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#!/usr/bin/env python3
import pickle
from copy import deepcopy
import matplotlib.pyplot as plt
import numpy as np
def unpickle(pkl):
with open(pkl, "rb") as f:
data = pickle.load(f)
return data
def fix_vp(data, procedures):
procs = deepcopy(procedures)
if data["train_0"]["procedure_order"] == data["test_0"]["procedure_order"]:
keys = list(data["train_0"].keys())
keys.remove("procedure_order")
keys.remove("water_sample")
for key in keys:
procs.remove(key)
proc_from = keys[2]
proc_to = procs[0]
for train in [x for x in data.keys() if x.startswith("train")]:
vp = deepcopy(data[train])
vp[proc_to] = vp.pop(proc_from)
data[train] = vp
return data
def block_vps(data, condition):
blocked_vps = {}
for vp in data[condition].keys():
blocked_vps[vp] = blocked_time(data[condition][vp])
return blocked_vps
def blocked_time(vp):
key_stem = list(vp.keys())[0].split("_")[0]
trial_count = len(vp.keys())
block_size = 5
block_count = trial_count / block_size
result = {}
sum_time = 0
block_i = 1
for trial in range(1, trial_count):
if trial % 5 == 0:
sum_time = 0
block_i += 1
sum_time += sum_time_over_trial(vp[f"{key_stem}_{trial}"])
result[block_i] = sum_time
return result
def sum_time_over_trial(trial):
total_time = 0
for proc in trial.keys():
if proc != "procedure_order" and proc != "water_sample":
total_time += trial[proc]["time"]
return total_time
def plot_vp(ax, data_dict):
x = data_dict.keys()
y = data_dict.values()
ax.scatter(x, y)
def plot_average_vps(ax, label, blocked_vps):
xlist = [list(blocked_vps[x].keys()) for x in blocked_vps]
ylist = [list(blocked_vps[x].values()) for x in blocked_vps]
x = xlist[0]
yarray = np.array(ylist)
y = np.average(yarray, axis=0)
ax.scatter(x, y, label=label)
def count_correct(vp, trials, procedures):
trials_correct = {}
for proc in procedures:
trials_correct[proc] = 0
for sample in trials:
for proc in vp[sample]["procedure_order"]:
vp_ans = vp[sample][proc]["answer"]
for c in vp_ans:
if not c.isdigit():
vp_ans = vp_ans.replace(c, "")
vp_ans = int(vp_ans)
if vp_ans == vp[sample]["water_sample"][proc][0]:
trials_correct[proc] += 1
return trials_correct
def total_accuracy(vp, procedures):
train = [x for x in vp.keys() if "train" in x]
test = [x for x in vp.keys() if "test" in x]
train_total = len(train) * len(vp[train[0]]["procedure_order"])
test_total = len(test) * len(vp[test[0]]["procedure_order"])
acc_train = count_correct(vp, train, procedures)
acc_test = count_correct(vp, test, procedures)
acc_train = sum([acc_train[x] for x in acc_train.keys()]) / train_total
acc_test = sum([acc_test[x] for x in acc_test.keys()]) / test_total
return acc_train, acc_test
def train_test_split(data):
def delete_trials(data, string):
new_dict = {}
for cond in data.keys():
new_dict[cond] = {}
for vp in data[cond].keys():
new_dict[cond][vp] = {}
for trial in data[cond][vp].keys():
if string in trial and trial != "train_0":
new_dict[cond][vp][trial] = data[cond][vp][trial]
return new_dict
data_train = delete_trials(data, "train")
data_test = delete_trials(data, "test")
return data_train, data_test
print("imported tools")
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