Evr2 to nuscribe

In [ ]:

import pandas as pd
import numpy as np
import datetime
import time

import pandas as pd import numpy as np import datetime import time

In [ ]:

filename = r'c:\Users\OZCAR_GEOPHY\Documents\Data\MontLozere\timestamp\OB_20241107100109.txt'

filename = r'c:\Users\OZCAR_GEOPHY\Documents\Data\MontLozere\timestamp\OB_20241107100109.txt'

In [ ]:

OB = pd.read_csv(filename,sep='\t')

OB = pd.read_csv(filename,sep='\t')

In [ ]:

# Convert GPS Time in UTC (for checking purposes)
# utc = 1980-01-06UTC + (gps - (leap_count(2017) - leap_count(1980)))
gps =  OB['Gps time(us)']
utc = datetime.datetime(1980, 1, 6) + datetime.timedelta(microseconds=int(gps[0]) - (37e6 - 19e6))

def unix_time_seconds(dt):
    # Function to convert utc to unix time (microseconds)
    # epoch = datetime.datetime.utcfromtimestamp(0)
    epoch = datetime.datetime(1970, 1, 1)

    return (dt - epoch).total_seconds()

# Convert GPS Time in UTC (for checking purposes) # utc = 1980-01-06UTC + (gps - (leap_count(2017) - leap_count(1980))) gps = OB['Gps time(us)'] utc = datetime.datetime(1980, 1, 6) + datetime.timedelta(microseconds=int(gps[0]) - (37e6 - 19e6)) def unix_time_seconds(dt): # Function to convert utc to unix time (microseconds) # epoch = datetime.datetime.utcfromtimestamp(0) epoch = datetime.datetime(1970, 1, 1) return (dt - epoch).total_seconds()

In [ ]:

# Merge utc date and time
OB['UTC'] = OB['UTC Date'].astype(str) + ' ' + OB["UTC Time"]
OB['UTC'] = pd.to_datetime(OB['UTC'], format='%Y-%m-%d %H:%M:%S.%f')

# Change date format
OB['DATE_OK'] = OB['UTC'].dt.strftime('%Y:%m:%d')

# Convert UTC in Unix time and add column
OB['UNIX'] = OB['UTC'].apply(unix_time_seconds)

# Merge utc date and time OB['UTC'] = OB['UTC Date'].astype(str) + ' ' + OB["UTC Time"] OB['UTC'] = pd.to_datetime(OB['UTC'], format='%Y-%m-%d %H:%M:%S.%f') # Change date format OB['DATE_OK'] = OB['UTC'].dt.strftime('%Y:%m:%d') # Convert UTC in Unix time and add column OB['UNIX'] = OB['UTC'].apply(unix_time_seconds)

In [ ]:

station = OB['Point'].astype(int)
tb_utc_date = OB['DATE_OK']
tb_utc_time = OB['UTC Time']
tb_utc = OB['UTC']
tb_unix = OB['UNIX']
tb_gps = OB['Gps time(us)']
line = OB['Line'].astype(int)
ffid = OB['FFID'].astype(int)
latitude_sexagesimal = OB['Latitude']
longitude_sexagesimal = OB['Longitude']

# Replace the 0 values with mean lat and lon
latitude_sexagesimal = latitude_sexagesimal.replace(0, latitude_sexagesimal[latitude_sexagesimal != 0].mean())
longitude_sexagesimal = longitude_sexagesimal.replace(0, longitude_sexagesimal[longitude_sexagesimal != 0].mean())

station = OB['Point'].astype(int) tb_utc_date = OB['DATE_OK'] tb_utc_time = OB['UTC Time'] tb_utc = OB['UTC'] tb_unix = OB['UNIX'] tb_gps = OB['Gps time(us)'] line = OB['Line'].astype(int) ffid = OB['FFID'].astype(int) latitude_sexagesimal = OB['Latitude'] longitude_sexagesimal = OB['Longitude'] # Replace the 0 values with mean lat and lon latitude_sexagesimal = latitude_sexagesimal.replace(0, latitude_sexagesimal[latitude_sexagesimal != 0].mean()) longitude_sexagesimal = longitude_sexagesimal.replace(0, longitude_sexagesimal[longitude_sexagesimal != 0].mean())

In [ ]:

def sexagesimal_to_decimal_degrees(sexagesimal):
    # Convert the latitude to a string
    coord_str = sexagesimal.astype(str)

    # Find the position of the decimal separator
    decimal_position = coord_str.str.find('.')

    df_coord = pd.DataFrame({'coord_str': coord_str, 'decimal_position': decimal_position})

    # Split the degrees, minutes, and seconds
    degrees = df_coord.apply(lambda row: row['coord_str'][:row['decimal_position']-4], axis=1).astype(int)
    minutes = df_coord.apply(lambda row: row['coord_str'][row['decimal_position']-4:row['decimal_position']-2], axis=1).astype(int)
    seconds = df_coord.apply(lambda row: row['coord_str'][row['decimal_position']-2:], axis=1).astype(float)

    # # Convert the minutes and seconds to decimaµl degrees
    decimal_minutes = minutes / 60
    decimal_seconds = seconds / 3600

    # # Add the decimal degrees to the degrees
    decimal = degrees + decimal_minutes + decimal_seconds
    
    return decimal

def sexagesimal_to_decimal_degrees(sexagesimal): # Convert the latitude to a string coord_str = sexagesimal.astype(str) # Find the position of the decimal separator decimal_position = coord_str.str.find('.') df_coord = pd.DataFrame({'coord_str': coord_str, 'decimal_position': decimal_position}) # Split the degrees, minutes, and seconds degrees = df_coord.apply(lambda row: row['coord_str'][:row['decimal_position']-4], axis=1).astype(int) minutes = df_coord.apply(lambda row: row['coord_str'][row['decimal_position']-4:row['decimal_position']-2], axis=1).astype(int) seconds = df_coord.apply(lambda row: row['coord_str'][row['decimal_position']-2:], axis=1).astype(float) # # Convert the minutes and seconds to decimaµl degrees decimal_minutes = minutes / 60 decimal_seconds = seconds / 3600 # # Add the decimal degrees to the degrees decimal = degrees + decimal_minutes + decimal_seconds return decimal

In [ ]:

latitude_decimal = sexagesimal_to_decimal_degrees(latitude_sexagesimal)
longitude_decimal = sexagesimal_to_decimal_degrees(longitude_sexagesimal)

latitude_decimal = sexagesimal_to_decimal_degrees(latitude_sexagesimal) longitude_decimal = sexagesimal_to_decimal_degrees(longitude_sexagesimal)

In [ ]:

def add_cardinal_direction(coordinate, is_latitude=True):
    # Check if the coordinate is positive or negative
    if coordinate >= 0:
        # If the coordinate is positive, append the appropriate cardinal direction for positive coordinates
        cardinal_direction = 'N' if is_latitude else 'E'
    else:
        # If the coordinate is negative, append the appropriate cardinal direction for negative coordinates
        cardinal_direction = 'S' if is_latitude else 'W'

    # Return the coordinate as a string with the cardinal direction appended
    if is_latitude:
        return f"{abs(coordinate*100):08.0f}{cardinal_direction}"
    else:
        return f"{abs(coordinate*100):09.0f}{cardinal_direction}"

# Add the cardinal direction to the latitude and longitude
latitude = latitude_sexagesimal.apply(add_cardinal_direction)
longitude = longitude_sexagesimal.apply(lambda coord: add_cardinal_direction(coord, is_latitude=False))

def add_cardinal_direction(coordinate, is_latitude=True): # Check if the coordinate is positive or negative if coordinate >= 0: # If the coordinate is positive, append the appropriate cardinal direction for positive coordinates cardinal_direction = 'N' if is_latitude else 'E' else: # If the coordinate is negative, append the appropriate cardinal direction for negative coordinates cardinal_direction = 'S' if is_latitude else 'W' # Return the coordinate as a string with the cardinal direction appended if is_latitude: return f"{abs(coordinate*100):08.0f}{cardinal_direction}" else: return f"{abs(coordinate*100):09.0f}{cardinal_direction}" # Add the cardinal direction to the latitude and longitude latitude = latitude_sexagesimal.apply(add_cardinal_direction) longitude = longitude_sexagesimal.apply(lambda coord: add_cardinal_direction(coord, is_latitude=False))

In [ ]:

d = {'STATION': station, 'TB UNIX': tb_unix, 'TB UTC': tb_utc_time, 'DATE': tb_utc_date, ' ' : '0 [OK]', '  ' : '0 [OK]', '   ' : '0.0 [OK]', 'LINE': line, 'FFID': ffid}
df = pd.DataFrame(data=d)
pd.set_option('display.float_format','{:.6f}'.format)
pd.set_option('display.max_rows', df.shape[0]+1)

d = {'STATION': station, 'TB UNIX': tb_unix, 'TB UTC': tb_utc_time, 'DATE': tb_utc_date, ' ' : '0 [OK]', ' ' : '0 [OK]', ' ' : '0.0 [OK]', 'LINE': line, 'FFID': ffid} df = pd.DataFrame(data=d) pd.set_option('display.float_format','{:.6f}'.format) pd.set_option('display.max_rows', df.shape[0]+1)

In [ ]:

sbs_ffid_file = filename[:-3]+'sbs_ffid'
df.to_csv(sbs_ffid_file,sep='\t',index=False,header=False,float_format='{:.6f}'.format)

sbs_ffid_file = filename[:-3]+'sbs_ffid' df.to_csv(sbs_ffid_file,sep='\t',index=False,header=False,float_format='{:.6f}'.format)

In [ ]:

df2 = pd.DataFrame({
    'Line': line,
    'Station': station,
    'Median Latitude WGS84': latitude,
    'Median Longitude WGS84': longitude,
    'Serial Number': 123456789,
    'Median Altitude_m': 0,
})
# Keep only unique pairs of Line and Station
df2 = df2.drop_duplicates(subset=['Line', 'Station'])

# Define or update the dictionary with the desired column widths
col_widths = {'Line': 17, 'Station': 7, 'Median Latitude WGS84': 8,
              'Median Longitude WGS84': 8, 'Serial Number': 26,
              'Median Altitude_m': 4}

# Define a dictionary to indicate which columns should have spaces around them
# True means add spaces around the column, False means do not add spaces
add_spaces = {'Line': True, 'Station': True, 'Median Latitude WGS84': False,
              'Median Longitude WGS84': False, 'Serial Number': True,
              'Median Altitude_m': False}

# Convert each column to a string and fix the width
for col in df2.columns:
    if add_spaces[col]:
        # Add spaces around the column content
        df2[col] = df2[col].astype(str).apply(lambda x: f" {x} ".rjust(col_widths[col] + 1, ' '))
    else:
        # Do not add spaces around the column content
        df2[col] = df2[col].astype(str).apply(lambda x: x.rjust(col_widths[col], ' '))

# Concatenate the columns to form the DataFrame string, selectively adding spaces
def concatenate_row(row):
    row_string = ''
    for col in df2.columns:
        if add_spaces[col]:
            # Add the column with spaces
            row_string += row[col]
        else:
            # Add the column without extra spaces
            row_string += row[col].strip()
    return row_string

df2_string = df2.apply(concatenate_row, axis=1).str.cat(sep='\n')

# Add a newline character to the end of the string
df2_string += '\n'

sp1_file = filename[:-3] + 'sp1'

# Open a file in write mode
with open(sp1_file, 'w') as f:
    # Write the string to the file
    f.write(df2_string)

df2 = pd.DataFrame({ 'Line': line, 'Station': station, 'Median Latitude WGS84': latitude, 'Median Longitude WGS84': longitude, 'Serial Number': 123456789, 'Median Altitude_m': 0, }) # Keep only unique pairs of Line and Station df2 = df2.drop_duplicates(subset=['Line', 'Station']) # Define or update the dictionary with the desired column widths col_widths = {'Line': 17, 'Station': 7, 'Median Latitude WGS84': 8, 'Median Longitude WGS84': 8, 'Serial Number': 26, 'Median Altitude_m': 4} # Define a dictionary to indicate which columns should have spaces around them # True means add spaces around the column, False means do not add spaces add_spaces = {'Line': True, 'Station': True, 'Median Latitude WGS84': False, 'Median Longitude WGS84': False, 'Serial Number': True, 'Median Altitude_m': False} # Convert each column to a string and fix the width for col in df2.columns: if add_spaces[col]: # Add spaces around the column content df2[col] = df2[col].astype(str).apply(lambda x: f" {x} ".rjust(col_widths[col] + 1, ' ')) else: # Do not add spaces around the column content df2[col] = df2[col].astype(str).apply(lambda x: x.rjust(col_widths[col], ' ')) # Concatenate the columns to form the DataFrame string, selectively adding spaces def concatenate_row(row): row_string = '' for col in df2.columns: if add_spaces[col]: # Add the column with spaces row_string += row[col] else: # Add the column without extra spaces row_string += row[col].strip() return row_string df2_string = df2.apply(concatenate_row, axis=1).str.cat(sep='\n') # Add a newline character to the end of the string df2_string += '\n' sp1_file = filename[:-3] + 'sp1' # Open a file in write mode with open(sp1_file, 'w') as f: # Write the string to the file f.write(df2_string)