This page highlights a few core DASCore concepts.


Any expression of time should use numpy time constructs, which include datetime64 and timedelta64.

For example:

import numpy as np

# create a datetime64 with a very precise time string
time_1 = np.datetime64('2022-01-01T15:12:11.172455')

# create a new time by adding some time to time_1
time_2 = time_1 + np.timedelta64(60, 's')

# get the number of hour separating them:
delta_1 = (time_2 - time_1) / np.timedelta64(1, 'h')

DASCore provides two convenience functions for working with times:

to_datetime64 which tries to convert most types of inputs expressing date times to the proper numpy type. to_timedelta64 performs a similar function for timedeltas. For example:

import dascore as dc

# convert a time string to a datetime64 object.
time_1 = dc.to_datetime64('2022-01-01T12:12:12.1212')

# convert a timestamp (seconds from 1970-01-01) to a datetime object
time_2 = dc.to_datetime64(610243200)

In general you should try to be as explicit as possible and use numpy’s time constructs directly, but the dascore time functions provide a helpful way to sanitize a variety of time inputs.

Dimension Selection

Most of DASCore’s processing methods can be applied along any dimension. For example, pass_filter can be applied along any dimension by passing a range and dimension name:

import dascore as dc
patch = dc.get_example_patch()

filtered_time = patch.pass_filter(time=(1, 5))
filtered_distance = patch.pass_filter(distance=(0.1, 0.2))

However, the meaning of the values (ie frequency vs period) depends on the function, so be sure to read the docs!

Data structures

DASCore has two key data structures; the Patch and the Spool. Both of these have their own tutorial pages.

The Patch contains a contiguous block of N dimensional data and metadata. The Spool manages a group of Patchs. These can be in memory, on disk, or a remote resource.

Figure 1: Patch (blue) and Spool (red)