package for encoding and decoding time series in JSON files into embedded base 16, 64, or 91 encodings.
Reason this release was yanked:
didn't run tests before, retesting and redeploying
Project description
A module for encoding timeseries data, with helper functions for parsing JSON and CSV files into smaller sizes for caching and transmitting through APIs.
Installation
pip install timeseriesencoder
Use
JSON
To use pass any json into TimeSeriesEncoder.encode_json(data, ts_key, ts_value) where ts_key and ts_value are the timestamp key and the value key in the json.
from timeseriesencoder import *
encoded = JSONEncoder.encode_json(myJson, ts_key='UTC', ts_value='Value')
To decode you can apply the reverse:
decoded = JSONEncoder.decode_json(encoded)
The encoder will encode all time series it finds in the json or csv file. Each will get their own encoding that is optimal for the data sparsity and values. Sorting the data before encoding can improve compression. If you'd like the encoder to sort for you, you can include sort_values = True on the encode_json call. This will sort each time series by the timeseries key before encoding.
CSV
from timeseriesencoder import *
csv = get_csv_sample()
encoded = CSVEncoder.encode_csv(csv, time_column="UTC", key_columns=["Attribute"])
To decode you can apply the reverse:
decoded = CSVEncoder.decode_csv(encoded)
Additionally, non time series data will be encoded in CSV files as able. Static columns will be compressed, and string value columns will be replaced with encoded lookups if it saves space in the encoded file size.
Updates
0.2.0
- Released a csv module that allows encoding of CSV time series files, it is accessible on CSVEncoder.
- Migrated JSON function from TimeSeriesEncoder to JSONEncoder, which extends TimeSeriesEncoder
- Fixed a few bugs, added more tests
0.1.17
- Added gzip compression as an optional output for all encoding and decoding calls
- Added tests for gzip
Tests
To run tests call pytests on the tests folder from the base package folder.
pytest ./tests/ -v -s
Examples
Example of encoding a json file, with the inputs/outputs below.
Note: The numpyencoder module is not required, but is used in the example to read/write the json to a file easily.
from timeseriesencoder import *
import json
from numpyencoder import NumpyEncoder
with open('sample.json', 'r') as rfile:
data = json.load(rfile)
data['Response'][0]['ForecastWeather'] = data['Response'][0]['ForecastWeather'][:3]
with open('data.json', 'w') as rfile:
json.dump(data, rfile, cls=NumpyEncoder)
encoded = TimeSeriesEncoder.encode_json(data, ts_key='UTC', ts_value='Value', sort_values=True)
with open('encoded.json', 'w') as rfile:
json.dump(encoded, rfile, cls=NumpyEncoder)
Size
Size before and after encoding. Encoding reduced package size by 10x.
In 0.1.17 the optional gzip parameter was added to encode_json and decode_json. This allows the final package to be smaller, which allows for lower data size for sending across networks, or storing in a cache system like Redis.
The raw sample.json had a size of 281KB. The encoded json that is output from the program that is sorted and is using base64 style encoding is 27KB. The raw zipped sample.json is 26KB The programs gzipped, encoded file is just 8KB. This is 1/3rd the size of the regular zip file, and just 1/35th of the original data size.
Data Output
{
"Request": {
"locations": [
"XXX"
],
"attributes": null,
"aggregate": true,
"startDate": "2021-04-12T01:46:08.8622635Z"
},
"Response": [
{
"LocationName": "XXX",
"EntityType": "WIND_TURBINE",
"DisplayName": "XXX",
"Latitude": -1,
"Longitude": -1,
"Timezone": "Europe/Brussels",
"DataSource": "ECMWF-IFS",
"AsOfDateUTC": "2021-04-08T00:00:00Z",
"ForecastWeather": [
{
"AttributeName": "fresh_snow_6h:cm",
"AttributeUnitOfMeasure": "cm",
"AttributeDescription": "fresh snow of previous 6h [cm]",
"AttributeDataType": "Numeric",
"Values": {
"encoder": "TimeSeriesEncoder",
"start": 1618192800.0,
"ts_key": "UTC",
"ts_value": "Value",
"interval": 3600.0,
"static_value": 0.0,
"static_count": 143
}
},
{
"AttributeName": "precip_1h:mm",
"AttributeUnitOfMeasure": "mm",
"AttributeDescription": "amount of precipitation in the previous 1h [mm]",
"AttributeDataType": "Numeric",
"Values": {
"encoder": "TimeSeriesEncoder",
"start": 1618192800.0,
"ts_key": "UTC",
"ts_value": "Value",
"encoding_size": 64,
"data": "00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000KKKKKK888888TTTTTT888888000000",
"interval": 3600.0,
"encoding_depth": 1,
"float_precision": 2,
"signed": false
}
},
{
"AttributeName": "relative_humidity_100m:p",
"AttributeUnitOfMeasure": "%",
"AttributeDescription": "relative humidity at 100m [%]",
"AttributeDataType": "Numeric",
"Values": {
"encoder": "TimeSeriesEncoder",
"start": 1618192800.0,
"ts_key": "UTC",
"ts_value": "Value",
"encoding_size": 64,
"data": "BkBUBJB7AyAA9Q8i887b726d6B5m6P747m808H8Y8F7x7c7u8B8U9LAAA-AzAyAy9u8r7q7t7w7z8V939e9h9l9o9Y9I918y8u8p8l8g8c8K837o7Y7J74797F7K7Q7W7c7p818E8S8g8u8t8t8t8t8t8s8h8W8K897-7p828I8X8m909G9kADAiBBBgCACGCMCSCYCeCjC4BSAqAE9e939H9W9l9zACARB6BnCTD9DrEXEgEpExF4FCFKEtEQDzDWD4CdCBBmBKAuATA1ADAQAcAoA-BA",
"interval": 3600.0,
"encoding_depth": 2,
"float_precision": 1,
"signed": false
}
}
]
}
]
}
Input
{
"Request": {
"locations": [
"XXX"
],
"attributes": null,
"aggregate": true,
"startDate": "2021-04-12T01:46:08.8622635Z"
},
"Response": [
{
"LocationName": "XXX",
"EntityType": "XXX",
"DisplayName": "BEZ_E01",
"Latitude": -1,
"Longitude": -1,
"Timezone": "Europe/Brussels",
"DataSource": "ECMWF-IFS",
"AsOfDateUTC": "2021-04-08T00:00:00Z",
"ForecastWeather": [
{
"AttributeName": "fresh_snow_6h:cm",
"AttributeUnitOfMeasure": "cm",
"AttributeDescription": "fresh snow of previous 6h [cm]",
"AttributeDataType": "Numeric",
"Values": [
{
"UTC": "2021-04-12T02:00:00Z",
"Value": 0.0
},
{
"UTC": "2021-04-12T03:00:00Z",
"Value": 0.0
},
{
"UTC": "2021-04-12T04:00:00Z",
"Value": 0.0
},
{
"UTC": "2021-04-13T03:00:00Z",
"Value": 0.0
},
{
"UTC": "2021-04-13T04:00:00Z",
"Value": 0.0
},
{
"UTC": "2021-04-13T05:00:00Z",
"Value": 0.0
},
{
"UTC": "2021-04-13T06:00:00Z",
"Value": 0.0
},
{
"UTC": "2021-04-13T07:00:00Z",
"Value": 0.0
},
{
"UTC": "2021-04-13T08:00:00Z",
"Value": 0.0
},
{
"UTC": "2021-04-13T09:00:00Z",
"Value": 0.0
},
{
"UTC": "2021-04-13T10:00:00Z",
"Value": 0.0
},
{
"UTC": "2021-04-13T11:00:00Z",
"Value": 0.0
},
{
"UTC": "2021-04-13T12:00:00Z",
"Value": 0.0
},
{
"UTC": "2021-04-13T13:00:00Z",
"Value": 0.0
},
{
"UTC": "2021-04-13T14:00:00Z",
"Value": 0.0
},
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"Value": 0.0
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"Value": 0.0
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"Value": 0.0
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"UTC": "2021-04-13T18:00:00Z",
"Value": 0.0
},
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"UTC": "2021-04-13T19:00:00Z",
"Value": 0.0
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"Value": 0.0
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"Value": 0.0
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}
]
},
{
"AttributeName": "precip_1h:mm",
"AttributeUnitOfMeasure": "mm",
"AttributeDescription": "amount of precipitation in the previous 1h [mm]",
"AttributeDataType": "Numeric",
"Values": [
{
"UTC": "2021-04-12T02:00:00Z",
"Value": 0.0
},
{
"UTC": "2021-04-12T03:00:00Z",
"Value": 0.0
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{
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}
]
}
]
}
]
}
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