Update STIS dependencies

notebooks/STIS/CoronagraphyViz/STIS_Coronagraphy_Visualization_v2.ipynb

@@ -32,7 +32,7 @@
     "tags": []
    },
    "source": [
-    "Some of the most important steps in planning and preparing coronagraphic observations with STIS involve:  \n",
+    "Some of the most important steps in planning and preparing coronagraphic observations with STIS involve:\n",
     "\n",
     "- 1. selecting the appropriate occulter position (from the various supported fiducial apertures, visualized in the figure below on an on-orbit lamp flatfield)\n",
     "- 2. determining the appropriate orientation of the observatory to conduct science observations, often at multiple telescope roll angles to provide angular diversity of imaging for the purposes of post-processing. \n",
@@ -494,7 +494,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,

notebooks/STIS/CoronagraphyViz/requirements.txt

@@ -1,2 +1,2 @@
-matplotlib==3.7.0
-numpy==1.23.4
+matplotlib>=3.7.0
+numpy>=1.23.4

notebooks/STIS/calstis/calstis_2d_ccd.ipynb

@@ -28,7 +28,7 @@
     "## 0 Introduction\n",
     "The STIS calibration pipeline, calstis, performs the calibration of STIS science data. Calstis consists of a series of individual modules that performs initial 2D image reduction, contemporaneous wavecal processing, spectroscopic calibration, extraction, rectification, and summation of images. In this notebook, we will go through the data flow through calstis of 2D CCD data reduction common to imaging and spectroscopy (for creating the `flt` data product from a `raw` file). This notebook also shows how negative count values are produced in the pipeline.\n",
     "\n",
-    "Some calibration process may require the application of calibration reference files. The names of which are found in the fits file header. To download reference files and configure reference environment variables, follow the steps in [HST Calibration Reference Data System (CRDS)](https://hst-crds.stsci.edu/docs/cmdline_bestrefs/) for personal or offsite use.\n",
+    "Some calibration process may require the application of calibration reference files. The names of which are found in the fits file header. To download reference files and configure reference environment variables, follow the steps in [HST Calibration Reference Data System (CRDS)](https://hst-crds.stsci.edu/docs/cmdline_bestrefs/) for personal or offsite use. \n",
     "\n",
     "For more information about calstis see:\n",
     "- [STIS Calibration in the STIS Data Handbook](https://hst-docs.stsci.edu/stisdhb/chapter-3-stis-calibration)\n",
@@ -705,7 +705,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.12.4"
   },
   "toc": {
    "base_numbering": "0",

notebooks/STIS/calstis/pre-requirements.sh

@@ -1 +1 @@
-conda install -y -c conda-forge hstcal==2.7.4
+conda install -y -c conda-forge hstcal

notebooks/STIS/calstis/requirements.txt

@@ -1,5 +1,5 @@
-astropy==5.3.3
-astroquery==0.4.6
-matplotlib==3.7.0
-stistools==1.4.4
+astropy>=5.3.3
+astroquery>=0.4.6
+matplotlib>=3.7.0
+stistools>=1.4.4
 crds>=11.17

notebooks/STIS/extraction/1D_Extraction.ipynb

@@ -27,7 +27,7 @@
    "source": [
     "## 0 Introduction\n",
     "\n",
-    "The `x1d` FITS file is the one-dimensional extracted spectra for individual imsets of `flt`, `sfl`, or `crj` images. The `x1d` file is in binary table with the science information stored in the 'SCI' extension. In this notebook, we will show how to visualize the extraction regions when generating the `x1d` extracted spectra from a `flt` image. In some cases when users work with images with multiple sources or extended background, they might want to customize extraction. The goal of visualizing extraction region is to help confirm that the proper extraction parameters are selected, and the extraction regions do not overlap.\n",
+    "The `x1d` FITS file is the one-dimensional extracted spectra for individual imsets of `flt`, `sfl`, or `crj` images. The `x1d` file is in binary table with the science information stored in the 'SCI' extension. In this notebook, we will show how to visualize the extraction regions when generating the `x1d` extracted spectra from a `flt` image. In some cases when users work with images with multiple sources or extended background, they might want to customize extraction. The goal of visualizing extraction region is to help confirm that the proper extraction parameters are selected, and the extraction regions do not overlap. \n",
     "\n",
     "For more information on extracted spectra, see the STIS Data Handbook: [5.5 Working with Extracted Spectra](https://hst-docs.stsci.edu/stisdhb/chapter-5-stis-data-analysis/5-5-working-with-extracted-spectra)"
    ]
@@ -426,7 +426,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.12.4"
   },
   "toc": {
    "base_numbering": "0",

notebooks/STIS/extraction/requirements.txt

@@ -1,4 +1,4 @@
-astropy==5.3.3
-astroquery==0.4.6
-matplotlib==3.7.0
-numpy==1.23.4
+astropy>=5.3.3
+astroquery>=0.4.6
+matplotlib>=3.7.0
+numpy>=1.23.4

notebooks/STIS/target_acquisition/requirements.txt

@@ -1,4 +1,4 @@
-astropy==5.3.3
-astroquery==0.4.6
-matplotlib==3.7.0
-stistools==1.4.4
+astropy>=5.3.3
+astroquery>=0.4.6
+matplotlib>=3.7.0
+stistools>=1.4.4

notebooks/STIS/target_acquisition/target_acquisition.ipynb

@@ -26,7 +26,7 @@
    "metadata": {},
    "source": [
     "## Introduction\n",
-    "There are two types of STIS target acquisitions: ACQ and ACQ/PEAKUP. The ACQ is used in almost all STIS observations to center the target. The ACQ/PEAKUP can be taken after the ACQ to further refine the centering and is recommended for observations using slits with widths less than 0.2”. In this notebook, we will go through the steps for ACQ observations, and explore some success and failure cases.\n",
+    "There are two types of STIS target acquisitions: ACQ and ACQ/PEAKUP. The ACQ is used in almost all STIS observations to center the target. The ACQ/PEAKUP can be taken after the ACQ to further refine the centering and is recommended for observations using slits with widths less than 0.2”. In this notebook, we will go through the steps for ACQ observations, and explore some success and failure cases. \n",
     "\n",
     "\n",
     "For ACQ observations, the target acquisition data has three science extensions:\n",
@@ -457,7 +457,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.12.4"
   },
   "toc": {
    "base_numbering": 1,

notebooks/STIS/view_data/requirements.txt

@@ -1,7 +1,7 @@
-astropy==5.3.3
-astroquery==0.4.6
-ipython==8.11.0
-matplotlib==3.7.0
-numpy==1.23.4
-pandas==1.5.3
-stistools==1.4.4
+astropy>=5.3.3
+astroquery>=0.4.6
+ipython>=8.11.0
+matplotlib>=3.7.0
+numpy>=1.23.4
+pandas>=1.5.3
+stistools>=1.4.4

notebooks/STIS/view_data/view_data.ipynb

@@ -64,7 +64,7 @@
     "\n",
     "**Defining some terms:**\n",
     "\n",
-    "* **HST:** Hubble Space Telescope\n",
+    "* **HST:** Hubble Space Telescope \n",
     "* **STIS:** Space Telescope Imaging Spectrograph on HST (https://www.stsci.edu/hst/instrumentation/stis)\n",
     "* **STIS/NUV-MAMA:** Cs2Te Multi-Anode Microchannel Array (MAMA) detector for observing mainly in the near ultraviolet (NUV)\n",
     "* **STIS/FUV-MAMA:** Solar-blind CsI Multi-Anode Microchannel Array (MAMA) detector for observing mainly in the far ultraviolet (FUV)\n",
@@ -1171,7 +1171,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.12.5"
   },
   "toc": {
    "base_numbering": 1,