Bash Script To Install Python Packages and Generate requirements.txt Inside Python Virtualenv.

pip-freezeWrapper.sh

#!/bin/bash
#
# Created By: misraX
# Github: github.com/misrax
# License: MIT
# CopyWrite: 2017
# Bash script to install and generate pip requirements.txt from inside a virtualenv.
# By using pip freeze to creat a list of all the virtualenviroment pip packeges.
# It's just a simple way of creating the pip requirements.txt for a development use.
# usage pip-install package1 package2 package3 .. package(n)... etc 
# 
packages=($1)

echo "Starting to install packages, Hooopa....." 
   #check virtualenv path 
if [[ "$VIRTUAL_ENV" != "" ]]; then 
    echo "You are in a working virtualenv $VIRTUAL_ENV";
   # virtual_env > check if packages is empty .. if [[ ]]; then
    if [[ "$packages" != "" ]]; then
    pip install "${packages[@]}";
    echo "Whre do u wanna save your requirements.txt type the path and click [ENTER]"
    ls "$VIRTUAL_ENV"
    read requirements_path
        # virtual_env > > package > if read is empty ... if [[ ]]; then 
        if [[ "$requirements_path" ==  ""  ]]; then
            echo "Oh pleae, Enter something :D, now to call this script again use prepare"
            exit 1;
        # virtual_env > packages > read > if virtualevn/read is a valid directory.. elif [[ ]]; then 
        elif [[ -d "$VIRTUAL_ENV/$requirements_path"  ]]; then
        #statements
            echo "creating requirements.txt";
            pip freeze > "$VIRTUAL_ENV/$requirements_path/requirements.txt"
        # > virtul_env > package > read > not a valid directory .. else; fi
        else 
        echo "This is not a vail directory under $(ls $VIRTUAL_ENV) path"
    fi
# virtual_env > packages .. else; fi 
  else 
    echo "Please enter at least one package to install";
    exit 1;
  fi 
# virtual_env .. else; fi
else 
    echo "You are not in a working virtualenv"
    echo "Exiting .........."
    exit 1;
fi 

#
#TODO
#adding enviroments variable to set DJANG_PATH and REQUIRMETS_PATH.
#

I'm trying to embed a requirements.txt file with the package variable but its showing an 'invalid requirement' error for the same. Please help. I want the packages to be installed via a text file, i.e. requirements.txt and the package dependencies via the generated additional_requirements.txt file.

P.S. My code works when only one package is specified in the requirements.txt file but fails when more than one package name is entered.

import os
import pandas as pd
import numpy as np
from math import radians, sin, cos, atan2, sqrt
from datetime import datetime

-----------------------------------------------------------------------------

CONFIGURATION

-----------------------------------------------------------------------------

DATA_FOLDER = "./" # Folder where your Excel files are stored
EXCEL_FILES = [
# List all your Excel filenames here:
"WK.xlsx",
"27727861641.xlsx",
"G(1).xlsx",
"Schalk Pienaar.xlsx",
"CR.xlsx",
"BP_1123383_-Police_Case_Query-_AMA.xlsx",
"summary of cell numbers.xlsx",
"Manie 27823078393_1.xlsx",
"Manie 27823078393_2.xlsx"
]

Update these lists as needed or glob them automatically if you prefer.

Phone number references (update to match your data format exactly)

MANIE = "+27823078393"
SP = "+27824634777"
GG = "+27637762604"
JU = "+27727861641"
CALLIE = "+27834124723"
WK = "+27825248358" # Example victim

Columns in your Excel data (update to match your actual column names)

COL_CALLING = "CallingNumber" # e.g. "MSISDN" or "CallingNumber"
COL_CALLED = "CalledNumber" # e.g. "Other Party"
COL_LAT = "Latitude"
COL_LON = "Longitude"
COL_DATE = "Start Date" # e.g. "StartDate", "Date/Time"
COL_DURATION = "Call Duration" # e.g. "Call Duration", "Duration"

-----------------------------------------------------------------------------

HELPER FUNCTIONS

-----------------------------------------------------------------------------

def load_all_excel(files, folder=DATA_FOLDER):
"""
Loads and concatenates all Excel files into a single pandas DataFrame.
"""
combined_df = pd.DataFrame()
for f in files:
path = os.path.join(folder, f)
if os.path.exists(path):
df = pd.read_excel(path)
combined_df = pd.concat([combined_df, df], ignore_index=True)
else:
print(f"WARNING: File not found -> {path}")
return combined_df

def haversine_distance(lat1, lon1, lat2, lon2):
"""
Calculate Haversine distance (in meters) between two lat/lon pairs.
"""
R = 6371_000 # Radius of Earth in meters
phi1, phi2 = radians(lat1), radians(lat2)
dphi = radians(lat2 - lat1)
dlambda = radians(lon2 - lon1)

a = sin(dphi / 2)**2 + cos(phi1) * cos(phi2) * sin(dlambda / 2)**2
c = 2 * atan2(sqrt(a), sqrt(1 - a))
return R * c

-----------------------------------------------------------------------------

MAIN ANALYSIS

-----------------------------------------------------------------------------

def main():
# 1. Load & clean data
df = load_all_excel(EXCEL_FILES, DATA_FOLDER)
if df.empty:
print("No data loaded. Please check your file paths and names.")
return

# Ensure consistent column names (if needed)
# E.g., rename columns if they differ:
# df.rename(columns={
#     "MSISDN": "CallingNumber",
#     "Other Party": "CalledNumber",
#     "StartDate": "Start Date",
#     "Call Duration": "Call Duration",
# }, inplace=True)

# 2. Convert date column to datetime
df[COL_DATE] = pd.to_datetime(df[COL_DATE], errors='coerce')

# -------------------------------------------------------------------------
# (A) Instances where 2+ phones were within 15m at the same time
# -------------------------------------------------------------------------
# Approach:
# 1) Sort df by time
# 2) For each record, compare location with other records at "roughly" the same time
# 3) If distance <= 15m, store in a results list
# 
# This can be time-consuming if you have large data sets. For large data, 
# consider more efficient methods (e.g., spatial indexing, chunking).
# 
# We'll do a naive approach for demonstration:

df_sorted = df.dropna(subset=[COL_LAT, COL_LON, COL_DATE]).sort_values(by=COL_DATE)
records = df_sorted.to_dict(orient='records')
proximity_results = []

# Let's define a time window (e.g., same minute or same second).
# For "at the same time," you might define a threshold, e.g. ±30 seconds.
TIME_THRESHOLD = pd.Timedelta(seconds=30)

for i in range(len(records)):
    r1 = records[i]
    for j in range(i+1, len(records)):
        r2 = records[j]
        
        # If r2 is beyond the time threshold, break (since it's sorted by time)
        if abs(r2[COL_DATE] - r1[COL_DATE]) > TIME_THRESHOLD:
            break
        
        # Calculate distance
        dist = haversine_distance(r1[COL_LAT], r1[COL_LON], r2[COL_LAT], r2[COL_LON])
        if dist <= 15:
            proximity_results.append({
                "Phone1": r1[COL_CALLING],
                "Phone2": r1[COL_CALLED],
                "Phone1_Lat": r1[COL_LAT],
                "Phone1_Lon": r1[COL_LON],
                "Phone2_Calling": r2[COL_CALLING],
                "Phone2_Called": r2[COL_CALLED],
                "Phone2_Lat": r2[COL_LAT],
                "Phone2_Lon": r2[COL_LON],
                "Distance_m": round(dist, 2),
                "Time1": r1[COL_DATE],
                "Time2": r2[COL_DATE],
            })

proximity_df = pd.DataFrame(proximity_results)
proximity_df.to_csv("proximity_15m.csv", index=False)
print(f"(A) Proximity analysis complete. Results -> proximity_15m.csv")

# -------------------------------------------------------------------------
# (B) Communication between Manie, SP, GG, and JU
# -------------------------------------------------------------------------
# We'll define a set of interest and see if either CallingNumber or CalledNumber is in that set.

interest_set = {MANIE, SP, GG, JU}

def in_interest_set(row):
    return (row[COL_CALLING] in interest_set) or (row[COL_CALLED] in interest_set)

df_comm = df[df.apply(in_interest_set, axis=1)].copy()
df_comm.to_csv("communication_manie_sp_gg_ju.csv", index=False)
print("(B) Communication between Manie, SP, GG, and JU -> communication_manie_sp_gg_ju.csv")

# -------------------------------------------------------------------------
# (C) Interactions between GG, Callie, and JU in 14/11/2022 - 22/11/2022
# -------------------------------------------------------------------------
# Filter by date range and phone numbers
start_date = pd.to_datetime("2022-11-14")
end_date = pd.to_datetime("2022-11-22")  # inclusive or exclusive, adjust as needed

# We consider calls from or to GG, Callie, JU
subset_phones = {GG, CALLIE, JU}
df_subset = df[
    (df[COL_DATE] >= start_date) &
    (df[COL_DATE] <= end_date) &
    (
        (df[COL_CALLING].isin(subset_phones)) |
        (df[COL_CALLED].isin(subset_phones))
    )
].copy()

df_subset.to_csv("gg_callie_ju_14_11_to_22_11.csv", index=False)
print("(C) Interactions for GG, Callie, JU (14/11/2022 - 22/11/2022) -> gg_callie_ju_14_11_to_22_11.csv")

# -------------------------------------------------------------------------
# (D) Interactions between GG and JU
# -------------------------------------------------------------------------
# Simple filter for calls or SMS between GG & JU in the entire dataset.

mask_gg_ju = (
    ((df[COL_CALLING] == GG) & (df[COL_CALLED] == JU)) |
    ((df[COL_CALLING] == JU) & (df[COL_CALLED] == GG))
)
df_gg_ju = df[mask_gg_ju].copy()

df_gg_ju.to_csv("gg_ju_interactions.csv", index=False)
print("(D) Interactions between GG and JU -> gg_ju_interactions.csv")

print("All analyses complete. CSV reports generated.")

if name == "main":
main()