Thank you so much for the prompt response!! I updated my code to consistently use Auto → JS, and now all of my Python code appears to translate successfully into JavaScript (i.e., I do not receive the message: /* Syntax Error: Fix Python code */). However, I now encounter a new error (attached) immediately upon starting the task. This error seems to relate to the portion of the code that scores participants’ responses (correct vs. incorrect) and provides feedback.
The specific Python code that I used is the following:
timer = core.Clock()
# Load your CSV once:
study_list_file = "lists/recall_stim_words.csv"
study_df = data.importConditions(study_list_file)
# Organize correct words and types per block
block_study_words = {}
block_trial_types = {}
for row in study_df:
blk = row["block"]
wd = row["word"]
t_type = row["trial_type"]
if blk not in block_study_words:
block_study_words[blk] = set()
block_trial_types[blk] = t_type # assumes consistent within block
block_study_words[blk].add(wd.lower())
print(block_study_words)
print(block_trial_types)
# Levenshtein functions
def levenshtein_distance(s1, s2):
if len(s1) < len(s2):
s1, s2 = s2, s1 # Swap without recursion
if len(s2) == 0:
return len(s1)
previous_row = list(range(len(s2) + 1))
for i in range(len(s1)):
c1 = s1[i]
current_row = [i + 1]
for j in range(len(s2)):
c2 = s2[j]
# Explicitly separate indices (JS compatible)
idx_insertions = j + 1
idx_deletions = j
insertions = previous_row[idx_insertions] + 1
deletions = current_row[idx_deletions] + 1
# Explicit conditional
if c1 == c2:
substitutions = previous_row[j] + 0
else:
substitutions = previous_row[j] + 1
current_row.append(min([insertions, deletions, substitutions]))
previous_row = current_row
return previous_row[-1]
def is_fuzzy_match(typed_word, correct_words, threshold=0.75):
correct_words_list = list(correct_words) # Ensure JS compatibility by explicitly converting to list
for i in range(len(correct_words_list)): # explicitly looping by index
correct_word = correct_words_list[i]
max_len = max(len(typed_word), len(correct_word))
if max_len == 0:
continue
similarity = 1 - (levenshtein_distance(typed_word, correct_word) / max_len)
if similarity >= threshold:
return True
return False
This Python code generated the following JavaScript:
var _pj;
function _pj_snippets(container) {
function in_es6(left, right) {
if (((right instanceof Array) || ((typeof right) === "string"))) {
return (right.indexOf(left) > (- 1));
} else {
if (((right instanceof Map) || (right instanceof Set) || (right instanceof WeakMap) || (right instanceof WeakSet))) {
return right.has(left);
} else {
return (left in right);
}
}
}
container["in_es6"] = in_es6;
return container;
}
_pj = {};
_pj_snippets(_pj);
timer = new util.Clock();
study_list_file = "lists/recall_stim_words.csv";
study_df = data.importConditions(study_list_file);
block_study_words = {};
block_trial_types = {};
for (var row, _pj_c = 0, _pj_a = study_df, _pj_b = _pj_a.length; (_pj_c < _pj_b); _pj_c += 1) {
row = _pj_a[_pj_c];
blk = row["block"];
wd = row["word"];
t_type = row["trial_type"];
if ((! _pj.in_es6(blk, block_study_words))) {
block_study_words[blk] = set();
block_trial_types[blk] = t_type;
}
block_study_words[blk].add(wd.toLowerCase());
}
console.log(block_study_words);
console.log(block_trial_types);
function levenshtein_distance(s1, s2) {
var c1, c2, current_row, deletions, idx_deletions, idx_insertions, insertions, previous_row, substitutions;
if ((s1.length < s2.length)) {
[s1, s2] = [s2, s1];
}
if ((s2.length === 0)) {
return s1.length;
}
previous_row = list(util.range((s2.length + 1)));
for (var i, _pj_c = 0, _pj_a = util.range(s1.length), _pj_b = _pj_a.length; (_pj_c < _pj_b); _pj_c += 1) {
i = _pj_a[_pj_c];
c1 = s1[i];
current_row = [(i + 1)];
for (var j, _pj_f = 0, _pj_d = util.range(s2.length), _pj_e = _pj_d.length; (_pj_f < _pj_e); _pj_f += 1) {
j = _pj_d[_pj_f];
c2 = s2[j];
idx_insertions = (j + 1);
idx_deletions = j;
insertions = (previous_row[idx_insertions] + 1);
deletions = (current_row[idx_deletions] + 1);
if ((c1 === c2)) {
substitutions = (previous_row[j] + 0);
} else {
substitutions = (previous_row[j] + 1);
}
current_row.push(Math.min([insertions, deletions, substitutions]));
}
previous_row = current_row;
}
return previous_row.slice((- 1))[0];
}
function is_fuzzy_match(typed_word, correct_words, threshold = 0.75) {
var correct_word, correct_words_list, max_len, similarity;
correct_words_list = list(correct_words);
for (var i, _pj_c = 0, _pj_a = util.range(correct_words_list.length), _pj_b = _pj_a.length; (_pj_c < _pj_b); _pj_c += 1) {
i = _pj_a[_pj_c];
correct_word = correct_words_list[i];
max_len = Math.max(typed_word.length, correct_word.length);
if ((max_len === 0)) {
continue;
}
similarity = (1 - (levenshtein_distance(typed_word, correct_word) / max_len));
if ((similarity >= threshold)) {
return true;
}
}
return false;
}
