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* 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 * Doctest whitespace error rectification to mirror_formulae.py * 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> Co-authored-by: Christian Clauss <cclauss@me.com>
128 lines
4.9 KiB
Python
128 lines
4.9 KiB
Python
"""
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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) # doctest: +NORMALIZE_WHITESPACE
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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) # doctest: +NORMALIZE_WHITESPACE
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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) # doctest: +NORMALIZE_WHITESPACE
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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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