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Added Mirror Formulae Equation (#9717)
* Python mirror_formulae.py is added to the repository * Changes done after reading readme.md * Changes for running doctest on all platforms * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Change 2 for Doctests * Changes for doctest 2 * updating DIRECTORY.md --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
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@ -170,6 +170,7 @@
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## Data Structures
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## Data Structures
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* Arrays
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* Arrays
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* [Median Two Array](data_structures/arrays/median_two_array.py)
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* [Permutations](data_structures/arrays/permutations.py)
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* [Permutations](data_structures/arrays/permutations.py)
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* [Prefix Sum](data_structures/arrays/prefix_sum.py)
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* [Prefix Sum](data_structures/arrays/prefix_sum.py)
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* [Product Sum](data_structures/arrays/product_sum.py)
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* [Product Sum](data_structures/arrays/product_sum.py)
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@ -185,6 +186,7 @@
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* [Diff Views Of Binary Tree](data_structures/binary_tree/diff_views_of_binary_tree.py)
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* [Diff Views Of Binary Tree](data_structures/binary_tree/diff_views_of_binary_tree.py)
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* [Distribute Coins](data_structures/binary_tree/distribute_coins.py)
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* [Distribute Coins](data_structures/binary_tree/distribute_coins.py)
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* [Fenwick Tree](data_structures/binary_tree/fenwick_tree.py)
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* [Fenwick Tree](data_structures/binary_tree/fenwick_tree.py)
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* [Flatten Binarytree To Linkedlist](data_structures/binary_tree/flatten_binarytree_to_linkedlist.py)
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* [Inorder Tree Traversal 2022](data_structures/binary_tree/inorder_tree_traversal_2022.py)
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* [Inorder Tree Traversal 2022](data_structures/binary_tree/inorder_tree_traversal_2022.py)
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* [Is Bst](data_structures/binary_tree/is_bst.py)
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* [Is Bst](data_structures/binary_tree/is_bst.py)
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* [Lazy Segment Tree](data_structures/binary_tree/lazy_segment_tree.py)
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* [Lazy Segment Tree](data_structures/binary_tree/lazy_segment_tree.py)
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@ -324,6 +326,7 @@
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* [Longest Common Substring](dynamic_programming/longest_common_substring.py)
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* [Longest Common Substring](dynamic_programming/longest_common_substring.py)
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* [Longest Increasing Subsequence](dynamic_programming/longest_increasing_subsequence.py)
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* [Longest Increasing Subsequence](dynamic_programming/longest_increasing_subsequence.py)
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* [Longest Increasing Subsequence O(Nlogn)](dynamic_programming/longest_increasing_subsequence_o(nlogn).py)
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* [Longest Increasing Subsequence O(Nlogn)](dynamic_programming/longest_increasing_subsequence_o(nlogn).py)
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* [Longest Palindromic Subsequence](dynamic_programming/longest_palindromic_subsequence.py)
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* [Longest Sub Array](dynamic_programming/longest_sub_array.py)
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* [Longest Sub Array](dynamic_programming/longest_sub_array.py)
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* [Matrix Chain Order](dynamic_programming/matrix_chain_order.py)
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* [Matrix Chain Order](dynamic_programming/matrix_chain_order.py)
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* [Max Non Adjacent Sum](dynamic_programming/max_non_adjacent_sum.py)
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* [Max Non Adjacent Sum](dynamic_programming/max_non_adjacent_sum.py)
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@ -539,6 +542,7 @@
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* [Average Mode](maths/average_mode.py)
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* [Average Mode](maths/average_mode.py)
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* [Bailey Borwein Plouffe](maths/bailey_borwein_plouffe.py)
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* [Bailey Borwein Plouffe](maths/bailey_borwein_plouffe.py)
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* [Basic Maths](maths/basic_maths.py)
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* [Basic Maths](maths/basic_maths.py)
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* [Bell Numbers](maths/bell_numbers.py)
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* [Binary Exp Mod](maths/binary_exp_mod.py)
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* [Binary Exp Mod](maths/binary_exp_mod.py)
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* [Binary Exponentiation](maths/binary_exponentiation.py)
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* [Binary Exponentiation](maths/binary_exponentiation.py)
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* [Binary Exponentiation 3](maths/binary_exponentiation_3.py)
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* [Binary Exponentiation 3](maths/binary_exponentiation_3.py)
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@ -690,6 +694,7 @@
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* [Matrix Class](matrix/matrix_class.py)
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* [Matrix Class](matrix/matrix_class.py)
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* [Matrix Operation](matrix/matrix_operation.py)
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* [Matrix Operation](matrix/matrix_operation.py)
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* [Max Area Of Island](matrix/max_area_of_island.py)
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* [Max Area Of Island](matrix/max_area_of_island.py)
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* [Median Matrix](matrix/median_matrix.py)
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* [Nth Fibonacci Using Matrix Exponentiation](matrix/nth_fibonacci_using_matrix_exponentiation.py)
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* [Nth Fibonacci Using Matrix Exponentiation](matrix/nth_fibonacci_using_matrix_exponentiation.py)
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* [Pascal Triangle](matrix/pascal_triangle.py)
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* [Pascal Triangle](matrix/pascal_triangle.py)
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* [Rotate Matrix](matrix/rotate_matrix.py)
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* [Rotate Matrix](matrix/rotate_matrix.py)
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@ -708,8 +713,8 @@
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* Activation Functions
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* Activation Functions
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* [Exponential Linear Unit](neural_network/activation_functions/exponential_linear_unit.py)
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* [Exponential Linear Unit](neural_network/activation_functions/exponential_linear_unit.py)
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* [Leaky Rectified Linear Unit](neural_network/activation_functions/leaky_rectified_linear_unit.py)
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* [Leaky Rectified Linear Unit](neural_network/activation_functions/leaky_rectified_linear_unit.py)
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* [Scaled Exponential Linear Unit](neural_network/activation_functions/scaled_exponential_linear_unit.py)
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* [Rectified Linear Unit](neural_network/activation_functions/rectified_linear_unit.py)
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* [Rectified Linear Unit](neural_network/activation_functions/rectified_linear_unit.py)
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* [Scaled Exponential Linear Unit](neural_network/activation_functions/scaled_exponential_linear_unit.py)
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* [Back Propagation Neural Network](neural_network/back_propagation_neural_network.py)
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* [Back Propagation Neural Network](neural_network/back_propagation_neural_network.py)
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* [Convolution Neural Network](neural_network/convolution_neural_network.py)
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* [Convolution Neural Network](neural_network/convolution_neural_network.py)
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* [Perceptron](neural_network/perceptron.py)
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* [Perceptron](neural_network/perceptron.py)
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@ -756,9 +761,11 @@
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* [Kinetic Energy](physics/kinetic_energy.py)
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* [Kinetic Energy](physics/kinetic_energy.py)
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* [Lorentz Transformation Four Vector](physics/lorentz_transformation_four_vector.py)
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* [Lorentz Transformation Four Vector](physics/lorentz_transformation_four_vector.py)
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* [Malus Law](physics/malus_law.py)
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* [Malus Law](physics/malus_law.py)
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* [Mirror Formulae](physics/mirror_formulae.py)
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* [N Body Simulation](physics/n_body_simulation.py)
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* [N Body Simulation](physics/n_body_simulation.py)
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* [Newtons Law Of Gravitation](physics/newtons_law_of_gravitation.py)
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* [Newtons Law Of Gravitation](physics/newtons_law_of_gravitation.py)
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* [Newtons Second Law Of Motion](physics/newtons_second_law_of_motion.py)
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* [Newtons Second Law Of Motion](physics/newtons_second_law_of_motion.py)
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* [Photoelectric Effect](physics/photoelectric_effect.py)
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* [Potential Energy](physics/potential_energy.py)
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* [Potential Energy](physics/potential_energy.py)
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* [Rms Speed Of Molecule](physics/rms_speed_of_molecule.py)
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* [Rms Speed Of Molecule](physics/rms_speed_of_molecule.py)
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* [Shear Stress](physics/shear_stress.py)
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* [Shear Stress](physics/shear_stress.py)
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127
physics/mirror_formulae.py
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physics/mirror_formulae.py
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"""
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This module contains the functions to calculate the focal length, object distance
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and image distance of a mirror.
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The mirror formula is an equation that relates the object distance (u),
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image distance (v), and focal length (f) of a spherical mirror.
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It is commonly used in optics to determine the position and characteristics
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of an image formed by a mirror. It is expressed using the formulae :
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-------------------
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| 1/f = 1/v + 1/u |
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-------------------
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Where,
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f = Focal length of the spherical mirror (metre)
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v = Image distance from the mirror (metre)
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u = Object distance from the mirror (metre)
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The signs of the distances are taken with respect to the sign convention.
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The sign convention is as follows:
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1) Object is always placed to the left of mirror
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2) Distances measured in the direction of the incident ray are positive
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and the distances measured in the direction opposite to that of the incident
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rays are negative.
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3) All distances are measured from the pole of the mirror.
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There are a few assumptions that are made while using the mirror formulae.
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They are as follows:
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1) Thin Mirror: The mirror is assumed to be thin, meaning its thickness is
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negligible compared to its radius of curvature. This assumption allows
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us to treat the mirror as a two-dimensional surface.
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2) Spherical Mirror: The mirror is assumed to have a spherical shape. While this
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assumption may not hold exactly for all mirrors, it is a reasonable approximation
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for most practical purposes.
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3) Small Angles: The angles involved in the derivation are assumed to be small.
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This assumption allows us to use the small-angle approximation, where the tangent
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of a small angle is approximately equal to the angle itself. It simplifies the
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calculations and makes the derivation more manageable.
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4) Paraxial Rays: The mirror formula is derived using paraxial rays, which are
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rays that are close to the principal axis and make small angles with it. This
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assumption ensures that the rays are close enough to the principal axis, making the
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calculations more accurate.
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5) Reflection and Refraction Laws: The derivation assumes that the laws of
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reflection and refraction hold.
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These laws state that the angle of incidence is equal to the angle of reflection
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for reflection, and the incident and refracted rays lie in the same plane and
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obey Snell's law for refraction.
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(Description and Assumptions adapted from
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https://www.collegesearch.in/articles/mirror-formula-derivation)
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(Sign Convention adapted from
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https://www.toppr.com/ask/content/concept/sign-convention-for-mirrors-210189/)
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"""
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def focal_length(distance_of_object: float, distance_of_image: float) -> float:
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"""
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>>> from math import isclose
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>>> isclose(focal_length(10, 20), 6.66666666666666)
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True
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>>> from math import isclose
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>>> isclose(focal_length(9.5, 6.7), 3.929012346)
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True
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>>> focal_length(0, 20)
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Traceback (most recent call last):
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...
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ValueError: Invalid inputs. Enter non zero values with respect
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to the sign convention.
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"""
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if distance_of_object == 0 or distance_of_image == 0:
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raise ValueError(
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"Invalid inputs. Enter non zero values with respect to the sign convention."
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)
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focal_length = 1 / ((1 / distance_of_object) + (1 / distance_of_image))
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return focal_length
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def object_distance(focal_length: float, distance_of_image: float) -> float:
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"""
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>>> from math import isclose
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>>> isclose(object_distance(30, 20), -60.0)
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True
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>>> from math import isclose
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>>> isclose(object_distance(10.5, 11.7), 102.375)
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True
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>>> object_distance(90, 0)
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Traceback (most recent call last):
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...
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ValueError: Invalid inputs. Enter non zero values with respect
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to the sign convention.
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"""
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if distance_of_image == 0 or focal_length == 0:
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raise ValueError(
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"Invalid inputs. Enter non zero values with respect to the sign convention."
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)
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object_distance = 1 / ((1 / focal_length) - (1 / distance_of_image))
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return object_distance
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def image_distance(focal_length: float, distance_of_object: float) -> float:
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"""
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>>> from math import isclose
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>>> isclose(image_distance(10, 40), 13.33333333)
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True
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>>> from math import isclose
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>>> isclose(image_distance(1.5, 6.7), 1.932692308)
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True
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>>> image_distance(0, 0)
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Traceback (most recent call last):
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...
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ValueError: Invalid inputs. Enter non zero values with respect
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to the sign convention.
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"""
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if distance_of_object == 0 or focal_length == 0:
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raise ValueError(
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"Invalid inputs. Enter non zero values with respect to the sign convention."
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)
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image_distance = 1 / ((1 / focal_length) - (1 / distance_of_object))
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return image_distance
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