8000 Add scale_voltage_current_power to ModelChain.pvwatts_dc by cwhanse · Pull Request #1138 · pvlib/pvlib-python · GitHub
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Add scale_voltage_current_power to ModelChain.pvwatts_dc #1138

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Merged
merged 10 commits into from
Jan 21, 2021
Merged

Add scale_voltage_current_power to ModelChain.pvwatts_dc #1138

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d210be9
add scale_voltage_current_power to ModelChain.pvwatts_dc, docstring e…
cwhanse Jan 20, 2021
a6140ed
extend pvsystem.scale_voltage_current_power to not require all column…
cwhanse Jan 20, 2021
917737a
handle tuples, use temporary DataFrame
cwhanse Jan 21, 2021
e078c0e
improve scale_voltage_current_power, handle tuples
cwhanse Jan 21, 2021
4e7f849
sort the new test
cwhanse Jan 21, 2021
fb04b7e
improve scale function, fix multiple string test
cwhanse Jan 21, 2021
18cb049
multiple string test runs
cwhanse Jan 21, 2021
bdc8120
add PR number
cwhanse Jan 21, 2021
478fb34
Maintain column order in returned DataFrame
cwhanse Jan 21, 2021
ad2fd80
whatsnew entry formatting
cwhanse Jan 21, 2021
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handle tuples, use temporary DataFrame
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@cwhanse
cwhanse committed Jan 21, 2021
commit 917737a9dab8fabc32db04e721fc169cc19f3568
13 changes: 11 additions & 2 deletions pvlib/modelchain.py
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Original file line number Diff line number Diff line change
Expand Up @@ -747,10 +747,19 @@ def pvwatts_dc(self):
"""
self.results.dc = self.system.pvwatts_dc(
self.results.effective_irradiance, self.results.cell_temperature)
self.results.dc = self.system.scale_voltage_current_power(
self.results.dc,
if isinstance(self.results.dc, tuple):
temp = tuple(
pd.DataFrame(s, columns=['p_mp']) for s in self.results.dc)
else:
temp = pd.DataFrame(self.results.dc, columns=['p_mp'])
scaled = self.system.scale_voltage_current_power(
temp,
unwrap=False
)
if isinstance(scaled, tuple):
self.results.dc = tuple(pd.Series(s) for s in scaled)
else:
self.results.dc = pd.Series(scaled)
return self

@property
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29 changes: 10 additions & 19 deletions pvlib/pvsystem.py
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Original file line number Diff line number Diff line change
Expand Up @@ -891,7 +891,7 @@ def scale_voltage_current_power(self, data):
Parameters
----------
data: DataFrame or tuple of DataFrame
Must contain columns `'v_mp', 'v_oc', 'i_mp' ,'i_x', 'i_xx',
May contain columns `'v_mp', 'v_oc', 'i_mp' ,'i_x', 'i_xx',
'i_sc', 'p_mp'`.

Returns
Expand Down Expand Up @@ -2631,18 +2631,17 @@ def scale_voltage_current_power(data, voltage=1, current=1):

Parameters
----------
data: DataFrame or Series
data: DataFrame
May contain columns `'v_mp', 'v_oc', 'i_mp' ,'i_x', 'i_xx',
'i_sc', 'p_mp'`. If Series, the content must be indicated using
one of these keys as the Series name.
'i_sc', 'p_mp'`.
voltage: numeric, default 1
The amount by which to multiply the voltages.
current: numeric, default 1
The amount by which to multiply the currents.

Returns
-------
scaled_data: DataFrame or Series
scaled_data: DataFrame
A scaled copy of the input data.
`'p_mp'` is scaled by `voltage * current`.
"""
Expand All @@ -2653,20 +2652,12 @@ def scale_voltage_current_power(data, voltage=1, current=1):
current_keys = ['i_mp', 'i_x', 'i_xx', 'i_sc']
power_keys = ['p_mp']
data = data.copy()
if isinstance(data, pd.DataFrame):
voltages = voltage_keys and data.columns
currents = current_keys and data.columns
powers = power_keys and data.columns
data[voltages] *= voltage
data[currents] *= current
data[powers] *= voltage * current
else:
if data.name in voltage_keys:
data *= voltage
elif data.name in current_keys:
data *= current
elif data.name in power_keys:
data *= voltage * current
voltages = voltage_keys and data.columns
currents = current_keys and data.columns
powers = power_keys and data.columns
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I was thinking of something like this:

In [28]: voltage = 5

In [29]: current = 10

In [30]: df = pd.DataFrame([[1, 1, 1]], columns=['p_mp', 'v_mp', 'i_mp'])

In [31]: df
Out[31]:
   p_mp  v_mp  i_mp
0     1     1     1

In [32]:     voltage_keys = ['v_mp', 'v_oc']
    ...:     current_keys = ['i_mp', 'i_x', 'i_xx', 'i_sc']
    ...:     power_keys = ['p_mp']

In [33]: df.filter(voltage_keys, axis=1) * voltage
Out[33]:
   v_mp
0     5

In [34]: df.filter(current_keys, axis=1) * current
Out[34]:
   i_mp
0    10

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In [35]: voltage_df = df.filter(voltage_keys, axis=1) * voltage

In [36]: current_df = df.filter(current_keys, axis=1) * current

In [37]: df = pd.concat([voltage_df, current_df], axis=1)

In [38]: df['p_mp'] = df['v_mp'] * df['i_mp']

In [39]: df
Out[39]:
   v_mp  i_mp  p_mp
0     5    10    50

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@wholmgren wholmgren Jan 21, 2021 •
edited
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I don't know how to scale a power other than p_mp without hugely complicating the code. I also don't know that there's a need for it at this time.

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The pvwatts methods don't produce voltage or current.

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@wholmgren wholmgren Jan 21, 2021 •
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I was still in the mindset that we could provide a DataFrame with p_mp=pdc, v_mp=pdc, and i_mp=1 as I suggested above. But yes it is better to just multiply the power keys by the voltage and current scale factors.

In [42]: df = pd.DataFrame([[1, 1, 1]], columns=['p_mp', 'v_mp', 'i_mp'])

In [43]: voltage_df = df.filter(voltage_keys, axis=1) * voltage

In [44]: current_df = df.filter(current_keys, axis=1) * current

In [45]: power_df = df.filter(power_keys, axis=1) * voltage * current

In [49]: concat_df = pd.concat([voltage_df, current_df, power_df], axis=1)

In [50]: concat_df = concat_df[df.columns]

In [51]: concat_df
Out[51]:
   p_mp  v_mp  i_mp
0    50     5    10

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One issue with the temporary DataFrame: when converting back to Series, the column label 'p_mp' is assigned to the Series name, which affect 8FB7 s testing but likely no user code. Any concern here?

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@wholmgren wholmgren Jan 21, 2021 •
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no concern. pandas assert_series_equal only recently started checking that attribute and it was a fair bit of work to fix all of the newly broken tests in the Arbiter.

data[voltages] *= voltage
data[currents] *= current
data[powers] *= voltage * current
return data


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