165 lines
4.9 KiB
Python
Executable File
165 lines
4.9 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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def io_model_form(args,
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# Trow_once,
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# Trow_col,
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Tio_row,
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# Tio_col_desc,
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Tio_row_col_desc,
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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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#
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# total_cost = Tstartup
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# total_cost += Nrow * (Trow_once + Ncol * Trow_col)
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io_cost = Nrow * Tio_row
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# io_cost -= Ncol * Tio_col_desc
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io_cost -= Nrow * Ncol * Tio_row_col_desc
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if io_cost < 0:
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io_cost = 0
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total_cost = io_cost
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return total_cost
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def io_model_arr(arg_sets,
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# Tstartup,
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# Trow_once,
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# Trow_col,
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Tio_row,
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# Tio_col_desc,
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Tio_row_col_desc
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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(io_model_form(single_arg_set,
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# Tstartup,
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# Trow_once,
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# Trow_col,
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Tio_row,
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# Tio_col_desc,
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Tio_row_col_desc
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))
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return np.array(res)
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io_model = Model(io_model_arr)
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# io_model.set_param_hint("Tstartup", min=0.0)
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# io_model.set_param_hint("Trow_once", min=0.0)
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# io_model.set_param_hint("Trow_col", min=0.0)
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io_model.set_param_hint("Tio_row", min=0.0)
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# io_model.set_param_hint("Tio_col_desc", min=0.0)
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io_model.set_param_hint("Tio_row_col_desc", min=0.0, max=0.07)
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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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model_file = None
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# sys.argv.extend("-i scan.W.small.prep -o scan.io.fit".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:m:")
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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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elif "-m" == op:
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model_file = 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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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[6])
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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 = io_model.fit(times_np, arg_sets=arg_sets_np,
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# Tstartup=10.0,
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# Trow_once=10.0,
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# Trow_col=1.0,
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Tio_row=1.0,
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# Tio_col_desc=1.0,
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Tio_row_col_desc=1.0
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)
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# Tstartup = result.best_values["Tstartup"]
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# Trow_once = result.best_values["Trow_once"]
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# Trow_col = result.best_values["Trow_col"]
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Tio_row = result.best_values["Tio_row"]
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# Tio_col_desc = result.best_values["Tio_col_desc"]
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Tio_row_col_desc = result.best_values["Tio_row_col_desc"]
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print result.fit_report()
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fit_res = io_model_arr(arg_sets_np,
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# Tstartup,
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# Trow_once,
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# Trow_col,
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Tio_row,
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# Tio_col_desc,
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Tio_row_col_desc
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)
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if output_fit_res:
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out_file = open(out_file_name, "w")
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for i in xrange(len(arg_sets_np)):
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a = list(arg_sets_np[i])
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b = [times_np[i]]
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c = [fit_res[i]]
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d = [(fit_res[i] - times_np[i]) * 100 / times_np[i]]
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out_file.write(','.join([str(i) for i in a + b + c + d]) + "\n")
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out_file.close()
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if model_file is not None:
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mf = open(model_file, 'w')
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mf.write(','.join([str(i) for i in [
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# Tstartup,
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# Trow_once,
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# Trow_col,
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Tio_row,
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# Tio_col_desc,
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Tio_row_col_desc
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]]))
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mf.close()
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