# TiSKitPy
Routines for time series data processing
Uses the [obspy](https://docs.obspy.org) Stream (data) and Inventory (metadata)
classes
[Documentation](https://tiskitpy.readthedocs.io)
## Classes
- `CleanRotator`: rotate data to minimize noise on vertical channel
- `DataCleaner`: Transfer_Function-based data cleaning
- `Decimator`: Decimate time series and update metadata with the decimator's
response
- `SpectralDensity`: Calculate and manipulate spectral density functions.
- `TimeSpans`: Specify time spans to be removed, kept, zeroed, etc.
- `ResponseFunctions`: Frequency response functions for a given input channel.
## Functions
- `FIR_corr`: transform zero-phase data to minimum phase (only works for
LCHEAPO loggers, need to update to calculate/work for any
zero-phase filter)
- `readMSEED`: read in MSEED data, including if the file is too big (> 2 GB)
for obspy's read() function
- `rptransient`: calculate and remove periodic transient (VERY manual!).
Based on Matlab code by E Wielandt, used in Deen et al., 2017
- `PetersonNoiseModel`: return the Peterson High and Low Noise Models
## Installation
First, install `obspy` using the instructions on their webpage.
Then, in the pip/conda environment that contains obspy...
### From this repository
Clone or download this repository, then from within the main repository directory, run:
`pip install .`
You can also install in editable mode (for developers), with:
`pip install -e .`
### Using `pip`
Type `pip install tiskitpy`
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