'''loads a saved AgentPopulation object from disk.'''

f = open(filename, 'rb')

pop = pickle.load(f)

f.close()

if chip[0] == 'A' or chip[0] == 'J' or chip[1:] =='0':

return -1

else:

row_convert = {'B':0, 'C':1, 'D':2, 'E':3, 'F':4, 'G':5, 'H':6, 'I':7}

row = row_convert[chip[0]]

col = int(chip[1:]) - 1

term_enum = np.array(pd.read_table(path.abspath("dict.txt")))

lang_dict = dict()

for lang_num in np.unique(term_enum[:,0])[1:]:

term_dict = dict()

for row in term_enum:

if row[0] == lang_num:

term_dict[row[3]] = int(row[1])

lang_dict[int(lang_num)] = term_dict

bct_data = np.array(pd.read_table(path.abspath("all_bcts.txt"), encoding='utf-8'))

bct_dict = dict()

for lang_num in np.unique(bct_data[:,0][1:]):

lang_bcts = []

for row in bct_data:

if row[0] == lang_num:

lang_bcts.append(int(row[1]))

bct_dict[int(lang_num)] = lang_bcts

#chip_index can be any number from 1 to 320

row = (chip_index-1)//40

col = (chip_index-1)%40

row_convert = {0:'B', 1:'C', 2:'D', 3:'E', 4:'F', 5:'G', 6:'H', 7:'I'}

def __init__(self, path_to_simulation_data_files):

'''Assumes path_to_simulation_data_files is a string specifying a path containing population snapshot files with .pkl extension'''

self.data_path = path_to_simulation_data_files

self.image_path = self.data_path + '/snapshots'

def plot_population_snapshots(self, extension='png'):

'''Creates a series of plots for a number of saved population objects that were generated during a simulation.

Saves the plots as image files in a new subpath specified by image_path.

Filetype can be specified using the extension parameter. Default is pdf.

Valid extensions are anything that is handled by your matplotlib installation.

All files are assumed to be binary pickled population objects.'''

# Get a list of all the files

files = [f for f in listdir(self.data_path) if isfile(join(self.data_path,f)) and f.split('.')[1]=='pkl']

# Check if the image_path directory exists and create if necessary

if not exists(self.image_path):

mkdir(self.image_path)

#Initialize the progress bar

progress = ProgressBar(widgets=[Percentage(), Bar(), ETA()], maxval=len(files)).start()

# Create all of the plots

for i, filename in enumerate(files):

# load the population data

try:

population = load_population(self.data_path + '/' + filename)

except:

print(self.data_path + '/' + filename)

raise

# The filename for the plot will be in the snapshots dir, using the same filename as the saved

# population datafile, but with the extension changed to reflect that it's an image file.

plot_filename = self.image_path + '/' + filename.split('.')[0] + '.' + extension

# Create and save the image

population.plot_population(filename=plot_filename)

#Update the status bar

progress.update(i + 1)

#End the status bar

progress.finish()

def movie_from_snapshots(self, filename='evo_movie', image_extension='png', frames_per_second=4):

'''Creates a movies from the population snapshot images.

Assumes the images already exist in self.image_path directory and all have extension image_extension.'''

# Get a list of the image filenames that have the extension specified by image_extension

image_files = [self.image_path+'/'+f for f in listdir(self.image_path) if isfile(join(self.image_path,f)) and f.split('.')[1]==image_extension]

# Sort the files in ascending order based on iteration number

self._sort_nicely(image_files)

# Create the image sequence

image_sequence = mpy.ImageSequenceClip(image_files, fps=frames_per_second)

# Save the movie

image_sequence.write_videofile(filename+'.mp4', fps=frames_per_second)

return image_sequence

def _tryint(self, s):

'''converts a string to integers or returns the string if string cannot be converted.'''

try:

return int(s)

except:

return s

def _alphanum_key(self, s):

'''Turn a string into a list of string and number chunks.

e.g. "z23a" -> ["z", 23, "a"]'''

return [ self._tryint(c) for c in re.split('([0-9]+)', s) ]

def _sort_nicely(self, l):

'''Sort the given list in the way that humans expect.'''

def __init__(self):

pass

def optimal_number_of_categories(self, num_chips, ksim):

'''Returns the optimal category size for the given parameters.

Based on Equation 8 from Komarova, N.L., Jameson, K.A. and Narens, L. (2007)

Evolutionary models of color categorization based on discrimination.

Journal of Mathematical Psychology 51(6):359--382.'''