PK QMY hkvsobekpy/__init__.pyimport fire
from hkvsobekpy.io.bui import __bui_class
from hkvsobekpy.io.his import __his_class
from hkvsobekpy.core.waterlevelstat import __waterlevelstat_class
from hkvsobekpy.core.plausibility import __plausibility_class
__doc__ = """package for water-statistics and plausibility checker using his- and bui-file"""
__version__ = '1.2.5'
# initiate class for .bui-files
__bui = __bui_class()
read_bui = __bui.read_bui
write_bui = __bui.write_bui
# initiate class for .his-files
read_his = __his_class()
# initiate class for waterlevelstats
waterlevelstat = __waterlevelstat_class()
# initiate class for plausibilitychecker
plausibility = __plausibility_class()
if __name__ == '__main__':
fire.Fire()PK QMY hkvsobekpy/__main__.pyimport fire
from hkvsobekpy.io.bui import __bui_class
from hkvsobekpy.io.his import __his_class
from hkvsobekpy.core.waterlevelstat import __waterlevelstat_class
from hkvsobekpy.core.plausibility import __plausibility_class
__doc__ = """package for water-statistics and plausibility checker using his- and bui-file"""
__version__ = '1.2.5'
# initiate class for .bui-files
__bui = __bui_class()
read_bui = __bui.read_bui
write_bui = __bui.write_bui
# initiate class for .his-files
read_his = __his_class()
# initiate class for waterlevelstats
waterlevelstat = __waterlevelstat_class()
# initiate class for plausibilitychecker
plausibility = __plausibility_class()
if __name__ == '__main__':
fire.Fire()PK QM hkvsobekpy/core/__init__.pyPK QMDzD D hkvsobekpy/core/plausibility.pyimport os
try:
from pathlib import Path
except:
from pathlib2 import Path
import argparse
import sys
import itertools
import geopandas as gpd
from datetime import datetime, timedelta
import copy
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.dates import date2num
import re
from matplotlib.ticker import ScalarFormatter, FormatStrFormatter
import numpy as np
import pandas as pd
from scipy import optimize
import fire
import warnings
try:
shell = get_ipython().__class__.__name__
if shell == 'ZMQInteractiveShell':
from tqdm import tqdm_notebook as tqdm # Jupyter notebook or qtconsole
elif shell == 'TerminalInteractiveShell':
from tqdm import tqdm # Terminal running IPython
else:
from tqdm import tqdm # Other type (?)
except NameError:
from tqdm import tqdm # Probably standard Python interpreter
#for debugging within notebook
try:
from IPython.core.debugger import set_trace
except:
pass
# for python 2/3 compatibility
try:
xrange
except NameError:
xrange = range
try:
get_ipython().magic('matplotlib inline')
except:
pass
from hkvsobekpy.io.his import __his_class
from hkvsobekpy.io.bui import __bui_class
from hkvsobekpy.core.utils import *
his_reader = __his_class()
bui_reader = __bui_class()
class __plausibility_class(object):
def __init__(self):
"""
SobekResultaten class. Class met methodes om een .his bestand
uit te lezen.
WaterstandStatistiek class. Class met methodes om een GumbleFit te bepalen
"""
# initatie van lege immutable objects
# zie: https://stackoverflow.com/a/22526544/2459096
# python2.7: obj = argparse.Namespace()
# python3.x: obj = types.SimpleNamespace()
# self.stats = argparse.Namespace()
# self.stats.plotposities = argparse.Namespace()
# self.stats.stap1 = argparse.Namespace()
# self.stats.stap2 = argparse.Namespace()
# self.stats.stap3 = argparse.Namespace()
# self.stats.stap4 = argparse.Namespace()
class __errors__(object):
"""
error class met verschillende foutmeldingen
"""
@staticmethod
def gewogenGemiddeldeError():
raise ValueError('Kan gewogen gemiddelde niet bepalen. Vereiste is minimaal 2 punten aan de linker en rechterzijde van de gekozen T')
def gebeurtenissenError():
raise ValueError('Meer gebeurtenissen dan N. Zorg ervoor dat je tijdreeks slechts 1 gebeurtenis per jaar heeft')
def schrijfTabelError():
raise ValueError('Kan deze tabel alleen wegschrijven met T10,25,50,100 voor Gumbel en T10 voor gewogen gemiddelde')
def prepare_bui_his(self, df_his, df_bui, bui_locations='auto', bui_locations_aantal=10):
"""
Prepare parameters from bui- and his-file for plotting purposes
Parameters
----------
his_file : str
name of his-file to query (e.g: 'reachseg', 'calcpnt', 'struc')
location : str
location to query in his-file
parameter : str
parameter to query in his-file
his_folder : str
path to folder containing the his_file
df_bui :
bui_locations : str or list
welke bui_locations worden gebruikt om een selectie te maken in de
bui-file, if 'bui_locations' is 'auto', dan worden de eerste x locations gebruikt
(waar x = 'bui_locations_aantal')
bui_locations_aantal : int
aantal locations mee te nemen tijdens plotten wanneer 'auto' is
opgegeven voor parameter 'bui_locations' (default: 10)
Returns
-------
df_his : pandas.DataFrame
timeseries containing all available timesteps in his-file
df_bui_sel : pandas.DataFrame
timeseries containing selection of bui-file, aligning with his-file
start_his : datetime
first datetime step of his-file
end_his : datetime
last datetime step of his-file
df_bui_std : float
standard deviation of timeseries selection bui-file
df_his_std : float
standard deviation of timeseries his-hile
"""
start_his = df_his.index[0]
end_his = df_his.index[-1]
df_bui_sel = df_bui.loc[start_his-pd.DateOffset(1):end_his+pd.DateOffset(1)]
if df_bui_sel.values.size == 0:
raise ValueError("""
cannot slice bui-file, seems time-period does not align with his-file
time-period his-file: {0} - {1}
time-period bui-file: {2} - {3}
""".format(df_his.index[0], df_his.index[-1], df_bui.index[0], df_bui.index[-1]))
# # create dummy waarden voor df_bui_sel
# for loc in df_bui_sel.columns:
# df_bui_sel.loc[df_bui_sel[loc] == 0,loc] = df_bui_sel[loc].apply(lambda x: np.random.normal(0,1))
if bui_locations == 'auto':
df_bui_sel = df_bui_sel.iloc[:, 0:bui_locations_aantal]
if isinstance(bui_locations, list):
df_bui_sel = df_bui_sel.loc[:, bui_locations]
df_his_std = np.std(df_his.values)
df_bui_std = np.std(df_bui_sel.values)
if df_bui_std == 0.:
df_bui_std = 0.5
if df_his_std == 0.:
df_his_std = 0.5
return df_bui_sel, start_his, end_his, df_bui_std, df_his_std
def plot_bui_his(self, df_his, df_bui_sel, start_his, end_his, df_bui_std, df_his_std, his_file, parameter, location, out_folder, savefigure, barthreshold=4, barwidth = 0.02, baroffset=0.01):
if barthreshold > 3:
barthreshold = 3
fig, ax1 = plt.subplots(figsize=(8,6))
ax2 = ax1.twinx()
# AXIS 1 :: his-file
ax1.plot(df_his.index, df_his.values,color='#BFD600',marker='o',lw=4,label=parameter)
ax1.set_xlim(start_his,end_his)
ax1.set_ylabel(parameter)
ax1.tick_params(labelbottom='on',labeltop='off', labelright="off",labelleft='on')
ax1.set_ylabel(parameter,rotation=90)
ax1.set_yticks(np.linspace(ax1.get_ybound()[0]-(df_his_std), ax1.get_ybound()[1]+df_his_std, 5))
ax1.grid(True, axis='y')
leg=ax1.legend(bbox_to_anchor=(0, 0.02),loc=3,prop={'size':10},ncol=5)
leg.draw_frame(False)
if len(df_bui_sel.columns) > barthreshold:
# AXIS 2 :: bui-file, width=barwidth, Draw line-chart
colors = [ cm.Blues(x) for x in np.linspace(0.2, 1.0, len(df_bui_sel.columns)) ]
for y_arr, label, color in zip(df_bui_sel.values.T, df_bui_sel.columns, colors):
ax2.plot(df_bui_sel.index, y_arr, label='P.{0} (mm)'.format(label), lw=1.5,color=color)
ax2.fill_between(df_bui_sel.index, 0, y_arr, color=color,alpha=0.05)
ax2_ylim = ax2.get_ylim()[::-1]
bottom = max(max(0,ax2_ylim[0]),max(df_bui_std,ax2_ylim[1]))
top = min(max(0,ax2_ylim[0]),max(df_bui_std,ax2_ylim[1]))
ax2.set_ylim(top=top, bottom=bottom)
ax2.set_xlim(start_his,end_his)
ax2.tick_params(labelbottom='off',labeltop='on', labelright="on",labelleft='off')
ax2.set_ylabel('Neerslag (mm)',rotation=90)
ax2.set_yticks(np.linspace(0, ax2.get_ybound()[1]+df_bui_std, 5))
leg=ax2.legend(bbox_to_anchor=(1.52, 0.02),loc=4,prop={'size':10},ncol=1)
leg.draw_frame(False)
else:
print('create a barchart for the bui-locations')
x = date2num(df_bui_sel.index.to_pydatetime())
if len(df_bui_sel.columns) == 3:
colors = [ cm.Blues(x) for x in np.linspace(0.25, 0.8, len(df_bui_sel.columns)) ]
series0 = df_bui_sel.iloc[:,0].tolist()
label0 = 'P.{0} (mm)'.format(df_bui_sel.iloc[:,0].name)
series1 = df_bui_sel.iloc[:,1].tolist()
label1 = 'P.{0} (mm)'.format(df_bui_sel.iloc[:,1].name)
series2 = df_bui_sel.iloc[:,2].tolist()
label2 = 'P.{0} (mm)'.format(df_bui_sel.iloc[:,2].name)
w = barwidth
offset = baroffset
ax2.bar(x - offset, series0, width=w, align='center', label=label0, color=colors[0], alpha=0.70)
ax2.bar(x, series1, width=w, align='center', label=label1 , color=colors[1], alpha=0.70)
ax2.bar(x + offset, series2, width=w, align='center', label=label2 , color=colors[2], alpha=0.70)
if len(df_bui_sel.columns) == 2:
colors = [ cm.Blues(x) for x in np.linspace(0.35, 0.75, len(df_bui_sel.columns)) ]
series0 = df_bui_sel.iloc[:,0].tolist()
label0 = 'P.{0} (mm)'.format(df_bui_sel.iloc[:,0].name)
series1 = df_bui_sel.iloc[:,1].tolist()
label1 = 'P.{0} (mm)'.format(df_bui_sel.iloc[:,1].name)
w = barwidth
offset = baroffset
ax2.bar(x - offset, series0, width=w, align='center', label=label0, color=colors[0], alpha=0.75)
ax2.bar(x, series1, width=w, align='center', label=label1 , color=colors[1], alpha=0.75)
if len(df_bui_sel.columns) == 1:
colors = [ cm.Blues(x) for x in np.linspace(0.45, 0.55, len(df_bui_sel.columns)) ]
series0 = df_bui_sel.iloc[:,0].tolist()
label0 = 'P.{0} (mm)'.format(df_bui_sel.iloc[:,0].name)
w = barwidth
offset = baroffset
ax2.bar(x, series0, width=w, align='center', label=label0 , color=colors[0])
ax2.xaxis_date()
ax2.autoscale(tight=True )
ax2_ylim = ax2.get_ylim()[::-1]
bottom = max(max(0,ax2_ylim[0]),max(df_bui_std,ax2_ylim[1]))
top = min(max(0,ax2_ylim[0]),max(df_bui_std,ax2_ylim[1]))
ax2.set_ylim(top=top, bottom=bottom)
ax2.set_xlim(start_his,end_his)
ax2.tick_params(labelbottom='off',labeltop='on', labelright="on",labelleft='off')
ax2.set_ylabel('Neerslag (mm)',rotation=90)
ax2.set_yticks(np.linspace(0, ax2.get_ybound()[1]+df_bui_std, 5))
leg=ax2.legend(bbox_to_anchor=(0.95, 0.02),loc=4,prop={'size':10},ncol=1)
leg.draw_frame(False)
# fix zorder
ax1.set_zorder(ax2.get_zorder()+1) # put ax1 in front of ax2
ax1.patch.set_visible(False) # hide the 'canvas'
#ax2.patch.set_visible(False) # hide the 'canvas'
plt.title('location: '+location)
plt.tight_layout()
if savefigure == False:
plt.show()
return
else:
his_file = "".join(re.findall("[A-Za-z0-9]", his_file))
location = "".join(re.findall("[A-Za-z0-9]", location))
parameter = "".join(re.findall("[A-Za-z0-9]", parameter))
path = os.path.join(out_folder,'{0}_{1}_{2}'.format(his_file,location,parameter))
#print(path)
plt.savefig(path, dpi=100, bbox_inches='tight')
plt.close(fig)
return
def table_bui_his(self, df_his, df_bui_sel, savetable=False, his_file='', his_parameter='', his_location='', out_folder=''):
"""
write table from combined his-file and bui-file
Parameters
----------
df_his : pandas.DataFrame
df_bui_sel : pandas.DataFrame
his_file : str
his_parameter : str
his_location : str
out_folder : str
Returns
-------
df_bui_his : pandas.DataFrame
"""
# include new index level on column
df_bui_sel = pd.concat([df_bui_sel], keys=['Precipitation (mm)'], names=['parameter'], axis=1)
# add column description
df_bui_sel.columns.levels[1].name='location'
# reorder levels of bui-file
df_bui_sel = df_bui_sel.reorder_levels(['location','parameter'], axis=1)
# concatenate both dataframes
df_bui_his = pd.concat((df_his,df_bui_sel), axis=1, join='outer')
# save to csv
if savetable==True:
his_file = "".join(re.findall("[A-Za-z0-9]", his_file))
his_location = "".join(re.findall("[A-Za-z0-9]", his_location))
his_parameter = "".join(re.findall("[A-Za-z0-9]", his_parameter))
path = os.path.join(out_folder,'{0}_{1}_{2}.csv'.format(his_file,his_location,his_parameter))
#print(path)
df_bui_his.to_csv(path, encoding='windows-1252')
return
else:
return df_bui_his
def EnsembleRunner(self, shp_file, bui_file, his_folder, out_folder,
shp_hiskey, shp_locationkey, shp_parameterkey, savefigure=True,
bui_locations='auto', bui_locations_aantal=10, savetable=True,
normalize_by_unicode=True, include_simularity=True, sequence_simularity=0.82,
threshold_bar=3, barwidth = 0.02, baroffset=0.01):
"""
Combine timeseries of his files and bui locations using a dbf/shp file
containing information about the mapping. Prepares figures and tables in batch function.
Parameters
----------
shp_file : str
path to dbf/shp file containing three columns to query his-file and location and parameter
bui_file : str
path to .bui file, containing a single precipitation event for 1 or more locations
his_folder : str
path to folder containing the his_file
out_folder : str
path to folder where to store output deriviates
shp_hiskey : str
his column key in shp/dbf file (contains name of his-file to query
(e.g: 'reachseg', 'calcpnt', 'struc'))
shp_locationkey : str
location column key in shp/dbf file
shp_parameterkey : str
parameter column key in shp/dbf file
savefigure : boolean
options to exlude or include export of figures
bui_locations : str or list
welke bui_locations worden gebruikt om een selectie te maken in de
bui-file, if 'bui_locations' is 'auto', dan worden de eerste x locations gebruikt
(waar x = 'bui_locations_aantal')
bui_locations_aantal : int
aantal locations mee te nemen tijdens plotten wanneer 'auto' is
opgegeven voor parameter 'bui_locations' (default: 10)
savetable : boolean
options to exlude or include export of table (csv format)
normalize_by_unicode : boolean (default: True)
inlcude this option to include NFKD unicode compatibility decomposition.
see: http://unicode.org/reports/tr15/
include_simluratiy : boolean (default: True)
include this option to include Ratcliff/Obershelp pattern recognition
sequence_simularity : float (default 0.82)
number between 0.0 and 1.0, function as threshold, where only a simularity above
this value is mapped
Returns
-------
None. Figures and tables are prepared in the out_folder
"""
print('start ensemble runner')
gdf = gpd.read_file(shp_file)
print('read shp-file')
df_bui = bui_reader.read_bui(bui_file)
print('read bui-file')
progress_bar = tqdm(gdf.T.columns.tolist())
for idx in progress_bar:
row = gdf.iloc[idx,:]
progress_bar.set_description("location %s" % row[shp_locationkey].ljust(20))
his_file = row[shp_hiskey]
location = row[shp_locationkey]
parameter = row[shp_parameterkey]
# read his-file
df_his, parameter = his_reader.read_series(his_file, location, parameter, his_folder,
normalize_by_unicode, include_simularity, sequence_simularity, return_matching_parameter=True)
# prepare files for plotting
df_bui_sel, start_his, end_his, df_bui_std, df_his_std = self.prepare_bui_his(
df_his, df_bui,
bui_locations=bui_locations,
bui_locations_aantal=bui_locations_aantal)
# plot the figure
self.plot_bui_his(df_his, df_bui_sel, start_his, end_his,
df_bui_std, df_his_std, his_file, parameter,
location, out_folder, savefigure)
# save the data tables
self.table_bui_his(df_his, df_bui_sel, savetable=savetable, his_file=his_file,
his_parameter=parameter, his_location=location, out_folder=out_folder)
return (print('done'))
PK QMb ( ( hkvsobekpy/core/utils.pyfrom difflib import SequenceMatcher
import unicodedata
def similar(a, b):
return SequenceMatcher(None, a, b).ratio()
def normalize_caseless(text):
return unicodedata.normalize("NFKD", text.casefold())
def caseless_equal(left, right):
return normalize_caseless(left) == normalize_caseless(right)
def compare_df_column_his_list(df, df_column_key, his_parameters, normalize_by_unicode=True,
include_simularity=False, sequence_simularity=0.82):
"""
function to apply unicode normalization and similarity checking for two list of columns
Parameters
----------
df : geopandas.GeoDataFrame
dataframe containing the input columns, normally is the source a shp or dbf file
df_column_key : str
name of column for usage as key
his_parameters : list
list of strings of locations/parameters of his-file to compare against the column
in the geodataframe
normalize_by_unicode : boolean
inlcude this option to include NFKD unicode compatibility decomposition.
see: http://unicode.org/reports/tr15/
include_simluratiy : boolean
include this option to include Ratcliff/Obershelp pattern recognition
sequence_simularity : float
number between 0.0 and 1.0, function as threshold, where only a simularity above
this value is mapped
Returns
df : geopandas.GeoDataFrame
dataframe where column matching the column key is updated with matching values
"""
for df_idx, df_parameter in enumerate(df[df_column_key]):
for his_parameter in his_parameters:
# option to have only a unicode normalization
if normalize_by_unicode == True and include_simularity == False:
if caseless_equal(his_parameter, df_parameter) == True:
df.loc[df_idx, df_column_key] = his_parameter
print('{0} changed into {1}'.format(
df_parameter, his_parameter))
# option to apply only parameter similarity functionality
if include_simularity == True and normalize_by_unicode == False:
smty = similar(his_parameter, df_parameter)
# print(smty)
if sequence_simularity <= smty < 1.0:
df.loc[df_idx, df_column_key] = his_parameter
print('{0} changed into {1}'.format(
df_parameter, his_parameter))
# option to apply both unicode normalization and similarity functionality
if include_simularity == True and normalize_by_unicode == True:
# first check unicode normalization
if caseless_equal(his_parameter, df_parameter) == True:
df.loc[df_idx, df_column_key] = his_parameter
print('{0} changed into {1}'.format(
df_parameter, his_parameter))
# if not succeed try similarity
else:
smty = similar(normalize_caseless(his_parameter), normalize_caseless(df_parameter))
#print(smty)
if sequence_simularity <= smty < 1.0:
df.loc[df_idx, df_column_key] = his_parameter
print('{0} changed into {1}'.format(
df_parameter, his_parameter))
return df
def compare_df_parameter_his_parameter(df_parameter, his_parameters, normalize_by_unicode=True,
include_simularity=False, sequence_simularity=0.82):
"""
function to apply unicode normalization and similarity checking for two list of columns
Parameters
----------
df_parameter : str
string of locations/parameter from a dbf of shp file
his_parameters : list
list of strings of locations/parameters of his-file to compare against the df value
normalize_by_unicode : boolean
inlcude this option to include NFKD unicode compatibility decomposition.
see: http://unicode.org/reports/tr15/
include_simluratiy : boolean
include this option to include Ratcliff/Obershelp pattern recognition
sequence_simularity : float
number between 0.0 and 1.0, function as threshold, where only a simularity above
this value is mapped
Returns
df_parameter : str
df parameter updated with matching values of his parmater/location if any
"""
for his_parameter in his_parameters:
if normalize_by_unicode == True and include_simularity == False:
if caseless_equal(his_parameter, df_parameter) == True:
print('Normalized unicode {0} matches {1}'.format(
df_parameter, his_parameter))
df_parameter = his_parameter
# option to apply only parameter similarity functionality
if include_simularity == True and normalize_by_unicode == False:
smty = similar(his_parameter, df_parameter)
# print(smty)
if sequence_simularity <= smty < 1.0:
print('{0} changed into {1}, since similarity is {2}'.format(
df_parameter, his_parameter, round(smty,2)))
df_parameter = his_parameter
# option to apply both unicode normalization and similarity functionality
if include_simularity == True and normalize_by_unicode == True:
# first check unicode normalization
if caseless_equal(his_parameter, df_parameter) == True:
print('Normalized unicode {0} matches {1}'.format(
df_parameter, his_parameter))
df_parameter = his_parameter
# if not succeed try similarity
else:
smty = similar(normalize_caseless(his_parameter), normalize_caseless(df_parameter))
#print(smty)
if sequence_simularity <= smty < 1.0:
print('{0} changed into {1}, since similarity is {2}'.format(
df_parameter, his_parameter, round(smty,2)))
df_parameter = his_parameter
return df_parameter PK QMoT( ( ! hkvsobekpy/core/waterlevelstat.pyimport os
try:
from pathlib import Path
except:
from pathlib2 import Path
import types
import argparse
import sys
import itertools
import geopandas as gpd
from datetime import datetime, timedelta
import copy
import matplotlib.pyplot as plt
from matplotlib.ticker import ScalarFormatter, FormatStrFormatter
import numpy as np
import pandas as pd
from scipy import optimize
import fire
import warnings
try:
shell = get_ipython().__class__.__name__
if shell == 'ZMQInteractiveShell':
from tqdm import tqdm_notebook as tqdm # Jupyter notebook or qtconsole
elif shell == 'TerminalInteractiveShell':
from tqdm import tqdm # Terminal running IPython
else:
from tqdm import tqdm # Other type (?)
except NameError:
from tqdm import tqdm # Probably standard Python interpreter
#for debugging within notebook
try:
from IPython.core.debugger import set_trace
except:
pass
# for python 2/3 compatibility
try:
xrange
except NameError:
xrange = range
try:
get_ipython().magic('matplotlib inline')
except:
pass
from hkvsobekpy.io.his import __his_class
his_reader = __his_class()
class __waterlevelstat_class(object):
def __init__(self):
"""
SobekResultaten class. Class met methodes om een .his bestand
uit te lezen.
WaterstandStatistiek class. Class met methodes om een GumbleFit te bepalen
"""
# initatie van lege immutable objects
# zie: https://stackoverflow.com/a/22526544/2459096
# python2.7: obj = argparse.Namespace()
# python3.x: obj = types.SimpleNamespace()
def _initNamespaces(self):
try:
self.stats = types.SimpleNamespace()
self.stats.plotposities = types.SimpleNamespace()
self.stats.stap1 = types.SimpleNamespace()
self.stats.stap2 = types.SimpleNamespace()
self.stats.stap3 = types.SimpleNamespace()
self.stats.stap4 = types.SimpleNamespace()
except Exception as e:
print(e)
self.stats = argparse.Namespace()
self.stats.plotposities = argparse.Namespace()
self.stats.stap1 = argparse.Namespace()
self.stats.stap2 = argparse.Namespace()
self.stats.stap3 = argparse.Namespace()
self.stats.stap4 = argparse.Namespace()
return self
class __errors__(object):
"""
error class met verschillende foutmeldingen
"""
@staticmethod
def gewogenGemiddeldeError():
raise ValueError('Kan gewogen gemiddelde niet bepalen. Vereiste is minimaal 2 punten aan de linker en rechterzijde van de gekozen T')
def gebeurtenissenError():
raise ValueError('Meer gebeurtenissen dan N. Zorg ervoor dat je tijdreeks slechts 1 gebeurtenis per jaar heeft')
def schrijfTabelError():
raise ValueError('Kan deze tabel alleen wegschrijven met T10,25,50,100 voor Gumbel en T10 voor gewogen gemiddelde')
def _best_fit_slope_and_intercept(self,xs,ys):
try:
warnings.filterwarnings("error")
m = (((xs.mean()*ys.mean()) - (xs*ys).mean()) / ((xs.mean()*xs.mean()) - (xs*xs).mean()))
except RuntimeWarning:
#set_trace()
m = 0#np.nan
b= ys.mean() - m*xs.mean()
return m, b
def _squared_error(self,ys_orig,ys_line):
return sum((ys_line - ys_orig) * (ys_line - ys_orig))
def _coefficient_of_determination(self,ys_orig,ys_line):
y_mean_line = [ys_orig.mean() for y in ys_orig]
squared_error_regr = self._squared_error(ys_orig, ys_line)
squared_error_y_mean = self._squared_error(ys_orig, y_mean_line)
try:
warnings.filterwarnings("error")
_r_squared = 1 - (squared_error_regr/squared_error_y_mean)
except RuntimeWarning:
_r_squared = 'undefined'
return _r_squared
def _r_squared(self,xs,ys):
"""
Bepaalt de r**2 waarde op basis van twee input arrays
Parameters
----------
xs : numpy.array
array met orginele waarden
ys : numpy.array
array met afgeleide waarden
Returns
-------
r_squared : float
de afgeleide r**2 waarde
"""
m, b = self._best_fit_slope_and_intercept(xs,ys)
regression_line = [(m*x)+b for x in xs]
r_squared = self._coefficient_of_determination(ys,regression_line)
return r_squared
def _igumbelFunc(self, arr, Td, t):
"""
Gumbel Functie
Parameters
----------
arr : numpy.array
array met gesorteerde waterstanden
Td :
Returns
-------
gumbelFuncFunc : numpy.array
Gumbel Functie
"""
a,d = arr
self.stats.plotposities.gumbelFunc = (float(-1.0)* float(a)*np.log(1.0/t * float(Td))+float(d))
return self.stats.plotposities.gumbelFunc
def _igumbelFuncFit(self, arr, h, Td, t):
"""
Gumbel Functie
Parameters
----------
arr : numpy.array
array met gesorteerde waterstanden
h : int/float
Td : int/float
T : int/float
Returns
-------
gumbelFuncFit : numpy.array
Fitted Gumbel Functie
"""
a,d = arr
self.stats.plotposities.gumbelFuncFit = h-(float(-1.0)* float(a)*np.log(1.0/t * float(Td))+float(d))
return self.stats.plotposities.gumbelFuncFit
def _gewogenGemiddelde4TOI(self, Tarray, WSarray, TOI):
"""
Functie om het gewogen gemiddelde te krijgen op basis van de vier dichtsbijzijnde waarden rondom de T of interest
Parameters
----------
Tarray : numpy.array
Array met de terugkeertijden,
WSarray : numpy.array
Array met de parameterwaarden, bijvoorbeeld waterstanden
TOI : int
Waarde met de terugkeertijd of interest, bijvoorbeeld 10 voor T=10
Returns
-------
"""
# get the index of the closest value to T10 on its left side,
# T is sorted in descending order so reverse array first to get ascending order
cl = self._find_closest(Tarray[::-1], TOI)
# get the indices of the four closest points
# [..] * * * * | * * [..]
# cl-2 cl-1 cl T10 cl+1 cl+2
# ---- -- ---- ----
try:
indices = np.array([cl-1,cl,cl+1,cl+2])
WSarray_sel = np.take(WSarray[::-1], indices)
Tarray_sel = np.take(Tarray[::-1], indices)
except:
self.__errors__.gewogenGemiddeldeError()
# inverse distance weighting
weights = 1 / abs(Tarray_sel - TOI)
weights /= weights.sum()
# weight_cl-1 * ws_cl-1 + weight_cl * ws_cl + weight_cl+1 * ws_cl+1 + weight_cl+2 * ws_cl+2
WSarray_TOI = np.dot(weights, WSarray_sel)
return WSarray_TOI, WSarray_sel, Tarray_sel
def _find_closest(self, Tarray, TOI):
# T must be sorted (in ascending order)
idx = Tarray.searchsorted(TOI)
idx = np.clip(idx, 1, len(Tarray)-1)
left = Tarray[idx-1]
right = Tarray[idx]
idx -= TOI - left < right - TOI
return idx
def _calcFi_T(self, len_arr,N, Ggi=0.44, GgN=0.12):
"""
Bepaal plotpositie jaarmaximum als kans
Parameters
----------
len_arr : int
aantal gebeurtenissen
N : int
aantal jaren waarover de Gumble fit bepaald moet worden
Ggi : float
Gringgorten plotposities i (standaard: 0.44)
GgN : float
Gringgorten plotposities N (standaard: 0.12)
Returns
-------
T : np.array
array van terugkeertijden
Fijaar : np.array
array van plotposities jaarmaxima
Literature
----------
Voor bepaling Gringorten coefficienten zie bijv: http://glossary.ametsoc.org/wiki/Gringorten_plotting_position
"""
Fi = []
T = []
for i in range(len_arr):
i += 1
f = float(1 - ((i - Ggi) / (N+GgN)))
Fi.append(f)
try:
warnings.filterwarnings("error")
Fijaar = (np.log(Fi) )* -1
except RuntimeWarning:
#set_trace()
pass
T = 1 / (Fijaar)
# set set self
self.stats.plotposities.Fijaar = Fijaar
self.stats.plotposities.T = T
return self.stats.plotposities.T, self.stats.plotposities.Fijaar
def PlotFiguur(self,stat_object,out_folder='none'):
"""
plot figuur
Parameters
----------
ID : str
locationnaam
## STEP1 Gumbel Fit voor T25,T50,T100 voor alle buien
S1_vAmin : int
venster array minimum
S1_vAmax : int
venster array maximum
S1_T : ndarray
Terugkeertijden van jaarmaxima gebeurtenissen genomen over gehele jaar
S1_ws_srt : ndarray
Waterstanden welke horen bij de terugkeertijden van jaarmaxima gebeurtenissen genomen over gehele jaar
S1_GumbelT : ndarray
Terugkeertijden welke horen bij de Gumbel fit (eg, 25,50,100)
S1_GumbelWS : ndarray
Waterstanden welke horen bij de terugkeertijden welke horen bij de Gumbel fit (eg, 25,50,100)
S1_GumbelWS_line : ndarray
Waterstanden welke horen bij de terugkeertijden welke horen bij de Gumbel fit vallend binnend het venster
S1_r_squared : float
r**2 tussen de Gumbel fit en gebeurtenenissed binnen je venster
## STEP2 Gumbel Fit voor T10 voor alleen zomerbuien
S2_T : ndarray
Terugkeertijden van jaarmaxima gebeurtenissen genomen over het groeiseizoen
S2_ws_srt : ndarray
Waterstanden welke horen bij de terugkeertijden van jaarmaxima gebeurtenissen genomen over het groeiseizoen
## STEP4 Gewogen gemiddelde voor T10 voor alleen de zomerbuien
S4_Tarray_sel_jaar : ndarray
Terugkeertijden van jaarmaxima gebeurtenissen genomen over het groeiseizoen welke meegenomen zijn voor bepaling gewogen gemiddeld
S4_WSarray_sel_jaar : ndarray
Waterstanden welke horen bij terugkeertijden van jaarmaxima gebeurtenissen welke meegenomen zijn voor bepaling gewogen gemiddeld
TOI : int
Terugkeertijd of interest voor bepaling van het gewogen gemiddelde (normaal gesproken 10)
S4_WSarray_TOI_jaar : float
Waterstand welke hoort bij de TOI
Returns
-------
Maakt figuur en slaat deze in de folder waar het script gerund wordt.
"""
ID = stat_object.stats.stap1.ID
S1_vAmin = stat_object.stats.stap1.vAmin
S1_vAmax = stat_object.stats.stap1.vAmax
S1_T = stat_object.stats.stap1.T
S1_ws_srt = stat_object.stats.stap1.ws_srt
S1_GumbelT = stat_object.stats.stap1.GumbelT
S1_GumbelWS = stat_object.stats.stap1.GumbelWS
S1_GumbelWS_line = stat_object.stats.stap1.GumbelWS_line
S1_r_squared = stat_object.stats.stap1.r_squared
S2_T = stat_object.stats.stap2.T
S2_ws_srt = stat_object.stats.stap2.ws_srt
S3_Tarray_sel_jaar = stat_object.stats.stap3.Tarray_sel_jaar
S3_WSarray_sel_jaar = stat_object.stats.stap3.WSarray_sel_jaar
TOI3 = stat_object.stats.stap3.TOI
S3_WSarray_TOI_jaar = stat_object.stats.stap3.WSarray_TOI_jaar
S4_Tarray_sel_jaar = stat_object.stats.stap4.Tarray_sel_jaar
S4_WSarray_sel_jaar = stat_object.stats.stap4.WSarray_sel_jaar
TOI = stat_object.stats.stap4.TOI
S4_WSarray_TOI_jaar = stat_object.stats.stap4.WSarray_TOI_jaar
# Voor het figuur mag de TOI niet geplot worden in de gumbel fit.
# Kijk of TOI binnen de GumbelT bestaan en verwijder dit punt als zo.
if TOI in S1_GumbelT:
idx = S1_GumbelT.index(TOI)
S1_GumbelT = np.delete(S1_GumbelT, idx)
S1_GumbelWS = np.delete(S1_GumbelWS, idx)
fig=plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set_xscale('log')
# plot jaarmaxima gebeurtenissen over gehele jaar ## OPEN CIRKELS
ax.plot(S1_T, S1_ws_srt, fillstyle='none', label='waterstanden (jaarmaxima)',
color='#2E589F', linestyle='', marker='o', markeredgewidth=2, markersize=10, markerfacecoloralt='gray')
# plot maxima gebeurtenissen over de zomerbuien ## DICHTE CIRKELS
ax.plot(S2_T, S2_ws_srt, fillstyle='full', label='waterstanden (maxima groeiseizoen)',
color='cornflowerblue', linestyle='', marker='o', markersize=8, markerfacecoloralt='gray', alpha=0.75)
# plot gumble fit voor T25, T50, T100 in lijn en punt
ax.plot(S1_T[S1_vAmin:S1_vAmax],S1_GumbelWS_line, '-', color='#3BBB75', label='Gumbel fit (venster)')
ax.scatter(S1_GumbelT, S1_GumbelWS,s=100, marker='s', facecolors='#3BBB75', zorder=10,label='waterstand op T25, T50 en T100')
# plot gewogen gemiddelde voor T10 op basis van geheel jaar in punt
ax.scatter(TOI3, S3_WSarray_TOI_jaar,s=100, marker='s',facecolors='#FF5AC3', zorder=10,label='waterstand op T10 (stedelijk)') # geheel jaar
# plot gewogen gemiddelde voor T10 op basis van geheel jaar in punt ## HALF-GEVULDE CIRKELS
ax.plot(S3_Tarray_sel_jaar,S3_WSarray_sel_jaar, fillstyle='right', label='gebeurtenissen voor bepaling T'+str(TOI3)+' (stedelijk)',
color='#865FC5', linestyle='none', marker='o', markersize=8, markerfacecoloralt='white')
# plot gewogen gemiddelde voor T10 op basis van alleen zomerbuien in punt
ax.scatter(TOI, S4_WSarray_TOI_jaar,s=100, marker='s',facecolors='#CE0002', zorder=10,label='waterstand op T10 (landelijk)') # zomerbuien
# plot gewogen gemiddelde voor T10 op basis van zomerbuien in punt ## HALF-GEVULDE CIRKELS
ax.plot(S4_Tarray_sel_jaar,S4_WSarray_sel_jaar, fillstyle='right', label='gebeurtenissen voor bepaling T'+str(TOI)+ ' (landelijk)',
color='#F5AC1B', linestyle='none', marker='o', markersize=8, markerfacecoloralt='white')
# plot r**2 rechtsbovenin
if type(S1_r_squared) is str:
label_r_squared = S1_r_squared
else:
label_r_squared = str(np.round(S1_r_squared,2))
ax.text(0.975, 0.975, '$r^2$: '+label_r_squared,
horizontalalignment='right',
verticalalignment='top',
transform=ax.transAxes)
# axes settings
ax.set_xlim(1,300)
Ymin = min(S1_ws_srt) - 0.05
Ymax = max(S1_ws_srt) + 0.25
ax.set_ylim(Ymin,Ymax)
ax.set_ylabel('Waterstand (m+NAP)')
ax.set_xlabel('Terugkeertijd (jaren)')
ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))
ax.grid(True,which='both', axis='both', linestyle='-', color= '0.75', zorder=0)
# legend settings
legend = plt.legend(loc='upper left',prop={'size':10})
legend.get_frame().set_facecolor('white')
legend.get_frame().set_alpha(1)
legend.get_frame().set_linewidth(1)
legend.get_frame().set_edgecolor('#8B8B8B')
# table settings
col_labels=['Terugkeertijd','Waterstand (m+NAP)']
table_vals = pd.DataFrame([['T'+str(i) for i in S1_GumbelT],S1_GumbelWS.round(decimals=2).tolist()]).T.values.tolist()
table_vals = [['T'+str(TOI)+' (landelijk)', str(round(S4_WSarray_TOI_jaar,2))]] + table_vals
table_vals = [['T'+str(TOI3)+' (stedelijk)', str(round(S3_WSarray_TOI_jaar,2))]] + table_vals
# the rectangle is where I want to place the table
the_table = ax.table(cellText=table_vals,
colWidths = [0.4]*2,
colLabels=col_labels,
loc='center right',bbox=[1.01, 0.0, 0.5, 0.5])
plt.title('location: '+ID)
plt.tight_layout()
if out_folder=='none':
plt.show()
else:
out_folder_png = os.path.join(out_folder,'png')
out_folder_png_file = os.path.join(out_folder_png, 'waterstandstatistiek_loc_'+ID+'.png')
plt.savefig(out_folder_png_file, bbox_inches='tight', dpi=90, pad_inches=0.2)
plt.clf()
plt.close(fig)
def _gewogenGemiddelde(self, df_enkel, N=109,TOI=10):
"""
"""
# lengte van array van waterstanden
Nws = df_enkel.size
T, Fijaar = self._calcFi_T(Nws,N)
# kijk eerst of de input dataframe een multi-column heeft en reduceerd indien ja
try:
df_enkel = df_enkel.iloc[:,0]
except:
pass
if len(df_enkel.name) == 2:
enkel_location = df_enkel.name[0]
enkel_parameter = df_enkel.name[1]
else:
enkel_location = enkel_parameter = 'niet gedefinieerd'
df_enkel_sorted = df_enkel.sort_values(ascending=False)
# bepaal gewogen gemiddelde voor parameter waarde voor terugkeertijd of interest op basis van 4 dichtsbijzijnde waarden
WSarray_TOI, WSarray_sel, Tarray_sel = self._gewogenGemiddelde4TOI(T, df_enkel_sorted.values, TOI)
return WSarray_TOI, WSarray_sel, Tarray_sel
def _enkeleGumbelFit(self, df_enkel, N, vensterArray=[0,9], GumbelT=[25,50,100.0], Ggi=0.44, GgN=0.12):
"""
TOI : int
Terugkeertijd of interest
N : int
Pas op: de bepaling voor over hoeveel jaren het aantal plotposities berekend moet worden,
moet gedaan worden op basis van het aantal mogelijke jaren in de gebeurtenissen reeks.
Bepaal dit op een gebeurtenissenreeks alvorens een periode filter is toegepast.
"""
# lengte van array van waterstanden
Nws = df_enkel.size
if Nws > N:
#set_trace()
self.__errors__.gebeurtenissenError()
T, Fijaar = self._calcFi_T(Nws, N, Ggi, GgN)
# kijk eerst of de input dataframe een multi-column heeft en reduceerd indien ja
try:
df_enkel = df_enkel.iloc[:,0]
except:
pass
if len(df_enkel.name) == 2:
enkel_location = df_enkel.name[0]
enkel_parameter = df_enkel.name[1]
else:
enkel_location = enkel_parameter = 'niet gedefinieerd'
# get min/max van venster array
vAmin = vensterArray[0]
vAmax = vensterArray[1]
df_enkel_sorted = df_enkel.sort_values(ascending=False)
# bepaal de terugkeertijden waarden en afgeleiden op basis van venster array voor Gumbel fit
dT = np.log(T[vAmin]) - np.log(T[vAmax])
dh = df_enkel_sorted.iloc[vAmin] - df_enkel_sorted.iloc[vAmax]
dTpunt = -np.log(T[vAmax])
dhpunt = (dh/dT)*dTpunt
a0 = [dh/dT, df_enkel_sorted.iloc[vAmax]+dhpunt]
# apply de gumbel fit functie
a_out, cov_x, infodict, msg, flag = optimize.leastsq(self._igumbelFuncFit, a0, args = (df_enkel_sorted.iloc[vAmin:vAmax], 1, T[vAmin:vAmax]), full_output=True)
# a_out holds the paramters of the fitted line [a and b]
a = a_out[0]
b = a_out[1]
# get the locations on the fit corresponding to the required return periods
GumbelWS = self._igumbelFunc(a_out, 1, np.array(GumbelT))
# get the locations on the fit corresponding to the slice on all Ts using the vensterArray
GumbelWS_lineVensterArray = self._igumbelFunc(a_out, 1, T[vAmin:vAmax])
#set_trace()
xs = df_enkel_sorted.iloc[vAmin:vAmax]
ys = GumbelWS_lineVensterArray
r_squared = self._r_squared(xs,ys)
# schrijf weg naar gumbel dataframe
T_columns = ['T'+str(int(item)) for item in GumbelT]
df_gumbel = pd.DataFrame(index=[enkel_location], columns=T_columns+['solution'])
df_gumbel.index.name='location'
df_gumbel.loc[enkel_location][T_columns] = GumbelWS
if flag > 4:
df_gumbel.loc[enkel_location]['solution'] = 'no solution'
else:
df_gumbel.loc[enkel_location]['solution'] = 'optimization succesful'
return (vAmin,vAmax, df_enkel_sorted.values, T, GumbelT, GumbelWS, enkel_location, a,b, GumbelWS_lineVensterArray,r_squared), df_gumbel
def AfleidingParameters(self, df_enkel, N, vensterArray, GumbelT, TOI, startMMdd=(1,1), endMMdd=(12,31),
jaarmax_as='date', Ggi=0.44, GgN=0.12):
"""
"""
self = self._initNamespaces()
# STEP 1 Gumbel Fit voor geselecteerde T's [eg. T10,T25,T50,T100] voor alle buien
S1_param4fig, S1_df_gumbel = self._enkeleGumbelFit(df_enkel, N=N, vensterArray=vensterArray, GumbelT=GumbelT,Ggi=Ggi,GgN=GgN)
self.stats.stap1.vAmin = S1_param4fig[0]
self.stats.stap1.vAmax = S1_param4fig[1]
self.stats.stap1.ws_srt = S1_param4fig[2]
self.stats.stap1.T = S1_param4fig[3]
self.stats.stap1.GumbelT = S1_param4fig[4]
self.stats.stap1.GumbelWS = S1_param4fig[5]
self.stats.stap1.ID = S1_param4fig[6]
self.stats.stap1.a = S1_param4fig[7]
self.stats.stap1.b = S1_param4fig[8]
self.stats.stap1.GumbelWS_line = S1_param4fig[9]
self.stats.stap1.r_squared = S1_param4fig[10]
# STEP 2 Gumbel Fit voor T10 voor alleen zomerbuien
df_enkel_groei = his_reader.SelectPeriodeWaardenArray(df_enkel, startMMdd=startMMdd, endMMdd=endMMdd, jaarmax_as=jaarmax_as)
S2_param4fig, S2_df_gumbel = self._enkeleGumbelFit(df_enkel_groei, N=N, GumbelT=[TOI], vensterArray = vensterArray,Ggi=Ggi,GgN=GgN)
self.stats.stap2.ws_srt = S2_param4fig[2]
self.stats.stap2.T = S2_param4fig[3]
self.stats.stap2.GumbelT = S2_param4fig[4]
self.stats.stap2.GumbelWS = S2_param4fig[5]
self.stats.stap2.startMMdd = startMMdd
self.stats.stap2.endMMdd = endMMdd
# STEP 3 Gewogen gemiddelde voor T10 voor alle buien
self.stats.stap3.WSarray_TOI_jaar, self.stats.stap3.WSarray_sel_jaar, self.stats.stap3.Tarray_sel_jaar = self._gewogenGemiddelde(df_enkel, N=N, TOI=TOI)
self.stats.stap3.TOI = TOI
self.stats.stap3.startMMdd = startMMdd
self.stats.stap3.endMMdd = endMMdd
# STEP 4 Gewogen gemiddelde voor T10 voor alleen de zomerbuien
self.stats.stap4.WSarray_TOI_jaar, self.stats.stap4.WSarray_sel_jaar, self.stats.stap4.Tarray_sel_jaar = self._gewogenGemiddelde(df_enkel_groei, N=N, TOI=TOI)
self.stats.stap4.TOI = TOI
self.stats.stap4.startMMdd = startMMdd
self.stats.stap4.endMMdd = endMMdd
return self
def EnsembleRunner(self, out_folder, his_file, shp_file, shp_key='nodeID', parameter='auto',
startMMdd=(5,15), endMMdd=(10,15), vensterArray=[0,10], GumbelT=[10,25,50,100],
TOI=10, draw_plot=True, write_table=True):
"""
Lees SOBEK weggeschreven variablene uit en schrijf de afgeleide waterstanden
toebehorend aan gebruikers gedefineerde terugkeertijden naar zowel csv/dbf
en png. Voor het afleiden van de waterstanden welke horen bij de verschillende
terugkeertijden kan gebruik gemaakt worden van plotposities/Gumbel fit en een
gewogen gemiddelde.
Input
-----
out_folder : path
een folder waarin de csv en shp/dbf bestand worden weggeschreven.
voor de figuren moet er ook een folder binnen de out_folder bestaan
getiteld 'png'
his_file : path
absoluut pad naar een his-file welke ingelezen moet gaan worden
shp_file : path
absoluut pad naar een shp-file van waaruit de locations gelinkt moeten
worden
shp_key : str
string met de naam van de kolom in de shp-file waarin de locations staan
parameter : str
naam van de parameter in het his-file welke gebruikt moet gaan worden.
Default is 'auto'. In dit geval zal de eerste parameter gebruikt worden
vanuit het his-file
startMMdd : tuple
Tuple in het formaat (maand,dag). Bepaling van de start datum van de
periode. Genoemde datum is inclusief. Voorbeeld (5,15) staat voor
maand 5, dag 15.
endMMdd : tuple
Tuple in het formaat (maand,dag). Bepaling van de eind datum van de
periode. Genoemde datum is inclusief. Voorbeeld (10,15) staat voor
maand 10, dag 15.
vensterArray : array
het venster welke gebruikt wordt als filter om de gebeurtenissen mee te
nemen voor de bepaling van de Gumbel fit. Het venster is een array van
twee waarden, vaak wordt [0,10] gekozen, waar 0 overeenkomt met de meest
extreme waarde en 10 de op 10 na meest extreme waarde. In ander woorden,
in dit geval is het venster de 10 meest extreme waarden.
GumbelT : array
een array met terugkeertijden welke meegenomen moet worden als locations
waarover een de waterstand moet bepaald worden aan de hand van de
Gumbel fit. Een vaak gebruikte array is [10,25,50,100] wat overeenkomt met
de T10, T25, T50 en T100
TOI : int
Waarde met de terugkeertijd of interest voor het bepalen van het gewogen
gemiddelde. Dit wordt gebruikt voor een terugkeertijd welke tenminste 2
gebeurtenissen en 2 gebeurtenissen na zicht heeft. Gewoonlijk kan dit
gebruikt worden voor het bepalen van de T10, welke soms nog in de
'knik' ligt.
draw_plot : boolean
Aangeven of je de figuren ook wilt wegschrijven naar png's. Indien zo,
moet er een 'png' folder bestaan binnen de out_folder. Voor het figuur
zal er gekeken worden of de TOI in de GumbelT bevindt. Indien dit zo is,
zal alleen de terugkeertijd van de TOI ingetekend worden.
write_table : boolean
Aangeven of je de waarden ook wilt wegschrijven naar tabellen. De tabellen
welke worden weggeschreven zijn 1 csv bestand en 1 shp/dbf bestand.
Return
------
geeft geen return binnen python
"""
print ('read his-file')
# create frame table
his_object = his_reader.LeesMetadata(his_file)
stat_init = self._initNamespaces()
stat_table1 = pd.DataFrame()#
stat_table2 = pd.DataFrame()#self._initTabellen(stat_init,shp_key)
print ('read shp-file')
locations_df = gpd.read_file(shp_file)
if parameter=='auto':
parameter = his_object.hisFile.variabeleInfo.variabelen[0]
print ('create png-file')
# iterate locationlist and create item in table
pbar = tqdm(locations_df[shp_key].tolist())
for location in pbar:
pbar.set_description("location %s" % location.ljust(21))
df_enkel = his_object.EnkeleWaardenArray(location, parameter, startMMdd=(1, 1), endMMdd=(12, 31), jaarmax_as='date')
if df_enkel.size > his_object.hisFile.tijdstapInfo.N:
#set_trace()
self.__errors__.gebeurtenissenError()
stat_object = self.AfleidingParameters(df_enkel, his_object.hisFile.tijdstapInfo.N, vensterArray, GumbelT, TOI, startMMdd, endMMdd, jaarmax_as='date')
if draw_plot == True:
self.PlotFiguur(stat_object, out_folder)
if write_table ==True:
stat_tables = self.SchrijfTabellen(stat_object, shp_key=shp_key, init_tabel=False)
#set_trace()
stat_table1 = stat_table1.append(stat_tables.stats.df_table1)
stat_table2 = stat_table2.append(stat_tables.stats.df_table2)
print ('save csv-file')
# join on input shapefile and write tables
# save table 1
stat_table1.to_csv(os.path.join(out_folder,'waterstandstatistiek_output_1.csv'),index=False)
print ('save shp-file')
# prepare table 2 and save
# set_trace()
locations_df = locations_df.set_index(shp_key).join(stat_table2.set_index(shp_key)).reset_index()
locations_df.to_file(os.path.join(out_folder,'waterstandstatistiek_output_2.shp'), driver='ESRI Shapefile')
stat_table2 = locations_df
print ('done')
def _initTabellen(self,stat_object,shp_key):
"""
Initieer tabelstructuur
Parameters
----------
shp_key : str
key kolom vanuit de shp/dbf bestand
"""
stat_object.stats.df_table1 = pd.DataFrame(columns=[shp_key,'H','T','seizoen','methode'])
stat_object.stats.df_table2 = pd.DataFrame(columns=[shp_key,'T10_LANDELIJK','T10_STEDELIJK',
'T25','T50','T100','r^2','a','b'])
return stat_object
def SchrijfTabellen(self,stat_object,shp_key,decimals=3, init_tabel=True):
"""
Schrijf afgeleide parameters naar tabel
Parameters
----------
shp_key : str
key kolom vanuit de shp/dbf bestand
decimals : int
aantal decimalen om af te ronden
init_tabel : boolean
parameter om aan te geven of de _initTabellen aangeroepen moet worden. Moet op True staan wanneer functie
buiten de SingleRunner functie aangeroepen wordt.
"""
stat_object._initTabellen(stat_object,shp_key)
# if init_tabel==True:
# stat_object._initTabellen(stat_object,shp_key)
# else:
# stat_empty_init = self._initNamespaces()
# stat_empty_table = self._initTabellen(stat_empty_init, shp_key)
# stat_object.stats.df_table1 = types.SimpleNamespace()
# stat_object.stats.df_table2 = types.SimpleNamespace()
# try:
# stat_object.stats.df_table1 = init_tabel.df_table1
# stat_object.stats.df_table2 = init_tabel.df_table2
# except Exception as e:
# print(e)
# set_trace()
table1_list = []
table2_list = []
# gewogen gemiddelde voor jaarmaxima alle buien (# STEP 3)
table1_list.append({shp_key:stat_object.stats.stap1.ID,
'H':round(stat_object.stats.stap3.WSarray_TOI_jaar,decimals),
'T':stat_object.stats.stap3.TOI,
'seizoen':'geheel jaar',
'methode':'gewogen gemiddelde'})
# gewogen gemiddelde voor maxima zomerbuien (# STEP 4)
table1_list.append({shp_key:stat_object.stats.stap1.ID,
'H':round(stat_object.stats.stap4.WSarray_TOI_jaar,decimals),
'T':stat_object.stats.stap4.TOI,
'seizoen':'alleen zomer',
'methode':'gewogen gemiddelde'})
# Gumbel fit voor enkel maxima zomerbuien (# STEP 2)
table1_list.append({shp_key:stat_object.stats.stap1.ID,
'H':round(stat_object.stats.stap2.GumbelWS[0],decimals),
'T':stat_object.stats.stap2.GumbelT[0],
'seizoen':'alleen zomer',
'methode':'Gumbel fit'})
# Gumbel fit voor jaarmaxima alle buien (# STEP 1)
for idx, T in enumerate(stat_object.stats.stap1.GumbelT):
item = {shp_key:stat_object.stats.stap1.ID,
'H':round(stat_object.stats.stap1.GumbelWS[idx],decimals),
'T':T,
'seizoen':'geheel jaar',
'methode':'Gumbel fit'}
table1_list.append(item)
# append to dataframe 1
stat_object.stats.df_table1 = stat_object.stats.df_table1.append(table1_list, ignore_index=True)
if type(stat_object.stats.stap1.r_squared) is not str:
label_r_squared_table = round(stat_object.stats.stap1.r_squared,decimals)
else:
label_r_squared_table = stat_object.stats.stap1.r_squared
try:
table2_list.append({shp_key:stat_object.stats.stap1.ID,
'T10_LANDELIJK':stat_object.stats.df_table1[(stat_object.stats.df_table1['T'] == 10) &
(stat_object.stats.df_table1['methode'] == 'gewogen gemiddelde') &
(stat_object.stats.df_table1['seizoen'] == 'alleen zomer') &
(stat_object.stats.df_table1[shp_key] == stat_object.stats.stap1.ID)]['H'].values[0],
'T10_STEDELIJK':stat_object.stats.df_table1[(stat_object.stats.df_table1['T'] == 10) &
(stat_object.stats.df_table1['methode'] == 'gewogen gemiddelde') &
(stat_object.stats.df_table1['seizoen'] == 'geheel jaar') &
(stat_object.stats.df_table1[shp_key] == stat_object.stats.stap1.ID)]['H'].values[0],
'T25':stat_object.stats.df_table1[(stat_object.stats.df_table1['T'] == 25) &
(stat_object.stats.df_table1[shp_key] == stat_object.stats.stap1.ID)]['H'].values[0],
'T50':stat_object.stats.df_table1[(stat_object.stats.df_table1['T'] == 50) &
(stat_object.stats.df_table1[shp_key] == stat_object.stats.stap1.ID)]['H'].values[0],
'T100':stat_object.stats.df_table1[(stat_object.stats.df_table1['T'] == 100) &
(stat_object.stats.df_table1[shp_key] == stat_object.stats.stap1.ID)]['H'].values[0],
'r^2':label_r_squared_table,
'a':round(stat_object.stats.stap1.a,decimals),
'b':round(stat_object.stats.stap1.b,decimals)})
except (ValueError,IndexError):
self.__errors__.schrijfTabelError()
# append to dataframe 2
stat_object.stats.df_table2 = stat_object.stats.df_table2.append(table2_list, ignore_index=True)
if init_tabel==True:
return stat_object.stats.df_table1, stat_object.stats.df_table2
else:
return stat_objectPK QM hkvsobekpy/io/__init__.pyPK QM nz.- .- hkvsobekpy/io/bui.pyimport argparse
import pandas as pd
import datetime
import numpy as np
import warnings
# import logging
# from importlib import reload
# # initiatie logging
# reload(logging)
# logging.basicConfig(
# #filename="{0}/{1}.log".format(logPath, fileName),
# format='%(asctime)s [%(threadName)-12.12s] [%(levelname)-5.5s] %(message)s',
# level=logging.INFO)
# # to close file:
# #logging.shutdown()
class __bui_class(object):
class __errors__(object):
"""
error class met verschillende foutmeldingen
"""
@staticmethod
def fileNotFound(file=None):
raise IOError('Bestand bestaat niet. Is je pad goed? {}'.format(file))
@staticmethod
def variableNotSupported(variable=None):
raise AttributeError('Variabele {} wordt (nog) niet ondersteund. Zie definition information'.format(variable))
@staticmethod
def builengteError(availablevalues=None,expectedvalues=None):
raise ValueError('Het verwachte aantal tijdstappen {} komt niet overeen met het aanwezige aantal tijdstappen {} in de dataframe'.format(expectedvalues,availablevalues))
# @staticmethod
# def metadataNotSet():
# raise AttributeError('Metadata is niet bekend. Zet met sobek.LeesMetadata(his_file)')
# @staticmethod
# def metadataError():
# raise AttributeError('Metadata kon tijdens de functie LeesMetadata() niet bepaald worden')
# @staticmethod
# def administratieError():
# raise AttributeError('Administratieblok van his-file kan niet worden uitgelezen')
# @staticmethod
# def locationNotFound():
# raise AttributeError('location niet gevonden. Is het een bestaande location?')
# @staticmethod
# def variabeleNotFound():
# raise AttributeError('Parameter niet gevonden. Is het een bestaande parameter?')
# @staticmethod
# def jaarmaxError():
# raise ValueError('Voor de MultiWaardenArray functie kan alleen `year` of `none` gebruikt worden als voor de jaarmax_as parameter')
# @staticmethod
# def gewogenGemiddeldeError():
# raise ValueError('Kan gewogen gemiddelde niet bepalen. Vereiste is minimaal 2 punten aan de linker en rechterzijde van de gekozen T')
# def gebeurtenissenError():
# raise ValueError('Meer gebeurtenissen dan N.')
def __init__(self):
return
def read_bui(self,filename):
"""
parse een bui-file naar een dataframe
Parameters
----------
filename : str
pad naar het .bui bestand
Returns
-------
df : pandas.DataFrame
dataframe met in de kolommen de neerslag in mm voor de stations
en in de rijen de waarnemingstijdstappen
"""
# initatie van lege immutable objects
# zie: https://stackoverflow.com/a/22526544/2459096
# python2.7: obj = argparse.Namespace()
# python3.x: obj = types.SimpleNamespace()
self.buiFile = argparse.Namespace()
with open(filename, 'r') as infile:
f = infile.readlines()
for i in range(len(f)):
#print ('regel {0}'.format(str(i)))
line = f[i]
if line[0] == '*':
# logging.info('regel {0} is comment'.format(str(i)))
if 'GEBRUIK DE DEFAULT DATASET' in line.upper():
self.buiFile.default_dataset = int(f[i+1])
elif 'AANTAL STATION' in line.upper():
self.buiFile.aantal_stations = int(f[i+1])
elif 'NAMEN VAN STATION' in line.upper():
stations = f[i+1:i+1+self.buiFile.aantal_stations]
self.buiFile.stations = [station.rstrip().replace("'","") for station in stations]
elif 'AANTAL SECONDEN' in line.upper():
geb_sec = f[i+1].split()
self.buiFile.aantal_gebeurtenissen = int(geb_sec[0])
self.buiFile.aantal_seconden = int(geb_sec[1])
elif 'HET FORMAT IS: YYYY' in line.upper():
T0_raw = f[i+2]#.split()
idx_block = i+1
# get data block
# voor elk station de neerslag in mm per tijdstap
s = pd.Series(f[idx_block+2:len(f)-1])
# parse datablock to DataFrame
df = s.str.rstrip().str.lstrip().str.split(' +', expand=True)
df = df.apply(pd.to_numeric)
# startdatum en -tijd
T0_str = T0_raw.rstrip().lstrip().split()
# Het format is: yyyymmdd:hhmmss
self.buiFile.start_datum = datetime.datetime(*list(map(int,T0_str[0:5]))) #seconden is eruit gehaald anders str[0:6]
# lengte van de gebeurtenis in dd hh mm ss
lengte_gebeurtenis = list(map(int,T0_str[-4::]))
self.buiFile.delta_dag = lengte_gebeurtenis[0]
self.buiFile.delta_uur = lengte_gebeurtenis[1]
self.buiFile.delta_minuut = lengte_gebeurtenis[2]
self.buiFile.delta_seconde = lengte_gebeurtenis[3]
# date_range gebeurtenis
gebeurtenis_date_range = pd.date_range(
start=self.buiFile.start_datum,
end=None,
periods=df.shape[0],
freq='{0}S'.format(self.buiFile.aantal_seconden)
)
df.set_index(gebeurtenis_date_range, inplace=True)
df.columns = self.buiFile.stations
return df
@staticmethod
def seconds_toperiods(total_seconds):
"""
Ontleedt een getal in seconden naar lijst met vaste periodes: days, hours, minutes, seconds
waarbij day = 24 * 60 * 60 = 86,400 seconden
hour = 60 * 60 = 3,600 seconden
minute = 60 = 60 seconden
Parameters
--------------
total_seconds : float or int
bijvoorbeeld Timedelta.total_seconds() van Pandas dataframe bv index[1]-index[0]
Returns
-------------
list : list of integer values [days, hours, minutes, seconds]
"""
#years, remainder = divmod(total_seconds,365.25*3600*24) #total seconds per year
days, remainder = divmod(total_seconds, 24*3600)
hours, remainder = divmod (remainder, 3600)
minutes, seconds = divmod(remainder, 60)
return [int(days), int(hours), int(minutes), int(seconds)]
def write_bui(self,df,filename,dataset="bui",comment=None):
"""
Maak van een pandas dataframe een sobek buifile
Parameters
----------
df : pandas dataframe
index df is datumtijdas. aanname is uniforme tijdstap delta time
in kolommen neerslag in mm/tijdstap
kolomnaam = stationnaam
filename: str
path + naam buifile "{str:8}.bui" lengte buinaam is 8 tekens
dataset: (optional) str [default = "bui"] options ["bui", "reeks"]
type dataset, dit is voorgedefinieerd door sobek.
NB. 'reeks' is nog niet ondersteund.
comment: (optional) str
extra opmerkingenregels die in de header van de buifile worden geplaatst
RETURNS
-----------
sobekbuifile : gebruiksklare buifile die door sobek2 kan worden ingelezen.
Standaard format sobekbuifile:
* user defined comment
* Gebruik de default dataset voor overige invoer (altijd 1 bij bui, 0 bij reeks)
1
* Aantal stations
2
* Namen van Stations
'Station1'
'Station2'
* Aantal gebeurtenissen en het aantal seconden per waarnemingsstap
1 3600
* Elke commentaarregel wordt begonnen met een * (asteriks).
* Meteo data: neerslag stations; voor elk station: neerslag intensiteit in mm
* Eerste record bevat start datum & tijd en lengte van de gebeurtenis in dd hh mm ss
* Het format is: yyyy mm dd hh mm ss
* Daarna voor elk station de neerslag in mm per tijdstap.
2010 11 07 00 00 00 14 00 00 00
... space seperated values in [mm/timestep] ...
"""
if dataset.upper() != "BUI":
self.__errors__.variableNotSupported(variabele=dataset)
with open(filename, 'w') as bui:
if not comment==None:
#optionele commentaarregel van gebruiker
bui.write("* "+comment+"\n")
bui.write("* Deze buifile is automatisch aangemaakt door de buifile generator van HKV Lijn in Water"+"\n")
bui.write("* Gebruik de default dataset voor overige invoer (altijd 1 bij bui, 0 bij reeks)"+"\n")
if dataset.upper() == "BUI":
bui.write("1"+"\n")
if dataset.upper() == "REEKS":
#throw error
print("not supported")
bui.write("* Aantal stations"+"\n")
bui.write(str(df.shape[1])+"\n")
bui.write("* Namen van stations"+"\n")
for station in df.columns:
bui.write("'{}'".format(str(station))+"\n")
bui.write("* Aantal gebeurtenissen en het aantal seconden per waarnemingsstap"+"\n")
timedelta = int(datetime.datetime.timestamp(df.index[1]) - datetime.datetime.timestamp(df.index[0]))
if dataset.upper() == "BUI":
bui.write("1 "+str(timedelta)+"\n")
bui.write("* Elke commentaarregel wordt begonnen met een * (asteriks)."+"\n")
bui.write("* Meteo data: neerslag stations; voor elk station: neerslag intensiteit in mm."+"\n")
bui.write("* Eerste record bevat start datum & tijd en lengte van de gebeurtenis in dd hh mm ss"+"\n")
bui.write("* Het format is: yyyy mm dd hh mm ss"+"\n")
bui.write("* Daarna voor elk station de neerslag in mm per tijdstap."+"\n")
lengte_bui = self.seconds_toperiods((df.index[-1] - df.index[0]).total_seconds())
bui.write(df.index[0].strftime('%Y %m %d %H %M %S ')+"{:0^2} {:0^2} {:0^2} {:0^2}".format(lengte_bui[0],lengte_bui[1],lengte_bui[2],lengte_bui[3])+"\n")
df.to_csv(bui, sep = " ", index = False, header=False)
bui.close()
#controleer tijdas bui
timesteps = (df.index[-1] - df.index[0]).total_seconds()/timedelta +1 # +1 om tijdstap t=0 te verreken.
if int(timesteps) != int(len(df.index)):
# ! error ! aantal tijdstappen en builengte komen niet met elkaar overeen
self.__errors__.builengteError(availablevalues=int(len(df.index)),expectedvalues=int(timesteps))
PK QMX7c 7c hkvsobekpy/io/his.pyimport os
try:
from pathlib import Path
except:
from pathlib2 import Path
import argparse
import sys
import itertools
import geopandas as gpd
from datetime import datetime, timedelta
import copy
import matplotlib.pyplot as plt
from matplotlib.ticker import ScalarFormatter, FormatStrFormatter
import numpy as np
import pandas as pd
from scipy import optimize
import fire
import warnings
from hkvsobekpy.core.utils import *
try:
shell = get_ipython().__class__.__name__
if shell == 'ZMQInteractiveShell':
from tqdm import tqdm_notebook as tqdm # Jupyter notebook or qtconsole
elif shell == 'TerminalInteractiveShell':
from tqdm import tqdm # Terminal running IPython
else:
from tqdm import tqdm # Other type (?)
except NameError:
from tqdm import tqdm # Probably standard Python interpreter
#for debugging within notebook
try:
from IPython.core.debugger import set_trace
except:
pass
# for python 2/3 compatibility
try:
xrange
except NameError:
xrange = range
try:
get_ipython().magic('matplotlib inline')
except:
pass
class __his_class(object):
class __errors__(object):
"""
error class met verschillende foutmeldingen
"""
@staticmethod
def fileNotFound():
raise IOError('HIS-bestand bestaat niet. Is je pad goed?')
@staticmethod
def metadataNotSet():
raise AttributeError('Metadata is niet bekend. Zet met sobek.LeesMetadata(his_file)')
@staticmethod
def metadataError():
raise AttributeError('Metadata kon tijdens de functie LeesMetadata() niet bepaald worden')
@staticmethod
def administratieError():
raise AttributeError('Administratieblok van his-file kan niet worden uitgelezen')
@staticmethod
def locationNotFound():
raise AttributeError('location niet gevonden. Is het een bestaande location?')
@staticmethod
def variabeleNotFound():
raise AttributeError('Parameter niet gevonden. Is het een bestaande parameter?')
@staticmethod
def jaarmaxError():
raise ValueError('Voor de MultiWaardenArray functie kan alleen `year` of `none` gebruikt worden als voor de jaarmax_as parameter')
@staticmethod
def gewogenGemiddeldeError():
raise ValueError('Kan gewogen gemiddelde niet bepalen. Vereiste is minimaal 2 punten aan de linker en rechterzijde van de gekozen T')
def gebeurtenissenError():
raise ValueError('Meer gebeurtenissen dan N.')
def __init__(self):
"""
SobekResultaten class. Class met methodes om een .his bestand
uit te lezen.
WaterstandStatistiek class. Class met methodes om een GumbleFit te bepalen
"""
# initatie van lege immutable objects
# zie: https://stackoverflow.com/a/22526544/2459096
# python2.7: obj = argparse.Namespace()
# python3.x: obj = types.SimpleNamespace()
self.hisFile = argparse.Namespace()
self.hisFile.tijdstapInfo = argparse.Namespace()
self.hisFile.variabeleInfo = argparse.Namespace()
self.hisFile.locationInfo = argparse.Namespace()
def _leesHeader(self, f):
"""
Lees de header van het bestand
Parameters
----------
f : BufferedReader
Leesbaar ruw IO object
Returns
-------
header : array
De offset van de location in bytes
"""
header = []
for i in range(4):
header.append(f.read(40).decode('windows-1252'))
return header
def _infoFromHeader(self,tijdstapInfo):
"""
Lees informatie uit de header
Parameters
----------
tijdstapInfo : str
De location in string format
Returns
-------
beginDate : datetime
De begindatum T0, waarop de andere datetime gebasseerd zijn
timeStepInterval : str
De eenheid waarin de stapgrote van de datetime objecten berekend zijn,
dit kan zijn s [seconds], m [minutes], h[hours], d[days]
timeStepFactor : int
Factor welke op de stapgrote toegepast moet worden
"""
#items = list(filter(None, str(tijdstapInfo).replace('.','').replace(':','').split(' '))) #old
items = list(filter(None, str(tijdstapInfo).replace('. ','.0').replace('.','').replace(':','').split(' ')))
#set_trace()
try:
beginDate = datetime(int(items[1]),int(items[2]),int(items[3]),int(items[4]),int(items[5]),int(items[6]))
except:
#set_trace()
beginDate = datetime.strptime(items[1],'%Y%m%d')
scu = items[-1].replace(')','').replace("'",'')
timeStepInterval = scu[(len(scu) - 1)]
timeStepFactor = int(scu[0:len(scu)-1])
return beginDate, timeStepInterval, timeStepFactor
def _leesAdmin(self, f):
"""
Lees het administratie blok
"""
self.hisFile.header = self._leesHeader(f)
# lees info van header
self.hisFile.tijdstapInfo.beginDate, self.hisFile.tijdstapInfo.timeStepInterval, self.hisFile.tijdstapInfo.timeStepFactor = self._infoFromHeader(self.hisFile.header[3])
# lees aantal variablelen
self.hisFile.variabeleInfo.numVar = np.fromfile(f,np.int,1)[0]
# lees aantal locations
self.hisFile.locationInfo.numLoc = np.fromfile(f,np.int,1)[0]
# lees variabelen
self.hisFile.variabeleInfo.variabelen = []
for i in range(self.hisFile.variabeleInfo.numVar):
self.hisFile.variabeleInfo.variabelen.append(f.read(20).decode('windows-1252'))
# Lees locations
self.hisFile.locationInfo.id = []
self.hisFile.locationInfo.locations = []
for i in range(self.hisFile.locationInfo.numLoc):
self.hisFile.locationInfo.id.append(np.fromfile(f,np.int,1)[0])
self.hisFile.locationInfo.locations.append(f.read(20).decode('windows-1252').rstrip())
#self.hisFile.locationInfo.locations.append(locationInfo)
# lees tijdstappen
self.hisFile.headerSize = f.tell()
self.hisFile.locationInfo.numTime = int((self.hisFile.hisFileSize - self.hisFile.headerSize) /
(self.hisFile.locationInfo.numLoc * self.hisFile.variabeleInfo.numVar + 1) / 4)
aantalBytesPerStap = int((self.hisFile.hisFileSize - self.hisFile.headerSize) / self.hisFile.locationInfo.numTime)
byteNr = self.hisFile.headerSize
self.hisFile.tijdstapInfo.moments = []
self.hisFile.tijdstapInfo.offset = []
for i in range(self.hisFile.locationInfo.numTime):
f.seek(byteNr)
moment = np.fromfile(f,np.int,1)[0]
if self.hisFile.tijdstapInfo.timeStepInterval == 's':
self.hisFile.tijdstapInfo.moments.append(self.hisFile.tijdstapInfo.beginDate + timedelta(seconds = (float(moment) * self.hisFile.tijdstapInfo.timeStepFactor)))
elif self.hisFile.tijdstapInfo.timeStepInterval == 'm':
self.hisFile.tijdstapInfo.moments.append(self.hisFile.tijdstapInfo.beginDate + timedelta(minutes = (float(moment) * self.hisFile.tijdstapInfo.timeStepFactor)))
elif self.hisFile.tijdstapInfo.timeStepInterval == 'h':
self.hisFile.tijdstapInfo.moments.append(self.hisFile.tijdstapInfo.beginDate + timedelta(hours= (float(moment) * self.hisFile.tijdstapInfo.timeStepFactor)))
elif self.hisFile.tijdstapInfo.timeStepInterval == 'd':
self.hisFile.tijdstapInfo.moments.append(self.hisFile.tijdstapInfo.beginDate + timedelta(days= (float(moment) * self.hisFile.tijdstapInfo.timeStepFactor)))
self.hisFile.tijdstapInfo.offset.append(f.tell())
byteNr += aantalBytesPerStap
self.hisFile.tijdstapInfo.N = np.array(self.hisFile.tijdstapInfo.moments).max().year - np.array(self.hisFile.tijdstapInfo.moments).min().year + 1
return True
def _locOffset(self, loc, var):
"""
Krijg de offset van de variabele
Parameters
----------
loc : string
De location in kwestie
var : string
De variabele in kwestie
Returns
-------
locOffset : int
De offset van de location in bytes
"""
# Zoek de index van de variabele
try:
varFound = self.hisFile.variabeleInfo.variabelen.index(var)
except :
self.__errors__.variabeleNotFound()
# Zoek de index/id van de location
try:
# removed dictionary
# locFound = next((item for item in self.hisFile.locationInfo.locations if item['location'] == loc))
locFound = self.hisFile.locationInfo.locations.index(loc)
except :
self.__errors__.locationNotFound()
locOffset = locFound * self.hisFile.variabeleInfo.numVar * 4 + varFound * 4
return locOffset
def KrijgLokaties(self):#, his_file):
"""
Krijg de locations beschikbaar in de his-file.
Parameters
----------
his_file : str
pad naar his_file
Returns
-------
locations : list
De locations welke bekend zijn binnen het his-bestand
"""
#self.LeesMetadata(his_file)
locations = self.hisFile.locationInfo.locations
return locations
def KrijgParameters(self):#, his_file):
"""
Krijg de parameters beschikbaar in de his-file.
Parameters
----------
his_file : str
pad naar his_file
Returns
-------
parameters : list
De parameters welke bekend zijn binnen het his-bestand
"""
#self.LeesMetadata(his_file)
parameters = self.hisFile.variabeleInfo.variabelen
return parameters
def KrijgTijdstappen(self):#, his_file):
"""
Krijg de tijdstappen beschikbaar in de his-file.
Parameters
----------
his_file : str
pad naar his_file
Returns
-------
tijdstappen : list
De tijdstappen welke bekend zijn binnen het his-bestand
"""
#self.LeesMetadata(his_file)
tijdstappen = self.hisFile.tijdstapInfo.moments
return tijdstappen
def LeesMetadata(self,his_file):
"""
Open het bestand
Parameters
----------
his_file : str
Volledige pad van het bestand
Returns
-------
Str : 'Metadata Ingelezen'
"""
self.hisFile.metaDataIngelezen = False
myHisFile = Path(his_file)
try:
p = myHisFile.resolve()
except:
# doesn't exist
self.__errors__.fileNotFound()
else:
# exists
self.hisFile.hisFileName = str(p)
# set length filesize (bytes)
statInfo = os.stat(his_file)
self.hisFile.hisFileSize = statInfo.st_size
#f = open(str(p), "rb")
with open(self.hisFile.hisFileName, "rb") as f:
try:
self._leesAdmin(f)
except:
self.__errors__.administratieError()
self.hisFile.metaDataIngelezen = True
return self
def SelectPeriodeWaardenArray(self, df, startMMdd=(1,1), endMMdd=(12,31), jaarmax_as='date'):
"""
Selecteer op basis van een DataFrame de gebeurtenissen binnen een bepaalde periode (bijv. een groeiseizoen).
Waarbij er de mogelijkheid is om de gebeurtenissen binnen deze periode te groeperen.
Pas op: in de `EnkeleWaardenArray` en `MultiWaardenArray` wordt deze functie intern ook aangeroepen.
Parameters
----------
df : pandas.DataFrame
DataFrame met datetime als index
startMMdd : tuple
Tuple in het formaat (M,d). Bepaling van de start datum van de periode. Genoemde datum is inclusief
endMMdd : tuple
Tuple in het formaat (M,d). Bepaling van de eind datum van de periode. Genoemde datum is inclusief
jaarmax_as : str
Mogelijkheden om de DataFrame te groeperen op basis van jaar om het jaarmaxima te bepalen.
Het jaarmaxima wordt bepaald nadat de slice van de jaarlijkse periode is toegepast.
Keuze bestaat uit:
'date' - bepaalt de jaarlijkse maxima en geeft de maximale waarde terug met de exacte datum van deze gebeurtenis
'year' - bepaalt de jaarlijkse maxima en geeft de maximale waarde terug met het jaar van de gebeurtenis
'none' - retourneert alle gebeurtenissen in elk jaar
Returns
-------
df : pandas.DataFrame
DataFrame met datetime als index
Examples
--------
Voor bepaling van het groeiseizoen:
Bijvoorbeeld het jaarlijks groeseizoen loopt van 15 april tot en met 11 oktober
startMMdd = (4,15)
endMMdd = (10,11)
betekent, slice elk jaar in april na de 15e inclusief tot aan oktober voor de 11e inclusief
Voor bepaling van het jaarmaxima:
groupby = 'date'
A
2012-10-06 1501
2013-04-22 1534
2014-04-18 1591
groupby = 'year'
A
2012 1501
2013 1534
2014 1591
"""
self.startMM, self.startdd = startMMdd
self.endMM, self.enddd = endMMdd
# https://stackoverflow.com/a/45996897/2459096
# maak een month_day dataframe van de MultiYear DataFrame
month_day = pd.concat([
df.index.to_series().dt.month,
df.index.to_series().dt.day
], axis=1).apply(tuple, axis=1)
# selecteer alleen de gebeurtenissen binnen een jaarlijks terugkerende periode
df = df[(month_day >= (self.startMM, self.startdd)) & (month_day <= (self.endMM, self.enddd))]
if jaarmax_as=='year':
# groupby year en selecteer de max.
# hetvolgende retourneert alleen jaren + max, willen van elk jaar de datum + max
df = df.groupby(df.index.year).max()
elif jaarmax_as=='date':
# groupy year en selecteer de max. Returned volle datums waar de max van dat jaar was
key = df.columns.levels[1][0]
level = df.columns.names[1]
slice_col = df.columns.levels[0][0]
# next get the year maxima of the remaining gebeurtenissen
idx = df.xs(key=key, level=level, axis=1).groupby([df.index.year])[slice_col].transform(max) == df.xs(key=key, level=level, axis=1)[slice_col]
df = df[idx]
# if there are multiple events in a single year with same value take the first
df_unique = pd.DataFrame(df.index, columns=['date'])
df_unique['index'] = df_unique['date'].apply(lambda x:x.year)
df_unique = df_unique.groupby('index').first()
slice_unique_values = df_unique['date'].values
df = df.loc[slice_unique_values]
elif jaarmax_as=='none':
#doe niets
pass
return df
def EnkeleWaardenArray(self, location, parameter, startMMdd=(1,1), endMMdd=(12,31), jaarmax_as='date'):
"""
Lees de waarden van een enkele variabele op een enkele location
Parameters
----------
location : string
De location in kwestie (alleen de eerste 21 karakters, anders cutoff)
parameter : string
De variabele in kwestie (alleen de eerste 21 karakters, anders cutoff)
startMMdd : tuple
Tuple in het formaat (M,d). Bepaling van de start datum van de periode. Genoemde datum is inclusief
endMMdd : tuple
Tuple in het formaat (M,d). Bepaling van de eind datum van de periode. Genoemde datum is inclusief
jaarmax_as : str
Mogelijkheden om de DataFrame te groeperen op basis van jaar om het jaarmaxima te bepalen.
Het jaarmaxima wordt bepaald nadat de slice van de jaarlijkse periode is toegepast.
Keuze bestaat uit:
'date' - bepaalt de jaarlijkse maxima en geeft de maximale waarde terug met de exacte datum van deze gebeurtenis
'year' - bepaalt de jaarlijkse maxima en geeft de maximale waarde terug met het jaar van de gebeurtenis
'none' - retourneert alle gebeurtenissen in elk jaar
Returns
-------
df : DataFrame
DataFrame met een multicolumn van variabele en locations met datetime index en
de bijbehorende waarden
Examples
-----------
location = '1'
parameter = 'Waterlevel maximum (mNAP)'
Intern worden alle inputs afgekort op [0:20] en ziet de tool het als:
location = '1'
parameter = 'Waterlevel maximum ('
Voor bepaling van het groeiseizoen:
Bijvoorbeeld het jaarlijks groeseizoen loopt van 15 april tot en met 11 oktober
startMMdd = (4,15)
endMMdd = (10,11)
betekent, slice elk jaar in april na de 15e inclusief tot aan oktober voor de 11e inclusief
Voor bepaling van het jaarmaxima:
groupby = 'date'
A
2012-10-06 1501
2013-04-22 1534
2014-04-18 1591
groupby = 'year'
A
2012 1501
2013 1534
2014 1591
"""
if not hasattr(self.hisFile, 'metaDataIngelezen'):
self.__errors__.metadataNotSet()
if self.hisFile.metaDataIngelezen == False:
self.__errors__.metadataError()
loc = location[0:20]
var = parameter[0:20]
with open(self.hisFile.hisFileName, "rb") as f:
varLocOffset = self._locOffset(loc, var)
values= []
for i in range(self.hisFile.locationInfo.numTime):
offset = self.hisFile.tijdstapInfo.offset[i] + varLocOffset
seek = f.seek(offset)
values.append(np.fromfile(f,np.float32,1)[0])
# maak dataframe
df = pd.DataFrame(data=values, index=self.hisFile.tijdstapInfo.moments, columns=[(loc,var)])
df.columns = pd.MultiIndex.from_tuples(df.columns, names=['location','parameter'])
df = self.SelectPeriodeWaardenArray(df, startMMdd=startMMdd, endMMdd=endMMdd, jaarmax_as=jaarmax_as)
return df#.T.squeeze()
def MultiWaardenArray(self, locations, parameters, startMMdd=(1,1), endMMdd=(12,31),
jaarmax_as='year', drop_lege_jaren=True):
"""
Lees de waarden van meerdere variabelen op meerdere locations
Parameters
----------
locations : list
Een list van de locations (type: str) in kwestie (alleen de eerste 21 karakters, anders cutoff)
parameters : list
Een list van de parameters in kwestie (alleen de eerste 21 karakters, anders cutoff)
startMMdd : tuple
Tuple in het formaat (M,d). Bepaling van de start datum van de periode. Genoemde datum is inclusief
endMMdd : tuple
Tuple in het formaat (M,d). Bepaling van de eind datum van de periode. Genoemde datum is inclusief
jaarmax_as : str
Mogelijkheden om de DataFrame te groeperen op basis van jaar om het jaarmaxima te bepalen.
Het jaarmaxima wordt bepaald nadat de slice van de jaarlijkse periode is toegepast.
Keuze bestaat uit:
'year' - bepaalt de jaarlijkse maxima en geeft de maximale waarde terug met het jaar van de gebeurtenis
'none' - retourneert alle gebeurtenissen in elk jaar
drop_lege_jaren : boolean
Mogelijkheid om de jaren te verwijderen welke geen gebeurtenissen bevatten voor de gegeven selectie.
Wanneer dit als `True` is geselecteerd zal het jaar alleen gedropt worden als deze niet bestaat voor
alle locations/parameters in de selectie
Returns
-------
df : DataFrame
DataFrame met een multicolumn van variabele en locations met datetime index en
de bijbehorende waarden
Examples
-----------
locations = ['1','6']
parameters = ['Waterlevel maximum (mNAP)']
Intern worden alle inputs afgekort op [0:20] en ziet de tool het als:
locations = ['1','6']
parameters = ['Waterlevel maximum (']
"""
# error checking
if jaarmax_as not in ('year', 'none'):
self.__errors__.jaarmaxError()
# define empty dataframe
clmns = pd.MultiIndex.from_product([parameters,locations], names=['parameters','locations'])
# voor een MultiWaardenArray wordt alleen een jaar meegenomen als index
# het hangt van een seizoen filter af of er gebeurtenissen voor dat jaar zijn. Bepaal dit eerst
loc0 = self.hisFile.locationInfo.locations[0]
par0 = self.hisFile.variabeleInfo.variabelen[0]
idx = self.EnkeleWaardenArray(loc0, par0, jaarmax_as=jaarmax_as).index
self.hisFile.tijdstapInfo.N
df_full = pd.DataFrame(index=idx, columns=clmns)
df_full.index.name='date'
for ix in clmns:
df = self.EnkeleWaardenArray(location=ix[1], parameter=ix[0],
startMMdd=startMMdd, endMMdd=endMMdd, jaarmax_as=jaarmax_as)
df_full.loc[:,(ix[0],ix[1])] = df.T.squeeze()
# bepaling of lege jaren gedropt moeten worden
if drop_lege_jaren:
df_full.dropna(axis=0, how='all', inplace=True)
return df_full
def read_series(self, his_file, location, parameter, his_folder, normalize_by_unicode=True,
include_simularity=True, sequence_simularity=0.82, return_matching_parameter=False):
"""
Extract timeseries from his-file
Parameters
----------
his_file : str
name of his-file to query (e.g: 'reachseg', 'calcpnt', 'struc')
location : str
location to query in his-file
parameter : str
parameter to query in his-file
his_folder : str
path to folder containing the his_file
normalize_by_unicode : boolean (default True)
inlcude this option to include NFKD unicode compatibility decomposition.
see: http://unicode.org/reports/tr15/
include_simluratiy : boolean (default True)
include this option to include Ratcliff/Obershelp pattern recognition
sequence_simularity : float (default 0.82)
number between 0.0 and 1.0, function as threshold, where only a simularity above
this value is mapped
Returns
-------
df : pandas.DataFrame
timeseries containing all available timesteps in his-file
"""
# get metadata from appropriate his-file
hisfile = self.LeesMetadata(os.path.join(his_folder, '{0}.his'.format(his_file).upper()))
his_parameters = hisfile.KrijgParameters()
his_locations = hisfile.KrijgLokaties()
# normalize unicode and/or check simularity of parameters
parameter = compare_df_parameter_his_parameter(parameter.ljust(20), his_parameters, normalize_by_unicode,
include_simularity, sequence_simularity)
# get dataframe for single location/parameter combination
df = self.EnkeleWaardenArray(
location=location,
parameter=parameter,
jaarmax_as='none')
if return_matching_parameter == True:
return df, parameter
else:
return df PK QMь " hkvsobekpy-1.2.5.dist-info/LICENSE GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
Copyright (C) 2007 Free Software Foundation, Inc.
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The GNU General Public License is a free, copyleft license for
software and other kinds of works.
The licenses for most software and other practical works are designed
to take away your freedom to share and change the works. By contrast,
the GNU General Public License is intended to guarantee your freedom to
share and change all versions of a program--to make sure it remains free
software for all its users. We, the Free Software Foundation, use the
GNU General Public License for most of our software; it applies also to
any other work released this way by its authors. You can apply it to
your programs, too.
When we speak of free software, we are referring to freedom, not
price. Our General Public Licenses are designed to make sure that you
have the freedom to distribute copies of free software (and charge for
them if you wish), that you receive source code or can get it if you
want it, that you can change the software or use pieces of it in new
free programs, and that you know you can do these things.
To protect your rights, we need to prevent others from denying you
these rights or asking you to surrender the rights. Therefore, you have
certain responsibilities if you distribute copies of the software, or if
you modify it: responsibilities to respect the freedom of others.
For example, if you distribute copies of such a program, whether
gratis or for a fee, you must pass on to the recipients the same
freedoms that you received. You must make sure that they, too, receive
or can get the source code. And you must show them these terms so they
know their rights.
Developers that use the GNU GPL protect your rights with two steps:
(1) assert copyright on the software, and (2) offer you this License
giving you legal permission to copy, distribute and/or modify it.
For the developers' and authors' protection, the GPL clearly explains
that there is no warranty for this free software. For both users' and
authors' sake, the GPL requires that modified versions be marked as
changed, so that their problems will not be attributed erroneously to
authors of previous versions.
Some devices are designed to deny users access to install or run
modified versions of the software inside them, although the manufacturer
can do so. This is fundamentally incompatible with the aim of
protecting users' freedom to change the software. The systematic
pattern of such abuse occurs in the area of products for individuals to
use, which is precisely where it is most unacceptable. Therefore, we
have designed this version of the GPL to prohibit the practice for those
products. If such problems arise substantially in other domains, we
stand ready to extend this provision to those domains in future versions
of the GPL, as needed to protect the freedom of users.
Finally, every program is threatened constantly by software patents.
States should not allow patents to restrict development and use of
software on general-purpose computers, but in those that do, we wish to
avoid the special danger that patents applied to a free program could
make it effectively proprietary. To prevent this, the GPL assures that
patents cannot be used to render the program non-free.
The precise terms and conditions for copying, distribution and
modification follow.
TERMS AND CONDITIONS
0. Definitions.
"This License" refers to version 3 of the GNU General Public License.
"Copyright" also means copyright-like laws that apply to other kinds of
works, such as semiconductor masks.
"The Program" refers to any copyrightable work licensed under this
License. Each licensee is addressed as "you". "Licensees" and
"recipients" may be individuals or organizations.
To "modify" a work means to copy from or adapt all or part of the work
in a fashion requiring copyright permission, other than the making of an
exact copy. The resulting work is called a "modified version" of the
earlier work or a work "based on" the earlier work.
A "covered work" means either the unmodified Program or a work based
on the Program.
To "propagate" a work means to do anything with it that, without
permission, would make you directly or secondarily liable for
infringement under applicable copyright law, except executing it on a
computer or modifying a private copy. Propagation includes copying,
distribution (with or without modification), making available to the
public, and in some countries other activities as well.
To "convey" a work means any kind of propagation that enables other
parties to make or receive copies. Mere interaction with a user through
a computer network, with no transfer of a copy, is not conveying.
An interactive user interface displays "Appropriate Legal Notices"
to the extent that it includes a convenient and prominently visible
feature that (1) displays an appropriate copyright notice, and (2)
tells the user that there is no warranty for the work (except to the
extent that warranties are provided), that licensees may convey the
work under this License, and how to view a copy of this License. If
the interface presents a list of user commands or options, such as a
menu, a prominent item in the list meets this criterion.
1. Source Code.
The "source code" for a work means the preferred form of the work
for making modifications to it. "Object code" means any non-source
form of a work.
A "Standard Interface" means an interface that either is an official
standard defined by a recognized standards body, or, in the case of
interfaces specified for a particular programming language, one that
is widely used among developers working in that language.
The "System Libraries" of an executable work include anything, other
than the work as a whole, that (a) is included in the normal form of
packaging a Major Component, but which is not part of that Major
Component, and (b) serves only to enable use of the work with that
Major Component, or to implement a Standard Interface for which an
implementation is available to the public in source code form. A
"Major Component", in this context, means a major essential component
(kernel, window system, and so on) of the specific operating system
(if any) on which the executable work runs, or a compiler used to
produce the work, or an object code interpreter used to run it.
The "Corresponding Source" for a work in object code form means all
the source code needed to generate, install, and (for an executable
work) run the object code and to modify the work, including scripts to
control those activities. However, it does not include the work's
System Libraries, or general-purpose tools or generally available free
programs which are used unmodified in performing those activities but
which are not part of the work. For example, Corresponding Source
includes interface definition files associated with source files for
the work, and the source code for shared libraries and dynamically
linked subprograms that the work is specifically designed to require,
such as by intimate data communication or control flow between those
subprograms and other parts of the work.
The Corresponding Source need not include anything that users
can regenerate automatically from other parts of the Corresponding
Source.
The Corresponding Source for a work in source code form is that
same work.
2. Basic Permissions.
All rights granted under this License are granted for the term of
copyright on the Program, and are irrevocable provided the stated
conditions are met. This License explicitly affirms your unlimited
permission to run the unmodified Program. The output from running a
covered work is covered by this License only if the output, given its
content, constitutes a covered work. This License acknowledges your
rights of fair use or other equivalent, as provided by copyright law.
You may make, run and propagate covered works that you do not
convey, without conditions so long as your license otherwise remains
in force. You may convey covered works to others for the sole purpose
of having them make modifications exclusively for you, or provide you
with facilities for running those works, provided that you comply with
the terms of this License in conveying all material for which you do
not control copyright. Those thus making or running the covered works
for you must do so exclusively on your behalf, under your direction
and control, on terms that prohibit them from making any copies of
your copyrighted material outside their relationship with you.
Conveying under any other circumstances is permitted solely under
the conditions stated below. Sublicensing is not allowed; section 10
makes it unnecessary.
3. Protecting Users' Legal Rights From Anti-Circumvention Law.
No covered work shall be deemed part of an effective technological
measure under any applicable law fulfilling obligations under article
11 of the WIPO copyright treaty adopted on 20 December 1996, or
similar laws prohibiting or restricting circumvention of such
measures.
When you convey a covered work, you waive any legal power to forbid
circumvention of technological measures to the extent such circumvention
is effected by exercising rights under this License with respect to
the covered work, and you disclaim any intention to limit operation or
modification of the work as a means of enforcing, against the work's
users, your or third parties' legal rights to forbid circumvention of
technological measures.
4. Conveying Verbatim Copies.
You may convey verbatim copies of the Program's source code as you
receive it, in any medium, provided that you conspicuously and
appropriately publish on each copy an appropriate copyright notice;
keep intact all notices stating that this License and any
non-permissive terms added in accord with section 7 apply to the code;
keep intact all notices of the absence of any warranty; and give all
recipients a copy of this License along with the Program.
You may charge any price or no price for each copy that you convey,
and you may offer support or warranty protection for a fee.
5. Conveying Modified Source Versions.
You may convey a work based on the Program, or the modifications to
produce it from the Program, in the form of source code under the
terms of section 4, provided that you also meet all of these conditions:
a) The work must carry prominent notices stating that you modified
it, and giving a relevant date.
b) The work must carry prominent notices stating that it is
released under this License and any conditions added under section
7. This requirement modifies the requirement in section 4 to
"keep intact all notices".
c) You must license the entire work, as a whole, under this
License to anyone who comes into possession of a copy. This
License will therefore apply, along with any applicable section 7
additional terms, to the whole of the work, and all its parts,
regardless of how they are packaged. This License gives no
permission to license the work in any other way, but it does not
invalidate such permission if you have separately received it.
d) If the work has interactive user interfaces, each must display
Appropriate Legal Notices; however, if the Program has interactive
interfaces that do not display Appropriate Legal Notices, your
work need not make them do so.
A compilation of a covered work with other separate and independent
works, which are not by their nature extensions of the covered work,
and which are not combined with it such as to form a larger program,
in or on a volume of a storage or distribution medium, is called an
"aggregate" if the compilation and its resulting copyright are not
used to limit the access or legal rights of the compilation's users
beyond what the individual works permit. Inclusion of a covered work
in an aggregate does not cause this License to apply to the other
parts of the aggregate.
6. Conveying Non-Source Forms.
You may convey a covered work in object code form under the terms
of sections 4 and 5, provided that you also convey the
machine-readable Corresponding Source under the terms of this License,
in one of these ways:
a) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by the
Corresponding Source fixed on a durable physical medium
customarily used for software interchange.
b) Convey the object code in, or embodied in, a physical product
(including a physical distribution medium), accompanied by a
written offer, valid for at least three years and valid for as
long as you offer spare parts or customer support for that product
model, to give anyone who possesses the object code either (1) a
copy of the Corresponding Source for all the software in the
product that is covered by this License, on a durable physical
medium customarily used for software interchange, for a price no
more than your reasonable cost of physically performing this
conveying of source, or (2) access to copy the
Corresponding Source from a network server at no charge.
c) Convey individual copies of the object code with a copy of the
written offer to provide the Corresponding Source. This
alternative is allowed only occasionally and noncommercially, and
only if you received the object code with such an offer, in accord
with subsection 6b.
d) Convey the object code by offering access from a designated
place (gratis or for a charge), and offer equivalent access to the
Corresponding Source in the same way through the same place at no
further charge. You need not require recipients to copy the
Corresponding Source along with the object code. If the place to
copy the object code is a network server, the Corresponding Source
may be on a different server (operated by you or a third party)
that supports equivalent copying facilities, provided you maintain
clear directions next to the object code saying where to find the
Corresponding Source. Regardless of what server hosts the
Corresponding Source, you remain obligated to ensure that it is
available for as long as needed to satisfy these requirements.
e) Convey the object code using peer-to-peer transmission, provided
you inform other peers where the object code and Corresponding
Source of the work are being offered to the general public at no
charge under subsection 6d.
A separable portion of the object code, whose source code is excluded
from the Corresponding Source as a System Library, need not be
included in conveying the object code work.
A "User Product" is either (1) a "consumer product", which means any
tangible personal property which is normally used for personal, family,
or household purposes, or (2) anything designed or sold for incorporation
into a dwelling. In determining whether a product is a consumer product,
doubtful cases shall be resolved in favor of coverage. For a particular
product received by a particular user, "normally used" refers to a
typical or common use of that class of product, regardless of the status
of the particular user or of the way in which the particular user
actually uses, or expects or is expected to use, the product. A product
is a consumer product regardless of whether the product has substantial
commercial, industrial or non-consumer uses, unless such uses represent
the only significant mode of use of the product.
"Installation Information" for a User Product means any methods,
procedures, authorization keys, or other information required to install
and execute modified versions of a covered work in that User Product from
a modified version of its Corresponding Source. The information must
suffice to ensure that the continued functioning of the modified object
code is in no case prevented or interfered with solely because
modification has been made.
If you convey an object code work under this section in, or with, or
specifically for use in, a User Product, and the conveying occurs as
part of a transaction in which the right of possession and use of the
User Product is transferred to the recipient in perpetuity or for a
fixed term (regardless of how the transaction is characterized), the
Corresponding Source conveyed under this section must be accompanied
by the Installation Information. But this requirement does not apply
if neither you nor any third party retains the ability to install
modified object code on the User Product (for example, the work has
been installed in ROM).
The requirement to provide Installation Information does not include a
requirement to continue to provide support service, warranty, or updates
for a work that has been modified or installed by the recipient, or for
the User Product in which it has been modified or installed. Access to a
network may be denied when the modification itself materially and
adversely affects the operation of the network or violates the rules and
protocols for communication across the network.
Corresponding Source conveyed, and Installation Information provided,
in accord with this section must be in a format that is publicly
documented (and with an implementation available to the public in
source code form), and must require no special password or key for
unpacking, reading or copying.
7. Additional Terms.
"Additional permissions" are terms that supplement the terms of this
License by making exceptions from one or more of its conditions.
Additional permissions that are applicable to the entire Program shall
be treated as though they were included in this License, to the extent
that they are valid under applicable law. If additional permissions
apply only to part of the Program, that part may be used separately
under those permissions, but the entire Program remains governed by
this License without regard to the additional permissions.
When you convey a copy of a covered work, you may at your option
remove any additional permissions from that copy, or from any part of
it. (Additional permissions may be written to require their own
removal in certain cases when you modify the work.) You may place
additional permissions on material, added by you to a covered work,
for which you have or can give appropriate copyright permission.
Notwithstanding any other provision of this License, for material you
add to a covered work, you may (if authorized by the copyright holders of
that material) supplement the terms of this License with terms:
a) Disclaiming warranty or limiting liability differently from the
terms of sections 15 and 16 of this License; or
b) Requiring preservation of specified reasonable legal notices or
author attributions in that material or in the Appropriate Legal
Notices displayed by works containing it; or
c) Prohibiting misrepresentation of the origin of that material, or
requiring that modified versions of such material be marked in
reasonable ways as different from the original version; or
d) Limiting the use for publicity purposes of names of licensors or
authors of the material; or
e) Declining to grant rights under trademark law for use of some
trade names, trademarks, or service marks; or
f) Requiring indemnification of licensors and authors of that
material by anyone who conveys the material (or modified versions of
it) with contractual assumptions of liability to the recipient, for
any liability that these contractual assumptions directly impose on
those licensors and authors.
All other non-permissive additional terms are considered "further
restrictions" within the meaning of section 10. If the Program as you
received it, or any part of it, contains a notice stating that it is
governed by this License along with a term that is a further
restriction, you may remove that term. If a license document contains
a further restriction but permits relicensing or conveying under this
License, you may add to a covered work material governed by the terms
of that license document, provided that the further restriction does
not survive such relicensing or conveying.
If you add terms to a covered work in accord with this section, you
must place, in the relevant source files, a statement of the
additional terms that apply to those files, or a notice indicating
where to find the applicable terms.
Additional terms, permissive or non-permissive, may be stated in the
form of a separately written license, or stated as exceptions;
the above requirements apply either way.
8. Termination.
You may not propagate or modify a covered work except as expressly
provided under this License. Any attempt otherwise to propagate or
modify it is void, and will automatically terminate your rights under
this License (including any patent licenses granted under the third
paragraph of section 11).
However, if you cease all violation of this License, then your
license from a particular copyright holder is reinstated (a)
provisionally, unless and until the copyright holder explicitly and
finally terminates your license, and (b) permanently, if the copyright
holder fails to notify you of the violation by some reasonable means
prior to 60 days after the cessation.
Moreover, your license from a particular copyright holder is
reinstated permanently if the copyright holder notifies you of the
violation by some reasonable means, this is the first time you have
received notice of violation of this License (for any work) from that
copyright holder, and you cure the violation prior to 30 days after
your receipt of the notice.
Termination of your rights under this section does not terminate the
licenses of parties who have received copies or rights from you under
this License. If your rights have been terminated and not permanently
reinstated, you do not qualify to receive new licenses for the same
material under section 10.
9. Acceptance Not Required for Having Copies.
You are not required to accept this License in order to receive or
run a copy of the Program. Ancillary propagation of a covered work
occurring solely as a consequence of using peer-to-peer transmission
to receive a copy likewise does not require acceptance. However,
nothing other than this License grants you permission to propagate or
modify any covered work. These actions infringe copyright if you do
not accept this License. Therefore, by modifying or propagating a
covered work, you indicate your acceptance of this License to do so.
10. Automatic Licensing of Downstream Recipients.
Each time you convey a covered work, the recipient automatically
receives a license from the original licensors, to run, modify and
propagate that work, subject to this License. You are not responsible
for enforcing compliance by third parties with this License.
An "entity transaction" is a transaction transferring control of an
organization, or substantially all assets of one, or subdividing an
organization, or merging organizations. If propagation of a covered
work results from an entity transaction, each party to that
transaction who receives a copy of the work also receives whatever
licenses to the work the party's predecessor in interest had or could
give under the previous paragraph, plus a right to possession of the
Corresponding Source of the work from the predecessor in interest, if
the predecessor has it or can get it with reasonable efforts.
You may not impose any further restrictions on the exercise of the
rights granted or affirmed under this License. For example, you may
not impose a license fee, royalty, or other charge for exercise of
rights granted under this License, and you may not initiate litigation
(including a cross-claim or counterclaim in a lawsuit) alleging that
any patent claim is infringed by making, using, selling, offering for
sale, or importing the Program or any portion of it.
11. Patents.
A "contributor" is a copyright holder who authorizes use under this
License of the Program or a work on which the Program is based. The
work thus licensed is called the contributor's "contributor version".
A contributor's "essential patent claims" are all patent claims
owned or controlled by the contributor, whether already acquired or
hereafter acquired, that would be infringed by some manner, permitted
by this License, of making, using, or selling its contributor version,
but do not include claims that would be infringed only as a
consequence of further modification of the contributor version. For
purposes of this definition, "control" includes the right to grant
patent sublicenses in a manner consistent with the requirements of
this License.
Each contributor grants you a non-exclusive, worldwide, royalty-free
patent license under the contributor's essential patent claims, to
make, use, sell, offer for sale, import and otherwise run, modify and
propagate the contents of its contributor version.
In the following three paragraphs, a "patent license" is any express
agreement or commitment, however denominated, not to enforce a patent
(such as an express permission to practice a patent or covenant not to
sue for patent infringement). To "grant" such a patent license to a
party means to make such an agreement or commitment not to enforce a
patent against the party.
If you convey a covered work, knowingly relying on a patent license,
and the Corresponding Source of the work is not available for anyone
to copy, free of charge and under the terms of this License, through a
publicly available network server or other readily accessible means,
then you must either (1) cause the Corresponding Source to be so
available, or (2) arrange to deprive yourself of the benefit of the
patent license for this particular work, or (3) arrange, in a manner
consistent with the requirements of this License, to extend the patent
license to downstream recipients. "Knowingly relying" means you have
actual knowledge that, but for the patent license, your conveying the
covered work in a country, or your recipient's use of the covered work
in a country, would infringe one or more identifiable patents in that
country that you have reason to believe are valid.
If, pursuant to or in connection with a single transaction or
arrangement, you convey, or propagate by procuring conveyance of, a
covered work, and grant a patent license to some of the parties
receiving the covered work authorizing them to use, propagate, modify
or convey a specific copy of the covered work, then the patent license
you grant is automatically extended to all recipients of the covered
work and works based on it.
A patent license is "discriminatory" if it does not include within
the scope of its coverage, prohibits the exercise of, or is
conditioned on the non-exercise of one or more of the rights that are
specifically granted under this License. You may not convey a covered
work if you are a party to an arrangement with a third party that is
in the business of distributing software, under which you make payment
to the third party based on the extent of your activity of conveying
the work, and under which the third party grants, to any of the
parties who would receive the covered work from you, a discriminatory
patent license (a) in connection with copies of the covered work
conveyed by you (or copies made from those copies), or (b) primarily
for and in connection with specific products or compilations that
contain the covered work, unless you entered into that arrangement,
or that patent license was granted, prior to 28 March 2007.
Nothing in this License shall be construed as excluding or limiting
any implied license or other defenses to infringement that may
otherwise be available to you under applicable patent law.
12. No Surrender of Others' Freedom.
If conditions are imposed on you (whether by court order, agreement or
otherwise) that contradict the conditions of this License, they do not
excuse you from the conditions of this License. If you cannot convey a
covered work so as to satisfy simultaneously your obligations under this
License and any other pertinent obligations, then as a consequence you may
not convey it at all. For example, if you agree to terms that obligate you
to collect a royalty for further conveying from those to whom you convey
the Program, the only way you could satisfy both those terms and this
License would be to refrain entirely from conveying the Program.
13. Use with the GNU Affero General Public License.
Notwithstanding any other provision of this License, you have
permission to link or combine any covered work with a work licensed
under version 3 of the GNU Affero General Public License into a single
combined work, and to convey the resulting work. The terms of this
License will continue to apply to the part which is the covered work,
but the special requirements of the GNU Affero General Public License,
section 13, concerning interaction through a network will apply to the
combination as such.
14. Revised Versions of this License.
The Free Software Foundation may publish revised and/or new versions of
the GNU General Public License from time to time. Such new versions will
be similar in spirit to the present version, but may differ in detail to
address new problems or concerns.
Each version is given a distinguishing version number. If the
Program specifies that a certain numbered version of the GNU General
Public License "or any later version" applies to it, you have the
option of following the terms and conditions either of that numbered
version or of any later version published by the Free Software
Foundation. If the Program does not specify a version number of the
GNU General Public License, you may choose any version ever published
by the Free Software Foundation.
If the Program specifies that a proxy can decide which future
versions of the GNU General Public License can be used, that proxy's
public statement of acceptance of a version permanently authorizes you
to choose that version for the Program.
Later license versions may give you additional or different
permissions. However, no additional obligations are imposed on any
author or copyright holder as a result of your choosing to follow a
later version.
15. Disclaimer of Warranty.
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
16. Limitation of Liability.
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
Copyright (C)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see .
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
Copyright (C)
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
.
PK !HJVS a hkvsobekpy-1.2.5.dist-info/WHEEL HM
K-*ϳR03rOK-J,/RH,rzd&Y)r$[)T&UD" PK !H_7K g # hkvsobekpy-1.2.5.dist-info/METADATAMP;o0+nlU9.:@$D;_\ؖ 5B { %0Yj& IA?Lѵ4xj^c G W : e
'=P 埦,lHHҗҝ/Vڍz)}p])HabM 64F,s]PÔ֎;!iD6@o%FLv`/u{՜`E8ְ~1m xZ YPK !H ! hkvsobekpy-1.2.5.dist-info/RECORD͒k@ @< 4A,"ABB bFFlJݹ5yN pge;!! E;QpM[wXV|ULq3Ӌ I-)eߊ"X4] +VVND
y4wq'=x-~wz cXA21",5m;8IHX0p:
D-\t<9r$ K{쬻g:NΉMl/ůVKLjd]
YIDfk]:vp)I~.g>G>@btX9
_}=I{i{v1R~Vǭ9\=]Q p