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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,133 @@ | ||
| from pyBL.fitting import BLRPRxConfig, BLRPRxFitter | ||
| import numba as nb | ||
| import numpy as np | ||
| import timeit | ||
| from pyBL.timeseries import IntensityMRLE | ||
| from pyBL.fitting import BLRPRxFitter | ||
| from pyBL.models import BLRPRx, Stat_Props, BLRPRx_params | ||
| import os | ||
| import pandas as pd | ||
|
|
||
| timescale = [1, 3600, 3 * 3600, 6 * 3600, 24 * 3600] | ||
| props = [ | ||
| Stat_Props.MEAN, | ||
| Stat_Props.CVAR, | ||
| Stat_Props.AR1, | ||
| Stat_Props.SKEWNESS, | ||
| Stat_Props.pDRY, | ||
| ] | ||
|
|
||
| # Set timezone to UTC | ||
| os.environ["TZ"] = "UTC" | ||
|
|
||
|
|
||
| def rain_timeseries(): | ||
| data_path = os.path.join(os.path.dirname(__file__), "data", "elmdon.csv") | ||
| data = pd.read_csv(data_path, parse_dates=["datatime"]) | ||
| data["datatime"] = data["datatime"].astype("int64") // 10**9 | ||
| time = data["datatime"].to_numpy() | ||
| intensity = data["Elmdon"].to_numpy() | ||
| return time, intensity | ||
|
|
||
|
|
||
| def month_start_end(): | ||
| # Generate first day of each month from 1980 to 2010 | ||
| day = pd.date_range(start="1980-01-01", end="2000-01-01", freq="MS") | ||
| # Convert to unix time | ||
| month_srt = day.astype("int64") // 10**9 | ||
| month_end = month_srt | ||
| # Stack them together | ||
| month_interval = np.stack((month_srt[:-1], month_end[1:]), axis=1) | ||
| # Group the month_interval by month | ||
| month_interval = np.reshape(month_interval, (-1, 12, 2)) | ||
| return month_interval | ||
|
|
||
|
|
||
| time, intensity = rain_timeseries() | ||
| mrle = IntensityMRLE(time, intensity / 3600) | ||
| month_interval_each_year = month_start_end() | ||
|
|
||
| # Segment the mrle timeseries into months from 1900 to 2100 | ||
| mrle_month_each = np.empty( | ||
| (12, len(month_interval_each_year), 5), dtype=IntensityMRLE | ||
| ) # (month, year, scale) | ||
| for i, year in enumerate(month_interval_each_year): | ||
| for j, month in enumerate(year): | ||
| for k, scale in enumerate(timescale): | ||
| mrle_month_each[j, i, k] = mrle[month[0] : month[1]].rescale( | ||
| scale | ||
| ) # 1s scale | ||
|
|
||
| # MRLE that stores the total of each month | ||
| mrle_month_total = np.empty((12, 5), dtype=IntensityMRLE) # (month, scale) | ||
| for i in range(12): | ||
| for j in range(len(mrle_month_each[0])): | ||
| for k, scale in enumerate(timescale): | ||
| if j == 0: | ||
| mrle_month_total[i, k] = IntensityMRLE(scale=scale) | ||
| mrle_month_total[i, k].add(mrle_month_each[i, j, k], sequential=True) | ||
|
|
||
| stats_month = np.zeros((12, 5, 5)) # (month, scale, stats) | ||
| for month in range(12): | ||
| for scale in range(5): | ||
| model = mrle_month_total[month, scale] | ||
| stats_month[month, scale, :] = [ | ||
| model.mean(), | ||
| model.cvar(), | ||
| model.acf(), | ||
| model.skewness(), | ||
| model.pDry(0), | ||
| ] | ||
|
|
||
| stats_month_seperate = np.zeros( | ||
| (12, len(month_interval_each_year), 5, 5) | ||
| ) # (month, year, scale, stats) | ||
| for month in range(12): | ||
| for year in range(len(month_interval_each_year)): | ||
| for scale in range(5): | ||
| model = mrle_month_each[month, year, scale] | ||
| stats_month_seperate[month, year, scale, :] = [ | ||
| model.mean(), | ||
| model.cvar(), | ||
| model.acf(), | ||
| model.skewness(), | ||
| model.pDry(0), | ||
| ] | ||
|
|
||
| stats_weight = 1 / np.nanvar( | ||
| stats_month_seperate, axis=1 | ||
| ) # (month, scale, stats) (12, 5, 5) | ||
|
|
||
| target_np = stats_month[0, 1:, :] | ||
| weight_np = stats_weight[0, 1:, :] | ||
|
|
||
| target = BLRPRxConfig.default_target([1, 3, 6, 24]) | ||
| target[Stat_Props.MEAN] = target_np[:, 0] | ||
| target[Stat_Props.CVAR] = target_np[:, 1] | ||
| target[Stat_Props.AR1] = target_np[:, 2] | ||
| target[Stat_Props.SKEWNESS] = target_np[:, 3] | ||
|
|
||
| weight = BLRPRxConfig.default_weight([1, 3, 6, 24]) | ||
| weight[Stat_Props.MEAN] = weight_np[:, 0] | ||
| weight[Stat_Props.CVAR] = weight_np[:, 1] | ||
| weight[Stat_Props.AR1] = weight_np[:, 2] | ||
| weight[Stat_Props.SKEWNESS] = weight_np[:, 3] | ||
|
|
||
| mask = BLRPRxConfig.default_mask([1, 3, 6, 24]) | ||
| mask[Stat_Props.MEAN] = [1, 0, 0, 0] | ||
| mask[Stat_Props.CVAR] = [1, 1, 1, 1] | ||
| mask[Stat_Props.AR1] = [1, 1, 1, 1] | ||
| mask[Stat_Props.SKEWNESS] = [1, 1, 1, 1] | ||
|
|
||
| config = BLRPRxConfig(target=target, weight=weight, mask=mask) | ||
| a = config.get_evaluation_func() | ||
| arr = np.array([0.016679733103341976, 0.08270236178820184, 0.34970877070925505, 9.017352714561754, 0.9931496975448589, 1.01, 0.971862948182735]) | ||
|
|
||
| old_config = BLRPRxFitter() | ||
| model = BLRPRx() | ||
|
|
||
| print(a(arr)) | ||
| print(old_config._evaluate(arr, target_np, weight_np, model)) | ||
|
|
||
| #print(timeit.timeit(lambda: a(arr), number=389253)) | ||
| #print(timeit.timeit(lambda: old_config._evaluate(arr, target_np, weight_np, model), number=389253)) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,99 @@ | ||
| from pyBL.timeseries import IntensityMRLE | ||
| from pyBL.fitting import BLRPRxFitter | ||
| from pyBL.models import BLRPRx, Stat_Props, BLRPRx_params | ||
| import os | ||
| import pandas as pd | ||
| import numpy as np | ||
|
|
||
| timescale = [1, 3600, 3 * 3600, 6 * 3600, 24 * 3600] | ||
| props = [ | ||
| Stat_Props.MEAN, | ||
| Stat_Props.CVAR, | ||
| Stat_Props.AR1, | ||
| Stat_Props.SKEWNESS, | ||
| Stat_Props.pDRY, | ||
| ] | ||
|
|
||
| # Set timezone to UTC | ||
| os.environ["TZ"] = "UTC" | ||
|
|
||
|
|
||
| def rain_timeseries(): | ||
| data_path = os.path.join(os.path.dirname(__file__), "data", "elmdon.csv") | ||
| data = pd.read_csv(data_path, parse_dates=["datatime"]) | ||
| data["datatime"] = data["datatime"].astype("int64") // 10**9 | ||
| time = data["datatime"].to_numpy() | ||
| intensity = data["Elmdon"].to_numpy() | ||
| return time, intensity | ||
|
|
||
|
|
||
| def month_start_end(): | ||
| # Generate first day of each month from 1980 to 2010 | ||
| day = pd.date_range(start="1980-01-01", end="2000-01-01", freq="MS") | ||
| # Convert to unix time | ||
| month_srt = day.astype("int64") // 10**9 | ||
| month_end = month_srt | ||
| # Stack them together | ||
| month_interval = np.stack((month_srt[:-1], month_end[1:]), axis=1) | ||
| # Group the month_interval by month | ||
| month_interval = np.reshape(month_interval, (-1, 12, 2)) | ||
| return month_interval | ||
|
|
||
|
|
||
| time, intensity = rain_timeseries() | ||
| mrle = IntensityMRLE(time, intensity / 3600) | ||
| month_interval_each_year = month_start_end() | ||
|
|
||
| # Segment the mrle timeseries into months from 1900 to 2100 | ||
| mrle_month_each = np.empty( | ||
| (12, len(month_interval_each_year), 5), dtype=IntensityMRLE | ||
| ) # (month, year, scale) | ||
| for i, year in enumerate(month_interval_each_year): | ||
| for j, month in enumerate(year): | ||
| for k, scale in enumerate(timescale): | ||
| mrle_month_each[j, i, k] = mrle[month[0] : month[1]].rescale( | ||
| scale | ||
| ) # 1s scale | ||
|
|
||
| # MRLE that stores the total of each month | ||
| mrle_month_total = np.empty((12, 5), dtype=IntensityMRLE) # (month, scale) | ||
| for i in range(12): | ||
| for j in range(len(mrle_month_each[0])): | ||
| for k, scale in enumerate(timescale): | ||
| if j == 0: | ||
| mrle_month_total[i, k] = IntensityMRLE(scale=scale) | ||
| mrle_month_total[i, k].add(mrle_month_each[i, j, k], sequential=True) | ||
|
|
||
| stats_month = np.zeros((12, 5, 5)) # (month, scale, stats) | ||
| for month in range(12): | ||
| for scale in range(5): | ||
| model = mrle_month_total[month, scale] | ||
| stats_month[month, scale, :] = [ | ||
| model.mean(), | ||
| model.cvar(), | ||
| model.acf(), | ||
| model.skewness(), | ||
| model.pDry(0), | ||
| ] | ||
|
|
||
| stats_month_seperate = np.zeros( | ||
| (12, len(month_interval_each_year), 5, 5) | ||
| ) # (month, year, scale, stats) | ||
| for month in range(12): | ||
| for year in range(len(month_interval_each_year)): | ||
| for scale in range(5): | ||
| model = mrle_month_each[month, year, scale] | ||
| stats_month_seperate[month, year, scale, :] = [ | ||
| model.mean(), | ||
| model.cvar(), | ||
| model.acf(), | ||
| model.skewness(), | ||
| model.pDry(0), | ||
| ] | ||
|
|
||
| stats_weight = 1 / np.nanvar( | ||
| stats_month_seperate, axis=1 | ||
| ) # (month, scale, stats) (12, 5, 5) | ||
|
|
||
| target = stats_month[:, 1:, :] | ||
| weight = stats_weight[:, 1:, :] |