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franckalbinet · Jan 23, 2025 · 1345cf1 · 1345cf1
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diff --git a/README.md b/README.md
@@ -3,47 +3,14 @@
 
 <!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
 
-> A Python package providing scikit-learn compatible transforms for
-> spectroscopic data preprocessing.
+> Spectral Processing Tools for Soil Spectroscopy
 
-It’s designed to work seamlessly with both MIR (Mid-Infrared) and VISNIR
-(Visible-Near Infrared) spectral data.
-
-**WORK IN PROGRESS**
-
-## Installation
-
-``` bash
-pip install soilspectfm
-```
-
-## Quick Start
-
-``` python
-from soilspectfm.core import (SNV, 
-                              TakeDerivative, 
-                              ToAbsorbance, 
-                              Resample, 
-                              WaveletDenoise)
-
-from sklearn.pipeline import Pipeline
-```
-
-### Loading OSSL dataset
-
-Let’s use OSSL dataset as an example using
-[SoilSpecData](https://fr.anckalbi.net/soilspecdata/) package.
-
-``` python
-from soilspecdata.datasets.ossl import get_ossl
-```
-
-``` python
-ossl = get_ossl()
-mir_data = ossl.get_mir()
-```
-
-### Preprocessing pipeline
+By translating specialized soil spectroscopy methods into the
+[`scikit-learn`](https://scikit-learn.org/stable/) framework,
+`SoilSpecTfm` and
+[`SoilSpecData`](https://fr.anckalbi.net/soilspecdata/) connect this
+niche domain with Python’s vast machine learning ecosystem, making
+advanced ML/DL tools accessible to soil scientists.
 
 Implemented transforms developed so far include:
 
@@ -55,17 +22,16 @@ Implemented transforms developed so far include:
   - [x]
     [`MSC`](https://franckalbinet.github.io/soilspectfm/core.html#msc):
     Multiplicative Scatter Correction
-  - [ ] `Detrend`: Detrend the spectrum (coming soon …)
-  - [ ] `ALS`: Asymmetric Least Squares detrend the spectrum (coming
-    soon …)
+  - [ ] `Detrend`: Detrend the spectrum (planned)
+  - [ ] `ALS`: Asymmetric Least Squares detrend the spectrum (planned)
 
 - **Derivatives**:
 
   - [x]
     [`TakeDerivative`](https://franckalbinet.github.io/soilspectfm/core.html#takederivative):
     Take derivative (1st, 2nd, etc.) of the spectrum and apply
     Savitzky-Golay smoothing
-  - [ ] `GapSegmentDerivative`: (coming soon …)
+  - [ ] `GapSegmentDerivative`: (planned)
 
 - **Smoothing**:
 
@@ -85,6 +51,53 @@ Implemented transforms developed so far include:
     [`Resample`](https://franckalbinet.github.io/soilspectfm/core.html#resample):
     Resample the spectrum to a new wavenumber range
 
+**Key Features**:
+
+- Seamless integration with scikit-learn’s machine learning ecosystem
+- Complement with [SoilSpecData](https://fr.anckalbi.net/soilspecdata/)
+  package for soil spectroscopy workflows
+- Pipeline-ready transformers with consistent API
+
+All transformers follow scikit-learn conventions:
+
+- Implement fit/transform interface
+- Support get_params/set_params for GridSearchCV
+- Provide detailed documentation and examples
+
+## Installation
+
+``` bash
+pip install soilspectfm
+```
+
+## Quick Start
+
+``` python
+from soilspectfm.core import (SNV, 
+                              TakeDerivative, 
+                              ToAbsorbance, 
+                              Resample, 
+                              WaveletDenoise)
+
+from sklearn.pipeline import Pipeline
+```
+
+### Loading OSSL dataset
+
+Let’s use OSSL dataset as an example using
+[SoilSpecData](https://fr.anckalbi.net/soilspecdata/) package.
+
+``` python
+from soilspecdata.datasets.ossl import get_ossl
+```
+
+``` python
+ossl = get_ossl()
+mir_data = ossl.get_mir()
+```
+
+### Preprocessing pipeline
+
 Transforms are fully compatible with
 [scikit-learn](https://scikit-learn.org/stable/) and can be used in a
 pipeline as follows:

diff --git a/nbs/index.ipynb b/nbs/index.ipynb
@@ -12,21 +12,59 @@
    "metadata": {},
    "source": [
     "\n",
-    "> A Python package providing scikit-learn compatible transforms for spectroscopic data preprocessing. "
+    "> Spectral Processing Tools for Soil Spectroscopy"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It's designed to work seamlessly with both MIR (Mid-Infrared) and VISNIR (Visible-Near Infrared) spectral data."
+    "By translating specialized soil spectroscopy methods into the [`scikit-learn`](https://scikit-learn.org/stable/) framework, `SoilSpecTfm` and [`SoilSpecData`](https://fr.anckalbi.net/soilspecdata/) connect this niche domain with Python's vast machine learning ecosystem, making advanced ML/DL tools accessible to soil scientists."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**WORK IN PROGRESS**"
+    "Implemented transforms developed so far include:\n",
+    "\n",
+    "- **Baseline corrections**:\n",
+    "  \n",
+    "  - [x] `SNV`: Standard Normal Variate\n",
+    "  - [x] `MSC`: Multiplicative Scatter Correction\n",
+    "  - [ ] `Detrend`: Detrend the spectrum (planned)\n",
+    "  - [ ] `ALS`: Asymmetric Least Squares detrend the spectrum (planned)\n",
+    "\n",
+    "- **Derivatives**:\n",
+    "  \n",
+    "  - [x] `TakeDerivative`: Take derivative (1st, 2nd, etc.) of the spectrum and apply Savitzky-Golay smoothing\n",
+    "  - [ ] `GapSegmentDerivative`: (planned)\n",
+    "\n",
+    "- **Smoothing**:\n",
+    "  - [x] `WaveletDenoise`: Wavelet denoising\n",
+    "  - [x] `SavGolSmooth`: Savitzky-Golay smoothing\n",
+    "  \n",
+    "\n",
+    "- **Other transformations**:\n",
+    "  - [x] `ToAbsorbance`: Transform the spectrum to absorbance\n",
+    "  - [x] `Resample`: Resample the spectrum to a new wavenumber range\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Key Features**:\n",
+    "\n",
+    "- Seamless integration with scikit-learn's machine learning ecosystem\n",
+    "- Complement with [SoilSpecData](https://fr.anckalbi.net/soilspecdata/) package for soil spectroscopy workflows\n",
+    "- Pipeline-ready transformers with consistent API\n",
+    "\n",
+    "All transformers follow scikit-learn conventions:\n",
+    "\n",
+    "- Implement fit/transform interface\n",
+    "- Support get_params/set_params for GridSearchCV\n",
+    "- Provide detailed documentation and examples"
    ]
   },
   {
@@ -111,34 +149,6 @@
     "### Preprocessing pipeline"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Implemented transforms developed so far include:\n",
-    "\n",
-    "- **Baseline corrections**:\n",
-    "  \n",
-    "  - [x] `SNV`: Standard Normal Variate\n",
-    "  - [x] `MSC`: Multiplicative Scatter Correction\n",
-    "  - [ ] `Detrend`: Detrend the spectrum (coming soon ...)\n",
-    "  - [ ] `ALS`: Asymmetric Least Squares detrend the spectrum (coming soon ...)\n",
-    "\n",
-    "- **Derivatives**:\n",
-    "  \n",
-    "  - [x] `TakeDerivative`: Take derivative (1st, 2nd, etc.) of the spectrum and apply Savitzky-Golay smoothing\n",
-    "  - [ ] `GapSegmentDerivative`: (coming soon ...)\n",
-    "\n",
-    "- **Smoothing**:\n",
-    "  - [x] `WaveletDenoise`: Wavelet denoising\n",
-    "  - [x] `SavGolSmooth`: Savitzky-Golay smoothing\n",
-    "  \n",
-    "\n",
-    "- **Other transformations**:\n",
-    "  - [x] `ToAbsorbance`: Transform the spectrum to absorbance\n",
-    "  - [x] `Resample`: Resample the spectrum to a new wavenumber range\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},

diff --git a/nbs/sidebar.yml b/nbs/sidebar.yml
@@ -6,3 +6,4 @@ website:
         contents:
           - 00_core.ipynb
           - 01_visualization.ipynb
+          - 02_utils.ipynb