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40058569.15, 20029284.575, 36853883.618, 43263254.682000004, 20029284.575, 33649198.086, 33649198.086, 28040998.405, 33649198.086, 40058569.15, 33649198.086, 33649198.086, 33649198.086, 33649198.086, 17625770.426, 24035141.49, 34450369.469000004, 24035141.49, 40058569.15, 40058569.15, 33649198.086, 43263254.682000004, 27239827.022000004, 17625770.426, 40058569.15, 40058569.15]}, "invalid_simulation_data": [], "tag": "[Complex(AND)] + [Complex(InB)] -> [Complex(AND_InB)]"}, "rsrxn37_[Complex(InB)] + [Complex(InB)] -> [Complex(InB)] + [Complex(InB)]": {"prob": "0.5 +/- 0.28867513459481287", "kcoll": "nan +/- inf", "k1": "nan +/- inf", "k2": "nan +/- inf", "simulation_data": {"valid": [], "tags": [], "times": [], "kcoll": []}, "invalid_simulation_data": [], "tag": "[Complex(InB)] + [Complex(InB)] -> [Complex(InB)] + [Complex(InB)]"}, "rsrxn35_[Complex(AND_Waste)] + [Complex(AND_Sig)] -> [Complex(AND_Waste)] + [Complex(AND_Sig)]": {"prob": "0.5 +/- 0.28867513459481287", "kcoll": "nan +/- inf", "k1": "nan +/- inf", "k2": "nan +/- inf", "simulation_data": {"valid": [], "tags": [], "times": [], "kcoll": []}, "invalid_simulation_data": [], "tag": "[Complex(AND_Waste)] + [Complex(AND_Sig)] -> [Complex(AND_Waste)] + [Complex(AND_Sig)]"}, "rsrxn41_[Complex(AND_Sig)] + [Complex(InB)] -> [Complex(AND_Sig)] + [Complex(InB)]": {"prob": "0.5 +/- 0.28867513459481287", "kcoll": "nan +/- inf", "k1": "nan +/- inf", "k2": "nan +/- inf", "simulation_data": {"valid": [], "tags": [], "times": [], "kcoll": []}, "invalid_simulation_data": [], "tag": "[Complex(AND_Sig)] + [Complex(InB)] -> [Complex(AND_Sig)] + [Complex(InB)]"}}, "initialization_params": {"kinda_params": {"stop_macrostate_mode": "ordered-complex", "start_macrostate_mode": "ordered-complex", "multistrand_similarity_threshold": 0.51, "nupack_similarity_threshold": 0.51, "multistrand_multiprocessing": true, "nupack_multiprocessing": true, "enable_unimolecular_reactions": false, "unimolecular_k1_scale": 1000, "max_concentration": 1e-07}, "multistrand_params": {"verbosity": 0, "dangles": "Some", "gt_enable": true, "simulation_time": 1.0, "output_interval": 0, "parameter_type": "Nupack", "substrate_type": "DNA", "temperature": 25, "sodium": 1.0, "magnesium": 0.0, "join_concentration": 1e-15, "rate_model": "DNA23Metropolis"}, "nupack_params": {"ensemble": "some", "material": "dna", "celsius": 25, "sodium": 1.0, "magnesium": 0.0}, "peppercorn_params": {"max_complex_size": 6, "max_reaction_count": 1000, "max_complex_count": 200, "release_cutoff_1_1": 8, "release_cutoff_1_N": 8, "reject_remote": false, "max_helix": true, "k_fast": 5e-07}}, "version": "v0.3"} \ No newline at end of file diff --git a/case_studies/Fig10_Groves2016/fig10_simulate_AND.py b/case_studies/Fig10_Groves2016/fig10_simulate_AND.py index 1d6f785..665386a 100644 --- a/case_studies/Fig10_Groves2016/fig10_simulate_AND.py +++ b/case_studies/Fig10_Groves2016/fig10_simulate_AND.py @@ -60,23 +60,25 @@ def simulate(sim_mode): return sstats, list(zip(rxns, rxn_times)) -if MODE == 'demo': - params = { - 'relative_error': 0.5, - 'init_batch_size': 50, - 'max_batch_size': 5000, - 'max_sims': 5000, +if __name__ == "__main__": + + if MODE == 'demo': + params = { + 'relative_error': 0.5, + 'init_batch_size': 50, + 'max_batch_size': 5000, + 'max_sims': 5000, } -elif MODE == 'publication': - params = { - 'relative_error': 0.025, - 'init_batch_size': 500, - 'max_batch_size': 10000, - 'max_sims': 500000, - 'sims_per_worker': 5 + elif MODE == 'publication': + params = { + 'relative_error': 0.025, + 'init_batch_size': 500, + 'max_batch_size': 10000, + 'max_sims': 500000, + 'sims_per_worker': 5 } -## Run simulations at each mode -sstats_disassoc = simulate('ordered-complex') -sstats_cbc = simulate('count-by-complex') -sstats_cbd = simulate('count-by-domain') + ## Run simulations at each mode + sstats_disassoc = simulate('ordered-complex') + sstats_cbc = simulate('count-by-complex') + sstats_cbd = simulate('count-by-domain') diff --git a/case_studies/Fig10_Groves2016/fig10_simulate_OR.py b/case_studies/Fig10_Groves2016/fig10_simulate_OR.py index 80b208b..86a4074 100644 --- a/case_studies/Fig10_Groves2016/fig10_simulate_OR.py +++ b/case_studies/Fig10_Groves2016/fig10_simulate_OR.py @@ -63,24 +63,25 @@ def simulate(sim_mode, **params): return sstats, list(zip(rxns, rxn_times)) -## Set up parameters dict -if MODE == 'demo': - params = { - 'relative_error': 0.5, - 'init_batch_size': 50, - 'max_batch_size': 5000, - 'max_sims': 5000, - } -elif MODE == 'publication': - params = { - 'relative_error': 0.025, - 'init_batch_size': 1000, - 'max_batch_size': 25000, - 'max_sims': 500000, - 'sims_per_worker': 25 - } - -## Run simulations at each mode -sstats_disassoc, rxn_times_disassoc = simulate('ordered-complex', **params) -sstats_cbc, rxn_times_cbc = simulate('count-by-complex', **params) -sstats_cbd, rxn_times_cbd = simulate('count-by-domain', **params) +if __name__ == "__main__": + ## Set up parameters dict + if MODE == 'demo': + params = { + 'relative_error': 0.5, + 'init_batch_size': 50, + 'max_batch_size': 5000, + 'max_sims': 5000, + } + elif MODE == 'publication': + params = { + 'relative_error': 0.025, + 'init_batch_size': 1000, + 'max_batch_size': 25000, + 'max_sims': 500000, + 'sims_per_worker': 25 + } + + ## Run simulations at each mode + sstats_disassoc, rxn_times_disassoc = simulate('ordered-complex', **params) + sstats_cbc, rxn_times_cbc = simulate('count-by-complex', **params) + sstats_cbd, rxn_times_cbd = simulate('count-by-domain', **params) diff --git a/case_studies/Fig10_Groves2016/fig10d.py b/case_studies/Fig10_Groves2016/fig10d.py index 5d6d58a..c8966e4 100644 --- a/case_studies/Fig10_Groves2016/fig10d.py +++ b/case_studies/Fig10_Groves2016/fig10d.py @@ -29,8 +29,8 @@ def plot_reaction_data(rxns_reactants, datasets, dataset_labels, dataset_colors, k2_err[:, i] = [rxn_stats.get_k2_error(max_sims=0) for rxn_stats in rxns_stats] left = np.arange(num_rxns)*(num_datasets+1) + i - axes_k1.bar(left = left, height = k1[:,i], width=1, yerr = k1_err[:,i], capsize = 5, color = dataset_colors[i], edgecolor = '0.0') - axes_k2.bar(left = left, height = k2[:,i], width=1, yerr = k2_err[:,i], capsize = 5, color = dataset_colors[i], edgecolor = '0.0') + axes_k1.bar(x = left, height = k1[:,i], width=1, yerr = k1_err[:,i], capsize = 5, color = dataset_colors[i], edgecolor = '0.0') + axes_k2.bar(x = left, height = k2[:,i], width=1, yerr = k2_err[:,i], capsize = 5, color = dataset_colors[i], edgecolor = '0.0') xticks = np.arange(num_rxns)*(num_datasets+1) + (num_datasets-1)/2. xlabels = [' + '.join(r) for r in rxns_reactants]