# -*- coding: utf-8 -*-
import neuropsydia as n
n.start()
n.newpage()
n.scale(title="Is Python great?",
y=3.3,
anchors=["", ""],
style="blue",
analog=False,
edges=[1, 5],
labels=["not at all", "not really", "maybe", "quite", "totally"],
labels_size=0.6)
n.scale(title="How is neuropsydia?",
y=-3.3,
line_length=12,
edges=[0, 100],
anchors=["atrocious", "brilliant"],
point_center=True,
separation_labels=["Bad", "Good"],
style="purple",
show_result=True,
show_result_shape_line_color="blue")
n.close()
# -*- coding: utf-8 -*-
import neuropsydia as n
n.start()
n.newpage()
response = n.choice(["Yes", "No"], y=5, title="Isn't it easy?")
response = n.choice(["Hell no", "Nope", "Dunno", "Sure"],
y=-5,
title="Am I better looking?",
height=-2,
boxes_edge_size=0,
boxes_background=["red", "amber", "teal", "blue"],
help_list=["means not at all", "means no", "means you don't know", "means yes"])
n.close()
while number_of_fails < 3: # Do it while the number of errors is smaller than 3
sequence = np.random.randint(10, size=span) # Generate sequence of size span with ints ranging from 0 to 9
good_answer = "" # Initiate an empty good_answer
for digit in sequence: # For every digit in the sequence...
good_answer = good_answer + str(digit) # Add the current stimulus to the good answer
n.newpage("grey") # Load a grey background
n.time.wait(250) # Display an empty screen for 250 ms
n.newpage("grey") # Load a grey background
n.write(digit, size=3) # Load the stimulus
n.refresh() # Display the stimulus on screen
n.time.wait(1000) # Wait 1000 ms
# Get answer
n.newpage("white")
answer = n.ask("Answer:")
# Manage result
if answer == good_answer:
span = span + 1 # Increase span
number_of_fails = 0 # Reset value
else:
number_of_fails = number_of_fails + 1
n.newpage() # Load a white background
n.write("Max span: " + str(span-1)) # Write task result
n.refresh() # Render it on screen
n.time.wait(3000) # Wait for 3s
n.close() # Close neuropsydia
n.refresh() # Diplay it on screen
response, RT = n.response(time_max=1500) # Wait until 1.5s and collect the response and its time
# Categorize the response
if response == "SPACE" and stimulus == "green":
response_type = "HIT" # Hit
if response != "SPACE" and stimulus == "green":
response_type = "MISS" # Miss
if response == "SPACE" and stimulus == "red":
response_type = "FA" # False Alarm
if response != "SPACE" and stimulus == "red":
response_type = "CR" # Correct Rejection
# Store data by appending each item to its list
data["Trial"].append(trial)
data["Stimulus"].append(stimulus)
data["ISI"].append(ISI)
data["RT"].append(RT)
data["Response"].append(response_type)
# Data saving
df = pd.DataFrame.from_dict(data) # Transform the data dictionary into a proper and savable dataframe
df.to_csv("data.csv") # Save it
# Quick analysis
RTs = df.query('Response=="HIT"')["RT"] # Select the Hits' RTs
print(np.mean(RTs), np.std(RTs)) # Print the mean and the standard deviation
print(len(df.query('Response=="FA"'))) # Print the number of intrusions (false alarms)
n.close() # Close neuropsydia