Analysing your results

[1]:

import matplotlib.pyplot as plt
import pandas as pd

import vstt

/home/docs/checkouts/readthedocs.org/user_builds/vstt/envs/stable/lib/python3.11/site-packages/psychopy/preferences/preferences.py:11: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  from pkg_resources import parse_version

pygame 2.6.1 (SDL 2.28.4, Python 3.11.12)
Hello from the pygame community. https://www.pygame.org/contribute.html

Import

To import an experiment from a psydat file:

[2]:

experiment = vstt.Experiment("example.psydat")

WARNING:root:Key 'turn_target_to_green_when_reached' missing, using default 'False'
WARNING:root:Key 'turn_target_to_green_when_reached' missing, using default 'False'
WARNING:root:Key 'area' missing, using default 'False'
WARNING:root:Key 'normalized_area' missing, using default 'False'
WARNING:root:Key 'peak_velocity' missing, using default 'False'
WARNING:root:Key 'peak_acceleration' missing, using default 'False'
WARNING:root:Key 'to_target_spatial_error' missing, using default 'False'
WARNING:root:Key 'to_center_spatial_error' missing, using default 'False'
WARNING:root:Key 'movement_time_at_peak_velocity' missing, using default 'False'
WARNING:root:Key 'total_time_at_peak_velocity' missing, using default 'False'
WARNING:root:Key 'movement_distance_at_peak_velocity' missing, using default 'False'
WARNING:root:Key 'rmse_movement_at_peak_velocity' missing, using default 'False'

Conditions

The trial conditions are in trial_list, each element in this list is a dict of trial conditions that defines a trial:

[3]:

experiment.trial_list

[3]:

[{'weight': 2,
  'condition_timeout': 0.0,
  'num_targets': 8,
  'target_order': 'clockwise',
  'target_indices': '0 1 2 3 4 5 6 7',
  'add_central_target': True,
  'hide_target_when_reached': True,
  'turn_target_to_green_when_reached': False,
  'show_target_labels': False,
  'target_labels': '0 1 2 3 4 5 6 7',
  'fixed_target_intervals': False,
  'target_duration': 5.0,
  'central_target_duration': 5.0,
  'pre_target_delay': 0.0,
  'pre_central_target_delay': 0.0,
  'pre_first_target_extra_delay': 0.0,
  'target_distance': 0.4,
  'target_size': 0.04,
  'central_target_size': 0.02,
  'show_inactive_targets': True,
  'ignore_incorrect_targets': True,
  'play_sound': True,
  'use_joystick': False,
  'joystick_max_speed': 0.02,
  'show_cursor': True,
  'cursor_size': 0.02,
  'show_cursor_path': True,
  'automove_cursor_to_center': False,
  'freeze_cursor_between_targets': False,
  'cursor_rotation_degrees': 0.0,
  'post_trial_delay': 0.0,
  'post_trial_display_results': False,
  'post_block_delay': 0.0,
  'post_block_display_results': True,
  'show_delay_countdown': True,
  'enter_to_skip_delay': True},
 {'weight': 2,
  'condition_timeout': 0.0,
  'num_targets': 6,
  'target_order': 'random',
  'target_indices': '2 3 0 4 5 1',
  'add_central_target': True,
  'hide_target_when_reached': True,
  'turn_target_to_green_when_reached': False,
  'show_target_labels': False,
  'target_labels': '0 1 2 3 4 5 6 7',
  'fixed_target_intervals': False,
  'target_duration': 5.0,
  'central_target_duration': 5.0,
  'pre_target_delay': 1.0,
  'pre_central_target_delay': 0.0,
  'pre_first_target_extra_delay': 0.0,
  'target_distance': 0.4,
  'target_size': 0.04,
  'central_target_size': 0.02,
  'show_inactive_targets': True,
  'ignore_incorrect_targets': True,
  'play_sound': True,
  'use_joystick': False,
  'joystick_max_speed': 0.02,
  'show_cursor': True,
  'cursor_size': 0.02,
  'show_cursor_path': True,
  'automove_cursor_to_center': True,
  'freeze_cursor_between_targets': True,
  'cursor_rotation_degrees': 0.0,
  'post_trial_delay': 0.0,
  'post_trial_display_results': False,
  'post_block_delay': 0.0,
  'post_block_display_results': True,
  'show_delay_countdown': True,
  'enter_to_skip_delay': True}]

This can be more easily viewed if converted to a pandas DataFrame:

[4]:

pd.DataFrame(experiment.trial_list)

[4]:
weight condition_timeout num_targets target_order target_indices add_central_target hide_target_when_reached turn_target_to_green_when_reached show_target_labels target_labels ... show_cursor_path automove_cursor_to_center freeze_cursor_between_targets cursor_rotation_degrees post_trial_delay post_trial_display_results post_block_delay post_block_display_results show_delay_countdown enter_to_skip_delay
0 2 0.0 8 clockwise 0 1 2 3 4 5 6 7 True True False False 0 1 2 3 4 5 6 7 ... True False False 0.0 0.0 False 0.0 True True True
1 2 0.0 6 random 2 3 0 4 5 1 True True False False 0 1 2 3 4 5 6 7 ... True True True 0.0 0.0 False 0.0 True True True

2 rows × 36 columns

The weight of a trial is how many times it should be repeated.

Results

The results of the trials are in stats which provides both the raw data and calculated statistics as a pandas DataFrame:

[5]:

stats = experiment.stats

[6]:

stats.head()

[6]:
i_trial i_rep i_target condition_index target_index target_pos target_radius to_target_timestamps to_target_mouse_positions to_target_success ... to_center_rmse to_center_spatial_error area normalized_area peak_velocity peak_acceleration movement_time_at_peak_velocity total_time_at_peak_velocity movement_distance_at_peak_velocity rmse_movement_at_peak_velocity
0 0 0 0 0 0 [0.0, 0.4] 0.04 [0.016512155532836914, 0.03316307067871094, 0.... [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0... True ... 0.033841 0 0.023840 0.034475 2.223369 76.701244 0.269374 0.602779 0.257532 0.036990
1 0 0 1 0 1 [0.282842712474619, 0.28284271247461906] 0.04 [1.269320011138916, 1.2859880924224854, 1.3026... [[0.013888888888888888, 0.0032407407407407406]... True ... 0.017958 0 0.016878 0.026436 2.546450 119.925339 0.733372 0.750040 0.626939 0.021135
2 0 0 2 0 2 [0.4, 2.4492935982947065e-17] 0.04 [2.1527011394500732, 2.16939115524292, 2.18605... [[-0.0032407407407407406, 0.008796296296296297... True ... 0.030604 0 0.012411 0.014747 3.017843 90.854791 0.249999 0.266689 0.217018 0.015429
3 0 0 3 0 3 [0.28284271247461906, -0.282842712474619] 0.04 [3.1360599994659424, 3.1527769565582275, 3.169... [[0.014351851851851852, -0.012037037037037037]... True ... 0.040075 0 0.015122 0.018269 3.348506 94.232559 0.266654 0.283371 0.086299 0.026286
4 0 0 4 0 4 [4.898587196589413e-17, -0.4] 0.04 [4.052838087081909, 4.0694849491119385, 4.0861... [[-0.0125, 0.01712962962962963], [-0.017592592... True ... 0.038653 0 0.018386 0.025205 2.846187 96.380859 0.750033 0.766680 0.507352 0.061012

5 rows × 37 columns

  • each row in this DataFrame contains the data from a single movement to a target (and optionally back to the center)

  • they are in the order that they were shown in the experiment

  • condition_index is the index of the corresponding trial conditions in trial_index

[7]:

stats.describe()

[7]:
i_trial i_rep i_target condition_index target_index target_radius to_target_num_timestamps_before_visible center_radius to_center_num_timestamps_before_visible to_target_distance ... to_center_rmse to_center_spatial_error area normalized_area peak_velocity peak_acceleration movement_time_at_peak_velocity total_time_at_peak_velocity movement_distance_at_peak_velocity rmse_movement_at_peak_velocity
count 28.000000 28.0 28.000000 28.000000 28.000000 28.00 28.000000 28.00 28.0 28.000000 ... 16.000000 28.0 28.000000 28.000000 28.000000 28.000000 28.000000 27.000000 28.000000 28.000000
mean 1.357143 0.0 3.071429 0.428571 3.071429 0.04 25.464286 0.02 0.0 0.414450 ... 0.030781 0.0 0.014558 0.019241 2.746698 88.582911 0.349513 0.407524 0.236214 0.030708
std 1.129218 0.0 2.159022 0.503953 2.159022 0.00 29.944989 0.00 0.0 0.050779 ... 0.017339 0.0 0.007793 0.009220 0.651132 23.619614 0.290741 0.276798 0.213964 0.024542
min 0.000000 0.0 0.000000 0.000000 0.000000 0.04 0.000000 0.02 0.0 0.364467 ... 0.007777 0.0 0.003810 0.006553 1.761711 55.124790 0.000000 0.099994 0.000000 0.000000
25% 0.000000 0.0 1.000000 0.000000 1.000000 0.04 0.000000 0.02 0.0 0.381896 ... 0.018277 0.0 0.009981 0.013583 2.234549 73.140230 0.195854 0.241679 0.085500 0.015340
50% 1.000000 0.0 3.000000 0.000000 3.000000 0.04 0.000000 0.02 0.0 0.397624 ... 0.031392 0.0 0.012561 0.018035 2.671863 82.988582 0.266655 0.283350 0.156280 0.021695
75% 2.000000 0.0 5.000000 1.000000 5.000000 0.04 59.000000 0.02 0.0 0.420149 ... 0.039008 0.0 0.017399 0.022368 3.192919 96.768431 0.362553 0.501398 0.261430 0.040291
max 3.000000 0.0 7.000000 1.000000 7.000000 0.04 60.000000 0.02 0.0 0.562351 ... 0.077367 0.0 0.035034 0.042204 4.206990 136.721870 1.166727 1.183390 0.841735 0.091925

8 rows × 29 columns

Plot statistics

Here we plot a few of the calculated statics for each target, identifying each set of conditions with a different color:

[8]:

fig, axs = plt.subplots(ncols=3, figsize=(18, 6))
for ax, statistic in zip(
    axs, ["to_target_reaction_time", "to_target_movement_time", "to_target_distance"]
):
    ax.set_title(f"{statistic}")
    for condition_index, df in stats.groupby("condition_index"):
        ax.plot(df.index, df[statistic], label=f"Condition {condition_index}")
    ax.legend()
plt.show()
../_images/notebooks_example_15_0.png 
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