PANDAS.py

public/PANDAS.py

+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+from __future__ import division
+__version__ = 20190626
+
+import re
+from time import time, sleep
+import datetime
+from datetime import datetime, timedelta
+from datetime import datetime
+import io
+import sys
+from datetime import date
+import csv
+import numpy as np
+import matplotlib
+import matplotlib.pyplot as plt
+import pandas as pd
+from pandas import read_csv
+import seaborn as sns
+
+DEBUG=False
+YOUR_PATH = "/dir/your_path"
+
+def PLOT(filepath_=None,title_=None,FLAG_=None):
+
+   df = read_csv(filepath_,sep=",", index_col = ['daily'], parse_dates=['daily'],na_values=['-999'])
+   #No,date,daily,year,month,day,hour,min,PM1,PM2.5,PM10,TEMP,Hum,PRES
+   if (FLAG_=="PM"):
+     df  = df.drop(['No','date','year','month','day','hour','min','TEMP','Hum','PRES'],axis=1)
+   if (FLAG_=="RH"):
+     df  = df.drop(['No','date','year','month','day','hour','min','PM1','PM2_5','PM10','TEMP','PRES'],axis=1)
+   if (FLAG_=="TEMP"):
+     df  = df.drop(['No','date','year','month','day','hour','min','PM1','PM2_5','PM10','Hum','PRES'],axis=1)
+   df = df.groupby('daily').mean()
+
+   if (FLAG_=="PM"):
+     pd.DataFrame({'PM2_5':100, 'PM10':100}, index=['PM2_5'])
+     ax = df.plot()
+     ax.grid()
+     ratio = 0.6
+     xleft, xright = ax.get_xlim()
+     ybottom, ytop = ax.get_ylim()
+     ax.set_aspect(abs((xright-xleft)/(ybottom-ytop))*ratio)
+
+     t0_= '2019-06-01'
+     now = datetime.utcnow().strftime('%Y-%m-%d')
+     ax.hlines(8.2,t0_,now, linestyles='dashed',color = 'darkorange')
+     ax.annotate('Very good',(t0_,6.0),color = 'darkorange')
+     ax.hlines(16.4,t0_,now, linestyles='dashed',color = 'darkorange')
+     ax.annotate('Good',(t0_,14.2),color = 'darkorange')
+     ax.hlines(25.1,t0_,now, linestyles='dashed',color = 'darkorange')
+     ax.annotate('Fair',(t0_,23.0),color = 'darkorange')
+     ax.hlines(37.4,t0_,now, linestyles='dashed',color = 'darkorange')
+     ax.annotate('Poor',(t0_,35.3),color = 'darkorange')
+     ax.annotate('Very Poor',(t0_,50.0),color = 'darkorange')
+     fig = ax.get_figure()
+
+     ax.set(xlabel="Date",
+     ylabel="average 24h polveri sottili (g/m3)",
+     title="Milano Scalo Romana - Air quality category (PM2.5 g/m3)\nTechnical data: PM sensor PM1\PM2.5 10%, PM10 30%");
+
+     now = datetime.utcnow().strftime('%Y%m')
+     filename=YOUR_PATH+"/PM/data/"+now+".png"
+     fig.savefig(filename)
+
+     filepath_ = YOUR_PATH+"pandas.log"
+     tmp=df.to_string()
+     OBJ_file_write_avg = open(filepath_, 'w')
+     OBJ_file_write_avg.write(tmp+"\n")
+     OBJ_file_write_avg.close()
+
+   if (FLAG_=="RH"):
+     pd.DataFrame({'Hum':100}, index=['Hum'])
+     ax = df.plot()
+     ax.grid()
+     ratio = 0.4
+     xleft, xright = ax.get_xlim()
+     ybottom, ytop = ax.get_ylim()
+     ax.set_aspect(abs((xright-xleft)/(ybottom-ytop))*ratio)
+
+     t0_= '2019-06-01'
+     now = datetime.utcnow().strftime('%Y-%m-%d')
+     fig = ax.get_figure()
+
+     ax.set(xlabel="Date",
+     ylabel=" Relative Humidity % ",
+     title="Milano Scalo Romana - Relative Humidity %\n 0%100%(20%80% 3% @25C,0%20% 80%100% @25C 5%)");
+
+     now = datetime.utcnow().strftime('%Y%m')
+     filename=YOUR_PATH+"/PM/data/RH_"+now+".png"
+     fig.savefig(filename)
+
+     filepath_ = YOUR_PATH+"pandas.log"
+     tmp=df.to_string()
+     OBJ_file_write_avg = open(filepath_, 'a')
+     OBJ_file_write_avg.write(tmp+"\n")
+     OBJ_file_write_avg.close()
+
+   if (FLAG_=="TEMP"):
+     pd.DataFrame({'TEMP':100}, index=['TEMP'])
+     ax = df.plot()
+     ax.grid()
+     ratio = 0.4
+     xleft, xright = ax.get_xlim()
+     ybottom, ytop = ax.get_ylim()
+     ax.set_aspect(abs((xright-xleft)/(ybottom-ytop))*ratio)
+
+     t0_= '2019-06-01'
+     now = datetime.utcnow().strftime('%Y-%m-%d')
+     fig = ax.get_figure()
+
+     ax.set(xlabel="Date",
+     ylabel=" Temperature C ",
+     title="Milano Scalo Romana - Temperature C \n -30  70C (0  65C 1.0C)");
+
+     now = datetime.utcnow().strftime('%Y%m')
+     filename=YOUR_PATH+"/PM/data/TEMP_"+now+".png"
+     fig.savefig(filename)
+
+     filepath_ = YOUR_PATH+"pandas.log"
+     tmp=df.to_string()
+     OBJ_file_write_avg = open(filepath_, 'a')
+     OBJ_file_write_avg.write(tmp+"\n")
+     OBJ_file_write_avg.close()
+
+Str_Anno = datetime.utcnow().strftime('%Y')
+filepath_ = YOUR_PATH+"/PM/data/MILANO_SCALO_ROMANA_PARTICULATE_MATTER_annual_series_"+ Str_Anno+".csv"
+PLOT(filepath_,"#FCUB","PM")
+PLOT(filepath_,"#FCUB","RH")
+PLOT(filepath_,"#FCUB","TEMP")
+