Arrakis Python client library
Project description
arrakis-python
Arrakis Python client library
Resources
Installation
With pip
:
pip install arrakis
With conda
:
conda install -c conda-forge arrakis-python
Features
- Query live and historical timeseries data
- Describe channel metadata
- Search for channels matching a set of conditions
- Publish timeseries data
Quickstart
Fetch timeseries
import arrakis
start = 1187000000
end = 1187001000
channels = [
"H1:CAL-DELTAL_EXTERNAL_DQ",
"H1:LSC-POP_A_LF_OUT_DQ",
]
block = arrakis.fetch(channels, start, end)
for channel, series in block.items():
print(channel, series)
where block
is a [arrakis.block.SeriesBlock][] and series
is a
[arrakis.block.Series][].
Stream timeseries
1. Live data
import arrakis
channels = [
"H1:CAL-DELTAL_EXTERNAL_DQ",
"H1:LSC-POP_A_LF_OUT_DQ",
]
for block in arrakis.stream(channels):
print(block)
2. Historical data
import arrakis
start = 1187000000
end = 1187001000
channels = [
"H1:CAL-DELTAL_EXTERNAL_DQ",
"H1:LSC-POP_A_LF_OUT_DQ",
]
for block in arrakis.stream(channels, start, end):
print(block)
Describe metadata
import arrakis
channels = [
"H1:CAL-DELTAL_EXTERNAL_DQ",
"H1:LSC-POP_A_LF_OUT_DQ",
]
metadata = arrakis.describe(channels)
where metadata
is a dictionary mapping channel names to
[arrakis.channel.Channel][].
Find channels
import arrakis
for channel in arrakis.find("H1:LSC-*"):
print(channel)
where channel
is a [arrakis.channel.Channel][].
Count channels
import arrakis
count = arrakis.count("H1:LSC-*")
Publish timeseries
from arrakis import Channel, Publisher, SeriesBlock, Time
import numpy
# admin-assigned ID
publisher_id = "my_producer"
# define channel metadata
metadata = {
"H1:FKE-TEST_CHANNEL1": Channel(
"H1:FKE-TEST_CHANNEL1",
data_type=numpy.float64,
sample_rate=64,
),
"H1:FKE-TEST_CHANNEL2": Channel(
"H1:FKE-TEST_CHANNEL2",
data_type=numpy.int32,
sample_rate=32,
),
}
publisher = Publisher(publisher_id)
publisher.register()
with publisher:
# create block to publish
series = {
"H1:FKE-TEST_CHANNEL1": numpy.array([0.1, 0.2, 0.3, 0.4], dtype=numpy.float64),
"H1:FKE-TEST_CHANNEL2": numpy.array([1, 2], dtype=numpy.int32),
}
block = SeriesBlock(
1234567890 * Time.SECONDS, # time in nanoseconds for first sample
series, # the data to publish
metadata, # the channel metadata
)
# publish timeseries
publisher.publish(block)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
arrakis-0.3.0.tar.gz
(27.6 kB
view details)
Built Distribution
arrakis-0.3.0-py3-none-any.whl
(32.8 kB
view details)
File details
Details for the file arrakis-0.3.0.tar.gz
.
File metadata
- Download URL: arrakis-0.3.0.tar.gz
- Upload date:
- Size: 27.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: python-httpx/0.27.2
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 |
51b964e85ccfd74c8b2bfa7b115a865671d107b9d72e94183e5bcae6288d7342
|
|
MD5 |
13d6109f5af920f2905a5e9d0125a312
|
|
BLAKE2b-256 |
58ca4521c36a53f426d2719e05db03b6cd397817b0fd6bc42f29780d595b0a10
|
File details
Details for the file arrakis-0.3.0-py3-none-any.whl
.
File metadata
- Download URL: arrakis-0.3.0-py3-none-any.whl
- Upload date:
- Size: 32.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: python-httpx/0.27.2
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 |
f5f207b7eb72dca3f1e4414cfdd776066c8b9a6bff6fb148dc25fd1e188167d5
|
|
MD5 |
32c57c03a2f5ee6b4aaf3024e3b317ab
|
|
BLAKE2b-256 |
a497bf33d229e572f6e32d8cc8091612a622d9ac42a06c85a3708667b8ad6814
|