def other():
# 根据说明文档的关键字查找相关函数
# np.lookfor('sort')
# 获取函数的帮助信息
# np.info('fft')
# 打印函数、类或模块的源码
np.source(np.info)
def test_nanquantile_1(self):
a = np.array([[10., 7., 4.], [3., 2., 1.]])
a[0][1] = np.nan
print(a)
b = np.quantile(a, 0.5)
print(b)
print(np.source(np.nanquantile))
c = np.nanquantile(a, 0.5)
print(c)
d = np.nanquantile(a, 0.5, axis=0)
print(d)
e = np.nanquantile(a, 0.5, axis=1, keepdims=True)
print(e)
m = np.nanquantile(a, 0.5, axis=0)
out = np.zeros_like(m)
f = np.nanquantile(a, 0.5, axis=0, out=out)
print(f)
print(m)
g = a.copy()
h = np.nanquantile(g, 0.5, axis=1, overwrite_input=True)
print(h)
assert not np.all(a==g)
return
import scipy as sp from scipy import integrate from scipy import cluster from scipy import fftpack import numpy as np #Help/doc help(integrate) np.info(fftpack) #Source code of subpackages np.source(cluster)
from __future__ import annotations
from io import StringIO
from typing import Any
import numpy as np
FILE = StringIO()
AR: np.ndarray[Any, np.dtype[np.float64]] = np.arange(10).astype(np.float64)
def func(a: int) -> bool:
...
np.deprecate(func)
np.deprecate()
np.deprecate_with_doc("test")
np.deprecate_with_doc(None)
np.byte_bounds(AR)
np.byte_bounds(np.float64())
np.info(1, output=FILE)
np.source(np.interp, output=FILE)
np.lookfor("binary representation", output=FILE)
import numpy as np np.source(np.interp) np.source(np.array)
integralSegunda = np.polyint(f3grau, 2)
integralTerceira = np.polyint(f3grau, 3)
#Tratamento de sinais - convolucao
#%%
sinalConvoluido = np.convolve([1, 2, 3], [0, 1, 0.5])
plt.hist(sinalConvoluido, bins=3, density=100) #altere os bins
plt.show()
#Pesquisa
print(np.info(np.polyval))
print(np.lookfor('binary representation'))
print(np.source(np.interp))#funcao Valida apenas para objetos escritos em python
'''Scipy
É uma biblioteca de computação científica, que junto ao Numpy é capaz
de realizar operações poderosas, tanto para o processamento de dados
quanto para a prototipagem de sistemas.'''
#%%
import scipy as sp
grafo = [[0,1,2,0],[0,0,0,1],[0,0,0,3],[0,0,0,0]]
def main():
np.source(ff.fft)
from io import StringIO
from typing import Any, Dict
import numpy as np
AR: np.ndarray[Any, np.dtype[np.float64]]
AR_DICT: Dict[str, np.ndarray[Any, np.dtype[np.float64]]]
FILE: StringIO
def func(a: int) -> bool: ...
reveal_type(np.deprecate(func)) # E: def (a: builtins.int) -> builtins.bool
reveal_type(np.deprecate()) # E: _Deprecate
reveal_type(np.deprecate_with_doc("test")) # E: _Deprecate
reveal_type(np.deprecate_with_doc(None)) # E: _Deprecate
reveal_type(np.byte_bounds(AR)) # E: Tuple[builtins.int, builtins.int]
reveal_type(np.byte_bounds(np.float64())) # E: Tuple[builtins.int, builtins.int]
reveal_type(np.who(None)) # E: None
reveal_type(np.who(AR_DICT)) # E: None
reveal_type(np.info(1, output=FILE)) # E: None
reveal_type(np.source(np.interp, output=FILE)) # E: None
reveal_type(np.lookfor("binary representation", output=FILE)) # E: None
reveal_type(np.safe_eval("1 + 1")) # E: Any
def main():
np.info(ff.fft) # <1>
print('-' * 60)
np.source(ff.fft) # <2>
# ( 52.292984, -183.492294, 1586.229614)]
# polygon_coordinates2 = [[ 52.40876 , -183.389725, 1586.93689 ],
# [ 52.143188, -183.361359, 1586.970337],
# [ 51.883614, -183.387726, 1586.961182],
# [ 51.659508, -183.51033 , 1586.876221],
# [ 51.502106, -183.61142 , 1586.804932],
# [ 51.338387, -183.783966, 1586.678711],
# [ 51.181816, -184.001953, 1586.517212],
# [ 51.176094, -184.210297, 1586.356445],
# [ 51.301521, -184.390747, 1586.21167 ],
# [ 51.513306, -184.485138, 1586.129517],
# [ 51.759201, -184.515472, 1586.095459],
# [ 52.098442, -184.500259, 1586.092529],
# [ 52.315491, -184.499954, 1586.083252],
# [ 52.619354, -184.382645, 1586.160767],
# [ 52.790848, -184.238586, 1586.264526],
# [ 52.845612, -184.011871, 1586.437378],
# [ 52.805977, -183.746689, 1586.643921],
# [ 52.657982, -183.519531, 1586.825806]]
# x1 = [i[0] for i in polygon_coordinates1]
# y1 = [i[1] for i in polygon_coordinates1]
# z1 = [i[2] for i in polygon_coordinates1]
# x2 = [i[0] for i in polygon_coordinates2]
# y2 = [i[1] for i in polygon_coordinates2]
# z2 = [i[2] for i in polygon_coordinates2]
# plot3DScatter(x1 + x2, y1 + y2, z1 + z2)
contour_point_list = get3DContourPointsList(vessels,
'Left Circumflex Artery')
drawSurfacePlt(contour_point_list)
(np.source(Axes3D.plot_trisurf))