Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
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An overview of the core data API of ObsPy, a Python framework for processing seismological data. It is useful for parsing common seismological file formats, or manipulating custom data into standard objects for downstream use cases such as ObsPy's signal processing routines or SeisBench's modeling API.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Seismograms of various formats (e.g. SAC, MiniSEED, GSE2, SEISAN, Q, etc.) can be imported into a Stream object using the read() function.
Streams are list-like objects which contain multiple Trace objects, i.e. gap-less continuous time series and related header/meta information.
Each Trace object has the attribute data pointing to a NumPy ndarray of the actual time series and the attribute stats which contains all meta information in a dict-like Stats object. Both attributes starttime and endtime of the Stats object are UTCDateTime objects.
A multitude of helper methods are attached to Stream and Trace objects for handling and modifying the waveform data.
Hierarchy: Stream → Trace (multiple)
Trace - DATA:
data → NumPy arraystats:
network, station, location, channel — Determine physical location and instrumentstarttime, sampling_rate, delta, endtime, npts — InterrelatedTrace - METHODS:
taper() — Tapers the data.filter() — Filters the data.resample() — Resamples the data in the frequency domain.integrate() — Integrates the data with respect to time.remove_response() — Deconvolves the instrument response.A Stream with an example seismogram can be created by calling read() without any arguments. Local files can be read by specifying the filename, files stored on http servers (e.g. at https://examples.obspy.org) can be read by specifying their URL.
>>> from obspy import read
>>> st = read()
>>> print(st)
3 Trace(s) in Stream:
BW.RJOB..EHZ | 2009-08-24T00:20:03.000000Z - ... | 100.0 Hz, 3000 samples
BW.RJOB..EHN | 2009-08-24T00:20:03.000000Z - ... | 100.0 Hz, 3000 samples
BW.RJOB..EHE | 2009-08-24T00:20:03.000000Z - ... | 100.0 Hz, 3000 samples
>>> tr = st[0]
>>> print(tr)
BW.RJOB..EHZ | 2009-08-24T00:20:03.000000Z - ... | 100.0 Hz, 3000 samples
>>> tr.data
array([ 0. , 0.00694644, 0.07597424, ..., 1.93449584,
0.98196204, 0.44196924])
>>> print(tr.stats)
network: BW
station: RJOB
location:
channel: EHZ
starttime: 2009-08-24T00:20:03.000000Z
endtime: 2009-08-24T00:20:32.990000Z
sampling_rate: 100.0
delta: 0.01
npts: 3000
calib: 1.0
...
>>> tr.stats.starttime
UTCDateTime(2009, 8, 24, 0, 20, 3)
Event metadata are handled in a hierarchy of classes closely modelled after the de-facto standard format QuakeML. See read_events() and Catalog.write() for supported formats.
Hierarchy: Catalog → events → Event (multiple)
Event contains:
origins → Origin (multiple)
latitude, longitude, depth, time, ...magnitudes → Magnitude (multiple)
mag, magnitude_type, ...picksfocal_mechanismsStation metadata are handled in a hierarchy of classes closely modelled after the de-facto standard format FDSN StationXML which was developed as a human readable XML replacement for Dataless SEED. See read_inventory() and Inventory.write() for supported formats.
Hierarchy: Inventory → networks → Network → stations → Station → channels → Channel
Network:
code, description, ...Station:
code, latitude, longitude, elevation, start_date, end_date, ...Channel:
code, location_code, latitude, longitude, elevation, depth, dip, azimuth, sample_rate, start_date, end_date, response, ...| Class/Function | Description |
|---|---|
read | Read waveform files into an ObsPy Stream object. |
Stream | List-like object of multiple ObsPy Trace objects. |
Trace | An object containing data of a continuous series, such as a seismic trace. |
Stats | A container for additional header information of an ObsPy Trace object. |
UTCDateTime | A UTC-based datetime object. |
read_events | Read event files into an ObsPy Catalog object. |
Catalog | Container for Event objects. |
Event | Describes a seismic event which does not necessarily need to be a tectonic earthquake. |
read_inventory | Function to read inventory files. |
Inventory | The root object of the Network → Station → Channel hierarchy. |
| Module | Description |
|---|---|
obspy.core.trace | Module for handling ObsPy Trace and Stats objects. |
obspy.core.stream | Module for handling ObsPy Stream objects. |
obspy.core.utcdatetime | Module containing a UTC-based datetime class. |
obspy.core.event | Module handling event metadata. |
obspy.core.inventory | Module for handling station metadata. |
obspy.core.util | Various utilities for ObsPy. |
obspy.core.preview | Tools for creating and merging previews. |
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Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
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AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching `acopf-math-model.md` and MATPOWER branch fields. Use when computing branch flows in either direction, aggregating bus injections for nodal balance, checking MVA (rateA) limits, computing branch loading %, or debugging sign/units issues in AC power flow.
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Redact text from PDF documents for blind review anonymization
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Use when checking simplified ADA-derived plan-view bathroom accessibility constraints such as turning space, door clear width, toilet centerline, grab bars, and lavatory knee/toe clearance.
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Analyze failed GitHub Action jobs for a pull request.
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Use when extracting plan-view architectural geometry from DXF files with semantic CAD layers, especially when outputs must normalize rooms, doors, fixtures, clearances, and grab bars into machine-checkable JSON.
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