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@ssolson
ssolson committed Jan 30, 2024
1 parent b74110e commit c2513a7
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10 changes: 6 additions & 4 deletions examples/power_example.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -151,10 +151,12 @@
],
"source": [
"# Read in time-series data of voltage (V) and current (I)\n",
"power_data = pd.read_csv('data/power/2020224_181521_PowRaw.csv',skip_blank_lines=True,index_col='Time_UTC')\n",
"power_data = pd.read_csv(\n",
" \"data/power/2020224_181521_PowRaw.csv\", skip_blank_lines=True, index_col=\"Time_UTC\"\n",
")\n",
"\n",
"# Convert the time index to type \"datetime\"\n",
"power_data.index=pd.to_datetime(power_data.index)\n",
"power_data.index = pd.to_datetime(power_data.index)\n",
"\n",
"# Display the data\n",
"power_data.head()"
Expand Down Expand Up @@ -498,8 +500,8 @@
}
],
"source": [
"# Finally we can compute the total harmonic current distortion as a percentage \n",
"THCD = power.quality.total_harmonic_current_distortion(h_s) \n",
"# Finally we can compute the total harmonic current distortion as a percentage\n",
"THCD = power.quality.total_harmonic_current_distortion(h_s)\n",
"THCD"
]
}
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160 changes: 95 additions & 65 deletions mhkit/power/characteristics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,9 +3,8 @@
import numpy as np
from scipy.signal import hilbert

def instantaneous_frequency(um, time_dimension="", to_pandas=True):

def instantaneous_frequency(um):
def instantaneous_frequency(um, time_dimension="", to_pandas=True):
"""
Calculates instantaneous frequency of measured voltage
Expand All @@ -15,7 +14,7 @@ def instantaneous_frequency(um):
Measured voltage (V) indexed by time
time_dimension: string (optional)
Name of the xarray dimension corresponding to time. If not supplied,
Name of the xarray dimension corresponding to time. If not supplied,
defaults to the first dimension. Does not affect pandas input.
to_pandas: bool (Optional)
Expand All @@ -26,31 +25,37 @@ def instantaneous_frequency(um):
frequency: pandas DataFrame or xarray Dataset
Frequency of the measured voltage (Hz) indexed by time
with signal name columns
"""
"""
if not isinstance(um, (pd.Series, pd.DataFrame, xr.DataArray, xr.Dataset)):
raise TypeError('um must be of type pd.Series, pd.DataFrame, ' +
f'xr.DataArray, or xr.Dataset. Got {type(um)}')
if not isinstance(to_pandas, bool):
raise TypeError(
f'to_pandas must be of type bool. Got: {type(to_pandas)}')
"um must be of type pd.Series, pd.DataFrame, "
+ f"xr.DataArray, or xr.Dataset. Got {type(um)}"
)
if not isinstance(to_pandas, bool):
raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")
if not isinstance(time_dimension, str):
raise TypeError(
f'time_dimension must be of type bool. Got: {type(time_dimension)}')
f"time_dimension must be of type bool. Got: {type(time_dimension)}"
)

# Convert input to xr.Dataset
um = _convert_to_dataset(um, 'data')

if time_dimension != '' and time_dimension not in um.coords:
raise ValueError('time_dimension was supplied but is not a dimension '
+ f'of um. Got {time_dimension}')
um = _convert_to_dataset(um, "data")

if time_dimension != "" and time_dimension not in um.coords:
raise ValueError(
"time_dimension was supplied but is not a dimension "
+ f"of um. Got {time_dimension}"
)

# Get the dimension of interest
if time_dimension == "":
time_dimension = list(um.coords)[0]

# Calculate time step
if isinstance(um.coords[time_dimension].values[0], np.datetime64):
t = (um[time_dimension] - np.datetime64('1970-01-01 00:00:00'))/np.timedelta64(1, 's')
t = (
um[time_dimension] - np.datetime64("1970-01-01 00:00:00")
) / np.timedelta64(1, "s")
else:
t = um[time_dimension]
dt = np.diff(t)
Expand All @@ -60,16 +65,21 @@ def instantaneous_frequency(um):
for var in um.data_vars:
f = hilbert(um[var])
instantaneous_phase = np.unwrap(np.angle(f))
instantaneous_frequency = np.diff(instantaneous_phase)/(2.0*np.pi) * (1/dt)
instantaneous_frequency = (
np.diff(instantaneous_phase) / (2.0 * np.pi) * (1 / dt)
)

frequency = frequency.assign({var: (time_dimension, instantaneous_frequency)})
frequency = frequency.assign_coords({time_dimension: um.coords[time_dimension].values[0:-1]})
frequency = frequency.assign_coords(
{time_dimension: um.coords[time_dimension].values[0:-1]}
)

if to_pandas:
frequency = frequency.to_pandas()

return frequency


def dc_power(voltage, current, to_pandas=True):
"""
Calculates DC power from voltage and current
Expand All @@ -91,43 +101,54 @@ def dc_power(voltage, current, to_pandas=True):
DC power [W] from each channel and gross power indexed by time
"""
if not isinstance(voltage, (pd.Series, pd.DataFrame, xr.DataArray, xr.Dataset)):
raise TypeError('voltage must be of type pd.Series, pd.DataFrame, ' +
f'xr.DataArray, or xr.Dataset. Got {type(voltage)}')
raise TypeError(
"voltage must be of type pd.Series, pd.DataFrame, "
+ f"xr.DataArray, or xr.Dataset. Got {type(voltage)}"
)
if not isinstance(current, (pd.Series, pd.DataFrame, xr.DataArray, xr.Dataset)):
raise TypeError('current must be of type pd.Series, pd.DataFrame, ' +
f'xr.DataArray, or xr.Dataset. Got {type(current)}')
if not isinstance(to_pandas, bool):
raise TypeError(
f'to_pandas must be of type bool. Got: {type(to_pandas)}')
"current must be of type pd.Series, pd.DataFrame, "
+ f"xr.DataArray, or xr.Dataset. Got {type(current)}"
)
if not isinstance(to_pandas, bool):
raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")

# Convert inputs to xr.Dataset
voltage = _convert_to_dataset(voltage, 'voltage')
current = _convert_to_dataset(current, 'current')
voltage = _convert_to_dataset(voltage, "voltage")
current = _convert_to_dataset(current, "current")

# Check that sizes are the same
if not (voltage.sizes == current.sizes and len(voltage.data_vars) == len(current.data_vars)):
raise ValueError('current and voltage must have the same shape')
if not (
voltage.sizes == current.sizes
and len(voltage.data_vars) == len(current.data_vars)
):
raise ValueError("current and voltage must have the same shape")

P = xr.Dataset()
gross = None

# Multiply current and voltage variables together, in order they're assigned
for i, (current_var, voltage_var) in enumerate(zip(current.data_vars,voltage.data_vars)):
temp = current[current_var]*voltage[voltage_var]
P = P.assign({f'{i}': temp})
for i, (current_var, voltage_var) in enumerate(
zip(current.data_vars, voltage.data_vars)
):
temp = current[current_var] * voltage[voltage_var]
P = P.assign({f"{i}": temp})
if gross is None:
gross = temp
else:
gross = gross + temp

P = P.assign({'Gross': gross})
P = P.assign({"Gross": gross})

if to_pandas:
P = P.to_dataframe()

return P

def ac_power_three_phase(voltage, current, power_factor, line_to_line=False, to_pandas=True):

def ac_power_three_phase(
voltage, current, power_factor, line_to_line=False, to_pandas=True
):
"""
Calculates magnitude of active AC power from line to neutral voltage and current
Expand All @@ -139,7 +160,7 @@ def ac_power_three_phase(voltage, current, power_factor, line_to_line=False, to_
current: pandas Series, pandas DataFrame, xarray DataArray, or xarray Dataset
Measured three phase current [A] indexed by time
power_factor: float
power_factor: float
Power factor for the efficiency of the system
line_to_line: bool (Optional)
Expand All @@ -151,36 +172,40 @@ def ac_power_three_phase(voltage, current, power_factor, line_to_line=False, to_
Returns
--------
P: pandas DataFrame or xarray Dataset
Magnitude of active AC power [W] indexed by time with Power column
Magnitude of active AC power [W] indexed by time with Power column
"""
if not isinstance(voltage, (pd.Series, pd.DataFrame, xr.DataArray, xr.Dataset)):
raise TypeError('voltage must be of type pd.Series, pd.DataFrame, ' +
f'xr.DataArray, or xr.Dataset. Got {type(voltage)}')
raise TypeError(
"voltage must be of type pd.Series, pd.DataFrame, "
+ f"xr.DataArray, or xr.Dataset. Got {type(voltage)}"
)
if not isinstance(current, (pd.Series, pd.DataFrame, xr.DataArray, xr.Dataset)):
raise TypeError('current must be of type pd.Series, pd.DataFrame, ' +
f'xr.DataArray, or xr.Dataset. Got {type(current)}')
if not isinstance(line_to_line, bool):
raise TypeError(
f'line_to_line must be of type bool. Got: {type(line_to_line)}')
"current must be of type pd.Series, pd.DataFrame, "
+ f"xr.DataArray, or xr.Dataset. Got {type(current)}"
)
if not isinstance(line_to_line, bool):
raise TypeError(f"line_to_line must be of type bool. Got: {type(line_to_line)}")
if not isinstance(to_pandas, bool):
raise TypeError(
f'to_pandas must be of type bool. Got: {type(to_pandas)}')
raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")

# Convert inputs to xr.Dataset
voltage = _convert_to_dataset(voltage, 'voltage')
current = _convert_to_dataset(current, 'current')
voltage = _convert_to_dataset(voltage, "voltage")
current = _convert_to_dataset(current, "current")

# Check that sizes are the same
if not len(voltage.data_vars) == 3:
raise ValueError('voltage must have three columns')
raise ValueError("voltage must have three columns")
if not len(current.data_vars) == 3:
raise ValueError('current must have three columns')
raise ValueError("current must have three columns")
if not current.sizes == voltage.sizes:
raise ValueError('current and voltage must be of the same size')
raise ValueError("current and voltage must be of the same size")

power = dc_power(voltage, current, to_pandas=False)['Gross']
power.name = 'Power'
power = power.to_dataset() # force xr.DataArray to be consistently in xr.Dataset format
power = dc_power(voltage, current, to_pandas=False)["Gross"]
power.name = "Power"
power = (
power.to_dataset()
) # force xr.DataArray to be consistently in xr.Dataset format
P = np.abs(power) * power_factor

if line_to_line:
Expand All @@ -191,60 +216,65 @@ def ac_power_three_phase(voltage, current, power_factor, line_to_line=False, to_

return P

def _convert_to_dataset(data, name='data'):

def _convert_to_dataset(data, name="data"):
"""
Converts the given data to an xarray.Dataset.
This function is designed to handle inputs that can be either a pandas DataFrame, a pandas Series,
an xarray DataArray, or an xarray Dataset. It ensures that the output is consistently an xarray.Dataset.
Parameters
----------
data: pandas DataFrame, pandas Series, xarray DataArray, or xarray Dataset
The data to be converted.
The data to be converted.
name: str (Optional)
The name to assign to the data variable in case the input is an xarray DataArray without a name.
Default value is 'data'.
Returns
-------
xarray.Dataset
The input data converted to an xarray.Dataset. If the input is already an xarray.Dataset,
it is returned as is.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
>>> ds = _convert_to_dataset(df)
>>> type(ds)
<class 'xarray.core.dataset.Dataset'>
>>> series = pd.Series([1, 2, 3], name='C')
>>> ds = _convert_to_dataset(series)
>>> type(ds)
<class 'xarray.core.dataset.Dataset'>
>>> data_array = xr.DataArray([1, 2, 3])
>>> ds = _convert_to_dataset(data_array, name='D')
>>> type(ds)
<class 'xarray.core.dataset.Dataset'>
"""
if not isinstance(data, (pd.DataFrame, pd.Series, xr.DataArray, xr.Dataset)):
raise TypeError("Input data must be of type pandas.DataFrame, pandas.Series, "
"xarray.DataArray, or xarray.Dataset")
raise TypeError(
"Input data must be of type pandas.DataFrame, pandas.Series, "
"xarray.DataArray, or xarray.Dataset"
)

if not isinstance(name, str):
raise TypeError("The 'name' parameter must be a string")
raise TypeError("The 'name' parameter must be a string")

# Takes data that could be pd.DataFrame, pd.Series, xr.DataArray, or
# Takes data that could be pd.DataFrame, pd.Series, xr.DataArray, or
# xr.Dataset and converts it to xr.Dataset
if isinstance(data, (pd.DataFrame, pd.Series)):
data = data.to_xarray()

if isinstance(data, xr.DataArray):
if data.name is None:
data.name = name # xr.DataArray.to_dataset() breaks if the data variable is unnamed
data.name = (
name # xr.DataArray.to_dataset() breaks if the data variable is unnamed
)
data = data.to_dataset()

return data
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