Introdução a Numpy¶

In [1]:

import numpy as np

import numpy as np

In [2]:

np.pi

np.pi

Out[2]:

3.141592653589793

In [8]:

a = np.arange(0,31,2)
print(a)
type(a)

a = np.arange(0,31,2) print(a) type(a)

[ 0  2  4  6  8 10 12 14 16 18 20 22 24 26 28 30]

Out[8]:

numpy.ndarray

In [9]:

b = np.linspace(0,10, 21)
print(b)
type(b)

b = np.linspace(0,10, 21) print(b) type(b)

[ 0.   0.5  1.   1.5  2.   2.5  3.   3.5  4.   4.5  5.   5.5  6.   6.5
  7.   7.5  8.   8.5  9.   9.5 10. ]

Out[9]:

numpy.ndarray

In [12]:

c = np.array([1,2,3])
c

c = np.array([1,2,3]) c

Out[12]:

array([1, 2, 3])

In [24]:

print("soma", b.sum()) # https://numpy.org/doc/stable/reference/generated/numpy.sum.html?highlight=sum
print("média", b.mean()) # https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean
print("desvio padrão", b.std()) # https://numpy.org/doc/stable/reference/generated/numpy.std.html
print("variancia", b.var()) # https://numpy.org/doc/stable/reference/generated/numpy.var.html#numpy.var
print("máximo", b.max()) # https://numpy.org/doc/stable/reference/generated/numpy.ndarray.max.html
print("mínimo", b.min()) # https://numpy.org/doc/stable/reference/generated/numpy.ndarray.min.html
print("indice máximo", b.argmax()) # https://numpy.org/doc/stable/reference/generated/numpy.argmax.html?highlight=numpy%20max
print("indice mínimo", b.argmin()) # https://numpy.org/doc/stable/reference/generated/numpy.argmin.html#numpy.argmin

print("soma", b.sum()) # https://numpy.org/doc/stable/reference/generated/numpy.sum.html?highlight=sum print("média", b.mean()) # https://numpy.org/doc/stable/reference/generated/numpy.mean.html#numpy.mean print("desvio padrão", b.std()) # https://numpy.org/doc/stable/reference/generated/numpy.std.html print("variancia", b.var()) # https://numpy.org/doc/stable/reference/generated/numpy.var.html#numpy.var print("máximo", b.max()) # https://numpy.org/doc/stable/reference/generated/numpy.ndarray.max.html print("mínimo", b.min()) # https://numpy.org/doc/stable/reference/generated/numpy.ndarray.min.html print("indice máximo", b.argmax()) # https://numpy.org/doc/stable/reference/generated/numpy.argmax.html?highlight=numpy%20max print("indice mínimo", b.argmin()) # https://numpy.org/doc/stable/reference/generated/numpy.argmin.html#numpy.argmin

soma 105.0
média 5.0
desvio padrão 3.0276503540974917
variancia 9.166666666666666
máximo 10.0
mínimo 0.0
indice máximo 20
indice mínimo 0

In [25]:

b.shape

b.shape

Out[25]:

(21,)

In [28]:

d = b.reshape(3,7)
print(d.shape)
d

d = b.reshape(3,7) print(d.shape) d

(3, 7)

Out[28]:

array([[ 0. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ],
       [ 3.5,  4. ,  4.5,  5. ,  5.5,  6. ,  6.5],
       [ 7. ,  7.5,  8. ,  8.5,  9. ,  9.5, 10. ]])

In [29]:

e = np.array([1,3,4])
f = np.array([7,8,9])

e = np.array([1,3,4]) f = np.array([7,8,9])

In [32]:

print(e + f)
print(e * f)
print(e @ f)

print(e + f) print(e * f) print(e @ f)

[ 8 11 13]
[ 7 24 36]
67

Funções de Algebra Linear¶

Documentação

In [36]:

mat1 = np.linspace(0,12, 9)
mat1 = mat1.reshape(3,3)
mat1

mat1 = np.linspace(0,12, 9) mat1 = mat1.reshape(3,3) mat1

Out[36]:

array([[ 0. ,  1.5,  3. ],
       [ 4.5,  6. ,  7.5],
       [ 9. , 10.5, 12. ]])

In [38]:

mat2 = np.arange(0,9, 1)
mat2 = mat2.reshape(3,3)
mat2

mat2 = np.arange(0,9, 1) mat2 = mat2.reshape(3,3) mat2

Out[38]:

array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8]])

In [44]:

print("Dor Product \n",np.dot(mat1, mat2))
print("Inner Product \n",np.inner(mat1, mat2))

print("Dor Product \n",np.dot(mat1, mat2)) print("Inner Product \n",np.inner(mat1, mat2))

Dor Product 
 [[ 22.5  27.   31.5]
 [ 63.   81.   99. ]
 [103.5 135.  166.5]]
Inner Product 
 [[  7.5  21.   34.5]
 [ 21.   75.  129. ]
 [ 34.5 129.  223.5]]

In [2]:

from skimage import io
from skimage import color
from matplotlib import pyplot as plt

from skimage import io from skimage import color from matplotlib import pyplot as plt

In [3]:

foto = io.imread('https://255ribeiro.github.io/curso_python_gis/cientif_py/bebe_01.jpeg')
plt.imshow(foto)
type(foto)

foto = io.imread('https://255ribeiro.github.io/curso_python_gis/cientif_py/bebe_01.jpeg') plt.imshow(foto) type(foto)

Out[3]:

numpy.ndarray

In [49]:

fotopb = color.rgb2gray(foto)
plt.imshow(fotopb.T, cmap=plt.cm.gray)
fotopb.shape

fotopb = color.rgb2gray(foto) plt.imshow(fotopb.T, cmap=plt.cm.gray) fotopb.shape

Out[49]:

(1197, 1600)

In [50]:

fotoT = np.transpose(foto, axes=(1,0,2))
plt.imshow(fotoT)

fotoT = np.transpose(foto, axes=(1,0,2)) plt.imshow(fotoT)

Out[50]:

<matplotlib.image.AxesImage at 0x137d7811b50>

In [51]:

foto_filtro = np.where(foto >150, 255, 0)
plt.imshow(foto_filtro)

foto_filtro = np.where(foto >150, 255, 0) plt.imshow(foto_filtro)

Out[51]:

<matplotlib.image.AxesImage at 0x137d7e0f6d0>

In [57]:

foto_cut = foto[0:700, 550:1050]
plt.imshow(foto_cut)

foto_cut = foto[0:700, 550:1050] plt.imshow(foto_cut)

Out[57]:

<matplotlib.image.AxesImage at 0x137d66c01f0>