OpenSesame version

[e.g. 4.0.24]

If you are running an online experiment, also provide the version of OSWeb, which is shown when you launch an experiment in a browser. We regularly fix bugs, so always make sure that you are running the latest version of OpenSesame and other relevant packages.

Operating system

[e.g. Windows 10 / macOS Ventura / Ubuntu 22.04]

If you are running an online experiment, also provide the browser version.

Backend

[PsychoPy / Expyriment / legacy / OSWeb]

You can find the backend under "General Properties / Run experiment".

Expected behavior

[e.g. "The experiment should play two sounds."]

Clearly explain what you would like to happen.

Actual behavior (what goes wrong)

[e.g. "The experiment only plays one sound."]

Clearly explain what happens, and in what way this deviates from what you would like to happen.

Error messages (if any)

[Error message here]

Copy-paste the full error message from the console output at the bottom of the OpenSesame window (for the desktop version) or from the browser console (for OSWeb). You can also attach a screenshot.

What did you try already?

[e.g. "I changed the backend, but it didn't help."]

Did you already reinstall OpenSesame, change the backend, check for similar issues on the forum, ask SigmundAI, or try anything else.?

Experiment file (optional)

Consider attaching attaching your .osexp file. If you do not want to share your experiment file, you can also attach a minimal version of the experiment that illustrates the issue.

I am working with a large dataset using the DataMatrix library & I have encountered an issue with handling missing values efficiently. Some columns have sporadic missing entries & I am unsure about the best approach to fill or exclude them without distorting my analysis. What are the most effective methods within DataMatrix to identify, filter / impute missing values while preserving data integrity?

I have seen some general Python approaches using NumPy and Pandas but I’d like to stay within the DataMatrix framework as much as possible. Should I replace missing values with column means, interpolate them / simply remove affected rows? Additionally, does DataMatrix provide built-in functions to handle these cases, or would I need to convert my dataset to another format for preprocessing? Checked https://datamatrix.cogsci.nl/ guide for reference .

If anyone has experience dealing with missing values in DataMatrix, I’d love to hear your approach. Are there any performance considerations when working with large CISSP Online Training datasets? Any tips on best practices for handling missing data in an experimental or psychological research context would be greatly appreciated!

Thank you !!

I am trying to build my routines and workflow to analyze pupillometry and eye tracking data, using a Tobii eye tracker. I tried to use some toolboxes in matlab for preprocessing of pupil data, but to no avail.

I saw Mr. Mathot's tutorial on using datamatrix to preprocess the data (thank you for the precious contributions by the way!) but couldn't implement it directly, as he uses eyelink parser. Is there a way to make a Tobii parser to preprocess the data with datamatrix? Alternatively, are there any suggestions for intuitive and easy-to-implement ways to preprocess the pupil data?

Thanks in advance!

I need someone's help, I have not been able to import a dataset that has 76,300 rows and 10 columns CSV type. I don't know why the error occurs. The software freezes. With smaller datasets the software imports them without problem, but the dataset that has 76,300 rows and 10 columns, JASP does not respond, does anyone know why? how could I import this dataset without problem? 

Thank you in advance for your help and comments.

Fraternally,

JADES

When I do a simple loop over a DataMatrix object in OpenSesame I get: RecursionError: maximum recursion depth exceeded.

Code:

from datamatrix import DataMatrix

dm = DataMatrix(length=5)
dm.fibonacci = 0, 1, 1, 2, 3
for row in dm:
  print(row.fibonacci)

Error:

File C:\Program Files (x86)\OpenSesame\Lib\site-packages\datamatrix\_datamatrix\_row.py:78, in Row.__getattr__(self, key)
     76 def __getattr__(self, key):
---> 78     return self.__getitem__(key)

File C:\Program Files (x86)\OpenSesame\Lib\site-packages\datamatrix\_datamatrix\_row.py:84, in Row.__getitem__(self, key)
     82 if isinstance(key, int):
     83     key = self._datamatrix.column_names[key]
---> 84 return self._datamatrix[key][self._index]

File C:\Program Files (x86)\OpenSesame\Lib\site-packages\datamatrix\_datamatrix\_row.py:78, in Row.__getattr__(self, key)
     76 def __getattr__(self, key):
---> 78     return self.__getitem__(key)

File C:\Program Files (x86)\OpenSesame\Lib\site-packages\datamatrix\_datamatrix\_row.py:84, in Row.__getitem__(self, key)
     82 if isinstance(key, int):
     83     key = self._datamatrix.column_names[key]
---> 84 return self._datamatrix[key][self._index]

    [... skipping similar frames: Row.__getattr__ at line 78 (1484 times), Row.__getitem__ at line 84 (1483 times)]

File C:\Program Files (x86)\OpenSesame\Lib\site-packages\datamatrix\_datamatrix\_row.py:84, in Row.__getitem__(self, key)
     82 if isinstance(key, int):
     83     key = self._datamatrix.column_names[key]
---> 84 return self._datamatrix[key][self._index]

File C:\Program Files (x86)\OpenSesame\Lib\site-packages\datamatrix\_datamatrix\_row.py:78, in Row.__getattr__(self, key)
     76 def __getattr__(self, key):
---> 78     return self.__getitem__(key)

RecursionError: maximum recursion depth exceeded

When I remove any file (tried multiple different ones) the error disappears and it works fine again. I accepted this as the limit, which is still found a little strange since 16 files is not that much, so I made an new folder, added the next 16 files in there, made the loading a function that uses the right paths and the eyelink parser. But then suddenly it can only take 10 files and not even 16 anymore. Made a new folder, that one could store 10 again, but the next one only 8. So i'm a little lost. It also does not seem to be caused by the size of the folder (they range from 800mb (do not work) to 1070 mb (still work)).

@fnc.memoize(persistent=True)
def get_data(folder_path):

    # The heavy lifting is done by eyelinkparser.parse()
    dm = parse(
        folder=folder_path,       # Folder path to .asc files
        traceprocessor=defaulttraceprocessor(
          blinkreconstruct=True,  # Interpolate pupil size during blinks
          downsample=10,          # Reduce sampling rate to 100 Hz,
          mode='advanced'         # Use the new 'advanced' algorithm
        )
    )
    dm = dm[dm.trialid, dm.blockcount, dm.invalid, dm.ptrace_bl, dm.ptrace_stim, dm.participant,
            dm.stim_con, dm.xtrace_stim, dm.ytrace_stim, dm.xtrace_bl, dm.ytrace_bl, dm.condition, dm.tact1]
    dm = dm.invalid == 0  # to filter all the invalid trials
    dm = dm.trialid != 0  # to filter the duplicate first trials

    return dm


get_data.clear()                                                           # clear data, otherwise is stored
left_folder = 'C:\\N&C Internship\\DATA_ANALYSIS\\Left_20_25'      # path for the left eye placebo
right_folder = 'C:\\N&C Internship\\DATA_ANALYSIS\\Right_20_25' 

this is the general code i use in which i can the folder paths to 1_8, 9_13, 14_19

Long story short, my question is: what are the limitations of the memoize function and how can i prevent this from happening? Is there a better way to load in all the data (I have 4 files per participant, 2 sessions x 2 eyes, 32 participants).

Any help is welcome, thank you in advance!

Question is 'how to do IRT plot in Jemerik].

I can make each item to category probability 'graph -> IRT plot',

but how to make each 'factor' category probability graph

sorry for posting like this, but if someone know how to do this things... please leave comments

the smoothing and blink_reconstruction functions are not working very well, and I have a difficult time tuning the hyperparameters. I get better results by using standard conv1D smoothing procedures. I was wondering if I am using bad hyperparameters, or something else is wrong. I am using Data Bins of 20ms in the raw csv file. attached and attached are some sample reconstructions. I tried a bunch of different hyperparameters and could not find something that worked.

I cannot parse the data correctly using the existing code. I noticed the ASC file has a different set of messages. For example, here is the messages in our file example (attached). I only could manage to parse the file by changing the source code to:

def is_start_trial(self, l):

     # MSG 6735155 start_trial 1

     if self.match(l, u'MSG', int, u'TRIALID', ANY_VALUE):

       self.trialid = l[3]

       self.current_phase = None

       return True

but this was enough and still the resulting datamatrix does not have the pupil data. Is there some workflow to change the defualt format created from an experiment by pylink only to be used and parsed?

Attached is some lines of the .asc file created

MSG 11751341 TRIALID 1

MSG 11751344 RECCFG CR 500 2 1 L

MSG 11751344 ELCLCFG MTABLER

MSG 11751344 GAZE_COORDS 0.00 0.00 1919.00 1199.00

MSG 11751344 THRESHOLDS L 54 206

MSG 11751344 ELCL_WINDOW_SIZES 176 188 0 0

MSG 11751344 CAMERA_LENS_FOCAL_LENGTH 38.00 

MSG 11751344 PUPIL_DATA_TYPE RAW_AUTOSLIP

MSG 11751344 ELCL_PROC CENTROID (3)

MSG 11751344 ELCL_PCR_PARAM 5 3.0

START 11751345 LEFT SAMPLES EVENTS

PRESCALER 1

VPRESCALER 1

PUPIL AREA

EVENTS GAZE LEFT RATE 500.00 TRACKING CR FILTER 2

SAMPLES GAZE LEFT RATE 500.00 TRACKING CR FILTER 2 INPUT

INPUT 11751345 127

MSG 11751345 !MODE RECORD CR 500 2 1 L

11751345  997.2  517.6 1390.0  127.0 ...

11751347  998.7  523.2 1393.0  127.0 ...

SFIX L  11751349

11751349  999.7  521.5 1394.0  127.0 ...

11751351 1001.5  518.4 1392.0  127.0 ...

MSG 11751352 trial_onset

11751353 1000.8  519.9 1391.0  127.0 ...

11751355  999.4  518.5 1389.0  127.0 ...

11751357  999.8  517.4 1391.0  127.0 ...

11751359 1001.7  517.5 1394.0  127.0 ...

I'm trying to run the code on: https://pydatamatrix.eu/eyelinkparser/

However, there seems to be a problem with the dependencies.

When I run this code:

from datamatrix import (

 operations as ops,

 functional as fnc,

 series as srs

)

from eyelinkparser import parse, defaulttraceprocessor

The last line returns this error:

ImportError: cannot import name 'cached' from 'datamatrix'

I have used pip install because conda was not available, but I usually use conda, so perhaps that is the problem. Are there any known workarounds?

Cheers,

Jasper

I was reading Methods in cognitive pupillometry: Design, preprocessing, and statistical analysis which uses DataMatrix library to preprocess the pupillometry data, and one question I have is regarding the preprocessing step of detecting blinks and artifacts.

What is the best approach for applying those algorithms, such as blinkreconstruct()? Should these algorithms be applied to the entire pupillometry data of an experiment or to each block or trial separately, and are there any differences between these approaches?

Thank you all in advance!

My question: Has anyone used Data Matrix for processing/analyzing data from a Tobii eyetracker?

In order to be able to compare the Tobii vs. Eyelink output files, I tried to read the Eyelink edf files of the semantic_pupil experiment https://github.com/smathot/semantic_pupil/tree/master/analysis, with no luck. I cannot convert the .edf files to .asc. Could someone, perhaps, upload one as .asc (or other readable format)?

I am also attaching two screenshots of a Tobii X3-120 output .tsv file.

Note that missing (-1) values in pupil size are due to Tobii X3-120 reporting pupil size only in bright pupil mode which is used only every third sample. Thus although this model records eye gaze at 120Hz, it records pupil size at only 40Hz -as this is only a pilot study, I guess this is not too important.

Any advice would be more than welcome!

Best,

Alexandra

I am trying to create a datamatrix and add a new row per trial. What happens so far is that I am adding a new row with x (number of trials) times the input of the latest row...

Here a piece of the code, where rate corresponds to an array saving 4 slider positions

and here some output of the command print(dm):

Cheers for any suggestions :-)

I recently downloaded the eyelinkparser. This generally looks like a very useful toolbox and I tried to read in my data, comparing it with the output of another script I had previously written and in which I mostly analyse fixations. I noticed some mismatches and after checking the original .asc file I saw that certain values in the lists for fixation onsets/ends/coordinates seem to correspond to values in ESACC lines, rather than EFIX. Could it be that those are similarly read in as fixations?

I'm not sure I have correctly understood what's going on, but I guess the match function in the Fixation class could be used to check this, but I don't see where it is used in the current code. Is there maybe something I would have to add or adjust?

Thanks a lot for any help :)

Maëlle

Problem is, there are tons of apps meant to read QR codes, but I have found absolutely none that can read these "unusual codes" like DataMatrix or Aztec codes. Any app that can read those?

EDIT: Answered.

The device gives out pupil size measures in the form of .csv files.

I need to know how to prepare (parse?) my data so that I can apply all the functions to it, such as srs.baseline, smooth, blinkreconstruct, etc.

A gist containing an example of my data is here:

https://gist.github.com/Zahra-on-Github/aa67a3e309fa66582a118f5c08509f77

pupil_positions.csv is my main data, in which pupil size in under the column: diameter_3d

I would appreciate some guidance on how to do the baseline correction as the first step.

I created the Tutorials on my computer running Windows 10, and my student is working through the tutorials on her computer running a Mac OS. Unfortunately, we haven't gotten very far, because my assistant is unable to create a DataMatrix. When typing in this code:

from datamatrix import DataMatrix

dm = DataMatrix(length=6)

she gets the following error:

I have a feeling that something is wrong with the installation of the DataMatrix package, but trying to upgrade tells us that it is the most recent version.

Here is her system information:

System

• System: Darwin-19.4.0-x86_64-i386-64bit
• Architektur: 64bit

Module and Pakete

• OpenSesame 3.2.8
• Python 2.7.15 |Anaconda, Inc.| (default, Nov 13 2018, 17:07:45) [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]
• QProgEdit 4.1.2
• datamatrix [version unknown]
• qdatamatrix 0.1.18
• pseudorandom [not available]
• fileinspector 1.0.2
• QNotifications 2.0.3
• QOpenScienceFramework 1.3.0
• opencv [not available]
• expyriment 0.9.1b2-11-gc100ee8
• IPython 5.5.0
• numpy 1.14.2
• scipy 1.0.1
• PIL/ PILLOW 1.1.7
• psychopy 1.85.3
• pygame 1.9.3
• pygaze 0.6.0a25
• pyglet 1.2.4
• PyQt 5.9.2
• serial 3.4
• markdown 2.6.9
• yaml 3.12

Apologies for posting on her behalf, she is brand new to Open Sesame and Python coding and I feel responsible for solving this problem.

Thank you in advance for any suggestions!

Katie Von Holzen

working on my master thesis I want to analyse some eyetracking data and found your toolbox, which seems pretty cool to me.

But sadly it does not work properly with my data. When calling eyelinkparser.parse on the folder containing my data it returns an empty list. Strangly it works fine with your example data.

I inspected your and my data files but cannot find any significant differences. The only difference I can find is in my data I have:

   10350642    864.3    542.7    2317.0

and in yours I have:

   1030724    223.7    381.1    1431.0   ...

Do those three dots make the difference? I tried adding those points to a couple of data points, but that did not change anything.

Here some header info, if it helps:

** CONVERTED FROM s1b1r1.edf using edfapi 3.1 Linux Mar 9 2017 on Thu Mar 19 09:42:35 2020 
** DATE: Mon Feb 3 10:28:19 2020 
** TYPE: EDF_FILE BINARY EVENT SAMPLE TAGGED 
** VERSION: EYELINK II 1 
** SOURCE: EYELINK CL 
** EYELINK II CL v5.12 May 12 2017 
** CAMERA: Eyelink GL Version 1.2 Sensor=AF7 
** SERIAL NUMBER: CLG-BEC12 
** CAMERA_CONFIG: BEC12200.SCD 
**

Cheers Vincent

Error Log:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-99-1398dfa59801> in <module>
      1 dm = DataMatrix(length=1500)
      2 dm.pupil = df['LEFT_PUPIL_SIZE'][1359:2859]
----> 3 dm.recon = blinkreconstruct(dm.pupil, smooth_winlen=21)

~/miniconda3/lib/python3.7/site-packages/datamatrix/_datamatrix/_datamatrix.py in __setattr__(self, name, value)
    571                         self._set_default_col_type(value)
    572                         return
--> 573                 self._set_col(name, value)
    574 
    575         def __delitem__(self, value):

~/miniconda3/lib/python3.7/site-packages/datamatrix/_datamatrix/_datamatrix.py in _set_col(self, name, value)
    472                 if name not in self:
    473                         self._cols[name] = self._default_col_type(self)
--> 474                 self._cols[name][:] = value
    475                 self._mutate()
    476 

~/miniconda3/lib/python3.7/site-packages/datamatrix/_datamatrix/_basecolumn.py in __setitem__(self, key, value)
    820                         self._setintkey(key, value)
    821                 elif isinstance(key, slice):
--> 822                         self._setslicekey(key, value)
    823                 elif isinstance(key, collections.Sequence):
    824                         self._setsequencekey(key, value)

~/miniconda3/lib/python3.7/site-packages/datamatrix/_datamatrix/_basecolumn.py in _setslicekey(self, key, value)
    521                         return
    522                 length = len(self._seq[key])
--> 523                 self._seq[key] = self._tosequence(value, length)
    524 
    525         def _setsequencekey(self, key, val):

~/miniconda3/lib/python3.7/site-packages/datamatrix/_datamatrix/_basecolumn.py in _tosequence(self, value, length)
    388                 if len(value) != length:
    389                         raise Exception('Sequence has incorrect length: %s' % len(value))
--> 390                 return [self._checktype(cell) for cell in value]
    391 
    392         def _getintkey(self, key):

~/miniconda3/lib/python3.7/site-packages/datamatrix/_datamatrix/_basecolumn.py in <listcomp>(.0)
    388                 if len(value) != length:
    389                         raise Exception('Sequence has incorrect length: %s' % len(value))
--> 390                 return [self._checktype(cell) for cell in value]
    391 
    392         def _getintkey(self, key):

~/miniconda3/lib/python3.7/site-packages/datamatrix/_datamatrix/_basecolumn.py in _checktype(self, value)
    326                         return value
    327                 if not isinstance(value, BASESTRING_OR_NUMBER):
--> 328                         raise TypeError('Invalid type: %s' % value)
    329                 if fastnumbers:
    330                         return self._checktype_fastnumber(value)

TypeError: Invalid type: 1599

What can I do to fix this bug, please?

Sincerely
Weizhe Li

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-3-d108d6f85b36> in <module>
----> 1 dm = get_data()
      2 print(dm)

~/miniconda3/lib/python3.7/site-packages/datamatrix/_functional/_memoize.py in _call_without_arguments(self, *args, **kwargs)
    223         return self._write_cache(
    224                         memkey,
--> 225                         self._fnc(*args, **kwargs)
    226         )
    227 

<ipython-input-2-107bd9e4e4c4> in get_data()
     21     dm = ops.keep_only(
     22         dm,
---> 23         dm.set_size, dm.correct, dm.ptrace_sounds, dm.ptrace_retention,
     24         dm.fixxlist_retention, dm.fixylist_retention
     25     )

~/miniconda3/lib/python3.7/site-packages/datamatrix/_datamatrix/_datamatrix.py in __getattr__(self, name)
    605                 if name in ('__getstate__', '_cols'):
    606                         raise AttributeError()
--> 607                 return self._getcolbyname(name)
    608 
    609         def __getitem__(self, key):

~/miniconda3/lib/python3.7/site-packages/datamatrix/_datamatrix/_datamatrix.py in _getcolbyname(self, key)
    388                 col = self._cols.get(key, None)
    389                 if col is None:
--> 390                         raise AttributeError(u'No column named "%s"' % key)
    391                 return col
    392 

AttributeError: No column named "set_size"

and the data type of my .asc file is:
** CONVERTED FROM E:\Git_liweizhe\Research\Papers\Journal Papers\2018\Pupil_color\Exp\code\data\edf\01_m_25F.EDF using edfapi 3.1 Win32 Oct 18 2018 on Fri Jan 18 16:55:01 2019
** DATE: Wed Dec 26 19:24:32 2018
** TYPE: EDF_FILE BINARY EVENT SAMPLE TAGGED
** VERSION: EYELINK II 1
** SOURCE: EYELINK CL
** EYELINK II CL v5.12 May 12 2017
** CAMERA: Eyelink GL Version 1.2 Sensor=AJ7
** SERIAL NUMBER: CLG-BEA17
** CAMERA_CONFIG: BEA17200.SCD
** Inspiration_Color
**

MSG 848272 DISPLAY_COORDS = 0 0 1919 1079
INPUT 900827 127
MSG 907988 !CAL

CALIBRATION (HV5,P-CR) FOR LEFT: <<<<<<<<<

MSG 907988 !CAL Calibration points:
MSG 907988 !CAL -33.5, -42.9 0, 108
MSG 907988 !CAL -33.6, -58.9 0, -2255
MSG 907988 !CAL -34.0, -29.1 0, 2399
MSG 907988 !CAL -69.1, -43.6 -4157, 108
MSG 907988 !CAL -1.6, -43.6 4157, 108
MSG 907988 !CAL 0.0, 0.0 0, 0
MSG 907988 !CAL eye check box: (L,R,T,B)
-76 7 -65 6
MSG 907988 !CAL href cal range: (L,R,T,B)
-6236 6236 -3418 3562
MSG 907988 !CAL Cal coeff:(X=a+bx+cy+dxx+eyy,Y=f+gx+goaly+ixx+jyy)
0.00031112 123.94 2.0783 0.20375 0.16679
107.89 0.3367 157.3 0.085003 0.57063
MSG 907988 !CAL Prenormalize: offx, offy = -33.517 -42.944
MSG 907988 !CAL Gains: cx:137.499 lx:120.471 rx:154.821
MSG 907988 !CAL Gains: cy:223.282 ty:171.314 by:253.597
MSG 907988 !CAL Resolution (upd) at screen center: X=1.9, Y=1.2
MSG 907988 !CAL Gain Change Proportion: X: 0.285 Y: 0.480
MSG 907988 !CAL Gain Ratio (Gy/Gx) = 1.624
MSG 907988 !CAL Cross-Gain Ratios: X=0.026, Y=0.002
MSG 907988 !CAL PCR gain ratio(x,y) = 2.201, 1.878
MSG 907988 !CAL CR gain match(x,y) = 1.010, 1.010
MSG 907988 !CAL Slip rotation correction OFF
MSG 907988 !CAL

CALIBRATION (HV5,P-CR) FOR RIGHT: <<<<<<<<<

MSG 907988 !CAL Calibration points:
MSG 907988 !CAL -46.7, -47.0 0, 108
MSG 907988 !CAL -46.5, -62.1 0, -2255
MSG 907988 !CAL -47.2, -33.2 0, 2399
MSG 907988 !CAL -78.4, -47.0 -4157, 108
MSG 907988 !CAL -15.5, -48.8 4157, 108
MSG 907988 !CAL 0.0, 0.0 0, 0
MSG 907988 !CAL eye check box: (L,R,T,B)
-86 8 -68 6
MSG 907988 !CAL href cal range: (L,R,T,B)
-6236 6236 -3418 3562
MSG 907988 !CAL Cal coeff:(X=a+bx+cy+dxx+eyy,Y=f+gx+goaly+ixx+jyy)
0.00031112 132.21 3.2536 0.041906 0.11144
107.89 4.5507 161.47 0.14073 0.32727
MSG 907988 !CAL Prenormalize: offx, offy = -46.681 -47.047
MSG 907988 !CAL Gains: cx:138.230 lx:133.009 rx:144.588
MSG 907988 !CAL Gains: cy:203.792 ty:178.628 by:228.657
MSG 907988 !CAL Resolution (upd) at screen center: X=1.9, Y=1.3
MSG 907988 !CAL Gain Change Proportion: X: 0.087 Y: 0.280
MSG 907988 !CAL Gain Ratio (Gy/Gx) = 1.474
MSG 907988 !CAL Cross-Gain Ratios: X=0.031, Y=0.031
MSG 907988 !CAL PCR gain ratio(x,y) = 2.205, 1.964
MSG 907988 !CAL CR gain match(x,y) = 1.010, 1.010
MSG 907988 !CAL Slip rotation correction OFF
MSG 908008 !CAL CALIBRATION HV5 LR LEFT GOOD
MSG 908008 !CAL CALIBRATION HV5 LR RIGHT GOOD
INPUT 909997 127
MSG 920294 !CAL VALIDATION HV5 LR LEFT GOOD ERROR 0.46 avg. 1.23 max OFFSET 0.39 deg. -0.6,20.5 pix.
MSG 920294 !CAL VALIDATION HV5 LR RIGHT GOOD ERROR 0.45 avg. 1.38 max OFFSET 0.15 deg. 5.0,6.1 pix.
MSG 920294 VALIDATE LR POINT 0 LEFT at 960,540 OFFSET 0.25 deg. -6.1,11.8 pix.
MSG 920294 VALIDATE LR 4POINT 0 RIGHT at 960,540 OFFSET 0.22 deg. 1.7,-11.6 pix.
MSG 920294 VALIDATE LR POINT 1 LEFT at 960,81 OFFSET 0.38 deg. 19.9,-1.8 pix.
MSG 920294 VALIDATE LR 4POINT 1 RIGHT at 960,81 OFFSET 0.23 deg. -0.2,-11.9 pix.
MSG 920294 VALIDATE LR POINT 2 LEFT at 960,998 OFFSET 0.38 deg. -7.8,18.1 pix.
MSG 920294 VALIDATE LR 4POINT 2 RIGHT at 960,998 OFFSET 0.22 deg. -5.2,-10.1 pix.
MSG 920294 VALIDATE LR POINT 3 LEFT at 144,540 OFFSET 0.35 deg. 15.5,9.9 pix.
MSG 920294 VALIDATE LR 4POINT 3 RIGHT at 144,540 OFFSET 0.46 deg. 14.7,18.9 pix.
MSG 920294 VALIDATE LR POINT 4 LEFT at 1775,540 OFFSET 1.23 deg. 1.2,64.3 pix.
MSG 920294 VALIDATE LR 4POINT 4 RIGHT at 1775,540 OFFSET 1.38 deg. 17.9,69.8 pix.
INPUT 925092 127
INPUT 1054798 127
INPUT 1054908 127
MSG 1056864 DRIFTCORRECT LR LEFT at 960,540 OFFSET 0.10 deg. -2.8,-4.3 pix.
MSG 1056864 DRIFTCORRECT LR RIGHT at 960,540 OFFSET 0.16 deg. 8.0,0.6 pix.
MSG 1057003 TRIALID
MSG 1057006 RECCFG CR 500 2 1 LR
MSG 1057006 ELCLCFG BTABLER
MSG 1057006 GAZE_COORDS 0.00 0.00 1919.00 1079.00
MSG 1057006 THRESHOLDS L 81 209 R 83 217
MSG 1057006 ELCL_WINDOW_SIZES 160 160 0 0
MSG 1057006 CAMERA_LENS_FOCAL_LENGTH 38.00
MSG 1057006 ELCL_PROC ELLIPSE (5)
MSG 1057006 ELCL_EFIT_PARAMS 1.01 4.00 0.15 0.05 0.65 0.65 0.00 0.00 0.30
MSG 1057007 !MODE RECORD CR 500 2 1 LR
START 1057008 LEFT RIGHT SAMPLES EVENTS
PRESCALER 1
VPRESCALER 1
PUPIL AREA
EVENTS GAZE LEFT RIGHT RATE 500.00 TRACKING CR FILTER 2
SAMPLES GAZE LEFT RIGHT RATE 500.00 TRACKING CR FILTER 2
INPUT 1057008 127
1057008 967.1 533.4 1559.0 964.1 547.3 1333.0
1057010 967.3 533.2 1558.0 964.4 547.6 1332.0
SFIX L 1057012
SFIX R 1057012
1057012 967.5 533.0 1557.0 964.5 548.1 1330.0
1057014 967.8 534.1 1556.0 964.5 548.4 1329.0
1057016 967.0 534.7 1556.0 964.5 549.0 1327.0
1057018 966.9 534.3 1557.0 964.1 549.9 1327.0
1057020 967.2 534.0 1557.0 964.3 550.0 1325.0
1057022 967.3 532.9 1556.0 964.3 549.7 1325.0
1057024 967.3 532.5 1553.0 964.3 549.9 1325.0

What should I change if I want to reconstruct the pupil?

I am using data matrix in order to fill loop in open sesame (I find it more convenient loading first the variable then filling the loop by using data matrix).
Anyway my issue is as follow. I have string of 64 characters made of 0 and 1, because I use this binary coding for filling a matrix of square with various patterns represented by these strings.
So I first created lists of stims, then I create a data matrix object from these lists that I use as a loop.
This strategy worked but not with so long strings apparently:

import numpy as np
from datamatrix import DataMatrix


dmSymPract = DataMatrix(length = 15)

dmPractice_Sym_Stim_Coord = list()
dmPractice_Sym_Stim_Correctness = list()

dmPractice_Sym_Stim_Coord.append('0001100000011000001111000111111011111111011001100011110000011000') #1
dmPractice_Sym_Stim_Correctness.append(True)
dmPractice_Sym_Stim_Coord.append('1100001111100111110110111011110110011001101111011100001111111111') #2
dmPractice_Sym_Stim_Correctness.append(True)

dmPractice_Sym_Stim_Coord.append('1111111100111100000110000011110000100100001001000011110001011010') #3
dmPractice_Sym_Stim_Correctness.append(True)

dmPractice_Sym_Stim_Coord.append('1100001111000011111111110011110000111100111111111100001111000011') #4
dmPractice_Sym_Stim_Correctness.append(True)

dmPractice_Sym_Stim_Coord.append('1111111101111110111111110001100000011000111111110111111011111111') #5
dmPractice_Sym_Stim_Correctness.append(True)

dmPractice_Sym_Stim_Coord.append('1111111111011011010000101101101111011011010000101101101111111111') #6
dmPractice_Sym_Stim_Correctness.append(True)

dmPractice_Sym_Stim_Coord.append('1100001101100110001111001111111111111111001111000110011011000011') #7
dmPractice_Sym_Stim_Correctness.append(True)

dmPractice_Sym_Stim_Coord.append('1111101100100110110000100111111101111111110000100010011011111011') #8
dmPractice_Sym_Stim_Correctness.append(False)

dmPractice_Sym_Stim_Coord.append('0000000111000011111001111111110110000001101111111110011111000011') #9
dmPractice_Sym_Stim_Correctness.append(False)

dmPractice_Sym_Stim_Coord.append('0010010011111111101011011011010110111101101111011111111100100100') #10
dmPractice_Sym_Stim_Correctness.append(False)

dmPractice_Sym_Stim_Coord.append('1011110101011010100110011111111111111111100010010100101010111101') #11
dmPractice_Sym_Stim_Correctness.append(False)

dmPractice_Sym_Stim_Coord.append('1110011110100110111111111010011010100110111111111010011011100111') #12
dmPractice_Sym_Stim_Correctness.append(False)

dmPractice_Sym_Stim_Coord.append('1111111111101011110100111100101111010011010010101101011111111111') #13
dmPractice_Sym_Stim_Correctness.append(False)

dmPractice_Sym_Stim_Coord.append('1111111101000110111001111110011111100111111001110100011011111111') #14
dmPractice_Sym_Stim_Correctness.append(False)

dmPractice_Sym_Stim_Coord.append('1111011111111111110000110111111001111110110000111111111111101111') #15
dmPractice_Sym_Stim_Correctness.append(False)

np_Practice_Sym_Stim_Coord = np.transpose([dmPractice_Sym_Stim_Coord])
np_Practice_Sym_Stim_Correctness = np.transpose([dmPractice_Sym_Stim_Correctness])

np_Practice_Sym_Stim = np.stack((np_Practice_Sym_Stim_Coord, np_Practice_Sym_Stim_Correctness), axis=-1)

np_Practice_Sym_Stim = np.squeeze(np_Practice_Sym_Stim, axis=1)



dmSymPract = DataMatrix(length = 15)
dmSymPract.StimSym = ''
dmSymPract.Correctness = ''

dmSymPract.StimSym[0] = np_Practice_Sym_Stim[0][0] # doesnt work
dmSymPract.StimSym[0] = str(np_Practice_Sym_Stim[0][0]) # doesnt work
dmSymPract.StimSym[0] = '0001100000011000001111000111111011111111011001100011110000011000' # doesnt work
dmSymPract.StimSym[0] = 0001100000011000001111000111111011111111011001100011110000011000 # doesnt work

So the traceback I have everytime is:
Traceback:
File "C:\Program Files (x86)\OpenSesame\lib\site-packages\libopensesame\inline_script.py", line 96, in run
self.experiment.python_workspace._exec(self.crun)
File "C:\Program Files (x86)\OpenSesame\lib\site-packages\libopensesame\python_workspace.py", line 174, in _exec
exec(bytecode, self._globals)
Inline script, line 11, in
File "C:\Program Files (x86)\OpenSesame\lib\site-packages\datamatrix_datamatrix_basecolumn.py", line 781, in setitem
self._setintkey(key, value)
File "C:\Program Files (x86)\OpenSesame\lib\site-packages\datamatrix_datamatrix_basecolumn.py", line 518, in _setintkey
self._seq[key] = self._checktype(value)
File "C:\Program Files (x86)\OpenSesame\lib\site-packages\datamatrix_datamatrix_basecolumn.py", line 344, in _checktype
raise TypeError('Invalid type: %s' % value)
TypeError: Invalid type: 1100000011000001111000111111011111111011001100011110000011000

Thanks for any help!

Sylvain

I am trying to use datamatrix within open sesame by manipulating dm. Is there a way I could get to shuffle a dm column with an inline?

Thank you!