rasmus/nafuma
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Add working unit conversion function for electrochemistry module

Browse source
This commit is contained in:
rasmusvt 2021-09-10 16:53:07 +02:00
parent e7f3bca376
commit d4b2832697
1 changed files with 37 additions and 10 deletions
Download patch file Download diff file Expand all files Collapse all files

45
beamtime/electrochemistry/io.py
View file

@ -10,7 +10,7 @@ def read_battsmall(path):
Output: Output:
df: pandas DataFrame containing the data as-is, but without additional NaN-columns.''' df: pandas DataFrame containing the data as-is, but without additional NaN-columns.'''
df = pd.read_csv(data1, skiprows=2, sep='\t') df = pd.read_csv(path, skiprows=2, sep='\t')
df = df.loc[:, ~df.columns.str.contains('^Unnamed')] df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
return df return df
@ -31,21 +31,29 @@ def clean_battsmall_data(df, t='ms', C='mAh/g', I='mA', U='V'):
df: A cleaned up DataFrame with desired units and additional column about charge/discharge. 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): def unit_conversion(df, t, C, I, U):
print('YAHOO!')
C, m = C.split('/')
# Get the units used in the data set # Get the units used in the data set
t_prev = df.columns[0].split()[-1].strip('[]') t_prev = df.columns[0].split()[-1].strip('[]')
U_prev = df.columns[1].split()[-1].strip('[]') U_prev = df.columns[1].split()[-1].strip('[]')
I_prev = df.columns[2].split()[-1].strip('[]') I_prev = df.columns[2].split()[-1].strip('[]')
C_prev_num, C_prev_den = df.columns[4].split()[-1].strip('[]').split('/') C_prev, m_prev = df.columns[4].split()[-1].strip('[]').split('/')
print(t_prev)
# Define matrix for unit conversion for time # 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 = {'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]}
@ -53,20 +61,39 @@ def unit_conversion(df, t, C, I, U):
t_units_df.index = ['h', 'min', 's', 'ms'] t_units_df.index = ['h', 'min', 's', 'ms']
# Define matrix for unit conversion for current # Define matrix for unit conversion for current
I_units_df = {'A': [1, 60, 3600, 3600000], 'mA': [1/60, 1, 60, 60000], 'uA': [1/3600, 1/60, 1, 1000]} 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 = pd.DataFrame(I_units_df)
I_units_df.index = ['A', 'mA', 'uA'] I_units_df.index = ['A', 'mA', 'uA']
# Define matrix for unit conversion for voltage # Define matrix for unit conversion for voltage
I_units_df = {'A': [1, 60, 3600, 3600000], 'mA': [1/60, 1, 60, 60000], 'uA': [1/3600, 1/60, 1, 1000]} U_units_df = {'V': [1, 1000, 1000000], 'mV': [1/1000, 1, 1000], 'uV': [1/1000000, 1/1000, 1]}
I_units_df = pd.DataFrame(I_units_df) U_units_df = pd.DataFrame(U_units_df)
I_units_df.index = ['A', 'mA', 'uA'] U_units_df.index = ['V', 'mV', 'uV']
# Define matrix for unit conversion for capacity # Define matrix for unit conversion for capacity
C_units_df = {'Ah': [1, 60, 3600, 3600000], 'mAh': [1/60, 1, 60, 60000], 'uAh': [1/3600, 1/60, 1, 1000]} 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 = pd.DataFrame(C_units_df)
C_units_df.index = ['Ah', 'mAh', 'uAh'] 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[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.columns = ['TT [{}]'.format(t), 'U [{}]'.format(U), 'I [{}]'.format(I), 'Z1', 'C [{}/{}]'.format(C, m), 'Comment']
return df