howto_matrix.py
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#! /usr/bin/env python
# ==========================================================================
# Scope
#
# This script provides an example for matrix handling with GammaLib
#
# Usage
# ./matrix_howto.py
#
# -------------------------------------------------------------------------
#
# Copyright (C) 2013 Juergen Knoedlseder
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# ==========================================================================
import gammalib
# =========================== #
# Illustrates matrix handling #
# =========================== #
def handle_matrix():
"""
Illustrates the handling of matrix. Although we use in this example the
GMatrix class, the same operations can be performed on a sparse matrix
GMatrixSparse or a symmetric matrix GMatrixSymmetric.
"""
# Create matrix with 4 rows and 5 columns
matrix = gammalib.GMatrix(4,5)
# Get matrix attributes
number_of_rows = matrix.rows() # Will be 4
number_of_columns = matrix.columns() # Will be 5
number_of_elements = matrix.size() # Will be 20=4*5
matrix_fill = matrix.fill() # Will be 0
matrix_minimum = matrix.min() # Will be 0
matrix_maximum = matrix.max() # Will be 0
matrix_sum = matrix.sum() # Will be 0
# Dump some information
print("Attributes of an empty GMatrix:")
print(gammalib.parformat("Matrix rows")+str(number_of_rows))
print(gammalib.parformat("Matrix columns")+str(number_of_columns))
print(gammalib.parformat("Matrix elements")+str(number_of_elements))
print(gammalib.parformat("Matrix fill")+str(matrix_fill))
print(gammalib.parformat("Smallest matrix element")+str(matrix_minimum))
print(gammalib.parformat("Largest matrix element")+str(matrix_maximum))
print(gammalib.parformat("Sum of all element")+str(matrix_sum))
# Set all matrix elements to 2.0
matrix.set(2.0)
# Now set all matrix elements to specific values
for row in range(matrix.rows()):
for column in range(matrix.columns()):
matrix[row,column] = row + column*matrix.rows() + 1.5
# Get matrix attributes
matrix_fill = matrix.fill() # Will be 1
matrix_minimum = matrix.min() # Will be 1.5
matrix_maximum = matrix.max() # Will be 20.5
matrix_sum = matrix.sum() # Will be 220.0
# Dump some information
print("Attributes of a filled GMatrix:")
print(gammalib.parformat("Matrix fill")+str(matrix_fill))
print(gammalib.parformat("Smallest matrix element")+str(matrix_minimum))
print(gammalib.parformat("Largest matrix element")+str(matrix_maximum))
print(gammalib.parformat("Sum of all element")+str(matrix_sum))
# Perform some matrix operations
other_matrix = matrix.copy() # Use copy() to create a deep copy
sum_matrix = matrix + other_matrix # Add two matrices
zero_matrix = matrix - other_matrix # Subtract two matrices
other_matrix += matrix # Add-on matrix
other_matrix -= matrix # Subtract off matrix
other_matrix *= 2.0 # Multiply elements by 2
other_matrix /= 2.0 # Divide elements by 2
neg_matrix = -matrix # Negate matrix
abs_matrix = neg_matrix.abs() # Absolute value of elements
# And now some more tricky operations
transposed = matrix.transpose() # First transpose matrix
mult_matrix = matrix * transposed # Now we can multiply it
other_matrix *= transposed # Multiply on matrix
#inverted = matrix.invert() # NOT YET IMPLEMENTED
vector = gammalib.GVector(5) # Allocate vector
multiplied = matrix * vector # Vector multiplication
# Compare matrices
if matrix == abs_matrix:
print("Yes, got it!")
if matrix != transposed:
print("Got it again!")
# Copy columns one-by-one from one matrix into another
destination = gammalib.GMatrix(4,5)
for column in range(matrix.columns()):
vector = matrix.column(column) # Extract column
destination.column(column, vector) # Set column
# Add columns one-by-one from one matrix into another
destination = matrix.copy()
for column in range(matrix.columns()):
vector = matrix.column(column) # Extract column
destination.add_to_column(column, vector) # Add column
# Copy rows one-by-one from one matrix into another
destination = gammalib.GMatrix(4,5)
for row in range(matrix.rows()):
vector = matrix.row(row) # Extract row
destination.row(row, vector) # Set row
# Add rows one-by-one from one matrix into another
destination = matrix.copy()
for row in range(matrix.rows()):
vector = matrix.row(row) # Extract row
destination.add_to_row(row, vector) # Add row
# Return
return
# ======================== #
# Illustrates matrix usage #
# ======================== #
def matrix_usage():
"""
Illustrates the usage of the different matrix storage classes.
"""
# Create matrices with 4 rows and 4 columns
matrix_generic = gammalib.GMatrix(4,4)
matrix_sparse = gammalib.GMatrixSparse(4,4)
matrix_symmetric = gammalib.GMatrixSymmetric(4,4)
# Now set all matrix elements to specific values
for row in range(matrix_generic.rows()):
for column in range(matrix_generic.columns()):
matrix_generic[row,column] = row * column
matrix_sparse[row,column] = row * column
matrix_symmetric[row,column] = row * column
# Convert from one storage class to another
matrix = gammalib.GMatrix(matrix_sparse)
matrix = gammalib.GMatrix(matrix_symmetric)
matrix = gammalib.GMatrixSparse(matrix_generic)
matrix = gammalib.GMatrixSparse(matrix_symmetric)
matrix = gammalib.GMatrixSymmetric(matrix_generic)
matrix = gammalib.GMatrixSymmetric(matrix_sparse)
# Return
return
# ======================== #
# Main routine entry point #
# ======================== #
if __name__ == '__main__':
"""
Example script for matrix handling.
"""
# Dump header
print("")
print("*******************************")
print("* Example for matrix handling *")
print("*******************************")
# Illustrate matrix handling
handle_matrix()
# Illustrate usage of different matrix classes
matrix_usage()