Python Var Global dan Telegram RX TX

import time, datetime
import RPi.GPIO as GPIO
import telepot
from telepot.loop import MessageLoop
import os
import sys
from picamera import PiCamera
from time import sleep
from sys import argv
import gps
import requests

GPIO.setwarnings(False)
GPIO.setmode(GPIO.BCM)

relay = 20
door1 = 21
door2 = 16
door3 = 12
door4 = 05

GPIO.setup(door1, GPIO.IN, pull_up_down=GPIO.PUD_UP)
GPIO.setup(door2, GPIO.IN, pull_up_down=GPIO.PUD_UP)
GPIO.setup(door3, GPIO.IN, pull_up_down=GPIO.PUD_UP)
GPIO.setup(door4, GPIO.IN, pull_up_down=GPIO.PUD_UP)

anow = datetime.datetime.now()
camera = PiCamera()
GPIO.setup(relay, GPIO.OUT)
GPIO.output(relay, 0) #Off initially

session = gps.gps("localhost", "2947")
session.stream(gps.WATCH_ENABLE | gps.WATCH_NEWSTYLE)

def action(msg):
    global out
    rep = session.next()
    print str(out)+"###"
    chat_id = msg['chat']['id']
    command = msg['text']
    print 'Received: %s' % command
    try :
        if command == 'lokasi'  and out==0:
            (rep["class"] == "TPV")
            addr=str(rep.lat) + "," + str(rep.lon)
            print(addr)
            URI="https://maps.google.com?q="+addr
            print(URI)
            telegram_bot.sendMessage (chat_id, text=URI)
            print "sukses terkirim"                         
            #os.system('curl -s -X POST https://api.telegram.org/bot763220421:AAHt3f2V5qnJ1sjSg2kRva1uxL4ytt5df6Q/sendMessage -d chat_id=647876166 -d text="'+URI+'"') #% (URI)
                           
                       
        if 'hidupkan' in command and out==0:
            out=0
            message = "hidupkan"
            if 'mesin' in command:
                message = message + "mesin"
                GPIO.output(relay, 0)
                os.system('curl -s -X POST https://api.telegram.org/bot841400617:AAGCmnMJlPrdVK5cvlI2ZHdgri6Pp7jUzes/sendMessage -d chat_id=444460946 -d text="Mesin hidup"')
               
        if 'matikan' in command and out==0:
            out=0
            message = "matikan"
            if 'mesin' in command:
                message = message + "mesin "
                GPIO.output(relay, 1)
                os.system('curl -s -X POST https://api.telegram.org/bot841400617:AAGCmnMJlPrdVK5cvlI2ZHdgri6Pp7jUzes/sendMessage -d chat_id=444460946 -d text="Mesin mati"')
               
        if command == 'foto'  and out==0:
            camera.start_preview()
            sleep(3)
            camera.capture('/home/pi/data/image.jpg')
            camera.stop_preview()
            telegram_bot.sendPhoto(chat_id=chat_id, photo=open('/home/pi/data/image.jpg', 'rb'))
            print "sukses terkirim"
            out=0
           
        if command == 'video'  and out==0:
            camera.start_preview()
            camera.start_recording('/home/pi/data/video.h264')
            sleep(5)
            camera.stop_recording()
            camera.stop_preview()
            telegram_bot.sendVideo(chat_id, video=open('/home/pi/data/video.h264'))
            print "sukses terkirim"
            out=0

     
       
        if command == 'tidak aktif':
            out=1
            print "Sistem tidak Aktif"

        if command == 'aktif':
            out=0
            print "Sistem Aktif"
            os.system('curl -s -X POST https://api.telegram.org/bot841400617:AAGCmnMJlPrdVK5cvlI2ZHdgri6Pp7jUzes/sendMessage -d chat_id=444460946 -d text="Sistem diaktifkan"')

         
    except Exception as e :
        print("Got exception " + str(e))
           
        return out


out=212
telegram_bot = telepot.Bot('841400617:AAGCmnMJlPrdVK5cvlI2ZHdgri6Pp7jUzes')
print (telegram_bot.getMe())
out=MessageLoop(telegram_bot, action).run_as_thread()
print 'Up and Running....'

while True:
    global out
    #print str(out)+"@@@"
    if out==0:
        if GPIO.input(door1):
           print("Pintu-1 Terbuka")
           os.system('curl -s -X POST https://api.telegram.org/bot841400617:AAGCmnMJlPrdVK5cvlI2ZHdgri6Pp7jUzes/sendMessage -d chat_id=444460946 -d text="Pintu-1 Terbuka"')
           time.sleep(1)
        if GPIO.input(door1) == False:
           #print("Pintu Tertutup")
           time.sleep(1)

        if GPIO.input(door2):
           print("Pintu-2 Terbuka")
           os.system('curl -s -X POST https://api.telegram.org/bot841400617:AAGCmnMJlPrdVK5cvlI2ZHdgri6Pp7jUzes/sendMessage -d chat_id=444460946 -d text="Pintu-2 Terbuka"')
           time.sleep(1)
        if GPIO.input(door2) == False:
           #print("Pintu Tertutup")
           time.sleep(1)

        if GPIO.input(door3):
           print("Pintu-3 Terbuka")
           os.system('curl -s -X POST https://api.telegram.org/bot841400617:AAGCmnMJlPrdVK5cvlI2ZHdgri6Pp7jUzes/sendMessage -d chat_id=444460946 -d text="Pintu-3 Terbuka"')
           time.sleep(1)
        if GPIO.input(door3) == False:
           #print("Pintu Tertutup")
           time.sleep(1)

        if GPIO.input(door4):
           print("Pintu-4 Terbuka")
           os.system('curl -s -X POST https://api.telegram.org/bot841400617:AAGCmnMJlPrdVK5cvlI2ZHdgri6Pp7jUzes/sendMessage -d chat_id=444460946 -d text="Pintu-4 Terbuka"')
           time.sleep(1)
        if GPIO.input(door4) == False:
           #print("Pintu Tertutup")
           time.sleep(1)

       

while 1:
    time.sleep(10)
   

Python Linier Regressi

Misalkan ini contoh datalatih:

No	Age	Sex	Job	Housing	Saving accounts	Checking account	Credit amount	Duration	Purpose	Risk
0	67	male	2	own	NA	little	1169	6	radio/TV	good
1	22	female	2	own	little	moderate	5951	48	radio/TV	bad
2	49	male	1	own	little	NA	2096	12	education	good
3	45	male	2	free	little	little	7882	42	furniture/equipment	good
4	53	male	2	free	little	little	4870	24	car	bad
5	35	male	1	free	NA	NA	9055	36	education	good
6	53	male	2	own	quite rich	NA	2835	24	furniture/equipment	good
7	35	male	3	rent	little	moderate	6948	36	car	good
8	61	male	1	own	rich	NA	3059	12	radio/TV	good
9	28	male	3	own	little	moderate	5234	30	car	bad
10	25	female	2	rent	little	moderate	1295	12	car	bad
11	24	female	2	rent	little	little	4308	48	business	bad
12	22	female	2	own	little	moderate	1567	12	radio/TV	good
13	60	male	1	own	little	little	1199	24	car	bad
14	28	female	2	rent	little	little	1403	15	car	good
15	32	female	1	own	moderate	little	1282	24	radio/TV	bad
16	53	male	2	own	NA	NA	2424	24	radio/TV	good
17	25	male	2	own	NA	little	8072	30	business	good
18	44	female	3	free	little	moderate	12579	24	car	bad
19	31	male	2	own	quite rich	NA	3430	24	radio/TV	good
20	48	male	2	own	little	NA	2134	9	car	good
21	44	male	2	rent	quite rich	little	2647	6	radio/TV	good
22	48	male	1	rent	little	little	2241	10	car	good
23	44	male	2	own	moderate	moderate	1804	12	car	good
24	26	male	2	own	NA	NA	2069	10	furniture/equipment	good
25	36	male	1	own	little	little	1374	6	furniture/equipment	good
26	39	male	1	own	little	NA	426	6	radio/TV	good
27	42	female	2	rent	rich	rich	409	12	radio/TV	good
28	34	male	2	own	little	moderate	2415	7	radio/TV	good
29	63	male	2	own	little	little	6836	60	business	bad

In [30]:

import pandas as pd
import numpy as np
from sklearn import preprocessing
import matplotlib.pyplot as plt 
plt.rc("font", size=14)
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import seaborn as sns
sns.set(style="white")
sns.set(style="whitegrid", color_codes=True)

In [31]:

def display(obj):
    cv2.imshow("show",obj)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    b=edges.shape[0]
    c=edges.shape[1]

    #for i in range(b): 
    #    for j in range(c):
    #        D=edges[i][j]
    #        #print(str(i)+","+str(j)+"="+str(D))
    return img   

def myresize(obj,b,c):
    dim = (b, c)
    resized = cv2.resize(obj, dim, interpolation = cv2.INTER_AREA)
    return resized

def resshape(img):
    width = int(img.shape[0])
    height = int(img.shape[1])
    wh=width * height
    dim = (wh, 1)
    resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
    return resized

def uk(obj):
    width = int(obj.shape[0])
    height = int(obj.shape[1])
    print (width ,' x ' , height)
    return (width ,' x ' , height)

def uk2(obj):
    width =len(obj[0])
    height = len(obj[1])
    print (width ,' x ' , height)
    return (width ,' x ' , height)

def uk3(obj):
    s =obj.shape()
    return s

def model(obj):
    lap=type(obj)
    print(lap)
    return lap

In [32]:

data=pd.read_csv('GD\\mycsv_test.csv',header=0) #pandas.core.frame.DataFrame 4521  x  17
data=data.dropna()
print(data.columns)
print(list(data.columns))
#['age;"job";"marital";"education";"default";"balance";"housing";"loan";"contact";"day";"month";"duration";"campaign";"pdays";"previous";"poutcome";"y"']
data.head()

Index(['No', 'Age', 'Sex', 'Job', 'Housing', 'Saving accounts',
       'Checking account', 'Credit amount', 'Duration', 'Purpose', 'Risk'],
      dtype='object')
['No', 'Age', 'Sex', 'Job', 'Housing', 'Saving accounts', 'Checking account', 'Credit amount', 'Duration', 'Purpose', 'Risk']

Out[32]:

	No	Age	Sex	Job	Housing	Saving accounts	Checking account	Credit amount	Duration	Purpose	Risk
1	1	22	female	2	own	little	moderate	5951	48	radio/TV	bad
3	3	45	male	2	free	little	little	7882	42	furniture/equipment	good
4	4	53	male	2	free	little	little	4870	24	car	bad
7	7	35	male	3	rent	little	moderate	6948	36	car	good
9	9	28	male	3	own	little	moderate	5234	30	car	bad

In [50]:

model(data)
list(data)

<class 'pandas.core.frame.DataFrame'>

Out[50]:

['No',
 'Age',
 'Sex',
 'Job',
 'Housing',
 'Saving accounts',
 'Checking account',
 'Credit amount',
 'Duration',
 'Purpose',
 'Risk']

In [37]:

data['Risk'].unique()

Out[37]:

array(['bad', 'good'], dtype=object)

In [19]:

#data['education']=np.where(data['education'] =='basic.9y', 'Basic', data['education'])
#data['education']=np.where(data['education'] =='basic.6y', 'Basic', data['education'])
#data['education']=np.where(data['education'] =='basic.4y', 'Basic', data['education'])
#data['y'].value_counts()

In [38]:

sns.countplot(x='Risk',data=data,palette='hls')
plt.show()
plt.savefig('count_plot')

<Figure size 432x288 with 0 Axes>

In [39]:

sns.countplot(x='Purpose',data=data,palette='hls')
plt.show()
plt.savefig('count_plot')

<Figure size 432x288 with 0 Axes>

In [40]:

jno= len(data[data['Risk']=='good'])
jyes = len(data[data['Risk']=='bad'])
total=jno + jyes
pct_of_no_sub = jno/(total)
print("percentage of no subscription is", pct_of_no_sub*100)
pct_of_sub = jyes/(total)
print("percentage of subscription", pct_of_sub*100)

percentage of no subscription is 55.74712643678161
percentage of subscription 44.252873563218394

In [42]:

data.groupby('Risk').mean()

Out[42]:

	No	Age	Job	Credit amount	Duration
Risk
bad	515.718615	34.147186	1.913420	3881.090909	25.445887
good	475.903780	35.477663	1.845361	2800.594502	18.079038

In [43]:

data.groupby('Purpose').mean()

Out[43]:

	No	Age	Job	Credit amount	Duration
Purpose
business	467.075472	35.188679	1.830189	4337.207547	28.452830
car	501.000000	36.861272	1.919075	3593.416185	20.381503
domestic appliances	452.333333	35.500000	1.833333	1333.500000	17.666667
education	518.607143	35.571429	1.928571	3394.107143	22.428571
furniture/equipment	492.485981	32.411215	1.934579	3111.028037	20.457944
radio/TV	500.765152	32.946970	1.787879	2265.916667	19.916667
repairs	473.714286	40.857143	1.285714	2821.142857	18.642857
vacation/others	391.888889	41.333333	2.666667	9495.444444	32.444444

In [44]:

data.groupby('Sex').mean()

Out[44]:

	No	Age	Job	Credit amount	Duration
Sex
female	519.226190	32.720238	1.803571	2937.202381	19.916667
male	481.324859	35.918079	1.909605	3440.833333	22.014124

In [45]:

%matplotlib inline
pd.crosstab(data.Sex,data.Risk).plot(kind='bar')
plt.title('Purchase Frequency for Job Title')
plt.xlabel('gender')
plt.ylabel('Frequency of Purchase')
plt.savefig('Gender of Purchase')

In [46]:

%matplotlib inline
pd.crosstab(data.Housing,data.Risk).plot(kind='bar')
plt.title('Purchase Frequency for Housing Title')
plt.xlabel('Housing')
plt.ylabel('Frequency of Purchase')
plt.savefig('purchase_fre_Housing')

In [52]:

%matplotlib inline
pd.crosstab(data.Housing,data.Risk).plot(kind='bar')
plt.title('Purchase Frequency for Credit amount Title')
plt.xlabel('Credit amount')
plt.ylabel('Frequency of Purchase')
plt.savefig('Purchase_Credit amount')

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