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%matplotlib inline
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from __future__ import print_function
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# import seaborn as sns
# sns.set_style('whitegrid')
from IPython.display import HTML
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flowdata = pd.read_pickle("./data/FlowData")
raindata = pd.read_pickle("./data/RainData")
Go to the hydropy website for more information on the package and the code on Github.
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#Loading the hydropy package
import hydropy as hp
We have a Dataframe with river discharge at different locations in the Maarkebeek basin (Belgium):
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HTML('<iframe src=http://biomath.ugent.be/~stvhoey/maarkebeek_data/ width=700 height=350></iframe>')
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Data downloaded from http://www.waterinfo.be/, made available by the Flemish Environmental Agency (VMM).
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flowdata.head()
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flowdata.tail()
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print(len(flowdata), 'records', 'from', flowdata.index[0], 'till', flowdata.index[-1])
Converting the dataframe to a hydropy time series datatype, provides extra functionalities:
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myflowserie = hp.HydroAnalysis(flowdata)
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myflowserie.get_year('2009').get_season('summer').plot(figsize=(12,6))
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myflowserie.get_year('2011').get_month("Jun").get_recess().plot(figsize=(12,6))
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fig, ax = plt.subplots(figsize=(13, 6))
myflowserie['LS06_347'].get_year('2010').get_month("Jul").get_highpeaks(150, above_percentile=0.9).plot(style='o', ax=ax)
myflowserie['LS06_347'].get_year('2010').get_month("Jul").plot(ax=ax)
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raindata.columns
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storms = myflowserie.derive_storms(raindata['P05_019'], 'LS06_347',
number_of_storms=3, drywindow=50,
makeplot=True)
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storms = myflowserie.derive_storms(raindata['P06_014'], 'LS06_347',
number_of_storms=3, drywindow=96,
makeplot=True)
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myflowserie.data.groupby('season').mean()
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Get the code on your computer
git clone https://github.com/yourname/hydropyRun the python setup script (install as development package):
python setup.py developImprove implementation, add functionalities,...
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import hydropy as hp
flowdata = pd.read_pickle("./data/FlowData")
raindata = pd.read_pickle("./data/RainData")
myflowserie = hp.HydroAnalysis(flowdata)
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# Data inspection
myflowserie.summary() #head(), tail(),
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# Resampling frequencies
temp1 = myflowserie.frequency_resample('7D', 'mean') # 7 day means
temp1.head()
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temp2 = myflowserie.frequency_resample("M", "max") # Monthly maxima
temp2.head()
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temp3 = myflowserie.frequency_resample("A", 'sum') # Yearly sums
temp3.head(6)
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#slicing of the dataframes
myflowserie['L06_347']['2009'].plot()
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# get_month, get_year, get_season, get_date_range
myflowserie.get_date_range("01/01/2010","03/05/2010").plot(figsize=(13, 6))
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# or combine different statements:
myflowserie.get_year('2010').get_month(6).plot(figsize=(13, 6))
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For the seasons some options are available: Meteorologic (first of the month) or astrologic (21st of the month)
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myflowserie.current_season_dates()
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myflowserie.info_season_dates('north', 'astro')
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# Peaks (high or low)
myflowserie['LS06_348'].get_year('2012').get_highpeaks(60, above_percentile=0.8).data.dropna().head()
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# Recessions and climbing periods get_recess, get_climbing
myflowserie.get_year("2012").get_month("april").get_climbing().plot(figsize=(13, 6))
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# above/below certain percentile values
myflowserie["LS06_347"].get_above_percentile(0.6).get_year('2011').get_season('summer').plot()
Furthermore:
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