Class 1 for course: "Programming, data analysis & data visualization in python". A course for the Champlimaud Neuroscience Program.

1. numpy
&
matplotlib

2. Installation...
in the ubuntu software 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 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()