SMDC_perftests¶

This is the documentation of SMDC_perftests, a small python module that provides a decorator for measuring the time a function needs to execute. It then stores the results in a SMDC_perftests.performance_tests.test_cases.TestResults object. These objects can be compared to each other to quickly find out if the measured time was significantly different using a 95% confidence interval.

The objects can also be stored to and restored from netCDF4 files on disk. There are also plotting functions for the TestResults object.

Requirements¶

This package was tested using python2.7 and requires the packages

netCDF4
pytest
matplotlib
pygeogrids

# optional
# seaborn for pretty plots

Contents¶

Examples¶

Basic Example¶

import smdc_perftests.performance_tests.test_cases as test_cases
import time
import numpy as np

# use measure decorator to run function multiple times
# and measure execution time of each run
# the returned results gets the name given in
# the decorator but can be changed later if necessary

@test_cases.measure('experiment', runs=50)
def experiment(sleeptime=0.01):
    time.sleep(sleeptime+np.random.rand(1)*sleeptime)

result1 = experiment()
result2 = experiment(0.05)
result2.name = "sleep 0.05"
result3 = experiment(0.011)
result3.name = "sleep 0.011"

# the results can be printed
print result1
print result3

Results experiment
50 runs
median 0.0158 mean 0.0157 stdev 0.0029
sum 0.7859
95%% confidence interval of the mean
upper 0.0165
       |
mean  0.0157
       |
lower 0.0149

Results sleep 0.011
50 runs
median 0.0158 mean 0.0163 stdev 0.0034
sum 0.8168
95%% confidence interval of the mean
upper 0.0173
       |
mean  0.0163
       |
lower 0.0154

# the results can also be compared based on the 95% confidence intervals.

print result1 < result2
print result2 < result1
print result1 < result3

True
False
False

# or then plotted as boxplots
import smdc_perftests.visual as vis
import matplotlib.pyplot as plt
%matplotlib inline

fig, axis = vis.plot_boxplots(result1, result3)
plt.show()

Example with Dataset¶

import smdc_perftests.performance_tests.test_runner as test_runner
import time
import datetime as dt
import numpy as np

# define a fake Dataset class that implements the methods
# get_timeseries, get_avg_image and get_data

class FakeDataset(object):

    """
    Fake Dataset that provides routines for reading
    time series and images
    that do nothing
    """

    def __init__(self):
        pass
        self.ts_read = 0
        self.img_read = 0
        self.cells_read = 0

    def get_timeseries(self, gpi, date_start=None, date_end=None):
        time.sleep(0.01*np.random.rand(1))
        self.ts_read += 1
        return None

    def get_avg_image(self, date_start, date_end=None, cell_id=None):
        """
        Image readers generally return more than one
        variable. This should not matter for these tests.
        """
        assert type(date_start) == dt.datetime
        self.img_read += 1
        time.sleep(0.01*np.random.rand(1))
        return None, None, None, None, None

    def get_data(self, date_start, date_end, cell_id):
        """
        Image readers generally return more than one
        variable. This should not matter for these tests.
        """
        assert type(date_start) == dt.datetime
        assert type(date_end) == dt.datetime
        self.cells_read += 1
        time.sleep(0.01*np.random.rand(1))
        return None, None, None, None, None

fd = FakeDataset()
# setup grid point index list, must come from grid object or
# sciDB
# this test dataset has 10000 gpis of which 1 percent will be read
gpi_list = range(10000)

@test_runner.measure('test_rand_gpi', runs=100)
def test_ts():
    test_runner.read_rand_ts_by_gpi_list(fd, gpi_list)

result_ts = test_ts()

print result_ts

Results test_rand_gpi
100 runs
median 0.5642 mean 0.5591 stdev 0.0334
sum 55.9069
95%% confidence interval of the mean
upper 0.5657
       |
mean  0.5591
       |
lower 0.5524

# setup datetime list
# this test dataset has 10000 days of dates of which 1 percent will be read
date_list = []
for days in range(10000):
    date_list.append(dt.datetime(2007, 1, 1) + dt.timedelta(days=days))

@test_runner.measure('test_rand_date', runs=100)
def test_img():
    test_runner.read_rand_img_by_date_list(fd, date_list)

result_img = test_img()
print result_img

Results test_rand_date
100 runs
median 0.5530 mean 0.5548 stdev 0.0343
sum 55.4800
95%% confidence interval of the mean
upper 0.5616
       |
mean  0.5548
       |
lower 0.5480

"""
Read data by cell list using fixed start and end date
1 percent of the cells are read with a minimum of 1 cell.
"""
fd = FakeDataset()
cell_list = range(10000)

@test_runner.measure('test_rand_cells', runs=100)
def test():
    test_runner.read_rand_cells_by_cell_list(fd,
                                             dt.datetime(2007, 1, 1), dt.datetime(2008, 1, 1), cell_list)

results_cells = test()
print results_cells

Results test_rand_cells
100 runs
median 0.5510 mean 0.5476 stdev 0.0368
sum 54.7624
95%% confidence interval of the mean
upper 0.5549
       |
mean  0.5476
       |
lower 0.5403

import smdc_perftests.visual as vis
import matplotlib.pyplot as plt
%matplotlib inline

fig, axis = vis.plot_boxplots(result_ts, result_img, results_cells)
plt.show()

Example of running the test suite and analyzing the results¶

import os
from datetime import datetime
from smdc_perftests.performance_tests import test_scripts
# the test_scripts module contains the function
# run performance tests which runs all the performance tests on a dataset

# in this example we will use the esa cci dataset class
from smdc_perftests.datasets.esa_cci import ESACCI_netcdf
from smdc_perftests import helper

#init the esa cci dataset
fname = os.path.join("/media", "sf_H", "Development", "python",
                     "workspace",
                     "SMDC", "SMDC_perftests", "tests", "test_data",
                     "ESACCI-2Images.nc")
# only read the sm variable for this testrun
ds = ESACCI_netcdf(fname, variables=['sm'])
# get the testname from the filename
testname = os.path.splitext(os.path.split(fname)[1])[0]

# generate a date range list using the helper function
# in this example this does not make a lot of sense
date_range_list = helper.generate_date_list(datetime(2013, 11, 30),
                                            datetime(2013, 12, 1),
                                            n=50)

# set a directory into which to save the results
# in this case the the tests folder in the home directory
res_dir = "/home/pydev/tests/"
# run the performance tests using the grid point indices from
# the dataset grid, the generated date_range_list and gpi read percentage
# of 0.1 percent and only one repeat
test_scripts.run_performance_tests(testname, ds, res_dir,
                                   gpi_list=ds.grid.land_ind,
                                   date_range_list=date_range_list,
                                   gpi_read_perc=0.1,
                                   repeats=1)

reading 245 out of 244243 time series
reading 1 out of 50 dates
reading 1 out of 50 dates

This creates the following files named using the name given to the test and the name of the test function that was run.

!ls /home/pydev/tests

ESACCI-2Images_test-rand-avg-img.nc    ESACCI-2Images_test-rand-gpi.nc
ESACCI-2Images_test-rand-daily-img.nc

Visualization of the results¶

%matplotlib inline
import glob
import smdc_perftests.performance_tests.analyze as analyze

# get all the files in the results folder
fs = glob.glob(os.path.join(res_dir, "*.nc"))
df = analyze.prep_results(fs)
# this returnes the mean times at the moment
print df
# and makes a very simple bar plot
ax = analyze.bar_plot(df)

                                       means
ESACCI-2Images_test-rand-avg-img    0.085946
ESACCI-2Images_test-rand-gpi        0.098265
ESACCI-2Images_test-rand-daily-img  0.059122

License¶

# Copyright (c) 2014,Vienna University of Technology,
# Department of Geodesy and Geoinformation
# All rights reserved.

# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#   * Redistributions of source code must retain the above copyright
#     notice, this list of conditions and the following disclaimer.
#    * Redistributions in binary form must reproduce the above copyright
#      notice, this list of conditions and the following disclaimer in the
#      documentation and/or other materials provided with the distribution.
#    * Neither the name of the Vienna University of Technology,
#      Department of Geodesy and Geoinformation nor the
#      names of its contributors may be used to endorse or promote products
#      derived from this software without specific prior written permission.

# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL VIENNA UNIVERSITY OF TECHNOLOGY,
# DEPARTMENT OF GEODESY AND GEOINFORMATION BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

smdc_perftests¶

smdc_perftests package¶

Subpackages¶

smdc_perftests.datasets package¶

Submodules¶

smdc_perftests.datasets.EQUI_7 module¶

Dataset Reader for EQUI-7 data Created on Mon Jun 8 17:30:19 2015

class smdc_perftests.datasets.EQUI_7.EQUI_7(fname, variables=None, avg_var=None, time_var='time', lat_var='x', lon_var='y')[source]¶

Bases: smdc_perftests.datasets.esa_cci.ESACCI_netcdf

Methods

smdc_perftests.datasets.ascat module¶

Module contains the Classes for reading ASCAT data Created on Fri Mar 27 15:12:18 2015

@author: Christoph.Paulik@geo.tuwien.ac.at

class smdc_perftests.datasets.ascat.ASCAT_netcdf(fname, variables=None, avg_var=None, time_var='time', gpi_var='gpis_correct', cell_var='cells_correct', get_exact_time=False)[source]¶

Bases: object

Class for reading ASCAT data from netCDF files

Caches the following: - time variable - keeps the dataset open as long as the instance exists

Methods

get_avg_image(date_start[, date_end, cellID]) Reads image from dataset, takes the average if more than one value is in the result array.

get_data(date_start, date_end[, cellID]) Reads date cube from dataset

get_timeseries(locationid[, date_start, ...])

Parameters:

get_avg_image(date_start, date_end=None, cellID=None)[source]¶

Reads image from dataset, takes the average if more than one value is in the result array.

Parameters:

date_start: datetime

start date of the image to get. If only one date is given then the whole day of this date is read

date_end: datetime, optional

end date of the averaged image to get

cellID: int, optional

cell id to which the image should be limited, for ESA CCI this is not defined at the moment.

get_data(date_start, date_end, cellID=None)[source]¶

Reads date cube from dataset

Parameters:

date_start: datetime

start date of the image to get. If only one date is given then the whole day of this date is read

date_end: datetime

end date of the averaged image to get

cellID: int

cell id to which the image should be limited, for ESA CCI this is not defined at the moment.

get_timeseries(locationid, date_start=None, date_end=None)[source]¶

Parameters:

locationid: int

location id as lat_index * row_length + lon_index

date_start: datetime, optional

start date of the time series

date_end: datetime, optional

end date of the time series

Returns:

ts : dict

smdc_perftests.datasets.esa_cci module¶

Module contains the Class for reading ESA CCI data in netCDF Format Created on Fri Mar 27 15:12:18 2015

@author: Christoph.Paulik@geo.tuwien.ac.at

class smdc_perftests.datasets.esa_cci.ESACCI_netcdf(fname, variables=None, avg_var=None, time_var='time', lat_var='lat', lon_var='lon')[source]¶

Bases: object

Class for reading ESA CCI data from netCDF files

Caches the following: - time variable - keeps the dataset open as long as the instance exists

Methods

get_avg_image(date_start[, date_end, cellID]) Reads image from dataset, takes the average if more than one value is in the result array.

get_data(date_start, date_end[, cellID]) Reads date cube from dataset

get_timeseries(locationid[, date_start, ...])

Parameters:

get_avg_image(date_start, date_end=None, cellID=None)[source]¶

Reads image from dataset, takes the average if more than one value is in the result array.

Parameters:

date_start: datetime

start date of the image to get. If only one date is given then the whole day of this date is read

date_end: datetime, optional

end date of the averaged image to get

cellID: int, optional

cell id to which the image should be limited, for ESA CCI this is not defined at the moment.

get_data(date_start, date_end, cellID=1)[source]¶

Reads date cube from dataset

Parameters:

date_start: datetime

start date of the image to get. If only one date is given then the whole day of this date is read

date_end: datetime

end date of the averaged image to get

cellID: int

cell id to which the image should be limited, for ESA CCI this is not defined at the moment.

get_timeseries(locationid, date_start=None, date_end=None)[source]¶

Parameters:

locationid: int

location id as lat_index * row_length + lon_index

date_start: datetime, optional

start date of the time series

date_end: datetime, optional

end date of the time series

Returns:

ts : dict

Module contents¶

smdc_perftests.performance_tests package¶

Submodules¶

smdc_perftests.performance_tests.analyze module¶

Module for analyzing and the test results Created on Thu Apr 2 14:30:51 2015

@author: christoph.paulik@geo.tuwien.ac.at

smdc_perftests.performance_tests.analyze.bar_plot(df, show=True)[source]¶

Make a bar plot from the gathered results

Parameters:

df: pandas.DataFrame

Measured data

show: boolean

if set then the plot is shown

Returns:

ax: matplotlib.axes

axes of the plot

smdc_perftests.performance_tests.analyze.esa_cci_grouping(n)[source]¶

smdc_perftests.performance_tests.analyze.esa_cci_name_formatter(n)[source]¶

smdc_perftests.performance_tests.analyze.prep_results(results_files, name_fm=None, grouping_f=None)[source]¶

Takes a list of results file names and bundles the results into a pandas DataFrame

Parameters:

results_files: list

list of filenames to load

name_fm: function, optional

if set a function that gets the name of the results and returns a more meaningful name. This is useful if the names of the results are very long or verbose.

grouping_f: function ,optional

can be used to assign groups according to the name of the results. Gets the name and returns a string.

Returns:

df : pandas.DataFrame

Results named and possibly grouped

smdc_perftests.performance_tests.test_cases module¶

This module contains functions that run tests according to specifications from SMDC Performance comparison document.

Interfaces to data should be interchangeable as long as they adhere to interface specifications from rsdata module

Created on Tue Oct 21 13:37:58 2014

@author: christoph.paulik@geo.tuwien.ac.at

class smdc_perftests.performance_tests.test_cases.SelfTimingDataset(ds, timefuncs=['get_timeseries', 'get_avg_image', 'get_data'])[source]¶

Bases: object

Dataset class that times the functions of a dataset instance it gets in it’s constructor

Stores the results as TestResults instances in a dictionary with the timed function names as keys.

Methods

gentimedfunc(funcname) generate a timed function that calls

gentimedfunc(funcname)[source]¶

generate a timed function that calls the function of the given dataset but returns the execution time

Parameters:

funcname: string

function to create/call of the timed dataset

class smdc_perftests.performance_tests.test_cases.TestResults(init_obj, name=None, ddof=1)[source]¶

Bases: object

Simple object that contains the test results and can be used to compare the test results to other test results.

Objects of this type can also be plotted by the plotting routines. Parameters ———- measured times or filename: list or string

list of measured times or netCDF4 file produced by to_nc of another TestResults object

ddof: int: difference degrees of freedom. This is used to calculate standard deviation and variance. It is the number that is subtracted from the sample number n when estimating the population standard deviation and variance. see bessel’s correction on e.g. wikipedia for explanation

Attributes

median: float	median of the measurements
n: int	sample size
stdev: float	standard deviation
var: float	variance
total: float	total time expired
mean: float	mean time per test run

Methods

`confidence_int`([conf_level])	Calculate confidence interval of the mean
`to_nc`(filename)	store results on disk as a netCDF4 file

confidence_int(conf_level=95)[source]¶

Calculate confidence interval of the mean time measured

Parameters:

conf_level: float

confidence level desired for the confidence interval in percent. this will be transformed into the quantile needed to get the z value for the t distribution. default is 95% confidence interval

Returns:

lower_mean : float

lower confidence interval boundary

mean : float

mean value

upper_mean : float

upper confidence interval boundary

to_nc(filename)[source]¶

store results on disk as a netCDF4 file

Parameters:

filename: string

path and filename

smdc_perftests.performance_tests.test_cases.measure(exper_name, runs=5, ddof=1)[source]¶

Decorator that measures the running time of a function and calculates statistics.

Parameters:

exper_name: string

experiment name, used for plotting and saving

runs: int

number of test runs to perform

ddof: int

difference degrees of freedom. This is used to calculate standard deviation and variance. It is the number that is subtracted from the sample number n when estimating the population standard deviation and variance. see bessel’s correction on e.g. wikipedia for explanation

Returns:

results: dict

TestResults instance

smdc_perftests.performance_tests.test_cases.read_rand_cells_by_cell_list(dataset, cell_date_list, cell_id, read_perc=1.0, max_runtime=None)[source]¶

reads data from the dataset using the get_data method. In this method the start and end datetimes are fixed for all cell ID’s that are read.

Parameters:

dataset: instance

instance of a class that implements a get_data(date_start, date_end, cell_id) method

date_start: datetime

start dates which should be read.

date_end: datetime

end dates which should be read.

cell_date_list: list of tuples, time intervals to read for each cell

cell_id: int or iterable

cell ids which should be read. can also be a list of integers

read_perc : float

percentage of cell ids to read from the

max_runtime: int, optional

maximum runtime of test in second.

smdc_perftests.performance_tests.test_cases.read_rand_img_by_date_list(dataset, date_list, read_perc=1.0, max_runtime=None, **kwargs)[source]¶

reads image data for random dates on a list additional kwargs are given to read_img method of dataset

Parameters:

dataset: instance

instance of a class that implements a read_img(datetime) method

date_list: iterable

list of datetime objects

read_perc: float

percentage of datetimes out of date_list to read

max_runtime: int, optional

maximum runtime of test in second.

**kwargs:

other keywords are passed to the get_avg_image method dataset

smdc_perftests.performance_tests.test_cases.read_rand_img_by_date_range(dataset, date_list, read_perc=1.0, max_runtime=None, **kwargs)[source]¶

reads image data between random dates on a list additional kwargs are given to read_img method of dataset

Parameters:

dataset: instance

instance of a class that implements a read_img(datetime) method

date_list: iterable

list of datetime objects The format is a list of lists e.g. [[datetime(2007,1,1), datetime(2007,1,1)], #reads one day

[datetime(2007,1,1), datetime(2007,12,31)]] # reads one year

read_perc: float

percentage of datetimes out of date_list to read

max_runtime: int, optional

maximum runtime of test in second.

**kwargs:

other keywords are passed to the get_avg_image method dataset

smdc_perftests.performance_tests.test_cases.read_rand_ts_by_gpi_list(dataset, gpi_list, read_perc=1.0, max_runtime=None, **kwargs)[source]¶

reads time series data for random grid point indices in a list additional kwargs are given to read_ts method of dataset

Parameters:

dataset: instance

instance of a class that implements a read_ts(gpi) method

gpi_list: iterable

list or numpy array of grid point indices

read_perc: float

percentage of points from gpi_list to read

max_runtime: int, optional

maximum runtime of test in second.

**kwargs:

other keywords are passed to the get_timeseries method dataset

smdc_perftests.performance_tests.test_scripts module¶

Module implements the test cases specified in the performance test protocol Created on Wed Apr 1 10:59:05 2015

@author: christoph.paulik@geo.tuwien.ac.at

smdc_perftests.performance_tests.test_scripts.run_ascat_tests(dataset, testname, results_dir, n_dates=10000, date_read_perc=0.1, gpi_read_perc=0.1, repeats=3, cell_read_perc=10.0, max_runtime_per_test=None)[source]¶

Runs the ASCAT tests given a dataset instance

Parameters:

dataset: Dataset instance

Instance of a Dataset class

testname: string

Name of the test, used for storing the results

results_dir: string

path where to store the test restults

n_dates: int, optional

number of dates to generate

date_read_perc: float, optioanl

percentage of random selection from date_range_list read for each try

gpi_read_perc: float, optional

percentage of random selection from gpi_list read for each try

repeats: int, optional

number of repeats of the tests

cell_list: list, optional

list of possible cells to read from. if given then the read_data test will be run

max_runtime_per_test: float, optional

maximum runtime per test in seconds, if given the tests will be aborted after taking more than this time

smdc_perftests.performance_tests.test_scripts.run_equi7_tests(dataset, testname, results_dir, n_dates=10000, date_read_perc=0.1, gpi_read_perc=0.1, repeats=3, cell_read_perc=100.0, max_runtime_per_test=None)[source]¶

Runs the ASAR/Sentinel 1 Equi7 tests given a dataset instance

Parameters:

dataset: Dataset instance

Instance of a Dataset class

testname: string

Name of the test, used for storing the results

results_dir: string

path where to store the test restults

n_dates: int, optional

number of dates to generate

date_read_perc: float, optioanl

percentage of random selection from date_range_list read for each try

gpi_read_perc: float, optional

percentage of random selection from gpi_list read for each try

repeats: int, optional

number of repeats of the tests

cell_list: list, optional

list of possible cells to read from. if given then the read_data test will be run

max_runtime_per_test: float, optional

maximum runtime per test in seconds, if given the tests will be aborted after taking more than this time

smdc_perftests.performance_tests.test_scripts.run_esa_cci_netcdf_tests(test_dir, results_dir, variables=['sm'])[source]¶

function for running the ESA CCI netCDF performance tests the tests will be run for all .nc files in the test_dir

Parameters:

test_dir: string

path to the test files

results_dir: string

path in which the results should be stored

variables: list

list of variables to read for the tests

smdc_perftests.performance_tests.test_scripts.run_esa_cci_tests(dataset, testname, results_dir, n_dates=10000, date_read_perc=0.1, gpi_read_perc=0.1, repeats=3, cell_read_perc=10.0, max_runtime_per_test=None)[source]¶

Runs the ESA CCI tests given a dataset instance

Parameters:

dataset: Dataset instance

Instance of a Dataset class

testname: string

Name of the test, used for storing the results

results_dir: string

path where to store the test restults

n_dates: int, optional

number of dates to generate

date_read_perc: float, optioanl

percentage of random selection from date_range_list read for each try

gpi_read_perc: float, optional

percentage of random selection from gpi_list read for each try

repeats: int, optional

number of repeats of the tests

cell_list: list, optional

list of possible cells to read from. if given then the read_data test will be run

max_runtime_per_test: float, optional

maximum runtime per test in seconds, if given the tests will be aborted after taking more than this time

smdc_perftests.performance_tests.test_scripts.run_performance_tests(name, dataset, save_dir, gpi_list=None, date_range_list=None, cell_list=None, cell_date_list=None, gpi_read_perc=1.0, date_read_perc=1.0, cell_read_perc=1.0, max_runtime_per_test=None, repeats=1)[source]¶

Run a complete test suite on a dataset and store the results in the specified directory

Parameters:

name: string

name of the test run, used for filenaming

dataset: dataset instance

instance implementing the get_timeseries, get_avg_image and get_data methods.

save_dir: string

directory to store the test results in

gpi_list: list, optional

list of possible grid point indices, if given the timeseries reading tests will be run

date_range_list: list, optional

list of possible dates, if given then the read_avg_image and read_data tests will be run. The format is a list of lists e.g. [[datetime(2007,1,1), datetime(2007,1,1)], #reads one day

[datetime(2007,1,1), datetime(2007,12,31)]] # reads one year

cell_list: list, optional

list of possible cells to read from. if given then the read_data test will be run

cell_date_list: list, optional

list of time intervals to read per cell. Should be as long as the cell list or longer.

gpi_read_perc: float, optional

percentage of random selection from gpi_list read for each try

date_read_perc: float, optioanl

percentage of random selection from date_range_list read for each try

cell_read_perc: float, optioanl

percentage of random selection from cell_range_list read for each try

max_runtime_per_test: float, optional

maximum runtime per test in seconds, if given the tests will be aborted after taking more than this time

repeats: int, optional

number of repeats for each measurement

Module contents¶

Submodules¶

smdc_perftests.helper module¶

Helper functions Created on Wed Apr 1 14:50:18 2015

@author: christoph.paulik@geo.tuwien.ac.at

smdc_perftests.helper.generate_date_list(minimum, maximum, n=500, max_spread=30, min_spread=None)[source]¶

Parameters:

minimum: datetime

minimum datetime

maximum: datetime

maximum datetime

n: int

number of dates to generate

max_spread: int, optional

maximum spread between dates

min_spread: int, optional

minimum spread between dates

Returns:

date_list: list

list of start, end lists The format is a list of lists e.g. [[datetime(2007,1,1), datetime(2007,1,1)],

[datetime(2007,1,1), datetime(2007,12,31)]]

smdc_perftests.visual module¶

Module for visualizing the test results Created on Tue Nov 25 13:44:56 2014

@author: Christoph.Paulik@geo.tuwien.ac.at

smdc_perftests.visual.plot_boxplots(*args, **kwargs)[source]¶

plots means and confidence intervals of given TestResults objects

Parameters:

*args: TestResults instances

any Number of TestResults instances that should be plotted side by side

conf_level: int, optional

confidence level to use for the computed confidence intervals

**kwargs: varied

all other keyword arguments will be passed on to the plt.subplots function

Returns:

fig: matplotlib.Figure

ax1: matplotlib.axes

SMDC_perftests¶

Requirements¶

Contents¶

Examples¶

Basic Example¶

Example with Dataset¶

Example of running the test suite and analyzing the results¶

Visualization of the results¶

License¶

smdc_perftests¶

smdc_perftests package¶

Subpackages¶

smdc_perftests.datasets package¶

smdc_perftests.performance_tests package¶

Submodules¶

smdc_perftests.helper module¶

smdc_perftests.visual module¶

Module contents¶

Indices and tables¶