def test_axis_None(self):
# Test axis=None (equal to axis=0 for 1-D input)
x = np.array((-2,-1,0,1,2,3)*4)**2
assert_allclose(mstats.normaltest(x, axis=None), mstats.normaltest(x))
assert_allclose(mstats.skewtest(x, axis=None), mstats.skewtest(x))
assert_allclose(mstats.kurtosistest(x, axis=None),
mstats.kurtosistest(x))
def test_axis_None(self):
# Test axis=None (equal to axis=0 for 1-D input)
x = np.array((-2,-1,0,1,2,3)*4)**2
assert_allclose(mstats.normaltest(x, axis=None), mstats.normaltest(x))
assert_allclose(mstats.skewtest(x, axis=None), mstats.skewtest(x))
assert_allclose(mstats.kurtosistest(x, axis=None),
mstats.kurtosistest(x))
def test_maskedarray_input(self):
# Add some masked values, test result doesn't change
x = np.array((-2, -1, 0, 1, 2, 3) * 4) ** 2
xm = np.ma.array(np.r_[np.inf, x, 10], mask=np.r_[True, [False] * x.size, True])
assert_allclose(mstats.normaltest(xm), stats.normaltest(x))
assert_allclose(mstats.skewtest(xm), stats.skewtest(x))
assert_allclose(mstats.kurtosistest(xm), stats.kurtosistest(x))
def test_maskedarray_input(self):
# Add some masked values, test result doesn't change
x = np.array((-2,-1,0,1,2,3)*4)**2
xm = np.ma.array(np.r_[np.inf, x, 10],
mask=np.r_[True, [False] * x.size, True])
assert_allclose(mstats.normaltest(xm), stats.normaltest(x))
assert_allclose(mstats.skewtest(xm), stats.skewtest(x))
assert_allclose(mstats.kurtosistest(xm), stats.kurtosistest(x))
def test_vs_nonmasked(self):
x = np.array((-2, -1, 0, 1, 2, 3) * 4) ** 2
assert_array_almost_equal(mstats.normaltest(x), stats.normaltest(x))
assert_array_almost_equal(mstats.skewtest(x), stats.skewtest(x))
assert_array_almost_equal(mstats.kurtosistest(x), stats.kurtosistest(x))
funcs = [stats.normaltest, stats.skewtest, stats.kurtosistest]
mfuncs = [mstats.normaltest, mstats.skewtest, mstats.kurtosistest]
x = [1, 2, 3, 4]
for func, mfunc in zip(funcs, mfuncs):
assert_raises(ValueError, func, x)
assert_raises(ValueError, mfunc, x)
def test_vs_nonmasked(self):
x = np.array((-2,-1,0,1,2,3)*4)**2
assert_array_almost_equal(mstats.normaltest(x), stats.normaltest(x))
assert_array_almost_equal(mstats.skewtest(x), stats.skewtest(x))
assert_array_almost_equal(mstats.kurtosistest(x),
stats.kurtosistest(x))
funcs = [stats.normaltest, stats.skewtest, stats.kurtosistest]
mfuncs = [mstats.normaltest, mstats.skewtest, mstats.kurtosistest]
x = [1, 2, 3, 4]
for func, mfunc in zip(funcs, mfuncs):
assert_raises(ValueError, func, x)
assert_raises(ValueError, mfunc, x)
def test_skewtest_result_attributes(self):
x = np.array((-2, -1, 0, 1, 2, 3)*4)**2
res = mstats.skewtest(x)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes, ma=True)
def test_skewtest_result_attributes(self):
x = np.array((-2, -1, 0, 1, 2, 3) * 4)**2
res = mstats.skewtest(x)
attributes = ('statistic', 'pvalue')
check_named_results(res, attributes, ma=True)
plt.setp(r2['boxes'], color='black',lw=1.5)
plt.setp(r2['whiskers'], color='black',lw=1.5)
plt.setp(r2['caps'], color='black',lw=1.5)
plt.setp(r2['medians'], color='black',lw=1.5)
ax.set_ylabel('TOTAL EDDY AREA, IN METERS SQUARED')
ax.get_yaxis().set_major_formatter(tkr.FuncFormatter(lambda x, p: format(int(x), ',')))
plt.tight_layout()
plt.savefig(r"C:\workspace\Time_Series\Output\Joes_Figs\grouped_mc_area_boxplot.png",dpi=600)
from scipy.stats.mstats import normaltest, skewtest
print 'old ', normaltest(area_old)
print 'combined ', normaltest(combined)
print 'old ', skewtest(area_old)
print 'combined ', skewtest(combined)
a = probplot(area_old,dist='norm', plot=None)
b= probplot(combined,dist='norm', plot=None)
colors = {'r':'red','s':'blue', 'u':'green'}
markers = {'r':'*','s':'x', 'u':'o'}
old_df = pd.DataFrame(area_old, columns=['Long Term Sites: N=12'])
old_df['Bar_Type'] = lt_bt
old_df = old_df.sort_values(by='Long Term Sites: N=12')
old_df['quart']=a[0][0]
combined_df = pd.DataFrame(combined, columns=['ALL SITES N=22'])
combined_df['Bar_Type'] = lt_bt + new_bt
combined_df = combined_df.sort_values(by='ALL SITES N=22')
args = parser.parse_args()
times = []
files = glob.glob("{0}/*.csv".format(args.dir))
for phile in files:
with open(phile, 'rb') as csvfile:
reader = csv.reader(csvfile, delimiter=',')
for row in reader:
if(len(row)>12):
nums = map(lambda x: int(x), row[12].split(":"))
seconds = nums[0] * 3600 + nums[1] * 60 + nums[2]
times.append(seconds)
print(normaltest(times))
print(mstats.skewtest(times))
print(stats.describe(times))
n, (smin, smax), sm, sv, ss, sk = stats.describe(times)
num_bins = 50
# the histogram of the data
n, bins, patches = plt.hist(times, num_bins, normed=1, facecolor='blue', alpha=0.5)
# add a 'best fit' line
y = mlab.normpdf(bins, sm, math.sqrt(sv))
plt.plot(bins, y, 'r--')
plt.xlabel('Time')
plt.ylabel('')
plt.title(r'2014 NYC Marathon Times')
