Let's define the test source Series as:
2019 - 02 - 20 13: 00: 49.268 40 2019 - 02 - 20 13: 00: 50.275 30 2019 - 02 - 20 13: 02: 51.397 18 2019 - 02 - 20 13: 02: 52.434 13 2019 - 02 - 20 13: 05: 53.542 21 2019 - 02 - 20 13: 05: 55.059 51 2019 - 02 - 20 13: 06: 56.169 32 2019 - 02 - 20 13: 06: 57.279 38 2019 - 02 - 20 13: 08: 58.408 48 2019 - 02 - 20 13: 08: 59.518 14 Name: Val, dtype: int64
and the list of intervals as:
intv = [(pd.to_datetime('2019-02-20 13:00'), pd.to_datetime('2019-02-20 13:01')),
(pd.to_datetime('2019-02-20 13:06'), pd.to_datetime('2019-02-20 13:07'))
]A preparatory step is to create an IntervalIndex:
intvInd = pd.IntervalIndex.from_tuples(intv)
I tried the above solution using Pandas version 0.24.2. As Inspi noticed, at least in version 0.25 the last instruction must be changed to:
s[[any(intvInd.contains(v)) for v in s.index.values]].mean()Assume your time series is indexed by date:
dates = pd.date_range('2019-07-01', '2019-07-25', freq = 'T')
s = pd.Series(np.random.uniform(1, 100, len(dates)), index = dates)Some sample data:
2019 - 07 - 01 00: 00: 00 54.851538 2019 - 07 - 01 00: 01: 00 82.493677 2019 - 07 - 01 00: 02: 00 80.589765 2019 - 07 - 01 00: 03: 00 54.973948 2019 - 07 - 01 00: 04: 00 18.216064
And your intervals are defined in a data frame:
intervals = pd.DataFrame([
['2019-07-01', '2019-07-02'],
['2019-07-02', '2019-07-10']
], columns = ['StartDate', 'EndDate'], dtype = 'datetime64[ns]')A preparatory step is to create an anycodings_python-3.x IntervalIndex:,What would be a short way to get the mean anycodings_pandas value of the Series in the combined anycodings_pandas intervals? (any interpolation function can anycodings_pandas be used here),Flutter streambuilder does not update a statefulwidget or statelesswidget,What is the use of the field generator in @GeneratedValue in JPA / Hibernate?
With an unevenly spaced datetime Series like anycodings_pandas so:
date 2019 - 02 - 20 13: 00: 49.268 41.177929 2019 - 02 - 20 13: 00: 50.275 12.431984 2019 - 02 - 20 13: 00: 51.397 18.042411 2019 - 02 - 20 13: 00: 52.434 13.144179 2019 - 02 - 20 13: 00: 53.542 21.349083 ... 2019 - 02 - 20 13: 05: 55.059 51.763360 2019 - 02 - 20 13: 05: 56.169 58.140644 2019 - 02 - 20 13: 05: 57.279 0.411533 2019 - 02 - 20 13: 05: 58.408 48.404780 2019 - 02 - 20 13: 05: 59.518 14.626680 Name: Values, Length: 285, dtype: float64
Let's define the test source Series as:
2019 - 02 - 20 13: 00: 49.268 40 2019 - 02 - 20 13: 00: 50.275 30 2019 - 02 - 20 13: 02: 51.397 18 2019 - 02 - 20 13: 02: 52.434 13 2019 - 02 - 20 13: 05: 53.542 21 2019 - 02 - 20 13: 05: 55.059 51 2019 - 02 - 20 13: 06: 56.169 32 2019 - 02 - 20 13: 06: 57.279 38 2019 - 02 - 20 13: 08: 58.408 48 2019 - 02 - 20 13: 08: 59.518 14 Name: Val, dtype: int64
and the list of intervals as:
intv = [(pd.to_datetime('2019-02-20 13:00'), pd.to_datetime('2019-02-20 13:01')),
(pd.to_datetime('2019-02-20 13:06'), pd.to_datetime('2019-02-20 13:07'))
]A preparatory step is to create an anycodings_python-3.x IntervalIndex:
intvInd = pd.IntervalIndex.from_tuples(intv)
I tried the above solution using Pandas anycodings_python-3.x version 0.24.2. As Inspi noticed, at anycodings_python-3.x least in version 0.25 the last anycodings_python-3.x instruction must be changed to:
s[[any(intvInd.contains(v)) for v in s.index.values]].mean()Assume your time series is indexed by anycodings_python-3.x date:
dates = pd.date_range('2019-07-01', '2019-07-25', freq = 'T')
s = pd.Series(np.random.uniform(1, 100, len(dates)), index = dates)Some sample data:
2019 - 07 - 01 00: 00: 00 54.851538 2019 - 07 - 01 00: 01: 00 82.493677 2019 - 07 - 01 00: 02: 00 80.589765 2019 - 07 - 01 00: 03: 00 54.973948 2019 - 07 - 01 00: 04: 00 18.216064
And your intervals are defined in a data anycodings_python-3.x frame:
intervals = pd.DataFrame([
['2019-07-01', '2019-07-02'],
['2019-07-02', '2019-07-10']
], columns = ['StartDate', 'EndDate'], dtype = 'datetime64[ns]')Now, we will aggregate the 1-second values into interval averages. To see how the averaging interval affects results, we’ll loop over a few common data intervals and accumulate the results.,Now, calculate the “ground truth” irradiance data. We’ll simulate clear-sky irradiance components at 1-second intervals and calculate the corresponding POA irradiance. At such a short timescale, the difference between instantaneous and interval-averaged irradiance is negligible.,It is important to account for this difference when using interval-averaged weather data for modeling. This example focuses on calculating solar position appropriately for irradiance transposition, but this concept is relevant for other steps in the modeling process as well.,We can also plot the underlying time series results of the last iteration (hourly in this case). The modeled irradiance using no shift is effectively time-lagged compared with ground truth. In contrast, the half-shift model is nearly identical to the ground truth irradiance.
import pvlib
import pandas as pd
import matplotlib.pyplot as pltdef transpose(irradiance, timeshift): "" " Transpose irradiance components to plane - of - array, incorporating a timeshift in the solar position calculation. Parameters -- -- -- -- -- irradiance: DataFrame Has columns dni, ghi, dhi timeshift: float Number of minutes to shift for solar position calculation Outputs: Series of POA irradiance "" " idx = irradiance.index # calculate solar position for shifted timestamps: idx = idx + pd.Timedelta(timeshift, unit = 'min') solpos = location.get_solarposition(idx) # but still report the values with the original timestamps: solpos.index = irradiance.index poa_components = pvlib.irradiance.get_total_irradiance( surface_tilt = 20, surface_azimuth = 180, solar_zenith = solpos['apparent_zenith'], solar_azimuth = solpos['azimuth'], dni = irradiance['dni'], ghi = irradiance['ghi'], dhi = irradiance['dhi'], model = 'isotropic', ) return poa_components['poa_global']
# baseline: all calculations done at 1 - second scale
location = pvlib.location.Location(40, -80, tz = 'Etc/GMT+5')
times = pd.date_range('2019-06-01 05:00', '2019-06-01 19:00',
freq = '1s', tz = 'Etc/GMT+5')
solpos = location.get_solarposition(times)
clearsky = location.get_clearsky(times, solar_position = solpos)
poa_1s = transpose(clearsky, timeshift = 0) # no shift needed
for 1 s datafig, ax = plt.subplots(figsize = (5, 3))
results = []
for timescale_minutes in [1, 5, 10, 15, 30, 60]:
timescale_str = f '{timescale_minutes}min'
# get the "true"
interval average of poa as the baseline
for comparison
poa_avg = poa_1s.resample(timescale_str).mean()
# get interval averages of irradiance components to use
for transposition
clearsky_avg = clearsky.resample(timescale_str).mean()
# low - res interval averages of 1 - second data, with NO shift
poa_avg_noshift = transpose(clearsky_avg, timeshift = 0)
# low - res interval averages of 1 - second data, with half - interval shift
poa_avg_halfshift = transpose(clearsky_avg, timeshift = timescale_minutes / 2)
df = pd.DataFrame({
'ground truth': poa_avg,
'modeled, half shift': poa_avg_halfshift,
'modeled, no shift': poa_avg_noshift,
})
error = df.subtract(df['ground truth'], axis = 0)
# add another trace to the error plot
error['modeled, no shift'].plot(ax = ax, label = timescale_str)
# calculate error statistics and save
for later
stats = error.abs().mean() # average absolute error across daylight hours
stats['timescale_minutes'] = timescale_minutes
results.append(stats)
ax.legend(ncol = 2)
ax.set_ylabel('Transposition Error [W/m$^2$]')
fig.tight_layout()
df_results = pd.DataFrame(results).set_index('timescale_minutes')
print(df_results) ground truth modeled, half shift modeled, no shift
timescale_minutes
1.0 0.0 0.012018 0.702429
5.0 0.0 0.021197 3.542882
10.0 0.0 0.062650 7.051620
15.0 0.0 0.142984 10.531453
30.0 0.0 0.581701 20.619625
60.0 0.0 1.955845 39.418585fig, ax = plt.subplots(figsize = (5, 3))
df_results[['modeled, no shift', 'modeled, half shift']].plot.bar(rot = 0, ax = ax)
ax.set_ylabel('Mean Absolute Error [W/m$^2$]')
ax.set_xlabel('Transposition Timescale [minutes]')
fig.tight_layout()jQuery Tutorial , Hire developers
Solution for get each 3 rows (if exist) per Name groups - first get counter by GroupBy.cumcount with integer division and pass it to named aggregations:
g = df.groupby('Name').cumcount() // 3
df = df.groupby(['Name', g]).agg(Start = ('Position', 'first'),
End = ('Position', 'last'),
Value = ('Value', 'mean')).droplevel(1).reset_index()
print(df)
Name Start End Value
0 A 1 3 10.333333
1 A 4 6 8.666667
2 A 7 9 9.333333
3 A 10 12 9.000000