quick_trial.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jul 27 21:26:57 2019

@author: ncs
"""

from swel.buoy import Buoy
import matplotlib.pyplot as plt

b = Buoy(46054)
print('b',type(b))
dest = b.get_data()
(E,f) = b.read_data(dest)
print('E',E.shape)
b.bootstrap(E, f)
swh = b.calc_swh()

signif_waves = swh[0:60]*3.28

fig, ax = plt.subplots()
ax.plot(signif_waves, 'b')
plt.gca().invert_xaxis()
ax.set(xlabel='hours', ylabel='interval (seconds)',
       title='Significant Wave height for the last 60 hours')
ax.grid()

# ===========================
#import matplotlib.pyplot as plt
#import numpy as np
#
#from scipy.signal import savgol_filter
#
#np.set_printoptions(precision=2)
#
#filename = '46054' # the buoy name
#dest = './data/{}.data_spec'.format(filename)
#
#dates = []
#energies = []
#frequencies = []
#
#with open(dest) as fp:
#    for _ in range(3):
#        next(fp)    
#    for l in fp:
#        dates.append(l.split()[0:5])
#        energies.append([float(e) for e in l.split()[6::2]])
#        freqs=l.split()[7::2]
#        frequencies.append([float(i[1:-1]) for i in freqs])
#    fp.close()
#
#E = np.array(energies)                       # E for 'Energy'
#f = np.array(frequencies)                    # f for 'frequency'
#df = np.diff(f)
#fmid = .5*(f[:,:-1] + f[:,1:])               # only used to consider frequencies by period bins
#Emid = .5*(E[:,:-1] + E[:,1:]) 
#
#p = np.array([0,5,7,9,11,13,15,17,19,21,35]) # arbitrary periods
#pmid = .5*(p[1:] + p[:-1])                   # mid-point resolution 
#
#Pf = 1./fmid[0,:]                            # convert from frequency to period
#
## plotting options
#fig, ax = plt.subplots()
#fig.set_size_inches(11,5)
#fig.savefig('SWH.png', dpi=100)
#
#for idx, _ in enumerate(pmid):                     # loop over period mid-point indexes 
#                                                   # (to make bins: period and neighbor)
#    period_mask = (Pf > p[idx]) & (Pf <= p[idx+1]) # create a boolean mask of which period data will fit in this bin
#    df_subset = df[:,period_mask]                  # subset of frequency 
#    Emid_subset = Emid[:,period_mask]              # subset Energy
#    variance = (df_subset*Emid_subset)              
#    SWH = (4*np.sqrt(variance.sum(axis=1)))        # 4*(sqrt of variance) rerturns the 'crest to trough' wave height
#    if idx == 0:                                   # creating a legend
#        label = 'less than 5'
#    else:
#        label = p[idx]
#    
#    signif = SWH[0:72]*3.28                        # convert meters to feet
#    smoothed = savgol_filter(signif, 7, 2)         # apply a Savitzky-Golay filter
#    ax.plot(smoothed,label=label)                  # add it to the plot 
#
#
#plt.gca().invert_xaxis()                          # invert the x axis (since it is looking back in time)
#ax.set(xlabel='hours', ylabel='Wave height (ft)',
#       title='Significant Wave height for the last 72 hours')
#ax.legend(bbox_to_anchor=(1.2, 1.05))             # add the legend
#ax.grid()