Hydropy-package¶
In [15]:
#Loading the hydropy package
import hydropy as hp
We have a Dataframe with river discharge at different locations in the Maarkebeek basin (Belgium):
In [16]:
HTML('<iframe src=http://biomath.ugent.be/~stvhoey/maarkebeek_data/ width=700 height=350></iframe>')
Out[16]:
Data downloaded from http://www.waterinfo.be/, made available by the Flemish Environmental Agency (VMM).
In [17]:
flowdata.head()
Out[17]:
In [18]:
flowdata.tail()
Out[18]:
In [19]:
print len(flowdata), 'records', 'from', flowdata.index[0], 'till', flowdata.index[-1]
Converting the dataframe to a hydropy time series datatype, provides extra functionalities:
In [20]:
myflowserie = hp.HydroAnalysis(flowdata)
Select the summer of 2009:¶
In [21]:
myflowserie.get_year('2009').get_season('summer').plot(figsize=(12,6))
Out[21]:
Select only the recession periods of the discharges (catchment drying) in June 2011:¶
In [22]:
myflowserie.get_year('2011').get_month("Jun").get_recess().plot(figsize=(12,6))
Out[22]:
Peak values above the 90th percentile for the station LS06_347 in July 2010:¶
In [23]:
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)
Out[23]:
Select storms and make plot¶
In [25]:
storms = myflowserie.derive_storms(raindata['P06_014'], 'LS06_347',
number_of_storms=3, drywindow=96,
makeplot=True)
Season averages (Pandas!)¶
In [26]:
myflowserie.data.groupby('season').mean()
Out[26]:
Goals¶
- Use the power of Python Pandas
- Provide domain specific (hydrological) functionalities
- Provide intuitive interface for hydrological time series (main focus on flow series)
- Combine different earlier written loose functionalities in package
- Independent, but useful in global Phd-developed framework: enables the user to quickly look at different properties of model behaviour
Where?¶
- Code : https://github.com/stijnvanhoey/hydropy --> Fork and contribute
- Website : https://stijnvanhoey.github.io/hydropy/
How to start?¶
- Fork the github repo
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,...
- Make a new branch
- Make improvements on this branch
- push the branch towards the repo and perform a push request
In [ ]:
Functionalities extended¶
Forwarding Pandas functionalities¶
In [26]:
# Data inspection
myflowserie.summary() #head(), tail(),
Out[26]:
In [27]:
# Resampling frequencies
temp1 = myflowserie.frequency_resample('7D', 'mean') # 7 day means
temp1.head()
Out[27]:
In [28]:
temp2 = myflowserie.frequency_resample("M", "max") # Monthly maxima
temp2.head()
Out[28]:
In [29]:
temp3 = myflowserie.frequency_resample("A", 'sum') # Yearly sums
temp3.head(6)
Out[29]:
In [30]:
#slicing of the dataframes
myflowserie['L06_347']['2009'].plot()
Out[30]:
Easy period selection¶
In [31]:
# get_month, get_year, get_season, get_date_range
myflowserie.get_date_range("01/01/2010","03/05/2010").plot(figsize=(13, 6))
Out[31]:
In [32]:
# or combine different statements:
myflowserie.get_year('2010').get_month(6).plot(figsize=(13, 6))
Out[32]:
For the seasons some options are available: Meteorologic (first of the month) or astrologic (21st of the month)
In [33]:
myflowserie.current_season_dates()
Out[33]:
In [34]:
myflowserie.info_season_dates('north', 'astro')
Out[34]:
'Hydrological' selections¶
In [35]:
# Peaks (high or low)
myflowserie['LS06_348'].get_year('2012').get_highpeaks(60, above_percentile=0.8).data.dropna().head()
Out[35]:
In [36]:
# Recessions and climbing periods get_recess, get_climbing
myflowserie.get_year("2012").get_month("april").get_climbing().plot(figsize=(13, 6))
Out[36]:
In [37]:
# above/below certain percentile values
myflowserie["LS06_348"].get_above_percentile(0.6).get_year('2011').get_season('summer').plot()
Out[37]:
Furthermore:
- get_storms_per_year (in combination with rain-data)
- get_above_baseflow (in combination with baseflow-data) => next on the list
- ...
In [ ]:
In [ ]: