Banda de 60m Agosto 2019

import datetime as dt

import matplotlib.pyplot as plt

import sys

import numpy as np

station=sys.argv[1]

print(station)

WSPR_file=open("/home/andres/WSPR/60m_dp0gvn/"+station,'r')

date_list=[]

hour_list=[]

SNR_list=[]

for l in WSPR_file:

    line=l.split()

    date_list.append(dt.datetime.strptime(line[0]+' '+line[1],"%Y-%m-%d %H:%M"))#convert string date to datetime format

    hour_list.append(line[1])

    SNR_list.append(line[4])

init_date=min(date_list) #search min and max dates of the list of reports, from datetime list of objects

print(init_date)

end_date=max(date_list)

print(end_date)

h_list=[]

hourly=[]

init_time=dt.datetime.strptime("00:00","%H:%M")

for i in hour_list:

    time=dt.datetime.strptime(i,"%H:%M")

    h_list.append(((time-init_time).total_seconds())/3600)

h=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]

h_m=[0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5,10.5,11.5,12.5,13.5,14.5,15.5,16.5,17.5,18.5,19.5,20.5,21.5,22.5,23.5]

h_m_ext=[0,0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5,10.5,11.5,12.5,13.5,14.5,15.5,16.5,17.5,18.5,19.5,20.5,21.5,22.5,23.5,24]

mean=[]

mean_ext=[]

stdev=[]

stdev_ext=[]

n_decodes=[]

min_signal=[]

max_signal=[]

min_signal_ext=[]

max_signal_ext=[]

color_decodes=[]

freq=[]

for i in range(0,24):

    WSPR_file2=open("/home/andres/WSPR/60m_dp0gvn/"+station,'r')

    signal=[]

    for l in WSPR_file2:

        a=l.split()

        freq.append(float(a[3]))

        if(dt.datetime.strptime(a[0]+' ' +a[1],"%Y-%m-%d %H:%M").hour==i):

            signal.append(int(a[4]))

    if (len(signal)!=0):

        mu=np.mean(signal)

        dev=np.std(signal)

        min_sig=mu-dev

        max_sig=mu+dev

        n=len(signal)

        print(str(i)+":"+str(len(signal)))

    else:

        mu=-30.0

        dev=0.0

        min_sig=mu-dev

        max_sig=mu+dev

        print(str(i)+":"+"none")

        n=0

    mean.append(mu)

    stdev.append(dev)

    min_signal.append(min_sig)

    max_signal.append(max_sig)

    n_decodes.append(n)

    WSPR_file.close()

m=(mean[0]+mean[-1])*0.5

mean_ext.append(m)

for i in range(0,len(mean)):

    mean_ext.append(mean[i])

mean_ext.append(m)

m=((mean[0]+mean[-1])*0.5)-((stdev[0]+stdev[-1])*0.5)

min_signal_ext.append(m)

for i in range(0,len(min_signal)):

    min_signal_ext.append(min_signal[i])

min_signal_ext.append(m)

m=((mean[0]+mean[-1])*0.5)+((stdev[0]+stdev[-1])*0.5)

max_signal_ext.append(m)

for i in range(0,len(max_signal)):

    max_signal_ext.append(max_signal[i])

max_signal_ext.append(m)

mean_freq=np.mean(freq)

max_decodes=max(n_decodes)

#Color represents porcentage of spots from total

#If lot of spots green, else degrades through yellow, orange and red

for i in n_decodes:

    if (float(i)>=max_decodes*0.75):

        color_decodes.append("g")

    elif (max_decodes*0.5<=float(i) and float(i)<max_decodes*0.75):

        color_decodes.append("y")

    #elif (max_decodes*0.25<=float(i) and float(i)<max_decodes*0.5):

        #color_decodes.append("orange")

    else:

        color_decodes.append("r")

print(color_decodes)

x=h_list

y=SNR_list

y_int=[]

x_min=0

x_max=24

for i in y:             #generate list in int format for SNR levels

    y_int.append(int(i))

y_min=min(y_int)    #Only works min() if integer

y_max=max(y_int)

fig, ax = plt.subplots(ncols=1, sharey=True, figsize=(7, 4))

fig.subplots_adjust(hspace=0.5, left=0.07, right=0.97, top=0.9)

#ax.errorbar(h_m,mean,xerr=0.5,yerr=stdev,fmt="o--",ecolor=color_decodes)

ax.errorbar(h_m_ext,mean_ext,xerr=0,yerr=0,fmt="-.")

ax.grid(linestyle='--',alpha=0.5)

#ax.fill_between(h_m,min_signal,max_signal,alpha=0.15,color='k')

#????????????????

#min_signal.append(-25)

#max_signal.append(-15)

#escalonado

#ax.fill_between(h_m_ext,min_signal_ext,max_signal_ext,alpha=0.15,color='k',step="mid")

#Interpolado linealmente

ax.fill_between(h_m_ext,min_signal_ext,max_signal_ext,alpha=0.15,color='k')

ax.scatter(h_m,mean,color=color_decodes,marker='o')

#ax.plot(min_signal)

#ax.plot(max_signal)

#Axis texts

ax.set_xlim(left=0,right=24)

ax.set_xlabel("UTC time\nVertical lines are local sunrise/sunset, Red:"+station+", Black:LU3HO")

ax.set_ylabel("Signal to Noise Ratio (dB)")

ax.set_title("WSPR Analysis of LU3HO received by "+station+" from "+str(init_date)+" to "+str(end_date)+"\nShadow is +/- sigma(SNR) - Dotted blue line is mean(SNR) - Dot is mean, if green: enough data, yellow: little data, red:insufficient data\nMean=-30dB is plot when there is no data - Numbers above mean dots are number of spots")

#Print above each mean point the number of received/transmitted spots.

for i in h:

    print(i,n_decodes[i])

    plt.text(i+0.35,mean[i]+0.5,n_decodes[i])

#Annotate Mean WSPR frequency on top of graph

ax.annotate("%.4f MHz"%mean_freq,

            xy=(0, -30), xycoords='data',

            xytext=(0.9, 0.95), textcoords='axes fraction',

            horizontalalignment='right', verticalalignment='top',fontsize=17)

#Hour axis

ax.set_xticks([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24])

#SNR Axis

ax.set_yticks([-30,-25,-20,-15])

#Sunrise/Sunset vertical lines....put a better approach

ax.axvline(9,color='r',linestyle='-.')

ax.axvline(16.25,color='r',linestyle='-.')

ax.axvline(11.2,color='k',linestyle='-.')

ax.axvline(22.2,color='k',linestyle='-.')

plt.show()