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LukeSeifertLukeSeifert
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Add relevant csv data for analysis
1 parent 28b5611 commit 35b830a

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lines changed

‎examples/phd_results/t_net_analysis/analysis.py‎

Lines changed: 72 additions & 29 deletions
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import matplotlib.pyplot as plt
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from collections import defaultdict
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from mosden.utils.csv_handler import CSVHandler
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import glob
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import os
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plt.style.use('mosden.plotting')
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def plot_data(data_vals):
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formatted_data = defaultdict(list)
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formatted_data['yields'] = defaultdict(list)
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formatted_data['hls'] = defaultdict(list)
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formatted_data["xs"] = []
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xlab = 'Irradiation Time [s]'
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endfb71_data_post_irrad = {
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"0.00001": {
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"yields": [0.000601, 0.00325528, 0.00323466, 0.00678078, 0.0036572234, 0.001167046],
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"hls": [55.047, 22.133, 5.6015, 1.9612, 0.4696, 0.095268]
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},
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"30": {
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"yields": [0.0005810767, 0.002995618, 0.00165845, 0.0065752589, 0.004701, 0.00214555],
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"hls": [55.395699, 22.939397, 8.79516, 2.8361, 0.82819, 0.15777]
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},
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"1200": {
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"yields": [0.000576267, 0.002988244, 0.0018862306, 0.0069244077, 0.0044298534, 0.001862336],
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"hls": [55.4938, 23.030678, 8.43461, 2.63272, 0.7163197, 0.139332515]
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}
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}
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endfb71_data_all = {
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"0.00001": {
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"yields": [0.000601, 0.00325528, 0.00323466, 0.00678078, 0.0036572234, 0.001167046],
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"hls": [55.047, 22.133, 5.6015, 1.9612, 0.4696, 0.095268]
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},
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"30": {
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"yields": [0.000591267, 0.00316419, 0.002834526, 0.007079536, 0.00482916, 0.00073507],
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"hls": [55.222525, 22.459077, 6.3348598, 2.0843, 0.349625, 0.006170445]
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},
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"1200": {
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"yields": [0.0005771185, 0.0030126616, 0.0020301955, 0.007109, 0.0043759, 0.0016164],
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"hls": [55.4809, 22.969864, 8.044375, 2.5102436, 0.637035, 0.1137935]
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}
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}
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for t_net, params in data_vals.items():
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formatted_data['xs'].append(t_net)
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for name, data in params.items():
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for group, val in enumerate(data):
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formatted_data[name][group].append(val)
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markers = ['.', '*', '>', '<', 'v', '^']
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for name, data in formatted_data.items():
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if type(data) is list:
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continue
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for group, params in data.items():
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plt.plot(formatted_data['xs'], params, label=f'Group {group+1}',
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marker=markers[group], linestyle='--', markersize=5,
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linewidth=1)
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plt.legend()
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plt.xlabel(xlab)
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if name == 'yields':
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ylab = 'Group Yield'
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elif name == 'hls':
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ylab = 'Group Half-life [s]'
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plt.ylabel(ylab)
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plt.savefig(f'{name}.png')
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plt.close()
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xs = formatted_data['xs']
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yields = formatted_data['yields']
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y_arrays = [yields[group] for group in sorted(yields.keys())]
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labels = [f'Group {group + 1}' for group in sorted(yields.keys())]
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plt.stackplot(xs, y_arrays, labels=labels)
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plt.xlabel(xlab)
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plt.ylabel('Yield')
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plt.legend(loc='upper left')
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plt.tight_layout()
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plt.savefig('stack_yields.png')
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plt.close()
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def build_data_dict(data_path=r'./'):
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def helper(pathmod):
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files = glob.glob(os.path.join(data_path, f"*{pathmod}.csv"))
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data = {}
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for file in files:
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file: str = file
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time = file.split('_')[1]
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data[time] = dict()
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file_data = CSVHandler(file).read_vector_csv()
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data[time]['yields'] = file_data['yield']
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data[time]['hls'] = file_data['half_life']
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data = dict(sorted(data.items()))
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return data
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post_data = helper('_post')
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all_data = helper('_all')
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return post_data, all_data
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if __name__ == '__main__':
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post_data, all_data = build_data_dict()
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plot_data(post_data)

‎examples/phd_results/t_net_analysis/intermediate_10_all.csv‎

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yield,sigma yield,half_life,sigma half_life
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0.0005960792791545259,0.0,55.135012580612894,0.0
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0.0032076980439315044,0.0,22.300052183480698,0.0
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0.002852067744538698,0.0,6.07979006431029,0.0
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0.0072500454214785865,0.0,2.068868164010149,0.0
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0.004558198674648902,0.0,0.34336751183229575,0.0
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0.001340870481079989,0.0,0.004507230924419563,0.0

‎examples/phd_results/t_net_analysis/intermediate_10_post.csv‎

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yield,sigma yield,half_life,sigma half_life
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0.0005870727013651158,0.0,55.290077835858426,0.0
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0.003091369998492769,0.0,22.664905168525173,0.0
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0.001968341704938068,0.0,7.5899319720354885,0.0
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0.006438600070596588,0.0,2.6418605208557473,0.0
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0.004438727502047097,0.0,0.80027907621914,0.0
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0.0021307909835243557,0.0,0.15804682735133815,0.0

‎examples/phd_results/t_net_analysis/intermediate_120_all.csv‎

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yield,sigma yield,half_life,sigma half_life
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0.0005823400215057117,0.0,55.38630382837625,0.0
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0.0030897459200327363,0.0,22.743864037034047,0.0
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0.0023312673910747105,0.0,7.260463540180148,0.0
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0.007188639984811037,0.0,2.281422548703631,0.0
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0.005299839054028378,0.0,0.37494942046447,0.0
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0.0003861811628406449,0.0,0.011808495575342024,0.0

‎examples/phd_results/t_net_analysis/intermediate_120_post.csv‎

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yield,sigma yield,half_life,sigma half_life
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0.0005767048630914441,0.0,55.477220312818744,0.0
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0.0029564455268097263,0.0,23.089996196122005,0.0
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0.0017864532832818157,0.0,8.839865917748451,0.0
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0.006860686320503778,0.0,2.7158202487143988,0.0
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0.004538628126891252,0.0,0.750955358584509,0.0
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0.0019453670322669935,0.0,0.1444922641142176,0.0

‎examples/phd_results/t_net_analysis/intermediate_30_all.csv‎

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yield,sigma yield,half_life,sigma half_life
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0.0005911850193901523,0.0,55.22393043614616,0.0
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0.003163109413726762,0.0,22.46245897522971,0.0
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0.0028222439571752517,0.0,6.349704456768178,0.0
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0.007080343614618728,0.0,2.0901662589500822,0.0
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0.004831262210233585,0.0,0.35113048748943776,0.0
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0.0007442719988219486,0.0,0.0062679624222023445,0.0

‎examples/phd_results/t_net_analysis/intermediate_30_post.csv‎

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yield,sigma yield,half_life,sigma half_life
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0.0005811024743039744,0.0,55.39526601544824,0.0
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0.002996173853064892,0.0,22.937958196181093,0.0
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0.0016607510618795024,0.0,8.78676461612742,0.0
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0.0065818428022054246,0.0,2.8330939407621774,0.0
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0.0046993321437068525,0.0,0.8258084004380751,0.0
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0.002138155087000719,0.0,0.157340620909389,0.0

‎examples/phd_results/t_net_analysis/pulse_0.00001_all.csv‎

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yield,sigma yield,half_life,sigma half_life
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0.0006483599627133113,0.0,54.249302430034945,0.0
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0.003603201783433704,0.0,20.75272053617694,0.0
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0.006892455512097611,0.0,3.418177728377391,0.0
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0.005634490916347498,0.0,0.8419229562786248,0.0
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0.0018999590391563853,0.0,0.13128248341716664,0.0
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2.8612956761155514e-05,0.0,0.004057200980891107,0.0

‎examples/phd_results/t_net_analysis/pulse_0.00001_post.csv‎

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yield,sigma yield,half_life,sigma half_life
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0.0006010003059852591,0.0,55.04723041412313,0.0
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0.003255280588350585,0.0,22.133220787208796,0.0
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0.0032346608600193516,0.0,5.601539640596733,0.0
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0.006780784272096461,0.0,1.9611956331472495,0.0
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0.0036572233997257285,0.0,0.46959941257806,0.0
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0.0011670464710297904,0.0,0.0952681389499337,0.0

‎examples/phd_results/t_net_analysis/saturation_1200_all.csv‎

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yield,sigma yield,half_life,sigma half_life
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0.0005771243371292638,0.0,55.48081611875357,0.0
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0.003012739319976157,0.0,22.969586143388984,0.0
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0.002030301243574989,0.0,8.043850048784337,0.0
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0.007109375113888054,0.0,2.510086618543929,0.0
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0.0043760294212489715,0.0,0.6368977344666057,0.0
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0.0016159408687693988,0.0,0.11373253387826857,0.0

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