Updated README, minor bug fix with dark_item in for loop

README.rst

@@ -24,7 +24,13 @@ Data reduction pipeline for VLT/CRIRES+
 .. image:: https://img.shields.io/github/license/tomasstolker/pycrires
    :target: https://github.com/tomasstolker/pycrires/blob/main/LICENSE
 
-*pycrires* is a Python wrapper for running the CRIRES+ recipes of *EsoRex*. The pipeline organizes the raw data, creates SOF and configuration files for *EsoRex*, runs the calibration and science recipes, applies a telluric correction with *Molecfit*, and creates plots of the images and extracted spectra.
+*pycrires* is a Python wrapper for running the CRIRES+ recipes of *EsoRex*.
+
+The pipeline organizes the raw data, creates SOF and configuration files for *EsoRex*, runs the calibration and science recipes, improves the wavelength solution, and creates plots of the images and extracted spectra.
+
+Additionally, there are dedicated routines for the extraction, calibration, and detection of spatially-resolved objects such as directly imaged planets.
+
+For spatially resolved objects, the telluric lines can typically be corrected with the stellar spectrum. Otherwise, it is possible to use an empirical modeling approach with the recipes of *MolecFit*. The pipeline interface of *pycrires* provides functionalities for both cases.
 
 Documentation
 -------------
@@ -34,7 +40,7 @@ Documentation can be found at `http://pycrires.readthedocs.io <http://pycrires.r
 Attribution
 -----------
 
-Please cite `Stolker & Landman, 2023, ascl: 3541 <https://ascl.net/code/v/3541>`_ when *pycrires* is used in a publication.
+Please cite `Stolker & Landman, 2023, ascl: 3541 <https://ascl.net/code/v/3541>`_ when *pycrires* is used in a publication and Landman et al. (in prep.) specifically when using the dedicated routines for spatially-resolved sources.
 
 Contributing
 ------------

docs/running.rst

@@ -80,7 +80,7 @@ Next, for spatially-resolved targets (e.g. directly imaged exoplanets), there ar
                                   collapse_spatially=True, collapse_exposures=True, accuracy=0.001)
 
   pipeline.remove_starlight(nod_ab, input_folder='correct_wavelengths_2d',
-                            telluric_mask=[0.6, 1.6], svd_broadening_kernel=True)
+                            telluric_mask=(0.6, 1.6), svd_broadening_kernel=True)
 
   pipeline.remove_systematics(nod_ab, n_modes=5, input_folder='remove_starlight')
 

pycrires/pipeline.py

@@ -396,10 +396,11 @@ def _update_files(self, sof_tag: str, file_name: str) -> None:
                     decimal = len(file_tmp.split(".")[-1])
 
                     if decimal == 5:
-                        for dark_item in self.file_dict["DARK"]:
+                        for dark_item in self.file_dict["DARK"].values():
                             if float(file_tmp) == round(dark_item["DIT"], 5):
                                 file_dict["DIT"] = dark_item["DIT"]
                                 break
+
                 else:
                     file_dict["DIT"] = float(file_tmp)
 
@@ -6519,7 +6520,11 @@ def detection_map(
             for which the cross-correlation map will be calculated.
             For each value of :math:`v\\sin\\,i`, a subplot will be
             created that shows the cross-correlation as function
-            of radial velocity and separation from the star.")
+            of radial velocity and separation from the star. When
+            using this parameter, make sure that the template of
+            ``model_flux`` has not been broadened and make sure
+            that the wavelengths of ``model_wavel`` are evenly
+            spaced.
         nod_ab : str
             Nod position which will be cross-correlated.
         input_folder : str