In [1]:

    

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


    

In [15]:

    

# 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


    

In [16]:

    

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

In [17]:

    

# 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

In [18]:

    

"""
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

In [19]:

    

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()


    

    




  agg_filter: unknown
  alpha: float (0.0 transparent through 1.0 opaque)         
  animated: [True | False]         
  axes: an :class:`~matplotlib.axes.Axes` instance         
  backgroundcolor: any matplotlib color         
  bbox: rectangle prop dict         
  clip_box: a :class:`matplotlib.transforms.Bbox` instance         
  clip_on: [True | False]         
  clip_path: [ (:class:`~matplotlib.path.Path`,         :class:`~matplotlib.transforms.Transform`) |         :class:`~matplotlib.patches.Patch` | None ]         
  color: any matplotlib color         
  contains: a callable function         
  family or fontfamily or fontname or name: [FONTNAME | 'serif' | 'sans-serif' | 'cursive' | 'fantasy' |                   'monospace' ]         
  figure: a :class:`matplotlib.figure.Figure` instance         
  fontproperties or font_properties: a :class:`matplotlib.font_manager.FontProperties` instance         
  gid: an id string         
  horizontalalignment or ha: [ 'center' | 'right' | 'left' ]         
  label: string or anything printable with '%s' conversion.         
  linespacing: float (multiple of font size)         
  lod: [True | False]         
  multialignment: ['left' | 'right' | 'center' ]         
  path_effects: unknown
  picker: [None|float|boolean|callable]         
  position: (x,y)         
  rasterized: [True | False | None]         
  rotation: [ angle in degrees | 'vertical' | 'horizontal' ]         
  rotation_mode: unknown
  size or fontsize: [size in points | 'xx-small' | 'x-small' | 'small' |                   'medium' | 'large' | 'x-large' | 'xx-large' ]         
  sketch_params: unknown
  snap: unknown
  stretch or fontstretch: [a numeric value in range 0-1000 | 'ultra-condensed' |                   'extra-condensed' | 'condensed' | 'semi-condensed' |                   'normal' | 'semi-expanded' | 'expanded' | 'extra-expanded' |                   'ultra-expanded' ]         
  style or fontstyle: [ 'normal' | 'italic' | 'oblique']         
  text: string or anything printable with '%s' conversion.         
  transform: :class:`~matplotlib.transforms.Transform` instance         
  url: a url string         
  variant or fontvariant: [ 'normal' | 'small-caps' ]         
  verticalalignment or va or ma: [ 'center' | 'top' | 'bottom' | 'baseline' ]         
  visible: [True | False]         
  weight or fontweight: [a numeric value in range 0-1000 | 'ultralight' | 'light' |                   'normal' | 'regular' | 'book' | 'medium' | 'roman' |                   'semibold' | 'demibold' | 'demi' | 'bold' | 'heavy' |                   'extra bold' | 'black' ]         
  x: float         
  y: float         
  zorder: any number