Encrypt Archives

Improvement of Pandas data processing performance using Multi-threading with the Queue (Another crossover of Space Stone, Reality Stone & Power Stone)

Posted on March 24, 2019September 23, 2019 by SatyakiDe in Data Science, function, member function, operating system, Pandas, Python

Today, we’ll discuss how to improve your panda’s data processing power using Multi-threading. Note that, we are not going to use any third party python package. Also, we’ll be using a couple of python scripts, which we’ve already discussed in our previous posts. Hence, this time, I won’t post them here.

Please refer the following scripts –

a. callClient.py
b. callRunServer.py
c. clsConfigServer.py
d. clsEnDec.py
e. clsFlask.py
f. clsL.py
g. clsParam.py
h. clsSerial.py
i. clsWeb.py

Please find the above scripts described here with details.

So, today, we’ll be looking into how the multi-threading really helps the application to gain some performance over others.

Let’s go through our existing old sample files –

And, we’ve four columns that are applicable for encryption. This file contains 10K records. That means the application will make 40K calls to the server for a different kind of encryption for each column.

Now, if you are going with the serial approach, which I’ve already discussed here, will take significant time for data processing. However, if we could club a few rows as one block & in this way we can create multiple blocks out of our data csv like this –

As you can see that blocks are marked with a different color. So, now if you send each block of data in parallel & send the data for encryption. Ideally, you will be able to process data much faster than the usual serial process. And, this what we would be looking for with the help of python’s multi-threading & queue. Without the queue, this program won’t be possible as the queue maintains the data & process integrity.

One more thing we would like to explain here. Whenever this application is sending the block of data. It will be posting that packed into a (key, value) dictionary randomly. Key will be the thread name. The reason, we’re not expecting data after process might arrive in some random order wrapped with the dictionary as well. Once the application received all the dictionary with dataframe with encrypted/decrypted data, the data will be rearranged based on the key & then joined back with the rest of the data.

Let’s see one sample way of sending & receiving random thread –

The left-hand side, the application is splitting the recordset into small chunks of a group. Once, those group created, using python multi-threading the application is now pushing them into the queue for the producer to produce the encrypted/decrypted value. Similar way, after processing the application will push the final product into the queue for consuming the final output.

This is the pictorial representation of dictionary ordering based on the key-value & then the application will extract the entire data to form the target csv file.

Let’s explore the script –

1. clsParallel.py (This script will consume the split csv files & send the data blocks in the form of the dictionary using multi-threading to the API for encryption in parallel. Hence, the name comes into the picture.)

import pandas as p
import clsWeb as cw
import datetime
from clsParam import clsParam as cf
import threading
from queue import Queue
import gc
import signal
import time
import os

# Declaring Global Variable
q = Queue()
m = Queue()
tLock = threading.Lock()
threads = []

fin_dict = {}
fin_dict_1 = {}
stopping = threading.Event()

# Disbling Warnings
def warn(*args, **kwargs):
    pass
import warnings
warnings.warn = warn

class clsParallel(object):
    def __init__(self):
        self.path = cf.config['PATH']
        self.EncryptMode = str(cf.config['ENCRYPT_MODE'])
        self.DecryptMode = str(cf.config['DECRYPT_MODE'])
        self.num_worker_threads = int(cf.config['NUM_OF_THREAD'])
        

    # Lookup Methods for Encryption
    def encrypt_acctNbr(self, row):
        # Declaring Local Variable
        en_AcctNbr = ''
        json_source_str = ''

        # Capturing essential values
        EncryptMode = self.EncryptMode
        lkp_acctNbr = row['Acct_Nbr']
        str_acct_nbr = str(lkp_acctNbr)
        fil_acct_nbr = str_acct_nbr.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_acct_nbr + '","dataTemplate":"subGrAcct_Nbr"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_acct_nbr)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            en_AcctNbr = x.getResponse(EncryptMode)
        else:
            en_AcctNbr = ''

        return en_AcctNbr

    def encrypt_Name(self, row):
        # Declaring Local Variable
        en_AcctName = ''

        # Capturing essential values
        EncryptMode = self.EncryptMode
        lkp_acctName = row['Name']
        str_acct_name = str(lkp_acctName)
        fil_acct_name = str_acct_name.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_acct_name + '","dataTemplate":"subGrName"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_acct_name)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            en_AcctName = x.getResponse(EncryptMode)
        else:
            en_AcctName = ''

        return en_AcctName

    def encrypt_Phone(self, row):
        # Declaring Local Variable
        en_Phone = ''

        # Capturing essential values
        EncryptMode = self.EncryptMode
        lkp_phone = row['Phone']
        str_phone = str(lkp_phone)
        fil_phone = str_phone.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_phone + '","dataTemplate":"subGrPhone"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_phone)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            en_Phone = x.getResponse(EncryptMode)
        else:
            en_Phone = ''

        return en_Phone

    def encrypt_Email(self, row):
        # Declaring Local Variable
        en_Email = ''

        # Capturing essential values
        EncryptMode = self.EncryptMode
        lkp_email = row['Email']
        str_email = str(lkp_email)
        fil_email = str_email.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_email + '","dataTemplate":"subGrEmail"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_email)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            en_Email = x.getResponse(EncryptMode)
        else:
            en_Email = ''

        return en_Email

    # Lookup Methods for Decryption
    def decrypt_acctNbr(self, row):
        # Declaring Local Variable
        de_AcctNbr = ''
        json_source_str = ''

        # Capturing essential values
        EncryptMode = self.DecryptMode
        lkp_acctNbr = row['Acct_Nbr']
        str_acct_nbr = str(lkp_acctNbr)
        fil_acct_nbr = str_acct_nbr.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_acct_nbr + '","dataTemplate":"subGrAcct_Nbr"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_acct_nbr)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            de_AcctNbr = x.getResponse(EncryptMode)
        else:
            de_AcctNbr = ''

        return de_AcctNbr

    def decrypt_Name(self, row):
        # Declaring Local Variable
        de_AcctName = ''

        # Capturing essential values
        EncryptMode = self.DecryptMode
        lkp_acctName = row['Name']
        str_acct_name = str(lkp_acctName)
        fil_acct_name = str_acct_name.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_acct_name + '","dataTemplate":"subGrName"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_acct_name)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            de_AcctName = x.getResponse(EncryptMode)
        else:
            de_AcctName = ''

        return de_AcctName

    def decrypt_Phone(self, row):
        # Declaring Local Variable
        de_Phone = ''

        # Capturing essential values
        EncryptMode = self.DecryptMode
        lkp_phone = row['Phone']
        str_phone = str(lkp_phone)
        fil_phone = str_phone.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_phone + '","dataTemplate":"subGrPhone"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_phone)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            de_Phone = x.getResponse(EncryptMode)
        else:
            de_Phone = ''

        return de_Phone

    def decrypt_Email(self, row):
        # Declaring Local Variable
        de_Email = ''

        # Capturing essential values
        EncryptMode = self.DecryptMode
        lkp_email = row['Email']
        str_email = str(lkp_email)
        fil_email = str_email.strip()

        # Forming JSON String for this field
        json_source_str = '{"dataGroup":"GrDet","data":"' + fil_email + '","dataTemplate":"subGrEmail"}'

        # Identifying Length of the field
        len_acct_nbr = len(fil_email)

        # This will trigger the service if it has valid data
        if len_acct_nbr > 0:
            x = cw.clsWeb(json_source_str)
            de_Email = x.getResponse(EncryptMode)
        else:
            de_Email = ''

        return de_Email

    def getEncrypt(self, df_dict):
        try:
            df_input = p.DataFrame()
            df_fin = p.DataFrame()

            # Assigning Target File Basic Name
            for k, v in df_dict.items():
                Thread_Name = k
                df_input = v

            # Checking total count of rows
            count_row = int(df_input.shape[0])
            # print('Part number of records to process:: ', count_row)

            if count_row > 0:

                # Deriving rows
                df_input['Encrypt_Acct_Nbr'] = df_input.apply(lambda row: self.encrypt_acctNbr(row), axis=1)
                df_input['Encrypt_Name'] = df_input.apply(lambda row: self.encrypt_Name(row), axis=1)
                df_input['Encrypt_Phone'] = df_input.apply(lambda row: self.encrypt_Phone(row), axis=1)
                df_input['Encrypt_Email'] = df_input.apply(lambda row: self.encrypt_Email(row), axis=1)

                # Dropping original columns
                df_input.drop(['Acct_Nbr', 'Name', 'Phone', 'Email'], axis=1, inplace=True)

                # Renaming new columns with the old column names
                df_input.rename(columns={'Encrypt_Acct_Nbr':'Acct_Nbr'}, inplace=True)
                df_input.rename(columns={'Encrypt_Name': 'Name'}, inplace=True)
                df_input.rename(columns={'Encrypt_Phone': 'Phone'}, inplace=True)
                df_input.rename(columns={'Encrypt_Email': 'Email'}, inplace=True)

                # New Column List Orders
                column_order = ['Acct_Nbr', 'Name', 'Acct_Addr_1', 'Acct_Addr_2', 'Phone', 'Email', 'Serial_No']
                df_fin = df_input.reindex(column_order, axis=1)

                fin_dict[Thread_Name] = df_fin

            return 0
        except Exception as e:
            df_error = p.DataFrame({'Acct_Nbr':str(e), 'Name':'', 'Acct_Addr_1':'', 'Acct_Addr_2':'', 'Phone':'', 'Email':'', 'Serial_No':''})
            fin_dict[Thread_Name] = df_error

            return 1

    def getEncryptWQ(self):
        item_dict = {}
        item = ''

        while True:
            try:
                #item_dict = q.get()
                item_dict = q.get_nowait()

                for k, v in item_dict.items():
                    # Assigning Target File Basic Name
                    item = str(k)

                if ((item == 'TEND') | (item == '')):
                    break

                if ((item != 'TEND') | (item != '')):
                    self.getEncrypt(item_dict)

                q.task_done()
            except Exception:
                break

    def getEncryptParallel(self, df_payload):
        start_pos = 0
        end_pos = 0
        l_dict = {}
        c_dict = {}
        min_val_list = {}
        cnt = 0
        num_worker_threads = self.num_worker_threads
        split_df = p.DataFrame()
        df_ret = p.DataFrame()

        # Assigning Target File Basic Name
        df_input = df_payload

        # Checking total count of rows
        count_row = df_input.shape[0]
        print('Total number of records to process:: ', count_row)

        interval = int(count_row / num_worker_threads) + 1
        actual_worker_task = int(count_row / interval) + 1

        for i in range(actual_worker_task):
            t = threading.Thread(target=self.getEncryptWQ)
            t.start()
            threads.append(t)
            name = str(t.getName())

            if ((start_pos + interval) < count_row):
                end_pos = start_pos + interval
            else:
                end_pos = start_pos + (count_row - start_pos)

            split_df = df_input.iloc[start_pos:end_pos]
            l_dict[name] = split_df

            if ((start_pos > count_row) | (start_pos == count_row)):
                break
            else:
                start_pos = start_pos + interval

            q.put(l_dict)
            cnt += 1

        # block until all tasks are done
        q.join()

        # stop workers
        for i in range(actual_worker_task):
            c_dict['TEND'] = p.DataFrame()
            q.put(c_dict)

        for t in threads:
            t.join()

        for k, v in fin_dict.items():
            min_val_list[int(k.replace('Thread-',''))] = v

        min_val = min(min_val_list, key=int)

        for k, v in sorted(fin_dict.items(), key=lambda k:int(k[0].replace('Thread-',''))):
            if int(k.replace('Thread-','')) == min_val:
                df_ret = fin_dict[k]
            else:
                d_frames = [df_ret, fin_dict[k]]
                df_ret = p.concat(d_frames)

        # Releasing Memory
        del[[split_df]]
        gc.collect()

        return df_ret

    def getDecrypt(self, df_encrypted_dict):
        try:
            df_input = p.DataFrame()
            df_fin = p.DataFrame()

            # Assigning Target File Basic Name
            for k, v in df_encrypted_dict.items():
                Thread_Name = k
                df_input = v

            # Checking total count of rows
            count_row = int(df_input.shape[0])

            if count_row > 0:

                # Deriving rows
                df_input['Decrypt_Acct_Nbr'] = df_input.apply(lambda row: self.decrypt_acctNbr(row), axis=1)
                df_input['Decrypt_Name'] = df_input.apply(lambda row: self.decrypt_Name(row), axis=1)
                df_input['Decrypt_Phone'] = df_input.apply(lambda row: self.decrypt_Phone(row), axis=1)
                df_input['Decrypt_Email'] = df_input.apply(lambda row: self.decrypt_Email(row), axis=1)

                # Dropping original columns
                df_input.drop(['Acct_Nbr', 'Name', 'Phone', 'Email'], axis=1, inplace=True)

                # Renaming new columns with the old column names
                df_input.rename(columns={'Decrypt_Acct_Nbr':'Acct_Nbr'}, inplace=True)
                df_input.rename(columns={'Decrypt_Name': 'Name'}, inplace=True)
                df_input.rename(columns={'Decrypt_Phone': 'Phone'}, inplace=True)
                df_input.rename(columns={'Decrypt_Email': 'Email'}, inplace=True)

                # New Column List Orders
                column_order = ['Acct_Nbr', 'Name', 'Acct_Addr_1', 'Acct_Addr_2', 'Phone', 'Email']
                df_fin = df_input.reindex(column_order, axis=1)

                fin_dict_1[Thread_Name] = df_fin

            return 0

        except Exception as e:
            df_error = p.DataFrame({'Acct_Nbr': str(e), 'Name': '', 'Acct_Addr_1': '', 'Acct_Addr_2': '', 'Phone': '', 'Email': ''})
            fin_dict_1[Thread_Name] = df_error

            return 1

    def getDecryptWQ(self):
        item_dict = {}
        item = ''

        while True:
            try:
                #item_dict = q.get()
                item_dict = m.get_nowait()

                for k, v in item_dict.items():
                    # Assigning Target File Basic Name
                    item = str(k)

                if ((item == 'TEND') | (item == '')):
                    return True
                    #break

                if ((item != 'TEND') | (item != '')):
                    self.getDecrypt(item_dict)

                m.task_done()
            except Exception:
                break


    def getDecryptParallel(self, df_payload):
        start_pos = 0
        end_pos = 0
        l_dict_1 = {}
        c_dict_1 = {}
        cnt = 0
        num_worker_threads = self.num_worker_threads
        split_df = p.DataFrame()
        df_ret_1 = p.DataFrame()

        min_val_list = {}

        # Assigning Target File Basic Name
        df_input_1 = df_payload

        # Checking total count of rows
        count_row = df_input_1.shape[0]
        print('Total number of records to process:: ', count_row)

        interval = int(count_row / num_worker_threads) + 1
        actual_worker_task = int(count_row / interval) + 1

        for i in range(actual_worker_task):
            t_1 = threading.Thread(target=self.getDecryptWQ)
            t_1.start()
            threads.append(t_1)
            name = str(t_1.getName())

            if ((start_pos + interval) < count_row):
                end_pos = start_pos + interval
            else:
                end_pos = start_pos + (count_row - start_pos)

            split_df = df_input_1.iloc[start_pos:end_pos]
            l_dict_1[name] = split_df

            if ((start_pos > count_row) | (start_pos == count_row)):
                break
            else:
                start_pos = start_pos + interval

            m.put(l_dict_1)
            cnt += 1

        # block until all tasks are done
        m.join()

        # stop workers
        for i in range(actual_worker_task):
            c_dict_1['TEND'] = p.DataFrame()
            m.put(c_dict_1)

        for t_1 in threads:
            t_1.join()

        for k, v in fin_dict_1.items():
            min_val_list[int(k.replace('Thread-',''))] = v

        min_val = min(min_val_list, key=int)

        for k, v in sorted(fin_dict_1.items(), key=lambda k:int(k[0].replace('Thread-',''))):
            if int(k.replace('Thread-','')) == min_val:
                df_ret_1 = fin_dict_1[k]
            else:
                d_frames = [df_ret_1, fin_dict_1[k]]
                df_ret_1 = p.concat(d_frames)

        # Releasing Memory
        del[[split_df]]
        gc.collect()

        return df_ret_1

Let’s explain the key snippet from the code. For your information, we’re not going to describe all the encryption methods such as –

# Encryption Method
encrypt_acctNbr
encrypt_Name
encrypt_Phone
encrypt_Email

# Decryption Method
decrypt_acctNbr
decrypt_Name
decrypt_Phone
decrypt_Email

As we’ve already described the logic of these methods in our previous post.

# Checking total count of rows
count_row = df_input.shape[0]
print('Total number of records to process:: ', count_row)

interval = int(count_row / num_worker_threads) + 1
actual_worker_task = int(count_row / interval) + 1

Fetching the total number of rows from the dataframe. Based on the row count, the application will derive the actual number of threads that will be used for parallelism.

for i in range(actual_worker_task):
    t = threading.Thread(target=self.getEncryptWQ)
    t.start()
    threads.append(t)
    name = str(t.getName())

    if ((start_pos + interval) < count_row):
        end_pos = start_pos + interval
    else:
        end_pos = start_pos + (count_row - start_pos)

    split_df = df_input.iloc[start_pos:end_pos]
    l_dict[name] = split_df

    if ((start_pos > count_row) | (start_pos == count_row)):
        break
    else:
        start_pos = start_pos + interval

    q.put(l_dict)
    cnt += 1

Here, the application is splitting the data into multiple groups of smaller data packs & then combining them into (key, value) dictionary & finally placed them into the individual queue.

# block until all tasks are done
q.join()

This will join the queue process. This will ensure that queues are free after consuming the data.

# stop workers
for i in range(actual_worker_task):
    c_dict['TEND'] = p.DataFrame()
    q.put(c_dict)

for t in threads:
    t.join()

The above lines are essential. As this will help the process to identify that no more data are left to send at the queue. And, the main thread will wait until all the threads are done.

for k, v in fin_dict.items():
    min_val_list[int(k.replace('Thread-',''))] = v

min_val = min(min_val_list, key=int)

Once, all the jobs are done. The application will find the minimum thread value & based on that we can sequence all the data chunks as explained in our previous image & finally clubbed them together to form the complete csv.

for k, v in sorted(fin_dict.items(), key=lambda k:int(k[0].replace('Thread-',''))):
    if int(k.replace('Thread-','')) == min_val:
        df_ret = fin_dict[k]
    else:
        d_frames = [df_ret, fin_dict[k]]
        df_ret = p.concat(d_frames)

As already explained, using the starting point of our data dictionary element, the application is clubbing the data back to the main csv.

Next method, which we’ll be explaining is –

getEncryptWQ

Please find the key lines –

while True:
    try:
        #item_dict = q.get()
        item_dict = q.get_nowait()

        for k, v in item_dict.items():
            # Assigning Target File Basic Name
            item = str(k)

        if ((item == 'TEND') | (item == '')):
            break

        if ((item != 'TEND') | (item != '')):
            self.getEncrypt(item_dict)

        q.task_done()
    except Exception:
        break

This method will consume the data & processing it for encryption or decryption. This will continue to do the work until or unless it receives the key value as TEND or the queue is empty.

Let’s compare the statistics between Windows & MAC.

Let’s see the file structure first –

Windows (16 GB – Core 2) Vs Mac (10 GB – Core 2):

Windows (16 GB – Core 2):

Mac (10 GB – Core 2):

Find the complete directory from both the machine.

Windows (16 GB – Core 2):

Mac (10 GB – Core 2):

Here is the final output –

So, we’ve achieved our target goal.

Let me know – how do you like this post. Please share your suggestion & comments.

I’ll be back with another installment from the Python verse.

Till then – Happy Avenging!

Pandas, Numpy, Encryption/Decryption, Hidden Files In Python (Crossover between Space Stone, Reality Stone & Mind Stone of Python-Verse)

Posted on February 3, 2019September 23, 2019 by SatyakiDe in Data Science, exposure, filepermission, function, numpy, objects, operating system, Pandas, Python, read, replace, Technology

So, here we come up with another crossover of Space Stone, Reality Stone & Mind Stone of Python-Verse. It is indeed exciting & I cannot wait to explore that part further. Today, in this post, we’ll see how one application can integrate all these key ingredients in Python to serve the purpose. Our key focus will be involving popular packages like Pandas, Numpy & Popular Encryption-Decryption techniques, which include some hidden files as well.

So, our objective here is to proceed with the encryption & decryption technique. But, there is a catch. We need to store some salt or tokenized value inside a hidden file. Our application will extract the salt value from it & then based on that it will perform Encrypt/Decrypt on the data.

Why do we need this approach?

The answer is simple. On many occasions, we don’t want to store our right credentials in configuration files. Also, we don’t want to keep our keys to open to other developers. There are many ways you can achieve this kind of security. Today, I’ll be showing a different approach to make the same.

Let’s explore.

As usual, I’ll provide the solution, which is tested in Windows & MAC & provide the script. Also, I’ll explain the critical lines of those scripts to understand it from a layman point of view. And, I won’t explain any script, which I’ve already explained in my earlier post. So, you have to refer my old post for that.

To encrypt & decrypt, we need the following files, which contains credentials in a csv. Please find the sample data –

Config_orig.csv

Please see the file, which will be hidden by the application process.

As you can see, this column contains the salt, which will be used in our Encryption/Decryption.

1. clsL.py (This script will create the csv files or any intermediate debug csv file after the corresponding process. Hence, the name comes into the picture.)

###########################################
#### Written By: SATYAKI DE        ########
#### Written On: 25-Jan-2019       ########
####                               ########
#### Objective: Log File           ########
###########################################
import pandas as p
import platform as pl
from clsParam import clsParam as cf

class clsL(object):
    def __init__(self):
        self.path = cf.config['PATH']

    def logr(self, Filename, Ind, df, subdir=None):
        try:
            x = p.DataFrame()
            x = df
            sd = subdir

            os_det = pl.system()

            if sd == None:
                if os_det == "Windows":
                    fullFileName = self.path + '\\' + Filename
                else:
                    fullFileName = self.path + '/' + Filename
            else:
                if os_det == "Windows":
                    fullFileName = self.path + '\\' + sd + "\\" + Filename
                else:
                    fullFileName = self.path + '/' + sd + "/" + Filename

            if Ind == 'Y':
                x.to_csv(fullFileName, index=False)

            return 0

        except Exception as e:
            y = str(e)
            print(y)
            return 3

2. clsParam.py (This is the script that will be used as a parameter file & will be used in other python scripts.)

###########################################
#### Written By: SATYAKI DE        ########
#### Written On: 25-Jan-2019       ########
#### Objective: Parameter File     ########
###########################################

import os
import platform as pl

class clsParam(object):

    config = {
        'FILENAME' : 'test.amca',
        'OSX_MOD_FILE_NM': '.test.amca',
        'CURR_PATH': os.path.dirname(os.path.realpath(__file__)),
        'NORMAL_FLAG': 32,
        'HIDDEN_FLAG': 34,
        'OS_DET': pl.system()
    }

3. clsWinHide.py (This script contains the core logic of hiding/unhiding a file under Windows OS. Hence, the name comes into the picture.)

###########################################
#### Written By: SATYAKI DE          ######
#### Written On: 25-Jan-2019         ######
####                                 ######
#### This script will hide or Unhide ######
#### Files in Windows.               ######
###########################################

import win32file
import win32con
from clsParam import clsParam as cp

class clsWinHide(object):
    def __init__(self):
        self.path = cp.config['CURR_PATH']
        self.FileName = cp.config['FILENAME']
        self.normal_file_flag = cp.config['NORMAL_FLAG']

    def doit(self):
        try:
            path = self.path
            FileName = self.FileName

            FileNameWithPath = path + '\\' + FileName
            flags = win32file.GetFileAttributesW(FileNameWithPath)
            win32file.SetFileAttributes(FileNameWithPath,win32con.FILE_ATTRIBUTE_HIDDEN | flags)

            return 0
        except Exception as e:
            x = str(e)
            print(x)

            return 1

    def undoit(self):
        try:
            path = self.path
            FileName = self.FileName
            normal_file_flag = self.normal_file_flag

            FileNameWithPath = path + '\\' + FileName
            win32file.SetFileAttributes(FileNameWithPath,win32con.FILE_ATTRIBUTE_NORMAL | int(normal_file_flag))

            return 0
        except Exception as e:
            x = str(e)
            print(x)

            return 1

Key lines that we would like to explore are as follows –

def doit()

flags = win32file.GetFileAttributesW(FileNameWithPath)
win32file.SetFileAttributes(FileNameWithPath,win32con.FILE_ATTRIBUTE_HIDDEN | flags)

The above two lines under doit() functions are changing the file attributes in Windows OS to the hidden mode by assigning the FILE_ATTRIBUTE_HIDDEN property.

def undoit()

normal_file_flag = self.normal_file_flag

FileNameWithPath = path + '\\' + FileName
win32file.SetFileAttributes(FileNameWithPath,win32con.FILE_ATTRIBUTE_NORMAL | int(normal_file_flag))

As the script suggested, the application is setting the file attribute of a hidden file to FILE_ATTRIBUTE_NORMAL & set the correct flag from parameters, which leads to the file appears as a normal windows file.

4. clsOSXHide.py (This script contains the core logic of hiding/unhiding a file under OSX, i.e., MAC OS. Hence, the name comes into the picture.)

###########################################
#### Written By: SATYAKI DE           #####
#### Written On: 25-Jan-2019          #####
####                                  #####
#### Objective: This script will hide #####
#### or Unhide the file in OSX.       #####
###########################################

import os
from clsParam import clsParam as cp

class clsOSXHide(object):
    def __init__(self):
        self.path = cp.config['CURR_PATH']
        self.FileName = cp.config['FILENAME']
        self.OSX_Mod_FileName = cp.config['OSX_MOD_FILE_NM']
        self.normal_file_flag = cp.config['NORMAL_FLAG']

    def doit(self):
        try:
            path = self.path
            FileName = self.FileName

            FileNameWithPath = path + '/' + FileName
            os.rename(FileNameWithPath, os.path.join(os.path.dirname(FileNameWithPath),'.'
                                                     + os.path.basename(FileNameWithPath)))

            return 0
        except Exception as e:
            x = str(e)
            print(x)

            return 1

    def undoit(self):
        try:
            path = self.path
            FileName = self.FileName
            OSX_Mod_FileName = self.OSX_Mod_FileName

            FileNameWithPath = path + '/' + FileName
            os.rename(OSX_Mod_FileName, FileNameWithPath)

            return 0
        except Exception as e:
            x = str(e)
            print(x)

            return 1

The key lines that we’ll be exploring here are as follows –

def doit()

FileNameWithPath = path + '/' + FileName
os.rename(FileNameWithPath, os.path.join(os.path.dirname(FileNameWithPath),'.'
                                         + os.path.basename(FileNameWithPath)))

In MAC or Linux, any file starts with ‘.’ will be considered as a hidden file. Hence, we’re changing the file type by doing this manipulation.

def undoit()

OSX_Mod_FileName = self.OSX_Mod_FileName

FileNameWithPath = path + '/' + FileName
os.rename(OSX_Mod_FileName, FileNameWithPath)

In this case, our application simply renaming a file with its the original file to get the file as a normal file.

Let’s understand that in Linux or MAC, you have a lot of other ways to restrict any files as it has much more granular level access control. But, I thought, why not take a slightly different & fun way to achieve the same. After all, we’re building an Infinity War for Python verse. A little bit of fun will certainly make some sense. 🙂

5. clsProcess.py (This script will invoke any of the hide scripts, i.e. clsWinHide.py or clsOSXHide.py based on the OS platform. Hence, the name comes into the picture.)

###########################################
#### Written By: SATYAKI DE          ######
#### Written On: 25-Jan-2019         ######
####                                 ######
#### Objective: Based on the OS, this######
#### script calls the actual script. ######
###########################################

from clsParam import clsParam as cp

plat_det = cp.config['OS_DET']

# Based on the platform
# Application is loading subprocess
# in order to avoid library missing
# case against cross platform

if plat_det == "Windows":
    import clsWinHide as win
else:
    import clsOSXHide as osx

# End of conditional class load

class clsProcess(object):
    def __init__(self):
        self.os_det = plat_det

    def doit(self):
        try:

            os_det = self.os_det
            print("OS Info: ", os_det)

            if os_det == "Windows":
                win_doit = win.clsWinHide()
                ret_val = win_doit.doit()
            else:
                osx_doit = osx.clsOSXHide()
                ret_val = osx_doit.doit()

            return ret_val
        except Exception as e:
            x = str(e)
            print(x)

            return 1

    def undoit(self):
        try:

            os_det = self.os_det
            print("OS Info: ", os_det)

            if os_det == "Windows":
                win_doit = win.clsWinHide()
                ret_val = win_doit.undoit()
            else:
                osx_doit = osx.clsOSXHide()
                ret_val = osx_doit.undoit()

            return ret_val
        except Exception as e:
            x = str(e)
            print(x)

            return 1

Key lines to explores are as follows –

from clsParam import clsParam as cp

plat_det = cp.config['OS_DET']

# Based on the platform
# Application is loading subprocess
# in order to avoid library missing
# case against cross platform

if plat_det == "Windows":
    import clsWinHide as win
else:
    import clsOSXHide as osx

This step is very essential to run the same python scripts in both the environments, e.g. in this case like MAC & Windows.

So, based on the platform details, which the application is getting from the clsParam class, it is loading the specific class to the application. And why it is so important.

Under Windows OS, this will work if you load both the class. But, under MAC, this will fail as the first program will try to load all the libraries & it may happen that the pywin32/pypiwin32 package might not available under MAC. Anyway, you are not even using that package. So, this conditional class loading is significant.

os_det = self.os_det
print("OS Info: ", os_det)

if os_det == "Windows":
    win_doit = win.clsWinHide()
    ret_val = win_doit.doit()
else:
    osx_doit = osx.clsOSXHide()
    ret_val = osx_doit.doit()

As you can see that, based on the OS, it is invoking the correct function of that corresponding class.

6. clsEnDec.py (This script will read the credentials from a csv file & then based on the salt captured from the hidden file, it will either encrypt or decrypt the content. Hence, the name comes into the picture.)

###########################################
#### Written By: SATYAKI DE        ########
#### Written On: 25-Jan-2019       ########
#### Package Cryptography needs to ########
#### install in order to run this  ########
#### script.                       ########
####                               ########
#### Objective: This script will   ########
#### encrypt/decrypt based on the  ########
#### hidden supplied salt value.   ########
###########################################

import pandas as p
from cryptography.fernet import Fernet

class clsEnDec(object):

    def __init__(self, token):
        # Calculating Key
        self.token = token

    def encrypt_str(self):
        try:
            # Capturing the Salt Information
            salt = self.token
            # Fetching the content of lookup file
            df_orig = p.read_csv('Config_orig.csv', index_col=False)

            # Checking Individual Types inside the Dataframe
            cipher = Fernet(salt)

            df_orig['User'] = df_orig['User'].apply(lambda x1: cipher.encrypt(bytes(x1,'utf8')))
            df_orig['Pwd'] = df_orig['Pwd'].apply(lambda x2: cipher.encrypt(bytes(x2,'utf8')))

            # Writing to the File
            df_orig.to_csv('Encrypt_Config.csv', index=False)

            return 0
        except Exception as e:
            x = str(e)
            print(x)
            return 1

    def decrypt_str(self):
        try:
            # Capturing the Salt Information
            salt = self.token
            # Checking Individual Types inside the Dataframe
            cipher = Fernet(salt)

            # Fetching the Encrypted csv file
            df_orig = p.read_csv('Encrypt_Config.csv', index_col=False)

            df_orig['User'] = df_orig['User'].apply(lambda x1: str(cipher.decrypt(bytes(x1[2:-1],'utf8'))).replace("b'","").replace("'",""))
            df_orig['Pwd'] = df_orig['Pwd'].apply(lambda x2: str(cipher.decrypt(bytes(x2[2:-1],'utf8'))).replace("b'","").replace("'",""))

            # Writing to the file
            df_orig.to_csv('Decrypt_Config.csv', index=False)

            return 0
        except Exception as e:
            x = str(e)
            print(x)
            return 1

Key lines from this script are as follows –

def encrypt_str()

# Checking Individual Types inside the Dataframe
cipher = Fernet(salt)

df_orig['User'] = df_orig['User'].apply(lambda x1: cipher.encrypt(bytes(x1,'utf8')))
df_orig['Pwd'] = df_orig['Pwd'].apply(lambda x2: cipher.encrypt(bytes(x2,'utf8')))

So, once you captured the salt from that hidden file, the application is capturing that value over here. And, based on that both the field will be encrypted. But, note that cryptography package is required for this. And, you need to pass bytes value to work this thing. Hence, we’ve used bytes() function over here.

def decrypt_str()

cipher = Fernet(salt)

# Fetching the Encrypted csv file
df_orig = p.read_csv('Encrypt_Config.csv', index_col=False)

df_orig['User'] = df_orig['User'].apply(lambda x1: str(cipher.decrypt(bytes(x1[2:-1],'utf8'))).replace("b'","").replace("'",""))
df_orig['Pwd'] = df_orig['Pwd'].apply(lambda x2: str(cipher.decrypt(bytes(x2[2:-1],'utf8'))).replace("b'","").replace("'",""))

Again, in this step, our application is extracting the salt & then it retrieves the encrypted values of corresponding fields & applies the decryption logic on top of it. Note that, since we need to pass bytes value to get it to work. Hence, your output will be appended with (b’xxxxx’). To strip that, we’ve used the replace() functions. You can use regular expression using pattern matching as well.

7. callEnDec.py (This script will create the split csv files or final merge file after the corresponding process. However, this can be used as normal verbose debug logging as well. Hence, the name comes into the picture.)

###########################################
#### Written By: SATYAKI DE           #####
#### Written On: 25-Jan-2019          #####
####                                  #####
#### Objective: Main calling function #####
###########################################

import clsEnDec as ed
import clsProcess as h
from clsParam import clsParam as cp
import time as t
import pandas as p

def main():
    print("")
    print("#" * 60)
    print("Calling (Encryption/Decryption) Package!!")
    print("#" * 60)
    print("")

    # Unhiding the file
    x = h.clsProcess()
    ret_val_unhide = x.undoit()

    if ret_val_unhide == 0:
        print("Successfully Unhide the file!")
    else:
        print("Unsuccessful to Unhide the file!")

    # To See the Unhide file
    t.sleep(10)

    print("*" * 60)
    print("Proceeding with Encryption...")
    print("*" * 60)

    # Getting Salt Value from the hidden files
    # by temporarily making it available
    FileName = cp.config['FILENAME']
    df = p.read_csv(FileName, index_col=False)
    salt = str(df.iloc[0]['Token_Salt'])
    print("-" * 60)
    print("Salt: ", salt)
    print("-" * 60)

    # Calling the Encryption Method
    x = ed.clsEnDec(salt)
    ret_val = x.encrypt_str()

    if ret_val == 0:
        print("Encryption Successful!")
    else:
        print("Encryption Failure!")

    print("")
    print("*" * 60)
    print("Checking Decryption Now...")
    print("*" * 60)

    # Calling the Decryption Method
    ret_val1 = x.decrypt_str()

    if ret_val1 == 0:
        print("Decryption Successful!")
    else:
        print("Decryption Failure!")

    # Hiding the salt file
    x = h.clsProcess()
    ret_val_hide = x.doit()

    if ret_val_hide == 0:
        print("Successfully Hide the file!")
    else:
        print("Unsuccessful to Hide the file!")

    print("*" * 60)
    print("Operation Done!")
    print("*" * 60)

if __name__ == '__main__':
    main()

And, here comes the final calling methods.

The key lines that we would like to discuss –

# Getting Salt Value from the hidden files
# by temporarily making it available
FileName = cp.config['FILENAME']
df = p.read_csv(FileName, index_col=False)
salt = str(df.iloc[0]['Token_Salt'])

As I’ve shown that, we have our hidden files that contain only 1 row & 1 column. To extract the specific value we’ve used iloc with the row number as 0 along with the column name, i.e. Token_Salt.

Now, let’s see how it runs –

Windows (64 bit):

Mac (32 bit):

So, from the screenshot, we can see our desired output & you can calculate the aggregated value based on our sample provided in the previous screenshot.

Let’s check the Encrypted & Decrypted values –

Encrypted Values (Encrypt_Config.csv):

Decrypted Values (Decrypt_Config.csv):

So, finally, we’ve achieved our target.

I hope this will give you some more idea about more insights into the Python verse. Let me know – how do you think about this post.

Till then – Happy Avenging!

	RAG implementation o… on RAG implementation of LLMs by…
	Realtime reading fro… on Live visual reading using Conv…
	Neural prophet… on Predicting real-time Covid-19…
	Projecting real-time… on Python-based dash framework vi…
	Displaying real-time… on Creating a real-time dashboard…

Tag: Encrypt

Improvement of Pandas data processing performance using Multi-threading with the Queue (Another crossover of Space Stone, Reality Stone & Power Stone)

Windows (16 GB – Core 2) Vs Mac (10 GB – Core 2):

Windows (16 GB – Core 2):

Mac (10 GB – Core 2):

Find the complete directory from both the machine.

Windows (16 GB – Core 2):

Mac (10 GB – Core 2):

Like this:

Pandas, Numpy, Encryption/Decryption, Hidden Files In Python (Crossover between Space Stone, Reality Stone & Mind Stone of Python-Verse)

Like this:

Windows (16 GB – Core 2) Vs Mac (10 GB – Core 2):

Windows (16 GB – Core 2):

Mac (10 GB – Core 2):

Find the complete directory from both the machine.

Windows (16 GB – Core 2):

Mac (10 GB – Core 2):

Share this:

Like this:

Share this:

Like this: