Insert version metadata into the RuneLite launcher

For easy identification of your local launcher version, since the app itself can't tell you this. A little more convenient than checking your list of installed programs.

https://github.com/runelite/launcher


BlockEnumerate does not work with Image or 2D/3D Buffer

BlockEnumerate operation fail if applied using Image object type. Though it works fine if used with Buffer object type.
Error caught when using Image:
Error CL_INVALID_COMMAND_QUEUE (-36)
        Description: the specified command-queue is not a valid command-queue
Exception caught in GPU class. Error when enqueuing Read instruction to Image.
Error CL_OUT_OF_RESOURCES (-5)
        Description: failure to allocate resources required by the OpenCL implementation on the device

Todo:

add error/warning message if Image object is used

Final feedback on this, the kernel is only working for 1D Buffer Image.


HTMLEditorField doesn't use field assigned configuration for sanitisation

Affected Version
tested on 4.12 and 5
Description
Saving an HTMLEditorField always uses the 'active' HTMLEditorConfig rather than the config defined on the field when sanitising the content for saving into the database.
I would have expected the field to sanitise its content using the config set on the field.
It means it is not possible to have mor than one kind of server side validation. This probably goes un-noticed as the client will prevent adding invalid content in the JS and the default valid elements is fairly permissive. However, it is possibly to bypass the client restrictions (eg with the source button)
Steps to Reproduce
This test will reproduce the issue:

expand code sample
<?php

namespace App\Tests\Forms;

use SilverStripe\Dev\SapphireTest;
use SilverStripe\Forms\HTMLEditor\HTMLEditorConfig;
use SilverStripe\Forms\HTMLEditor\HTMLEditorField;
use SilverStripe\ORM\DataObject;

class HTMLEditorFieldTest extends SapphireTest
{

    public function testHTMLEditorFieldSave(): void
    {
        $defaultValidElements = [
            '@[id|class|style|title|data*]',
            'a[id|rel|dir|tabindex|accesskey|type|name|href|target|title|class]',
            '-strong/-b[class]',
            '-em/-i[class]',
            '-ol[class]',
            '#p[id|dir|class|align|style]',
            '-li[class]',
            'br',
            '-span[class|align|style]',
            '-ul[class]',
            '-h3[id|dir|class|align|style]',
            '-h2[id|dir|class|align|style]',
            'hr[class]',
        ];

        $cmsConfig = HTMLEditorConfig::get('cms');
        $cmsConfig->setOption('valid_elements', implode(',', $defaultValidElements));

        HTMLEditorConfig::set_active_identifier('cms');
        $field = HTMLEditorField::create('Standard');

        $htmlValue = '
standard text

Header

';
        $expectedHtmlString = '
standard text
Header';
        $field->setValue($htmlValue);

        $dummyObject = new class extends DataObject {
        };

        $field->saveInto($dummyObject);

        $this->assertEquals($expectedHtmlString, $dummyObject->Standard);

        $tableValidElements = [
            'table',
            'thead',
            'tbody',
            'tr',
            'th',
            'td',
        ];

        $tableConfig = HTMLEditorConfig::get('table');
        $tableConfig->setOption('valid_elements', implode(',', $tableValidElements));
        // HTMLEditorConfig::set_active_identifier('table');
        $tableField = HTMLEditorField::create('Standard');
        $tableField->setEditorConfig($tableConfig);

        $htmlValue = '
standard text

Header

';
        $expectedHtmlString = '
standard text

'
            .'
Header

';
        $tableField->setValue($htmlValue);

        $dummyObject2 = new class extends DataObject {
        };

        $tableField->saveInto($dummyObject2);

        $this->assertEquals($expectedHtmlString, $dummyObject2->Standard);
    }
}

if you uncomment HTMLEditorConfig::set_active_identifier('table'); then the test will pass.
Setting the active identifier works unless you have multiple different configurations within the same form - as there can only be one active config at time.
Save into could instead use the config attached to the field and fall back to the active one?
PRs

https://github.com/silverstripe/silverstripe-framework/pull/10977

Thanks for reporting this.

Save into could instead use the config attached to the field and fall back to the active one?

Yup that sounds like the correct course of action. Would you be willing to raise a PR to implement this fix?

Thanks for reporting this.

Save into could instead use the config attached to the field and fall back to the active one?

Yup that sounds like the correct course of action. Would you be willing to raise a PR to implement this fix?

PR attached now :)

PR was merged. This will be automatically tagged by GitHub Actions.

This was reverted - see https://github.com/silverstripe/silverstripe-framework/issues/11141 for details


Compliance Summary Page for Regulated Operations

Description:
As a Regulated Operation I want a Compliance Summary page so that I can see final emissions totals.
WIREFRAME
Question: what is initial_compliance_period?
The spreadsheet says it has a value of 2024, but should that be pulled from the first year the facility reported rather than hardcoded?
Answer: Yes it will always be 2024. Hardcode it.
Acceptance Criteria:
Given I am a Regulated Operation (Regulated, Opt-in or New Entrant)
When I have filled out all my required forms including emissions data, production data, allocation of emissions and any additional reporting data
Then I need to see a summary page with compliance information/totals (calculated from data input on previous forms).
This is required as part of my compliance report, including my excess or credited emissions.
Given I am a Developer
When I am building the Compliance Summary page
Then I need to reference the "Emissions Summary" spreadsheet saved in the SME teams channel under Files > Reporting App
in order to create the required calculations/totals.
naics_code_regulatory_value model:
id pkey
naics_code fkey -> naics_code
reduction_factor decimal
tightening_rate decimal
valid_from timestamptz
valid_to timestamptz
product_emission_intensity model:
id pkey
product_id fkey -> product
product_weighted_average_emission_intensity decimal
valid_from timestamptz
valid_to timestamptz
Development Checklist:

[ ] Compliance summary values are calculated using the parameters in the 'Emissions Summary' spreadsheet
[ ] Regulatory values are defined by NAICS code as per the 'Registration fields pt 2' spreadsheet
[ ] A naics_code_regulatory_value model exists as described above to hold the regulatory values & valid to/from dates
[ ] A product_emission_intensity model exists as described above to hold the PWAEI values & valid to/from dates (values in Registration fields pt 2 spreadsheet)
[ ] For each product the annual amount & PWAEI is shown (from database values)
[ ] For each product, the allocated industrial process emissions & compliance emissions are calculated & shown
[ ] The compliance summary page has a continue & a back button (but no save button)
[ ] Meets the DOD

Definition of Ready (Note: If any of these points are not applicable, mark N/A)

[ ] User story is included
[ ] User role and type are identified
[ ] Acceptance criteria are included
[ ] Wireframes are included (if required)
[ ] Design / Solution is accepted by Product Owner
[ ] Dependencies are identified (technical, business, regulatory/policy)
[ ] Story has been estimated (under 13 pts)

Definition of Done (Note: If any of these points are not applicable, mark N/A)

[ ] Acceptance criteria are tested by the CI pipeline
[ ] UI meets accessibility requirements
[ ] Configuration changes are documented, documentation and designs are updated
[ ] Passes code peer-review
[ ] Passes QA of Acceptance Criteria with verification in Dev and Test
[ ] Ticket is ready to be merged to main branch
[ ] Can be demoed in Sprint Review
[ ] Bugs or future work cards are identified and created
[ ] Reviewed and approved by Product Owner

Notes:

@dleard to write AC and dev checklist

Blocked by #237

Note from Collab: Please add another data field under each Product: Production {April 1 - Dec 31 2024}

Based on my understanding, this page is working well. We will have UAT testers try and recalculate this to demonstrate that the calculations in the system are correct.


question about correlation matrix calculation

Hi,
Thanks for your great work. I tried to compute the correlation matrix from the provided time series using the following code.
"
import numpy as np
from nilearn import connectome
def get_network(X, kind="correlation"):
conn_measure = connectome.ConnectivityMeasure(kind=kind)
connectivity = conn_measure.fit_transform(X)
return connectivity
data = np.load(data_path + "abide.npy", allow_pickle=True).tolist()
time_series=data["timeseires"].transpose(0,2,1)
correlation_matrix = get_network(time_series)
correlation_matrix = np.arctanh(correlation_matrix)
correlation_matrix[correlation_matrix==np.inf] = 0
"
But, I find the calculated one is slightly different to that you provided. And, using this as feature will lead to performance that is much lower than you report.
This code follows your previous repository. Could you tell me where is wrong?

Hi，I'm not author yet I think I know what's error in your codes.

Hi, I'd like to talk to you some questions about this.
In the abide.npy, the provided data by this paper, the length of timeseries of each ROI per subject is 100, however the init timeseries is not same for different sites, reasoning from ABIDE fMRI project have multiple sites participation. Some sites's data_timeseries length is nearly 300 yet some sites data_timeseries length is 100. So not use sufficient data_timeseries may cause decline in model effect. I think the author's abide.npy use sufficient data_timeseries(conn_measure.fit_transform also support this condition). So I think you can apply for init ABIDE data_timeseries and reprocess data

Hi, I'd like to talk to you some questions about this.
In the abide.npy, the provided data by this paper, the length of timeseries of each ROI per subject is 100, however the init timeseries is not same for different sites, reasoning from ABIDE fMRI project have multiple sites participation. Some sites's data_timeseries length is nearly 300 yet some sites data_timeseries length is 100. So not use sufficient data_timeseries may cause decline in model effect. I think the author's abide.npy use sufficient data_timeseries(conn_measure.fit_transform also support this condition).
So I think you can apply for init ABIDE data_timeseries and reprocess data

Hi, I'd like to talk to you some questions about this.
In the abide.npy, the provided data by this paper, the length of timeseries of each ROI per subject is 100, however the init timeseries is not same for different sites, reasoning from ABIDE fMRI project have multiple sites participation. Some sites's data_timeseries length is nearly 300 yet some sites data_timeseries length is 100. So not use sufficient data_timeseries to calculate pearson matrix may cause model effect decline. I think the author's abide.npy use sufficient data_timeseries
and function: conn_measure.fit_transform() also support this condition.
So I think you can apply for init ABIDE data_timeseries and reprocess data

Hi,
Thanks for your great work. I tried to compute the correlation matrix from the provided time series using the following code. " import numpy as np from nilearn import connectome def get_network(X, kind="correlation"): conn_measure = connectome.ConnectivityMeasure(kind=kind) connectivity = conn_measure.fit_transform(X) return connectivity
data = np.load(data_path + "abide.npy", allow_pickle=True).tolist() time_series=data["timeseires"].transpose(0,2,1) correlation_matrix = get_network(time_series) correlation_matrix = np.arctanh(correlation_matrix) correlation_matrix[correlation_matrix==np.inf] = 0 " But, I find the calculated one is slightly different to that you provided. And, using this as feature will lead to performance that is much lower than you report.
This code follows your previous repository. Could you tell me where is wrong?

Hi, I'd like to talk to you some questions about this.
In the abide.npy, the provided data by this paper, the length of timeseries of each ROI per subject is 100, however the init timeseries is not same for different sites, reasoning from ABIDE fMRI project have multiple sites participation. Some sites's data_timeseries length is nearly 300 yet some sites data_timeseries length is 100. So not use sufficient data_timeseries to calculate pearson matrix may cause model effect decline. I think the author's abide.npy use sufficient data_timeseries
and function: conn_measure.fit_transform() also support this condition.
So I think you can apply for init ABIDE data_timeseries and reprocess data

Hi,
Thanks for your great work. I tried to compute the correlation matrix from the provided time series using the following code. " import numpy as np from nilearn import connectome def get_network(X, kind="correlation"): conn_measure = connectome.ConnectivityMeasure(kind=kind) connectivity = conn_measure.fit_transform(X) return connectivity
data = np.load(data_path + "abide.npy", allow_pickle=True).tolist() time_series=data["timeseires"].transpose(0,2,1) correlation_matrix = get_network(time_series) correlation_matrix = np.arctanh(correlation_matrix) correlation_matrix[correlation_matrix==np.inf] = 0 " But, I find the calculated one is slightly different to that you provided. And, using this as feature will lead to performance that is much lower than you report.
This code follows your previous repository. Could you tell me where is wrong?

Hi, I'd like to talk to you some questions about this.
In the abide.npy, the provided data by this paper, the length of timeseries of each ROI per subject is 100, however the init timeseries is not same for different sites, reasoning from ABIDE fMRI project have multiple sites participation. Some sites's data_timeseries length is nearly 300 yet some sites data_timeseries length is 100. So not use sufficient data_timeseries to calculate pearson matrix may cause model effect decline. I think the author's abide.npy use sufficient data_timeseries and function: conn_measure.fit_transform() also support this condition.
So I think you can apply for init ABIDE data_timeseries and reprocess data

Thank you so much for your reply, I have tried to follow the source code to download the time series and build the connectome with the raw series. But, the classification performance is still the same. Another thing is in the source code, there are 1035 time series, but in the provided data, there are 1009 time series.

Hi,
Thanks for your great work. I tried to compute the correlation matrix from the provided time series using the following code. " import numpy as np from nilearn import connectome def get_network(X, kind="correlation"): conn_measure = connectome.ConnectivityMeasure(kind=kind) connectivity = conn_measure.fit_transform(X) return connectivity
data = np.load(data_path + "abide.npy", allow_pickle=True).tolist() time_series=data["timeseires"].transpose(0,2,1) correlation_matrix = get_network(time_series) correlation_matrix = np.arctanh(correlation_matrix) correlation_matrix[correlation_matrix==np.inf] = 0 " But, I find the calculated one is slightly different to that you provided. And, using this as feature will lead to performance that is much lower than you report.
This code follows your previous repository. Could you tell me where is wrong?

Hi, I'd like to talk to you some questions about this.
In the abide.npy, the provided data by this paper, the length of timeseries of each ROI per subject is 100, however the init timeseries is not same for different sites, reasoning from ABIDE fMRI project have multiple sites participation. Some sites's data_timeseries length is nearly 300 yet some sites data_timeseries length is 100. So not use sufficient data_timeseries to calculate pearson matrix may cause model effect decline. I think the author's abide.npy use sufficient data_timeseries and function: conn_measure.fit_transform() also support this condition.
So I think you can apply for init ABIDE data_timeseries and reprocess data

Thank you so much for your reply, I have tried to follow the source code to download the time series and build the connectome with the raw series. But, the classification performance is still the same. Another thing is in the source code, there are 1035 time series, but in the provided data, there are 1009 time series.

Yeah, there exists this question.

Yeah, there really exists this problem
First:
The init ABIDE data have 1035 subjects. Yet a site named as 'OHSU', it has 26 subjects, and it's data_timeseries length is 76 less than 100. May be author team consider these data is not favourable due to too small data_timeseries length. And the author team may delete all subjects belonging to this site, since I didn't find this site data in abide.npy provided by this paper.
Second:
Referring to classification performance, cause ABIDE dataset I can access has some damaged data(some subjects' data_timeseries is incomplete), therefore I can't decide whether classification performance can promote given intact and undamaged ABIDE data.But I think the model performence of this paper may be correct causeI I found if I use processed pearson matrix using incomplete data, almost all model I knew ,their performence will all decline.
Hope some explains can resolve your doubts.

Hi,
Thanks for your great work. I tried to compute the correlation matrix from the provided time series using the following code. " import numpy as np from nilearn import connectome def get_network(X, kind="correlation"): conn_measure = connectome.ConnectivityMeasure(kind=kind) connectivity = conn_measure.fit_transform(X) return connectivity
data = np.load(data_path + "abide.npy", allow_pickle=True).tolist() time_series=data["timeseires"].transpose(0,2,1) correlation_matrix = get_network(time_series) correlation_matrix = np.arctanh(correlation_matrix) correlation_matrix[correlation_matrix==np.inf] = 0 " But, I find the calculated one is slightly different to that you provided. And, using this as feature will lead to performance that is much lower than you report.
This code follows your previous repository. Could you tell me where is wrong?

Hi, I'd like to talk to you some questions about this.
In the abide.npy, the provided data by this paper, the length of timeseries of each ROI per subject is 100, however the init timeseries is not same for different sites, reasoning from ABIDE fMRI project have multiple sites participation. Some sites's data_timeseries length is nearly 300 yet some sites data_timeseries length is 100. So not use sufficient data_timeseries to calculate pearson matrix may cause model effect decline. I think the author's abide.npy use sufficient data_timeseries and function: conn_measure.fit_transform() also support this condition.
So I think you can apply for init ABIDE data_timeseries and reprocess data

Thank you so much for your reply, I have tried to follow the source code to download the time series and build the connectome with the raw series. But, the classification performance is still the same. Another thing is in the source code, there are 1035 time series, but in the provided data, there are 1009 time series.

Yeah, there really exists this problem
First:
The init ABIDE data have 1035 subjects. Yet a site named as 'OHSU', it has 26 subjects, and it's data_timeseries length is 76 less than 100. May be author team consider these data is not favourable due to too small data_timeseries length. And the author team may delete all subjects belonging to this site, since I didn't find this site data in abide.npy provided by this paper.
Second:
Referring to classification performance, cause ABIDE dataset I can access has some damaged data(some subjects' data_timeseries is incomplete), therefore I can't decide whether classification performance can promote given intact and undamaged ABIDE data.But I think the model performence of this paper may be correct causeI I found if I use processed pearson matrix using incomplete data, almost all model I knew ,their performence will all decline.
Hope some explains can resolve your doubts.

Hi,
Thanks for your great work. I tried to compute the correlation matrix from the provided time series using the following code. " import numpy as np from nilearn import connectome def get_network(X, kind="correlation"): conn_measure = connectome.ConnectivityMeasure(kind=kind) connectivity = conn_measure.fit_transform(X) return connectivity
data = np.load(data_path + "abide.npy", allow_pickle=True).tolist() time_series=data["timeseires"].transpose(0,2,1) correlation_matrix = get_network(time_series) correlation_matrix = np.arctanh(correlation_matrix) correlation_matrix[correlation_matrix==np.inf] = 0 " But, I find the calculated one is slightly different to that you provided. And, using this as feature will lead to performance that is much lower than you report.
This code follows your previous repository. Could you tell me where is wrong?

Hi, I'd like to talk to you some questions about this.
In the abide.npy, the provided data by this paper, the length of timeseries of each ROI per subject is 100, however the init timeseries is not same for different sites, reasoning from ABIDE fMRI project have multiple sites participation. Some sites's data_timeseries length is nearly 300 yet some sites data_timeseries length is 100. So not use sufficient data_timeseries to calculate pearson matrix may cause model effect decline. I think the author's abide.npy use sufficient data_timeseries and function: conn_measure.fit_transform() also support this condition.
So I think you can apply for init ABIDE data_timeseries and reprocess data

Thank you so much for your reply, I have tried to follow the source code to download the time series and build the connectome with the raw series. But, the classification performance is still the same. Another thing is in the source code, there are 1035 time series, but in the provided data, there are 1009 time series.

Yeah, there really exists this problem
First:
The init ABIDE data have 1035 subjects. Yet a site named as 'OHSU', it has 26 subjects, and it's data_timeseries length is 76 less than 100. May be author team consider these data is not favourable due to too small data_timeseries length. And the author team may delete all subjects belonging to this site, since I didn't find this site data in abide.npy provided by this paper.
Second:
Referring to classification performance, cause ABIDE dataset I can access has some damaged data(some subjects' data_timeseries is incomplete), and complete data application unsuccessful for now, therefore I can't decide whether classification performance can promote given intact and undamaged ABIDE data.But I think the model performence of this paper may be correct causeI I use processed pearson matrix using incomplete data, almost all model I knew ,their performence will all decline.
Hope these explains can resolve your doubts.

Hi,
Thanks for your great work. I tried to compute the correlation matrix from the provided time series using the following code. " import numpy as np from nilearn import connectome def get_network(X, kind="correlation"): conn_measure = connectome.ConnectivityMeasure(kind=kind) connectivity = conn_measure.fit_transform(X) return connectivity
data = np.load(data_path + "abide.npy", allow_pickle=True).tolist() time_series=data["timeseires"].transpose(0,2,1) correlation_matrix = get_network(time_series) correlation_matrix = np.arctanh(correlation_matrix) correlation_matrix[correlation_matrix==np.inf] = 0 " But, I find the calculated one is slightly different to that you provided. And, using this as feature will lead to performance that is much lower than you report.
This code follows your previous repository. Could you tell me where is wrong?

Hi, I'd like to talk to you some questions about this.
In the abide.npy, the provided data by this paper, the length of timeseries of each ROI per subject is 100, however the init timeseries is not same for different sites, reasoning from ABIDE fMRI project have multiple sites participation. Some sites's data_timeseries length is nearly 300 yet some sites data_timeseries length is 100. So not use sufficient data_timeseries to calculate pearson matrix may cause model effect decline. I think the author's abide.npy use sufficient data_timeseries and function: conn_measure.fit_transform() also support this condition.
So I think you can apply for init ABIDE data_timeseries and reprocess data

Thank you so much for your reply, I have tried to follow the source code to download the time series and build the connectome with the raw series. But, the classification performance is still the same. Another thing is in the source code, there are 1035 time series, but in the provided data, there are 1009 time series.

Yeah, there really exists this problem
First:
The init ABIDE data have 1035 subjects. Yet a site named as 'OHSU', it has 26 subjects, and it's data_timeseries length is 76 less than 100. May be author team consider these data is not favourable due to too small data_timeseries length. And the author team may delete all subjects belonging to this site, since I didn't find this site data in abide.npy provided by this paper.
Second:
Referring to classification performance, cause ABIDE dataset I can access has some damaged data(some subjects' data_timeseries is incomplete), and complete data application still be unsuccessful for now, therefore I can't decide whether classification performance can promote given intact and undamaged ABIDE data.But I think the model performence of this paper may be correct causeI I use processed pearson matrix using incomplete data, almost all model I knew ,their performence will all decline.
Hope these explains can resolve your doubts.

Hi,
Thanks for your great work. I tried to compute the correlation matrix from the provided time series using the following code. " import numpy as np from nilearn import connectome def get_network(X, kind="correlation"): conn_measure = connectome.ConnectivityMeasure(kind=kind) connectivity = conn_measure.fit_transform(X) return connectivity
data = np.load(data_path + "abide.npy", allow_pickle=True).tolist() time_series=data["timeseires"].transpose(0,2,1) correlation_matrix = get_network(time_series) correlation_matrix = np.arctanh(correlation_matrix) correlation_matrix[correlation_matrix==np.inf] = 0 " But, I find the calculated one is slightly different to that you provided. And, using this as feature will lead to performance that is much lower than you report.
This code follows your previous repository. Could you tell me where is wrong?

Hi, I'd like to talk to you some questions about this.
In the abide.npy, the provided data by this paper, the length of timeseries of each ROI per subject is 100, however the init timeseries is not same for different sites, reasoning from ABIDE fMRI project have multiple sites participation. Some sites's data_timeseries length is nearly 300 yet some sites data_timeseries length is 100. So not use sufficient data_timeseries to calculate pearson matrix may cause model effect decline. I think the author's abide.npy use sufficient data_timeseries and function: conn_measure.fit_transform() also support this condition.
So I think you can apply for init ABIDE data_timeseries and reprocess data

Thank you so much for your reply, I have tried to follow the source code to download the time series and build the connectome with the raw series. But, the classification performance is still the same. Another thing is in the source code, there are 1035 time series, but in the provided data, there are 1009 time series.

Yeah, there really exists this problem
First:
The init ABIDE data have 1035 subjects. Yet a site named as 'OHSU', it has 26 subjects, and it's data_timeseries length is 76 less than 100. May be author team consider these data is not favourable due to too small data_timeseries length. And the author team may delete all subjects belonging to this site, since I didn't find this site data in abide.npy provided by this paper.
Second:
Referring to classification performance, cause ABIDE dataset I can access has some damaged data(some subjects' data_timeseries is incomplete), and complete data application still be unsuccessful for now, therefore I can't decide whether classification performance can promote given intact and undamaged ABIDE data.But I think the model performence of this paper may be correct cause I use processed pearson matrix using incomplete data, almost all model I knew ,their performence will all decline.
Hope these explains can resolve your doubts.

Hi,
Thanks for your great work. I tried to compute the correlation matrix from the provided time series using the following code. " import numpy as np from nilearn import connectome def get_network(X, kind="correlation"): conn_measure = connectome.ConnectivityMeasure(kind=kind) connectivity = conn_measure.fit_transform(X) return connectivity
data = np.load(data_path + "abide.npy", allow_pickle=True).tolist() time_series=data["timeseires"].transpose(0,2,1) correlation_matrix = get_network(time_series) correlation_matrix = np.arctanh(correlation_matrix) correlation_matrix[correlation_matrix==np.inf] = 0 " But, I find the calculated one is slightly different to that you provided. And, using this as feature will lead to performance that is much lower than you report.
This code follows your previous repository. Could you tell me where is wrong?

Hi, I'd like to talk to you some questions about this.
In the abide.npy, the provided data by this paper, the length of timeseries of each ROI per subject is 100, however the init timeseries is not same for different sites, reasoning from ABIDE fMRI project have multiple sites participation. Some sites's data_timeseries length is nearly 300 yet some sites data_timeseries length is 100. So not use sufficient data_timeseries to calculate pearson matrix may cause model effect decline. I think the author's abide.npy use sufficient data_timeseries and function: conn_measure.fit_transform() also support this condition.
So I think you can apply for init ABIDE data_timeseries and reprocess data

Thank you so much for your reply, I have tried to follow the source code to download the time series and build the connectome with the raw series. But, the classification performance is still the same. Another thing is in the source code, there are 1035 time series, but in the provided data, there are 1009 time series.

Oh, I forget to say: the data I can download is from BrainGB, it has damaged entries, maybe we have same dataset or not.You can examine your ABIDE data whether there is the same data corruption, for example: some subjects from same site yet have significantly smaller data_timeseries.


Single-select combobox expand on focus

This might be a feature request, but if you disagree, I can try to implement a javascript bandaid on my end.
When using a single-select in combobox mode, currently the list expands if the user clicks the arrow, or if the user clicks the textbox and starts typing.
Is there any way to expand the dropdown immediately upon the textbox receiving focus?
Basically let the user click the component, and then once they see the list, decide if they're going to click an item, or start typing to narrow the options.

I agree, it would be more comfortable if the dropdown opened automatically. I'll implement that.

Done.

Works great! thanks!