semvec/pypi-packages huggingface dataset:
htmloverpdf 0.0.3
Render a HTML overlay over existing PDF files.
Requires Python: >=3.7
Keywords: pdf

===========
htmloverpdf
===========
.. image:: https://readthedocs.org/projects/htmloverpdf/badge/?version=latest
    :target: https://htmloverpdf.readthedocs.io/en/latest/?badge=latest

.. image:: https://badge.fury.io/py/htmloverpdf.svg
    :target: https://badge.fury.io/py/htmloverpdf

Render a HTML overlay over existing PDF files.

Install
-------

::

    sudo apt install libgirepository1.0-dev gir1.2-poppler-0.18 gir1.2-pango-1.0
    python3 -m pip install htmloverpdf

A wrapper for https://weasyprint.org/ which allows compositing with existing PDF files.
            
It parses the HTML looking for <img> tags with src urls ending ".pdf". Each one begins a new page and copies all source pages overlaying the weasyprint output.
The magic value "blank.pdf" outputs sections HTML without overlaying.

Usage
-----

::

    htmloverpdf < test.html > test.pdf
pogmake 0.0.7
I needed a cross platform makefile
Requires Python: >=3.6

# Pogmake

Peterino's cross platform make replacement and automation tooler.

I'm just putting this on Pypi because I hate having to git clone this repo around so much.

Doing a big boy release in mid Feb.

Documentation available here:

[Pogmake Documentation](https://pogmake.readthedocs.io/en/latest/)

## Quickstart

`pip install pogmake`

`pog --list` - to show all jobs or `pog --init` to create a sample starter file
ucla-geotech-tools-response-spectrum 1.0.5
Python package for computing acceleration response spectra.

# Welcome to response_spectrum

response_spectrum computes a psuedo-acceleration response spectrum from an input ground motion or set of ground motions sampled at a constant frequency. Calculates are performed in the frequency domain by

1. taking the Fourier transform of the input motion(s)
2. multiplying by the transfer function for computing the motion of a single-degree-of-freedom (SDOF) oscillator with natural period T and damping D
3. computing the peak acceleration amplitude for the SDOF oscillator
4. repeating steps 2 and 3 for a range of user-specified periods

## Installation  
```bash
pip install ucla_geotech_tools.response_spectrum
```

## Command
```bash
get_response_spectrum(**kwargs)
get_ngawest2_T()
```

## Input parameters.
```python
motion(s) = an M x N list or python array containing acceleration data, where M is the number of motions and N is the number of data points in each motion. All motions must have the same number of data points. (required)  
dt = time step (required)
D = damping (optional, default = 0.05)
T = array of natural periods (optional, default = array used for NGAwest2 project: 
T = [0.01, 0.02, 0.022, 0.025, 0.029, 0.030, 0.035, 0.036, 0.040, 0.042, 0.044, 0.045, 0.046, 0.048, 0.050, 0.055, 0.060, 0.065, 0.067, 0.070, 0.075, 0.080, 0.085, 0.090, 0.095, 0.100, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.20, 0.22, 0.24, 0.25, 0.26, 0.28, 0.29, 0.30, 0.32, 0.34, 0.35, 0.36, 0.38, 0.40, 0.42, 0.44, 0.45, 0.46, 0.48, 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.2, 2.4, 2.5, 2.6, 2.8, 3.0, 3.2, 3.4, 3.5, 3.6, 3.8, 4.0, 4.4, 4.6, 4.8, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 20.0])
zeropad = 1: apply zero padding to speed up FFT operation, 0: do not pad with zeros (optional, default = 1)
verbose = 1: print output message when default values are being used, 0: suppress output messages (optional, default = 1)
```

## Example

### Python script
```python
import numpy as np
import ucla_geotech_tools.response_spectrum as ars
import matplotlib.pyplot as plt

N = 4000               # Number of time steps
M = 3                  # Number of motions
dt = 0.005             # Time step in seconds
D = 0.05               # Damping
freq = [1.0, 3.0, 5.0] # Frequencies for three different harmonic motions

motions = np.empty([M,N],dtype="float64")
for i in range(M):
    for j in range(N):
        motions[i][j] = np.sin(2*np.pi*freq[i]*j*dt)

# Get spectral periods used in NGAWest2 project
T = ars.get_ngawest2_T()

# Get Python array containing pseudo-spectral acceleration values
Sa = ars.get_response_spectrum(motions=motions, dt=dt, D=D, zeropad=0, verbose=0)

for i in range(M):
    plt.loglog(T,Sa[i])
plt.xlabel('Natural Period (s)')
plt.ylabel('Spectral Acceleration (g)')
```

### Output