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Add plot-function to ec-module

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rasmusvt 2021-09-14 16:22:24 +02:00
parent 795c111807
commit 994a7d8c54
1 changed files with 105 additions and 34 deletions
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139
beamtime/electrochemistry/io.py
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@ -1,4 +1,6 @@
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def read_battsmall(path):
@ -16,36 +18,10 @@ def read_battsmall(path):
return df
def clean_battsmall_data(df, t='ms', C='mAh/g', I='mA', U='V'):
''' Takes BATTSMALL-data in the form of a DataFrame and cleans the data up and converts units into desired units.
Also adds a column indicating whether or not it is charge or discharge.
Input:
df (required): A pandas DataFrame containing BATTSMALL-data, as obtained from read_battsmall().
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.
U (optional): Unit for voltage. Defaults to V.
def unit_conversion(df, units):
Output:
df: A cleaned up DataFrame with desired units and additional column about charge/discharge.
'''
df = unit_conversion(df=df, t=t, C=C, I=I, U=U)
return df
def test_print():
print('IT WORKS!')
def unit_conversion(df, t, C, I, U):
print('YAHOO!')
C, m = C.split('/')
C, m = units['C'].split('/')
# Get the units used in the data set
t_prev = df.columns[0].split()[-1].strip('[]')
@ -53,7 +29,6 @@ def unit_conversion(df, t, C, I, U):
I_prev = df.columns[2].split()[-1].strip('[]')
C_prev, m_prev = df.columns[4].split()[-1].strip('[]').split('/')
print(t_prev)
# 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]}
@ -84,17 +59,101 @@ def unit_conversion(df, t, C, I, U):
#print(df["TT [{}]".format(t_prev)])
df["TT [{}]".format(t_prev)] = df["TT [{}]".format(t_prev)] * t_units_df[t_prev].loc[t]
df["U [{}]".format(U_prev)] = df["U [{}]".format(U_prev)] * U_units_df[U_prev].loc[U]
df["I [{}]".format(I_prev)] = df["I [{}]".format(I_prev)] * I_units_df[I_prev].loc[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["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[units['C']] / m_units_df[m_prev].loc[units['m']])
df.columns = ['TT [{}]'.format(t), 'U [{}]'.format(U), 'I [{}]'.format(I), 'Z1', 'C [{}/{}]'.format(C, m), 'Comment']
df.columns = ['TT', 'U', 'I', 'Z1', 'C', 'Comment']
return df
#def process_battsmall_data(df, t='ms', C='mAh/g', I='mA', U='V'):
def process_battsmall_data(df, units=None):
''' Takes BATTSMALL-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().
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.
U (optional): Unit for voltage. Defaults to V.
Output:
cycles: A list with
'''
required_units = ['t', 'I', 'U', 'C']
default_units = {'t': 'h', 'I': 'mA', 'U': 'V', 'C': 'mAh/g'}
if not units:
units = default_units
if units:
for unit in required_units:
if unit not in units.values():
units[unit] = default_units[unit]
# Convert all units to the desired units.
df = unit_conversion(df=df, units=units)
# 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 and
df[["Comment"]] = df[["Comment"]].fillna(value={'Comment': ''})
df = df[df["Comment"].str.contains("program")==False]
# Creates masks for
chg_mask = df['I'] >= 0
dchg_mask = df['I'] < 0
# Initiate cycles list
cycles = []
# Loop through all the cycling steps, change the current and capacities in the
for i in range(df["Z1"].max()):
sub_df = df.loc[df['Z1'] == i].copy()
sub_df.loc[dchg_mask, 'I'] *= -1
sub_df.loc[dchg_mask, 'C'] *= -1
chg_df = sub_df.loc[chg_mask]
dchg_df = sub_df.loc[dchg_mask]
cycles.append((chg_df, dchg_df))
return cycles
def plot_gc(cycles, which_cycles='all', chg=True, dchg=True, colours=None, x='C', y='U'):
fig, ax = prepare_gc_plot()
if which_cycles == 'all':
which_cycles = [i for i, c in enumerate(cycles)]
if not colours:
chg_colour = (40/255, 70/255, 75/255) # Dark Slate Gray #28464B
dchg_colour = (239/255, 160/255, 11/255) # Marigold #EFA00B
for i, cycle in cycles:
if i in which_cycles:
if chg:
cycle[0].plot(ax=ax)
@ -102,3 +161,15 @@ def unit_conversion(df, t, C, I, U):
def prepare_gc_plot(figsize=(14,7), dpi=None):
fig, ax = plt.subplots(figsize=figsize, dpi=dpi)
return fig, ax