import matplotlib.dates as mdates
from scipy.interpolate import griddata
import pandas as pd
import pdb
import numpy as np
import matplotlib.pyplot as plt
#from datetime import datetime, timedelta
import datetime
import sys
def process_waves_app():
n_days = 7
# Read data from CSV
df = pd.read_csv(
'/tmp/Waves_edge_all.txt',
delimiter=',', # Specify delimiter if needed
header=None
)
df.columns = ['NMEA_Type','Date','Time','Hs','Hmax','Current_mean','Current_bearing','zero_crossing_frequency','Peak_wave_frequency_mlm','wave_bearing_mlm','Peak_wave_frequency','Wave_bearing','Checksum']
df['datetime_str'] = df['Date'].astype(str) + ',' + df['Time'].astype(str).str.zfill(6)
df['Datetime'] = pd.to_datetime(df['datetime_str'], format="%y%m%d,%H%M%S")
# Prepare data for plots
times = mdates.date2num(pd.to_datetime(df['Datetime'])) # Convert timestamps to numerical format for plotting
# Save the dataframe to useable csv file
df = df[['Datetime','Hs','Hmax','Current_mean','Current_bearing','zero_crossing_frequency','Peak_wave_frequency_mlm','wave_bearing_mlm','Peak_wave_frequency','Wave_bearing']]
df.to_csv('/tmp/L4_buoy_sonardyne_waves.csv', index=False)
# Filter data to get the last n_days
end_time = np.max(times)
start_time = end_time - n_days
# Create a mask for the time
time_mask = (times >= start_time)
Hs = df['Hs']
Hmax = df['Hmax']
peak_period = df['Peak_wave_frequency']
direction = df['Wave_bearing']
current = df['Current_mean']
current_bearing = df['Current_bearing']
# Filter the data based on the time mask
filtered_time = times[time_mask]
filtered_Hs = Hs[time_mask]
filtered_Hmax = Hmax[time_mask]
filtered_peak_period = peak_period[time_mask]
filtered_current = current[time_mask]
filtered_current_bearing = current_bearing[time_mask]
# Wave height (Significant height, Max height)
plt.figure(figsize=(7.0,2.20), dpi=100)
fig, ax1 = plt.subplots(figsize=(7.0,2.20))
ax1.plot_date(filtered_time, filtered_Hs, '+', color='purple', markersize=5, label='Sig Height')
ax1.plot_date(filtered_time, filtered_Hmax, '*', color='blue', markersize=5, label='Max Height')
ax1.set_ylabel('Wave Height (m)', color='black', fontsize=8)
ax1.tick_params(axis='y', labelcolor='black', size=7)
ax1.set_title('L4 Buoy (Sonardyne)', fontsize=10)
ax1.set_ylim(0, 10)
# Set custom y-ticks and add grid lines
#plt.yticks(np.arange(0, 1, 5)) # Set y-tick marks
plt.grid(axis='y', linestyle='--', linewidth=0.5) # Grid lines only for y-axis
# Formatting the x-axis
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%d/%m/%y \n %j'))
ax1.xaxis.set_major_locator(mdates.DayLocator(interval=1))
#plt.gca().xaxis.set_minor_locator(mdates.HourLocator(interval=6)) # Minor ticks for every 6 hours
plt.minorticks_on() # Enable minor ticks
plt.xticks(fontsize=8)
# Add grid and legend
ax1.grid(True)
fig.tight_layout() # Adjust layout
ax1.legend(loc='upper right', fontsize=7)
#ax2.legend(loc='lower right', fontsize=7)
# Save the plot
plt.savefig('/tmp/wave_height_sonardyne.png', format='png', dpi=100)
#pdb.set_trace()
# Wave Period
plt.figure(figsize=(7.0,2.20), dpi=100)
fig, ax1 = plt.subplots(figsize=(7.0,2.20))
ax1.plot_date(filtered_time, filtered_peak_period, '+', color='purple', markersize=5, label='Peak Period')
ax1.set_ylabel('Wave Period (s)', color='black', fontsize=8)
ax1.tick_params(axis='y', labelcolor='black', size=7)
ax1.set_title('L4 Buoy (Sonardyne)', fontsize=10)
ax1.set_ylim(0, 20)
# Set custom y-ticks and add grid lines
#plt.yticks(np.arange(0, 1, 5)) # Set y-tick marks
plt.grid(axis='y', linestyle='--', linewidth=0.5) # Grid lines only for y-axis
# Formatting the x-axis
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%d/%m/%y \n %j'))
ax1.xaxis.set_major_locator(mdates.DayLocator(interval=1))
#plt.gca().xaxis.set_minor_locator(mdates.HourLocator(interval=6)) # Minor ticks for every 6 hours
plt.minorticks_on() # Enable minor ticks
plt.xticks(fontsize=8)
# Add grid and legend
ax1.grid(True)
fig.tight_layout() # Adjust layout
ax1.legend(loc='upper right', fontsize=7)
# Save the plot
plt.savefig('/tmp/wave_period_sonardyne.png', format='png', dpi=100)
# Plot current speed and direction
plt.figure(figsize=(7.0,2.20), dpi=100)
fig, ax1 = plt.subplots(figsize=(7.0,2.20))
ax1.plot_date(filtered_time, filtered_current, '-', color='purple', label='Current speed')
ax1.set_ylabel('Current Speed (ms$^{-1}$)', color='purple', fontsize=8)
ax1.tick_params(axis='y', labelcolor='purple', size=7)
ax1.set_title('L4 Buoy (Sonardyne)', fontsize=10)
ax1.set_ylim(0,1)
# Set custom y-ticks and add grid lines
plt.grid(axis='y', linestyle='--', linewidth=0.5) # Grid lines only for y-axis
# Create a second y-axis for current bearing
ax2 = ax1.twinx()
ax2.plot_date(filtered_time, filtered_current_bearing, '.', color='blue', markersize=5, label='Bearing')
ax2.set_ylabel('Bearing ($^{o}$)', color='b', fontsize=8)
ax2.tick_params(axis='y', labelcolor='b', size=7)
ax2.set_ylim(0,360)
# Formatting the x-axis
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%d/%m/%y \n %j'))
ax1.xaxis.set_major_locator(mdates.DayLocator(interval=1))
#plt.gca().xaxis.set_minor_locator(mdates.HourLocator(interval=6)) # Minor ticks for every 6 hours
plt.minorticks_on() # Enable minor ticks
plt.xticks(fontsize=8)
# Add grid and legend
ax1.grid(True)
fig.tight_layout() # Adjust layout
#ax1.legend(loc='lower left', fontsize=7)
#ax2.legend(loc='lower right', fontsize=7)
# Save the plot
plt.savefig('/tmp/L4_buoy_currents_sonardyne.png', format='png', dpi=100)
latest_Hs = np.array(Hs[times == end_time])
latest_Hmax = np.array(Hmax[times == end_time])
latest_peak_period = np.array(peak_period[times == end_time])
latest_direction = np.array(direction[times == end_time])
latest_current = np.array(current[times == end_time])
latest_current_bearing = np.array(current_bearing[times == end_time])
return latest_Hs[0], latest_Hmax[0], latest_peak_period[0], latest_direction[0], latest_current[0], latest_current_bearing[0]
def main():
Hs, Hmax, peak_period, wave_direction, current_speed, current_bearing = process_waves_app()
sys.exit(0)
if __name__=='__main__':
main()