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Fourier Series Solved Problem: Even Function (bn = 0)

In Mathematical Physics, understanding symmetry is the key to simplifying complex Fourier integrals. When a function is Even, its Fourier series contains only cosine terms, and the sine coefficients (bn) automatically become zero.

The Problem

Find the Fourier series coefficients for the function f(x) = x² in the interval -π < x < π.

Detailed Explanation & Solution

Step 1: Symmetry Analysis

First, we check if the function is even or odd.

f(x) = x²
f(-x) = (-x)² = x²

Since f(-x) = f(x), the function is Even.

Result: For any even function, the sine coefficient bn = 0. We only need to calculate a0 and an.

Step 2: Calculating a0 (The DC Component)

The formula for a0 is:

a0 = (1/π) ∫ f(x) dx from -π to π.

Using the property of even functions:

a0 = (2/π) ∫ x² dx from 0 to π.

Integration:

∫ x² dx = x³ / 3
Applying limits (0 to π): (π³ / 3) – 0

a0 = (2/π) * (π³ / 3) = 2π² / 3

Step 3: Calculating an (The Cosine Coefficient)

The formula for an is:

an = (2/π) ∫ x² cos(nx) dx from 0 to π.

To solve this, we use Bernoulli’s Rule (Integration by Parts):

Formula: ∫ u v dx = uv₁ – u’v₂ + u”v₃ …

u = x² => u’ = 2x => u” = 2
v = cos(nx) => v₁ = (sin nx)/n => v₂ = -(cos nx)/n² => v₃ = -(sin nx)/n³

Applying the rule:

an = (2/π) [ x²(sin nx / n) – (2x)(-cos nx / n²) + (2)(-sin nx / n³) ] from 0 to π.

Substituting the limits:

Since sin(nπ) = 0 and sin(0) = 0, the 1st and 3rd terms become zero.
We are left with: an = (2/π) [ 2x cos(nx) / n² ] from 0 to π.
an = (2/π) [ (2π cos nπ / n²) – 0 ]
an = (4 / n²) * cos(nπ)

Since cos(nπ) = (-1)^n:

an = 4(-1)^n / n²

Final Fourier Series Representation

Substituting a0 and an into the general Fourier series equation:

f(x) = (a0 / 2) + Σ [an * cos(nx)]

x² = (π² / 3) + Σ [ 4(-1)^n / n² * cos(nx) ]

Expanded Form:

x² = (π² / 3) – 4 [ cos(x) – (cos 2x / 4) + (cos 3x / 9) – … ]

Key Takeaway

In problems where bn = 0, the resulting Fourier series is called a Fourier Cosine Series. This occurs whenever the function is symmetric about the y-axis. This simplification is vital in heat conduction and wave equation problems in Physics.

What is vector? Claude answers

I — What Is a Vector, Really?

Before a physicist can describe a single phenomenon in the universe — before she can write down how a planet orbits the sun, how a charged particle moves through a magnetic field, or how light bends around a massive object — she must first master the concept of a vector. It is not an exaggeration to say that vectors are the skeleton of physics. Without them, the language of nature collapses into noise.

At the most elementary level, a vector is a mathematical object that carries two inseparable pieces of information: magnitude and direction. This is what separates it from a simple number — a scalar — which carries magnitude alone. The temperature of a room is a scalar: 25°C tells you everything. But the wind blowing at 25 km/h tells you almost nothing until you add “blowing northeast.” That full description — speed plus direction — is a vector.

Physicists represent vectors as arrows. The length of the arrow encodes magnitude; the orientation of the arrow encodes direction. This geometric picture is not merely decorative. It is the most honest way to see what a vector truly is — an instruction: go this far, in this direction.

II — Notation & Components

In practice, physicists decompose vectors into components — projections along mutually perpendicular axes. In three-dimensional space, we choose three orthogonal directions, conventionally labeled x, y, and z, with unit vectors î, ĵ, and k̂ pointing along each. Any vector A can then be written as:

Vector Decomposition A = Aₓ î + A_y ĵ + A_z k̂

This decomposition is powerful because it reduces vector arithmetic to ordinary number arithmetic, done component by component. It is also deeply physical: the components tell you how much of the vector lies along each independent direction of space. A force of 10 N directed 30° above the horizontal has a horizontal component of 8.66 N and a vertical component of 5 N — each independently producing its own physical effect.

The magnitude of a vector is recovered through the Pythagorean theorem, extended to three dimensions:

Magnitude | A | = \sqrt(Aₓ² + A_y² + A_z²)

III — Vector Addition: Forces in Nature

The most immediate physical application of vectors is the addition of forces. If two forces act on an object simultaneously, the net effect is not simply the sum of their magnitudes. Direction matters profoundly. Two people pulling a box with equal strength in opposite directions produce zero net force. Two people pulling at right angles produce a force at 45° between them, with a magnitude larger than either alone.

This is captured by the parallelogram law of vector addition: place two vectors tail to tail, complete the parallelogram, and the diagonal is their sum. Algebraically, you simply add corresponding components:

Vector Addition A + B = (Aₓ + Bₓ) î + (A_y + B_y) ĵ + (A_z + B_z) k̂

Nature does not add forces like bank balances — it adds them like arrows in space, and the direction of each arrow is as important as its length.

This principle underlies Newton’s Second Law itself. The acceleration of a body is determined not by any single force but by the vector sum of all forces acting upon it. Gravity pulls down; the normal force pushes up; friction acts horizontally — and the body obeys their combined vector instruction.

IV — The Dot Product: Measuring Alignment

Vectors can be multiplied in two fundamentally different ways, each capturing a different physical truth. The first is the dot product (also called the scalar product), defined as:

Dot Product A \cdot B = | A | | B | cos θ

The result is a scalar — a single number. Geometrically, it measures how much one vector projects onto another. When θ = 0° (vectors point the same way), the dot product is maximized. When θ = 90° (vectors are perpendicular), the dot product is zero. This zero result is extraordinarily important in physics: it means the two vectors have absolutely no component in common.

The most celebrated physical example is work. Work is not simply force times distance — it is force dot displacement. Only the component of force parallel to the motion does work. A waiter carrying a tray horizontally across a room does no work against gravity, because the gravitational force (downward) is perpendicular to the displacement (horizontal). The dot product makes this precise and automatic.

Dot Product in Physics

Work: W = F · d — only force along displacement counts
Power: P = F · v — force aligned with velocity delivers power
Electric Flux: Φ = E · A — field lines passing through a surface
Magnetic force on a wire: involves dot product of current and field geometry

V — The Cross Product: Measuring Rotation

The second multiplication is the cross product, and it produces not a scalar but a new vector — one perpendicular to both of the originals:

Cross Product | A \times B | = | A | | B | sin θ

The direction of this new vector is given by the right-hand rule: curl the fingers of your right hand from A toward B, and your thumb points in the direction of A × B. This built-in handedness — called chirality — reflects something deep about three-dimensional space and appears throughout physics.

The cross product is the natural language of rotation and circulation. Torque — the rotational effect of a force — is the cross product of the position vector and the force: τ = r × F. If you push a door at its edge (large |r|) perpendicular to it (sin 90° = 1), you get maximum torque. Push along the door’s surface (sin 0° = 0) and it won’t rotate at all, regardless of how hard you push.

The cross product also governs the magnetic force on a moving charge: F = q(v × B). A particle moving parallel to a magnetic field feels no force. Moving perpendicular, it feels maximum force, deflected into a circular orbit. The entire science of particle accelerators, cyclotrons, and aurora borealis rests on this single cross product.

VI — Vector Fields: Vectors Everywhere in Space

A physicist does not just work with a single vector at a single point. In most situations, a physical quantity has a direction and magnitude at every point in space. This is a vector field — one of the most powerful and beautiful concepts in all of physics.

The gravitational field of the Earth is a vector field: at every point in space around it, there is a gravitational force vector pointing toward the Earth’s center, decreasing in magnitude with distance. The electric field around a charge, the magnetic field around a current-carrying wire, the velocity field inside a flowing river — all are vector fields.

A vector field is nature’s way of filling space with intention — assigning every point its own direction, its own pull, its own private instruction about where things should go.

Vector fields are analyzed using two key operations from vector calculus: the divergence (which measures how much a field spreads outward from a point, like water from a source) and the curl (which measures how much a field rotates around a point, like water in a whirlpool). These operations are the heart of Maxwell’s equations of electromagnetism — arguably the most elegant system of equations ever written down.

VII — Vectors in Kinematics: Position, Velocity, Acceleration

In mechanics, the three most fundamental quantities describing motion are all vectors. Position (r) tells you where a particle is relative to some origin. Velocity (v) tells you how fast and in what direction it is moving — it is the time derivative of position. Acceleration (a) tells you how velocity is changing — the time derivative of velocity.

Crucially, acceleration is a vector: it can change a body’s speed, its direction, or both. A car moving at constant speed around a circular track has zero change in speed — yet it is continuously accelerating, because its direction is continuously changing. This centripetal acceleration always points toward the center of the circle, perpendicular to the velocity. Without this concept — impossible to express without vectors — circular motion makes no sense at all.

Newton’s second law, written properly as F = m a, is a vector equation. It encodes not one but three scalar equations simultaneously — one for each spatial direction. The force and acceleration must point in the same direction; nature does not mix them across axes independently.

· · ·

VIII — Why Vectors Are Indispensable

One might ask: could physics be done without vectors? Could we not just use numbers and angles and trigonometry for everything? In principle, perhaps — but the cost would be staggering. Every equation would bloat into a tangle of sines and cosines. The deep symmetries of nature — its indifference to our choice of coordinate axes — would be buried under a mess of conventions. Physics would lose its elegance and its clarity.

Vectors exist because space has direction, and physics happens in space. Every time you ask “which way does it go?” you are demanding a vector. And the universe, obligingly, answers in the same language.

Learning vectors, truly learning them — not just the algebra, but the geometry, the intuition for direction and projection and rotation — is the single most important step a student of physics can take. Everything else in the discipline is, in some sense, an elaboration of this one idea: that to describe nature completely, magnitude alone is never enough.

To a physicist, a vector is not a symbol on a page. It is a fundamental truth about the fabric of the universe: that space has structure, that direction matters, and that nature keeps a precise accounting of both.

Why Do We Need Differentiation?

The Question Behind the Question

Before we learn how to differentiate, it is worth pausing to ask a more elementary question — why should we bother at all? Mathematics, after all, is not done for its own sake alone. It is done because the world demands it. And the world, as it turns out, is not a static, frozen thing. It moves, it changes, it accelerates, it cools down, it grows, it decays. Differentiation is simply our most powerful tool for describing, measuring, and predicting that change. Without it, modern science and engineering would collapse like a building without a foundation.

What Differentiation Actually Does

At its heart, differentiation answers one deceptively simple question: how fast is something changing at this particular moment? Not on average, not roughly, but precisely, at an exact instant.

Consider a Delhi auto-rickshaw moving through traffic. Its position changes with time — that much is obvious. But the driver does not just care about where the auto is. He cares about how fast it is moving right now, so he knows whether to brake before the signal. That instantaneous speed is nothing but the derivative of position with respect to time. Differentiation is what converts “where are you?” into “how fast are you going?” — a far more useful and urgent question in real life.

Rates of Change Are Everywhere

Once you start looking, rates of change appear in virtually every domain of human activity.

A farmer in Punjab wants to know how quickly the water level in his field is rising during monsoon. A doctor in a Chennai hospital wants to know how fast a patient’s blood pressure is dropping after medication. A software engineer in Bengaluru wants to know how quickly a computer’s memory usage is growing as more users log in. A student preparing for JEE wants to know at what angle a projectile reaches maximum height. All of these are questions about rates of change. All of them are, mathematically speaking, questions about derivatives.

Without differentiation, we could only say “things are changing.” With differentiation, we can say “things are changing at exactly this rate, in exactly this direction, and here is what will happen next.” That precision is the difference between guessing and knowing.

Finding Maxima and Minima — Optimisation

Perhaps the single most practically important application of differentiation is optimisation — finding the best possible value of something. Nature herself seems obsessed with optimisation. Light travels the path that takes the least time. A soap bubble takes the shape that minimises surface area. A falling object reaches a terminal velocity where forces balance. Everywhere we look, things settle into optimal states, and differentiation is how we find those states mathematically.

In business, a shopkeeper in Chandni Chowk wants to know what price maximises his profit. In engineering, a designer wants to know what shape of a steel beam carries maximum load with minimum material. In economics, a policymaker wants to know what tax rate maximises government revenue without killing growth. In all these cases, the method is the same: write down a function, differentiate it, set the derivative equal to zero, and solve. The point where the derivative vanishes is exactly the peak or the valley — the maximum or the minimum.

This is not a mathematical trick. It is a profound insight: at the top of a hill, the slope is zero. Differentiation finds the slope; setting it to zero finds the top.

Understanding Motion — Newton’s Great Insight

Isaac Newton invented calculus — and with it, differentiation — precisely because he needed it to understand motion. His laws of motion are written in the language of derivatives. Force equals mass times acceleration, and acceleration is the derivative of velocity, which is itself the derivative of position. So force is actually the second derivative of position with respect to time.

This means that when ISRO launches a rocket from Sriharikota, every calculation of its trajectory — how fast it is going, how quickly it is accelerating, when to fire the engines, how to correct its path — is built on differentiation. The entire space programme, in a very real sense, runs on derivatives.

Without Newton’s calculus, we could observe the planets moving in the sky but we could not predict exactly where they would be tomorrow, or calculate how to send a spacecraft to meet them.

Heat, Electricity, and Waves

Differentiation is equally indispensable in understanding how heat spreads through a material, how electric current flows through a circuit, and how sound and light travel as waves. All of these phenomena are governed by differential equations — equations that relate a quantity to its own rate of change.

For instance, Newton’s law of cooling says that the rate at which a hot cup of chai loses heat is proportional to the difference between its temperature and the room temperature. This is a statement about a derivative. Solving it tells you exactly how long before your chai becomes too cold to enjoy — which, in India, is a matter of some urgency.

Similarly, the equations governing electricity in your home, the signal in your mobile phone, and the light from your LED bulb are all differential equations. Every engineer who designs these systems must understand differentiation deeply.

Economics and Social Sciences

Differentiation is not confined to the physical sciences. Economists use a concept called marginal analysis, which is pure differentiation in disguise. The marginal cost of producing one more unit of a good, the marginal utility of consuming one more roti, the marginal revenue from selling one more ticket — all of these are derivatives. They tell producers and consumers how the next small step will change their situation, which is exactly the information needed to make rational decisions.

India’s GDP growth rate — that number economists and finance ministers argue about endlessly on television — is itself a derivative. It is the rate of change of economic output with respect to time. When we say India’s economy is growing at 7% per year, we are making a statement that is, at its core, about differentiation.

Medicine and Biology

In medicine, differentiation helps model how diseases spread. The rate at which a viral infection grows through a population depends on how many people are currently infected — a relationship captured by a differential equation. During the COVID-19 pandemic, the models that governments used to predict hospital requirements and plan lockdowns were built on exactly this kind of mathematics.

In biology, the rate at which a cell divides, the rate at which a drug is absorbed into the bloodstream, and the rate at which an ecosystem recovers after a drought — all are questions answered by differentiation.

A Tool That Mirrors How We Think

There is something deeply natural about differentiation, beyond its technical applications. Human beings are instinctively sensitive to rates of change. When a student’s marks improve from 60% to 75%, the parents are happy. But they are even more interested in the trend — is she improving fast, or has the improvement slowed down? That instinct about trends, about momentum, about acceleration, is precisely what differentiation formalises.

We do not just want to know where things are. We want to know where they are going, and how quickly. Differentiation is the mathematical answer to that most human of questions.

In Summary

We need differentiation because the world does not stand still. Change is the fundamental character of reality — in physics, in biology, in economics, in engineering, in daily life. Differentiation gives us the language to describe that change precisely, the tools to find optimal solutions, and the power to predict the future from the present. It is not an abstract luxury of mathematics. It is as practical and as essential as arithmetic itself — only more powerful, and considerably more beautiful.

Partial vs. Total Differentiation

Differentiation, at its core, is the mathematical art of measuring change. But when a quantity depends on more than one variable simultaneously, a crucial question arises: change with respect to what? This is precisely where the distinction between partial and total differentiation becomes not just useful, but necessary.
The Setting: Functions of Several Variables
Consider a simple function of one variable, say f(x) = x^2. Its derivative df/dx = 2x is unambiguous — there is only one direction in which things can change. But now imagine a function like f(x, y) = x^2 + xy + y^2, which lives on a two-dimensional landscape. If you stand at a point on this surface and ask “how steep is the slope?”, the answer depends entirely on which direction you choose to walk. Partial and total derivatives are two different, principled answers to this directional question.
Partial Differentiation: Freezing the World
The partial derivative isolates the effect of one variable by treating all others as constants, frozen in place. For f(x, y), the partial derivative with respect to x asks: if y is held fixed and x alone is nudged, how does f respond?
For our example, d(f)/d(x) = 2x + y, where y simply tags along as a constant. Likewise, d(f)/d(y) = x + 2y. Each partial derivative gives you the slope of the surface along one axis-aligned direction, as if you were walking strictly north or strictly east, never diagonally.
The round symbol “d” used in partial derivatives signals this restriction deliberately. It is a reminder that the derivative is partial — incomplete — because it only tells part of the story of how f changes.
Total Differentiation: Letting Everything Move
The total derivative, by contrast, allows all variables to change simultaneously, and accounts for their combined effect. If x and y are themselves functions of some parameter t (think of t as time), then the total derivative of f with respect to t is:
df/dt = (df/dx)(dx/dt) + (df/dy)(dy/dt)
This is the chain rule in its full generality. It says: the total rate of change of f is the sum of contributions from each variable, each weighted by how fast that variable itself is changing. You are no longer walking along a fixed axis — you are moving along some curved path across the landscape, and the total derivative captures the slope you actually experience along that path.
In its infinitesimal form, the total differential is written:
df = (df/dx)dx + (df/dy)dy
This expression is strikingly honest — it says that any small change in f is built from small changes in x and y, each scaled by the appropriate partial derivative.
The Crucial Difference in Spirit
The deepest distinction between the two is philosophical as much as mathematical. A partial derivative asks a hypothetical, controlled question: what if only this one thing changed? It is the mathematical equivalent of a controlled experiment. A total derivative asks a realistic, unconstrained question: given how everything is actually moving, what is the net change? It is the mathematics of the real, coupled world.
This difference becomes especially vivid in physics and thermodynamics. The internal energy U of a gas depends on temperature T and volume V. The partial derivative d(U)/d(T) at constant volume is a well-defined material property. But in a real process, both T and V may change together, and the total derivative dU/dt must account for both. Confusing the two has led to genuine conceptual errors in the history of science.
Returning to Poisson Brackets
This distinction is precisely why Poisson brackets are so carefully constructed. In Hamiltonian mechanics, an observable f(q, p, t) depends on coordinates, momenta, and possibly time explicitly. Its total rate of change is:
df/dt = (df/dq)(dq/dt) + (df/dp)(dp/dt) + df/dt (explicit)
Substituting Hamilton’s equations and reorganizing gives exactly {f, H} + df/dt (explicit). The Poisson bracket captures the dynamical part — how f changes because the system evolves — while the explicit df/dt captures only the direct time dependence. Separating these two contributions is only possible because partial and total derivatives mean fundamentally different things.
In Summary
Partial differentiation is a scalpel — it isolates one variable’s influence with surgical precision. Total differentiation is a wide-angle lens — it captures the full, combined rate of change as the entire system moves. One is a tool for analysis; the other, for synthesis. Together, they form the language in which most of modern physics and applied mathematics is written.

Hoping for something better (23/02/2026)

As a physics student, I usually how the world works. Upon closer interest towards electronics I finally ended up being a Science writer.

I got a opportunity at a open source forum from india called Kaniyam. They introduced the opensource tech, the god father of the world internet.

I tried to promote it with my articles, but that’s not enough for me to accomplish as a developer. Finally I got a opportunity to talk with Mr. Ivan from openscience labs. He guided me towards the boost up the learning process.

Finally with some momentum I started formulon. A opensource project to create physics computation easier and effective for students.Its already released in the pypi and you can easily install it using pip install formulon-physics

But, we need to develop it further into a deep module. With lot of advance features. Now,the list of mentoring organisations for the Google summer of code has been released.

Open science labs also marked as the mentoring organisation. Also I got interest over the deep physics computation using sympy. I’m planning to contribute to anyone of the project to gain my knowledge that I can implement of formulon in future. If you’re reading leave your suggestions.

One day formulon will do something better.

If you really think I’m joking, running the modules with a decade old cranky ThinkPad machine is still a night mare. Ha ha … But it does the job. See you in another blog post

Literally need some help !!!(29/01/2026)

After successfully writing the code for the python library. I tried to publish the python package into the pypi repository. But currently I’m facing a lot of terminal bugs and issues.

First of all I created a account on a pypi. I Used hatch for the python package creation. But afterwards, I seek help from chat gpt. I used the twine command (showed in image) push my file into pypi. Mean time I have created a clean repo in github too.

When run the command it showed a name,version error. Afterwards I can’t paste the token in the terminal too. I tried the alternative GitHub trusted publisher technique that too makes lot of errors and stuff. I have created a workflow there too.

Now I have the clean package but need a proper guidance on uploading the file onto pypi. Even in the chaos, I named my little package as “formulon”. A tiny particle is waiting to emerge for a blast.

Your valuable assistance is needed thank you.

Mail me on : srikaleeswarartamil@gmail.com

Finally I found out that, I have a fingerprint scanner in my laptop.

I bought the Lenovo T460 Laptop from the second hand market in 2024. It’s been around 1.5 years since I didn’t notice anything about the finger print sensor present in my laptop

Although, it’s an older model. Think pad series laptops have some unique features like simcard slots,rugged design,dual batteries and some other features. I finally confirmed it’s a fingerprint scanner.

So, i headed to the internet and finally found out how to configure and register your finger print in the security settings in the terminal itself.

dpkg -l | grep fprint

This command shows all the external and internal sensors and devices present in the desktop

If nothing is showing, try this command.

sudo apt install fprintd

It will install the finger print configuration files

lsusb or fprintd-list

Will show the list

fprintd-enroll

This command will enroll your fingerprint

fprintd-enroll username

This command is for particular user

fprintd-verify

Place your finger again and run the command it will verify your fingerprint

sudo pam-auth-update

All set welcome to the world of fingerprint verification in Linux mint.

Beta version is almost ready (23/01/2026)

I’m heading towards my python library development project. After some mistakes. and chaos, finally I have integrated numba and Numpy in my modules

Today I pushed the file for the error handler for my modules. Which would find the errors like type error,value error,zero division error and so on. I would call the error handler as a decorator function. Since, it’s a beginner level code. I don’t want to make it more and more complex

Using the stream lit library, I have created 5 web apps for the formulas like force,angular frequency and so on. It was quite good to compute and I had demonstrated them to my classmates using the library projector.Initially I had faced some issues with screen mirroring, but with the projector. The Linux mint showed the options for screen mirror and extended display. I choose mirror and it worked fine.

Then, I explained the code and other stuff. My friends asked for help for the data analysis they need for their projects.I think in future I should learn more about data bases and machine learning too. My professor hinted that a quantum computing lecture should be taken by me for another college.

Things going well having a lot of ideas. Stay tuned

learned about Hatch and Stream lit and solved a problem in git(19/01/2026)

Now, I have successfully pushed the 13th formula for my python library. I am planning to release the beta version with limited 15 formulas on pypi.

For the python package manager, MR. Ivan from open science labs recommended the Hatch package manager. He shared a YouTube tutorial which is attached below.

https://youtu.be/XAq-HnPU4XM?si=XrzooyrHdshmSxRg

Afterwards, I had a session with the Kaniyam editor. He suggested the stream lit for faster web applications and implementation. I had to go through some tutorials and got an idea of what it is.

Also, he suggested the unit testing of the functions and trying to avoid crashing. Will implement it and release the beta version on 21/01/2026. On the 22nd of January,Thursday, Will take a seminar on the modules at my class

Meantime, I faced an issue with my GitHub and git. When I pushed my file, it showed a repo fatal error. Then with the help of chatgpt I figured out that this problem occurred due to the stream lit integration with my GitHub repository and I have locally edited some files in my repo.

So, I run the command git pull –rebase origin main. This command basically rebooted the connection and now things work fine. I also got a contact about telehealthai.org, which is an open source organisation powered by the open science labs which deals with clinical softwares.

This is the update for now. Will meet in another blog.

Issues that i have faced while installing python package manager -” hatch” and solutions (16/01/2026)

Well currently im using the same git hub repository for all the python codes i push.Since im dvveloping a small scale physics library (frame work). I have should have a perfect separate repository in order to publish it into PYPI.

Ivan from open science labs shared about hatch, which is perfectly suitable to do my work. upon watching the tutorial in the (link here) :https://www.youtube.com/watch?v=XAq-HnPU4XM

I finally headed towards my terminal, already i faced a hard time with installation process over the poetry(alternative to hatch). since, Linux mint does not allow other python files to be installed via pip install, i need to setup a virtual environment there.(hatch is a externally managed python package).

So, i headed towards chat gpt for help, then i installed pipx using sudo apt install pipx

Then used pipx to install hatch using command pipx install hatch.Before that, you should run pipx ensurepath .

After that, if you type hatch –version it didn’t showed any response. In my case the package is installed in the .Bin folder. So, to resolve this you have to run

echo ‘export PATH=”$HOME/.local/bin:$PATH”‘ >> ~/.bashrc
~/.local/bin$ source ~/.bashrc

This command will help you to run the hatch in any file directory

Then, you can simply run cd ~ and then hatch –version you will find the exact hatch version of yours.

lastly, just run hatch new package_name you will create a network of files in a single command. Useful screen shots are shared below,

Learning only comes from “chaos”,you will surely find some difficulties while youre learning. Happy coding ………