folders = glob.glob("./csvs/*")

folders = filter(lambda folder: not ("aggregate" in folder), folders)

agg_dir = "csvs/aggregate/"

if os.path.isdir(agg_dir) == False:

os.mkdir(agg_dir, 0777)

agg_csv = open(agg_dir+"times_results.csv", 'w')

for folder in folders:

if 'aggregate' in folder:

continue

team_csv = open(folder+"/times_results.csv",'r')

content = team_csv.read()

fig = plt.figure()

times, results = get_data(team_alias)

plt.hist(times, bins=8*10, range=(0,8), facecolor='green')

plt.xlabel("Time to cross halfcourt (s)")

plt.ylabel("Normalized frequency")

plt.title("Histogram of time to cross halfcourt for %s" % (team_alias))

fig = plt.figure()

times, results = get_data(team_alias)

hist, bins= np.histogram(times, bins=8*10,range=(0.00, 8.00), weights=results)

hist1, bins1= np.histogram(times, bins=8*10, range=(0.00, 8.00))

pdb.set_trace()

hist1 = np.array(map(lambda x: 1.0 if abs(x) == 0 else float(x), hist1))

final_hist = hist/hist1

bar_x = bins[:-1]

plt.scatter(bar_x, final_hist)

# plt.bar(left=bar_x, height=final_hist, width=0.1)

plt.xlabel("Time to cross halfcourt (s)")

plt.ylabel("Average points per possession")

plt.title("Avg points vs time to cross halfcourt for %s" % (team_alias))

fig = plt.figure(figsize=(15,6))

ax = fig.add_subplot(121)

times_to_cross, results = get_data(team_alias)

ax.hist(times_to_cross, bins=8*10, range=(0,8), facecolor='green')

plt.xlabel("Time to cross halfcourt (s)")

plt.ylabel("Normalized frequency")

plt.title("Histogram of time to cross halfcourt for %s" % (team_alias))

mean = np.mean(times_to_cross)

std = np.std(times_to_cross)

n = len(times_to_cross)

text = ax.text(0.05, 0.85,'Mean: {:.2f} \nStd Dev: {:.2f} \n# Possessions: {: d}'.format(mean,std,n),transform = ax.transAxes)

ax2 = fig.add_subplot(122)

points, times= np.histogram(times_to_cross, bins=8*10,range=(0.00, 8.00), weights=results)

num_crosses, times= np.histogram(times_to_cross, bins=8*10, range=(0.00, 8.00))

to_remove = []

for index, ncross in enumerate(num_crosses):

if abs(ncross) == 0:

to_remove.append(index)

points = np.delete(points, to_remove)

num_crosses = np.delete(num_crosses, to_remove)

times = np.delete(times, to_remove)

# hist1 = np.array(map(lambda x: 1.0 if abs(x) == 0 else float(x), hist1))

avg_points = points/num_crosses

times = np.delete(times, -1)

ax2.scatter(times, avg_points)

#avg = np.mean(avg_points)

#std = np.std(avg_points)

#corr, pvals = pearsonr(times, avg_points)

#ax2.text(0.05,0.85, 'Avg: {:.2f} \nStd Dev: {:.2f} \nPearson\' corr: {:.2f}'.format(avg, std, corr), transform=ax2.transAxes)

avg_all = np.mean(avg_points)

std = np.std(avg_points)

pcorr_all, p_val = pearsonr(times_to_cross, results)

spcorr_all, sp_val = spearmanr(times_to_cross, results)

ax2.text(0.05,0.85, 'Pearson Corr: {:.2f}\n\

Pearson 2t_val: {:.2f}\n\

Spearman Corr: {:.2f}\n\

Spearman 2t_val: {:.2f}'.format(pcorr_all, p_val, spcorr_all, sp_val), transform=ax2.transAxes)

# plt.bar(left=bar_x, height=final_hist, width=0.1)

plt.xlabel("Time to cross halfcourt (s)")

plt.ylabel("Average points per possession")

plt.title("Avg points vs time to cross halfcourt for %s" % (team_alias))

fig.savefig("csvs/%s/graphs.png" % (team_alias))

# plt.show()