refactor: rename module to image and add citation

docs/source/refs.bib

@@ -1,14 +1,13 @@
 @article{Ishida2018,
-  author   = {Ishida, Y. and Shin, S.},
-  title    = {Functions to map photoelectron distributions in a variety of setups in angle-resolved photoemission spectroscopy},
-  journal  = {Rev. Sci. Instrum.},
-  volume   = {89},
-  number   = {4},
-  pages    = {043903},
-  keywords = {photoelectron microscopy},
-  doi      = {10.1063/1.5007226},
-  year     = {2018},
-  type     = {Journal Article}
+  author  = {Ishida, Y. and Shin, S.},
+  title   = {Functions to map photoelectron distributions in a variety of setups in angle-resolved photoemission spectroscopy},
+  journal = {Rev. Sci. Instrum.},
+  volume  = {89},
+  number  = {4},
+  pages   = {043903},
+  doi     = {10.1063/1.5007226},
+  year    = {2018},
+  type    = {Journal Article}
 }
 
 @article{Seah1979,
@@ -38,3 +37,27 @@ @inproceedings{Schirra2008
   year      = {2008},
   type      = {Conference Proceedings}
 }
+
+@article{Zhang2011,
+  author  = {Zhang, P. and Richard, P. and Qian, T. and Xu, Y.-M. and Dai, X. and Ding, H.},
+  title   = {A precise method for visualizing dispersive features in image plots},
+  journal = {Rev. Sci. Instrum.},
+  volume  = {82},
+  number  = {4},
+  pages   = {043712},
+  doi     = {10.1063/1.3585113},
+  year    = {2011},
+  type    = {Journal Article}
+}
+
+@article{He2017,
+  author  = {He, Yu and Wang, Yan and Shen, Zhi-Xun},
+  title   = {Visualizing dispersive features in 2D image via minimum gradient method},
+  journal = {Rev. Sci. Instrum.},
+  volume  = {88},
+  number  = {7},
+  pages   = {073903},
+  doi     = {10.1063/1.4993919},
+  year    = {2017},
+  type    = {Journal Article}
+}

src/erlab/analysis/__init__.py

@@ -12,8 +12,8 @@
    fit
    mask
    correlation
-   derivative
    gold
+   image
    interpolate
    kspace
    transform

src/erlab/analysis/derivative.py → src/erlab/analysis/image.py

@@ -1,4 +1,6 @@
-"""Tools for visualizing dispersive features."""
+"""
+Various image processing functions including tools for visualizing dispersive features.
+"""
 
 import numpy as np
 import numpy.typing as npt
@@ -74,7 +76,8 @@ def _kernel(values_ptr, len_values, result, data):
 def minimum_gradient(
     darr: xr.DataArray, mode: str = "nearest", cval: float = 0.0
 ) -> xr.DataArray:
-    """Minimum gradient method of a 2D DataArray.
+    """Minimum gradient method for detecting dispersive features in 2D data as described
+    in Ref. :cite:p:`He2017`.
 
     Parameters
     ----------