Merge pull request #13 from LSSTDESC/JaimeRZP-patch-2

Update README.md

README.md

@@ -8,4 +8,62 @@ A DESC Package to turn an ensemble of redshift distributions into a prior for ph
 
 ![image](https://github.com/user-attachments/assets/db975890-934e-4db1-aefd-51f6709aac29)
 
+## Usage
 
+`nz_prior` can either be run on a `QP` ensemble or on a `sacc` file with `QPnz` tracers. Here is an example of the simplest use when a `QP` ensemble is provided:
+
+```python
+import nz_prior as nzp
+X = nzp.PriorX(q) 
+#  X is the method to capture photometric uncertainty
+#  q is the sacc file with QPnz tracers
+```
+
+Here is an example when a `sacc` file is provided instead:
+
+```python
+X = nzp.PriorSacc(sacc_file, 
+    model="Model",
+    compute_crosscorrs="Full",
+    zgrid=zgrid)
+```
+A couple of running options are important to note:
+- model: the model used to capture the uncertainty in the n(z)'s within the `sacc` file.
+    - "Shifts":  the n(z)'s are shifted by a constant amount.
+    - "ShiftsWidths": the n(z)'s are shifted by a constant amount and broadened by a constant width.
+    - "PCA": the n(z)'s are represented as a linear combination of the principal components.
+    - "Fourier": the n(z)'s are represented as a linear combination of Fourier modes.
+    - "GP": the model assumes that the n(z)'s are a Gaussian Process.
+    - "Comb": the n(z)'s are represented as a linear combination of evenly spaced Gaussians.
+- compute_crosscorrs: whether to compute the cross-correlations between the n(z)'s or not.
+    - "None": do not compute the cross-correlations.
+    - "BinWise": compute the cross-correlations between the parameters associated with the same tomographic bin.
+    - "Full": compute the cross-correlations between all the parameters across tomographic bins.
+- zgrid: the zgrid on which to evaluiate the n(z)'s. If None, the zgrid of the `sacc` file is used.
+
+Upon initializing the class, Gaussian priors for the model parameters will be calibrated. The prior can then be accessed as an attribute of the class:
+
+```python
+mean, cov, chol = X.get_prior()
+```
+which returns the mean, covariance, and Cholesky decomposition of the prior.
+
+Moreover, it is possible to sample from the prior:
+
+```python
+samples = X.sample_prior()
+```
+which procudes a dictionary with the following structure `Dict{param_names: value}`. The keys are the names of the parameters and the values are the samples.
+
+The samples can then be used to generate n(z)'s using the models in the `nz_prior.models` module.
+
+Checkout the examples folder for more detailed use cases.
+
+## Installation
+
+To install the package, run the following command in the root directory of the package:
+
+```bash
+pip install .
+```
+Note: make sure that you have installed sacc version 0.16 or higher.