load_mossbauer.py

from objects import mossbauer_sample as m
from objects import mossbauer_sample_set as mset
from cacheout import Cache
import urllib.parse
import numpy as np
import xlrd
import os.path
import csv
import re
import os.path, time
from datetime import datetime
import glob
cache = Cache()

#SPECTRA_PATH = 'A:/UMass Projects/Superman - Mats/Spectrum Explorer/spectra/';
SPECTRA_PATH = '/srv/nfs/common/spectra/';

def to_digit(text):
	return int(text) if text.isdigit() else text

def get_rows_from_excel():
	file = SPECTRA_PATH + 'Mossbauer/MHC/mlogbook.xlsx'
	offset = 1;
	workbook = xlrd.open_workbook(file)
	worksheet = workbook.sheet_by_index(0)

	rows = []
	for i, row in enumerate(range(worksheet.nrows)):
		if i <= offset:  # (Optionally) skip headers
			continue
		r = []
		for j, col in enumerate(range(worksheet.ncols)):
			r.append(worksheet.cell_value(i, j))
		rows.append(r)
	return rows

def list_badfiles():
	ls_not_in_server = []
	ls_files_in_book = []
	rows = get_rows_from_excel()
	for row in rows:
		sample = m.mossbauer_sample()
		sample.sample_no = str(row[0]).replace('.0','')
		sample.sampleurl = SPECTRA_PATH + 'Mossbauer/MHC/original/' + sample.sample_no + '.cnt'
		ls_files_in_book.append(sample.sampleurl)
		if not os.path.exists(sample.sampleurl):
		#File does not exist
			ls_not_in_server.append(sample.sampleurl)
	ls_files_in_dir  = glob.glob(SPECTRA_PATH+'Mossbauer/MHC/original/*.cnt')
	ls_not_book =  [item for item in ls_files_in_dir if item not in ls_files_in_book]
	return ls_not_book, ls_not_in_server

def load_data():
	mossbauer_sample_list = cache.get('moss_sample_list')
	if mossbauer_sample_list is not None:
		return mossbauer_sample_list
	else:
		mossbauer_sample_list = []
		rows = get_rows_from_excel()

		for row in rows:
			sample = m.mossbauer_sample()
			sample.sample_no = str(row[0]).replace('.0','')
			sample.temperature = to_digit(str(row[1]).replace('.0','')) 
			sample.sample_name = row[2]
			sample.weight = row[3]
			sample.is_post = row[4]
			sample.dana_group = row[5]
			sample.group_folder = row[6]
			sample.perc_Comp = row[7]
			sample.owner = row[9]
			sample.pubs = row[10]			
			sample.multitemp = row[11]
			sample.datafile_display_link = '/datafile/'+sample.sample_no
			sample.textfile_display_link = '/textfile/'+sample.sample_no
			sample.sampleurl = SPECTRA_PATH + 'Mossbauer/MHC/original/' + sample.sample_no + '.cnt'
			try:
				sample.sampletakentime = datetime.strftime(datetime.strptime(sample.sample_no[:6], '%y%m%d'),'%b %d, %Y')
			except ValueError:
				sample.sampletakentime = 'TBD'
			sample.last_modified_time = "No File"
			if os.path.exists(sample.sampleurl):
				sample.last_modified_time = time.ctime(os.path.getmtime(sample.sampleurl))

			mossbauer_sample_list.append(sample)
		cache.set('moss_sample_list',mossbauer_sample_list,10000)
	return mossbauer_sample_list

def get_data_file(cnt_no):
	return SPECTRA_PATH + 'Mossbauer/MHC/original/' + cnt_no + '.cnt'

def get_text_file(cnt_no):
	return SPECTRA_PATH + 'Mossbauer/MHC/original/' + cnt_no + '.txt'

def get_group_names():
	moss_list = load_data()
	seen = set()
	unique = [mbs.group_folder for mbs in moss_list if mbs.group_folder not in seen and not seen.add(mbs.group_folder)]
	unique.sort()
	group_list = []
	for g in unique:
		group_list.append({'groupname':g, 'groupurl': urllib.parse.quote_plus(g, safe='')})
	return group_list

def get_samples_for_group(group_folder):
	decoded_group_folder = urllib.parse.unquote_plus(group_folder)
	moss_list = load_data()
	seen = set()
	samples = [mbs for mbs in moss_list if mbs.group_folder.lower() == decoded_group_folder.lower() and mbs.sample_name not in seen and not seen.add(mbs.sample_name) and mbs.is_post == 'Y']
	samples.sort(key=lambda x: x.sample_name, reverse=False)
	samples_set = []
	for s in samples:
		sampleset = mset.mossbauer_sample_set()
		sampleset.sample_name = s.sample_name
		sampleset.dana_group = s.dana_group
		sampleset.group_folder = s.group_folder
		sampleset.weight = s.weight
		sampleset.is_post = s.is_post
		sampleset.perc_Comp = s.perc_Comp
		sampleset.owner = s.owner
		sampleset.temp_class = 'bolden' if s.multitemp == 'Y' else 'unbolden'

		samples_set.append(sampleset)
	return samples_set, decoded_group_folder

def get_sample(sample_name):
	decoded_sample = sample_name
	moss_list = load_data()
	sample_list = [mbs for mbs in moss_list if mbs.sample_name.lower() == decoded_sample.lower()]
	sample_list.sort(key=lambda x: x.temperature, reverse=False)
	name = sample_list[0].sample_name
	group = sample_list[0].group_folder
	dana_group = sample_list[0].dana_group
	owner = sample_list[0].owner
	pubs = sample_list[0].pubs
	return sample_list, name, group, dana_group,owner, pubs

def get_sample_temperature(sample_no):
	moss_list = load_data()
	sample_temperature = [s for s in moss_list if s.sample_no.lower() == sample_no.lower()]
	name = sample_temperature[0].sample_name
	temperature = sample_temperature[0].temperature
	plot_data = get_sample_plot_data(sample_temperature[0])
	return {'sample_no':sample_no, 'sample_name':name, 'temperature':temperature, 'plot':plot_data}

def spectrum_plot_data(sample_name):
	decoded_sample = sample_name
	moss_list = load_data()
	sample_list = [mbs for mbs in moss_list if mbs.sample_name.lower() == decoded_sample.lower()]
	sample_list.sort(key=lambda x: x.temperature, reverse=False)
	sample_set_plot = [];
	for sample in sample_list:
		plot_data = get_sample_plot_data(sample)
		sample_set_plot.append({'sample_no':sample.sample_no, 'plot': plot_data, 'temperature':sample.temperature, 'sample_name':decoded_sample})

	return sample_set_plot

def get_sample_plot_data(sample):
	file = sample.sampleurl
	intensity_list = []
	plot_data = []
	midpoint = 0
	gradient = 0

	# SOme files are not found and this affects all the other samples in sample set
	if not os.path.exists(file):
		return []

	with open(file,'r') as tsvin:
	    tsvin = csv.reader(tsvin, delimiter='\t')

	    for i,row in enumerate(tsvin):
	    	for col in row:
	    		if i == 9:
	    			listvals = col.split(' ')
	    			midpoint = float(listvals[2])
	    			gradient = float(listvals[4])
	    		
	    		if i > 9:
	    			intensity_list.append(float(col.strip()))

	max_spec_intensity = max(intensity_list)
	for i, channel_intensity in enumerate(intensity_list):
		channel = i+1
		x_val = (channel - midpoint)*gradient
		y_val = 1-(channel_intensity/max_spec_intensity)
		plot_data.append({'x':x_val, 'y':y_val})
	
	return plot_data

def searchResult(query):
	#Only on sample name and group folder
	moss_list = load_data()
	seen = set()
	search = []
	group = [mbs.group_folder for mbs in moss_list if query.lower() in mbs.group_folder.lower() and mbs.group_folder not in seen and not seen.add(mbs.group_folder) ]
	group.sort()

	sample_seen = set()
	samples = [mbs for mbs in moss_list if mbs.sample_name not in sample_seen and not sample_seen.add(mbs.sample_name) and mbs.is_post == 'Y' and query.lower() in mbs.sample_name.lower()]
	samples.sort(key=lambda x: x.sample_name, reverse=False)
	samples_set = []
	for s in samples:
		sampleset = mset.mossbauer_sample_set()
		sampleset.sample_name = s.sample_name
		sampleset.dana_group = s.dana_group
		sampleset.group_folder = s.group_folder
		sampleset.weight = s.weight
		sampleset.is_post = s.is_post
		sampleset.perc_Comp = s.perc_Comp
		sampleset.owner = s.owner
		sampleset.url = urllib.parse.quote_plus(s.sample_name)        
		sampleset.temp_class = 'bolden' if s.multitemp == 'Y' else 'unbolden'

		samples_set.append(sampleset)

	return group,samples_set