Merge pull request #94 from spacetelescope/ci-fix

Fixing errors in CI

notebooks/IUE/exploring_UV_extinction_curves/exploring_UV_extinction_curves.ipynb

@@ -165,15 +165,13 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "fc1e4e13",
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# We use a radius of zero to find exact matches\n",
     "obs_table_nodust = Observations.query_criteria(obs_collection=\"IUE\",\n",
-    "                                           objectname=target_nodust,\n",
-    "                                           radius = \"0m\")\n",
+    "                                               objectname=target_nodust,\n",
+    "                                               radius=\"0m\")\n",
     "len(obs_table_nodust)"
    ]
   },
@@ -185,7 +183,7 @@
    "outputs": [],
    "source": [
     "# Display the matching observations, if you want\n",
-    "obs_table_nodust.show_in_notebook()"
+    "obs_table_nodust"
    ]
   },
   {
@@ -216,7 +214,7 @@
    "outputs": [],
    "source": [
     "# Display the products, if you want\n",
-    "data_products_nodust.show_in_notebook()"
+    "data_products_nodust"
    ]
   },
   {
@@ -235,8 +233,8 @@
    "outputs": [],
    "source": [
     "filtered_products_nodust = Observations.filter_products(data_products_nodust,\n",
-    "                                                     productType='SCIENCE',\n",
-    "                                                     extension = '.fits')\n",
+    "                                                        productType='SCIENCE',\n",
+    "                                                        extension='.fits')\n",
     "len(filtered_products_nodust)"
    ]
   },
@@ -248,7 +246,7 @@
    "outputs": [],
    "source": [
     "# Display the results, if you want\n",
-    "filtered_products_nodust.show_in_notebook()"
+    "filtered_products_nodust"
    ]
   },
   {
@@ -286,18 +284,18 @@
    "outputs": [],
    "source": [
     "obs_table_dusty = Observations.query_criteria(obs_collection=\"IUE\",\n",
-    "                                            objectname=target_dusty,\n",
-    "                                            radius = \"0m\",\n",
-    "                                            proposal_pi=\"Prevot\") # We specify the PI since this search returns more results\n",
+    "                                              objectname=target_dusty,\n",
+    "                                              radius=\"0m\",\n",
+    "                                              proposal_pi=\"Prevot\") # We specify the PI since this search returns more results\n",
     "\n",
     "\n",
     "data_products_dusty = Observations.get_product_list(obs_table_dusty)\n",
     "\n",
     "# Note that you can skip the 'filter products' step\n",
     "# Instead, you can pass the filters directly to 'download_products'\n",
     "manifest_dusty = Observations.download_products(data_products_dusty,\n",
-    "                                             productType='SCIENCE',\n",
-    "                                             extension = \".fits\")"
+    "                                                productType='SCIENCE',\n",
+    "                                                extension=\".fits\")"
    ]
   },
   {
@@ -356,9 +354,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "897a2252",
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "with fits.open(lw_dusty) as hdulist: \n",
@@ -417,7 +413,7 @@
     "        \n",
     "    # Extract our desired data from the corresponding columns\n",
     "    wav = spectrum[0][0]   # wavelength, angstrom, A\n",
-    "    flux = spectrum [0][1] # flux, ergs/cm2/sec/A\n",
+    "    flux = spectrum[0][1] # flux, ergs/cm2/sec/A\n",
     "\n",
     "    return wav, flux"
    ]
@@ -434,9 +430,7 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "781f9315",
-   "metadata": {
-    "scrolled": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Let's run our helper function on our data\n",
@@ -511,30 +505,31 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Create a helper function to plot the data. We want to plot inverse wavelength against log of flux\n",
+    "def Plot(wav, flux, style, lbl):\n",
+    "    plt.plot(10**4/wav, np.log10(flux), style, label=lbl)\n",
+    "\n",
+    "\n",
     "# Set up the plot size, labels, units\n",
     "fig = plt.figure()\n",
     "ax = plt.subplot(111)\n",
-    "ax.set_xlabel('1/$\\lambda$ ($\\mu m^{-1}$)')\n",
+    "ax.set_xlabel(r'1/$\\lambda$ ($\\mu m^{-1}$)')\n",
     "ax.set_ylabel(r'log(Flux (ergs $cm^{-2}$ $s^{-1}$ $\\AA^{-1}$))')\n",
-    "ax.set_xlim([3,8])\n",
+    "ax.set_xlim([3, 8])\n",
     "ax.set_ylim([-13.6, -11.5])\n",
     "\n",
-    "# Create a helper function to plot the data. We want to plot inverse wavelength against log of flux\n",
-    "def Plot(wav, flux, style, lbl):\n",
-    "    plt.plot(10**4/wav, np.log10(flux), style, label=lbl)\n",
-    "\n",
     "# Call the helper function four times: for long/short wavelengths of both targets\n",
     "Plot(wav_lw_dusty, flux_lw_dusty, 'r:', 'lw_dusty')\n",
     "Plot(wav_sw_dusty, flux_sw_dusty, 'r', 'sw_dusty')\n",
     "Plot(wav_lw_nodust, flux_lw_nodust, 'b:', 'lw_nodust')\n",
     "Plot(wav_sw_nodust, flux_sw_nodust, 'b', 'sw_nodust')\n",
     "\n",
     "# Add some labels to our plot to clarify which spectrum is which\n",
-    "plt.text(6.7, -13.4, target_dusty, fontsize = 11, color='r')\n",
-    "plt.text(6.1, -12.6, target_nodust, fontsize = 11, color='b')\n",
+    "plt.text(6.7, -13.4, target_dusty, fontsize=11, color='r')\n",
+    "plt.text(6.1, -12.6, target_nodust, fontsize=11, color='b')\n",
     "\n",
     "# Create the legend and show our plot\n",
-    "plt.legend(loc='best',frameon=False)\n",
+    "plt.legend(loc='best', frameon=False)\n",
     "plt.show()"
    ]
   },
@@ -564,7 +559,7 @@
    "outputs": [],
    "source": [
     "# Request the B and V magnitudes\n",
-    "Simbad.add_votable_fields('flux(B)','flux(V)')\n",
+    "Simbad.add_votable_fields('flux(B)', 'flux(V)')\n",
     "\n",
     "# Query for dusty star, in our case AvZ 456\n",
     "table_dusty = Simbad.query_object(target_dusty)"
@@ -578,7 +573,7 @@
    "outputs": [],
    "source": [
     "# Examine the data, if you want\n",
-    "table_dusty.show_in_notebook()"
+    "table_dusty"
    ]
   },
   {
@@ -615,11 +610,11 @@
    "outputs": [],
    "source": [
     "# Extract these values from the table\n",
-    "V_dusty = table_dusty['FLUX_V'][0]\n",
-    "B_dusty = table_dusty['FLUX_B'][0]\n",
+    "V_dusty = table_dusty['V'][0]\n",
+    "B_dusty = table_dusty['B'][0]\n",
     "\n",
-    "V_nodust = table_nodust['FLUX_V'][0]\n",
-    "B_nodust = table_nodust['FLUX_B'][0]\n",
+    "V_nodust = table_nodust['V'][0]\n",
+    "B_nodust = table_nodust['B'][0]\n",
     "\n",
     "# Plug into the formula\n",
     "EBV = (B_dusty-V_dusty)-(B_nodust-V_nodust)\n",
@@ -647,17 +642,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "V_rat = V_dusty/V_nodust\n",
-    "\n",
     "# Another helper function to create our plots, and save the output data for further analysis\n",
     "def Extinction(wav, flux1, flux2):\n",
+    "    V_rat = V_dusty/V_nodust\n",
     "    flux_rat = flux1/flux2\n",
     "    # Calcuating extinction from the formula \n",
     "    ext = np.log(V_rat/flux_rat)/EBV\n",
-    "    inv_wav= 10**4/wav\n",
+    "    inv_wav = 10**4/wav\n",
     "    plt.plot(inv_wav, ext, 'o', markersize=2)\n",
     "    return inv_wav, ext \n",
     "\n",
+    "\n",
     "# Create the figure, then use the helper function to create the plots and save the data\n",
     "plt.figure()\n",
     "s_inv_wav, s_ext = Extinction(wav_sw_dusty, flux_sw_dusty, flux_sw_nodust)\n",
@@ -778,7 +773,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "myenv",
    "language": "python",
    "name": "python3"
   },
@@ -792,7 +787,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,

notebooks/IUE/exploring_UV_extinction_curves/requirements.txt

@@ -1,5 +1,5 @@
 astropy >= 5.3.3
 astroquery >= 0.4.6
 matplotlib >= 3.6.2
-numpy >= 1.23.5
+numpy >= 1.23.5, < 2.0.0
 scipy >= 1.10.1

notebooks/JWST/download_by_program_id/download_by_program_id.ipynb

@@ -162,9 +162,9 @@
     "obs_list = Observations.query_criteria(proposal_id=2733)\n",
     "\n",
     "# We can chooose the columns we want to display in our table\n",
-    "disp_col = ['dataproduct_type','calib_level','obs_id',\n",
-    "            'target_name','filters','proposal_pi', 'obs_collection']\n",
-    "obs_list[disp_col].show_in_notebook()"
+    "disp_col = ['dataproduct_type', 'calib_level', 'obs_id',\n",
+    "            'target_name', 'filters', 'proposal_pi', 'obs_collection']\n",
+    "obs_list[disp_col]"
    ]
   },
   {
@@ -234,8 +234,8 @@
     "filtered_prod = Observations.filter_products(data_products, calib_level=[2], productType=\"SCIENCE\")\n",
     "\n",
     "# Again, we choose columns of interest for convenience\n",
-    "disp_col = ['obsID','dataproduct_type','productFilename','size','calib_level']\n",
-    "filtered_prod[disp_col].show_in_notebook(display_length=10)"
+    "disp_col = ['obsID', 'dataproduct_type', 'productFilename', 'size', 'calib_level']\n",
+    "filtered_prod[disp_col][:10]"
    ]
   },
   {
@@ -374,7 +374,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "myenv",
    "language": "python",
    "name": "python3"
   },
@@ -388,7 +388,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,