AreaCovered_lastVersion

from matplotlib import pyplot as plt
import numpy as np
import os
from glob import glob
import csv
import matplotlib.cm as cm
from collections import OrderedDict
import ast
import re
import fnmatch

# data decription for plot titles
dataSetName = "ScavengeTroph2017-05-28_"

plot_title = "UT60LT30"
'''
# the path to the data from where the current file is saved
dirname = os.path.abspath(os.path.join(
    os.path.dirname( __file__ ),
    '../../../..',
    "self_energy_robot"
    + "/" + "steptaken_infinitefood"
    ))

path = dirname + "/" + dataSetName
#print(path)
extension   = "*Summary.csv"
files = []

for dir,_,_ in os.walk(path):
    files.extend(glob(os.path.join(dir,extension)))
'''

data_to_import = ['n_food_cells_initial',
                'total_area_covered',
                'n_bots']

# ->
# 1 for Default avg area plot
# 2 for Normalised area plot (Avg / (food/bots))
drawY = 2

# ->
# 0 don't draw error bars
# 1 draw error bars
drawError = 1

data = {}

for _data in data_to_import:
    data[_data] = []

#UTfolder = ['No_Trophallaxis','Trophallaxis_generous', 'Th50', 'Th70', 'Th90']
#UTfolder = ['UT90LT40', 'UT90LT40_more']


path = []
#testNumber = 'Troph_Th_average'
testNumber = 'test9'


UTfolder = ['UT60LT30']
#UTfolder = ['No_Trophallaxis','Trophallaxis_generous', 'Th50','Th70','Th90',]

trophFolder = 'test9FiniteTroph'
#cwd='/Users/ammacpro1/AnacondaProjects/self_energy_robot/'+testNumber+'/'+UTfolder+'/'+trophFolder
cwd='/Users/ammacpro1/simulation_results/self_energy_robot/'+testNumber
#cwd='/Users/ammacpro1/AnacondaProjects/result_increase_initial_energy/'+testNumber
#cwd = os.getcwd()
for f in UTfolder:
    path.append(cwd+'/'+f+'/'+trophFolder+'/Bot*/*Summary.csv')
#init_food_per_groups = [32, 40, 48, 56, 64]
#

fig, ax = plt.subplots()
def analyseFolders(folder, curr_p, drawY, drawError):
    files = glob(folder)
    for file in files:
        with open(file, 'rt') as f:
            reader = csv.reader(f, delimiter=',')
            for row in reader:
                for _data in data_to_import:
                    if row[0] in [_data]:
                        data[_data].append(row[1])

    sorted_data = dict.fromkeys(data.keys(),[])
    data_to_plot = {}

    keys = [key for key, value in data.items()]
    #print(keys)

    foodPatchSize = sorted(list(set(data["n_food_cells_initial"])), key=int)
    swarmSize =     sorted(list(set(data["n_bots"])), key=int)

    for PSize in foodPatchSize:
        for SSize in swarmSize:
            for _data in [key for key, value in data.items()]:
                #print(PSize, _data)
                data_to_plot[_data + "_food" + PSize + "_bots" + SSize] = \
                        [float(i)
                         for i, j, k in zip(
                            data[_data],
                            data["n_bots"],
                            data["n_food_cells_initial"])
                         if j == SSize and k == PSize]

    #print(data_to_plot)
    #colors = iter(cm.Greys(np.linspace(0.4, 1, len(foodPatchSize))))
    colors = cm.rainbow(np.linspace(0.4, 1, len(UTfolder)))
    widths = np.linspace(2, 0.5, len(UTfolder))

    #fig, ax = plt.subplots()
    data_dict = {k:v for (k,v) in data_to_plot.items() if _data in k}

    Markers = ['+','+','o','o','o','^','o','p','s']
    LineStyles = ['--','--','-','-','-','-.',':','--']
    #MarkerIndex = 0;

    data_mean = []
    data_sd = []

    for PSize in foodPatchSize:
        c = colors[curr_p]
        w = widths[curr_p]
        food_dict = {k: v for (k, v) in data_dict.items() if PSize in k}
        vals = []
        sd = []
        vals = np.mean(data_to_plot["total_area_covered_food" + PSize + "_bots" + SSize])
        if (drawY == 2):
            vals /= (int(PSize)/int(SSize))
        if (drawError):
            sd = np.std(data_to_plot["total_area_covered_food" + PSize + "_bots" + SSize])
        else:
            sd = 0
        data_mean.append(vals)
        data_sd.append(sd)

    foodPatchSize = [int(x) for x in foodPatchSize]
    ax.errorbar(foodPatchSize,
                data_mean,
                data_sd,
                #sd, fmt = 'o',
                capsize = 2,
                linestyle = LineStyles[curr_p % len(LineStyles)],
                #linestyle = "None",
                linewidth = w,
                marker = Markers[curr_p % len(Markers)],
                #marker='o',
                color=c,
                #label="Number of food patches = " + str(PSize))
                label=UTfolder[curr_p])

    ax.legend(frameon=False, loc="best", prop={'size': 10})
    ax.set_xlabel("Food Boundary Offset")
    if (drawY == 1):
        ax.set_ylabel("Average Area Covered")
    elif (drawY == 2):
        ax.set_ylabel("Average Covered/(food/bots)")

    ax.set_title(plot_title)

    #plotname = path + '/' + plot_title + "_" + _data
    #fig.savefig(plotname + '.pdf', orientation='landscape')
    #fig.savefig(plotname + '.png', orientation='landscape')


curr_p = 0
for p in path:
    analyseFolders(p, curr_p, drawY, drawError)
    curr_p += 1

plt.show()