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![numpy arrays
---- plots.py ----
import numpy as np
# creating an new empty 2-dimensional array/matrix:
a = np.zeros([3, 10])
# creating an new 2-dimensional matrix from array/lists:
a = np.array([[1,10,4],[3,9,2]])
# accessing array elements: indexes and slicing
print a.shape
# accessing array elements: indexes and slicing
print a[1,2] # row 1, element 2
print a[1] # row 1
print a[:,1] # column 1
print a[:,0:2] # remember that x:y selects x to y-1
A one-dimensional array:
a = np.array(list)
A two-dimensional array:
a = np.array(list-of-lists)
A two-dimensional array of zeros:
a = np.zeros([3, 10])](https://image.slidesharecdn.com/zyyw9t4vs06cq4bwtq1u-signature-8b30a4dc7f388ca820bf77ce8d424819e6bc869ded42016f2d1bbd7f84298e80-poli-160419081002/75/Class-8b-Numpy-Matplotlib-4-2048.jpg)
![numpy arrays
---- plots.py ----
import numpy as np
# creating an new 2-dimensional matrix from array/lists:
a = np.array([[1,10,4],[3,9,2]])
# changing a single values:
a[1,1] = 50
print a
# changing an entire column:
column = a[:,0]
a[:,1] = column
# basic math:
b = a[:,0] * 5
c = b + 9
# numpy math:
b = np.log10(a)
A single entry [row:col]
a[x:y]
A single row [row:col]
a[x,:]
A single column [row:col]
a[:,y]
see the full list of numpy mathfunctions:
http://docs.scipy.org/doc/numpy/reference/routines.math.html](https://image.slidesharecdn.com/zyyw9t4vs06cq4bwtq1u-signature-8b30a4dc7f388ca820bf77ce8d424819e6bc869ded42016f2d1bbd7f84298e80-poli-160419081002/75/Class-8b-Numpy-Matplotlib-5-2048.jpg)
![Heatmap!
Transpose an array:
data = data.transpose()
absolute value of matrix:
data = np.abs(data)
log10 of a matrix:
data = np.log10(data)
1. Read the matrix from the file (use np.loadtxt(“data.txt”))
2. Transpose the matrix
3. Apply a log10 transformation to all values
4. Copy row 11 to 15 and 18 (start counting at zero) (don’t forget n -1!)
5. Copy column 8 to columns 9 to 11 (start counting at zero) (don’t forget n -1!)
6. Transform all numbers to positive
7. Multiply all numbers between coordinates (2,3) and (18,7) by 20
8. Display the final result as a heatmap. It should be obvious if you got it right :P .
displaying a heatmap:
plt.pcolor(data)
plt.show()
selecting value, rows, columns...
data[x:y] (value)
data[x,:] (row)
data[:,y] (column)](https://image.slidesharecdn.com/zyyw9t4vs06cq4bwtq1u-signature-8b30a4dc7f388ca820bf77ce8d424819e6bc869ded42016f2d1bbd7f84298e80-poli-160419081002/75/Class-8b-Numpy-Matplotlib-6-2048.jpg)
![making plots just a little prettier!
---- plots.py ----
import numpy as np
import matplotlib.pyplot as plt
# Compute the x and y coordinates for points on sine and cosine curves
x = np.arange(0, 3 * np.pi, 0.1)
y_sin = np.sin(x)
y_cos = np.cos(x)
# Plot the points using matplotlib
plt.plot(x, y_sin)
plt.plot(x, y_cos)
plt.xlabel('x axis label')
plt.ylabel('y axis label')
plt.title('Sine and Cosine')
plt.legend(['Sine', 'Cosine'])
plt.show()](https://image.slidesharecdn.com/zyyw9t4vs06cq4bwtq1u-signature-8b30a4dc7f388ca820bf77ce8d424819e6bc869ded42016f2d1bbd7f84298e80-poli-160419081002/75/Class-8b-Numpy-Matplotlib-7-2048.jpg)
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Class 8b: Numpy & Matplotlib
- 1.
- 2. Installation... in the ubuntusoftware center install: - python-numpy - python-matplotlib www.virtualbox.org
- 3. numpy and matplotlib:a quick plot! ---- plots.py ---- import numpy as np import matplotlib.pyplot as plt # let's create some data x = np.arange(0, 10) y = np.sin(x) # and now let's plot it! plt.plot(x, y) plt.show()
- 4. numpy arrays ---- plots.py---- import numpy as np # creating an new empty 2-dimensional array/matrix: a = np.zeros([3, 10]) # creating an new 2-dimensional matrix from array/lists: a = np.array([[1,10,4],[3,9,2]]) # accessing array elements: indexes and slicing print a.shape # accessing array elements: indexes and slicing print a[1,2] # row 1, element 2 print a[1] # row 1 print a[:,1] # column 1 print a[:,0:2] # remember that x:y selects x to y-1 A one-dimensional array: a = np.array(list) A two-dimensional array: a = np.array(list-of-lists) A two-dimensional array of zeros: a = np.zeros([3, 10])
- 5. numpy arrays ---- plots.py---- import numpy as np # creating an new 2-dimensional matrix from array/lists: a = np.array([[1,10,4],[3,9,2]]) # changing a single values: a[1,1] = 50 print a # changing an entire column: column = a[:,0] a[:,1] = column # basic math: b = a[:,0] * 5 c = b + 9 # numpy math: b = np.log10(a) A single entry [row:col] a[x:y] A single row [row:col] a[x,:] A single column [row:col] a[:,y] see the full list of numpy mathfunctions: http://docs.scipy.org/doc/numpy/reference/routines.math.html
- 6. Heatmap! Transpose an array: data= data.transpose() absolute value of matrix: data = np.abs(data) log10 of a matrix: data = np.log10(data) 1. Read the matrix from the file (use np.loadtxt(“data.txt”)) 2. Transpose the matrix 3. Apply a log10 transformation to all values 4. Copy row 11 to 15 and 18 (start counting at zero) (don’t forget n -1!) 5. Copy column 8 to columns 9 to 11 (start counting at zero) (don’t forget n -1!) 6. Transform all numbers to positive 7. Multiply all numbers between coordinates (2,3) and (18,7) by 20 8. Display the final result as a heatmap. It should be obvious if you got it right :P . displaying a heatmap: plt.pcolor(data) plt.show() selecting value, rows, columns... data[x:y] (value) data[x,:] (row) data[:,y] (column)
- 7. making plots justa little prettier! ---- plots.py ---- import numpy as np import matplotlib.pyplot as plt # Compute the x and y coordinates for points on sine and cosine curves x = np.arange(0, 3 * np.pi, 0.1) y_sin = np.sin(x) y_cos = np.cos(x) # Plot the points using matplotlib plt.plot(x, y_sin) plt.plot(x, y_cos) plt.xlabel('x axis label') plt.ylabel('y axis label') plt.title('Sine and Cosine') plt.legend(['Sine', 'Cosine']) plt.show()