import jcamp as jc
import numpy as np
from tempfile import NamedTemporaryFile
class JcampParser:
'''This module is designed to help retrieve spectral data as well as metadata of smaples from jcamp file'''
def __init__(self, path):
#self.__path = path.replace('\\','/')
self.__path = path
self.__dxfile = jc.jcamp_readfile(self.__path)
# Access samples data
self.__nb = self.__dxfile['blocks'] # Get the total number of blocks = The total number of scanned samples
self.__list_of_blocks = self.__dxfile['children'] # Store all blocks within a a list
self.__wl = self.__list_of_blocks[0]["x"] # Wavelengths/frequencies/range
def parse(self):
# Start retreiving the data
specs = np.zeros((self.__nb, len(self.__list_of_blocks[0]["y"])), dtype=float) # preallocate a np matrix for sotoring spectra
self.idx = np.arange(self.__nb) # This list is designed to store samples name
self.__met = {}
for i in range(self.__nb): # Loop over the blocks
specs[i] = self.__list_of_blocks[i]['y']
block = self.__list_of_blocks[i]
block_met = { 'name': block['title'],
'origin': block['origin'],
'date': block['date'],
#'time': block['time'],
'spectrometer': block['spectrometer/data system'].split('\n$$')[0],
'n_scans':block['spectrometer/data system'].split('\n$$')[6].split('=')[1],
'resolution': block['spectrometer/data system'].split('\n$$')[8].split('=')[1],
#'instrumental parameters': block['instrumental parameters'],
'xunits': block['xunits'],
'yunits': block['yunits'],
#'xfactor': block['xfactor'],
#'yfactor': block['yfactor'],
'firstx': block['firstx'],
'lastx': block['lastx'],
#'firsty':block['firsty'],
#'miny': block['miny'],
#'maxy': block['maxy'],
'npoints': block['npoints'],
'concentrations':block['concentrations'],
#'deltax':block['deltax']
}
self.__met[f'{i}'] = block_met
from pandas import DataFrame
self.metadata_ = DataFrame(self.__met).T
self.spectra = DataFrame(np.fliplr(specs), columns= self.__wl[::-1], index = self.metadata_['name']) # Storing spectra in a dataframe
#### Concentrarions
self.pattern = r"\(([^,]+),(\d+(\.\d+)?),([^)]+)"
aa = self.__list_of_blocks[0]['concentrations']
a = '\n'.join(line for line in aa.split('\n') if "NCU" not in line and "<<undef>>" not in line)
n_elements = a.count('(')
## Get the name of analyzed chamical elements
import re
elements_name = []
for match in re.findall(self.pattern, a):
elements_name.append(match[0])
## Retrieve concentrations
df = self.metadata_['concentrations']
cc = {}
for i in range(self.metadata_.shape[0]):
cc[df.index[i]] = self.conc(df[str(i)])
### dataframe conntaining chemical data
self.chem_data = DataFrame(cc, index=elements_name).T.astype(float)
self.chem_data.index = self.metadata_['name']
### Method for retrieving the concentration of a single sample
def conc(self,sample):
import re
prep = '\n'.join(line for line in sample.split('\n') if "NCU" not in line and "<<undef>>" not in line)
c = []
for match in re.findall(self.pattern, prep):
c.append(match[1])
concentration = np.array(c)
return concentration
@property
def specs_df_(self):
return self.spectra
@property
def meta_data_st_(self):
me = self.metadata_.drop("concentrations", axis = 1)
me = me.drop(me.columns[(me == '').all()], axis = 1).applymap(lambda x: x.upper() if isinstance(x, str) else x)
meta_data_st = me.loc[:,me.nunique(axis=0) > 1]
return meta_data_st
@property
def meta_data(self):
return self.metadata_.drop("concentrations", axis = 1)
@property
def chem_data_(self):
return self.chem_data
class CsvParser:
import clevercsv
def __init__(self, file):
with NamedTemporaryFile(delete = False, suffix = ".csv") as tmp:
tmp.write(file.read())
self.file = tmp.name
def parse(self, decimal, separator, index_col, header):
from pandas import read_csv
self.df = read_csv(self.file, decimal = decimal, sep = separator, index_col = index_col, header = header)
if len(set(self.df.index))<self.df.shape[0]:
self.df = read_csv(self.file, decimal = decimal, sep = separator, index_col = None, header = header)
self.float, self.non_float = self.df.select_dtypes(include='float'), self.df.select_dtypes(exclude='float')
@property
def meta_data_st_(self):
me = self.non_float.applymap(lambda x: x.upper() if isinstance(x, str) else x)
meta_data_st = me.loc[:,me.nunique(axis=0) > 1]
return meta_data_st
@property
def meta_data(self):
return self.non_float
# def parse(self):
# import pandas as pd
# dec_dia = ['.', ',']
# sep_dia = [',', ';']
# dec, sep = [], []
# with open(self.file, mode = 'r') as csvfile:
# lines = [csvfile.readline() for i in range(3)]
# for i in lines:
# for j in range(2):
# dec.append(i.count(dec_dia[j]))
# sep.append(i.count(sep_dia[j]))
# if dec[0] != dec[2]:
# header = 0
# else:
# header = 0
# semi = np.sum([sep[2*i+1] for i in range(3)])
# commas = np.sum([sep[2*i] for i in range(3)])
# if semi>commas:separator = ';'
# elif semi<commas: separator = ','
# elif semi ==0 and commas == 0: separator = ';'
# commasdec = np.sum([dec[2*i+1] for i in range(1,3)])
# dot = np.sum([dec[2*i] for i in range(1,3)])
# if commasdec>dot:decimal = ','
# elif commasdec<=dot:decimal = '.'
# if decimal == separator or len(np.unique(dec)) <= 2:
# decimal = "."
# df = pd.read_csv(self.file, decimal=decimal, sep=separator, header=None, index_col=None)
# try:
# rat = np.mean(df.iloc[0,50:60]/df.iloc[5,50:60])>10
# header = 0 if rat or np.nan else None
# except:
# header = 0
# from pandas.api.types import is_float_dtype
# if is_float_dtype(df.iloc[1:,0]):
# index_col = None
# else:
# try:
# te = df.iloc[1:,0].to_numpy().astype(float).dtype
# except:
# te = set(df.iloc[1:,0])
# if len(te) == df.shape[0]-1:
# index_col = 0
# elif len(te) < df.shape[0]-1:
# index_col = None
# else:
# index_col = None
# # index_col = 0 if len(set(df.iloc[1:,0])) == df.shape[0]-1 and is_float_dtype(df.iloc[:,0])==False else None
# df = pd.read_csv(self.file, decimal=decimal, sep=separator, header=header, index_col=index_col)
# # st.write(decimal, separator, index_col, header)
# if df.select_dtypes(exclude='float').shape[1] >0:
# non_float = df.select_dtypes(exclude='float')
# else:
# non_float = pd.DataFrame()
# if df.select_dtypes(include='float').shape[1] >0:
# float_data = df.select_dtypes(include='float')
# else:
# float_data = pd.DataFrame()
# return float_data, non_float
# ############## new function
# def csv_loader(file):
# import clevercsv
# import numpy as np
# import pandas as pd
# dec_dia = ['.',',']
# sep_dia = [',',';']
# dec, sep = [], []
# with open(file, mode = 'r') as csvfile:
# lines = [csvfile.readline() for i in range(3)]
# for i in lines:
# for j in range(2):
# dec.append(i.count(dec_dia[j]))
# sep.append(i.count(sep_dia[j]))
# if dec[0] != dec[2]:
# header = 0
# else:
# header = 0
# semi = np.sum([sep[2*i+1] for i in range(3)])
# commas = np.sum([sep[2*i] for i in range(3)])
# if semi>commas:separator = ';'
# elif semi<commas: separator = ','
# elif semi ==0 and commas == 0: separator = ';'
# commasdec = np.sum([dec[2*i+1] for i in range(1,3)])
# dot = np.sum([dec[2*i] for i in range(1,3)])
# if commasdec>dot:decimal = ','
# elif commasdec<=dot:decimal = '.'
# if decimal == separator or len(np.unique(dec)) <= 2:
# decimal = "."
# df = pd.read_csv(file, decimal=decimal, sep=separator, header=None, index_col=None)
# try:
# rat = np.mean(df.iloc[0,50:60]/df.iloc[5,50:60])>10
# header = 0 if rat or np.nan else None
# except:
# header = 0
# from pandas.api.types import is_float_dtype
# if is_float_dtype(df.iloc[1:,0]):
# index_col = None
# else:
# try:
# te = df.iloc[1:,0].to_numpy().astype(float).dtype
# except:
# te = set(df.iloc[1:,0])
# if len(te) == df.shape[0]-1:
# index_col = 0
# elif len(te) < df.shape[0]-1:
# index_col = None
# else:
# index_col = None
# # index_col = 0 if len(set(df.iloc[1:,0])) == df.shape[0]-1 and is_float_dtype(df.iloc[:,0])==False else None
# df = pd.read_csv(file, decimal=decimal, sep=separator, header=header, index_col=index_col)
# # st.write(decimal, separator, index_col, header)
# if df.select_dtypes(exclude='float').shape[1] >0:
# non_float = df.select_dtypes(exclude='float')
# else:
# non_float = pd.DataFrame()
# if df.select_dtypes(include='float').shape[1] >0:
# float_data = df.select_dtypes(include='float')
# else:
# float_data = pd.DataFrame()
# return float_data, non_float