Change unit conversion function and add time string conversions
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rasmusvt
parent
2e910a2afb
commit
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1 changed files with 192 additions and 85 deletions
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@ -19,15 +19,166 @@ def read_battsmall(path):
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def unit_conversion(df, units):
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C, m = units['C'].split('/')
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def read_neware(path, summary=False, active_material_weight=None, molecular_weight=None):
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''' Reads electrochemistry data, currently only from the Neware battery cycler. Will convert to .csv if the filetype is .xlsx,
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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
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type is .csv, it will just open the datafile and it does not matter if summary is False or not.'''
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# Convert from .xlsx to .csv to make readtime faster
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if filename.split('.')[-1] == 'xlsx':
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csv_details = ''.join(filename.split('.')[:-1]) + '_details.csv'
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csv_summary = ''.join(filename.split('.')[:-1]) + '_summary.csv'
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Xlsx2csv(filename, outputencoding="utf-8").convert(csv_summary, sheetid=3)
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Xlsx2csv(filename, outputencoding="utf-8").convert(csv_details, sheetid=4)
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if summary:
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df = pd.read_csv(csv_summary)
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else:
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df = pd.read_csv(csv_details)
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elif filename.split('.')[-1] == 'csv':
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df = pd.read_csv(filename)
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return df
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#def process_battsmall_data(df, t='ms', C='mAh/g', I='mA', U='V'):
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def process_battsmall_data(df, units=None, splice_cycles=None, molecular_weight=None):
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''' Takes BATTSMALL-data in the form of a DataFrame and cleans the data up and converts units into desired units.
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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.
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For this to work, the cycling program must be set to use the counter.
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Input:
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df (required): A pandas DataFrame containing BATTSMALL-data, as obtained from read_battsmall().
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t (optional): Unit for time data. Defaults to ms.
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C (optional): Unit for specific capacity. Defaults to mAh/g.
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I (optional): Unit for current. Defaults mA.
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U (optional): Unit for voltage. Defaults to V.
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Output:
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cycles: A list with
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'''
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#########################
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#### UNIT CONVERSION ####
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#########################
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# Complete the list of units - if not all are passed, then default value will be used
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required_units = ['t', 'I', 'U', 'C']
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default_units = {'t': 'h', 'I': 'mA', 'U': 'V', 'C': 'mAh/g'}
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if not units:
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units = default_units
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if units:
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for unit in required_units:
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if unit not in units.values():
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units[unit] = default_units[unit]
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# Get the units used in the data set
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t_prev = df.columns[0].split()[-1].strip('[]')
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U_prev = df.columns[1].split()[-1].strip('[]')
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I_prev = df.columns[2].split()[-1].strip('[]')
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C_prev, m_prev = df.columns[4].split()[-1].strip('[]').split('/')
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prev_units = {'t': t_prev, 'I': I_prev, 'U': U_prev, 'C': C_prev}
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# Convert all units to the desired units.
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df = unit_conversion(df=df, units=units)
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# 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
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df[["Comment"]] = df[["Comment"]].fillna(value={'Comment': ''})
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df = df[df["Comment"].str.contains("program")==False]
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# Creates masks for charge and discharge curves
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chg_mask = df['I'] >= 0
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dchg_mask = df['I'] < 0
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# Initiate cycles list
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cycles = []
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# Loop through all the cycling steps, change the current and capacities in the
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for i in range(df["Z1"].max()):
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sub_df = df.loc[df['Z1'] == i].copy()
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sub_df.loc[dchg_mask, 'I'] *= -1
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sub_df.loc[dchg_mask, 'C'] *= -1
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chg_df = sub_df.loc[chg_mask]
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dchg_df = sub_df.loc[dchg_mask]
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# Continue to next iteration if the charge and discharge DataFrames are empty (i.e. no current)
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if chg_df.empty and dchg_df.empty:
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continue
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cycles.append((chg_df, dchg_df))
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return cycles
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def process_neware_data(df, units=None, splice_cycles=None, active_material_weight=None, molecular_weight=None):
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#########################
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#### UNIT CONVERSION ####
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#########################
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# Complete the list of units - if not all are passed, then default value will be used
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required_units = ['t', 'I', 'U', 'C']
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default_units = {'t': 'h', 'I': 'mA', 'U': 'V', 'C': 'mAh/g'}
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if not units:
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units = default_units
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if units:
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for unit in required_units:
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if unit not in units.values():
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units[unit] = default_units[unit]
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# Get the units used in the data set
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t_prev = 's' # default in
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U_prev = df.columns[1].split()[-1].strip('[]')
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I_prev = df.columns[2].split()[-1].strip('[]')
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C_prev, m_prev = df.columns[4].split()[-1].strip('[]').split('/')
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prev_units = {'t': t_prev, 'I': I_prev, 'U': U_prev, 'C': C_prev}
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# Convert all units to the desired units.
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df = unit_conversion(df=df, units=units)
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if active_material_weight:
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df["SpecificCapacity(mAh/g)"] = df["Capacity(mAh)"] / (active_material_weight / 1000)
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if molecular_weight:
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faradays_constant = 96485.3365 # [F] = C mol^-1 = As mol^-1
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seconds_per_hour = 3600 # s h^-1
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f = faradays_constant / seconds_per_hour * 1000.0 # [f] = mAh mol^-1
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df["IonsExtracted"] = (df["SpecificCapacity(mAh/g)"]*molecular_weight)/f
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def unit_conversion(df, units, prev_units, kind):
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C, m = units['C'].split('/')
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C_prev, m_prev = prev_units['C'].split('/')
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# Define matrix for unit conversion for time
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@ -55,14 +206,11 @@ def unit_conversion(df, units):
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m_units_df = pd.DataFrame(m_units_df)
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m_units_df.index = ['kg', 'g', 'mg', 'ug']
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#print(df["TT [{}]".format(t_prev)])
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df["TT [{}]".format(t_prev)] = df["TT [{}]".format(t_prev)] * t_units_df[t_prev].loc[units['t']]
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df["U [{}]".format(U_prev)] = df["U [{}]".format(U_prev)] * U_units_df[U_prev].loc[units['U']]
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df["I [{}]".format(I_prev)] = df["I [{}]".format(I_prev)] * I_units_df[I_prev].loc[units['I']]
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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']])
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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])
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df.columns = ['TT', 'U', 'I', 'Z1', 'C', 'Comment']
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@ -70,86 +218,60 @@ def unit_conversion(df, units):
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return df
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#def process_battsmall_data(df, t='ms', C='mAh/g', I='mA', U='V'):
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def process_battsmall_data(df, units=None):
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''' Takes BATTSMALL-data in the form of a DataFrame and cleans the data up and converts units into desired units.
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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.
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For this to work, the cycling program must be set to use the counter.
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Input:
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df (required): A pandas DataFrame containing BATTSMALL-data, as obtained from read_battsmall().
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t (optional): Unit for time data. Defaults to ms.
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C (optional): Unit for specific capacity. Defaults to mAh/g.
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I (optional): Unit for current. Defaults mA.
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U (optional): Unit for voltage. Defaults to V.
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Output:
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cycles: A list with
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'''
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required_units = ['t', 'I', 'U', 'C']
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default_units = {'t': 'h', 'I': 'mA', 'U': 'V', 'C': 'mAh/g'}
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if not units:
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units = default_units
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if units:
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for unit in required_units:
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if unit not in units.values():
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units[unit] = default_units[unit]
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# Convert all units to the desired units.
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df = unit_conversion(df=df, units=units)
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def convert_time_string(time_string, unit='ms'):
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''' Convert time string from Neware-data with the format hh:mm:ss.xx to any given unit'''
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# 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
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df[["Comment"]] = df[["Comment"]].fillna(value={'Comment': ''})
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df = df[df["Comment"].str.contains("program")==False]
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h, m, s = time_string.split(':')
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ms = int(s)*1000 + int(m)*1000*60 + int(h)*1000*60*60
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# Creates masks for
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chg_mask = df['I'] >= 0
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dchg_mask = df['I'] < 0
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factors = {'ms': 1, 's': 1/1000, 'min': 1/(1000*60), 'h': 1/(1000*60*60)}
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# Initiate cycles list
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cycles = []
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t = ms*factors[unit]
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# Loop through all the cycling steps, change the current and capacities in the
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for i in range(df["Z1"].max()):
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sub_df = df.loc[df['Z1'] == i].copy()
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sub_df.loc[dchg_mask, 'I'] *= -1
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sub_df.loc[dchg_mask, 'C'] *= -1
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chg_df = sub_df.loc[chg_mask]
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dchg_df = sub_df.loc[dchg_mask]
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cycles.append((chg_df, dchg_df))
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return cycles
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return t
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def plot_gc(cycles, which_cycles='all', chg=True, dchg=True, colours=None, x='C', y='U'):
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def convert_datetime_string(datetime_string, reference, unit='s'):
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''' Convert time string from Neware-data with the format yyy-mm-dd hh:mm:ss to any given unit'''
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fig, ax = prepare_gc_plot()
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from datetime import datetime
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# Parse the
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cur_date, cur_time = datetime_string.split()
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cur_y, cur_mo, cur_d = cur_date.split('-')
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cur_h, cur_m, cur_s = cur_time.split(':')
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cur_date = datetime(int(cur_y), int(cur_mo), int(cur_d), int(cur_h), int(cur_m), int(cur_s))
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if which_cycles == 'all':
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which_cycles = [i for i, c in enumerate(cycles)]
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ref_date, ref_time = reference.split()
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ref_y, ref_mo, ref_d = ref_date.split('-')
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ref_h, ref_m, ref_s = ref_time.split(':')
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ref_date = datetime(int(ref_y), int(ref_mo), int(ref_d), int(ref_h), int(ref_m), int(ref_s))
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if not colours:
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chg_colour = (40/255, 70/255, 75/255) # Dark Slate Gray #28464B
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dchg_colour = (239/255, 160/255, 11/255) # Marigold #EFA00B
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days = cur_date - ref_date
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s = days.seconds
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factors = {'ms': 1000, 's': 1, 'min': 1/(60), 'h': 1/(60*60)}
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for i, cycle in cycles:
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if i in which_cycles:
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if chg:
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cycle[0].plot(ax=ax)
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t = s * factors[unit]
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return t
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def splice_cycles(first, second):
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first_chg = first[0]
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first_dchg = first[1]
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first
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second_chg = second[0]
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second_dchg = second[1]
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chg_df = first[0].append(second[0])
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return True
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@ -158,18 +280,3 @@ def plot_gc(cycles, which_cycles='all', chg=True, dchg=True, colours=None, x='C'
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def prepare_gc_plot(figsize=(14,7), dpi=None):
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fig, ax = plt.subplots(figsize=figsize, dpi=dpi)
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return fig, ax
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