我是 python 新手，并试图让 Kauffman 的 NK 模型在其中工作......我在网上找到了代码，并希望我可以随着时间的推移进行一些更改，但我无法运行代码......它给出了错误第 31 行 ...'f = open(options.in_filenames)'....我确定我遗漏了一些非常小的东西....任何帮助将不胜感激...
import csv
from numpy import *
import Gnuplot
import time
from optparse import OptionParser
from pylab import *
# set up and read command line options
parser = OptionParser()
parser.add_option("-f", "--file", dest="in_filenames",
help="read data from FILE - enclose comma-separated file list in quotes e.g. \"FILE1, FILE2\"", metavar="FILE")
(options, args) = parser.parse_args()
# set up constants
# column titles
columnvar_titles = (["A", "N", "K"])
series_titles = (["Average Fitness", "Maximum Fitness","Minimum Fitness",
"Average Wait Before Move","Maximum Wait Before Move",
"Minimum Wait Before Move","Average Number of Fitter Neighbours",
"Maximum Number of Fitter Neighbours",
"Minimum Number of Fitter Neighbours"])
f = open(options.in_filenames)
reader = csv.reader(f)
floats = []
options_dict={}
# start from the first line in the file
# read lines until we hit a blank
# lines will be in form "Key: Value"
# so split them and build a dictionary
while (1):
readstring = reader.next()
if len(readstring)==0:
break
dict_entry = readstring[0].split(': ', 1)
options_dict[dict_entry[0]] = dict_entry[1]
#print readstring
#print reader.line_num
#print options_dict
#print len(options_dict)
#print options_dict['Fitness_method']
# after the model parameters, we have blank line(s) before the data headers
# keep skipping blanks, then grab the first non-blank line
# then read the first line into a list of strings.
while (1):
readstring = reader.next()
if len(readstring) > 0:
column_headers = readstring;
break
#print column_headers
# need to check if we have a 'run' column
# single run gui output doesn't produce one, so need to add to column headers
# First six cols are "run, tick, A_size_of, RNGseed, [N & K]_size_of"
# "tick is already contained in the data, but is overwritten with K_size_of
# as it needs to be moved
# set add_run_data - flag to insert corresponding columns into the numeric data
add_run_data = 0
if column_headers[0]!="run":
column_headers[0] = "K_size_of"
column_headers = ["run", "tick", "A_size_of", "RngSeed", "N_size_of"] + column_headers
add_run_data = 1
print "Processing one run GUI output format..."
else:
print "Processing batch mode output..."
#print column_headers
# read lines from the data until we hit a blank.
# if data is numeric, put it into out 2d list of floats
countlines=0
while (1):
try:
readstring=reader.next()
countlines = countlines + 1
if len(readstring)==0:
print "Stopped reading"
break
try:
floats.append(map(float, readstring))
except:
print "Bad data - not adding"
except StopIteration:
#print "Read:", countlines
break
print "Read", countlines, "lines of data from file"
#print floats
xdata=array(floats)
#print xdata
# if we needed to add column headers before, we are dealing with single run
# output. If so, we need to add columns at the left of the data.
# First six cols are "run, tick, A_size_of, RNGseed, [N & K]_size_of"
# We add five (not six) cols, since "tick" is currently in the data already
# Other values are run = 1 (by def), RNGSeed (doesn't matter)
# A, N, K values are taken from the dictionary made from the header data in the file
if add_run_data == 1:
newcol = ones((xdata.shape[0],5), dtype="float")
xdata = concatenate((newcol, xdata), axis=1)
A_val = float(options_dict['A_size_of'])
N_val = float(options_dict['N_size_of'])
K_val = float(options_dict['K_size_of'])
#print A_val, N_val, K_val
xdata[:,1] = xdata[:,5] # ticks - already there but needs to move
xdata[:,2] = A_val
xdata[:,3] = 0 #RNG seed doesn't matter
xdata[:,4] = N_val
xdata[:,5] = K_val #overwrites original tick column
#print column_headers
#print xdata[1]
A_uniques = unique(xdata[:,2])
N_uniques = unique(xdata[:,4])
K_uniques = unique(xdata[:,5])
series_total = len(A_uniques) * len(N_uniques) * len(K_uniques)
# set up an array to hold averages (no columns for run number or rng seed)
# needs to move to handle multiple variables
maxticks = xdata[:,1].max()
#print series_total
#print maxticks
#print xdata.shape[1]-2
averages = zeros((series_total, maxticks, xdata.shape[1]-2), float)
# three loop setup for varying A / N / K values
series_counter = 0
Aseries_name=""
Nseries_name=""
Kseries_name=""
series_keys=[]
for A_value in A_uniques:
if len(A_uniques)>1:
Aseries_name = "A=" + str(A_value) + ", "
dataA = compress(xdata[:,2]==A_value, xdata, axis=0)
for N_value in N_uniques:
#if len(N_uniques)>1:
Nseries_name = "N=" + str(N_value)
dataAN = compress(dataA[:,4]==N_value, dataA, axis = 0)
for K_value in K_uniques:
#if len(K_uniques)>1:
Kseries_name = ", " + "K=" + str(K_value)
dataANK = compress(dataAN[:,5]==K_value, dataAN, axis = 0)
series_keys.append(Aseries_name + Nseries_name + Kseries_name)
# when multiple variables are used, run values continue to count from the
# previous variable value (e.g. A=2 (runs 1-100) A=3 (runs 101-200))
# we need to number the runs in ascending order from 1.
firstrun=dataANK[:,0].min()
lastrun=dataANK[:,0].max()
totalruns = 1 + lastrun - firstrun
#print firstrun, lastrun, totalruns
# for each run, find the last actual tick data
last_tick_array = zeros((totalruns, dataANK.shape[1]), float)
#print last_tick_array.shape
for k in arange(totalruns): # for each run get the data for the last tick
this_run=compress(dataANK[:,0]==k+firstrun, dataANK, axis=0)
last_tick_array[k]=this_run[-1]
#print "Last tick array"
#print last_tick_array[-1]
print "Processing simulation " + str(series_counter+1) + "/" + str(series_total)
for i in arange(maxticks): # for each tick value up to the maximum
# array to hold one tick from each run for averaging
# will contain either actual or extrapolated data
selected_ticks = zeros((totalruns, dataANK.shape[1]), float)
#print selected_ticks
# get dataANK for this tick from all runs. May be empty.
this_tick = compress(dataANK[:,1]==i+1, dataANK, axis=0)
#print "this tick"
#print this_tick
for j in arange(totalruns): # for each run
if (i+1) < last_tick_array[j,1]:# do we have actual data?
#print "Using real data"
# if so, get it
selected_ticks[j] = compress(this_tick[:,0]==j+firstrun, this_tick, axis=0)
#print selected_ticks
else:
# if not, use the last tick we do have
#print "Using last tick"
selected_ticks[j] = last_tick_array[j]
#print "selected_ticks"
#print selected_ticks[0]
averages[series_counter][i][0]=i+1 # tick number
averages[series_counter][i][1]=selected_ticks[:,2].max() #A_size_of
averages[series_counter][i][2]=selected_ticks[:,4].min() #N_size_of
averages[series_counter][i][3]=selected_ticks[:,5].min() #K_size_of
for m in xrange(6,16):
#print m
averages[series_counter][i][m-2]=selected_ticks[:,m].mean()
# increment to fill next index
series_counter = series_counter + 1
# matplotlib plots
print "Plotting graphs..."
matplotlib.use('Agg')
for graph_num in (4,5,6,7,8,9,10,11,12,13):
ylabel(column_headers[graph_num+2])
for ser in range(series_total):
plot(averages[ser][:,0], averages[ser][:,graph_num], label=series_keys[ser])
legend(loc='center right').draw_frame(0)
show()
savefig('nk' + column_headers[graph_num+2] + '.png')
clf()
print "Writing CSV files"
f1 = open("nk_allticks.csv","wt")
csv1 = csv.writer(f1)
f2 = open("nk_finaltick.csv","wt")
csv2 = csv.writer(f2)
column_headers.remove('RngSeed')
# replace spaces for underscores in column headers for better file compatability
for i in range(len(column_headers)):
column_headers[i]=column_headers[i].replace(' ','_')
try:
csv1.writerow(column_headers[1:])
csv2.writerow(column_headers[1:])
for series in range(series_total):
csv1.writerows(averages[series])
csv2.writerow(averages[series][-1])
finally:
f1.close
f2.close
f3 = open("nk_allticks_crosstab.csv","wt")
csv3 = csv.writer(f3)
out_array = zeros((averages[0].shape[0], 1 +(series_total * 10)), float)
out_array[:,0] = averages[0][:,0]
headers=[1000]#[8 * series_total]
headers[0]="tick"
datacol = 1
for column_num in (3, 4,5,6,7,8,9,10,11,12):
for ser in range (series_total):
headers.append((column_headers[column_num+2] + " " + series_keys[ser]).replace(' ','_'))
out_array[:,datacol] = averages[ser][:,column_num+1]
#print averages[ser][0]
datacol = datacol + 1
#print headers
#print out_array.shape
try:
csv3.writerow(headers)
csv3.writerows(out_array)
finally:
f3.close