本文整理汇总了Python中scipy.interpolate方法的典型用法代码示例。如果您正苦于以下问题:Python scipy.interpolate方法的具体用法?Python scipy.interpolate怎么用?Python scipy.interpolate使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类scipy
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在下文中一共展示了scipy.interpolate方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_interpolate_index_values
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import interpolate [as 别名]
def test_interpolate_index_values(self):
s = Series(np.nan, index=np.sort(np.random.rand(30)))
s[::3] = np.random.randn(10)
vals = s.index.values.astype(float)
result = s.interpolate(method='index')
expected = s.copy()
bad = isna(expected.values)
good = ~bad
expected = Series(np.interp(vals[bad], vals[good],
s.values[good]),
index=s.index[bad])
assert_series_equal(result[bad], expected)
# 'values' is synonymous with 'index' for the method kwarg
other_result = s.interpolate(method='values')
assert_series_equal(other_result, result)
assert_series_equal(other_result[bad], expected)
示例2: test_interp_limit
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import interpolate [as 别名]
def test_interp_limit(self):
s = Series([1, 3, np.nan, np.nan, np.nan, 11])
expected = Series([1., 3., 5., 7., np.nan, 11.])
result = s.interpolate(method='linear', limit=2)
assert_series_equal(result, expected)
# GH 9217, make sure limit is an int and greater than 0
methods = ['linear', 'time', 'index', 'values', 'nearest', 'zero',
'slinear', 'quadratic', 'cubic', 'barycentric', 'krogh',
'polynomial', 'spline', 'piecewise_polynomial', None,
'from_derivatives', 'pchip', 'akima']
s = pd.Series([1, 2, np.nan, np.nan, 5])
msg = (r"Limit must be greater than 0|"
"time-weighted interpolation only works on Series or"
r" DataFrames with a DatetimeIndex|"
r"invalid method '(polynomial|spline|None)' to interpolate|"
"Limit must be an integer")
for limit in [-1, 0, 1., 2.]:
for method in methods:
with pytest.raises(ValueError, match=msg):
s.interpolate(limit=limit, method=method)
示例3: test_interp_limit_before_ends
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import interpolate [as 别名]
def test_interp_limit_before_ends(self):
# These test are for issue #11115 -- limit ends properly.
s = Series([np.nan, np.nan, 5, 7, np.nan, np.nan])
expected = Series([np.nan, np.nan, 5., 7., 7., np.nan])
result = s.interpolate(method='linear', limit=1,
limit_direction='forward')
assert_series_equal(result, expected)
expected = Series([np.nan, 5., 5., 7., np.nan, np.nan])
result = s.interpolate(method='linear', limit=1,
limit_direction='backward')
assert_series_equal(result, expected)
expected = Series([np.nan, 5., 5., 7., 7., np.nan])
result = s.interpolate(method='linear', limit=1,
limit_direction='both')
assert_series_equal(result, expected)
示例4: test_interp_timedelta64
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import interpolate [as 别名]
def test_interp_timedelta64(self):
# GH 6424
df = Series([1, np.nan, 3],
index=pd.to_timedelta([1, 2, 3]))
result = df.interpolate(method='time')
expected = Series([1., 2., 3.],
index=pd.to_timedelta([1, 2, 3]))
assert_series_equal(result, expected)
# test for non uniform spacing
df = Series([1, np.nan, 3],
index=pd.to_timedelta([1, 2, 4]))
result = df.interpolate(method='time')
expected = Series([1., 1.666667, 3.],
index=pd.to_timedelta([1, 2, 4]))
assert_series_equal(result, expected)
示例5: test_interp_basic
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import interpolate [as 别名]
def test_interp_basic(self):
df = DataFrame({'A': [1, 2, np.nan, 4],
'B': [1, 4, 9, np.nan],
'C': [1, 2, 3, 5],
'D': list('abcd')})
expected = DataFrame({'A': [1., 2., 3., 4.],
'B': [1., 4., 9., 9.],
'C': [1, 2, 3, 5],
'D': list('abcd')})
result = df.interpolate()
assert_frame_equal(result, expected)
result = df.set_index('C').interpolate()
expected = df.set_index('C')
expected.loc[3, 'A'] = 3
expected.loc[5, 'B'] = 9
assert_frame_equal(result, expected)
示例6: test_interp_rowwise
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import interpolate [as 别名]
def test_interp_rowwise(self):
df = DataFrame({0: [1, 2, np.nan, 4],
1: [2, 3, 4, np.nan],
2: [np.nan, 4, 5, 6],
3: [4, np.nan, 6, 7],
4: [1, 2, 3, 4]})
result = df.interpolate(axis=1)
expected = df.copy()
expected.loc[3, 1] = 5
expected.loc[0, 2] = 3
expected.loc[1, 3] = 3
expected[4] = expected[4].astype(np.float64)
assert_frame_equal(result, expected)
result = df.interpolate(axis=1, method='values')
assert_frame_equal(result, expected)
result = df.interpolate(axis=0)
expected = df.interpolate()
assert_frame_equal(result, expected)
示例7: test_interp_ignore_all_good
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import interpolate [as 别名]
def test_interp_ignore_all_good(self):
# GH
df = DataFrame({'A': [1, 2, np.nan, 4],
'B': [1, 2, 3, 4],
'C': [1., 2., np.nan, 4.],
'D': [1., 2., 3., 4.]})
expected = DataFrame({'A': np.array(
[1, 2, 3, 4], dtype='float64'),
'B': np.array(
[1, 2, 3, 4], dtype='int64'),
'C': np.array(
[1., 2., 3, 4.], dtype='float64'),
'D': np.array(
[1., 2., 3., 4.], dtype='float64')})
result = df.interpolate(downcast=None)
assert_frame_equal(result, expected)
# all good
result = df[['B', 'D']].interpolate(downcast=None)
assert_frame_equal(result, df[['B', 'D']])
示例8: make_lookup_table
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import interpolate [as 别名]
def make_lookup_table(self):
"""Construct lookup table radiance <-> BT
To convert a channel radiance to a brightness temperature,
applying the (inverse) Planck function is not correct, because the
Planck function applies to monochromatic radiances only. Instead,
to convert from radiance (in W m^-2 sr^-1 Hz^-1) to brightness
temperature, we use a lookup table. This lookup table is
constructed by considering blackbodies at a range of temperatures,
then calculating the channel radiance. This table can then be
used to get a mapping from radiance to brightness temperature.
This method does not return anything, but fill self.lookup_table.
"""
self.lookup_table = numpy.zeros(
shape=(2, self.T_lookup_table.size), dtype=numpy.float64)
self.lookup_table[0, :] = self.T_lookup_table
self.lookup_table[1, :] = self.blackbody_radiance(self.T_lookup_table)
self.L_to_T = scipy.interpolate.interp1d(self.lookup_table[1, :],
self.lookup_table[0, :],
kind='linear',
bounds_error=False,
fill_value=(0, 2000))
示例9: __call__
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import interpolate [as 别名]
def __call__(self, pkg):
pkg = format_package(pkg)
wav = pkg['chunk']
wav = wav.data.numpy()
factor = random.choice(self.factors)
x_lr = decimate(wav, factor).copy()
x_lr = torch.FloatTensor(x_lr)
x_ = F.interpolate(x_lr.view(1, 1, -1),
scale_factor=factor,
align_corners=True,
mode='linear').view(-1)
if self.report:
if 'report' not in pkg:
pkg['report'] = {}
pkg['report']['resample_factor'] = factor
pkg['chunk'] = x_
return pkg
示例10: test_interpolate
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import interpolate [as 别名]
def test_interpolate(self):
ts = Series(np.arange(len(self.ts), dtype=float), self.ts.index)
ts_copy = ts.copy()
ts_copy[5:10] = np.NaN
linear_interp = ts_copy.interpolate(method='linear')
tm.assert_series_equal(linear_interp, ts)
ord_ts = Series([d.toordinal() for d in self.ts.index],
index=self.ts.index).astype(float)
ord_ts_copy = ord_ts.copy()
ord_ts_copy[5:10] = np.NaN
time_interp = ord_ts_copy.interpolate(method='time')
tm.assert_series_equal(time_interp, ord_ts)
# try time interpolation on a non-TimeSeries
# Only raises ValueError if there are NaNs.
non_ts = self.series.copy()
non_ts[0] = np.NaN
pytest.raises(ValueError, non_ts.interpolate, method='time')
示例11: test_interp_limit
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import interpolate [as 别名]
def test_interp_limit(self):
s = Series([1, 3, np.nan, np.nan, np.nan, 11])
expected = Series([1., 3., 5., 7., np.nan, 11.])
result = s.interpolate(method='linear', limit=2)
assert_series_equal(result, expected)
# GH 9217, make sure limit is an int and greater than 0
methods = ['linear', 'time', 'index', 'values', 'nearest', 'zero',
'slinear', 'quadratic', 'cubic', 'barycentric', 'krogh',
'polynomial', 'spline', 'piecewise_polynomial', None,
'from_derivatives', 'pchip', 'akima']
s = pd.Series([1, 2, np.nan, np.nan, 5])
for limit in [-1, 0, 1., 2.]:
for method in methods:
with pytest.raises(ValueError):
s.interpolate(limit=limit, method=method)
示例12: _skip_if_no_pchip
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import interpolate [as 别名]
def _skip_if_no_pchip():
try:
from scipy.interpolate import pchip_interpolate # noqa
except ImportError:
import pytest
pytest.skip('scipy.interpolate.pchip missing')
示例13: _skip_if_no_akima
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import interpolate [as 别名]
def _skip_if_no_akima():
try:
from scipy.interpolate import Akima1DInterpolator # noqa
except ImportError:
import pytest
pytest.skip('scipy.interpolate.Akima1DInterpolator missing')
示例14: test_interpolate
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import interpolate [as 别名]
def test_interpolate(self, datetime_series, string_series):
ts = Series(np.arange(len(datetime_series), dtype=float),
datetime_series.index)
ts_copy = ts.copy()
ts_copy[5:10] = np.NaN
linear_interp = ts_copy.interpolate(method='linear')
tm.assert_series_equal(linear_interp, ts)
ord_ts = Series([d.toordinal() for d in datetime_series.index],
index=datetime_series.index).astype(float)
ord_ts_copy = ord_ts.copy()
ord_ts_copy[5:10] = np.NaN
time_interp = ord_ts_copy.interpolate(method='time')
tm.assert_series_equal(time_interp, ord_ts)
# try time interpolation on a non-TimeSeries
# Only raises ValueError if there are NaNs.
non_ts = string_series.copy()
non_ts[0] = np.NaN
msg = ("time-weighted interpolation only works on Series or DataFrames"
" with a DatetimeIndex")
with pytest.raises(ValueError, match=msg):
non_ts.interpolate(method='time')
示例15: test_interpolate_pchip
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import interpolate [as 别名]
def test_interpolate_pchip(self):
_skip_if_no_pchip()
ser = Series(np.sort(np.random.uniform(size=100)))
# interpolate at new_index
new_index = ser.index.union(Index([49.25, 49.5, 49.75, 50.25, 50.5,
50.75]))
interp_s = ser.reindex(new_index).interpolate(method='pchip')
# does not blow up, GH5977
interp_s[49:51]