138 lines
3.8 KiB
Python
Executable File
138 lines
3.8 KiB
Python
Executable File
#!/bin/env python
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__author__ = 'dongyun.zdy'
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import math
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import numpy as np
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from scipy.optimize import leastsq
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from scipy.optimize import curve_fit
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import sys
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from lmfit import Model
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import getopt
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STARTUP = 0.0
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def get_model_form(args,
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# Tstartup,
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Tper_row,
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Tper_col,
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Tcorrection1,
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Tcorrection2,
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# Tlog3
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):
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(
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Nrow,
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Ncol,
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) = args
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global STARTUP
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total_cost = STARTUP#Tstartup
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total_cost += Nrow * (Tper_row + Ncol * Tper_col)
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total_cost += Tcorrection1 * math.log(Tcorrection2 * Nrow , 2)
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return total_cost
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def get_model_arr(arg_sets,
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# Tstartup,
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Tper_row,
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Tper_col,
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Tcorrection1,
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Tcorrection2,
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# Tlog3
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):
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res = []
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for single_arg_set in arg_sets:
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res.append(get_model_form(single_arg_set,
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# Tstartup,
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Tper_row,
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Tper_col,
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Tcorrection1,
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Tcorrection2,
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# Tlog3
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))
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return np.array(res)
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get_model = Model(get_model_arr)
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# get_model.set_param_hint("Tstartup", min=0.0)
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get_model.set_param_hint("Tper_row", min=0.0)
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get_model.set_param_hint("Tper_col", min=0.0)
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get_model.set_param_hint("Tcorrection1", min=0.0)
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get_model.set_param_hint("Tcorrection2", min=0.0)
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# get_model.set_param_hint("Tlog3", min=0.0)
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def extract_info_from_line(line):
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splited = line.split(",")
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line_info = []
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for item in splited:
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line_info.append(float(item))
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return line_info
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if __name__ == '__main__':
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file_name = "scan_model.res.formal.prep"
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out_file_name = "scan_model.fit"
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# sys.argv.extend("-i get.IO.prep -o get.IO.model".split(" "))
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output_fit_res = False
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wrong_arg = False
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opts,args = getopt.getopt(sys.argv[1:],"i:o:")
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for op, value in opts:
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if "-i" == op:
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file_name = value
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elif "-o" == op:
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output_fit_res = True
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out_file_name = value
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else:
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wrong_arg = True
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if wrong_arg:
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print "wrong arg"
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sys.exit(1)
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if file_name.find('rc') != -1:
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STARTUP = 170.0
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elif file_name.find('bc') != -1:
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STARTUP = 210.0
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else:
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STARTUP = 520.0
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file = open(file_name, "r")
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arg_sets = []
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times = []
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case_params = []
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for line in file:
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if line.startswith('#'):
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continue
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case_param = extract_info_from_line(line)
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case_params.append(case_param)
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arg_sets.append((case_param[0], case_param[1]))
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times.append(case_param[4])
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file.close()
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arg_sets_np = np.array(arg_sets)
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times_np = np.array(times)
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#10, 0.20406430879623488, 0.016618100054245379, 14.0, 4.5, 37.0, -0.005, 0.5, -7.0
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result = get_model.fit(times_np, arg_sets=arg_sets_np,
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# Tstartup=10.0,
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Tper_row=10.0,
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Tper_col=1.0,
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Tcorrection1=1.0,
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Tcorrection2=1.0,
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# Tlog3=1.0,
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)
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# res_line = str(result.best_values["Tstartup"]) + ","
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res_line = str(result.best_values["Tper_row"]) + ","
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res_line += str(result.best_values["Tper_col"]) + ","
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res_line += str(result.best_values["Tcorrection1"]) + ","
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res_line += str(result.best_values["Tcorrection2"]) #+ ","
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# res_line += str(result.best_values["Tlog3"])
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print result.fit_report()
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if output_fit_res:
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out_file = open(out_file_name, "w")
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out_file.write(res_line)
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out_file.close()
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