Update unit conversion functions
This commit is contained in:
rasmusvt
parent
5241776df7
commit
4a987808fc
3 changed files with 160 additions and 121 deletions
|
|
@ -1,10 +1,11 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import os
|
||||
|
||||
|
||||
def read_battsmall(path):
|
||||
''' Reads BATTSMALL-data into a DataFrame.
|
||||
def read_batsmall(path):
|
||||
''' Reads BATSMALL-data into a DataFrame.
|
||||
|
||||
Input:
|
||||
path (required): string with path to datafile
|
||||
|
|
@ -21,47 +22,46 @@ def read_battsmall(path):
|
|||
|
||||
|
||||
def read_neware(path, summary=False, active_material_weight=None, molecular_weight=None):
|
||||
''' Reads electrochemistry data, currently only from the Neware battery cycler. Will convert to .csv if the filetype is .xlsx,
|
||||
which is the file format the Neware provides for the backup data. In this case it matters if summary is False or not. If file
|
||||
type is .csv, it will just open the datafile and it does not matter if summary is False or not.'''
|
||||
|
||||
''' Reads electrochemistry data, currently only from the Neware battery cycler. Will convert to .csv if the filetype is .xlsx,
|
||||
which is the file format the Neware provides for the backup data. In this case it matters if summary is False or not. If file
|
||||
type is .csv, it will just open the datafile and it does not matter if summary is False or not.'''
|
||||
from xlsx2csv import Xlsx2csv
|
||||
|
||||
# Convert from .xlsx to .csv to make readtime faster
|
||||
if filename.split('.')[-1] == 'xlsx':
|
||||
csv_details = ''.join(filename.split('.')[:-1]) + '_details.csv'
|
||||
csv_summary = ''.join(filename.split('.')[:-1]) + '_summary.csv'
|
||||
# Convert from .xlsx to .csv to make readtime faster
|
||||
if path.split('.')[-1] == 'xlsx':
|
||||
csv_details = ''.join(path.split('.')[:-1]) + '_details.csv'
|
||||
csv_summary = ''.join(path.split('.')[:-1]) + '_summary.csv'
|
||||
|
||||
Xlsx2csv(filename, outputencoding="utf-8").convert(csv_summary, sheetid=3)
|
||||
Xlsx2csv(filename, outputencoding="utf-8").convert(csv_details, sheetid=4)
|
||||
|
||||
if summary:
|
||||
df = pd.read_csv(csv_summary)
|
||||
else:
|
||||
df = pd.read_csv(csv_details)
|
||||
if not os.path.isfile(csv_summary):
|
||||
Xlsx2csv(path, outputencoding="utf-8").convert(csv_summary, sheetid=3)
|
||||
|
||||
elif filename.split('.')[-1] == 'csv':
|
||||
if not os.path.isfile(csv_details):
|
||||
Xlsx2csv(path, outputencoding="utf-8").convert(csv_details, sheetid=4)
|
||||
|
||||
df = pd.read_csv(filename)
|
||||
|
||||
|
||||
return df
|
||||
if summary:
|
||||
df = pd.read_csv(csv_summary)
|
||||
else:
|
||||
df = pd.read_csv(csv_details)
|
||||
|
||||
elif path.split('.')[-1] == 'csv':
|
||||
df = pd.read_csv(path)
|
||||
|
||||
|
||||
return df
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
#def process_battsmall_data(df, t='ms', C='mAh/g', I='mA', U='V'):
|
||||
|
||||
def process_battsmall_data(df, units=None, splice_cycles=None, molecular_weight=None):
|
||||
''' Takes BATTSMALL-data in the form of a DataFrame and cleans the data up and converts units into desired units.
|
||||
def process_batsmall_data(df, units=None, splice_cycles=None, molecular_weight=None):
|
||||
''' Takes BATSMALL-data in the form of a DataFrame and cleans the data up and converts units into desired units.
|
||||
Splits up into individual charge and discharge DataFrames per cycle, and outputs a list where each element is a tuple with the Chg and DChg-data. E.g. cycles[10][0] gives the charge data for the 11th cycle.
|
||||
|
||||
For this to work, the cycling program must be set to use the counter.
|
||||
|
||||
Input:
|
||||
df (required): A pandas DataFrame containing BATTSMALL-data, as obtained from read_battsmall().
|
||||
df (required): A pandas DataFrame containing BATSMALL-data, as obtained from read_batsmall().
|
||||
t (optional): Unit for time data. Defaults to ms.
|
||||
C (optional): Unit for specific capacity. Defaults to mAh/g.
|
||||
I (optional): Unit for current. Defaults mA.
|
||||
|
|
@ -71,32 +71,13 @@ def process_battsmall_data(df, units=None, splice_cycles=None, molecular_weight=
|
|||
cycles: A list with
|
||||
'''
|
||||
|
||||
#########################
|
||||
#### UNIT CONVERSION ####
|
||||
#########################
|
||||
|
||||
# Complete the list of units - if not all are passed, then default value will be used
|
||||
required_units = ['t', 'I', 'U', 'C']
|
||||
default_units = {'t': 'h', 'I': 'mA', 'U': 'V', 'C': 'mAh/g'}
|
||||
# Complete set of new units and get the units used in the dataset, and convert values in the DataFrame from old to new.
|
||||
new_units = set_units(units=units)
|
||||
old_units = get_old_units(df, kind='batsmall')
|
||||
df = unit_conversion(df=df, new_units=new_units, old_units=old_units, kind='batsmall')
|
||||
|
||||
if not units:
|
||||
units = default_units
|
||||
|
||||
if units:
|
||||
for unit in required_units:
|
||||
if unit not in units.values():
|
||||
units[unit] = default_units[unit]
|
||||
|
||||
|
||||
# Get the units used in the data set
|
||||
t_prev = df.columns[0].split()[-1].strip('[]')
|
||||
U_prev = df.columns[1].split()[-1].strip('[]')
|
||||
I_prev = df.columns[2].split()[-1].strip('[]')
|
||||
C_prev, m_prev = df.columns[4].split()[-1].strip('[]').split('/')
|
||||
prev_units = {'t': t_prev, 'I': I_prev, 'U': U_prev, 'C': C_prev}
|
||||
|
||||
# Convert all units to the desired units.
|
||||
df = unit_conversion(df=df, units=units)
|
||||
df.columns = ['TT', 'U', 'I', 'Z1', 'C', 'Comment']
|
||||
|
||||
# Replace NaN with empty string in the Comment-column and then remove all steps where the program changes - this is due to inconsistent values for current
|
||||
df[["Comment"]] = df[["Comment"]].fillna(value={'Comment': ''})
|
||||
|
|
@ -138,93 +119,98 @@ def process_neware_data(df, units=None, splice_cycles=None, active_material_weig
|
|||
#### UNIT CONVERSION ####
|
||||
#########################
|
||||
|
||||
# Complete set of new units and get the units used in the dataset, and convert values in the DataFrame from old to new.
|
||||
new_units = set_units(units=units)
|
||||
old_units = get_old_units(df, kind='neware')
|
||||
df = unit_conversion(df=df, new_units=new_units, old_units=old_units, kind='neware')
|
||||
|
||||
|
||||
# if active_material_weight:
|
||||
# df["SpecificCapacity(mAh/g)"] = df["Capacity(mAh)"] / (active_material_weight / 1000)
|
||||
|
||||
# if molecular_weight:
|
||||
# faradays_constant = 96485.3365 # [F] = C mol^-1 = As mol^-1
|
||||
# seconds_per_hour = 3600 # s h^-1
|
||||
# f = faradays_constant / seconds_per_hour * 1000.0 # [f] = mAh mol^-1
|
||||
|
||||
# df["IonsExtracted"] = (df["SpecificCapacity(mAh/g)"]*molecular_weight)/f
|
||||
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def unit_conversion(df, new_units, old_units, kind):
|
||||
from . import unit_tables
|
||||
|
||||
if kind == 'batsmall':
|
||||
|
||||
df["TT [{}]".format(old_units["time"])] = df["TT [{}]".format(old_units["time"])] * unit_tables.time()[old_units["time"]].loc[new_units['time']]
|
||||
df["U [{}]".format(old_units["voltage"])] = df["U [{}]".format(old_units["voltage"])] * unit_tables.voltage()[old_units["voltage"]].loc[new_units['voltage']]
|
||||
df["I [{}]".format(old_units["current"])] = df["I [{}]".format(old_units["current"])] * unit_tables.current()[old_units["current"]].loc[new_units['current']]
|
||||
df["C [{}/{}]".format(old_units["capacity"], old_units["mass"])] = df["C [{}/{}]".format(old_units["capacity"], old_units["mass"])] * (unit_tables.capacity()[old_units["capacity"]].loc[new_units["capacity"]] / unit_tables.mass()[old_units["mass"]].loc[new_units["mass"]])
|
||||
|
||||
|
||||
if kind == 'neware':
|
||||
df['Current({})'.format(old_units['current'])] = df['Current({})'.format(old_units['current'])] * unit_tables.current()[old_units['current']].loc[new_units['current']]
|
||||
df['Voltage({})'.format(old_units['voltage'])] = df['Voltage({})'.format(old_units['voltage'])] * unit_tables.voltage()[old_units['voltage']].loc[new_units['voltage']]
|
||||
df['Capacity({})'.format(old_units['capacity'])] = df['Capacity({})'.format(old_units['capacity'])] * unit_tables.capacity()[old_units['capacity']].loc[new_units['capacity']]
|
||||
df['Energy({})'.format(old_units['energy'])] = df['Energy({})'.format(old_units['energy'])] * unit_tables.energy()[old_units['energy']].loc[new_units['energy']]
|
||||
|
||||
df['RelativeTime({})'.format(new_units['time'])] = df.apply(lambda row : convert_time_string(row['Relative Time(h:min:s.ms)'], unit=new_units['time']), axis=1)
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def set_units(units=None):
|
||||
|
||||
# Complete the list of units - if not all are passed, then default value will be used
|
||||
required_units = ['t', 'I', 'U', 'C']
|
||||
default_units = {'t': 'h', 'I': 'mA', 'U': 'V', 'C': 'mAh/g'}
|
||||
required_units = ['time', 'current', 'voltage', 'capacity', 'mass', 'energy']
|
||||
default_units = {'time': 'h', 'current': 'mA', 'voltage': 'V', 'capacity': 'mAh', 'mass': 'g', 'energy': 'mWh'}
|
||||
|
||||
if not units:
|
||||
units = default_units
|
||||
|
||||
if units:
|
||||
for unit in required_units:
|
||||
if unit not in units.values():
|
||||
if unit not in units.keys():
|
||||
units[unit] = default_units[unit]
|
||||
|
||||
|
||||
return units
|
||||
|
||||
|
||||
|
||||
def get_old_units(df, kind):
|
||||
|
||||
if kind=='batsmall':
|
||||
time = df.columns[0].split()[-1].strip('[]')
|
||||
voltage = df.columns[1].split()[-1].strip('[]')
|
||||
current = df.columns[2].split()[-1].strip('[]')
|
||||
capacity, mass = df.columns[4].split()[-1].strip('[]').split('/')
|
||||
old_units = {'time': time, 'current': current, 'voltage': voltage, 'capacity': capacity, 'mass': mass}
|
||||
|
||||
# Get the units used in the data set
|
||||
t_prev = 's' # default in
|
||||
U_prev = df.columns[1].split()[-1].strip('[]')
|
||||
I_prev = df.columns[2].split()[-1].strip('[]')
|
||||
C_prev, m_prev = df.columns[4].split()[-1].strip('[]').split('/')
|
||||
prev_units = {'t': t_prev, 'I': I_prev, 'U': U_prev, 'C': C_prev}
|
||||
if kind=='neware':
|
||||
|
||||
for column in df.columns:
|
||||
if 'Voltage' in column:
|
||||
voltage = column.split('(')[-1].strip(')')
|
||||
elif 'Current' in column:
|
||||
current = column.split('(')[-1].strip(')')
|
||||
elif 'Capacity' in column:
|
||||
capacity = column.split('(')[-1].strip(')')
|
||||
elif 'Energy' in column:
|
||||
energy = column.split('(')[-1].strip(')')
|
||||
|
||||
# Convert all units to the desired units.
|
||||
df = unit_conversion(df=df, units=units)
|
||||
|
||||
|
||||
|
||||
if active_material_weight:
|
||||
df["SpecificCapacity(mAh/g)"] = df["Capacity(mAh)"] / (active_material_weight / 1000)
|
||||
|
||||
if molecular_weight:
|
||||
faradays_constant = 96485.3365 # [F] = C mol^-1 = As mol^-1
|
||||
seconds_per_hour = 3600 # s h^-1
|
||||
f = faradays_constant / seconds_per_hour * 1000.0 # [f] = mAh mol^-1
|
||||
|
||||
df["IonsExtracted"] = (df["SpecificCapacity(mAh/g)"]*molecular_weight)/f
|
||||
|
||||
|
||||
def unit_conversion(df, units, prev_units, kind):
|
||||
|
||||
C, m = units['C'].split('/')
|
||||
C_prev, m_prev = prev_units['C'].split('/')
|
||||
|
||||
|
||||
# Define matrix for unit conversion for time
|
||||
t_units_df = {'h': [1, 60, 3600, 3600000], 'min': [1/60, 1, 60, 60000], 's': [1/3600, 1/60, 1, 1000], 'ms': [1/3600000, 1/60000, 1/1000, 1]}
|
||||
t_units_df = pd.DataFrame(t_units_df)
|
||||
t_units_df.index = ['h', 'min', 's', 'ms']
|
||||
|
||||
# Define matrix for unit conversion for current
|
||||
I_units_df = {'A': [1, 1000, 1000000], 'mA': [1/1000, 1, 1000], 'uA': [1/1000000, 1/1000, 1]}
|
||||
I_units_df = pd.DataFrame(I_units_df)
|
||||
I_units_df.index = ['A', 'mA', 'uA']
|
||||
|
||||
# Define matrix for unit conversion for voltage
|
||||
U_units_df = {'V': [1, 1000, 1000000], 'mV': [1/1000, 1, 1000], 'uV': [1/1000000, 1/1000, 1]}
|
||||
U_units_df = pd.DataFrame(U_units_df)
|
||||
U_units_df.index = ['V', 'mV', 'uV']
|
||||
|
||||
# Define matrix for unit conversion for capacity
|
||||
C_units_df = {'Ah': [1, 1000, 1000000], 'mAh': [1/1000, 1, 1000], 'uAh': [1/1000000, 1/1000, 1]}
|
||||
C_units_df = pd.DataFrame(C_units_df)
|
||||
C_units_df.index = ['Ah', 'mAh', 'uAh']
|
||||
|
||||
# Define matrix for unit conversion for capacity
|
||||
m_units_df = {'kg': [1, 1000, 1000000, 1000000000], 'g': [1/1000, 1, 1000, 1000000], 'mg': [1/1000000, 1/1000, 1, 1000], 'ug': [1/1000000000, 1/1000000, 1/1000, 1]}
|
||||
m_units_df = pd.DataFrame(m_units_df)
|
||||
m_units_df.index = ['kg', 'g', 'mg', 'ug']
|
||||
|
||||
#print(df["TT [{}]".format(t_prev)])
|
||||
df["TT [{}]".format(t_prev)] = df["TT [{}]".format(t_prev)] * t_units_df[t_prev].loc[units['t']]
|
||||
df["U [{}]".format(U_prev)] = df["U [{}]".format(U_prev)] * U_units_df[U_prev].loc[units['U']]
|
||||
df["I [{}]".format(I_prev)] = df["I [{}]".format(I_prev)] * I_units_df[I_prev].loc[units['I']]
|
||||
df["C [{}/{}]".format(C_prev, m_prev)] = df["C [{}/{}]".format(C_prev, m_prev)] * (C_units_df[C_prev].loc[C] / m_units_df[m_prev].loc[m])
|
||||
|
||||
df.columns = ['TT', 'U', 'I', 'Z1', 'C', 'Comment']
|
||||
|
||||
|
||||
|
||||
return df
|
||||
old_units = {'voltage': voltage, 'current': current, 'capacity': capacity, 'energy': energy}
|
||||
|
||||
|
||||
return old_units
|
||||
|
||||
def convert_time_string(time_string, unit='ms'):
|
||||
''' Convert time string from Neware-data with the format hh:mm:ss.xx to any given unit'''
|
||||
|
||||
h, m, s = time_string.split(':')
|
||||
ms = int(s)*1000 + int(m)*1000*60 + int(h)*1000*60*60
|
||||
ms = float(s)*1000 + int(m)*1000*60 + int(h)*1000*60*60
|
||||
|
||||
factors = {'ms': 1, 's': 1/1000, 'min': 1/(1000*60), 'h': 1/(1000*60*60)}
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue