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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,212 @@ | ||
| #!/usr/bin/env python3 | ||
| import sys | ||
| from os import path | ||
| import pickle | ||
| import numpy as np | ||
|
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||
|
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||
| def help_message(): | ||
| print("Usage: {0} database_file".format(sys.argv[0])) | ||
| exit(0) | ||
|
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||
|
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| centralityRange = 1. | ||
| Reg_centrality_cut_list = [ | ||
| 0., 5., 10., 20., 30., 40., 50., 60., 70., 80., 90., 100. | ||
| ] | ||
| centralityCutList = Reg_centrality_cut_list | ||
| # centralityCutList = [0, 1, 2, 3, 4, 6, 8, 10, 15, 20, 30, 40, 50, 60, | ||
| # 70, 80, 90, 100] | ||
| dNcutList = [] # pre-defined Nch cut if simulation is not minimum bias | ||
|
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||
|
|
||
| def computeJKMeanandErr(dataArr): | ||
| nev, nEta = dataArr.shape | ||
| dataMean = np.mean(dataArr, axis=0) | ||
| dataErr = np.sqrt((nev - 1)/nev*np.sum((dataArr - dataMean)**2, axis=0)) | ||
| return dataMean, dataErr | ||
|
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||
|
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||
| def calculate_photon_dNdy(yArr, pTArr, data_ypTdiff, | ||
| outputFileName: str) -> None: | ||
| """ | ||
| this function calculate the photon dN/dy | ||
| """ | ||
| dpT = pTArr[1] - pTArr[0] | ||
| nev = data_ypTdiff.shape[0] | ||
| dNdy = np.sum(data_ypTdiff*pTArr, axis=2)*2*np.pi*dpT | ||
| dNdy_mean = np.mean(dNdy, axis=0) | ||
| dNdy_err = np.std(dNdy, axis=0)/np.sqrt(nev) | ||
| results = np.array([yArr, dNdy_mean, dNdy_err]) | ||
| np.savetxt(outputFileName, | ||
| results.transpose(), | ||
| fmt="%.4e", | ||
| delimiter=" ", | ||
| header="y dN/dy dN/dy_err") | ||
|
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|
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||
| def calculate_photon_pTSpectra(yArr, pTArr, data_ypTdiff, | ||
| outputFileName: str) -> None: | ||
| """ | ||
| this function calculate the photon pT spectra | ||
| """ | ||
| nev = data_ypTdiff.shape[0] | ||
| idx = np.abs(yArr) < 0.5 | ||
| dy = yArr[1] - yArr[0] | ||
| Yinterval = len(yArr[idx])*dy | ||
| dNd2pT = np.sum(data_ypTdiff[:, idx, :], axis=1)*dy/Yinterval | ||
| dNd2pT_mean = np.mean(dNd2pT, axis=0) | ||
| dNd2pT_err = np.std(dNd2pT, axis=0)/np.sqrt(nev) | ||
| results = np.array([pTArr, dNd2pT_mean, dNd2pT_err]) | ||
| np.savetxt(outputFileName, | ||
| results.transpose(), | ||
| fmt="%.4e", | ||
| delimiter=" ", | ||
| header="pT(GeV) dN/d2pT dN/d2pT_err") | ||
|
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|
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||
| def calculate_photon_vnpT(yArr, pTArr, photon_dN, photon_v2, rapInterval, | ||
| dataRef, etaRef, nOrder, | ||
| outputFileName: str) -> None: | ||
| """ | ||
| this function compute the v_n(p_T) according to the scalar product | ||
| method | ||
| """ | ||
| nev, nQn, nEta = dataRef.shape | ||
| etaArr = np.linspace(-7, 7, nEta) | ||
| etaRefMin = etaRef[0] | ||
| etaRefMax = etaRef[1] | ||
| etaRef1Interp = np.linspace(etaRefMin, etaRefMax, 16) | ||
| etaRef2Interp = np.linspace(-etaRefMax, -etaRefMin, 16) | ||
| QnRef1 = [] | ||
| QnRef2 = [] | ||
| dNRef1 = [] | ||
| dNRef2 = [] | ||
| for iev in range(nev): | ||
| Qn1_interp = np.interp(etaRef1Interp, etaArr, | ||
| dataRef[iev, -1, :]*dataRef[iev, nOrder + 1, :]) | ||
| Qn2_interp = np.interp(etaRef2Interp, etaArr, | ||
| dataRef[iev, -1, :]*dataRef[iev, nOrder + 1, :]) | ||
| Q01_interp = np.interp(etaRef1Interp, etaArr, dataRef[iev, -1, :]) | ||
| Q02_interp = np.interp(etaRef2Interp, etaArr, dataRef[iev, -1, :]) | ||
| QnRef1.append(np.sum(Qn1_interp)) | ||
| QnRef2.append(np.sum(Qn2_interp)) | ||
| dNRef1.append(np.sum(Q01_interp)) | ||
| dNRef2.append(np.sum(Q02_interp)) | ||
|
|
||
| QnRef1 = np.array(QnRef1).reshape((nev, 1)) | ||
| QnRef2 = np.array(QnRef2).reshape((nev, 1)) | ||
| dNRef1 = np.array(dNRef1).reshape((nev, 1)) | ||
| dNRef2 = np.array(dNRef2).reshape((nev, 1)) | ||
|
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||
| idx = (yArr < rapInterval[1]) & (yArr > rapInterval[0]) | ||
| dy = yArr[1] - yArr[0] | ||
| dNd2pT = np.sum(photon_dN[:, idx, :], axis=1) | ||
| QnpT = np.sum(photon_v2[:, idx, :]*photon_dN[:, idx, :], axis=1) | ||
|
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||
| vnpTNum = np.real(QnpT*np.conj(QnRef1 + QnRef2)) | ||
| n2Num = dNd2pT*(dNRef1 + dNRef2) + 1e-16 | ||
| vnpTDen = np.real(QnRef1*np.conj(QnRef2)) | ||
| n2Den = dNRef1*dNRef2 + 1e-16 | ||
|
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||
| vnpT_arr = np.zeros([nev, npT]) | ||
| for iev in range(nev): | ||
| array_idx = [True]*nev | ||
| array_idx[iev] = False | ||
| array_idx = np.array(array_idx) | ||
|
|
||
| vnpT_arr[iev, :] = (np.mean(vnpTNum[array_idx, :], axis=0) | ||
| /np.mean(n2Num[array_idx, :], axis=0)/(np.sqrt( | ||
| np.mean(vnpTDen[array_idx], axis=0) | ||
| /np.mean(n2Den[array_idx], axis=0)) + 1e-16)) | ||
|
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||
| vnpT_mean, vnpT_err = computeJKMeanandErr(vnpT_arr) | ||
| dNpT_mean = np.mean(dNd2pT, axis=0)*dy | ||
| dNpT_err = np.std(dNd2pT, axis=0)*dy/np.sqrt(nev) | ||
|
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||
| results = np.array([pTArr, dNpT_mean, dNpT_err, vnpT_mean, vnpT_err]) | ||
| np.savetxt(outputFileName, | ||
| results.transpose(), | ||
| fmt="%.4e", | ||
| delimiter=" ", | ||
| header="pT (GeV) dN/d2pT dN/d2pT_err vn(pT) vn(pT)_err") | ||
|
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|
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| try: | ||
| database_file = str(sys.argv[1]) | ||
| except IndexError: | ||
| help_message() | ||
|
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||
| with open(database_file, "rb") as pf: | ||
| data = pickle.load(pf) | ||
|
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||
| dNdyList = [] | ||
| for event_name in data.keys(): | ||
| dNdyList.append(data[event_name]['Nch']) | ||
| dNdyList = -np.sort(-np.array(dNdyList)) | ||
| print(f"Number of good events: {len(dNdyList)}") | ||
|
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||
| for icen in range(len(centralityCutList) - 1): | ||
| if centralityCutList[icen + 1] < centralityCutList[icen]: | ||
| continue | ||
| selected_events_list = [] | ||
|
|
||
| dN_dy_cut_high = dNdyList[int(len(dNdyList)*centralityCutList[icen]/100.)] | ||
| dN_dy_cut_low = dNdyList[min( | ||
| len(dNdyList) - 1, int(len(dNdyList)*centralityCutList[icen + 1]/100.))] | ||
|
|
||
| if len(dNcutList) == len(centralityCutList): | ||
| dN_dy_cut_high = dNcutList[icen] | ||
| dN_dy_cut_low = dNcutList[icen + 1] | ||
|
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||
| for event_name in data.keys(): | ||
| if (data[event_name]['Nch'] > dN_dy_cut_low | ||
| and data[event_name]['Nch'] <= dN_dy_cut_high): | ||
| selected_events_list.append(event_name) | ||
|
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||
| nev = len(selected_events_list) | ||
| if nev <= 0: | ||
| continue | ||
|
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||
| cenLabel = "{:d}-{:d}".format( | ||
| int(centralityCutList[icen]*centralityRange), | ||
| int(centralityCutList[icen + 1]*centralityRange)) | ||
| cenBinMid = (centralityCutList[icen] | ||
| + centralityCutList[icen + 1])/2.*centralityRange | ||
| print("analysis {}%-{}% nev = {}...".format( | ||
| centralityCutList[icen]*centralityRange, | ||
| centralityCutList[icen + 1]*centralityRange, nev)) | ||
| print(f"dNdy: {dN_dy_cut_low:.2f} - {dN_dy_cut_high:.2f}") | ||
|
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||
| photon_dN_ypTDiff = [] | ||
| photon_v2_ypTDiff = [] | ||
| QnArrEta = [] | ||
| Ncoll = [] | ||
| pTArr = data[selected_events_list[0]]['photon_pTArr'] | ||
| yArr = data[selected_events_list[0]]['photon_yArr'] | ||
| npT = len(pTArr); ny = len(yArr) | ||
| for event_name in selected_events_list: | ||
| Ncoll.append(data[event_name]['Ncoll']) | ||
| photon_dN_ypTDiff.append(data[event_name]['photon_ypTdiff'][:, 0]) | ||
| photon_v2_ypTDiff.append(data[event_name]['photon_ypTdiff'][:, 3] | ||
| + 1j*data[event_name]['photon_ypTdiff'][:, 4]) | ||
| QnArrEta.append(data[event_name]['chVneta_pT_0p15_2']) | ||
| Ncoll = np.array(Ncoll) | ||
| photon_dN_ypTDiff = np.array(photon_dN_ypTDiff).reshape(-1, ny, npT) | ||
| photon_v2_ypTDiff = np.array(photon_v2_ypTDiff).reshape(-1, ny, npT) | ||
| QnArrEta = np.array(QnArrEta) | ||
|
|
||
| if icen == 0: | ||
| f = open("Ncoll.dat", 'w') | ||
| f.write("# centrality Ncoll Ncoll_err\n") | ||
| else: | ||
| f = open("Ncoll.dat", 'a') | ||
| f.write(f"{cenBinMid} {np.mean(Ncoll):.3e} {np.std(Ncoll)/np.sqrt(nev):.3e}\n") | ||
|
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||
| calculate_photon_dNdy(yArr, pTArr, photon_dN_ypTDiff, | ||
| f"photon_dNdy_C{cenLabel}.dat") | ||
| calculate_photon_pTSpectra(yArr, pTArr, photon_dN_ypTDiff, | ||
| f"photon_pTSpectra_C{cenLabel}.dat") | ||
| calculate_photon_vnpT(yArr, pTArr, photon_dN_ypTDiff, photon_v2_ypTDiff, | ||
| [-0.5, 0.5], QnArrEta, [0.5, 1.], 2, | ||
| f"photon_v2pT_C{cenLabel}.dat") |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,3 @@ | ||
| #!/usr/bin/env bash | ||
|
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||
| wget --no-check-certificate 'https://www.dropbox.com/scl/fi/4so8e9jxb9wnuddrb84df/posteriorChain.pkl?rlkey=h2i9jpg3u56f3zaz9v0uud1o3&dl=0' -O posteriorChain.pkl |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,84 @@ | ||
| #!/usr/bin/env python3 | ||
| """ | ||
| This script translates the posterior chain files in the parameters | ||
| can be read in by the iEBE-MUSIC package. | ||
| """ | ||
|
|
||
| import pickle | ||
| import sys | ||
| import numpy as np | ||
|
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||
| parameterName = [ | ||
| 'BG', 'shadowing_factor', 'ylossParam4At2', 'ylossParam4At4', | ||
| 'ylossParam4At6', 'ylossParam4var', 'remnant_energy_loss_fraction', | ||
| 'lambdaB', 'string_source_sigma_x_200', 'string_source_sigma_x_19p6', | ||
| 'string_source_sigma_x_7p7', 'string_source_sigma_eta_200', | ||
| 'string_source_sigma_eta_19p6', 'string_source_sigma_eta_7p7', | ||
| 'stringTransverseShiftFrac', 'stringPreEqFlowFactor', | ||
| 'Shear_to_S_ratio', 'shear_muB_0p2', 'shear_muB_0p4', | ||
| 'bulk_viscosity_10_max', 'bulk_viscosity_10_T_peak', | ||
| 'bulk_viscosity_10_width_high', 'bulk_viscosity_10_width_low', | ||
| 'eps_switch' | ||
| ] | ||
|
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||
| outputParameterName = [ | ||
| 'BG', 'shadowing_factor', 'ylossParam4At2', 'ylossParam4At4', | ||
| 'ylossParam4At6', 'ylossParam4var', 'remnant_energy_loss_fraction', | ||
| 'lambdaB', 'string_source_sigma_x', 'string_source_sigma_eta', | ||
| 'stringTransverseShiftFrac', 'stringPreEqFlowFactor', 'Shear_to_S_ratio', | ||
| 'shear_muBf0p2', 'shear_muBf0p4', 'bulk_viscosity_10_max', | ||
| 'bulk_viscosity_10_T_peak', 'bulk_viscosity_10_width_high', | ||
| 'bulk_viscosity_10_width_low', 'eps_switch', | ||
| ] | ||
|
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||
| setId = int(sys.argv[1]) | ||
| paramFile = str(sys.argv[2]) | ||
| ecm = float(sys.argv[3]) | ||
|
|
||
| with open("posteriorChain.pkl", 'rb') as f: | ||
| data = pickle.load(f) | ||
| nParamSets = data['chain'].shape[0] | ||
| setId = setId % nParamSets | ||
| print(f"Using parameter set: {setId}") | ||
|
|
||
| paramSet = data['chain'][setId, :] | ||
| paramDict = {} | ||
| for i, param_i in enumerate(parameterName): | ||
| paramDict[param_i] = paramSet[i] | ||
|
|
||
| paramDict['shear_muBf0p2'] = (paramDict['shear_muB_0p2'] | ||
| /paramDict['Shear_to_S_ratio']) | ||
| paramDict['shear_muBf0p4'] = (paramDict['shear_muB_0p4'] | ||
| /paramDict['Shear_to_S_ratio']) | ||
| if ecm < 7.7: | ||
| paramDict['string_source_sigma_x'] = paramDict['string_source_sigma_x_7p7'] | ||
| paramDict['string_source_sigma_eta'] = ( | ||
| paramDict['string_source_sigma_eta_7p7']) | ||
| elif ecm < 19.6: | ||
| frac = (np.log(ecm) - np.log(7.7)) / (np.log(19.6) - np.log(7.7)) | ||
| paramDict['string_source_sigma_x'] = ( | ||
| (1 - frac) * paramDict['string_source_sigma_x_7p7'] | ||
| + frac * paramDict['string_source_sigma_x_19p6'] | ||
| ) | ||
| paramDict['string_source_sigma_eta'] = ( | ||
| (1 - frac) * paramDict['string_source_sigma_eta_7p7'] | ||
| + frac * paramDict['string_source_sigma_eta_19p6'] | ||
| ) | ||
| elif ecm < 200.: | ||
| frac = (np.log(ecm) - np.log(19.6)) / (np.log(200.) - np.log(19.6)) | ||
| paramDict['string_source_sigma_x'] = ( | ||
| (1 - frac) * paramDict['string_source_sigma_x_19p6'] | ||
| + frac * paramDict['string_source_sigma_x_200'] | ||
| ) | ||
| paramDict['string_source_sigma_eta'] = ( | ||
| (1 - frac) * paramDict['string_source_sigma_eta_19p6'] | ||
| + frac * paramDict['string_source_sigma_eta_200'] | ||
| ) | ||
| else: | ||
| paramDict['string_source_sigma_x'] = paramDict['string_source_sigma_x_200'] | ||
| paramDict['string_source_sigma_eta'] = ( | ||
| paramDict['string_source_sigma_eta_200']) | ||
|
|
||
| with open(paramFile, "w") as f: | ||
| for param_i in outputParameterName: | ||
| f.write("{} {}\n".format(param_i, paramDict[param_i])) |
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