Scipy curve fit

[ca999]

Description

1. Add a new entry in scipy.optimize
2. Create a new directory: content/scipy/concepts/scipy-optimize/terms/curve-fit
3. Added curve-fit documentation under curve-fit.md

Issue Solved

closes #5848

Type of Change

• Adding a new entry

Checklist

• All writings are my own. - the code example was taken from https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html with minor alterations. Chatgpt was used for better coherence for certain explanations.
• My entry follows the Codecademy Docs style guide.
• My changes generate no new warnings.
• I have performed a self-review of my own writing and code.
• I have checked my entry and corrected any misspellings.
• I have made corresponding changes to the documentation if needed.
• I have confirmed my changes are not being pushed from my forked main branch.
• I have confirmed that I'm pushing from a new branch named after the changes I'm making.
• I have linked any issues that are relevant to this PR in the Issues Solved section.

[CLAassistant]

Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you all sign our Contributor License Agreement before we can accept your contribution.
10 out of 11 committers have signed the CLA.

✅ mamtawardhani
✅ ca999
✅ tommaso-berti
✅ Nil-Morah
✅ vlrnsnk
✅ Maverick073
✅ ebikatsudon
✅ hitomipupil
✅ jrood
✅ SaviDahegaonkar
❌ codecademy-gh-api
_{You have signed the CLA already but the status is still pending? Let us recheck it.}

[mamtawardhani]

Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you all sign our Contributor License Agreement before we can accept your contribution.1 out of 2 committers have signed the CLA.✅ ca999❌ Nayana

Nayana seems not to be a GitHub user. You need a GitHub account to be able to sign the CLA. If you have already a GitHub account, please add the email address used for this commit to your account.
You have signed the CLA already but the status is still pending? Let us recheck it.

Hey @ca999, looks like you have made a commit from a local ID as well, could you please use that to sign the CLA?

[mamtawardhani]

Thank you for contributing to Codecademy Docs @ca999😄

The entry looks good for a second round of review! 🚀

[ca999]

Hi @mamtawardhani I am unable to get the CLA for the local account. Can you please guide me on how I can get the CLA agreement to the account

[ca999]

Hi @mamtawardhani So I update the commit email using the ones related to this account, I think it did the job, I no longer see the local account, but I think all the other commiter also have been added in to the CLA

[avdhoottt]

Hey @ca999, I've suggested some changes, please make them as soon as possible. Thank you!!

[avdhoottt]

Suggested change

	- `f`: This is the function to fit to the data.
	- `xdata`: An array-like input representing the independent variable values (e.g., time, x-coordinates, etc.).
	- `ydata`: An array-like input representing the dependent variable values (e.g., measured data corresponding to `xdata`).
	- `p0`: Inital guess of the parameters.
	- `sigma`: Defines the uncertainty in `ydata`.
	- `absolute_sigma`: If `True`, `sigma` is interpreted absolutely, and the parameter covariance pcov reflects absolute values. If `False` which is the default, `sigma` is scaled to normalize residual variance. Here, pcov(absolute_sigma=False) = pcov(absolute_sigma=True) \* chisq(popt)/(M-N).
	- `check_finite`: Ensures input arrays do not contain `NaN` or `inf`. If `True`, a `ValueError` is raised when such values are found. Defaults to `True` unless `nan_policy` is explicitly specified.
	- `bounds`: Specifies parameter bounds. Defaults to no bounds. Options include:
	- An instance of the `Bounds` class.
	- A 2-tuple of array-like objects or scalars: Scalars apply bounds uniformly and `np.inf` can disable bounds partially.
	- `method` - Optimization method. Choices are:
	- `'lm'` (default for unconstrained problems): Levenberg-Marquardt.
	- `'trf'` (default if bounds are set): Trust Region Reflective.
	- `'dogbox'`: Dogleg.
	`'lm'` cannot handle cases where observations < variables. Use `'trf'` or `'dogbox'` instead.
	- `jac`: Jacobian matrix computation for `jac(x, ...)`. Defaults to numerical estimation if None. Supports finite difference schemes for `'trf'` and `'dogbox'` methods.
	- `full_output`: If `True`, returns additional information such as infodict, mesg, ier.
	- `nan_policy`: Decides behavior when `NaN` values exist in input data:
	- None (default): No special handling, behavior depends on implementation.
	- `'raise'`: Throws an error.
	- `'omit'`: Ignores NaN values during computation.
	- `**kwargs`: Additional keyword arguments passed to leastsq (if method = 'lm') or least_squares otherwise.
	- `f`: This is the function to fit to the data.
	- `xdata`: An array-like input representing the independent variable values (e.g., time, x-coordinates, etc.).
	- `ydata`: An array-like input representing the dependent variable values (e.g., measured data corresponding to `xdata`).
	- `p0`: Initial guess of the parameters.
	- `sigma`: Defines the uncertainty in `ydata`.
	- `absolute_sigma`: If `True`, `sigma` is interpreted absolutely, and the parameter covariance pcov reflects absolute values. If `False` which is the default, `sigma` is scaled to normalize residual variance. Here, pcov(absolute_sigma=False) = pcov(absolute_sigma=True) \* chisq(popt)/(M-N).
	- `check_finite`: Ensures input arrays do not contain `NaN` or `inf`. If `True`, a `ValueError` is raised when such values are found. Defaults to `True` unless `nan_policy` is explicitly specified.
	- `bounds`: Specifies parameter bounds. Defaults to no bounds. Options include:
	- An instance of the `Bounds` class.
	- A 2-tuple of array-like objects or scalars: Scalars apply bounds uniformly, and `np.inf` can disable bounds partially.
	- `method` - Optimization method. Choices are:
	- `'lm'` (default for unconstrained problems): Levenberg-Marquardt.
	- `'trf'` (default if bounds are set): Trust Region Reflective.
	- `'dogbox'`: Dogleg.
	`'lm'` cannot handle cases where observations < variables. Use `'trf'` or `'dogbox'` instead.
	- `jac`: Jacobian matrix computation for `jac(x, ...)`. Defaults to numerical estimation if None. Supports finite difference schemes for `'trf'` and `'dogbox'` methods.
	- `full_output`: If `True`, returns additional information such as infodict, mesg, ier.
	- `nan_policy`: Decides behavior when `NaN` values exist in input data:
	- None (default): No special handling; behavior depends on implementation.
	- `'raise'`: Throws an error.
	- `'omit'`: Ignores NaN values during computation.
	- `**kwargs`: Additional keyword arguments passed to `leastsq` (if method = 'lm') or least_squares otherwise.

[avdhoottt]

The output differs every time we run the code. We should add a note here saying that.

[ca999]

Hi @avdhoottt I have added in a note saying that the output will differ for each run

[ca999]

Hi @avdhoottt Is there anything else that I need to do to get this PR merged ?