, amount)) + target_dict[frag1]=0 + + while remain > 0: + frag1=FragList.random_pick_frag(target_dict) + + if target_dict[frag1] >= remain: + target_dict[frag1] -= remain + additional_frag_list.append((frag1, remain)) + remain=0 + + else: + frag_amt=target_dict[frag1] + target_dict[frag1]=0 + additional_frag_list.append((frag1, frag_amt)) + remain=remain - frag_amt + return additional_frag_list + + def pair_CL4s(self): + ''' + pairs all 4-cutting label fragments with other randomly picked 1-cutting label fragments, creating 3-cutting label fragments + ''' + + for species, amount in self.CL4: # 4R, 3R1L, 2R2L, 1R3L, 4L + ount_of_R_labels=len(re.findall(r'R', species)) + count_of_L_labels=len(re.findall(r'L', species)) + if count_of_R_labels == 4 and count_of_L_labels == 0: + paired_frag_list=FragList.pair_frag(amount, self.L1dict) + for frag_amt in paired_frag_list: + frag=frag_amt[0] + amt=frag_amt[1] + frag1=frag # 1L + frag2=species # 4R + frag_new=merge_frag_to_frag(frag1, frag2, 'R') # L,R,R -> 3 + self.CL3.append((frag_new, amt)) + + elif count_of_R_labels == 0 and count_of_L_labels == 4: + paired_frag_list = FragList.pair_frag(amount, self.R1dict) + for frag_amt in paired_frag_list: + frag = frag_amt[0] + amt = frag_amt[1] + frag1 = frag # 1R + frag2 = species # 4L + frag_new = merge_frag_to_frag(frag2, frag1, 'R') # L,R,R -> 3L + self.CL3.append((frag_new, amt)) + + elif count_of_R_labels == 2 and count_of_L_labels == 2: + paired_frag_list = FragList.pair_frag(amount, self.L1dict) + for frag_amt in paired_frag_list: + frag = frag_amt[0] + amt = frag_amt[1] + frag1 = frag # 1L + frag2 = species # 2R2L + frag_new = merge_frag_to_frag(frag1, frag2, 'R') # L,R,R -> 1R2L + self.CL3.append((frag_new, amt)) + + elif count_of_R_labels == 3 and count_of_L_labels == 1: + paired_frag_list = FragList.pair_frag(amount, self.R1dict) + for frag_amt in paired_frag_list: + frag = frag_amt[0] + amt = frag_amt[1] + frag1 = frag # 1R + frag2 = species # 3R1L + frag_new = merge_frag_to_frag(frag2, frag1, 'R') # L,R,R -> 3R + self.CL3.append((frag_new, amt)) + + elif count_of_R_labels == 1 and count_of_L_labels == 3: + paired_frag_list = FragList.pair_frag(amount, self.L1dict) + for frag_amt in paired_frag_list: + frag = frag_amt[0] + amt = frag_amt[1] + frag1 = frag # 1L + frag2 = species # 1R3L + frag_new = merge_frag_to_frag(frag1, frag2, 'R') # L,R,R -> 3L + self.CL3.append((frag_new, amt)) + + def pair_CL3s(self): + ''' + pairs all 3-cutting label fragments with other randomly picked 1-cutting label fragments, creating 2-cutting label fragments + ''' + + for species, amount in self.CL3: + count_of_R_labels = len(re.findall(r'R', species)) + count_of_L_labels = len(re.findall(r'L', species)) + if count_of_R_labels == 2 and count_of_L_labels == 1: + paired_frag_list = FragList.pair_frag(amount, self.R1dict) + for frag_amt in paired_frag_list: + frag = frag_amt[0] + amt = frag_amt[1] + frag1 = frag # 1R + frag2 = species # 2R1L + frag_new = merge_frag_to_frag(frag2, frag1, 'R') # L,R,R + self.Rlist.append((frag_new, amt * 2)) + self.RRLLlist.append(frag_new) + + elif count_of_R_labels == 1 and count_of_L_labels == 2: + paired_frag_list = FragList.pair_frag(amount, self.L1dict) + for frag_amt in paired_frag_list: + frag = frag_amt[0] + amt = frag_amt[1] + frag1 = frag # 1L + frag2 = species # 1R2L + frag_new = merge_frag_to_frag(frag1, frag2, 'R') # L,R,R + self.Llist.append((frag_new, amt * 2)) + self.RRLLlist.append(frag_new) + + elif count_of_R_labels == 3 and count_of_L_labels == 0: + paired_frag_list = FragList.pair_frag(amount, self.L1dict) + for frag_amt in paired_frag_list: + frag = frag_amt[0] + amt = frag_amt[1] + frag1 = frag # 1L + frag2 = species # 3R + frag_new = merge_frag_to_frag(frag1, frag2, 'R') # L,R,R + self.Rlist.append((frag_new, amt * 2)) + self.RRLLlist.append(frag_new) + + # 3L + elif count_of_R_labels == 0 and count_of_L_labels == 3: + paired_frag_list = FragList.pair_frag(amount, self.R1dict) + for frag_amt in paired_frag_list: + frag = frag_amt[0] + amt = frag_amt[1] + frag1 = frag # 1R + frag2 = species # 3L + frag_new = merge_frag_to_frag(frag2, frag1, 'R') # L,R,R + self.Llist.append((frag_new, amt * 2)) + self.RRLLlist.append(frag_new) + + def update_lists(self): + ''' + adds the 1-cuttinglabel fragments remaining after pairing 4- and 3-cutting label fragments to their corresponding list + ''' + for one_R_frag, amt in self.R1dict.items(): + self.Rlist.append((one_R_frag, amt)) + for one_L_frag, amt in self.L1dict.items(): + self.Llist.append((one_L_frag, amt)) + + def grind(conc, size): + ''' + Split fragment concentrations into several repeating concentration units with specified size + ''' + grinded_conc = [] + for label, c in conc: + times = int(c / size) + grinded_conc.extend([(label, size)] * times) + + if c - size * times > 0: + grinded_conc.append((label, c - size * times)) + + return grinded_conc + + + def grind_endcaps(self, grindsize=1, repeats=1): + ''' + splits each fragment concentration in to many subconcentrations of a maximum of grindsize + ''' + print('grinding endcaps to {}'.format(grindsize)) + self.repeats = repeats + if self.repeats == 1: + self.grindsize = grindsize + seed = np.random.randint(0, 100) + grmoles = FragList.grind(self.Rlist, self.grindsize) + glmoles = FragList.grind(self.Llist, self.grindsize) + self.glmoles = shuffle(glmoles, seed) + self.grmoles = shuffle(grmoles, seed+1) + else: + print('repeats > 1 not supported') + + def pair_endcaps(self): + ''' + randomly pairs subconcentrations of endcaps and assigns the correct amount for each pair + 2L and 2R fragments will be paired with 2 1-cuttinglabel fragments + the remaining fragment subconcentrations are middle pieces + ''' + matches = match_concentrations_with_same_sums(self.glmoles, + self.grmoles, + rtol=1e-3) + self.endcaps = [] + self.middles = [] + for match in matches: + pair = match[0] + value = match[1] + if value > 0.0: + lfrag, rfrag = pair + if lfrag not in self.RRLLlist: + if rfrag not in self.RRLLlist: + self.endcaps.append((pair, value)) + elif rfrag in self.RRLLlist: + self.endcaps.append(((lfrag, rfrag, lfrag), value/2)) + else: + raise Error + elif lfrag in self.RRLLlist: + if rfrag not in self.RRLLlist: + self.endcaps.append(((rfrag, lfrag, rfrag), value/2)) + elif rfrag in self.RRLLlist: + self.middles.append((pair, value/2)) + else: + raise Error + else: + raise Error + + def grind_middles(self): + ''' + grinds the middle 1L1R fragments into subconcentrations + the same grindsize is used as for the endcaps + ''' + print('grinding middle pieces to {}'.format(self.grindsize)) + if self.repeats == 1: + grinded_r_l_moles = FragList.grind(self.RLlist, self.grindsize) + seed = np.random.randint(0, 100) + self.middles = self.middles + \ + shuffle(grinded_r_l_moles, seed) + else: + print('repeats > 1 not supported') + + def distribute_middles(self): + ''' + loop through the middle pieces + randomly pick an endcap pair to add to + repeat until no more middle pieces + ''' + + r_l_frag_distri_amt = [0 for i in range(len(self.endcaps))] + r_l_frag_distri_dict = [{} for i in range(len(self.endcaps))] + for i, r_l_tup in enumerate(self.middles): + r_l_frag = r_l_tup[0] + r_l_frag_amount = r_l_tup[1] + rand_idx = random.randrange(len(self.endcaps)) + threshold = 1e-15 + while self.endcaps[rand_idx][1] <= threshold: + rand_idx = random.randrange(len(self.endcaps)) + r_l_frag_distri_amt[rand_idx] += r_l_frag_amount + r_l_dict = r_l_frag_distri_dict[rand_idx] + if r_l_frag in r_l_dict.keys(): + r_l_dict[r_l_frag] += r_l_frag_amount + else: + r_l_dict[r_l_frag] = r_l_frag_amount + matches_random = [] + for i in range(len(self.endcaps)): + pair = self.endcaps[i] + if r_l_frag_distri_dict[i].keys(): + r_l_matched = [] + for r_l_frag, amt in r_l_frag_distri_dict[i].items(): + r_l_matched.append((r_l_frag, amt)) + match_list = match_concentrations_with_different_sums([ + pair], r_l_matched) + for tups in match_list: + matches_random.append(tups) + else: + matches_random.append(pair) + flattened_matches_random = [ + (tuple(flatten(m[0])), m[1]) for m in matches_random] + + self.grouped = [] + + for non_cut_mole, val in self.molesremain: + self.grouped.append(((non_cut_mole, ), val)) + + self.grouped.extend(flattened_matches_random) + + def get_mwd(self, bins=10, fname='mwd.png'): + ''' + loop through molecules i.e. grouped fragments + calculate molecular weight + create histogram of molecular weights, weighted by molar amount + store histogram data in histdata + ''' + self.mwd_amts = [x[1] for x in self.grouped] + self.mwd_mws = [] + for fraglist, amt in self.grouped: + mw = sum([Fragment().from_smiles_like_string( + frag).get_molecular_weight()*1000 for frag in fraglist]) + self.mwd_mws.append(mw) + + self.histdata = plt.hist( + self.mwd_mws, bins=bins, weights=self.mwd_amts) + plt.xlabel("Molecular Weight (g/mol)") + plt.ylabel("Moles") + + def reattach(self, grindsize = 1): + ''' + this parent function combines the steps of reattachment into one + ''' + self.sort() # sort the fragments by number and type of cutting labels + self.pair_CL4s() # reattach 4-cutting label fragments to make 3-cutting label fragments + self.pair_CL3s() # reattach 3-cutting label fragments to make 2-cutting label fragments + self.update_lists() # add 1-cutting label fragments to either Rlist or Llist + self.grind_endcaps(grindsize=grindsize) # grind the concentrations of fragments into smaller sub-concentrations + self.pair_endcaps() # pair together 1R and 1L fragments to make endcap pairs + self.grind_middles() # grind the concentrations of middle LR fragments into smaller sub-concentrations of same size as above + self.distribute_middles() # randomly select an endcap pair for each middle LR fragment + return self def match_sequences(seq1, seq2, rtol=1e-6): ''' @@ -98,7 +498,7 @@ def match_concentrations_with_same_sums(conc1, conc2, rtol=1e-6): seq1 = [tup[1] for tup in conc1] seq2 = [tup[1] for tup in conc2] - matches_seq = FragList.match_sequences(seq1, seq2, rtol) + matches_seq = match_sequences(seq1, seq2, rtol) matches_conc = [] for match_seq in matches_seq: @@ -184,7 +584,7 @@ def match_concentrations_with_different_sums(conc1, conc2): # let matches_conc match with remaining seq2 elif pin1 == len(seq1) and pin2 < len(seq2): remain_conc2 = [(labels2[pin2], val2)] + conc2[(pin2 + 1):] - matches_conc = FragList.match_concentrations_with_different_sums( + matches_conc = match_concentrations_with_different_sums( matches_conc, remain_conc2 ) @@ -216,7 +616,7 @@ def flatten(combo): return_list = [] for i in combo: if isinstance(i, tuple): - return_list.extend(FragList.flatten(i)) + return_list.extend(flatten(i)) else: return_list.append(i) return return_list @@ -224,8 +624,6 @@ def flatten(combo): # label should match the desired merging l/'abel on frag2 def merge_frag_to_frag(frag1, frag2, label): - from rmgpy.molecule import Bond - from rmgpy.molecule.fragment import Fragment, CuttingLabel frag_spe1 = Fragment().from_smiles_like_string(frag1) frag_spe2 = Fragment().from_smiles_like_string(frag2) @@ -264,7 +662,6 @@ def merge_frag_to_frag(frag1, frag2, label): def merge_frag_list(to_be_merged): - import os # merges fragments in list from right to left species_list = [] ethylene = [] @@ -278,10 +675,10 @@ def merge_frag_list(to_be_merged): frag2 = to_be_merged[-1].smiles # last fragment in list if 'R' in frag1 and 'L' in frag2: - newfrag = FragList.merge_frag_to_frag(frag1, frag2, 'L') + newfrag = merge_frag_to_frag(frag1, frag2, 'L') elif 'L' in frag1 and 'R' in frag2: - newfrag = FragList.merge_frag_to_frag(frag1, frag2, 'R') + newfrag = merge_frag_to_frag(frag1, frag2, 'R') # warn user if last two fragments in list cannot be merged (no R/L # combo to be made) @@ -290,7 +687,7 @@ def merge_frag_list(to_be_merged): frag1, frag2)) if 'L' in frag1 and 'L' in frag2: - newfrag = FragList.merge_frag_to_frag( + newfrag = merge_frag_to_frag( frag1.replace('L', 'R'), frag2, 'L') if len(to_be_merged) > 2: cut = len(to_be_merged) - 2 @@ -308,5 +705,4 @@ def merge_frag_list(to_be_merged): # structure to list of smiles structures # print('{}% of fragments fully merged...'.format(np.round(100*(i+1)/len(flattened_matches_random)),1)) -# print(newfraglist) return newfraglist From 67d2895915982ac5b15d5742f889c26768dd7f2c Mon Sep 17 00:00:00 2001 From: jonwzheng Date: Wed, 9 Jul 2025 20:29:43 -0400 Subject: [PATCH 17/17] keep full fragment reattachment example output --- ipython/fragment_reattachment_example.ipynb | 473 +++++++++++++++++++- 1 file changed, 466 insertions(+), 7 deletions(-) diff --git a/ipython/fragment_reattachment_example.ipynb b/ipython/fragment_reattachment_example.ipynb index 6267985844..8d7b0c6cda 100644 --- a/ipython/fragment_reattachment_example.ipynb +++ b/ipython/fragment_reattachment_example.ipynb @@ -194,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "id": "ebe147a1-ad99-4320-8813-45b2141353f8", "metadata": { "tags": [] @@ -214,7 +214,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -248,13 +248,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "id": "83a0e056-66dd-4e79-b4b0-f6b74c518a58", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -280,10 +280,469 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "c2b46571-b217-4962-9578-631740a17466", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "species mole percent\n", + "C.......................................................................................................24.35%\n", + "C=CC....................................................................................................20.71%\n", + "miscellaneous large molecules............................................................................8.93%\n", + "CCC......................................................................................................1.53%\n", + "C=CC=CC...................................................................................................1.2%\n", + "C=CCCC...................................................................................................1.11%\n", + "C=C(C)C=CC...............................................................................................0.96%\n", + "C=CCC(C)C=CC..............................................................................................0.8%\n", + "CC=CC(C)CCC..............................................................................................0.73%\n", + "C=CCC(C)CC(C)CC(C)CCC....................................................................................0.73%\n", + "CC=CCC...................................................................................................0.72%\n", + "C=C(C)CCC.................................................................................................0.7%\n", + "C=CCC(=C)C...............................................................................................0.62%\n", + "C=C(C)CC(C)C=CC...........................................................................................0.6%\n", + "C=CCC(C)CC(=C)C...........................................................................................0.6%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC.....................................................................0.57%\n", + "C=CCC(C)CC(C)CC(C)CC(C)C=CC..............................................................................0.57%\n", + "CC1=CCCC1................................................................................................0.57%\n", + "CCCCC....................................................................................................0.56%\n", + "C=CCC(C)CC(C)CC(C)C=CC...................................................................................0.55%\n", + "C=C(C)C..................................................................................................0.54%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC................................................................0.51%\n", + "CC=CC(C)C.................................................................................................0.5%\n", + "C=C(C)CC(C)CC(C)CC(C)CCC.................................................................................0.48%\n", + "C=CCC(C)CC(C)CC(C)C......................................................................................0.46%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC...............................................................0.45%\n", + "CCCC(C)CC(C)CC(C)CCC.....................................................................................0.44%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CCC...............................................................................0.43%\n", + "C=CCC=C..................................................................................................0.43%\n", + "CC=CC(C)CC(C)C=CC........................................................................................0.43%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC.....................................................................0.43%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC..................................................................0.43%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)C=CC...........................................................................0.43%\n", + "C=CCC(C)CC(C)CC(C)CC(=C)C................................................................................0.42%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC....................................................................0.41%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(=C)C.................................................................0.41%\n", + "CC=CC(C)CC(C)CC(C)CC(C)C.................................................................................0.39%\n", + "C=CCC(C)C................................................................................................0.39%\n", + "CC1=CC=CC1...............................................................................................0.39%\n", + "CC=CC(C)CC(C)CC(C)CCC....................................................................................0.38%\n", + "CC=CC(C)C=CC.............................................................................................0.38%\n", + "C=CCC(C)CCC..............................................................................................0.38%\n", + "CCCC(C)CC(C)CC(C)C.......................................................................................0.36%\n", + "C=CCC(C)CC(C)CC(C)CC=C...................................................................................0.36%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC......................................................0.35%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C.......................................................................0.35%\n", + "CCCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C........................................................................0.34%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC............................................................0.32%\n", + "C=C(C)CC(=C)C............................................................................................0.32%\n", + "C=C(C)CC(C)CCC...........................................................................................0.32%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC.................................................0.32%\n", + "CCCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC......................................................................0.32%\n", + "CC=CC(C)CC(C)CC(C)CC(C)C=CC...............................................................................0.3%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC................................................................0.3%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC..................................................................0.3%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC.................................................0.3%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(=C)C...........................................................................0.29%\n", + "C=C(C)CC(C)CC(C)C=CC.....................................................................................0.29%\n", + "C=C(C)CC(C)CC(C)CC(C)C=CC................................................................................0.29%\n", + "C=CCC(C)CC(C)C=CC........................................................................................0.28%\n", + "CC1=CCC=C1...............................................................................................0.28%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(=C)C............................................................0.27%\n", + "CCCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C.........................................................0.26%\n", + "C=CCC(C)CC(C)CC(C)CC(C)C.................................................................................0.25%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC.............................................................0.25%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC.....................................................0.25%\n", + "C=C(C)CC(C)C.............................................................................................0.25%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(=C)C..................................................0.24%\n", + "C=C(C)CC(C)CC(C)CC(C)C...................................................................................0.23%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC...................................................0.23%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C..................................................................0.23%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC=C....................................................................0.23%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C........................................................0.23%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CCC............................................................................0.22%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC..................................................0.22%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CC(C)C=CC..........................................................................0.2%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC.......................................................0.2%\n", + "C=CCC(C)CC=C..............................................................................................0.2%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC.......................................................................0.2%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC=C..............................................................................0.19%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CCC...............................................................................0.19%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC................................................0.19%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C..................................................................0.19%\n", + "C1=CCCC1.................................................................................................0.18%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC=C...............................................................0.18%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC.............................................0.18%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)C=CC.........................................................................0.18%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C....................................................................0.17%\n", + "C=C(C)CC(C)CC(=C)C.......................................................................................0.17%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C...................................................0.17%\n", + "CCC=C(C)C................................................................................................0.16%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC=C.....................................................0.16%\n", + "CC=CC(C)CC(C)C...........................................................................................0.16%\n", + "CCCC(C)C.................................................................................................0.16%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC..........................................................0.15%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C.....................................................0.15%\n", + "CCCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC.......................................................0.15%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CC(C)C............................................................................0.15%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)C..............................................................................0.15%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC................................................................0.14%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(=C)C.............................................................................0.14%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC.......................................................0.14%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC..........................................................0.14%\n", + "C=CC=C(C)C...............................................................................................0.14%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(=C)C.............................................0.13%\n", + "CC=CC(C)CC(C)CCC.........................................................................................0.13%\n", + "C=C......................................................................................................0.13%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC..............................................0.12%\n", + "C1=CCC=C1................................................................................................0.12%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC........................................0.11%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(=C)C..............................................................0.11%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C...................................................0.11%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC.................................................0.11%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC=C................................................0.11%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(=C)C.........................................................................0.1%\n", + "CCCCC(C)CC(C)CC(C)C.......................................................................................0.1%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C..............................................................0.1%\n", + "C=CC=CCC(C)CC(C)CC(C)C....................................................................................0.1%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC...........................................0.09%\n", + "CCCC(C)CC(C)CC(C)C=C(C)C.................................................................................0.09%\n", + "CCCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=C(C)C..................................................................0.09%\n", + "CCCC(C)CC(C)CC(C)CCC(C)CC(C)CC(C)C.......................................................................0.09%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC...........................................0.09%\n", + "C=CC=CCC(C)CC(C)CC(C)CCC.................................................................................0.08%\n", + "C=CC=CCCC................................................................................................0.08%\n", + "CCC=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC................................................................0.08%\n", + "C=CC=CCC(=C)C............................................................................................0.08%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC...........................................................0.08%\n", + "C=CCC(C)CC(C)CC=C........................................................................................0.08%\n", + "C=CCC(C)CC(C)CC(C)CC(C)=CCC..............................................................................0.08%\n", + "C=CC=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC=C.................................................................0.08%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CCC..........................................................................0.07%\n", + "CC=CC(C)CC(C)CC(C)C=CC...................................................................................0.07%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(=C)C.......................................................0.07%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C...............................................................0.07%\n", + "CC=CC(C)CC(C)CC(C)CC(C)=CCC..............................................................................0.07%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C................................................0.07%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)=CCC...............................................................0.07%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(=C)C.........................................................0.07%\n", + "CC(C)C...................................................................................................0.07%\n", + "C=CC=CCC(C)CC(C)CC(C)C=CC................................................................................0.07%\n", + "CCC=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CCC.................................................0.07%\n", + "C=CCC(C)CC(C)CC(=C)C.....................................................................................0.07%\n", + "CCC=C(C)CCC..............................................................................................0.06%\n", + "CC=CCC(C)CC(C)CC(C)C.....................................................................................0.06%\n", + "C=CC=CC=CC...............................................................................................0.06%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)C............................................................................0.06%\n", + "CCCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C..........................................0.06%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)=CCC...........................................................................0.06%\n", + "C=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C.............................................................0.06%\n", + "C=CC=CCC=C...............................................................................................0.06%\n", + "C=CC=CCC(C)CC(C)CC(C)CC=C................................................................................0.06%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(=C)C...............................................0.06%\n", + "C=CC=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)C=CC.................................................................0.06%\n", + "C=CCC(C)CC(C)CC(C)C=C(C)C................................................................................0.06%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)=CCC..........................................................0.06%\n", + "C=C(C)C=C(C)C............................................................................................0.06%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)=CCC...........................................0.06%\n", + "C=CC=CCC(C)CC(C)CC(C)CC(=C)C.............................................................................0.05%\n", + "CCCC(C)CC(C)CC(C)CC(C)C..................................................................................0.05%\n", + "C=CC=CCC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC=C..................................................0.05%\n", + "CCC=C(C)CC(C)CC(C)CC(C)CCC...............................................................................0.05%\n", + "CC=CC(C)=CCC.............................................................................................0.05%\n", + "C=C(C)C=CCC(C)CC(C)CC(C)C=CC.............................................................................0.05%\n", + "C=C(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(C)CC(=C)C..........................................0.05%\n", + "CC=CC(C)CC(C)CC(C)CC(C)CC(C)C=C(C)C......................................................................0.05%\n", + 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"C=CC=CCCC=CCC.............................................................................................0.0%\n", + "CC1=CC(C2C=C(C)CC2)CC1....................................................................................0.0%\n", + "C=CCC(C)CCC(C)CC=C........................................................................................0.0%\n", + "C=CC=CCCC=C...............................................................................................0.0%\n", + "C=CCCC1=CC=CC1............................................................................................0.0%\n" + ] + } + ], "source": [ "results_dictionary = {}\n", "for fraglist, amt in fl.grouped:\n", @@ -293,7 +752,7 @@ " elif len(fraglist) < 6:\n", " new = frag.merge_frag_list(fraglist)[0].smiles\n", " else:\n", - " new = 'miscillaneous large molecules'\n", + " new = 'miscellaneous large molecules'\n", " if new in results_dictionary.keys():\n", " results_dictionary[new] += amt\n", " else:\n",