def test_rf_negative_4():
'''
A test with negatives
'''
test_list = [ 2, 3, 4, -10, 5, 6, -10, 4, 4, 4]
expect = test_list[2]
observ = rf.rfmean( test_list )
assert_almost_equal( observ, expect )
def test_rf_99_2():
'''
A test with 99s
'''
test_list = [ 2, 3, 4, 99, 5, 6, 99, 4, 4, 4]
expect = test_list[1]
observ = rf.rfmean( test_list )
assert_almost_equal( observ, expect )
def test_rf_simple_1():
'''
A simple test which has no negative and no '99's
'''
test_list = [ 1 ]
expect = test_list[0]
observ = rf.rfmean( test_list )
assert_almost_equal( observ, expect )
def test_rf_negative_3():
'''
A test with negatives
'''
test_list = [ -1, 2, 3, 1, 4, 5]
expect = test_list[2]
observ = rf.rfmean( test_list )
assert_almost_equal( observ, expect )
def test_rf_negative_99_start_2():
'''
A test with no valid data points
'''
test_list = [-1, -10, 99]
expect = "No valid data points"
observ = rf.rfmean( test_list )
assert observ == expect
def test_rf_99_start():
'''
A test where first value is 99
'''
test_list = [99]
expect = "First value 99"
observ = rf.rfmean( test_list )
assert observ == expect
def test_rf_empty():
'''
A test with empty list
'''
test_list = []
expect = "Empty list"
observ = rf.rfmean( test_list )
assert observ == expect
def test_rf_both():
'''
A test with negatives and 99s
'''
test_list = [ 2, 3, 4, 99, 5, -1, 6, 99, 4, 4, 4]
expect = test_list[1]
observ = rf.rfmean( test_list )
assert_almost_equal( observ, expect )
def test_rnd_99():
'''
Generate a list of random integers:
- of length [0:50]
- in the range [0:99)
In this notation the square bracket indicates inclusive, the parentheses exlusive i.e.
Find the mean then add a terminating 99 and further 'fluff' values,
Then disperse negative values in the resulting list.
Finally test rfmean against the expected value.
'''
rnd.seed()
# Conduction 50 tests of:
for itest in range(50):
# Generate number of values
nvalues = rnd.randrange(51)
# Generate list of non-negative random numbers less thann 99
values = []
for ivalue in range(nvalues):
values.append( rnd.randrange(99) )
# Calculate expected mean
expect = sum(values) / nvalues
# Add terminating 99
values.append(99)
# Add some fluff after 99
fluff = []
for ivalue in range(50):
fluff.append( rnd.randrange(100) )
values.extend( fluff )
# Disperse some (fewer than 11) negative numbers [-10:0)
for x in range( rnd.randrange(1,11) ):
insert_value = rnd.randrange(-10,0)
insert_pos = rnd.randrange( len(values) )
values.insert( insert_pos, insert_value )
assert_almost_equal( rf.rfmean(values), expect)
