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How can I improve the launch time of my flutter firebase app?
Improving the launch time of your Flutter Firebase app involves optimizing the initialization and loading processes. Here are some steps you can take to improve the launch time: Delay initialization of Firebase services: Instead of initializing Firebase services at the start of the app, consider iniRead more
Improving the launch time of your Flutter Firebase app involves optimizing the initialization and loading processes. Here are some steps you can take to improve the launch time:
main()function. This can help reduce the initial startup time.--aotflag when building the release version of your app.Remember to profile and measure the performance of your app after implementing these optimizations to ensure they have the desired impact. Monitor the app’s startup time and make iterative improvements as needed.
See lessBottle server with WebSocket (without other third-party libraries)
Currently, as of my knowledge cutoff in September 2021, the Bottle framework itself does not provide native WebSocket support. To handle WebSocket connections in Bottle without relying on third-party libraries, you would need to implement the WebSocket protocol manually. The WebSocket protocol involRead more
Currently, as of my knowledge cutoff in September 2021, the Bottle framework itself does not provide native WebSocket support. To handle WebSocket connections in Bottle without relying on third-party libraries, you would need to implement the WebSocket protocol manually.
The WebSocket protocol involves an initial HTTP handshake and then transitioning to a persistent connection between the client and the server. This process requires managing the low-level details of the protocol, which can be quite complex.
However, I can provide you with an example using the Python
socketmodule to create a simple WebSocket server within your Bottle application. Please note that this approach is a basic demonstration and may not be suitable for production use. Here’s an example of how you could modify your code:In this example, we manually handle the WebSocket handshake by constructing the necessary HTTP headers and sending them back to the client. The
ws()function reads and sends WebSocket messages using the low-level socket operations.Keep in mind that manually implementing the WebSocket protocol can be error-prone and may not support all the features provided by third-party WebSocket libraries. If possible, using a well-maintained library like
See lessgevent-websocketorwebsocket-serveris generally recommended for WebSocket functionality in a Bottle application.Can an Application token be used instead of a Personal Access Token for github/checkout?
When using a GitHub Application token for authentication in your workflow, you need to make a few adjustments to the checkout step. The actions/checkout action does not support using the token parameter with a GitHub Application token directly. To authenticate with a GitHub Application token, you caRead more
When using a GitHub Application token for authentication in your workflow, you need to make a few adjustments to the checkout step. The
actions/checkoutaction does not support using thetokenparameter with a GitHub Application token directly.To authenticate with a GitHub Application token, you can use the
@actions/checkoutaction with a specific version (v2.3.4 or higher) that supports therepositoryandrefparameters. Here’s an example of how you can modify your workflow:- name: Checkout repository uses: actions/checkout@v2.3.4 with: repository: owner/repo ref: ${{ github.ref }} token: ${{ secrets.GITHUB_TOKEN }}In the above example, replace
owner/repowith the actual repository you want to check out.By specifying the
tokenparameter as${{ secrets.GITHUB_TOKEN }}, it will automatically use the token provided by GitHub for the workflow, which should have sufficient permissions for most operations.Make sure to use the
@v2.3.4version or a higher version that supports therepositoryandrefparameters for using the GitHub Application token in the checkout action.Remember to grant the necessary permissions to your GitHub Application to perform the required actions on the repository. You can manage the application’s permissions in the repository’s settings under “Installed GitHub Apps.”
With these modifications, you should be able to use a GitHub Application token for higher permissions during the checkout and subsequent steps in your workflow.
See lessTMX(Translation Memory eXchange) files in python
there is a module in Python for handling TMX (Translation Memory eXchange) files called tmx which you can use to work with TMX files. This module provides functions to read and write TMX files and perform various operations on the translation units. To install the tmx module, you can use pip: pip inRead more
there is a module in Python for handling TMX (Translation Memory eXchange) files called
tmxwhich you can use to work with TMX files. This module provides functions to read and write TMX files and perform various operations on the translation units.To install the
tmxmodule, you can use pip:Here’s an example of how you can use the
tmxmodule to read a TMX file:import tmx # Read a TMX file with open('file.tmx', 'rb') as f: tmx_data = f.read() # Parse the TMX data tmx_file = tmx.to_object(tmx_data) # Access translation units for tu in tmx_file.units: source = tu.source target = tu.target # Do something with the source and target translationsRegarding the size of your 2GB TMX file, it’s recommended to handle large files in a memory-efficient way to avoid memory issues. You may consider processing the file in smaller chunks instead of loading the entire file into memory at once. You can read and process the TMX file in smaller portions or use a streaming approach where you process the data line by line or unit by unit.
For example, you can modify the above code to process the TMX file in chunks:
with open('file.tmx', 'rb') as f: chunk_size = 1024 # adjust the chunk size as per your requirements while True: chunk = f.read(chunk_size) if not chunk: break # Process the chunkBy reading and processing the TMX file in smaller chunks, you can handle large files more efficiently and avoid memory limitations.
If you find that the
See lesstmxmodule doesn’t meet your specific needs or if you prefer an alternative approach, you can also parse and process TMX files using other XML parsing libraries in Python, such asxml.etree.ElementTreeorlxml. These libraries provide XML parsing capabilities that allow you to extract and manipulate data from TMX files.how to disable button clicks via .preventDefault()
The preventDefault() method is used to prevent the default behavior of an event, such as submitting a form or following a link. However, it does not disable the button or make it unclickable. It only prevents the default action associated with the event. If you want to disable the button and make itRead more
The
preventDefault()method is used to prevent the default behavior of an event, such as submitting a form or following a link. However, it does not disable the button or make it unclickable. It only prevents the default action associated with the event.If you want to disable the button and make it unclickable, you can use a reactive variable to control the button’s state. Here’s an updated code snippet that demonstrates how to achieve this:
<template> <div> <button v-on:click="handleClick" :disabled="isButtonDisabled">warn</button> </div> </template> <script> import { ref } from 'vue'; export default { name: 'App', setup() { const isButtonDisabled = ref(false); const handleClick = (event) => { event.preventDefault(); if (!isButtonDisabled.value) { // Perform your desired action here console.log('Button clicked!'); } }; return { isButtonDisabled, handleClick, }; }, }; </script>In this example, we use the
isButtonDisabledreactive variable to control thedisabledattribute of the button. By default, the button is not disabled (isButtonDisabledisfalse). When the button is clicked, thehandleClickfunction is triggered. We check if the button is not disabled before performing any action. By callingevent.preventDefault(), we prevent the default behavior of the click event.To disable the button, you can set
isButtonDisabledtotruein your code when necessary. For example, if you want to disable the button after the first click, you can modify thehandleClickfunction as follows:const handleClick = (event) => { event.preventDefault(); if (!isButtonDisabled.value) { // Perform your desired action here console.log('Button clicked!'); isButtonDisabled.value = true; // Disable the button } };By setting
isButtonDisabled.valuetotrue, the button will become disabled, and subsequent clicks will not trigger the action inside theifstatement.Remember to import the necessary dependencies (
See lessref) and adjust the component name and any other dependencies according to your specific setup.How to create a small python code to get a list of participants of a teams call?
To retrieve the list of participants in an ongoing Microsoft Teams call using Python, you can utilize the Microsoft Graph API with the help of the requests library. Here's a code snippet that demonstrates how to achieve this: import requests # Microsoft Graph API endpoint for retrieving call particiRead more
To retrieve the list of participants in an ongoing Microsoft Teams call using Python, you can utilize the Microsoft Graph API with the help of the
requestslibrary. Here’s a code snippet that demonstrates how to achieve this:import requests # Microsoft Graph API endpoint for retrieving call participants call_participants_url = 'https://graph.microsoft.com/v1.0/communications/calls/{call_id}/participants' # Azure AD application credentials client_id = 'your_client_id' client_secret = 'your_client_secret' tenant_id = 'your_tenant_id' # Get an access token using client credentials flow token_url = f'https://login.microsoftonline.com/{tenant_id}/oauth2/v2.0/token' token_payload = { 'client_id': client_id, 'client_secret': client_secret, 'scope': 'https://graph.microsoft.com/.default', 'grant_type': 'client_credentials' } token_response = requests.post(token_url, data=token_payload) access_token = token_response.json().get('access_token') # Make a GET request to obtain the list of participants call_id = 'your_call_id' headers = {'Authorization': f'Bearer {access_token}'} participants_response = requests.get(call_participants_url.format(call_id=call_id), headers=headers) participants = participants_response.json() # Process the participant data for participant in participants['value']: print(participant['displayName'])Before running the code, make sure you replace the following placeholders with your own values:
your_client_id: Your Azure AD application ID (client ID).your_client_secret: Your Azure AD application client secret.your_tenant_id: Your Azure AD tenant ID.your_call_id: The ID of the Teams call you want to retrieve participants for.Please note that you need the necessary permissions and valid Azure AD application credentials to access the Microsoft Graph API. Also, ensure that you have the
See lessrequestslibrary installed (pip install requests) to make HTTP requestsundeclared variable in a do-while loop in C
In C, variable declarations should be placed outside the loop in which they are used. In your code, the variable sp is declared inside the do-while loop, making it inaccessible in the condition part of the loop. To fix the error, you need to declare the variable sp before the do-while loop: int sp;Read more
In C, variable declarations should be placed outside the loop in which they are used. In your code, the variable
spis declared inside thedo-whileloop, making it inaccessible in the condition part of the loop.To fix the error, you need to declare the variable
spbefore thedo-whileloop:int sp; // Declare the variable before the loop do { sp = get_int("Enter the start size of llama population:"); } while (sp >= 9);By moving the declaration of
spoutside the loop, it becomes accessible in both the loop body and the condition part of the loop.Now, the code will prompt the user to enter a valid number (greater or equal to 9) until the condition is satisfied.
Make sure you have the necessary header files included and the
See lessget_intfunction defined or declared correctly before using it in your code.Cypress use clock between each test without visit again
It seems that the issue lies in the way Cypress handles the setInterval function and the clock. Currently, Cypress's cy.tick() function is not designed to advance the clock for intervals set using setInterval. To work around this limitation, you can use the cy.wait() command instead of cy.tick() toRead more
It seems that the issue lies in the way Cypress handles the
setIntervalfunction and the clock. Currently, Cypress’scy.tick()function is not designed to advance the clock for intervals set usingsetInterval.To work around this limitation, you can use the
cy.wait()command instead ofcy.tick()to advance the clock. Here’s an updated example of how you can modify your tests:describe('test clock', () => { beforeEach(() => { cy.clock(); // Initialize the clock cy.visit('http://localhost:3000/#/index/dashboardview2'); }); it('test1', () => { cy.get('[data-test="number"]').should('have.text', '0'); cy.wait(1000); // Advance the clock by 1 second using cy.wait() cy.get('[data-test="number"]').should('have.text', '1'); }); it('test2', () => { cy.get('[data-test="number"]').should('have.text', '0'); cy.wait(2000); // Advance the clock by 2 seconds using cy.wait() cy.get('[data-test="number"]').should('have.text', '2'); }); });By replacing
cy.tick()withcy.wait(), you can manually control the clock advancement and verify the expected behavior in your component.Please give this approach a try and let me know if it resolves the issue for you.
See lessHow to exclude @Bean from library configuration
@Configuration @Import({ClientConfig.class}) public class ValidatorsConfiguration { @Bean @Conditional(ExcludeBeanCondition.class) public Parser excludedParser() { // Return a dummy instance or null if you want to exclude the bean return null; } // Other beans and configuration... } In the example aRead more
@Configuration @Import({ClientConfig.class}) public class ValidatorsConfiguration { @Bean @Conditional(ExcludeBeanCondition.class) public Parser excludedParser() { // Return a dummy instance or null if you want to exclude the bean return null; } // Other beans and configuration... }In the example above, a new
Parserbean calledexcludedParser()is defined. The@Conditional(ExcludeBeanCondition.class)annotation is used to conditionally include or exclude this bean based on the implementation of theExcludeBeanConditionclass.You need to create the
ExcludeBeanConditionclass implementing theConditioninterface. Here’s an example implementation that excludes the bean based on a specific condition:public class ExcludeBeanCondition implements Condition { @Override public boolean matches(ConditionContext context, AnnotatedTypeMetadata metadata) { // Implement your custom logic here to determine if the bean should be excluded // Return true to exclude the bean, false to include it // You can access environment variables, properties, etc., using the ConditionContext // Example: Exclude the bean if a specific property is set String excludeProperty = context.getEnvironment().getProperty("exclude.bean"); return "true".equalsIgnoreCase(excludeProperty); } }In the
ExcludeBeanConditionclass, you can define your custom logic to determine if the bean should be excluded. In this example, the bean will be excluded if the propertyexclude.beanis set to"true".Remember to adjust the logic in
See lessExcludeBeanConditionaccording to your specific conditions for excluding beans.How can I use another df to calculate a new variable in my df, using time intervals?
To calculate the new variable in your dataframe based on time intervals and information from another dataframe, you can use a combination of filtering and aggregation functions. Here's an example of how you can approach this task: library(dplyr) # Assuming your dataframes are named 'exposition' andRead more
To calculate the new variable in your dataframe based on time intervals and information from another dataframe, you can use a combination of filtering and aggregation functions. Here’s an example of how you can approach this task:
library(dplyr) # Assuming your dataframes are named 'exposition' and 'pregnancy_interval' # Initialize a new column for the pollution dataframe pollution$sum <- 0 # Loop over each row in the exposition dataframe for (i in 1:nrow(exposition)) { # Get the code_muni and time interval for the current row in exposition code_muni <- exposition$code_muni[i] dt_concep <- exposition$dt_concep[i] data_nasc <- exposition$data_nasc[i] # Filter the pollution dataframe based on the code_muni and time interval filtered_data <- pollution %>% filter(code_muni == code_muni, date >= dt_concep, date < data_nasc) # Calculate the sum for the specified time interval sum_value <- sum(filtered_data$neg_to_7) # Update the 'sum' column in the pollution dataframe for the matching rows pollution$sum[code_muni == code_muni & pollution$date >= dt_concep & pollution$date < data_nasc] <- sum_value }This code loops over each row in the
expositiondataframe and performs the following steps:code_muni,dt_concep, anddata_nascvalues from the current row.pollutiondataframe based on thecode_muniand the time interval.neg_to_7values for the filtered data.sumcolumn in thepollutiondataframe for the matching rows.Note that this approach can be time-consuming if your dataframes are large. Consider optimizing it if needed, such as by using vectorized operations or parallel processing, depending on the size and complexity of your data.
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