From e381a1e64ddb9e3bacba25389214cfd4756bba06 Mon Sep 17 00:00:00 2001
From: "Dobbertin, Niclas" <niclas.dobbertin@gmx.de>
Date: Tue, 28 Nov 2023 12:00:55 +0100
Subject: add jupyter notebook, RTsum plot

---
 experiment/analysis/tools.py | 72 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 72 insertions(+)

(limited to 'experiment/analysis/tools.py')

diff --git a/experiment/analysis/tools.py b/experiment/analysis/tools.py
index d32ccd3..1dffc9a 100644
--- a/experiment/analysis/tools.py
+++ b/experiment/analysis/tools.py
@@ -2,6 +2,9 @@
 
 import pickle
 from copy import deepcopy
+import matplotlib.pyplot as plt
+import numpy as np
+
 
 def unpickle(pkl):
     with open(pkl, "rb") as f:
@@ -9,6 +12,73 @@ def unpickle(pkl):
     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 = 0
+    for trial in range(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:
@@ -52,9 +122,11 @@ def train_test_split(data):
                     if string in trial:
                         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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