Tag: Social

I'm trying to create a table thats like the github contributions horizontal scroll and data should go column down in order. <ScrollView horizontal showsHorizontalScrollIndicator={false}> <FlatList data={[...Array(100).keys()]} renderItem={({item}) => ( <SmallHistoryBlock /> )} scrollEnabled={false} numColumns={Math.ceil(100 / 7)} contentContainerStyle={{ justifyContent: 'center', paddingHorizontal: 10, }} /> </ScrollView> however if this code isnt divisible by full number, it will create a new row and add the remainder which I dont want. Is the only way to implement this is by Creating a Flatlist inside another flatlist? I dont want to do that because preformace wise its not that efficient. Any help would be appreciated!

I have an ES 6.5 index with the following mappings - { "hotel": { "mappings": { "hotel": { "properties": { "hotelAmenities": { "type": "nested", "properties": { "enabled": { "type": "boolean" }, "featured": { "type": "boolean" }, "key": { "type": "text", "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } }, "name": { "type": "text", "fields": {

I'm working on a Three.js project where I need to add custom fonts. I have placed my .json font file inside a fonts folder located in the static directory. When attempting to load the font using FontLoader, the console logs an error. I've ensured the path to the .json file is correct. I am encountering a syntax error in the console, specifically an Uncaught (in promise) SyntaxError: Unexpected token '<', "<!DOCTYPE "... is not valid JSON. This suggests that the file being loaded is not parsed as JSON. I suspect it might be returning an HTML page instead. What might be causing this issue, and how can I resolve it? Here is the snippet of my code that loads the font: import { FontLoader } from 'three/examples/jsm/loaders/FontLoader.js'; const fontLoader = new FontLoader(); fontLoader.load( '/fonts/helvetiker_regular.typeface.json', () => { console.log('font loaded'); } ); Here is my full code:

Apache IoTDB often get stuck in SessionPool, and the system reported org.apache.iotdb.session.pool.SessionPool: current occupied size 50, queue size 0, considered size 50. After this information, the system also mentioned that SessionPool has wait for 60 seconds to get a new connection. I checked the official guide and did not find any relevant instruction for this kind of message. Please ask me how to set and configure the parameters for this kind of problem?

I'm having a hard time converting a Series of strings into an array to iterate in a for loop. Each item in the Series should be passed to the lit expression, to limit the DataFrame on the specific date. Then, the DataFrames are converted into an ndarray for matrix alg, then passed into a new DataFrame that is pushed & collected into a Vec. Here is what I'm trying to do when trying to iterate let dates = &unique_dates["date"]; This will return an error on lit(date) which requires a bound (not being passed as a string). When printing the first iteration, this will return String("2023-02-02") instead of "2023-02-02" #[pyfunction] fn factor_returns(pydf: PyDataFrame, endog: Vec) -> PyResult { let endog_str: Vec<&str> = endog.iter().map(|s| s.as_str()).collect(); let df: DataFrame = pydf.into(); let unique_dates = df.clone() .lazy() .

I'm trying to copy and paste the values of cells containing the formula =IMAGE(Url) in Excel. The result is an image embedded in the cell and no longer linked to the Url. I am able to do it with pywin32 with the following script: ws.Range(f"C2:C{last_row}").PasteSpecial(Paste=win32.constants.xlPasteValues) Given my goal to automate the entire script within an Azure Function App, I understand that pywin32 isn't the ideal solution? Is there an equivalent of the above script using openpyxl or another library? Thanks!

I tried to train sequential (LSTM and GRU) models to predict the chemical structure of drugs for drug discovery process. I got very large cross entropy loss (>100). The input to the model is SMILES string of chemical structures encoded as a one-hot encoded array. I tried to change parameters like number of epochs, learning rate, played with train/test data split, etc., and also tried to add more layers to the model but the cross-entropy loss still doesn't drop below 100. What do you think it is? I'm stuck guys

I’m currently developing an extension for JupyterLab. As part of this process, I’m utilizing the InversifyJS library from npm. To use it, I have to import ‘reflect-metadata’ in my index.ts file, which I have done. However, when I create an injectable class and use it in my extension, I encounter the following error: TypeError: getOwnMetadataKeys is not a function at Object.isProviderFor (Reflect.js:1135:1) at getProviderNoCache (Reflect.js:941:1) at Object.getProvider (Reflect.js:955:1) at GetMetadataProvider (Reflect.js:1158:1) at OrdinaryDefineOwnMetadata (Reflect.js:611:1) at decorator (Reflect.js:195:1) at DecorateConstructor (Reflect.js:549:1) at Reflect.decorate (Reflect.js:143:1) at push.../shared/components/localTelemetry.ts.__decorate (icons.ts:4:72) at ../shared/components/localTelemetry.ts (localTelemetry.ts:7:28) This is part of my code @injectable() export class LocalTelemetry

I have created an npm package where I defined custom error classes and exported them. However, when importing these classes into another project, TypeScript shows an error despite successful import. Here's the structure of my npm package: NotFoundError.ts: import { CustomError } from "./custom-error"; export class NotFoundError extends CustomError { statusCode = 404; constructor() { let message = "Route not found"; super(message); Object.setPrototypeOf(this, NotFoundError.prototype); } serializeErrors() { return [{ message: this.message }]; } } CustomError.ts: export abstract class CustomError extends Error { abstract statusCode: number; constructor(message: string) { super(message); Object.setPrototypeOf(this, CustomError.prototype); } abstract serializeErrors(): { message: string; fields?: string }[]; }

i have data that is coming to me as it appears in a spreadsheet. WHen it arrives it looks like this: import pandas as pd, numpy as np data1 = [['symbol', 'appl', 'goog', None, 'msft', None, None, None], ['date' , 'close', 'close', 'volume', 'close', 'open', 'high', 'low'], ['1999-01-10', 100, 101, 10000, 102, 102, 104, 105], ['1999-01-11', 200, 201, 10000, 202, 202, 204, 205]] df = pd.DataFrame(data1) df this generates a table that looks like this: I basically need to iterate over this data by splitting or grouping by symbol. So the first dataframe would consist of only [date, close] column for appl, The 2nd woudl be [date, close, volume] for goog