Working with datetime python module.

This module is very good and I worked with this issue by using MySQL and python.
The version of python I used is 2.7 under Fedora distro.
You can take a look at the official page.
I use the pip and not the DNF fedora Linux tool.

[root@localhost lucru]# pip install datetime
Collecting datetime
Downloading DateTime-4.1.1.zip (66kB)
100% |████████████████████████████████| 71kB 703kB/s 
Collecting zope.interface (from datetime)
Downloading zope.interface-4.3.3.tar.gz (150kB)
100% |████████████████████████████████| 153kB 2.2MB/s 
Collecting pytz (from datetime)
Downloading pytz-2016.10-py2.py3-none-any.whl (483kB)
100% |████████████████████████████████| 491kB 2.4MB/s 
Requirement already satisfied: setuptools in /usr/lib/python2.7/site-packages (from zope.interface->datetime)
Installing collected packages: zope.interface, pytz, datetime
Running setup.py install for zope.interface ... done
Running setup.py install for datetime ... done
Successfully installed datetime-4.1.1 pytz-2016.10 zope.interface-4.3.3

I solve this problem:

• conversion using the lambda function
  

  parser.add_argument('date', type=lambda s: datetime.datetime.strptime(s, '%Y-%m-%d'))

• solve last day
  

  datetime.datetime.strptime(new_value, '%Y-%m-%d %H:%M:%S')-timedelta(days=1)

• print the today date
  

  print date.today()

• show date using an explicit format string
  

  today=date.today()
  today.strftime("%A %d. %B %Y")
  'Sunday 05. March 2017'
  

• using epoch issue [1]
  

  from datetime import datetime
  now_epoch = (datetime.utcnow() - datetime(1970, 1, 1)).total_seconds()
  datetime.utcfromtimestamp(now_epoch)
  datetime.datetime(2017, 3, 4, 22, 35, 13, 463409)
  datetime.fromtimestamp(now_epoch)
  datetime.datetime(2017, 3, 5, 0, 35, 13, 463409)
  import pytz
  datetime.fromtimestamp(now_epoch, pytz.utc)
  datetime.datetime(2017, 3, 4, 22, 35, 13, 463409, tzinfo=)
  

[1] The Unix epoch is the time 00:00:00 UTC on 1 January 1970. There is a problem with this definition, in that UTC did not exist in its current form until 1972;

Using pygeoip and maxmin database.

I try to locate one IP using the databases from maxmind website and is not good for me.
The database records show me the output from country area.
I read the docs from here.
This is the python script I used:

#wget -N -q http://geolite.maxmind.com/download/geoip/database/GeoLiteCity.dat.gz
import pygeoip 
gip = pygeoip.GeoIP('GeoLiteCity.dat')
rec = gip.record_by_addr('___________________')
for key,val in rec.items():
    print "%s: %s" %(key,val)

Linux: OpenCV and using Lucas-Kanade Optical Flow function.

Fist I install OpenCV python module and I try using with Fedora 25.
I used python 2.7 version.

[root@localhost mythcat]# dnf install opencv-python.x86_64 
Last metadata expiration check: 0:21:12 ago on Sat Feb 25 23:26:59 2017.
Dependencies resolved.
================================================================================
 Package              Arch          Version                Repository      Size
================================================================================
Installing:
 opencv               x86_64        3.1.0-8.fc25           fedora         1.8 M
 opencv-python        x86_64        3.1.0-8.fc25           fedora         376 k
 python2-nose         noarch        1.3.7-11.fc25          updates        266 k
 python2-numpy        x86_64        1:1.11.2-1.fc25        fedora         3.2 M

Transaction Summary
================================================================================
Install  4 Packages

Total download size: 5.6 M
Installed size: 29 M
Is this ok [y/N]: y
Downloading Packages:
(1/4): opencv-python-3.1.0-8.fc25.x86_64.rpm    855 kB/s | 376 kB     00:00    
(2/4): opencv-3.1.0-8.fc25.x86_64.rpm           1.9 MB/s | 1.8 MB     00:00    
(3/4): python2-nose-1.3.7-11.fc25.noarch.rpm    543 kB/s | 266 kB     00:00    
(4/4): python2-numpy-1.11.2-1.fc25.x86_64.rpm   2.8 MB/s | 3.2 MB     00:01    
--------------------------------------------------------------------------------
Total                                           1.8 MB/s | 5.6 MB     00:03     
Running transaction check
Transaction check succeeded.
Running transaction test
Transaction test succeeded.
Running transaction
  Installing  : python2-nose-1.3.7-11.fc25.noarch                           1/4 
  Installing  : python2-numpy-1:1.11.2-1.fc25.x86_64                        2/4 
  Installing  : opencv-3.1.0-8.fc25.x86_64                                  3/4 
  Installing  : opencv-python-3.1.0-8.fc25.x86_64                           4/4 
  Verifying   : opencv-python-3.1.0-8.fc25.x86_64                           1/4 
  Verifying   : opencv-3.1.0-8.fc25.x86_64                                  2/4 
  Verifying   : python2-numpy-1:1.11.2-1.fc25.x86_64                        3/4 
  Verifying   : python2-nose-1.3.7-11.fc25.noarch                           4/4 

Installed:
  opencv.x86_64 3.1.0-8.fc25            opencv-python.x86_64 3.1.0-8.fc25       
  python2-nose.noarch 1.3.7-11.fc25     python2-numpy.x86_64 1:1.11.2-1.fc25    

Complete!
[root@localhost mythcat]# 

This is my test script with opencv to detect flow using Lucas-Kanade Optical Flow function.
This tracks some points in a black and white video.
First you need:
- one black and white video;
- not mp4 file type file;
- the color args need to be under 4 ( see is 3);
- I used this video:
I used cv2.goodFeaturesToTrack().
We take the first frame, detect some Shi-Tomasi corner points in it, then we iteratively track those points using Lucas-Kanade optical flow.
The function cv2.calcOpticalFlowPyrLK() we pass the previous frame, previous points and next frame.
The returns next points along with some status numbers which has a value of 1 if next point is found, else zero.
That iteratively pass these next points as previous points in next step.
See the code below:

import numpy as np
import cv2

cap = cv2.VideoCapture('candle')

# params for ShiTomasi corner detection
feature_params = dict( maxCorners = 77,
                       qualityLevel = 0.3,
                       minDistance = 7,
                       blockSize = 7 )

# Parameters for lucas kanade optical flow
lk_params = dict( winSize  = (17,17),
                  maxLevel = 1,
                  criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

# Create some random colors
color = np.random.randint(0,255,(100,3))

# Take first frame and find corners in it
ret, old_frame = cap.read()
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)

# Create a mask image for drawing purposes
mask = np.zeros_like(old_frame)

while(1):
    ret,frame = cap.read()
    frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # calculate optical flow
    p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)

    # Select good points
    good_new = p1[st==1]
    good_old = p0[st==1]

    # draw the tracks
    for i,(new,old) in enumerate(zip(good_new,good_old)):
        a,b = new.ravel()
        c,d = old.ravel()
        mask = cv2.line(mask, (a,b),(c,d), color[i].tolist(), 2)
        frame = cv2.circle(frame,(a,b),5,color[i].tolist(),-1)
    img = cv2.add(frame,mask)

    cv2.imshow('frame',img)
    k = cv2.waitKey(30) & 0xff
    if k == 27:
        break

    # Now update the previous frame and previous points
    old_gray = frame_gray.copy()
    p0 = good_new.reshape(-1,1,2)

cv2.destroyAllWindows()
cap.release()

The output of this file is: