OpenGL texture compression library for Ubuntu for better graphics experience

If you are using Intel graphics chip for your graphics needs on Ubuntu then probably you need to install OpenGL texture compression library to gain proper graphics performance and quality out of your graphics card. By default this library is not available on Ubuntu due to license, IP or distribution restrictions. The library provides the following OpenGL extensions under Mesa3D OpenGL implementation:

GL_EXT_texture_compression_s3tc
GL_S3_s3tc

These OpenGL extensions provide support for efficient graphics texture uploading to your graphics processing unit making graphics applications utilize its memory bandwidths efficiently and productively to deliver best performance. Consequently, almost all games and applications make use of these OpenGL extensions. The library can be compiled from the source code under a GNU Linux distribution of choice easily. For Ubuntu based distributions the pre compiled and ready to be installed 32 Bit and 64 Bit libraries packages can be downloaded from the following link:

http://onthim.blogspot.com/p/onthim-downloads.html
 

Cheers,
Imam

Free secure file sharing using Snap Image Share

Sometimes we want to share files with our friends, colleagues and mates but also want to limit the access someway like how long the file will be available, number of downloads for the file, and so on. We can easily share files on Facebook and Instagram too, but to share files on these platforms the people you are sharing files to also need to have accounts on these platforms. Snap Image Share is a new free file sharing platform by SALHOS Engineering with which you can share as many files as you want with file size up-to 100 MB without any accounts or signup and also limit file sharing access like availability and downloads. Just like Google Drive and Dropbox file sharing services people do not need any account in Snap Share Image to download images. So, start sharing files now with Snap Image Share:

Snap Image Share

Cheers!
Imam

Use Python objects to process and store csv file data

In Python data from csv files can be easily loaded and processed for analysis or automating other jobs. In this tutorial, we will see how data from a csv file can be stored easily in Python objects and spit out the processed data from an object as necessary. For this exercise, we assume we have a csv file containing temperature data for 28 days where the first column contains days records and the second column contains temperatures records. First we will load the csv file's temperatures data into an object and then show the data and temperature average from the object. After that, we find and store the differences in days temperature from the object to another object. Finally, we save the differences in days temperature data from the object to a csv file. A sample of the csv file contents is shown below:

1 27
2 25
3 23
4 30
5 19
6 20
7 13
8 30

Now, in Python first we will have to create an object for holding these data from the csv file. For this, in the Python object we need to have two lists, one for days records and the other one for temperatures records. Now, we define the Python object as below:

# define object for holding a set of temperature data

class TempData():

    def __init__(self):

        self.day = [] # list of days

        self.temp = [] # list of temperature

Now, we create two object instance variables of this type where one will hold temperatures data as in the csv file and the other one will hold temperature differences data.

csv_imported_data = TempData() # create object for holding csv file temperature data

processed_data = TempData() # create object for holding processed data

Now, we can open the csv file and load the csv columns data into the object lists for days and temperatures data.

csv_file = open('data.csv', 'r') # open the csv file for reading

reader = csv.reader(csv_file) # pass the file to csv reader object for extracting data

for row in reader: # get the row data from the csv file

    csv_imported_data.day.append(float(row[0])) # get the first column data

    csv_imported_data.temp.append(float(row[1])) # get the second column data

csv_file.close() # close the csv file

Now, lets show the temperature data from the object:

# print temperature data from the temperature object

for i in range(len(csv_imported_data.day)):

    print(csv_imported_data.day[i], csv_imported_data.temp[i])

Now, lets show the average temperature from the object's temperature data:

# show average temperature from the temperature object

print('Average temperature for', len(csv_imported_data.day), 'days was', sum(csv_imported_data.temp)/len(csv_imported_data.temp))

Now, lets find the temperature difference from the object's temperature data and store difference data into another object of same type:

# find difference between temperatures and store in temperature data object

for i in range(len(csv_imported_data.day)):

    if (i == 0): # no difference for first data

        processed_data.day.append(csv_imported_data.day[i])

        processed_data.temp.append(0)

    else:

        processed_data.day.append(abs(csv_imported_data.day[i] - csv_imported_data.day[i-1]))

        processed_data.temp.append(abs(csv_imported_data.temp[i] - csv_imported_data.temp[i-1]))

Now, we show the temperature difference data from the object:

# print temperature difference data from the temperature object

for i in range(len(processed_data.day)):

    print(processed_data.day[i], processed_data.temp[i])

Finally, we save the temperature difference data from the object to a new csv file so that we can retrieve the temperature difference data without running the Python script again:

csv_file = open('processed_temp_data.csv', 'w', newline='') # create a new csv file for writing

csv_writer = csv.writer(csv_file) # pass the file to csv writer object for writing data

for row in zip(processed_data.day, processed_data.temp): # create the row data from the the temperature object

    csv_writer.writerow(row)

csv_file.close()

Download the full example code and CSV files:

csv_process_data.zip

Cheers!
Imam