#!/usr/bin/env python3
#
# witzit-load
#
# Copyright (C) 2022, 2023, Jeff Moe
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
# witzit-load
# Load a sample from a Olympus Vanta XRF
#
# Usage:
# witzit-load [filename]
# Examples:
# witzit-load ./examples/olympus-vanta.csv
# witzit-load ./examples/sciaps-x555.mca

import pandas as pd
import sys
import re

# Determine what type of file to load to dataframe
datafile=(sys.argv[1])

with open(datafile) as f:
    firstline = f.readline().rstrip()

if firstline == 'File Version = 2':
    print('SciAps X-555 XRF MCA')
    template = pd.read_csv('template/sciaps-x555-ev.csv', header=0,
                     skiprows=0,
                     usecols = [0])
    mca = pd.read_csv(datafile, skiprows=21, header=0,
                     usecols = [0])
    df=template.join(mca)
    df.rename(columns = {'2048':'Counts'}, inplace = True)

elif re.match(re.compile('Date,*'), firstline):
    print('Olympus Vanta-M XRF')
    df = pd.read_csv(datafile, header=0,
                     skiprows=39,
                     names=('energy (eV)', 'Counts'),
                     usecols = [0, 1])
    df['energy (eV)'] = df['energy (eV)'] * 1000

elif re.match(re.compile('"Date",*'), firstline):
    print('Possibly SciAps X-555 XRF CSV, not processed. Use .mca file instead of .csv.')
    exit()

else:
    print('Unknown file type.')
    exit()

# Print the Dataframe
with pd.option_context('display.max_rows', None,
                       'display.max_columns', None
                       ):
    print(df)