def matlab2datetime(tindex):
""" Transform a matlab time to a datetime, rounded to seconds
"""
day = Timestamp.fromordinal(int(tindex))
dayfrac = Timedelta(days=float(tindex) % 1) - Timedelta(days=366)
return day + dayfrac
def test_constructor_fromordinal(self):
base = datetime(2000, 1, 1)
ts = Timestamp.fromordinal(base.toordinal(), freq='D')
assert base == ts
assert ts.freq == 'D'
assert base.toordinal() == ts.toordinal()
ts = Timestamp.fromordinal(base.toordinal(), tz='US/Eastern')
assert Timestamp('2000-01-01', tz='US/Eastern') == ts
assert base.toordinal() == ts.toordinal()
# GH#3042
dt = datetime(2011, 4, 16, 0, 0)
ts = Timestamp.fromordinal(dt.toordinal())
assert ts.to_pydatetime() == dt
# with a tzinfo
stamp = Timestamp('2011-4-16', tz='US/Eastern')
dt_tz = stamp.to_pydatetime()
ts = Timestamp.fromordinal(dt_tz.toordinal(), tz='US/Eastern')
assert ts.to_pydatetime() == dt_tz
def test_constructor_fromordinal(self):
base = datetime(2000, 1, 1)
ts = Timestamp.fromordinal(base.toordinal(), freq='D')
assert base == ts
assert ts.freq == 'D'
assert base.toordinal() == ts.toordinal()
ts = Timestamp.fromordinal(base.toordinal(), tz='US/Eastern')
assert Timestamp('2000-01-01', tz='US/Eastern') == ts
assert base.toordinal() == ts.toordinal()
# GH#3042
dt = datetime(2011, 4, 16, 0, 0)
ts = Timestamp.fromordinal(dt.toordinal())
assert ts.to_pydatetime() == dt
# with a tzinfo
stamp = Timestamp('2011-4-16', tz='US/Eastern')
dt_tz = stamp.to_pydatetime()
ts = Timestamp.fromordinal(dt_tz.toordinal(), tz='US/Eastern')
assert ts.to_pydatetime() == dt_tz
def mavg(x, y, window):
"compute moving average"
x, y = map(_plot_friendly, [x, y])
x_is_date = _isdate(x.iloc[0])
if x_is_date:
x = np.array([i.toordinal() for i in x])
std_err = pd.rolling_std(y, window)
y = pd.rolling_mean(y, window)
y1 = y - std_err
y2 = y + std_err
if x_is_date:
x = [Timestamp.fromordinal(int(i)) for i in x]
return (x, y, y1, y2)
def mavg(x,y, window):
"compute moving average"
x, y = map(_plot_friendly, [x,y])
x_is_date = _isdate(x.iloc[0])
if x_is_date:
x = np.array([i.toordinal() for i in x])
std_err = pd.rolling_std(y, window)
y = pd.rolling_mean(y, window)
y1 = y - std_err
y2 = y + std_err
if x_is_date:
x = [Timestamp.fromordinal(int(i)) for i in x]
return (x, y, y1, y2)
def lm(x, y, alpha=ALPHA):
"fits an OLS from statsmodels. returns tuple."
x_is_date = _isdate(x.iloc[0])
if x_is_date:
x = np.array([i.toordinal() for i in x])
X = sm.add_constant(x)
fit = sm.OLS(y, X).fit()
prstd, iv_l, iv_u = wls_prediction_std(fit)
_, summary_values, summary_names = summary_table(fit, alpha=alpha)
df = pd.DataFrame(summary_values, columns=map(_snakify, summary_names))
# TODO: indexing w/ data frame is messing everything up
fittedvalues = df['predicted_value'].values
predict_mean_ci_low = df['mean_ci_95%_low'].values
predict_mean_ci_upp = df['mean_ci_95%_upp'].values
predict_ci_low = df['predict_ci_95%_low'].values
predict_ci_upp = df['predict_ci_95%_upp'].values
if x_is_date:
x = [Timestamp.fromordinal(int(i)) for i in x]
return (x, fittedvalues, predict_mean_ci_low, predict_mean_ci_upp)
def lm(x, y, alpha=ALPHA):
"fits an OLS from statsmodels. returns tuple."
x_is_date = _isdate(x.iloc[0])
if x_is_date:
x = np.array([i.toordinal() for i in x])
X = sm.add_constant(x)
fit = sm.OLS(y, X).fit()
prstd, iv_l, iv_u = wls_prediction_std(fit)
_, summary_values, summary_names = summary_table(fit, alpha=alpha)
df = pd.DataFrame(summary_values, columns=map(_snakify, summary_names))
# TODO: indexing w/ data frame is messing everything up
fittedvalues = df['predicted_value'].values
predict_mean_ci_low = df['mean_ci_95%_low'].values
predict_mean_ci_upp = df['mean_ci_95%_upp'].values
predict_ci_low = df['predict_ci_95%_low'].values
predict_ci_upp = df['predict_ci_95%_upp'].values
if x_is_date:
x = [Timestamp.fromordinal(int(i)) for i in x]
return (x, fittedvalues, predict_mean_ci_low, predict_mean_ci_upp)
def lowess(x, y, span=SPAN):
"returns y-values estimated using the lowess function in statsmodels."
"""
for more see
statsmodels.nonparametric.smoothers_lowess.lowess
"""
x, y = map(_plot_friendly, [x, y])
x_is_date = _isdate(x.iloc[0])
if x_is_date:
x = np.array([i.toordinal() for i in x])
result = smlowess(np.array(y), np.array(x), frac=span)
x = pd.Series(result[::, 0])
y = pd.Series(result[::, 1])
lower, upper = stats.t.interval(span, len(x), loc=0, scale=2)
std = np.std(y)
y1 = pd.Series(lower * std + y)
y2 = pd.Series(upper * std + y)
if x_is_date:
x = [Timestamp.fromordinal(int(i)) for i in x]
return (x, y, y1, y2)
def lowess(x, y, span=SPAN):
"returns y-values estimated using the lowess function in statsmodels."
"""
for more see
statsmodels.nonparametric.smoothers_lowess.lowess
"""
x, y = map(_plot_friendly, [x,y])
x_is_date = _isdate(x.iloc[0])
if x_is_date:
x = np.array([i.toordinal() for i in x])
result = smlowess(np.array(y), np.array(x), frac=span)
x = pd.Series(result[::,0])
y = pd.Series(result[::,1])
lower, upper = stats.t.interval(span, len(x), loc=0, scale=2)
std = np.std(y)
y1 = pd.Series(lower * std + y)
y2 = pd.Series(upper * std + y)
if x_is_date:
x = [Timestamp.fromordinal(int(i)) for i in x]
return (x, y, y1, y2)
def time_fromordinal(self):
Timestamp.fromordinal(730120)
def time_fromordinal(self):
Timestamp.fromordinal(730120)
