Python Qt5 : Use QStandardItem with Images.

This tutorial show you how to use QStandardItem with Images.

The source code is simple to understand.

import sys
from PyQt5.QtWidgets import QApplication, QMainWindow, QTreeView
from PyQt5.QtCore import  Qt
from PyQt5.QtGui import QFont, QColor, QImage, QStandardItemModel, QStandardItem

class ItemImage(QStandardItem):
    def __init__(self, txt='', image_path='', set_bold=False, color=QColor(0, 0, 0)):
        super().__init__()

        self.setEditable(False)
        self.setForeground(color)
        self.setText(txt)

        if image_path:
            image = QImage(image_path)
            self.setData(image, Qt.DecorationRole)

class MyApp(QMainWindow):
    def __init__(self):
        super().__init__()
        self.resize(1200, 1200)

        treeView = QTreeView()
        treeView.setHeaderHidden(True)
        treeView.header().setStretchLastSection(True)

        treeModel = QStandardItemModel()
        rootNode = treeModel.invisibleRootItem()

        robots = ItemImage('Robots', '', set_bold=True)

        aaa = ItemImage('aaa.jpg', 'aaa.jpg', 14)
        robots.appendRow(aaa)

        bbb = ItemImage('bbb.jpg', 'bbb.jpg', 16)
        robots.appendRow(bbb)

        robots2 = ItemImage('Robots 2', '', set_bold=False)

        aaa = ItemImage('ccc.png', 'ccc.png', 14)
        robots2.appendRow(aaa)

        bbb = ItemImage('ddd.jpg', 'ddd.jpg', 16)
        robots2.appendRow(bbb)

        rootNode.appendRow(robots)
        rootNode.appendRow(robots2)
        treeView.setModel(treeModel)
        treeView.expandAll()

        self.setCentralWidget(treeView)

app = QApplication(sys.argv)        
demo = MyApp()
demo.show()
sys.exit(app.exec_())

The result is this:

Python 3.8.5 : A sphere in Cartesian coordinates - part 001.

I like the equation of a sphere of radius R centered at the origin is given in Cartesian coordinates:

x*x + y*y + z*z = r*r

It is one of the first elements that helped me better understand mathematics and later the dynamics and theory of electromagnetic fields.

I did not find a graphical representation using python as accurately as possible without eliminating the discretion of the range from -1 and 1 and radius * radius = 1.

The main reason is the plot_surface from matplotlib python package.

This is output of my script:

[mythcat@desk ~]$ python sphere_xyz.py 
[-1.         -0.91666667 -0.83333333 -0.75       -0.66666667 -0.58333333
 -0.5        -0.41666667 -0.33333333 -0.25       -0.16666667 -0.08333333
  0.          0.08333333  0.16666667  0.25        0.33333333  0.41666667
  0.5         0.58333333  0.66666667  0.75        0.83333333  0.91666667
  1.        ]
sphere_xyz.py:7: RuntimeWarning: invalid value encountered in sqrt
  return np.sqrt(1-x**2 - y**2)
sphere_xyz.py:18: UserWarning: Z contains NaN values. This may result in rendering artifacts.
  surface1 = ax.plot_surface(X2, Y2, -Z2,rstride=1, cstride=1, linewidth=0,antialiased=True)
sphere_xyz.py:19: UserWarning: Z contains NaN values. This may result in rendering artifacts.
  surface2 = ax.plot_surface(X2, Y2, Z2,rstride=1, cstride=1, linewidth=0,antialiased=True)

The image result is this:

The source code is this:

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LightSource

#@np.vectorize
def solve_Z2(x,y):
    return np.sqrt(1-x**2 - y**2)

fig = plt.figure()
ax = fig.gca(projection='3d')

xrange2 = np.linspace(-1.0, 1.0, 25)
yrange2 = np.linspace(-1.0, 1.0, 25)
print(xrange2)
X2, Y2 = np.meshgrid(xrange2, yrange2)
Z2 = solve_Z2(X2, Y2)

surface1 = ax.plot_surface(X2, Y2, -Z2,rstride=1, cstride=1, linewidth=0,antialiased=True)
surface2 = ax.plot_surface(X2, Y2, Z2,rstride=1, cstride=1, linewidth=0,antialiased=True)

plt.show()

Python 3.8.5 : Linked List - part 001.

In computer science, a linked list is a linear collection of data elements whose order is not given by their physical placement in memory. see wikipedia.org.

In this tutorial I will show you how these linked list works and how can build with python programming language.
Let's start with some basic elements:

• Linked List is a linear data structure;
• Linked List are not array;
• Linked List are not stored at a contiguous location because use pointes;
• Each element use a linked pointe;
• Linked List has a dynamic size;
• Linked List use operations like insertion and deletion for each element;
• The access to elements is sequentially;
• Using reference to pointer foe each element needs extra memory space and is not cache friendly;

The Linked List consists of at least two parts: data and pointer to the next node.
The first element of the list is called the head.

The last node has a reference to null.
The each element named node of the list has a similar structure like Linked List: data node and pointer to the next node.
The data from Linked list is represent like a sequence.

A number of operations are required to use the Linked List.

The basic operations supported by a list can be:

• insertion - adds an element at the beginning of the list;
• deletion - deletes an element at the beginning of the list;
• display - displays the complete list;
• search - searches an element using the given key;
• delete - deletes an element using the given key.

The Advantages of Linked List elements can be easily inserted or removed without reallocation or reorganization of the entire structure because the data items need not be stored contiguously in memory or on disk using operations.

The Linked List structure allows several ways to link nodes resulting in a number of types of Linked List:

• Simple linked list;
• Doubly linked list;
• Multiply linked list;
• Circular linked list;
• Sentinel nodes;
• Empty lists;
• Hash linking;
• List handles;
• Combining alternatives;

Let's start with first source code for the first type of Linked List named Simple linked list.

Python 3.8.5 (default, Aug 12 2020, 00:00:00) 
[GCC 10.2.1 20200723 (Red Hat 10.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> # create a node class 
>>> class Node:
...     # first initialise of the node object
...     def __init__(self, data):
...             # assign the node data 
...             self.data = data
...             # initialize next element as null
...             self.next = None
... 
>>> # create a simple linked list
>>> class Simple_LinkedList:
...     # first initialize of element named head
...     def __init__(self):
...             self.head = None
>>> # use the list 
>>> if __name__=='__main__': 
...     simple_list = Simple_LinkedList()
...     simple_list.head = Node(1)
...     second = Node(2)
...     third = Node(3)
...     #link first node with the second
...     simple_list.head.next = second
...     #link second  node with the third
...     second.next = third
...     # now the list is linked as a simple Linked List

This source code don't include any basic operations.

If you put this source code into python script and run it you will see this:

[mythcat@desk ~]$ python simple_LinkedList.py 
<__main__.Node object at 0x7f35163e1ac0>
<__main__.Node object at 0x7f351638b0a0>
<__main__.Node object at 0x7f351638b130>

I create a basic operation for this simple Linked List to display the content of this list:

# create a simple linked list with a basic diplay operation
class Simple_LinkedList:
    # first initialize of element named head
    def __init__(self):
            self.head = None
    # use the basic operation for display
    def display_Simple_LinkedList(self): 
        points_to_node = self.head
	#will traverse the list to the last node. 
        while (points_to_node): 
            # print data linked to points_to_node
            print (points_to_node.data) 
            # print next node linked to points_to_node
            points_to_node = points_to_node.next

I run with this new python source code and result is this:

[mythcat@desk ~]$ python simple_LinkedList_basic_display.py 
1
2
3 

Python 3.8.5 : Get Sentinel-3 satellite data from Eutelsat.

The tutorial for today is about Eutelsat satellites.
I used Sentinel-3 with these features:

• Instrument: SLSTR;
• Mode: EO;
• Satellite: Sentinel-3

You need to install xarray and netcdf4 python packages:

[mythcat@desk TLauncher]$ pip install xarray
...
[mythcat@desk ~]$ pip install netcdf4

Defaulting to user installation because normal site-packages is not writeable
Collecting netcdf4
  Downloading netCDF4-1.5.4-cp38-cp38-manylinux1_x86_64.whl (4.3 MB)
     |████████████████████████████████| 4.3 MB 649 kB/s 
Collecting cftime
  Downloading cftime-1.2.1-cp38-cp38-manylinux1_x86_64.whl (271 kB)
     |████████████████████████████████| 271 kB 30.5 MB/s 
Requirement already satisfied: numpy>=1.9 in /usr/lib64/python3.8/site-packages (from netcdf4) (1.18.4)
Installing collected packages: cftime, netcdf4
Successfully installed cftime-1.2.1 netcdf4-1.5.4 

You need to get data from the official webpage.

After I download the nc file with all data I used this source code:

Python 3.8.5 (default, Aug 12 2020, 00:00:00) 
[GCC 10.2.1 20200723 (Red Hat 10.2.1-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import xarray as xr
>>> dir = '/home/mythcat/Downloads/'
>>> file_xr = xr.open_dataset(dir+'FRP_in.nc')
>>> file_xr
<xarray .dataset="">
Dimensions:                 (columns: 1500, fires: 23, rows: 2000)
Dimensions without coordinates: columns, fires, rows
Data variables:
    i                       (fires) int16 ...
    j                       (fires) int32 ...
    time                    (fires) datetime64[ns] ...
    latitude                (fires) float64 ...
    longitude               (fires) float64 ...
    FRP_MWIR                (fires) float64 ...
    FRP_uncertainty_MWIR    (fires) float64 ...
    transmittance_MWIR      (fires) float64 ...
    FRP_SWIR                (fires) float64 ...
    FRP_uncertainty_SWIR    (fires) float64 ...
    FLAG_SWIR_SAA           (fires) int16 ...
    transmittance_SWIR      (fires) float64 ...
    confidence              (fires) float64 ...
    classification          (fires) uint8 ...
    S7_Fire_pixel_radiance  (fires) float32 ...
    F1_Fire_pixel_radiance  (fires) float32 ...
    used_channel            (fires) uint8 ...
    Radiance_window         (fires) float32 ...
    Glint_angle             (fires) float64 ...
    IFOV_area               (fires) float64 ...
    TCWV                    (fires) float64 ...
    n_window                (fires) int16 ...
    n_water                 (fires) int16 ...
    n_cloud                 (fires) int16 ...
    n_SWIR_fire             (fires) float32 ...
    flags                   (rows, columns) uint32 ...
Attributes:
    title:                  SLSTR Level 2 Product, Fire Radiative Power measu...
    comment:                 
    netCDF_version:         4.2 of Jul  5 2012 17:07:43 $
    product_name:           S3B_SL_2_FRP____20200910T082906_20200910T083406_2...
    institution:            MAR
    source:                 IPF-SL-2-FRP 02.00
    history:                 
    references:             S3MPC ACR FRP 003 - i1r2 - SLSTR L2 Product Data ...
    contact:                ops@eumetsat.int
    creation_time:          2020-09-10T10:38:54Z
    resolution:             [ 1000 1000 ]
    absolute_orbit_number:  12385
    start_time:             2020-09-10T08:29:06.288252Z
    stop_time:              2020-09-10T08:34:06.277355Z
    track_offset:           998
    start_offset:           14032

Let's parse size by latitude and longitude:

>>> lat = file_xr['latitude']
>>> long = file_xr['longitude']
>>> lat , long 
(<xarray.DataArray 'latitude' (fires: 23)>
array([52.992509, 52.98967 , 48.447335, 44.00415 , 48.443263, 48.439204,
       48.438141, 48.434069, 48.430012, 48.430012, 43.99905 , 43.993966,
       43.996166, 43.991064, 43.991064, 43.985972, 43.986568, 43.981467,
       43.976379, 43.978613, 43.973495, 43.973495, 43.968419])
Dimensions without coordinates: fires
Attributes:
    long_name:      Latitude
    standard_name:  latitude
    units:          degrees_north
    valid_min:      -90.0
    valid_max:      90.0, 
array([38.424813, 38.441404, 29.594317, 26.774829, 29.607258, 29.620235,
       29.592402, 29.605285, 29.618236, 29.618236, 26.787083, 26.799272,
       26.77044 , 26.782656, 26.782656, 26.7949  , 26.769496, 26.781764,
       26.794036, 26.765122, 26.777358, 26.777358, 26.789596])
Dimensions without coordinates: fires
Attributes:
    long_name:      Longitude
    standard_name:  longitude
    units:          degrees_east
    valid_min:      -180.0
    valid_max:      180.0) 

Get FRP_SWIR and FRP_MWIR data from satelite:

>>> FRP_SWIR = file_xr['FRP_SWIR']
>>> FRP_SWIR 

array([-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,
       -1., -1., -1., -1., -1., -1., -1., -1., -1.])
Dimensions without coordinates: fires
Attributes:
    long_name:  Fire radiative power computed from SWIR channel (S6)
    units:      MW 
>>> FRP_MWIR = file_xr['FRP_MWIR']
>>> FRP_MWIR

array([ 10.290943,  11.447042, 179.555982,  84.84376 ,  48.277547,   9.320155,
        17.840467,  38.242334,  18.615514,  18.611452,  14.36118 ,   4.440371,
         3.06999 ,   5.008403,   5.005609,   4.452938,   8.228399,   8.442025,
         5.631591,   3.531036,   3.509205,   3.507225,   2.876292])
Dimensions without coordinates: fires
Attributes:
    long_name:  Fire radiative power computed from MWIR channels (S7 and F1)
    units:      MW