@neurodevs/node-csv
v1.0.3
Published
Load and extract data from CSV files
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node-csv
Load and extract data from CSV files
Installation
Install the package using npm:
npm install @neurodevs/node-csv
Or yarn:
yarn add @neurodevs/node-csv
Usage
CsvLoader
CsvLoader - Code Example
To load a CSV file:
import { CsvLoaderImpl } from '@neurodevs/node-csv'
async function loadCsv() {
const loader = CsvLoaderImpl.Create()
const data = await loader.load('/path/to/csv')
console.log(data)
}
CsvLoader - Disable Path Validation
This class automatically performs the following validations on the path you pass to the load method:
- Asserts that you passed a path
- Asserts that the file exists at the path
- Asserts that the file extension is '.csv'
To turn off path validation (e.g., when you are certain of the file's existence or extension):
import { CsvLoaderImpl } from '@neurodevs/node-csv'
const loader = CsvLoaderImpl.Create({ shouldValidatePath: false })
CsvExtractor
To extract data from a CSV file based on a set of rules:
import { CsvExtractorImpl } from '@neurodevs/node-csv'
async function extractCsvData() {
const extractor = await CsvExtractorImpl.Create('/path/to/csv')
const extractedRecord = extractor.extract([
{
column: 'The column you want to search',
value: 'The value to match in the column',
extract: 'The column to extract data from'
},
{
column: 'Another column to search',
value: 'Another value to match',
extract: 'Another column to extract'
}
])
return extractedRecord
}
Example Use Case for a Stroboscopic Experiment with EEG
Example CSV Format
| phase-name | mean-alpha-band-power | ... | |-----------------|-----------------------|-----| | eyes-closed-1 | 12.345 | ... | | eyes-open-1 | 10.987 | ... | | ... | ... | ... | | eyes-closed-5 | ... | ... | | eyes-open-5 | ... | ... |
Example Code
import { CsvExtractorImpl, ExtractionRule } from '@neurodevs/node-csv'
async function extractStroboscopicData() {
const extractor = await CsvExtractorImpl.Create('/path/to/csv')
const rules = []
const numTrials = 5
for (let i = 1; i <= numTrials; i++) {
rules.push({
column: 'phase-name', // The column to search
value: `eyes-closed-${i}`, // The value to match
extract: 'mean-alpha-band-power' // The column to extract
})
rules.push({
column: 'phase-name',
value: `eyes-open-${i}`,
extract: 'mean-alpha-band-power'
})
}
const extractedRecord = extractor.extract(rules)
return extractedRecord
}
// Example value of extractedRecord:
//
// {
// "eyes-closed-1": 12.345,
// "eyes-open-1": 10.987,
// ...
// "eyes-closed-5": ...,
// "eyes-open-5": ...
// }
Testing
You can use the following test doubles for unit testing purposes:
CsvLoader
FakeCsvLoader
: Provides a fake implementation to simulate CSV loading during testing.SpyCsvLoader
: Wraps the real implementation with enhanced visibility for inspecting internal behavior during testing.
import {
CsvLoaderImpl,
FakeCsvLoader,
SpyCsvLoader
} from '@neurodevs/node-csv'
// Use FakeCsvLoader for simulating inputs and outputs in tests
CsvLoaderImpl.Class = FakeCsvLoader
// Use SpyCsvLoader to test real behavior with enhanced internal visibility
CsvLoaderImpl.Class = SpyCsvLoader
const loader = CsvLoaderImpl.Create()
CsvExtractor
FakeCsvExtractor
: Provides a fake implementation to simulate data extraction during testing.SpyCsvExtractor
: Wraps the real implementation with enhanced visibility for inspecting internal behavior during testing.
import {
CsvExtractorImpl,
FakeCsvExtractor,
SpyCsvExtractor
} from '@neurodevs/node-csv'
async function createTestDouble() {
// Use FakeCsvExtractor for simulating the extraction process in tests
CsvExtractorImpl.Class = FakeCsvExtractor
// Use SpyCsvExtractor to test real behavior with enhanced internal visibility
CsvExtractorImpl.Class = SpyCsvExtractor
const extractor = await CsvExtractorImpl.Create('/path/to/csv')
}