heavelock

int nwd(int a,int b)
{
	int c;
	while(b != 0)
	{
		c = a % b;
		a = b;
		b = c;
	}
	return a;

heavelock

import os
from shutil import copy

folder1 = 'C:\\bezwgledna\\sciezka\\do\\folderu1'
folder2 = 'C:\\bezwgledna\\sciezka\\do\\folderu2'

lista_poczatkow = ['poczatek1', 'poczatek2']

lista_plikow = os.listdir(folder1)

heavelock

lis3 = []
appended = False
for i in lis1:
  for j in lis2:
    if i[0] == j[0] and i[1] == j[1]:
      lis3.append(j)
      appended = True
      break
  if appended:
    appended = False

heavelock

import pandas as pd

df1 = pd.DataFrame([['adam','slodowy',1],['karol','karolowy',2],['igor','iksinski',3]])
df2 = pd.DataFrame([['adam','slodowy',13,'L'],['zdzisiek','jozek',13,'m'],['dupa','dupa',3,'k']])

df = pd.merge(a,b,how='left',on=[0,1])

heavelock

def plot_map(df, aax):
    aax.clear()
    aax.scatter(x = df.x,
                y =df.y,
                c = df['log_eng'],
                s = df['log_eng'],
                cmap = 'viridis_r',
                alpha = 0.5)
    aax.set_xlim(lim_rudna['xlim'])
    aax.set_ylim(lim_rudna['ylim'])

heavelock

As part of the HebMarine project a number of scripts were developed for processing the data recieved from the Datawell Waverider buoys and latterly the Nortek AWAC ADCPs.

Spectral polar plot

A number of approaches were taken with the various data outputs from the buoys. Visual representations of 3 minutely spectral data took place every half hour after the sync had taken place with the recieving station. This produced a polar contour plot for each buoy based on the direction, power spectral density, spread and skew. See Spectral Polar Plot repository and accompanying plot_spectra.py. The colorbar represents power spectral density (m2/Hz), the peak value on the plot can deviate from the max psd in the spectral file due to a hard to fit surface. ![example plot](https://b

heavelock

As part of the HebMarine project a number of scripts were developed for processing the data recieved from the Datawell Waverider buoys and latterly the Nortek AWAC ADCPs.

Spectral polar plot

A number of approaches were taken with the various data outputs from the buoys. Visual representations of 3 minutely spectral data took place every half hour after the sync had taken place with the recieving station. This produced a polar contour plot for each buoy based on the direction, power spectral density, spread and skew. See Spectral Polar Plot repository and accompanying plot_spectra.py. The colorbar represents power spectral density (m2/Hz), the peak value on the plot can deviate from the max psd in the spectral file due to a hard to fit surface. ![example plot](https://b

heavelock

lsblk
sudo badblocks -wsv /dev/sdc
sudo parted /dev/sdc mktable msdos
sudo parted -a optimal /dev/sdc mkpart primary 0% 100%
sudo mkfs.vfat -F 32 /dev/sdc1

sudo mkfs -t ext3 /dev/sdc1

heavelock

{% set name = "environs" %}
{% set version = "4.1.0" %}

package:
  name: "{{ name|lower }}"
  version: "{{ version }}"

source:
  url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.tar.gz
  sha256: a69695630145bf559baedd1ac99312abd27cca55cbc1dca7f7a6a2bae7e1570c

heavelock

import obspy

# Reading a miniseed file
st = obspy.read('filepath.miniseed')

# Reading dataless file
inv = obspy.read_inventory('filepath.dataless')

# The easiest way to get station's position is to do that:
inv[0][0][0].longitude