#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
ENGG1811. 

numpy Boolean indexing - exercise
"""
import numpy as np 

# %% Problem parameters 
# 
# Create a two dimensional numpy array   
temp_array = np.array(
             [[ 0.4,  0.4,  0.6,  0.5,  0.7,  0.8,  0.8,  0.5,  0.0,  0.7],
              [ 0.4,  0.4,  0.8,  0.4,  0.8,  1.1,  0.9,  0.4,  1.1,  1.1],
              [ 0.4,  1.1,  0.8,  0.3,  0.7,  1.1,  0.9,  0.5,  1.1,  0.6],
              [ 0.4,  0.5,  0.6,  0.4,  0.9,  1.2,  0.8,  0.5,  0.1,  0.6],
              [ 0.3,  0.4,  0.8,  0.3,  0.8,  0.7,  0.7,  0.4,  0.2,  0.7]]
             )
# Each row contains readings from a sensor 
# Each column contains the readings from five sensors at a given time 

# %%
# You want to compute the mean of each column after excluding the values >= 1
# 
# The final answer you want is:
# [0.38,  0.425,  0.72,  0.38,  0.78,  0.75,  0.82,  0.46,  0.1,  0.65 ]

# Hint: remember mean = sum of readings / number of readings 
#       You on want to use the valid readings in the calculations

# Step 1: Sum all the valid readings in a column
# 
# The expected answer is:
# [ 1.9,  1.7,  3.6,  1.9,  3.9,  1.5,  4.1,  2.3,  0.3,  2.6]


# Step 2: Count the number valid readings 
# 
# The expected answer is:
# [5, 4, 5, 5, 5, 2, 5, 5, 3, 4]

# Step 3:
# Compute the mean 


# Method 1
# Create an array of the same size as temp_array and fill it with 1's
mask = np.ones_like(temp_array, dtype = 'int')
# Set the elements in the mask to zero if the corresponding element
# in temp_array is >= 1
mask[temp_array >= 1] = 0
# Sum the valid readings
sum_valid_readings = np.sum(temp_array * mask, axis = 0)
# Number of valid readings
num_valid_readings = np.sum(mask > 0, axis = 0)
# Mean of valid readings
mean_valid_readings = sum_valid_readings / num_valid_readings

# %% 
# Method 2:
# Make a copy of temp_array
temp_array_copy = np.copy(temp_array)
# Set invalid readings to zero in temp_array_copy 
temp_array_copy[temp_array >= 1] = 0
# Sum the valid reading
sum_valid_readings_alt = np.sum(temp_array_copy, axis = 0)
# Count the number of valid readings
num_valid_readings_alt = np.sum(temp_array < 1, axis = 0)
# Mean of valid readings
mean_valid_readings_alt = sum_valid_readings_alt / num_valid_readings_alt

# %% 
# Note: This won't work:
# np.sum(temp_array[temp_array < 1],axis = 0)