PlotDataQA.py

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import os
import sys
import subprocess
import datetime
import time
#sys.path.append('/Users/athula/Desktop/MuonMonitor/ACNETdata/PhysicsStudies/Utility/')
import PlotUtilityNew as pu
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
#import seaborn as sns
import matplotlib.dates as mdates
import matplotlib 
from datetime import datetime

#matplotlib.use('GTK')
#matplotlib.use('Agg')
plt.switch_backend('agg')
#----------------------------
level0_list = ['MM1XAV','MM2XAV','MM3XAV','MM1YAV','MM2YAV','MM3YAV',
               'TORTGT','TRTGTD']
level1_list = ['MM1COR_CAL','MM2COR_CAL','MM3COR_CAL']

plt.rcParams["font.weight"] = "bold"
plt.rcParams["axes.labelweight"] = "bold"
plt.rcParams['figure.figsize'] = 12, 8
plt.rcParams.update({'font.size': 12})
plt.rcParams["lines.linewidth"] = 2
my_markersize = 1.0
#----------------------------
def MakePlots(unixtime,pldir):
    timein = datetime.fromtimestamp(unixtime)
    timein = timein.replace(minute=0,second=0)

    timepast = datetime.fromtimestamp(int(unixtime) - 60*60*24)
    timepast = timepast.replace(minute=0,second=0)

    myFmt = mdates.DateFormatter('%Y-%m-%d %H:%M')

    d2 = str(timein.year)+'-'+str(timein.month)+'-'+str(timein.day)
    d1 = str(timepast.year)+'-'+str(timepast.month)+'-'+str(timepast.day)    
    t2 = str(timein.time())
    t1 = str(timepast.time())
    #print(timein.replace(minute=0,second=0))
    print(d1,t1,d2,t2)
#-------------------MMAVE Plots:

    #==== Setting limits: ========
    mm1xavMin = 1.7
    mm1xavMax = 2.0
    mm2xavMin = 5.6
    mm2vavMax = 6.1
    
    mm1yavMin = -1.5
    mm1yavMax = -1.0
    mm2yavMin = 0.0
    mm2yavMax = 0.3
    
    mm1corMin = 1.7
    mm1corMax = 2.0
    mm2corMin = 5.6
    mm2corMax = 6.1
    
    hornIMin = -200.
    hornIMax = -198.
    #=============================
#-------------------MMAVE Plots:
    var_array = ['MM1XAV', 'MM1YAV','MM2XAV','MM2YAV']
    
    df_result =pu.pull_MultiData(d1,t1,d2,t2,var_array)
    df_result['time_new'] = pd.to_datetime(df_result['date'] +' '+df_result['time'])
    
    df_result['mm1ymin'] = mm1yavMin
    df_result['mm1ymax'] = mm1yavMax
    df_result['mm2ymin'] = mm2yavMin
    df_result['mm2ymax'] = mm2yavMax
    var_array = ['MM1XAV', 'MM1YAV','MM2XAV','MM2YAV','mm1ymin','mm1ymax','mm2ymin','mm2ymax']
    
    #-- Setting up status:
    std_mm1y = df_result['MM1YAV'].std()
    std_mm2y = df_result['MM2YAV'].std()
    std_mm1x = df_result['MM1XAV'].std()
    std_mm2x = df_result['MM2XAV'].std()
    
    ave90_mm2y = df_result['MM2YAV'].quantile(0.95)
    ave90_mm1y = df_result['MM1YAV'].quantile(0.95)
    
    ave10_mm2y = df_result['MM2YAV'].quantile(0.1)
    ave10_mm1y = df_result['MM1YAV'].quantile(0.1)

    print(df_result.describe(percentiles=[.9]))

    stat_mm1y = 'Good'
    face_colr = 'g'
    if (ave10_mm1y < mm1yavMin or ave90_mm1y > mm1yavMax) or (ave10_mm2y < mm2yavMin or ave90_mm2y > mm2yavMax) or std_mm1x > 10.0 or std_mm2x > 10:
        stat_mm1y = 'Not Good'
        face_colr = 'r'
    #-----------------------
    props = dict(boxstyle='round', facecolor=face_colr, alpha=0.7)
    
    fig, ax = plt.subplots()
    
    df_result.plot(x='time_new',y = var_array,ax=ax, markersize = my_markersize,label=[r'MM1 $X_{ave}$',r'MM1 $Y_{ave}$',r'MM2 $X_{ave}$',r'MM2 $Y_{ave}$','','','',''], marker='o', linestyle=' ',color = ['b','r','g','orange','magenta','magenta','coral','coral'])
    plt.ylim((-2., 1.5))
    ax.text(0.05, 0.95, 'Status: '+str(stat_mm1y), transform=ax.transAxes, fontsize=14,verticalalignment='top', bbox=props)

    plt.ylabel('Muon Monitor Centroid Position [in]')
    plt.xlabel('Time')
    plt.grid(True)
    plt.title(r'Muon Monitor 1 & 2 $X_{ave}$ and $Y_{ave}$ Data         Last refreshed:'+str(d2)+"-"+str(t2), fontsize=14)
    plt.legend(loc='upper right', numpoints=5, fontsize=15)
    plt.gcf().autofmt_xdate()
    plt.gca().xaxis.set_major_formatter(myFmt)
    plt.savefig(str(pldir)+'MMAVE.png')

    #======================== MMCOR : ===================================

    fig, ax1 = plt.subplots()
    ax2 = ax1.twinx()
    ax2.tick_params(axis='y', labelcolor='green')
    var_array = ['MM1COR_CAL', 'MM2COR_CAL','TRTGTD']

    df_result =pu.pull_MultiData(d1,t1,d2,t2,var_array)
    df_result['time_new'] = pd.to_datetime(df_result['date'] +' '+df_result['time'])

    df_result['MM1COR_norm'] = df_result['MM1COR_CAL']/df_result['TRTGTD']
    df_result['MM2COR_norm'] = df_result['MM2COR_CAL']/df_result['TRTGTD']

    df_result['mm1cormin'] = mm1corMin
    df_result['mm1cormax'] = mm1corMax
    df_result['mm2cormin'] = mm2corMin
    df_result['mm2cormax'] = mm2corMax
    #-- Setting up status:
    
    ave90_mm1cor = df_result['MM1COR_norm'].quantile(0.98)
    ave90_mm2cor = df_result['MM2COR_norm'].quantile(0.98)
    ave10_mm1cor = df_result['MM1COR_norm'].quantile(0.1)
    ave10_mm2cor = df_result['MM2COR_norm'].quantile(0.1)
    
    print(df_result.describe(percentiles=[.9]))
    print(ave90_mm1cor,ave90_mm2cor)

    stat_cor = 'Good'
    face_colr = 'g'
    if (ave10_mm1cor < mm1corMin or ave90_mm1cor > mm1corMax) or (ave10_mm2cor < mm2corMin or ave90_mm2cor > mm2corMax):
        stat_cor = 'Not Good'
        face_colr = 'r'
    #-----------------------
    props = dict(boxstyle='round', facecolor=face_colr, alpha=0.7)

    df_result.plot(x='time_new',y = ['MM1COR_norm','MM2COR_norm','mm1cormin','mm1cormax','mm2cormin','mm2cormax'],ax = ax1,color = ['b','r','cyan','cyan','magenta','magenta'], label=['Muon Monitor 1','Muon Monitor 2','','','',''],markersize = my_markersize, marker='o', linestyle=' ')
    ax1.text(0.4, 0.95, 'Gain Status: '+str(stat_cor), transform=ax.transAxes, fontsize=14,verticalalignment='top', bbox=props)

    ax1.set_ylim(1,8)
    ax2.set_ylim(-205,-190)
    ax1.tick_params(axis='y', labelcolor='b')
    ax1.set_ylabel('Calibrated Signal Gain/POT[E12]')
    ax1.set_xlabel('Time')
    ax1.grid(True)
    plt.gcf().autofmt_xdate()
    plt.gca().xaxis.set_major_formatter(myFmt)
    ax1.legend(loc='upper left', numpoints=5, fontsize=15)

#---------- horn current plots:

    var_array = ['NSLINA','NSLINB','NSLINC','NSLIND']
    df_cur =pu.pull_MultiData(d1,t1,d2,t2,var_array)
    df_cur['HornI'] = df_cur.iloc[:,2:6].sum(axis = 1)

    df_cur['hornImin'] = hornIMin
    df_cur['hornImax'] = hornIMax
    #-- Setting up status:

    cuartile10_hornI = df_cur['HornI'].quantile(0.05)
    cuartile90_hornI = df_cur['HornI'].quantile(0.98)

    print(df_cur.describe(percentiles=[.9]))

    stat_I = 'Good'
    face_I = 'g'
    if (cuartile10_hornI < hornIMin or cuartile90_hornI > hornIMax):
        stat_I = 'Not Good'
        face_I = 'r'
#-----------------------
    props = dict(boxstyle='round', facecolor=face_I, alpha=0.7)
    print(df_cur)
    df_cur['time_new'] = pd.to_datetime(df_cur['date'] +' '+df_cur['time'])
    df_cur.plot(x='time_new',y = ['HornI','hornImin','hornImax'], ax = ax2,color=['green','lime','lime'],label=['Horn Current','',''],markersize = my_markersize, marker='o', linestyle=' ')
    ax2.text(0.4, 0.88, 'Horn I Status: '+str(stat_I), transform=ax.transAxes, fontsize=14,verticalalignment='top', bbox=props)

    ax2.set_ylabel('Horn Current [kA]')
    ax2.set_xlabel('Time')
    plt.grid(True)
    plt.gcf().autofmt_xdate()
    plt.gca().xaxis.set_major_formatter(myFmt)
    plt.title(r'Muon Monitor 1 & 2 signal gain and Horn Current data      Last refreshed:'+str(d2)+"-"+str(t2), fontsize=14)
    ax2.legend(loc=0, numpoints=5, fontsize=15)
    plt.savefig(str(pldir)+'MMCOR.png')