From 1f6419d8593e38e84de240ab55ccb4b758b0aa36 Mon Sep 17 00:00:00 2001 From: Ori Date: Mon, 13 Dec 2021 09:45:46 -0500 Subject: [PATCH 1/8] Added blackbody and polynomial fitting within Jdaviz --- .../JWST_Mstar_dataAnalysis_analysis.ipynb | 434 +++++++++++++----- 1 file changed, 316 insertions(+), 118 deletions(-) diff --git a/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb b/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb index e7a55987..7b4cc08f 100644 --- a/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb +++ b/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb @@ -35,7 +35,7 @@ "\n", "This is the second notebook, which will perform data analysis using `specutils`. Specifically, it will fit a model photosphere/blackbody to the spectra. Then it will calculate the centroids, line integrated flux and equivalent width for each dust and molecular feature. \n", "\n", - "**This notebook assumes you have run the first notebook.**\n", + "**This notebook utilizes reduced data created in the first notebook (JWST_Mstar_dataAnalysis_runpipeline.ipynb), although you don't have to run that notebook to use this notebook. All data created in notebook number 1 are available for download within this noteobok.**\n", "\n", "## To Do:\n", "- Make function to extract spectra from datacube using a different means of extraction.\n", @@ -128,8 +128,17 @@ "metadata": {}, "outputs": [], "source": [ - "import warnings\n", - "warnings.simplefilter('ignore')" + "# Save and Load Objects Using Pickle\n", + "import pickle\n", + "\n", + "def save_obj(obj, name):\n", + " with open(name, 'wb') as f:\n", + " pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n", + "\n", + "\n", + "def load_obj(name):\n", + " with open(name, 'rb') as f:\n", + " return pickle.load(f)" ] }, { @@ -138,16 +147,15 @@ "metadata": {}, "outputs": [], "source": [ - "# Check if Pipeline 3 Reduced data exists and, if not, download it\n", - "import os\n", - "import urllib.request\n", - "\n", - "if os.path.exists(\"combine_dithers_all_exposures_ch1-long_s3d\"):\n", - " print(\"Pipeline 3 Data Exists\")\n", - "else:\n", - " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MRS_Mstar_analysis/reduced.tar.gz'\n", - " urllib.request.urlretrieve(url, './reduced.tar.gz')\n", - " " + "def checkKey(dict, key):\n", + " \n", + " if key in dict.keys():\n", + " print(\"Present, \", end =\" \")\n", + " print(\"value =\", dict[key])\n", + " return(True)\n", + " else:\n", + " print(\"Not present\")\n", + " return(False)" ] }, { @@ -156,17 +164,8 @@ "metadata": {}, "outputs": [], "source": [ - "# Unzip Tar Files\n", - "\n", - "import tarfile\n", - "\n", - "# Unzip files if they haven't already been unzipped\n", - "if os.path.exists(\"reduced/\"):\n", - " print(\"Pipeline 3 Data Exists\")\n", - "else:\n", - " tar = tarfile.open('./reduced.tar.gz', \"r:gz\")\n", - " tar.extractall()\n", - " tar.close()" + "import warnings\n", + "warnings.simplefilter('ignore')" ] }, { @@ -175,9 +174,29 @@ "metadata": {}, "outputs": [], "source": [ - "# Move Files \n", + "# Check if Pipeline 3 Reduced data exists and, if not, download it\n", + "import os\n", + "import urllib.request\n", "\n", - "os.system('mv reduced/*fits .')" + "if os.path.exists(\"combine_dithers_all_exposures_ch1-long_s3d.fits\"):\n", + " print(\"Pipeline 3 Data Exists\")\n", + "else:\n", + " url = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MRS_Mstar_analysis/reduced.tar.gz'\n", + " urllib.request.urlretrieve(url, './reduced.tar.gz')\n", + " # Unzip Tar Files\n", + "\n", + " import tarfile\n", + "\n", + " # Unzip files if they haven't already been unzipped\n", + " if os.path.exists(\"reduced/\"):\n", + " print(\"Pipeline 3 Data Exists\")\n", + " else:\n", + " tar = tarfile.open('./reduced.tar.gz', \"r:gz\")\n", + " tar.extractall()\n", + " tar.close()\n", + " \n", + " # Move Files \n", + " os.system('mv reduced/*fits .')" ] }, { @@ -271,6 +290,62 @@ "plt.close()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Specviz Visualization\n", + "\n", + "You can also visualize the spectrum list inside a Jupyter notebook using Specviz" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Video1:\n", + " \n", + "This Specviz Instructional Demo is from STScI's official YouTube channel and provides an introduction to Cubeviz." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import HTML, YouTubeVideo\n", + "\n", + "vid = YouTubeVideo(\"zLyRnfG32Bo\")\n", + "display(vid)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Open these spectra up in Specviz\n", + "from jdaviz import Specviz\n", + "\n", + "specviz = Specviz()\n", + "specviz.app" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load in the spectrum list from above. Note, only the first spectrum in your list is displayed automatically.\n", + "# You will need to turn on the remaining spectra and scale our plot accordingly to see the other spectra.\n", + "specviz.load_spectrum(splist)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -284,7 +359,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Video1:\n", + "## Video2:\n", " \n", "This Cubeviz Instructional Demo is from STScI's official YouTube channel and provides an introduction to Cubeviz." ] @@ -328,7 +403,8 @@ "metadata": {}, "outputs": [], "source": [ - "# Here, we load the data into the Cubeviz app for visual inspection. In this case, we're just looking at a single channel, because Cubeviz can only load a single cube at a time.\n", + "# Here, we load the data into the Cubeviz app for visual inspection. \n", + "# In this case, we're just looking at a single channel because, unlike Specviz, Cubeviz can only load a single cube at a time.\n", "\n", "ch1short_cubefile = 'combine_dithers_all_exposures_ch1-long_s3d.fits'\n", "cubeviz.load_data(ch1short_cubefile)" @@ -345,7 +421,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Video2:\n", + "## Video3:\n", " \n", "Here is a video that quickly shows how to select a spatial region in Cubeviz." ] @@ -374,10 +450,15 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Developer Note: Since Cubeviz only displays a single cube at a time, you can't extract a full spectrum at the current time. So, you should use the spectrum defined above ('spec')\n" + ] + }, + { + "cell_type": "raw", "metadata": {}, - "outputs": [], "source": [ "# Check to see if user drew a subset in cubeviz. If not, define spec_agb as just the original spectrum.\n", "if not spec_agb:\n", @@ -388,33 +469,21 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "## Specviz Visualization\n", - "\n", - "You can visualize the extracted spectrum inside Specviz" + "spec_agb = spec" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Video3:\n", - " \n", - "This Specviz Instructional Demo is from STScI's official YouTube channel and provides an introduction to Cubeviz." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import HTML, YouTubeVideo\n", + "## Specviz Visualization\n", "\n", - "vid = YouTubeVideo(\"zLyRnfG32Bo\")\n", - "display(vid)" + "You can visualize the extracted spectrum inside Specviz" ] }, { @@ -426,8 +495,6 @@ "outputs": [], "source": [ "# Open these spectra up in Specviz\n", - "from jdaviz import Specviz\n", - "\n", "specviz = Specviz()\n", "specviz.app" ] @@ -487,41 +554,60 @@ "source": [ "## Data analysis \n", "\n", - "Analyze the spectrum1d object (spec) created from the spectrumlist list above.\n", - "\n", - "#### Developer Note: Add blackbody fitting to the modeling. And, for that matter, more of the astropy models: https://docs.astropy.org/en/stable/modeling/physical_models.html Can't do the next step in Specviz because don't have a blackbody fitter." + "Analyze the spectrum1d object (spec) created from the spectrumlist list above." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Fit a continuum - find the best-fitting template (stellar photosphere model or blackbody)\n", + "#### Fit a continuum - find the best-fitting template (stellar photosphere model or blackbody)\n", "\n", "For AGB stars with a photosphere component fit a stellar photosphere model or a blackbody to short wavelength end of the spectra.\n", "\n", "#### Developer Note: Would idealy like to fit the photosphere with a set of Phoenix Models.\n", "I think `template_comparison` may be a good function here to work with the Phoenix Models which have been setup to\n", - "interface with `pysynphot`.\n", - "\n", + "interface with `pysynphot`.
\n", "For now switching to a blackbody." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Video5:\n", + " \n", + "Here is a video that shows how to fit a blackbody model to the spectrum" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Video showing how to fit a blackbody \n", + "HTML('')" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "def blackbody_Fnu(lam, T, A):\n", - " \"\"\" Blackbody as a function of wavelength (um) and temperature (K).\n", - " Function returns the Planck function in f_nu units\n", - " # [Y Jy] = 1.0E+23 * [X erg/cm^2/s/Hz] = 10E+26 [X Watts/m^2/Hz]\n", - " \"\"\"\n", - " from scipy.constants import h, k, c\n", - " lam = 1e-6 * lam # convert to metres\n", - " bb_nu = 2*h*c / (lam**3 * (np.exp(h*c / (lam*k*T)) - 1)) # units of W/m^2/Hz/Steradian ; f_nu units\n", - " return A * bb_nu" + "spectra = specviz.get_spectra()\n", + " \n", + "a = checkKey(spectra, \"BB1\")\n", + "if a is True:\n", + " # Extract Blackbody fit from Specviz\n", + " blackbody = spectra[\"BB1\"]\n", + "if a is False:\n", + " print(\"No Blackbody\")\n", + " fn = download_file('https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MRS_Mstar_analysis/blackbody.fits', cache=False)\n", + " blackbody = Spectrum1D.read(fn)" ] }, { @@ -530,12 +616,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Only want to fit to a small wavelength range at the start of the spectra\n", - "phot_fit_region = [5.0, 8.0]# Microns\n", - "\n", - "# Trim the specrum to the region showing a stellar photosphere\n", - "sub_region_phot = SpectralRegion([(phot_fit_region[0], phot_fit_region[1])] * u.micron)\n", - "sub_spectrum_phot = extract_region(spec, sub_region_phot)" + "# Delete any existing output in current directory\n", + "if os.path.exists(\"blackbody.fits\"):\n", + " os.remove(\"blackbody.fits\")\n", + "else:\n", + " print(\"The blackbody.fits file does not exist\")" ] }, { @@ -544,32 +629,33 @@ "metadata": {}, "outputs": [], "source": [ - "# fit BB to the data\n", - "def phot_fn(wa, T1, A):\n", - " return blackbody_Fnu(wa, T1, A) \n", - "\n", - "\n", - "popt, pcov = curve_fit(phot_fn, sub_spectrum_phot.spectral_axis.value,\n", - " sub_spectrum_phot.flux.value, p0=(3000, 10000),\n", - " sigma=sub_spectrum_phot.uncertainty.quantity)\n", - "\n", - "# Get the best fitting parameter value and their 1 sigma errors\n", - "best_t1, best_a1 = popt\n", - "sigma_t1, sigma_a1 = np.sqrt(np.diag(pcov))\n", - "\n", - "ybest = blackbody_Fnu(spec.spectral_axis.value, best_t1, best_a1)\n", - "\n", - "print('Parameters of best-fitting model:')\n", - "print('T1 = %.2f +/- %.2f' % (best_t1, sigma_t1))\n", - "\n", - "degrees_of_freedom = len(sub_spectrum_phot.spectral_axis.value) - 2\n", - "\n", - "resid = (sub_spectrum_phot.flux.value - phot_fn(sub_spectrum_phot.spectral_axis.value, *popt)) \\\n", - " / sub_spectrum_phot.uncertainty.quantity\n", - "\n", - "chisq = np.dot(resid, resid)\n", - "\n", - "print('nchi2 %.2f' % (chisq.value / degrees_of_freedom))" + "# Save if you so desire. Keep commented otherwise.\n", + "# blackbody.write('blackbody.fits')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#rename blackbody.flux as ybest\n", + "ybest = blackbody.flux" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "def blackbody_Fnu(lam, T, A):\n", + " \"\"\" Blackbody as a function of wavelength (um) and temperature (K).\n", + " Function returns the Planck function in f_nu units\n", + " # [Y Jy] = 1.0E+23 * [X erg/cm^2/s/Hz] = 10E+26 [X Watts/m^2/Hz]\n", + " \"\"\"\n", + " from scipy.constants import h, k, c\n", + " lam = 1e-6 * lam # convert to metres\n", + " bb_nu = 2*h*c / (lam**3 * (np.exp(h*c / (lam*k*T)) - 1)) # units of W/m^2/Hz/Steradian ; f_nu units\n", + " return A * bb_nu" ] }, { @@ -593,7 +679,7 @@ "\n", "# Now subtract the BB and plot the underlying dust continuum\n", "plt.figure(figsize=(8,4))\n", - "plt.plot(spec.spectral_axis, spec.flux.value - ybest, color='purple', label='Dust spectra')\n", + "plt.plot(spec.spectral_axis, spec.flux.value - ybest.value, color='purple', label='Dust spectra')\n", "plt.axhline(0, color='r', linestyle='dashdot', alpha=0.5)\n", "plt.xlabel('Wavelength (microns)')\n", "plt.ylabel(\"Flux ({:latex})\".format(spec.flux.unit))\n", @@ -649,7 +735,86 @@ "outputs": [], "source": [ "# Subtract the continuum and plot in a new instance of specviz\n", - "bbsub_spectra = spec - ybest # continuum subtracted spectra - Dust only" + "bbsub_spectra = spec - ybest.value # continuum subtracted spectra - Dust only" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "specviz = Specviz()\n", + "specviz.app" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "specviz.load_spectrum(bbsub_spectra)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Video6:\n", + " \n", + "Here is a video that shows how to fit a polynomial to two separate spectral regions within a single subset to remove more underlying continuum" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Video showing how to fit a polynomial to two separate spectral regions within a single subset\n", + "HTML('')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "spectra = specviz.get_spectra()\n", + " \n", + "a = checkKey(spectra, \"PolyFit\")\n", + "if a is True:\n", + " # Extract Blackbody fit from Specviz\n", + " blackbody = spectra[\"PolyFit\"]\n", + "if a is False:\n", + " print(\"No Polyfit\")\n", + " fn = download_file('https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MRS_Mstar_analysis/poly.fits', cache=False)\n", + " poly = Spectrum1D.read(fn)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Delete any existing output in current directory\n", + "if os.path.exists(\"poly.fits\"):\n", + " os.remove(\"poly.fits\")\n", + "else:\n", + " print(\"The poly.fits file does not exist\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Save if you so desire. Keep commented otherwise.\n", + "#poly.write('poly.fits')" ] }, { @@ -658,10 +823,6 @@ "metadata": {}, "outputs": [], "source": [ - "# Fit a spline to the 10 micron feature to isolate it.\n", - "\n", - "bbsub_spectra = spec - ybest # continuum subtracted spectra - Dust only\n", - "\n", "# Fit a local continuum between the flux densities at: 8.0 - 8.1 & 14.9 - 15.0 microns\n", "# (i.e. excluding the line itself)\n", "\n", @@ -673,15 +834,8 @@ "# Zoom in on the line complex & extract\n", "line_reg_10 = SpectralRegion([(sw_region*u.um, lw_region*u.um)])\n", "line_spec = extract_region(bbsub_spectra, line_reg_10)\n", - "\n", - "# Fit a local continuum - exclude the actual dust feature when doing the fit\n", - "\n", - "lgl_fit = fit_generic_continuum(line_spec, \n", - " exclude_regions = SpectralRegion([(sw_line*u.um, \n", - " lw_line*u.um)])) \n", - "\n", - "# Determine Y values of the line continuum\n", - "line_y_continuum = lgl_fit(line_spec.spectral_axis) \n", + "polysub = extract_region(poly, line_reg_10)\n", + "line_y_continuum = polysub.flux\n", "\n", "#-----------------------------------------------------------------\n", "# Generate a continuum subtracted and continuum normalised spectra\n", @@ -729,6 +883,49 @@ "metadata": {}, "outputs": [], "source": [ + "#Load 10 um feature back into specviz and calculate the Line flux; Line Centroid; Equivalent width\n", + "specviz = Specviz()\n", + "specviz.app" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "specviz.load_spectrum(line_spec_consub,data_label='Continuum Subtraction')\n", + "specviz.load_spectrum(line_spec_norm,data_label='Normalized')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Video7:\n", + " \n", + "Here is a video that shows how to make line analysis measurements within specviz (i.e., line flux, line centroid, equivalent width)
\n", + "Note: You want to calculate your equivalent width on the normalized spectrum
\n", + "Note: You can also hack to convert the line flux value into more conventional units
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Video showing how to measure lines within specviz\n", + "HTML('')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Alternative method to analyze the 10um line within the notebook.\n", "# Calculate the Line flux; Line Centroid; Equivalent width\n", "\n", "line_centroid = centroid(line_spec_consub, SpectralRegion(sw_line*u.um, lw_line*u.um))\n", @@ -810,9 +1007,10 @@ }, "source": [ "## About this notebook\n", - "**Author:** Olivia Jones, Project Scientist, UK ATC.\n", - "**Updated On:** 2020-08-11\n", - "**Later Updated On:** 2021-09-06 by B. Sargent, STScI Scientist, Space Telescope Science Institute" + "**Author:** Olivia Jones, Project Scientist, UK ATC.
\n", + "**Updated On:** 2020-08-11
\n", + "**Updated On:** 2021-09-06 by B. Sargent, STScI Scientist, Space Telescope Science Institute (added MRS Simulated Data)
\n", + "**Updated On:** 2021-12-12 by O. Fox, STScI Scientist (added blackbody and polynomial fitting within the notebook)
" ] }, { @@ -846,7 +1044,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.8.10" } }, "nbformat": 4, From 55ec0602ed2096d99c7a651dce624d1f8d621846 Mon Sep 17 00:00:00 2001 From: Ori Date: Tue, 14 Dec 2021 10:21:48 -0500 Subject: [PATCH 2/8] some added text, plus one small variable change --- .../JWST_Mstar_dataAnalysis_analysis.ipynb | 109 +++++++++++++++--- 1 file changed, 91 insertions(+), 18 deletions(-) diff --git a/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb b/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb index 7b4cc08f..d4e8b9a9 100644 --- a/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb +++ b/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "slideshow": { "slide_type": "fragment" @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "jupyter": { "outputs_hidden": false @@ -91,7 +91,15 @@ "name": "#%%\n" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: AstropyDeprecationWarning: block_reduce was moved to the astropy.nddata.blocks module. Please update your import statement. [astropy.nddata.utils]\n" + ] + } + ], "source": [ "# Import astropy packages \n", "from astropy import units as u\n", @@ -124,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -143,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -160,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -170,9 +178,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pipeline 3 Data Exists\n" + ] + } + ], "source": [ "# Check if Pipeline 3 Reduced data exists and, if not, download it\n", "import os\n", @@ -209,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -255,9 +271,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "wav = wlall*u.micron # Wavelength: microns\n", "fl = fnuall*u.Jy # Fnu: Jy\n", @@ -310,9 +339,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/jpeg": 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\n", + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from IPython.display import HTML, YouTubeVideo\n", "\n", @@ -322,11 +374,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "aa4adad0c7204fb8bb657b262afd27e3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Application(config='specviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_selected', 'd…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Open these spectra up in Specviz\n", "from jdaviz import Specviz\n", @@ -335,14 +402,20 @@ "specviz.app" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load in the spectrum list from above. Note, only the first spectrum in your list is displayed automatically. You will need to turn on the remaining spectra in the \"DATA\" drop-down, then hit the \"Home\" button in the toolbar, and scale our plot accordingly to see the other spectra." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "# Load in the spectrum list from above. Note, only the first spectrum in your list is displayed automatically.\n", - "# You will need to turn on the remaining spectra and scale our plot accordingly to see the other spectra.\n", + "# Load in the spectrum list from above. \n", "specviz.load_spectrum(splist)" ] }, From 71d9c7fc47b5c7314b3a0eef037cbb876caa265b Mon Sep 17 00:00:00 2001 From: Ori Date: Tue, 14 Dec 2021 14:25:57 -0500 Subject: [PATCH 3/8] some added text, moved some cells around --- .../JWST_Mstar_dataAnalysis_analysis.ipynb | 231 ++++++++++-------- 1 file changed, 135 insertions(+), 96 deletions(-) diff --git a/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb b/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb index d4e8b9a9..b859d878 100644 --- a/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb +++ b/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb @@ -266,66 +266,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Developer Note: At this point, the 12 extracted 1D spectra need to get spliced together with a specialty function written for MRS. " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "wav = wlall*u.micron # Wavelength: microns\n", - "fl = fnuall*u.Jy # Fnu: Jy\n", - "efl = dfnuall*u.Jy # Error flux: Jy\n", - "\n", - "# Make a 1D spectrum object\n", - "spec = Spectrum1D(spectral_axis=wav, flux=fl, uncertainty=StdDevUncertainty(efl))\n", - "\n", - "# Apply a 5 pixel boxcar smoothing to the spectrum\n", - "spec_bsmooth = box_smooth(spec, width=5) \n", - "\n", - "# Plot the spectrum & smoothed spectrum to inspect features \n", - "plt.figure(figsize = (8,4))\n", - "plt.plot(spec.spectral_axis, spec.flux, label='Source')\n", - "plt.plot(spec.spectral_axis, spec_bsmooth.flux, label='Smoothed')\n", - "plt.xlabel('Wavelength (microns)')\n", - "plt.ylabel(\"Flux ({:latex})\".format(spec.flux.unit))\n", - "plt.ylim(-0.05,0.15)\n", - "\n", - "# Overplot the original input spectrum for comparison\n", - "origspecfile = fn = download_file('https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MRS_Mstar_analysis/63702662.txt', cache=False)\n", - "origdata = ascii.read(origspecfile)\n", - "wlorig = origdata['col1']\n", - "fnujyorig = origdata['col2']*0.001 # comes in as mJy, change to Jy to compare with pipeline output\n", - "plt.plot(wlorig,fnujyorig, '.', color='grey', markersize=1, label='Original Input')\n", - "\n", - "plt.legend(frameon=False, fontsize='medium')\n", - "plt.tight_layout()\n", - "plt.show()\n", - "plt.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Specviz Visualization\n", + "## Specviz Visualization of the SpectrumList\n", "\n", - "You can also visualize the spectrum list inside a Jupyter notebook using Specviz" + "You can visualize the spectrum list inside a Jupyter notebook using Specviz" ] }, { @@ -339,32 +282,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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\n", - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from IPython.display import HTML, YouTubeVideo\n", "\n", @@ -372,9 +292,16 @@ "display(vid)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read in the SpectrumList (12 unique spectra)" + ] + }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": { "scrolled": false }, @@ -382,7 +309,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aa4adad0c7204fb8bb657b262afd27e3", + "model_id": "fd43f7b81a474d15bef7f14b499267ab", "version_major": 2, "version_minor": 0 }, @@ -411,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -439,9 +366,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/jpeg": 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1ThOnUck9ZSj7MF3sbbGPrQxDyTcYWjKCjorSV7vmL3/JcP14TXHpxcnaKcnySbf0PRbGwscO5VKrXaqKeTjCLaV3ybOVLbOJas6srfBRi+qR0cHXdbDZa87OpVjTpzsr3XrJSfFXVvmP5fKz2Xx+OuTtCGXEVl/8k/zNGB9TD4iq+Mexj8XLf9BMXSqV8XOMYNVJTtlvfK1o23y4htesoZcPTd4Uk0379V+0/wBivuTkvq3pZtpfbL8Kl+k5rRu25L7aP4VL9JhTuV8f9Ynv+1FjoYH1qGJp8oqtHvjv+jMBu2I74hR4ThUg/nF+A+/60uPtz7q41SXIzTlZ68iJTOTuf7no/F1vMWZtSajutDNnuXwQK3GacWLSzX0NVRCJ23CtVLqylSd7t/LgLipLgVVKzfEzSrGfvV3qZkXxNtOcWtd5ghO3xLlUTW4jqarmrqlTLuKp4hPRr5kSlcrcNScVe/0JIZsLCghDJiRLhr8uRCZNE+18VzElEaExZMjWuelbYrRMxFIokyF1JciEwDk3JuK2Sjq1xLIGtyTy2SVopacXzZlprUsUhieluYlSKrkpjgrRCPEZsojMfOTZWvPUTIRg2CiI90iY6iyUkMoEXpXPx6iKRcnoVqJdSp3Ita+P4ggh1Fkxp8C3cheXtXj6Vq6V/kSqnNBKS077Ezhob8VzdzPpdB8BMRJLvIUrJZdG39BJUL6mv5TvpRmuyyIk6WRlkY3RPXY5jt7C9ur/ANPW/JGKLNXk9L+0Qi901OHWL/exks1o960Oj47v/wAcHy/f/bQmSV02WFsW+X9yj+PL9BXszF9hVjP+V6S/pf8A5csl/co/jv8AQc9smTZYu9ZZXb2h2dTNQk40Z5+0zW+yq3Wkr8NCnaeAquFCSi5tU8knD116r0enwZTbt8N/8tDrKj/+BSqS8zlklKMqdVSvGTi8slbh8URObz9fhV6nX3+WKOBqt6Uqn+hnUxWAaw9CNScaMYqc5uT9bNJ7lFat2OZ59XlaPbVXdpW7SW9/M0bVhKtjeyhq1kpR47lr+5fXlbNLnMrpS2pGNKtXpxtmtTjOS9erUslm+CS1PMS4m7alaLnGlT+6orJH/M/5pfNmKSH8XMk39l8nW3P037c+/j+FS/SYYm/bv38fwqX6TAiuP6xPf9qls3bC/vlHvl+lnPbN+xHatKfCnSqzfyjb9w7/AK0cf2jk4hFMJK+pZXksu95rrS2ludzHc5+5/udnx3/bG2EUhnN3M1JtmiKuR9Nfta05WKasco2e2hFSd1Yi1UYZybe8TIy6UEhZSJ1UhqbL4vmilSVt2pfF6EWtIZQTGcLFV7EvEac0TdOYZiNFqytaPrvIlEk81nJLOzHjEVonNVPRCZi6aKpkqLIrY7EHBSjxQKJYkK0+Y4gyQqGR1xxLEhkREYZAlAkPGI5RmoQ6LFSsrsUNPxsGUYhMlGdac32VDqTBoItGday4en8TQpFVONzXTw7ZnbPy1lpYQbFqqxsjQlbcSqbim2TO5qrzsxg0tp3jSlqNVWt0vkVKS38Tq46cvf2ujHQug1a1vmUU3c0whc0t2M56rNi4XWnArpw0Or5nfUR4ZLcjn6+SNMUYOq6dSElvjKMujOhtTDqOIqW9mT7SPJxlr+4lHZk5S+V38FyN1WHaYaL/AJ6DyS/Dfsv5bjp+D5ZZ6cf+o4yuVlsTCpcfiVvRnZHFa6U5f2KP47/Qc2Ruq/3GP/UP9BijT3cxc/n/AJV1+F2FrypyU4O0l0a5P4HWwcaFVVo032c6lN3py1gpLXMny+Bj8zhTSeIn2d9VTSzVWu7h8x8DjcPCvTVOi9ZKLqTm3JJ6blpxI7zqbF8TL7Ts/Zj7enJ1aEoxlnajUUm0tdEU4jG06faOg3OrUcs1ZrKopu7UF+5to1403im8PRj2UXD1U4uTcsqTdzmqeEqaOFSg+cJdpD5p69CZvV2quSZGC1hZM14rZ86cc6calF7qkNY9z5Mxs6JZfpjZZ9ujt37+P4VL9Jzjo7df28fwqX6TnE/H/WH3/ag6GGfZ4OvU41JRox7val9LGCMXJpRV22klzb3I17eqKmoYeLuqMbSfOq9ZP9g7v4PiflxasrsRxBRJictu13czJiyG6xopOxTTXM2xpqxFrSRTVjexnqNpG1x0M87O6I089sM6vMRyLKsLFDFpw6lfQtpNriyiD1L1VtvFVxbvISCEtB3G+52J3F5qI67ixNi0/V36jSfEm1UmDOMplTYNk017kuRS2iM4jYsh2pktCtlgjiFTEwHYqViUzOteXFSHQJDJHfHmpiOQkSUEothIqSHQsGrXMrkyGK2KnprjxK7jxZNOU9/mKkOkTlZlW09r6Bvpb9DDSRvoW4nN8jr4vp0YNuNlvMtdvVM1UN5GNSVtFqc/x9Z0XTBKhdXsY1S1+J26Mborr7P4o7OPmm5XP3zrJQiuJdT32+I6wluJHYtao38pYydOnTzWSHlhVDfzKsLj4xVmrNcTe5QqQ9pWfx1TOCzq9Y2tki6jCyvz1MNb7CrntenJONSPOL3m9PKkuSsUV1nTTOz/AE/F4v8Ahx/N1Op/lx8dhuynZO8Gs0JcJRe4wVHZnZg1bsKztC96c/8ADl4M5WNw06U3Gas/o1zXNHq89fiuG8/mNNSV8DD/AKl/oLXPzSK0TxUkm76qhF/9z+hXgNo0qdOMatKU3Cr2sbSSV7W1RM8VhZylKVKs5Sbk32q1ZGXcz0vZm77YKmZ3bbberb1bYi5p+tvXedVVMI/+TV/3EL/ZP8Gr/urwK8v8Iz/LdtdJUZTW7EThU/8AqoL92edloduvtHDzhThKlVy004x+0W7/AMRjdbB/4Nb/AHV4E/HvMyxXedX7ZsFjJ0ZZoPR6Si9YzXJou2hhoOCr0FalJ5ZQ3ulU93u5D9tg/wDArf7q8C2jj8LCNSKo1ctSOWSdRNPk+9FW3dkKSZlqnbv38fwqX6TnGvaWKjWq54xcY5YxSbu9FYfA4JSTrVnlw8Xq+M37kebHzfHmaVnl16W4CKoU3ipr1tY0IvjPjPuRwcRNzk9b8W+bNu1NoSrT0VopZYQW6EFuSOZFamPfX/10/Hx+UqXAvpUbsolEdTurGFdDQpZXzLqmJVtOBjkr2Y8KbZNOWxasTfQSUQVBkS3vkZ6uz0SrBMxVI20NyViqvTCU8Y0x1MSejsQpFWqkXxmWRqGNz1LYMiq1pzpkuppZFCFzWJVtaMzJuZ+0GjK4YWtGRsXKKlYsUuZNVPYiRIlIJLQm1UitsCAuRVxzkiUBKR6LyzEohDIZmSJBAGjEMRjNikU0IeIthkI19NmhyVkkZYF8EZ9RrxVtNm2kZadjTCRz/JHTw6GHFx09YiU6thMS72Ofjjei7uNWHqW3GunK+85lKZphUsXfjusb3GmVJFcIZW09w0ao82uJpzzYx/kijE4S6zR4b1zOSqklLj3HbnUsvgcjETWa6Lkxc6108LiamVWenKWtjoYWv2iaek1vXB/FHnqeMtwGljWpKUdH8Dbn/DPqS/bv18OpKzRzKmLjF+b4mLlTVskl95TvxjzXwGhtSo4+1qLLD+c6NpTs7S/Y6J8mTLGH8Pv1WbFbOlCOeDVWi91SGq/+y3xfeZotIrw+Pq4ao8snGS0lxT+DW5nQjjcNX+9punN750PZffB/sdPPyf8AbHv4sY8+ujLY1TR6MjP7nEUqnKMn2U+jFlsbFR/5Mn8YtS/Jl+fP7ZeFZqkypmuOy8S/+RU/0MujsTEWvKCguc5xivzDz5n5Hj1+nNBRbaSTbe5LVtnR8zoU/vsSpP3KCzv/AFPQJbVjTTWGpqlwc369V/N7vkK/JPwc401PAQopTxV098aEX9pL+r3UY8bjJ15pO0YRVoQjpCC5JGKWKk5Zm223dtu7bNUKilLVb7M5u/l/Tp4+L9sdSk03oURhdm/Geq3Z3TMluRjOm9FOnd/AtnRVtN5bQotqyTzDShkUlOOrWn+VkXpWMkYPd0NNKN3ZiR0+RdCV9RWnE1pZbIzyqJMurO5kqRJi9O5FNQa5EmJWsdanbUzM3VfZMckXKCJFzdkVoEFOHU2hXILkSYhqyLLYFEGWZxU4uzk5ilMsUiTWxmNm0KEwzEVpKdkEpDZTJp9sFgGsQeq8kIa5BFyKuLYXeiV+Om8VsWM2tU7P4EXFoSShUMmGhIIARNOLIF8GURZbFkVpGiDNNNXMcGbqEklqu74GXUaeS+KsLXe4a+hTVkLnj3rLvtZBmiEzDCRcpmni5eum6Ei6MtDDSmaITLnLnvViyVHNxKZYK3A0wmWRmPwi58txya2AlfRXKvR9V7os7qkT2vI0nJX5r+XFw2Ar5suVrnfRJHVlDzWm6jkpTtaPK/M2UpuWjPIbUr1O1nGcszUmvgl8EHUbfB1e7ay42tmm5c3cWjUfOxEYriQ5pbiLcdeftfOpOPxQ9PaEr6Nx7m0JSlm3dw1XCrLKT0SXLiVPl6n5ZX4+L+FvpSpe0pzt/XKxtw9RTi29Xbfe552/A0YLEZW47789R35O/wBl/FxPw146UoZG+P1LVK2q3W3GXEwvZXb0v3GnBNZWpct5HXVs9nzzJ9IqRWmmhNOSTsTKGZP4FSWpMHX2sxkufIqoTytK1y/ELPBabuJNClazAVuo1VBvRJNfMmaz8LoKsEoxnfW+q+A1Kad7EKjNWoZUVR3fI11Vcpyk2qzWKtdbiqUrrVbjfUpbzLK24cp2YzymNwKK+jJpz0CjlFTcZpI0X1M03qxxZbEMlyEbKAAFILgMSDbJsTZEmRSaNEamhRx+BdZMVEOmMt5XHQdE05WiA8olEGaqeqMOpjo5uuWAEM9SvKiBSWQRVJJIAAZEikoDMCBEk0aZDplaZr2Zg5YivTox0c5WvyW9vpcWHqaT+bNEJ8OJ9AhSwuz6F7RpwVk5NXnJ/m2ZnjsBjac8zhJQi5SzRcJxjzXHoTZo14+MxJs9B5NV8JGNbtXTS7T7PtcubJbQ9HQoYapHNCFKUdVdQi0GYjNfOrlkGz3U6uBjJxl5upJ2aahdM4nkzsmNXNVqK8FLLGPBvi2Nn1x7xyae74GiEtD1eJ2ph6M+ylJKXuxi2l323HM27DD5ITppZ56rJonHi2iuay+T48myuVGZZmKIm3ZsoqtB1LZLu+bduZq5ctuKlPQaEz1UaFJpSUINNXTyq1jzm0pwdaTpuOW0bZbW3Bz3vrGvyfDeJtqyk5ZZSSu4xcraK9lc8Piq7nNye9u56+njF2kqSl63YV5SXL1NDxlClm1e4i3a7vinhx/y01tnV40qdV032VS2Waaau9yfL5l9TyYx8U28PKy5Sg30TOhLZteGGoVM7lh5TjLJd2pyvbcetxWFxDx1KrCbjh4wtUWfR+1/L81qRWnlr5pgcNVrVVRpJ9q2/Vby2stb33bicU6lKdSjV9uLyyTd7PvPbYRU6dbaGPiouKbhTfCUrLNbvlY53lRsqNfGYStH7rE9nCbXO61+cX9BH5ODs7YGLxMc9Kk8nCcmoRfdfeTDYGL7eVFUW6sEpuOaHst2Tvex1/LTa1WniFhqM5UqNOENKbcLtrmuCVtC7yHxdSviq0qs3OSw8YZpb7KWl3x3vUr3idZ6+x69Kk5VaeRbleUX+TEwGy6taTdKOa1syulZPvOlXwtSlRtUxixF3aym5NPXXVsxbOxM4TSjKSu0nZtX1IAqbBxMJZnTahdL2o727LjzJfk/i1d9i7LX2oeJ3dtyn55SipyUH2TcbvK/X5Fu2MJUlVlKOLVKOVfZ52t3wvxAsedWCn2Maso/Zy0jK61evDfwZbh9mV6lNSp08yu1dSivzZ0K7/4Xh/6/3kaNnUpT2c4wq9lLO/Xva3rcwDjzwdWM1RnB9pJXjFNO/wAdO4etsqtQjmnC0OLTUrd9jVgqywuNXb11VTpWVS7ko3e6/wAvqPtPZ1ZRlUp1nVoTmpSWa9tdHyaV+Aqc9MmFwNasn2cG1z0S6sz4vBVKMstSNpPdZp3XyO7tuvKjGnRpNwioXdtG+G8o2FJ18QnVk59nBuObV6szz8NJfyxx2JiZRv2fDc5JPocWWEqOqqKg+1vZRfqu/wAzrYvbNeVVzVSUdXlinZJcrcfmdavarPZuJslUlNRl8U4Sf5r6j5k/Au/l5HHbBxcIucqEsq1bTjKy7k7leH2Bi61KNSlRcoSvllngr2duL+B7ihRr0sZia9apbBuPqqU7rctbcNz6mHD0ZVtlUVRxHm16lSSm5OHq9pO0dGvh0LTrxeJwtTDTdOvHJUSTtdPR7txglLU6W3MNOFeSlX7eSjFupmzXVt12zkthIvTAKgzDPUshgmRJiGnzDGdsFIWFLF+axbFmdSL6feKqjQoXJ7MTtEhZVGTlaep9rc9g84lfQoTbH4Csi51VTIZYxGd9eZCMglimdUkkgBGYkW5NwBrhcW5AEsTO55J4iNPH0nJ2Us0E3zasvrp8zhRLEGFr6h5RbIeMoqEZZZxlnjf2W7NWfU8Fjtl18M7VqbityktYP5o6myvLCtSShWj20FpmvlqJd+5nssHi6OMoZo+tTleMoyXHimifcP10+Zxke/8AJJ3wUf6p/meK2zglh8VVpL2U04/0tXS+tvkez8j3/YYf1z/UF+k8z28ttaX9rr/iS/M9X5K1U8LlXtRlK679UeP2zL+11/xZ/mJgcdUoTz05We58U1yaDxLyyvUbV2FUdWVWn66k8zjukn8OZzqGGqTn2ai8/FPTL38jq7M8p4VHGFaOST0Uk7wb/Y7+VXvZXta/GwvoeHPfuVw6Xk7p69XX/LHT6nM2ngnQmo5syaunaxp2ptSpKpOMZOMYycVldm7b2zm4vGTqZc7u4qyfFr4mnGuf5fDMk9vYYT+7U/wo/pPF0p6ns8E/7LT/AAo/pPDQY/j/ACfz/XJNjYWp55Wk02nRxGu/Vx0MGGoOMFc9JsiX2svwqn6Tn1oLLddAvOVc+W9Sa6HnlKns+FGMs06klKSvdU1mTf5fUXymxMatVOnUzQ7NJ5X6t7sxvZ9SFGFaSTpz3NO9vgzVgtjVq0VKMUoPc5O1+4ixttaqu1I4XC0aWG7KrJK88yco33vc1xf0MNbygjicBU7WVKhiqU1OjFeqpONmrJvvRHoav28qMVHNGOf2kk4t2uZV5OVsVTVSlGLTbV3JR3OxKprTjZ4DaihVqYhYXExjlmpuKTXztfjZp8dQ2DXwODxlaMMTF0Xh4R7WcladTM724brbjjLyWxTxEsOlTVWNPtbOejje2jS33OPGlLPkaanmyuL0ale1n8ypD162OBwtCnfD4uNd3tlSWi56EYBLMm/eX5mX0TVw0o0qkV2kkmlF5r3dl+R3sLsHEKF3GKfuuXreBNL2bbGMpyxdKUZxcF2d5J6K0m2PtOng69Z1fO4KWVLKrPcc/wBDYiss9OMcrutZJO6dmczHYKpQqZKqSllT0d9H/wChG9HQqYetgaNKpXjTlFuTWl1rLT6jx8281nh5YmMVnbUtLtXT3HAoYCcqE6yS7ODtJ314cPmPtLZ9WjCDnbLJXjKLuu5/EDaKeGwUMRknXc6cqbtUVkozba1+R0J1qGFwlWlTrqtOpe2WzSurX03aHnMNgKtSnUqxScKes7uz3X3G3ZuxMRXp9pBRUHucnbN3Cpu1HE0MZSgq1Tsq0VbM9FLqZFiKWCxFN0p9rHK1Vatrd8O4ow+xsRLMlFJwllknJLWyf5NEYjYOITissbyllj6y32b/AGZHtUxuxGDwFafa+cqEW80oXUXfja+qKMVtqlPGYWMGoYajJvM9FfK1fuW75mKn5O4mebLGPqycX663oWp5OYpSjBxjmkpW9dcLX/MoemDyixCq4utKFRypuScbSbj7K3HXw8sJX2ZQw9bFQpSjKUmtG160rKz7zg0cFOpW7GKXaOUoWbssyvfX5M2w8lcXPNljD1ZOL9dLVf8AsNGRyNrYelSquFCqq1NKLU1azfFaHOkj0NXySxiqQg4wzTzZftFwV2JjfJfE4elKrVjBQja9ppvV23DDgIg0ukVZbMNVipAWuBDiBYRIbIrED8BVXMIok3JZMI3AqspuPEa6KpLUi4YerpSQKZSmNEVip0tkytsZsRnVa4oGRYYGQotiQYtwBkgIuCQgkZIEhkhgJHZ8mtlwxeIUKk8sYrM43tKaXBfuchIuoVJQlGcG4zi7xktGmBPTba8k60arlhYKdKWqgmlKD5a8D0XkvsueFw7jUt2k5OckndR0SSv8jh4Py2mo2rUVOXvQllv8jPtLyuq1ouFKPZRejlfNNrv4E5T2T2w+UeKVXG1pRd4pqCfPKrP63PXeR39xh/XU/UfPkjubK8pKmFoqlGnCSTk7ybvq7js9Jl96y7Y/veI/Fn+Z39k+T1Otg3NzTq1FeMluptcO/meYxeIdWrOpJJOUnKy3K5s2TtirhW8jTg/ahL2W+fwY89I2b7b8N5N4l1lGcFGCavPMnG1+HH6Htzy/8ZK2lB5v69PyOVids161SNTNlcfYUdFHx+YrLfs51zx9OztXYtV1ZTopSjJ3tdJxb37zm4jZNWNSnSbj2lRNpX0W/S/yNuG8qKii1UhGTto1pd/Ew1dtVJYiFeSi3BNRitFbXxHNjPvwvt7DC0XGhCD9qNOMXyuo2PE4rDyozdOdsytezutUdJeVdX/Ch1ZzcZi3XqupJJNpaLdorD42UvmvPUmfhq2O/tZfhVf0mOSurGjZlaNOcnN2Tp1I7m9WtDOmUyn1HQpbJn2dCefNRnOOaN7ZG3a/7fMnyoxk1XjRi3GnGMdIuybZbLGwhg40oN9pJ5pf5Xe/7IatiMJjIxdeTpVoqzaWjXS1jGu3n6R5MVJSr1HOTk1Rypt3ds264YOjGpsqMZV1QXaS+0btb13pvQYHG4TD4ibjUfZdko5mpNynfXgc/CbQwU9nrDYmvKnLPKTywk37ba1ytAqL/JmlGntKtGNfzhLDJ9ondazXq73u/cw7Tw0MdCntDDRSqRnCOKpLemmvW/8AN67iNh43A4LHVJQxEpUJUElOcJX7TPdxso33JcDleTe1ZYXEOau6ctKkPejff3oZvoMqUXtHM1dww6ce9ykr9PzPIvalapW7R1JKV7q0mlH4JcjpY/yhpwx9OtRl2lPslCaV1dZm7a8dzGlS2ZObrdtKKbzSpWa146Wv0EG6jRVTZyUqyopzb7R6fzPTejzG06Sp1csa3bKy9dO/y3s72HxmFng1Qq1XD1m/VjJtLM2uDR5ra8aMKqWHqSnTsruSs82t1uQjd7Z0v+E4p/53+UTsYyrSlChhqy9WtT9WXuzSVvzPMYDaVKOzcRRlO1Wcm4RtLVWjxtbgx9v7SpVlhlSnmcINT0krO0ea+DAOlhcFPD4PaFOe9RlZ8JLJo0GEqUcdhKFDtnSrU1FKO7M0rXtx/MWG3Y1MBWp1pWrdnKEW1ftNNPmZcHHZ01RqOpKjOnlzwd3nkne97c+QA+CpVqO0IU6sm5OabeZtTTW/4m9zb2rlu7KW6+n3fI5tXa0Ku0YV9Y0oNJNp3yq+tu9lqx9L0l2+b7LNfNZ7slt1r7yKtdgpy9MVFd5c1TS7tfLyDZEpPa1ZOUml29k22l6y3FGHxtKO0ZV3K1JubUrPc46aWuUUdpxo46VdetTlOe7e4Se/8mLyPxZtjx/4rH8et/3nUwlSXpucc0suafq3eX7vkNSqbOpYiWLjXcneU40sr0lK9+F+L3nN2btKPpHzms8sZSqNuzdrxaS0+Q9DdslP0xWvKTV61k5Npa8FwMe29nQgq1RY1VJOo32Oa9ry3WzcO7gWbO2hShtKpWlO1KTq2llk73emlrle1aOBcalSjXnOs5OSg4tRu5a/y9/EW+lZ7edlAz1ocTotFdSBM6XeXPIsXSpWZGSxWoxncBoQ+g1TQItjOeqWEblijYiMbEqYiqqomVtmxwujPOnZlSpsIkMpakxiQ4gZ2wsFgN3Ki5DJYolQXBgSoiMJDJEDICMkOkQh0MkJFiQqHAgSgJSAgh0RYEwKnAINX1WnG2jAaVkS+mjPEuhINZ9Q8mShWxooEmii6CKkWxkCadoVSsF7iSHow+fUqqrfYhsaTvF87EdN/jv7VVYW0TzK2+1jnVaCZthUT04lVREt3MqUuRXRlaRsqWMk1qM2ijL1joRsznUd5qjOzEGum+ZnxsddCyUtzEq6xJNjix3ITiMihGmMtCt6MmM7RtbXTXkJJ3JUvpvU1UnzMKkaqL4mfTSL61rGWpPQ1SloYqu5kcrqh6jqRVcm5pURojIllUJDZzOxrKlitEuQEqVTjcVxVlb5l1hGrFSlVEqZWqeprsLKJUqbFOUV0y5ogepxUtCKiuWNEOIaMUJETLJCTXEosDIGIsdLkQK0OFhGRDE2CwsAGSISHQjTGJYkKhrjJNgRFyUBGQ6FQ1xAMi4BYCxKY6FiOhppojoRDJiSdMdMrGuCLFmYaDK4se404uUhZMrTJYaJEDQZXcmO8VXIy1llqacWTUrZSNo0mmppmNzzImOmHlNO/MzzpPeaKMN5ZKNg1UYoSZpcyurC27iRm0Fqsae0urCwd9CqnI0QhZiJnkrMaxdVp2YlhkL6EqBMEMnwJVCN2LsPPgUTRFOVpCs9Kn221XZFLY0p3RU2RGlK0RlBsZILcEmkyNMYa4rJ1WBFsFdpFQ8HYKqLsTh5U5OMlaS363KBpSbIEeKxhZLUm40oaEW8snPRLlfgVNjFhmVyGuLIAqYr3FsoiSiVqcKgZh88lyX1DzyXKP1Onyjk8a2pEmLz2XKP1I88lyj9Q8oXjW4DD55LlH6k+ey5R6PxF5Q/FvRKOf59LlHo/EPPpco9H4i08dElM53n0+Uej8SPPpco9H4hox1EOjlekJ8o9H4h6Rnyj0fiGljrgcpbTnyj0fiHpOfKPR+ItPHWJRyPSc+Uej8SfSlTlHo/ENLxddDpnF9K1OUOj8SfS1TlDo/EepvFdxIZI4S2xU92HR+JPpqr7sOj8R7EX4+nfsFjhenKvuw6PxB7bq+7Do/EVsH8fTvRY1zz3pur7sOj8Q9N1fdh0fiLR/HXogR5707V92HR+JPp2r7tPpLxDRPjr0FhonnPTtX3afR+JPp2r7tPo/EVVPjrvY37tnHT1M9TbdWSs4wt3PxM6x0r3tHo/EJ6a47VMaRx1tWpyh0fiHpWpyj0fiAx0qy0KDHLac3vUej8RFj58o9H4gHSpbzZDcjhx2lNcI9H4li2xUX8sOj8RB3Zq6M/E5fpmr7sOj8RHtSo+EOj8QN2IEzON6Vqco9H4kva1R8IdH4ixWuwtUVNWZy47VqLhHo/EHtWo/5YdH4iyq2OrmBs5PpOfKPR+Iek58o9H4h40eUdUa5yPSc+Uej8SPSU+Uej8SbzVTuR12MzkelKnuw6PxD0rU92HR+JPhVfycutYeJxvStTlDo/EPStTlHo/EfhSnyR2xbnH9LVOUOj8RXtSpyj0fiL+Oq/l5deQtzlek6nKPR+JHpKfKPR+I/Cl/Jy6jYrOb6Rnyj0fiHpGfKPR+I/Cj+Tl0UxmjlraM+Uej8SfSU+Uej8ReFH8nLoJEPRmB7Rnyj0fiRLaE3wj0fiPxpefLIAAasAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAB//2Q==\n", + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from IPython.display import HTML, YouTubeVideo\n", "\n", @@ -451,11 +401,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9f9c5b9aeb884619b0337fb938349af0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Application(config='cubeviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_selected', 'd…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from jdaviz import Cubeviz\n", "\n", @@ -472,9 +437,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:specutils.spectra.spectrum1d:Input WCS indicates that the spectral axis is not last. Reshaping arrays to put spectral axis last.\n", + "WARNING:specutils.spectra.spectrum1d:Input WCS indicates that the spectral axis is not last. Reshaping arrays to put spectral axis last.\n", + "WARNING:specutils.spectra.spectrum1d:Input WCS indicates that the spectral axis is not last. Reshaping arrays to put spectral axis last.\n" + ] + } + ], "source": [ "# Here, we load the data into the Cubeviz app for visual inspection. \n", "# In this case, we're just looking at a single channel because, unlike Specviz, Cubeviz can only load a single cube at a time.\n", @@ -514,7 +489,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -526,7 +501,64 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Developer Note: Since Cubeviz only displays a single cube at a time, you can't extract a full spectrum at the current time. So, you should use the spectrum defined above ('spec')\n" + "#### Developer Note: Since Cubeviz only displays a single cube at a time, you can't extract a full spectrum at the current time. So, you should use the spectrum defined above ('spec')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "wav = wlall*u.micron # Wavelength: microns\n", + "fl = fnuall*u.Jy # Fnu: Jy\n", + "efl = dfnuall*u.Jy # Error flux: Jy\n", + "\n", + "# Make a 1D spectrum object\n", + "spec = Spectrum1D(spectral_axis=wav, flux=fl, uncertainty=StdDevUncertainty(efl))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Apply a 5 pixel boxcar smoothing to the spectrum\n", + "spec_bsmooth = box_smooth(spec, width=5) \n", + "\n", + "# Plot the spectrum & smoothed spectrum to inspect features \n", + "plt.figure(figsize = (8,4))\n", + "plt.plot(spec.spectral_axis, spec.flux, label='Source')\n", + "plt.plot(spec.spectral_axis, spec_bsmooth.flux, label='Smoothed')\n", + "plt.xlabel('Wavelength (microns)')\n", + "plt.ylabel(\"Flux ({:latex})\".format(spec.flux.unit))\n", + "plt.ylim(-0.05,0.15)\n", + "\n", + "# Overplot the original input spectrum for comparison\n", + "origspecfile = fn = download_file('https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MRS_Mstar_analysis/63702662.txt', cache=False)\n", + "origdata = ascii.read(origspecfile)\n", + "wlorig = origdata['col1']\n", + "fnujyorig = origdata['col2']*0.001 # comes in as mJy, change to Jy to compare with pipeline output\n", + "plt.plot(wlorig,fnujyorig, '.', color='grey', markersize=1, label='Original Input')\n", + "\n", + "plt.legend(frameon=False, fontsize='medium')\n", + "plt.tight_layout()\n", + "plt.show()\n", + "plt.close()" ] }, { @@ -554,7 +586,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Specviz Visualization\n", + "## Visualize for Analysis the Single Spectrum1D Object Created Above from All 12 Individual Spectra\n", "\n", "You can visualize the extracted spectrum inside Specviz" ] @@ -731,6 +763,13 @@ " return A * bb_nu" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Developer Note: At this point, the 12 extracted 1D spectra need to get spliced together with a specialty function written for MRS. " + ] + }, { "cell_type": "code", "execution_count": null, From 3d70191c5b25aaccd35b88b1b8eff7eeeddddc61 Mon Sep 17 00:00:00 2001 From: Ori Date: Wed, 15 Dec 2021 15:16:58 -0500 Subject: [PATCH 4/8] new requirements removed alply --- .../JWST_Mstar_dataAnalysis_analysis.ipynb | 156 ++++-------------- .../MRS_Mstar_analysis/requirements.txt | 1 - 2 files changed, 34 insertions(+), 123 deletions(-) diff --git a/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb b/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb index b859d878..5dca2329 100644 --- a/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb +++ b/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb @@ -20,7 +20,7 @@ "**Data:** Simulated [MIRI MRS](https://jwst-docs.stsci.edu/mid-infrared-instrument/miri-observing-modes/miri-medium-resolution-spectroscopy) spectrum of AGB star.
\n", "**Source of Simulations:** [MIRISim](https://www.stsci.edu/jwst/science-planning/proposal-planning-toolbox/mirisim)
\n", "**Pipeline Version:** [JWST Pipeline](https://jwst-docs.stsci.edu/jwst-data-reduction-pipeline)
\n", - "**Tools:** specutils, jwst, photutils, astropy, aplpy, scipy.
\n", + "**Tools:** specutils, jwst, photutils, astropy, scipy.
\n", "**Cross-intrument:** NIRSpec, MIRI.
\n", "**Documentation:** This notebook is part of a STScI's larger [post-pipeline Data Analysis Tools Ecosystem](https://jwst-docs.stsci.edu/jwst-post-pipeline-data-analysis) and can be [downloaded](https://github.com/spacetelescope/dat_pyinthesky/tree/main/jdat_notebooks/MRS_Mstar_analysis) directly from the [JDAT Notebook Github directory](https://github.com/spacetelescope/jdat_notebooks).
\n", "\n", @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "jupyter": { "outputs_hidden": false @@ -91,15 +91,7 @@ "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: AstropyDeprecationWarning: block_reduce was moved to the astropy.nddata.blocks module. Please update your import statement. [astropy.nddata.utils]\n" - ] - } - ], + "outputs": [], "source": [ "# Import astropy packages \n", "from astropy import units as u\n", @@ -123,16 +115,13 @@ "from specutils.spectra import SpectralRegion\n", "from specutils import SpectrumList\n", "\n", - "# To make nice plots with WCS axis\n", - "import aplpy\n", - "\n", "# To fit a curve to the data\n", "from scipy.optimize import curve_fit" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -151,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -168,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -178,17 +167,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pipeline 3 Data Exists\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Check if Pipeline 3 Reduced data exists and, if not, download it\n", "import os\n", @@ -225,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -301,26 +282,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "fd43f7b81a474d15bef7f14b499267ab", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Application(config='specviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_selected', 'd…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Open these spectra up in Specviz\n", "from jdaviz import Specviz\n", @@ -338,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -366,32 +332,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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\n", - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from IPython.display import HTML, YouTubeVideo\n", "\n", @@ -401,26 +344,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9f9c5b9aeb884619b0337fb938349af0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Application(config='cubeviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_selected', 'd…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from jdaviz import Cubeviz\n", "\n", @@ -437,19 +365,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:specutils.spectra.spectrum1d:Input WCS indicates that the spectral axis is not last. Reshaping arrays to put spectral axis last.\n", - "WARNING:specutils.spectra.spectrum1d:Input WCS indicates that the spectral axis is not last. Reshaping arrays to put spectral axis last.\n", - "WARNING:specutils.spectra.spectrum1d:Input WCS indicates that the spectral axis is not last. Reshaping arrays to put spectral axis last.\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Here, we load the data into the Cubeviz app for visual inspection. \n", "# In this case, we're just looking at a single channel because, unlike Specviz, Cubeviz can only load a single cube at a time.\n", @@ -489,7 +407,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -504,6 +422,13 @@ "#### Developer Note: Since Cubeviz only displays a single cube at a time, you can't extract a full spectrum at the current time. So, you should use the spectrum defined above ('spec')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make a 1D spectrum object" + ] + }, { "cell_type": "code", "execution_count": null, @@ -520,22 +445,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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9ZrHvSDo1X65Ha9axmjYB051yyikluv3iRbWIiqLEH6rBUcoFW7fvBGCjuz4f/+/fdDyhOvfddD1pnvzFZ3eom2dMzcqpbDM16cPf+BfjBPj111/Ztm2bmhgjINI6TeUNfSspSnRRAUcpF6SnW74fR901fTZbf+4ZeGLQcYm3zKIBe2nIlpxGY9i6dSujR49m586dxbJfJXbQHDSKUjZRAUcpFzgSvPWkcpLaud05F67khOAfhdp1GrBRTuBKplCJ/TkHjMHpcrFnz57i2G5cUh4UOMGEZ0VRSgcVcJRyQYLTSi7nSKgU9Hh+F6bjZ71GAnAHY/E3VYkxHD5c9iNWIjWNLN56iCe+XUl6VnQSi8UTarJUlNhBBRwl7nG53LSxbwCgTvUcZ2KbTRh9dXc+ufbkfMc3bG0ddwDtWBpwrEqV8ldT6YI3f+ej3zfy1vT1pb2VMk+gPJSfcKSCU3lh27ZtnH/++bRs2ZLmzZtz5513hiz0u2PHDi666KIC5zz77LM5dOhQofbz2GOP8eKLL4bdXlRGjx7Njh07ojKXCjhK3LNv/14yPcn9kqvUDDjW78TanNKyZrBhPqpUrsgrDV/lmEniAmZi88tuHK306GULN01se9m7bhEuV06yvqysLMaNGxfXVdcLomgmKjVvlXeMMQwZMoQLLriAdevWsXbtWtLS0vjPf/6Tp6/T6aR+/fpMnDixwHl/+OEHqlatWgw7jj4q4ChKBPw5dzZraIODLPqcNqBQc9x93dU86RyBA2js53D8/fffc/z48SjttHSIRDfgdDoZ4FhJv8TNpOxf41eh3UrTvmHDhpDp2tU/RVHy57fffiM5OZmrr74asGpBvfLKK3z00Uekp6czevRozjvvPE4//XQGDBjApk2baN++PQDp6elccskltG3blgsvvJCTTz6ZBQusLNxNmjRh3759bNq0iTZt2nD99dfTrl07Bg0a5Pv+ev/99+nevTudOnVi6NChpKenB99kEPr168cDDzxAjx49aNWqFbNnzwYsYeX888+nX79+tGzZkscffxwgYN8AL774Io899hgTJ05kwYIFXH755XTu3LnI360q4Chxz6IlKwBwio0mjQqfNbdj78GAf/EGi7fffrvQc8YKFUnHRsFFMKdOncoJjhxHbf+Eh95fiMX9S9HlNiXq6yLFuVbA3GqGKosYY9i1a1dU3pMrVqyga9euAW2VK1emUaNGvjpQCxcuZOLEicycOTOg31tvvUW1atVYuXIlTz75JH///XfQNdatW8ett97KihUrqFq1Kl999RUAQ4YMYf78+SxZsoQ2bdrw4YcfRrR3p9PJX3/9xauvvuoTZAD++usvvvrqK5YuXcqXX37pE7qCcdFFF9GtWzc+/fRTFi9eTEpK3rI6kaACjhL32D23SewrkhahZ49eTHd1oiKBvyrKel2q/3w6g+XJ1/FdYl41uJeMjAxefvnlPNWDV65c6btfuXLlgNviwOlyc/LT0zj/zd8L7lwm8Lso5nOB1FD12GX37t188cUX7N69u0TWGzhwINWrV8/TPmfOHIYPHw5A+/bt6dixY9DxTZs2pXPnzgB07dqVTZs2AbB8+XL69u1Lhw4d+PTTT1mxYkVE+xoyZEieOb37rVGjBikpKQwZMoQ5c+ZENG9RUAGnHGCMYePGjbz++utl3pxSGLx6iWyqFmmeZrUqsj2pGWcxkyRyKvCeeGLwHDplhe62NQC0tW0O2efdd9/l6NGjAT43AOvXr8ftduNyudi82Rq/d+9eMjMzmThxItnZ2VHd664jGexLy2TptrIfvabEB3Xq1OGSSy6hTp06RZ6rbdu2eTQvR44cYcuWLbRo0QKAChUqFGmNpKQk33273e7zIxw5ciRvvPEGy5Yt49FHHyUjIyPUFPnO6z8n5DVNiwgOhwO3O0djHOla4aICTjlgy5YtjB07loMHD/L888+X9nZKnJqez3MDwrcph8JJAg7gbj4gmUMYU/wmmeImHJ3W0aNHQx5btmwZ06dP9wk4q1atYsyYMaxYsYIXX3wxql9ecefHY8LT4Cixi4hQt27dqLw3BwwYQHp6OmPHjgXA5XJx7733MnLkSFJTU/Md26dPH7744gvA0qwuWxZZnbejR49Sr149srOz+fTTTwt3AkH45ZdfOHDgAMePH2fy5Mn06dOHOnXqsGfPHvbv309mZibfffedr3+lSpXy/b6JBBVw4pysrCxGjx4d0BbtX9WxjDGGfWn7AUONKslFnm/r8UQAkoC2xrKJb926NY9mI55wuVwBX95OJ8zIqk5mQlUAJk+eHOBsDJCQYHkqZWVl8c4770RtL6Uj3oQveES+PxVqlBxEhEmTJvHll1/SsmVLWrVqRXJyMk8//XSBY2+55Rb27t1L27Ztefjhh2nXrl1EaSyefPJJTj75ZPr06UPr1q2LchoB9OjRg6FDh9KxY0eGDh1Kt27dSEhI4JFHHqFHjx4MHDgwYL2RI0dy0003RcXJOK6KbYqIDbgTuBFoAuwFvgAeMcYcy2eod/xDwElAV6ApsNkY0ySf/icD/wNOxvqmmgs8aIxZXJTziCbjx4/P0/bpp58ycuTIkt9MKbB5fzrVSOMA1bCnFE21C9DlwjvZ9+031JQjVBLrw7dp0yZmz55Nv379ijx/6RD8Ipudnc2UKVOoXr26T+WckJDAx8dbABU4qUo2yfsOBR27ZUtOpFlZT4ZYUv4vJr91VLtTbjjhhBP49ttvgx4bOXJkwHd3kyZNWL58OQDJycl88sknJCcns379es444wwaN24M4POJqVmzpq8/wL/+9S/f/Ztvvpmbb745z5qPPfZY0L34t8+YMcN3v2bNmgE+OA0bNmTy5Ml5xt9xxx3ccccdedqHDh3K0KFDg64ZKfGmwXkFeBlYCdwOfAncAXzrEX4K4mngdGA9cDC/jiLSE5iJJQg9AjwKtARmi0iHwp5AtDlw4ECeNq8poTyQ5XLTwbEZEDISqxV5vnO7tWJIlhUhIBxBPB4+f/75Z5nN/xJK6zB+/HhWrFjhC/msUaMGDz74IGAJiu6kiiFGBtKoUSOcTqen2GYalzgWAmmF22ucWahUcFGiRXp6OqeccgqdOnXiwgsv5K233iIxMbG0t1WqxI0GR0TaYQk1Xxtjhvq1bwReB4YDedUZgTQ3xmzwjFsO5PcN/jqQBZxqjNnuGfMFsAp4CRhUyFOJCk6nk19++SWiXAbxiPvYAd/v4oTKtaIyZ5eGlWAvzGQQXvEgIyODUaNGceedd+JwlK2PVW4NhdPp5LPPPgv4FQbWL0SbLed3gt2Z973VvHlz1q8PzHC8ZcsWfvjhB6AilzhWk+qAoc5VTHB3K8ReS0PCKSkhRIUdpfBUqlQp3xDs0iC3xqmkiScNzqVYV5tXc7W/D6QDVxQ0gVe4KQgRaQF0B770Cjee8duxtEZniEjd8LZdPPz444/89ddfce0bEg5rt+5kD1am4gqV8oZWFobbh5xh3fIG/rWp0tLS+P7776OyRmnhdDp588032bAh70ch057Kk9+tpK1s4lHHGOZtzik+mpKSwgMPPMAJJ5wQdN5FixbRKWMRRzyP9/uC98s74Qk1GiauKJETTwJOd6yI4L/8G40xGcBiz/ForgUwL8ixP7AEra5BjpUYx47l73IUKglUvDHjl+9YTWvasYr+A86IypzN61ZlUOZzVAMSCfQviVaK8ZKkry2nvtZXX30VvGaNMWRsmM2HczbyQ9K/udrxE4PsqwGw2WxcfvnlJCcnc8opp9DIk0wxOTnQqbsWadT2KLeOUrhcOXFtoio4z6KiKBEQTwJOfWCfMSZY1rXtQE0RiZZBsr7fvMHWAmgQbKCI3CAixa5HbNmyZb7H/cPy4pmrHD8B0JIN2O3R0RqICGuNpalIoezXohrqmM4HXMxxYPXq1QHH/DUyze2Bb3ebR9pITEykfn3rI2G322nSpAkA3bt3D8h0DNYXzj5nAn+7Gxdqr/Em3wSiWhpFiSbxJOCkAqFSymb49YnWWoRYL9+1jDHvGWMidz6IkGDOxeWRGhwkkSxq5O8zHjHndqzHHlOVmxiPf9T9nj17ylwBzrFczHYa8jy3+9qygY+Pt+ZgRo6J0w40lxwhJ8VjcGrQoEFAGLk3h0VaWlpQJ8cajmwa2zZzmWMBEPsRVlKscocKNYpSXMSTgJOOlZ4kGMl+faK1FiHWi/ZaEeGt6Dx37tx8+8VdwrQQ7KcaWSSyn6JHUPnzxmUnUVsOkQxc6xgXcGzKlClRXau4acZGz70c5+hfj9dmsG0labW7UTUBurKQs5jJtKT7fH3usn9Ku3btGDZsWMB8XgHn6NGjXH/99XnWE6Cv4yCJDrjCsY4jR47k6ROSWH/bRro/rUWlKMVGPAk4O7DMUMGEjgZY5qtoxfF6HS2CmaG8bcHMV8XO559/HtRBFKx8BF6MMezZs6ektlVq7KEyIJ7b4qEJe6lQIaco3PLly4OWxch2ubnl07/5/K8tuacoFYwxbNu2jS00wP/iWq9ePWYl/Yd3El/l0eX9ONU5lXOZmSfkMgGrOJ43qZ8Xr//XsWPHSE5O5l//+hd2u92n7vQquJxOcDjglVdeYd26dXmKFX6xYCsPT14W0F46UVQlQ755cBRFiZh4EnDmY51PD/9GEUkGOgPR9HuZ77ntFeRYT6yrRYl78WZnZ5OWZuUXCVavZOTIkQF+KPFQBbsg/qRbwG1xIEDPo4GJuQ4ePMi7774b0PbTil38sGwXD34dWQr14mLDhg18+OGHbKEZ/qqH4zsWkSg5pqku7pVBRofmqquuokGDBlx11VWA9V58+OGHOeDRom0GMp3wg6uG75I+fvx4du7cGTDP/ROX8skfW5i5dq+vrXQUj8UneBgt1aAoxUY8CTgTsL6J7srVfj2WP4yvuIaINBeRQueiNsb8gyUwXSwiXodjPPcvBn4zxuwq7PyFZdKkSezda10MckdRtWvXDrvdnudXcrxzkkeuPSmq8m1esiUxzwWqZs2aAY8zsmMrTObzzz/P22gMfYr4XCUlJXHdddcFFPUDWOVozXpnNWY7O/GluzsHTBO+zfR9fEJqFA8fz3Fyijv9TTn7PCpKSRI3Ao4xZhnwJjBERL4WketE5CWszMYzCUzyNw0rIV8AIjJCRB4WkYeBWkAV72MRGZGr+51YPjizReQuEbkLmI31nN4b7fMLhxo1ahR4zL+Ca3nAIQmAYXnFM6M+961ZOWnGvbl2LPdci9wRRPYY+bR5/bRSUlIC2oUMzuN7TmJt1NY6lJ7FhW/9zpcLtkJCArOdzbGMW9aKB0x9evWyFKGLFi2K6bxNh9KzeHP6P+w5UjyVj/PXFKkgpCiRUrZSrhbMXcAm4AbgHGAfMAqrFlU4V/ZrgdNytT3puZ0J+LxJjTFzRaQf8JTnz1uL6mJjzJLCnkBRCCdrcePGjctVqYYMkgChQb36BfaNlFTJudD15m/+oTlOY/OpGbZu3RrQ3wY86hjDIndLrLdn6RAsSzFAbfbRxU+4mezqzQX2/J3VC+KdmRtYtOUQi7YconGN4EGM3tD0LVu28NRTT+FwOEhNTWWAw8k0Z4uQDvHGmBJ1lv/Xl0v4ddUefli2k+/v6BulWU3Qu4qiFJ0Y+U0ZHYwxLmPMS8aYE40xScaYBsaYe4wxabn6NTHG5PlmNMb0M8ZIiL9+QfrPM8YMMMZUNMZUMsacaYxZWIynmC8NGgRNvQPgqyp78GB0w6VjHTuWRsDmSCigZ+Qsdrfw3W/IXq7lC1LZ5TM75A7Vr73vD0Y4fuKsxGWlFkq+d+9en3BjDLgNwHHqso2r+RKA4yaRThnvcVf2bUVeL9NZsEYmd6SV0+nkyJEjnOBIZ4BjXchxJWXd8WYRnr/J+uys2BFB1FdBhBtFpcKPokRMXAk45R1veG4wtm+3grpuuummgPZ4z2h80FjRU5m5IpqiwTrTkHMynwYspU1d9ubEahnjS34HMG3VbsZOX8pbXMZfnOQr6bDnSAZz1++L+t6C4XK5eOutt3yPBYMNN9fzFTfypS/nge3hnYy99UzWPDU4quuf4N7Opwn/o4cEWodTUlJo2rRp0DFHSAzwuynNoGpbcSuL8pXYVMJRlEhRASeOqFw5dCi0V5WfkpIS4HsR1xmN3S7Eq8GxR98a+9rwznTo1pff2zzsaxvJl1RlNzacdOlyks+p+9oxf1LVBgepA8DixYvJysqix9PTuOz9P/lrY/EnZvRWBTfkXEsdZOdx3E1KcNDphKokOSLP/Lxwy0F+/ydHYPMP6744+xv62FfwRdKTecYNHz6cJk2acNZZZ9GgQQOfg/KJHCYtLbjgXtIO88VjDgu3FpWiKJGiAk4cESphWmpqKoMH5/waL2uZdgtL1vEjvgtDpYp5w+aLyvmdG/Ds0I4YP8ftJOAifsGGi++nTmXWYsunpb19J0mOnI+bMfDRRx/5Hi/Zesh3PyPbxQezN7D1QHRzRfrKMBjfPzqxnLrsDTnm2qx7+cKZ2y0tNEPemsvlH/xJWmbe91gHVx6/fh+JiYlcddVV9OjRg+uuu44rrrgCJ1aenLW/fh6Wf1nxYT1X4QgZEQsiAVHiqqVRlGiiAk4ckTtqx0vnzp1xOHI0GGecEZ2ik7HO+n/+YR1WTa5q1aKbydgfV67r0gGq4SQR43Ty41ef8/jjj9Nadvld/KyLvzekPzevTVvHU9+v4qzXZkdlf06nk0mTJrF7924ABBcCVGY/ZzEz4KL8bLvJAWOnubtyv/PGiNc85hFwPvp9o68t8OJvaCo7kRAVJhs0aMDi7Jzq7y+++CLZ2dkBVpySFgeipcDZczSDc16fzVd/b9PkfopSjKiAE0esWxfcITN3uzcZoJds/2JKccSfCxYCDqrIQfr2jVbUS1421RnMZndt3+N2rOUET/mDZI9cWcnhfyGznm+rTlPeC/yybVZ9pmBakMIwefJkli71VAw3hu78TjM2cCtjyW2EeuCiflFZ0+XO/8J9kW0G05Pu5R7HxKDHRYTlribsclqmKmMMX331VUCfkld4FCzh2HDTSf7BkU8R1ld/XceKHUe498sl+Itp+c+ugpCiRIoKOHFE7rwmXjIyAvN27N+/P+DxxInBLzJlnW3brJIIddw7olZJPBgX9mnLHbU+4uLMRwDrQ3WQKiF6O2mElbE3IyODJjZrj/6/5KP9q37FihUBjzfTlBFMIW8ZzLx+JmOu6cHN/ZpHvKY7iPRh/Oa+326lpbrdMTmfWWxMdbZGEqzybi57QrE+TwURjpPxrUxgStIjPOoYG7JPltNPqFWzlKIUGyrgxBGhnCCTk5MDHl9wwQUBj9eujV5it1iit/kDgDpSvA68lZMTmHJ7X7qempPbJgunL7w4Byf3MIrLmYK4LC1OL1veCKrVO0NHw0VKoHYury/JUZPCHhPafHdaq1o8MLi1ld/Hg7+2KhTBrtuBTeHaexLYba+LMfDFksBMxyUXJu65DWPLw/gJgBGOXyNeJ7xUXYqihIsKOHFEpUqVgrbnzo+TkJDAfffdF7SvUngu6tqQO7Ks3DFOjwZHyKRnz55U4hD3MYpKQCLQyz4PgGW5hIvl2w+z/1i0asLC119/nfPAQCLHfPluAP48bRyzTWfrvjt09ZIdJidL9mlZr+Qc8HOw9neSDabBKWws0M506/moTGapZuIOp9CnM+LcqarBUZTiIt4yGZdrgmlwKleuzDnn5M2am5oamFX28OHDvmSA8cJRKgTcFjctalfktjsf5OM3NlDNvo/9NKQGezjzzDM5c94lAX1rYUW8pZnAvf2xIdB8WBTcbrevsnw2VoGEZmzx5bs58K89nFExia7Tj/FHdkt+cXVlcYi5JNejTOMgSZzgzgabNaO/282m/ek0rhF4bsZvlkgu63uoRjY7aOY4zLqVKwoeUEyEo8FxFymgO/SzklcbqChKQagGJ46oXr16nrbKlSsHRFCF4tVXXy2GHZUuJtdtSdCqTiXaNanP9XxJA7ZxPV/y/jcz8vQ77EkJ2Nq2D/JxSC0Ky5cvJysrC4yhhVlJO1YxhKlMNSfzU4+PqF7REkzSSeZLVz8OEVwDGIxsz28jV3Ymxhj+2ZNGtsvNU44PeSPhNUZ+9EfUzuMwFVnrrIoAhw7nZOIuMfeV4lwozGri6qqjKJFTKA2OiLQC2gG1sa4fe4HlxpjQedWVYqd9+/ZMmjQpoK285LwJxnFSAm5LimxHBZKA6zymoOsXnp+njzfZXl1HBlczC0xbILrJ5Pyrc5/APgZ4qoQPfPQn7DZ/bUrkV8/jJFKRDDbv3MOSw0e4e8ISGrCX35OnAfCcc3ieMYFZiAs+z7sdE+lpW8mNPOJrO5aWBh4dVHE5GRtjuGHc37zveRyJ9iSc88o9Iu89RVGiQdgaHBFpIyKvich2rErcE4G3gXc891eLyA4ReVVE2hTPdpX8sNlseXLhjBw5MmT/O++8s5h3VLrUwXLgTXcVXwRVMHZv31hgH++OBAOOFGr+83XIvk6Xm+XbD+MuIPTaH7fbzbx58/BeNvd6qp1nGUeAcFNYaollYps+4TXunmDVln0t8U3fcRuGDXvTco2KbN07HV9zsm01nbMX+cbu37sXPNmpvVqNzMxMRo0axeuvv87xKJTk2H8si19W7s7TXix5jP1eUlE1jaJElQIFHBFpLiITgeVY1baXAo8DVwJnY5VFvhJ4AlgCXAcsF5EvRaRZcW1cCU6nTp1894cNG+ZLeR+MqlWrlsCOShHjAgyVOw8t0WUz2wwJeWx48tsA9GEBdrzh+w6WbTsWcsx/Ji3n3FFzeHP6P2HvYdGiRZZDrueaWQHrwr/6oulhzxFALs3SIk+h0VOPTwMMtThIN1tONF4iTk5/aSaVSOfdhJcZapsVMD6SS7kDN3vdlTDAvj276OtYwmDHEiZ/NZHHH3+cZ599lgMHDnDw4EHeeeedsObMdrl59sfVzN+UN8IuuIN0eBRFg5Mf6oOjKJETjolqJbAMGAl8bYwJ/U0MiEgF4CLgTs/Y5Pz6K9Glf//+JCcn06tXr4hzvxw4cCCoH09ZZa/UAARnZkaBfaPJsPMvsER9YJ27AS1tVqHTbwfP5fOe7ch+9DYSxMXdvMVL3I71MXSzb5+lcRpim8Uq05hVpjEAExZsBWDsH5u5fUDLsPYwffp0vBfPBI5xFjMB6NihY6HOyWaTgGvxaOcguiT+Q0vbdjYlX56nfxJW5NPYxGfpYvuHM+0LWEvk+XTASp63yV2TXs4tJCdAc4cLcPHPujV5+rZv3z6sOSfM38o7M9fzzsz1bHo2rxN+ICXkg6NCjKJElXBMVBcbY7oZY8YVJNwAGGOOGWPGGGNOAoYVfYtKJNjtdk455ZSwhRt/k9aoUaOKa1slTkZGBquNZSldv6Fgk1E0sfmZgCa1eZktt21n3c3bOPdky8/m/T4zAKgI9Gaep6eDN998k2q75vJy4jv8mPQQdo8ppjDUqFHDM+tx7uE9Zrq68M0pk4L2LYzCYqvJPxfOdY4fcOCkiy2U1il8TYelvbDxmbO1b2Tu0QkJCYBVxDQzM7PAOXccityUVTzFNsNdvPSWVpSySoECjjHmm8JOboyZUtixSslwyy23lPYWioW333kHxAG4GHbJJQX2jzbf9xjH162e5/5LB9OoZkVa1qnku0DePLCDr18djuD/y33Zin84CozjfJ5LeCtgznCvcb8s3sj6LVvAQDLpJANNhz3HeWecXujzedZxKwD3Zd8AwN6QmZotLrDPZVXS1QFthdVPuH1fUxW5/PJLgePYsH5r1axZk3//+9/cf//9JCUlkZ6ezujRowu5UhE3WtTF8pM0VbmjKBGjeXDKObnLO2RkZOTJfFwWyTy8GahCTXbSomV4Zp1ocs7Z54U85q8JaM9aMoAfzZkglhbiFW7CkELjQvhG7zpwmJkTx+LNDFAbK5IqtVrdyCfzY6G9A82OfuITNvabgnMmJUigBsrfP6Uy4VcH9x9XqWIFHuUdXMD03l/Q//QzfNrK7t27M2fOHFq0aFHgnJEoY4rT/0UdixWl+Ig4D46IrBWRB0SkaN+YSkzy3HPPlfYWokIzsxmAeuwpXdNCAdiAHqzlRkZZSfOMwYZlYsldArWg09hx6DjPvzwKh8O6KNdiG8OZCkDVWvVDjqtewapKlZKQv0Tl9vu6SCe083pock4gScIv8BqQPM9tCU12oEfXzgGmWO99EeHPP//kiSee4LXXXgsaWVVcckXkTsaKohQXhUn0lw08A2wRkckicq6IaMLAMszNN99c2luIOsme5HllRUVZF+gslj+OC8svagcn8Pjjj3OFYwHnOBZgd+YVCpxOJ1988QXPPPMMX81djQOXT+PQgD0sd7fg7ytWkpyYEHLtj0Z25+Sm1ZlwY88Idix84Dwrgv6Fv/j7Czg/LtvudyCwbIPXQX727NlMnToVYwyHDh3i3XffjXCfJUmYJipFUSImYsHEGNMO6A2MAfoDU4CtIvI/ESlcmIRSqtSuXXDxxLJGQ3YCxnMbe+wweaPVlnKy555XLLOEkgSHobbDcKb5OaC/2+3mgw8+YNWqVWRlZXFg/jek+kl0B6jKimbX0rVFYC2y3LSpV5kJN/aiY8OqEZ3D+6nX8etJbxXc0UOmMzyn6Z2Hj3PN6Pl+LTkCztt+ofLGHZjEsn379kG1dX379g17jyWNCVuoUeFHUSKlUJoXY8wfxpjrgXpYeW82Ag8Ba0XkNxG5TEQKo8NWSgn/aKqDBw/m07NsYJVCEF9JhJhD8pqDbudNIAv/0g0B1z93YG2npUuXsnt3TkK67OxAf5EaHGLQ6YV3LA7YbhDlS0qCHfKpgJ1pQmuNcpPldLPzsGVK6vXMb/y2OicLs78Gx+af+TeXcGCz2bjzzjvJBo5nw2FSSUxMpE6dOmHvo6jkp6XKdrn5a+MBnK4Qz5lqcBQlqhTJtGSMSTfGfGyMOQVoDXwO9APGAd6sxo2Kvk2luElLy8k6+/rrrzNx4sQyXeZho6kHwA5PBt9Ywx3ko1cVeJQ3+S+jcHAUACEDf4HnqaeeYv369Tz++ONMmZITpGiAnJJjTrqykLOZiSSFX1+qMAihBZz/OS8LeNzZtj5k32HvzaPXM7+xcocVVfZmwqu+Y/7PlV38q5fnXbtKlSpMyOrO585uLMysTVZWFkuWLImoCnkoOaOorlxPfbeSS96dx+TFO/xXC2tsVpjaL0VRciiy74yI2EXkQuBlrLw3BpgO/AHcBqwSkbzFeJSYZsWKFUycOLG0t1Eo0jKy2COWYPOPtC3l3QTHlc9HzwY0ZRsALdnAA4wCnCAOXC4Xn3zySU5nY8BkI85NCNmAk9sZxbnMxAEUt3vcsZR6IY/VOLE3j2ZfFdY8i7YcAmDq8p1U5hjn2P/yHfMXAfwFKnG7MMaw/dBxHp2ynBEf/onbbXwCyhFjRQMuWLCAl156iS+++ILs7PCdm3PWDJ/8xJUx8zYHGeBfiyq0EPb35rKvVVWUkqbQPpgi0hqrdMMIrKKbe4AXgfeNMes9fVoAXwDPY/nqKDHKTTfdlCfN/Zo1eTPFlgUObVlJpifKp4DAoFIjtwZnq7sW8+qN4JLdLwNwMVOZApzPVI8njpO8H1cDOLmZUdQOcZ4mJTqZqYNpLwxwqFIr7si6jV62FVzqCCwDcVa3NjQcNhSeGRP2OtluQ26RIgEnw+zTme7qHGiicrt447d/eOmXnBIRC/wEgQOmgpULyThJT09n1apVuN1uhg/PWwg0X2LAdKSlGhQlcgoTJn6tiPwOrADuwUpKfzHQ0BjzoFe4ATDG/AO8DoXM0a6UGHXq1OGssyKLiolVps3+E0ghkeMMHxo6H01p4u9X8nT2pZyT9TQX3/QIcy5ZDljuxRf5hBu4m7exAhi9FzqD3WRyD6PIz0W8UnLxx5F94+7NCtPE93iWqwNr3Q2oWr85qUkJvJh9cdhzGZP3Yn6j/XueS3ifyUn/xY6/icoVINwAfDhng99oYX5mtYDjiYlWSHx+4kKoKuVFMVGF50ysQoyiRJPC6K/fB5oCzwLNjTFnGmO+MsaEcthYieWTo8Q4PXr0oFq1agV3jHGSHdaFojsLqFajVinvJjj+GpzRrsEcoQIiwiltTwjavzLwMG/Qw8zGYdI523zPv3mLgjxskqOkwpICDDX+gseV2Q8xKOsFxG6JZ817/V/IcXtzJQw0xuQp73CSp4hnfTkQKPwEqa7uNoHCxHJXQxz1Wvty5Cxbtoy//vorIp+cQPIXQoI5GR9Kz+KkJ38JMcBPHMtHCFINjqJETmEEnCHACcaY/xhjNhXU2RjzlzHm6oL6KbGBw1FWMseEZt1WKwJnKw1IqhybTsb+uLAFJNl7xxkoEBwyVvSUHTiLBfyHd+jOWt+H99aGXxX7Hp8dapWXeH5o8GKd9iD+IymJ1jl1aBBaaK5ABrd++jdDbbMYm/AMCdlHGJ34fECfLHKisQKjqPL+psorI9ipcWI37r//fl/Ljz/+SNb8LxngWAmeoqDh0Nm1nCVJ13Om7a+CO/sxadF2DqYX7PuTnwij6QMVJXIKkwdnsjFGXfrjlNatW/vuR1qNPFaomG0lg0sjkdQYLTvxGz18913YAswirziHMsPVieeyh9Ml4x2+dJ0Wcp53O3/Nm9edwZobtxTrfns3r8n6p8/mku7BNUz+TtMfXtWNcdf2oGKSJSzb7KGF5lTJ5M11p/NS4jucal/GSXu+znduh38B0hAh6sEEhcTERO68886AthMc6VzmWJon03FeIclqeCDrLapIOu8mvhrqdIIKIvlbp0zQu4qiFJ2wfq6LyD0RzusCDgHLjDELI92UUnqcdtppbNiwge3bt3PyyScXPCAGOSyWxuBAjIaIA3zN6dyIN0pNGNg2p/JJJomMzH7A9/gr16lc7/ghzxzrRi7jxiZWFoYT61Xhr8pn0uPIT4Bl+nkkeyRvR3HPdltoPcLPrm7c6/iC71w9Gdyomq/8A4DNFigobzc1aSD7gs6Tmp03Wsjf7PNj0kM5B/zMTLfYp9DVtpYJJnSpkapVq1K/fn127MgJ0050wFtvvcUtt9zC+++/z/XXXx9yfH4h8cH2mtOWlxoc5gCVwk70pyYqRYmccO0RLxZyfiMiC4HzjDGxmVJWCcBut9OsWTO2b99eZjU4DdnINtrQkI2lvZWQ+F8IX7q4E2e2D13abbVpxKmZr7DPVKGWHCKVTNJI5uPUQNOP/+vVPfMtituw0fmEqr77O6lBl8z3cGHjwlx+PzZ74D62ST0aEFzAcQfZczVJC9ITMG46yAb+mzCOHjYr4m/p8b/52TQOuecrr7ySjz76iD2Hssk8dpAkB6SlZ/P885ZZ7Pnnn+eKG24POtZF4T4PuYWYk2QtXyc9xo+u7mD8y0ioEKMo0SRcE1X/CP9OBy7ACg/vBLwUzU0rxcvRo0cDbssSxzOdNPVcPBub/aW8m9D4m6SGdm3oM+cAXNDZKoyZ4BEMLuhcnyYt2/PUJSdTtcGJrDKN2Wrq5PGx/a3uNWwzNflP9jUUp3Bza38rKPKJ89oHtFsCgPh8b7zY7Dk+NN+6ejJdehCKA+lODpiKIY/7Y9xuXk8Y5RNuABKM5euS5Odb4y9fJCUlcfPNN5PcZRA/uVrhBIwrM2DeT94bxWDHCrytXu2JCePrMhwR5WL7TADOss8PGKE+OIoSXcLS4BhjZhZy/m9EJBEIL9uXEhN4NQHbt2/H6XSWKcfjJbMmUR0rG+6m+rEZIl4QzwzpyP91qk+fFjVxug0VEu2+GksXdmnAf6cs5589abSoFSgIHEqsyymZrxf7/u47szX3ndm64I4ebH7JBsc6B9HZEVqZe27axPCv5sZJU9vugCa32LjUPo1nEj7kxqy7+MmdjzBlKuFy+meAzqGu4zgvcDP/4m0OuitGUDMqyDZzDXWG0ATlF0Wl2h1FiZySqAK+oITWUaLE4MGDSU1NZe/evUyePLm0txMRjrSdvktBq5bhX4RLmn1UBWC3qZrnWEqinQFt6pCcYKdikiOggKSI8NQFHfj8hl7Y8vGJiSXsjpwLejATVGExQcLEjYFnEj4E4N3EVznXNi94Xhvjpp9tMT8565HhhOQKlbjrrrsCurhI5jlu58fsbjzxxBP8yYkUJroi9/qBfjoquChKcVHgT3MRGWCMmVaYyUXkDGPMZ8BnhRmvlA4Oh4OsLEvFv2LFCtavX89tt91GhQoVChhZ+hw5coTZDASEA4dD+G7EAE4cnJgxGhc2/im4e5nGZsv5mglWgwss09X/2f+IaN7Fq1YzMFebw50R8PiNxFF8e6AV0CKgvfWB33gg8QW2mxr0yRzFZc0bUaVKFe6++25eeeUVsp2WA7L/V+QqOvIUjYAUEp95hjZt2rBkyRIcDoevbls1BnEjn+JfaTiIHJZDgNZGhR1FiSbhaFameiqEnysSpARyLkQkQUQuFJGZQN7QD6VM4F9oMyMjg9dfL37TRzSYu8kFJGIji3POObe0t5MvmSTiLHy1lDzkzrT7vwvbB+8YRZIcYXyF+GmaQmlwbGFEKOVm4NK8wZ3rd+R1Xq6anrcG1AmHreDOBmL5aY3/0wqzr1y5MtfecR+fOtuTyG4gEyuDtG82IMlXxBMCPysHqc2z3I6/91puy1MoMcblcjJnzhxcrrx6Io2iUpTICefbtQtWIc1vgL0i8ivwF7AeOIBlMa8OtAR6AgOwvgV+BjpHfcdKqVBWKot7Lw02jpKQmJhv31KlmK9X//zvLBz24rcMn9+5Ad8u2cmKHYdDJ7Pz88FxY+MoeZ2I/RP47TTV+c3VhcsdkSuOUyUzT1sw35aClSrJPMR4AFY52vJO4s1UP7bB08MJfskH8+LgFW7hmm3bOHjwILYsJx8kvMA/piHdbasDztV/J0vW7WDlluUsWbKEG3KtUDaMkYoSWxT4DWiMWW6MGQT0wRJa/g94Bat45mxgFjAZK5R8kKe9pzHmLGPMymLat1LCFD61fcnicB0AwLhj3O2rmK9YJSHcgFUK4pPrTuaKnqFDs/MIOE0H8a2rJx87z/S12yXnQt8r8w3+47y2UPt5KuHjIK1BxJkICkulZTqZsr8qJ7qXUIvd3MMoGjduTFJSEsOGDaNRo0Z07dqVe++9FxtpnhUT+fDDD/n666+Rv77hd3tn9jpOoLLtEJ39S1EYb4QWrN1qvXf37dvHJAaHvT9FUYITtn7cGDMPmOcxU3UF2gK1sD6be4HlwCJjQqQXVcoUV155JWPHji3tbUTMibaNrKAarW2xmwMHKFfuFv6W7Zv7t+RwlQbcvvIOusoarnZYiQn3V2gFxxew2N2c14Z35s7PF4ecr0nGeDYlX+Z7PPn/FsPkW7jAPjf4AI8Q8eWCrUxatJ33ruxGJBKm1dNON1bTgt8AGDlypO+4f/bvK/mG3+jJFpr52na6soGquIHxXM5IPqUxe/23xg5q4fR7T7RgA/70tq0Ie7+KolgUplSDy1NfarQx5gVjzIvGmDHGmL9VuIkfmjZtSt26gcnnsrKyGDdunM8BORZx5LpVYgA/DU6nRtV99/2jiX5P7su9WTdxb/ZNnN+5Qb7TzfhXP352dfU9/r8uTVjgPjH08h5p8r6JS5m7fj9j520KmnE4GtgQqnIoV6v33egEhNEM55jvmLW39VgaMG+x2yNU9vVwAQdtNdlOLYzn8Uy6MYtuhYrqUpTyQozr8cNHRGwicreIrBaRDBHZKiIviUhYoT+RjBeRGSJiQvx1i/7ZlQ5XXx1YI3X8+PFs2LCB8ePHl9KO8mfr7v0c8IRfe2/LE17BoFezGqW8k0CMf5i7zT9kPNB09ZX7VNYb6xxG5GPyalKzAimJOWPtNmGCq3/I/rlFmWOZxetPlk0KAMnJydSvXx9w0opV1MCbeNLB61xn3fWEWFXkCADVq1sC4GYa+ISXuXTjN/oymkvYSS3m0o0Z9GU6ffmd0F83xhi2b9/O7NmzgzouK0q8E08/dF8B7gAmYWVObuN53MUTrl6QdinS8fuAu4PMsyFIW5kkMTERm82G2+3GZrOR6HHaTYxR5920DX+yG+sCkUReZ9N4p3uT6sx98HRqV0oquHNJ4l+LSoJHVOXWqDSqnprvlCkJdvzVF9k46Jv5CrOT8n4kc0doCRIgdIVLuFqfC5mKDTj/nk9ISEhg3uiH6LVpKhnAi9yAiwokkc5rXMyhj74EbgBP8r9jxyzdziaa8QWnsY2WXMAUhJ44SWAv1WjGZqZzMoYEKnsEo2Ds2rWLMWPGkJ1tOX/37ds34nNWlLJMXAg4ItIOuB342hgz1K99I/A6MBwIqXYo5PhjxphPonYSMUr37t35888/SU5OJiXF+mVasWJ4qfRLmjmL1pLl0dzsdVct1b2UFvWrppT2FvLiZ6JC7IhHUPAXGJy5ksWc3Kw6HneX4FPmemy3CdvdtYL23XXImw/JkIDLUzQ0r7Cy8/Bx6lZOxricvJ+QU34vkhBtgxX9dBFTIcEbB2WtlQw8yHu8wmUcpY7fqBwlccWKFalQoQLH0tJYy0kAfMZQBDcG+JWeNGQPffmTWZySrwreGIMxhoSEBJo2bRr2OShKvBAvJqpLsb5FXs3V/j6QDlxRHOM9Zq3KIoX4OVhG2L3bSoWfnp7O6tWrAVi6dCkHDhxg4sSJHDhwgGeeeSYm6lbt2HsYgASO8qn9nFLejeLDT8ARm79ZKudj06t5bQA6Nqxi9UNwmtBfT7mFjkWPDGTGfQOC9t285xDrdh/lWcf7LEq6gUrO4DXKej3zG6/+uo7kzdMZaF9YwEkFJ5go5H+e1i/K4NqpBBtccskl1K5dO9ecCbg9QeNp1GA1bVhHAxLJojp5q6+7XC5fPh2bzYbdbsdmi5evekUJn3h513cH3Fj5eXwYYzKAxZ7j0R7fAEgDDgNpIvK1iMRubYBC0qlTJ999r3Oxy+Vi1KhRrFixglGjRpGVlcXLL79MZmYpm4Vclio+mxQeH1LQS66UGP75QSVHaeyvwRncoR5f3NiL8df3tLoJ/OFuE/YSlZMTaFQjuOBQlaOs25PGcMcMKkoG7fZ8h78oYsflM2O9Nm0df6zdHva6uQluxgpsu4UPsHGUKuymXu3qdGQhbVjFhX1akpCQwPDhw6nJBiCDSmzDEotsAfPspR5ZJDKd3lbBUGADtXiJq/nmm2+YNm0a06ZNQ0Sw2WzE8W8wRQlJvAg49YF9xphgV9jtQE1P0c9ojd+IVSn9auBi4C3gLOBPEelQmBOIVTp27Bh232effZbXX3+dzZs3F6k4YWE4evQo2Kw167KUszvUK9H1I6Vlndg08xULARocfx+cnHabzUaPptUDqqoHExb+mz3SmifEUttMzTxtVzl+4Z9tOQU+E93HA5L/LU26jmccH/ge/7h8V+D2ixjTn9vfpwLwXz7gLsZz+qk9uZCZXMJUEjwmrcTERG5lCo/yNi6q5JrN6fmfQjKH2UAzxnA+nzKYcVxGGlVZunQpAJs3b8blctGzZ0/q1KmDopQ34kXASYWQXqUZfn2iMt4Yc7Ux5j/GmAnGmInGmPuwkhxWxMr6HBIRuUFEFuTXJ5aIVLV98OBBRo8ezQcffFCi4eSjRo3Cm/s1sQxEUD10dvjaiTKPvw8OwU1UInnfZ8EEnD1BipP6854zuGnyjj9zoqwS3Jn4a3AqSCbDHDNCzplCJlfYf6G25DUH5SGoLBRae5Ltl/xm9a7DeX4Y3MIHJHOUM/metqxiJJ/TyBPHkODxst5GM9bTBuu5DYwQc7vd/PHHHz5Ts6KUJyIWcETksfx8TkSkuohMLtKuIicdCBU6kuzXp7jGY4zxZnXuLyIhPT2NMe8ZY+ImlDwUO3bs4JlnnuGll17iu+++K/ZSD95IETvHqJBPZEms4K+piHf8/W6MLURl8VyV0UN9w/jHXQVjrOtMnszO3+UuwZ0ZkVamjW0rTyV8TBXJ+QrIdgUPygw2a37RV3d8vsh3f/KiHfywLFB7VAF4gA/oYdZxMVNpzF66sgrBcCp/UIdtWLWyDKkcpRFWTa3UVOv3mNvtJj09nc8++4y33nqLjIzAYqSKEs8URoPzCDBdRPJk4xKR04AlWOaakmQHlhkpmJDSAMv8lJ86oajjvWzCivesFkbfMkNSUuHDjtPS0vj777/58ccfo7ijQJxOp8//xoWDZh1OLba1lOA0r5WPyc3PB8dfUxNw4Q9TgxMOaeT8vgjmx7P/0KG8FTCBs21/UDH/3zE+uj75C+lZBQvtxph8Q9L9BS3BMGvt3qD9jvqdU3vWMoQfOIm19GIJXtflQczicqbQrFkz+vXrFzj+6FH27t3LBx98gKKUFwoj4NyE5XS7RETOB1800ZPAr1g60tOit8WwmI91Lj38G0UkGavgZ0EmoaKO99IS6/wPhNm/TBDKnyY5OTloezByR1llZ2czceJEn+alKHw95TuweTUiNqRu8VfRLirx5vN5Xqf6PH5eO366K4hwGSKKyl+AkVxfRUkOW/4CTj4KGH+h4ajJq0w95dgvdN3zVZ72txJf57+OT8LS7hzPyGDRlkMs3XaI5g9+w/TVe/L0WbbtMJ0e/5klWw8VOF9B+D8TNiwhxwYBGX4ESARGjBjh09S0adOGFi1a+Pq0a9euxP3jFKW0iFhPbox5T0R+ByYAX4vI+0B7oDcwEbjeGHM4utsskAnAv4G7sAqAerkey3fmU2+DiDQHEowxqws5vgqQZowJSA0qIudgFST90RN9FTf4+9Kcd955rFmzhooVKzJ48GA27T3K/Fm/cN655/D8i2+QmZ2BIyFvreV169Zx8OBBsuwp3PLpQnq4lpOxfyfHjx9nxIgRRdrfquUL8fof3MEbVOn1VJHmKwni7RpjswlX9W4S9FiARduWAFjvpwAfnFy+Xs1rVWRrsLk8woed0NoTfwElNTnRsuCEyTDHDMRZ8IuzLvlKnls2laXzZ7I++RkeGXcVLe591kpM7Dmt2z9bSNXM7RzPPhay+Hhr2RKwbxNCuKoswTVLO6kHCM3YQHvW+tp79+6NiNCrVy8Afv/9d4wxLFiwgNatW1OvXmgn/OzsbKZMmcL555/vc3xWlLJIoZyMjTErgG7APCwhoBfwb2PMJaUg3GCMWQa8CQzxhGtfJyIvYTn8ziQwSd80YFURxvcH1onIayJyp4jcKiJjgG+wshvfVTxnWXrcfvvtvtsuXbowfPhwEjb/yhcfPE/yO91g2Xh27DvEwczD3Gz7hG2ZkFKxSp6v6tdff51RrzxHm+2/cHSXFdWyYcOGIufRqWp2WLfsohrWxVaJUew5F0x3Pl8/odz8vMLLvGQrK+845xlB+uSQ5HeBbpvxUVhbvMQxM6x+DywZzKeJzwDwRMIYFr4ylOa2nGitGgcWMSvpbu5P+CLkHM38+p8ge/hh2S6OZ4VfVmEQM+nBQi5lSsCzabfbOeWUU7Db7djtdk499VRatmyJ2+0mOzubH3/8EafTSWZmJu+//z5btmzxaXYmT57MihUr+Pzzz1Xbo5RpCuXpKCIJWGHSvYH1QCPgNhGZZ4wJ79sh+tyF5QNzA3AOlrAxCngkzCKg4Y5fg2WyOheog/XbbBvwDvC0MabwSTRilOrVq/Poo4/6Hs/7+UvO3Pux9UDgUsd0+Lgtr3sC6UcnvsxPh7rRx76Al+SegLkcvn85ePPoXHvttTRo0CCinB1Op5NDWEVBD1GHL0/4DxdHeoKlQLyZqAriNecQEsnmgsRU8JSadBfBB+e3lMH8tKcaa8wJ5Nb/+WtwnJIj4Dx+UQ/4rlDbD4vzc1Uzv9A+p8Axdzm+9t3/b8KnzM9szX+n1OXFfMb44wDOIryvXBHBbrczd+5c1qxZw/r16xER9u3bx5gxY7juuuuoV68eNWpYtcy2bNnCzp07PfW0ikZWVhYTJkxg2LBhMVvqRYk/ChNF1Qr4E7gNeBvLPHUqlt75VxF5QoLFfBYznirnLxljTjTGJBljGhhj7jHGpOXq18QYk+ebM4LxqzyaqubGmIqevs2NMbfGo3CTm+lv30mvudcV2O9M+wIqAveaXFHz+fwg/PDDD3niiSfYsyfQnyEt08m4PzazLy0wkn/K4u3c+fRb5EhMbk4759KCT0IpUUSEV5wX8ZzzUgTxCXcBH8Mg6QjyE3CMCAtNK44R6GMjEijg/FD1Mo6aFJ7MvpyLu51QtBOJkMH2+RGPOdf+BxP/3lYMu4E6deowaNAg3+P9+/ezb98+wIq2mjdvHu+//z6VKlVCRHA6nezdG9zpORycTifff/89ixYt4r333mPDhg189tlngCXwjB49mp9//pkPPvig9JOE+uFyuZg1axZLly7F7Q7nt7ESqxRGEFkINAGGei7qmcaYP4FOwFfAw8CMqO1QiSn67x4dUf+KAMbff8Agnr9Qjihvv/12wOPHvlnBfycv55rRgReM+z//mtrZXmHIyV28Re26JXsRUyLDX3OVn5OxdTzI+ILmz/V4R2JTOmW+z4eu6JXuuDJlVFj9akrk6QrsFN8Fdffu3UydOjWP2clbW27ZsmXs2LGDH374wZf/qigZkH/++WcWLFjAN998w/79VnmMDh2sPKgTJkxg8+bNzJs3j+3bt/Phhx/GjDls7ty5TJ8+nUmTJrFixYrS3o5SBAoj4CwGOhtjJvk3GmOOGmOGY/nknBSFvSlxwj28AyYbY/wvQE6aMBtMNriyMVjyTrDvuN//sX5lLt2W49514NBhLkveDfYce9d/sm4vtnOINvbyZqPy4H/W+TkZQwFh4iGuhR0aVs0VBWXy9fUpDCf36M0/7kCzzW+uzlGZ+1rHj7yeEJ4AFSnGGDIzM1m7di2dOnXypX9wOPJ6KjRp0gSHw0HFihULHe0YzJH52LFjuN1uKlWqFNB+4MABdu7cmad/adCsWTPsdjuJiYlUr169tLejFIHCfPJPM8ZsCXXQGPMh0LXwW1LKCl80e5qJyRcV2M/6KvMmV7OiX05iKVexgEcZBbZsxBjEme27OK1atcqnuva/zGW73Hy3dAe//pzbmSKNq6+9NQpnVTK0qF2RczrU49b+zUt7K8VObjGldiXrwhogeAQV+PK2ed8foaKN3r2ieL96xjtP57IejVhtGgW0V6tSKcSIyDnPPi9qc+XGa3Jp2rQp9957L+3atfOVY0lNTfUlCNy5cydOp5Nx48axYsUKvvrqK5YvXx6RySZ3FvQTTzyRefPmsWzZMlauXOlrT0lJweVy5TFNlxb16tWjb9++WqA0DihMmHiBLv7GmDWF245SVlhb+0wuufJW9h/Yz6y/zqTDKefxwzefc/naO4P2781C5tKXHswDKjDQzzHyHt7hDW7C4UgnHcvB8YsvrMiTZ599FqEeVr5F+HDORp79cTVXO1aB55dnMke5nY9Jbf5qsZ1vtBER3ry8HCo6BU5vXZu7zmhJs+RjVuYsrOrhuQluosrfjFG3SjL9WtW0qsUR/XD8sa5BXFYhkWpVq4Jf4J+xla1wamMMCQkJXHTRRbhcLhISEujVq5dPqBkwYABjx4719U9OTuarr75i06ZNnH322QVe/F0uF2vWBF4GRITjx4+zZcsWsrOz6du3LxkZGaSkpDBr1iwOHy7xANyg7N69m7lz55KVlcX+/ftp0CBPTluljFAYJ+PfwvibVhybVUof02owAC1PvwqAGtVrcOrgS6hWMZnLLxvJgx1m0ytjFLOGLmLbNYtZaiwNxeksYACzGcQCzmJmgGRdCXiId2hErl9wHptVf3ZytX0SDdjL3HW7Oc22BBxep8Tj3M8HPJx1U/GeuBIVLCdj4a4zWtGjWY2cA/a8v7WCmaiqpVoROPkJLkUpjnnQ5F8E9ZCpAMDiCn0D2ncktSz0miWFMcbn5+IvTPiHlCclJXHdddfRpEkTRowY4cuDc+jQIQD+/vtvli9f7hubkZHhKwHhcrn4+eefeeGFFxg7diyrVq2iZs2c4qe1atUiMTGRChWs57BGjRqcffbZVKtmJX6vUiV3YVFLUJozZw4uV/ih85GQmZkZ0snZ4XCoiaqMUxgdXDOgaa6/lliRVP2woqqaRWl/SozhrcIcKlDu2aEdmffslZzaoRkNGzWl4+MLAat+xSkswB50lMUQptKAVYDTcwWzzFkJxkBCXT5PfIq223+kWaJ/6TDrt/+KCj2CT6qUOv7WpwBLVECG47wakGACjsl1GxQ/6eeeQa0AuH/wiWHsFC7KepSeGQX7wCxL7RnweG9iw6D9PnEOCGvdkuDAgQMYY3A4HAHZjYMhIjRr1oyRI0eSmppK1apVfce8gtKGDRt47rnn2Lt3L6+99hpz5sxh3rx5pKens2WL5cXQqlUr+vfvz4UXXkj16tXJysoiLS0gMBW73fpW2LFjR4AJzO1289VXXzFt2jTmzCk45D5S3G43b7/9Ntu3b2fMmDG+9jp16tCxY0ecTidLlizRSKoyTGFMVE2CtXvqON0DXE3Jl2pQSgzPxSOCTACzTriZU7cGRkatczegpS0wqj4BuI6pZJipfMRlnMovfMVwvG/Tj2zDPRevnLdtd+Yz1dWdd289szAno5Qw/iJLQBRVUA1OXryyS34RN/4anHb1q7D+6bOxh5n88ZCpyH4qhzx+c39LMBCbkG3sJIiLWa4OuPyKiM53t6K7zcoq/IrzIq5wxIZCu127doClOckvk7E/9erVY+DAgXzzzTe+NrfbzZQpU1iyZImvLSMjg3Xr1uUZn5mZycCBAwF8/Tdv3pxnX1u2bGH+/PnUrVuX3bt3M3DgQFauXMmqVVZO1kgirNxuNytXrqRt27Y+DVPfvn3Zv38/gwYN8jlVr1ixwqfJqlKlCm63G5vNxu7du31aqgULFtCgQQM6d+4c9vpK7BA1LypPuPgzWDlyXi6ov1JG8eU8DD8KqP1ZNwQ8/snVjayknHqkxx7aT/Z/D/KTyyqyngzcwnja4Z+Dw+H358XJ51n9uMV5NyfUqBDJWSixQHIVVrgb86e7dVAn42AaHAkrjDrwYhiucANww2ktGdm7acjj53awoqdE4Nrsf/GnuzX/dl6H2083+Y2rt+9+dq7fkJmm9KrI22w2OnToQP369cMO/xYRtm7dijGGOnXqkJyczPbt2wOEm4YNLe2VN9LqrLPOok6dOnTt2pXBgwcHrA+WJslut1OrVi1fu1fgmjVrFn/99RcfffSRL7QcLJPa7NmzwzJVLVmyhK+++oq///6bV155hWPHjjF16lTmz5/P999/7+tXvXp1EhISaNq0KWvWrPGFhBtjEBEaN24MwLZtxZOXSCl+iuPTNgd4phjmVWIBE7kGp3r9Zvx9+qdsOCI8vQAeGdKJNb8+Sbvs5ewy1aibZL0N33eezZn2nLqmAtzEKN7BG/7t93Z1OfkyO4WXR1zLe61qRXQRU0oWfwfiwLpUds7JegYwLAnTyTgcE1VRfHCGdG9MrZq1rWxfQSfP2ecsdydmZXUCwG1yLoJL3DmRccN6tYBFOcNXmCbMdnWgk2ygkqTT1ZZX6xFrHD9+HMAXVu4VMpKSkrjzzjtZu3Yt27Zt80VBJSUlcdNNeX3i2rVrx8yZM9m/fz+1a9cO0CKtX78eyPEN2rlzZ0DY+OLFiwFL+Dj11CAFXf1YunQpAD/88EOeY5s3b8blcmG32xEREhIS6NChA1u3bmXPnj388MMPdOjQwSfgbN682SfAKWWP4hBwmmIVtVXiEa8GJ0J5ouup59IVGHq2wWYTLv/zMhYeSuFX10n84enz5kO3cvfEDryyJafYQh3gUUZxCHiN27HespncdO/D3J+aTEpifl49SqwRqsJUsANBfXBM4G1QihA5Jbbw3k+5o74qpyb57i81zbk/+3pWuxsx/sz2AQKOAK84rfd3NY6wKDn2neMvvPBCbDYbzZs355tvvmHDhg0A9OzZk5SUFDp06MDs2bPZv38/NpvNp5nJjc1m44YbbvCVbPAXdnv16hUQOh6KhIQEXnrpJYYMGUKTJk0wxrB48WJWrFjBsGHDSEhIoFGjRmzatMk3pnbt2mRmZmKM4eDBg/z+++8BQtLhw4dxOp0+P599+/Zhs9moXr06ycnJIc9HiX0KE0XVKMRfZxH5F3AHMCv6W1ViAp+AUzjrprcQZrYk8olrILvIiaSpUzmZp68YwNvO/+NbV086ZrznO1YVuI9RVOIQ9/EWdapWUOGmDBLKMhJp3sP8NTihzVi3Z93GNFeXkMdt9oLCvYNvtHW9QL+dL1z9WWqa5wmnTrTnjD+Yj69PLOENJ69Tpw4AR44cwWaz0bKlFTlms9lo1MjKC9SyZct8/XsSExMZMWJEnnpUDRo04KqrrqJWrVrcddddVK1alaSkJE455ZSA+WbMmEFaWhpjx45l27ZtLF++nG+//ZYNGzbw6aefsnPnTubPn0/Pnj1JSkqicePGXHvttdx1112cfvrpQE60lrc2V27/nm3btjFo0CBq1aqFw+HwOWcrZY/CaHA2Efr7RbCKUd5R2A0psU7kJqpgVAghnCQ5bLzC5STbbSx98kxef/cgd+x8AIBU4B4+5rGEe3isSKsrJYp/FFVEqr/8MhmHvuAcT6gW8ti37t586+7NJvtlAHznOpn3nOfyTdJ/AbCFqcE5rVUtvl9mmVBeG94Zc/Av37GODav4sm7bRDgt82VmJllFZx1+H5vb+rdg5Mz7GJ34Qlhrljb16tWjbt267Nq1izp16gQU4Tz77LNJSEhg4MCBhSrvICI0adKEW265BYA778zJp9W3b1/ef/999u3bR1ZWlq997ty51K5d2/d437597Ny5k+PHj1OvXj3OPDMw8KBmzZokJCSwadMmtm3bxsCBAxk0aFCArw9YvkTZ2dnUq1ePQYMG8fPPP1OzZs2wHbOV2KEwAs4T5BVwDHAAWAv8Gmb1bqUs4ruwFM3n5Ynz23PbZ4u44/TAcFWbTVj22CDfhfDGa69n8c7hfD1nGS1XvMpE16l8+XDZKcmghCbANyfI8WAijDuMH9Krag1m0+pFTHd1ZkIBfWe5O7LKNM7ZR5BorgA8AspFXRtSq1ISNSom0rFhVRbMyJFc/GUvEdhs6uY89jurK3s3psf0LqSZZCpKRoHnVdqICFdffXVQE5PD4eCss84qlnUTExO54IIL+OCDDwBo0aIF//zzD1WqVGHevJysz+np6fz666/5llhwuVw+f54lS5aQkJDA8OHDMcawefNmn2lrwYIFnHTSSdSsWbNI9biU0qUwYeKPFcM+lLJCIZyMg3FC9VSm3Non6LEkhz3gfucTqnLC//XgEfMf/t2zMYmOqAX/KSVNSBNVeFFU3vIO+brgiINnneFXlc/2i4Dy5mQpCJtN6N86R3vgb8JITsh5f9pynZdgmP6vfhxMz6J2pWSa1EjFllZ2zB9eE1NJ42/qO3DgAGA5HmdnZ5OcnEy3bt2YM2cOGRkZvj65nYP37dsXkNPGq6k5ePAg/fr1w+12s2jRIhYtWuQ7R28F9jp16uByuZg7dy7NmjWLKBJNKT1KL2ZRKZv4fHBK9sNdo2ISb15WDksbxAEhE/359wnSFlzA8RyLqkwgdMl4B4DfC3xbh+qQs6H+rWtTs2ISfVrUDCrgNK1ZgaZYaQ0+v6EXtpdV4V0QdevW5eqrr2bmzJlcfPHFTJo0ibVrrVxD3bp1o1+/fmzevJmtW7cCBE3O5y8kJSYmkpWVhcPh8GVbttlsdO3ala5dc+qZ7dmzh99++42aNWuyZs0aZsyYwaxZs7j66qsDTHRKbFKggCMiVxZmYmPM2IJ7KWWP6GhwlPJJJGLxT65unG+fy3J3E9rbNgE5JqpQxTYLux+vw29ugSRcXPZk3/0Em423PUU/czunHpPAfE11qySTFVZun/KNiNCoUSOfZsWbDblSpUr069cPu90eUKH8yJEjeebwJhRcsGABgwcPJi0tjWbNmuXrW1O7dm1OP/10atWqxYwZMwBwOp3s27dPBZwyQDganNFYV7VIPvkGUAEnHilEoj9F8eKv1i9Is/OD+2TOz3yCdaYhK5OvASA5xRIQoqXBced6Hxcs3wTvcLh6Zz5z9me5aUrTgPOyHlybdS93Or7mtdQ7+CjXWFuUhLXyxJVXXsm4ceMYMWKEz6x4wQUX4Ha7qVu3Ln365DV/22w2zjrrLBo3bkzbtm3Dqha+e/dufv75Z9xut694qMPhoEaNGgWMVGKBcASc/sW+C6XsECUfHKX8EI4oHDy6SlhiLCf0h7OvpqdtFVtrWFmC8xVwItDCuE3g+7iwGhyD8JDzegB+bpU3b8o0d1emZXWlnSNvaLgKOJHjLQrqT0JCAsOGDct3nM1mo3379hGvd/DgQQCaNm3qy5NTEmRlZfH555/Tv39/GjZsqH4/EVKggGOMmVkSG1HKCKXkg6PEB6HeNQW9nT5xDeQT10D6ivVrfWSfJtw/cSkXdS1altncGhx7ARsJfYHJEVJa1akUok/w87SJCjixitfJ2FuGokOHDiQkJPhyAhU3EyZMYOPGjWzcuJGRI0f6ykeEi9Pp5JdffmHgwIG+GlzlibDEUBHpISJaN14BRzI4UkA0yZ4SOUWVi72am0u6ncCs+/rz/NCOhZpnrHMg6931+NndLcL9RX4CKx7PyccSWR4gpbTZvXs3U6dO9ZmnDh8+zM8//8yWLVv44IMPyMzMDHuu48eP89prr7Fp06agiQONMb4SFd7jbdu29R3/6aefIt7/jz/+yF9//cVrr70WkEOovBCunm0e4KuaJiIVRWS8iLTNZ4wSj1z9PTy8Cxp2LbivopDL76aA3DeR0KhGqi8zdqQ84ryaAVkvcoyUgHbvXj9yDmaZu0nY8+VnMquQVP5+OccLxhgyMjJ8As7Ro0dxuVx8//33bN++nbFj87qaZmdnM3HiRJ/WxzvP22+/zaFDhxgzZgw7duzIM27Hjh18+OGHjB49mh07dmCM4dixY77j9evXDxodlt/evSH1aWlpfPbZZ2GPjRfCFXByf4skAcOBukH6KoqiRETpWDxDL/qE80r+L+vpvCNC+F6Ea2RSy27ZwhjjKy5ar149unTpgoiQkmIJxnXq1AnQxhhjGD9+PCtWrGDSpEm+9l27dvkivwD++eefPGutX78el8tFVlYW//zzD7t27eKvv/6iX79+dOvWjYULF/oqnofDzp07fWHzYJnXyhvqKaooSokROg9OeFf+jg2rRHE3Fr2aFT0iJtyoLpVvyhZeDQhA5cqVqVevHo0aNWLr1q00bdqUdevWsWvXLl+fnTt3+rIhV6lShTFjxrB48WKcTieJiYmcdJKVy6tq1ap51qpcuXLAfbfbjcvlokWLFnTu3JmkpKSQGZr9yczM5P3332fVqlW4XC5OPPFEAH7++Wc2btxYrupqqYCjKEqxEs2LercmoetMFYaXL+nEi5d0imBE0c5Go2DKFu3ateO8886jXbt2DB06lFWrVrFq1SrAEmDcbjfGGLKyshg3bhw7d+70jV2zZg2bNm1iypQpzJ49m8zMTCpWrEhiYiI1atTA5XIxZ84cMjMz+fHHHwPMT0eOHGH//v1kZGTkqZWVG2MMW7ZsYdy4cb597Nixw1cd3attyszMZOzYsXz77bc+rZQXp9PJjz/+iNPpjMrzFiuocVhRlBIj4PoeRobj4sZeSB+e3ISbeDDc5e7LvoEXEt4rwo6UaGCz2ejSpQtdulgV6Nu2bcvvv//Orl27OHDgAG63m2XLlvHHH38A+LQ3jRs3ZvPmzb551q1bB1i+MFlZWaxdu5bvvvuO3bt3s2zZMvbs2UNqaqqvf1paGk2bNvXV1fIXdho0aODr53a7mTNnDtOnTwdg/PjxDBgwIMA3yGazMWLECMaNGwfAokWLqFKlCqeddpqvz08//cSCBQtwu92cc8450XwKS5VIBJyzRcTrc5OKZXa+WEQ6B+lrjDGvFHVziqLEF6FMUaWp14hEZR/KqTmcIqAQfp6dnUYTycUiNpuNa665hilTptCjRw/GjRvnE24gp0REYmKiry0hIcHncOzNtjx79mzfca+GJj09ncaNG1OjRg2fsJGVlcWBAweoVs3SXP7555+0atWK1atXs2PHDurWresTbgAaNWrEtm3bfI/r1avHWWedhcPh4P777+fll1/G6XTmec97szLXrVuX5cuXh50IMdaJRMC5zPPnz40h+hpABRxFUcLSzgQz3bSuW4nVu45Gvl7EI8KnSkpC0HanK7zolnAFnNz5eZTYISEhgYsuuogdO3b4BBqHw0FSUhLHjh1DRHzamMTERJo2beqLwqpWrRqpqamkp6f75sttLqpQwcrWvXDhQsASwL2aoR07dvDBBx/4hKLcPjn79u1jy5YtnHbaadjtdnr37u3L9JySksI555zDlClT8vgAefts376dRYsWsXLlSoYOHRp28dlYJVwRrX+Ef6dHfaeKopRJ/LUbkZiESqJqvE0kwrIPITRQBZzWCxd1pEaFRB4/v11YqwQrNKrELueeey533HEHTZs25eqrr/blnGnYsCF161qGjzZt2tChQwduvfVWkpKSOOGEE6hSxXKar1y5Ms2aNeOyyy7L43NTq1YtevfuTcuWLQECTFTeQqFgaWtWrVpFVlYWLVu2pG/fvnkEFO/jNWvWMGvWLFwul0+bk5yc7HOqXrVqFb///nvUnp/SIiwNjmYzVhSlsCT5CSr+Ak40c+KEy5AuDfh60facdaO08Fnt6/HJH1s4o03wDLcXdzuBi7qGn2o/dwkJJfZwu90+DY4xhsTERK680qpNXbduXWw2G+effz42mw2Hw0GvXr2w2Wykpqby4IMPApbjr7emVlJSEmDV1Dp+/Dh79+5lyJAh1KtXDxGhffv2rFu3ziek2O126tat66uqXqdOHXbt2kV2dnYeXx0v7dq1Y/ny5axevZrVq1czf/58LrroIqZOnYqIULVqVZ/vkH9UV1lFnYwVRSlWEuw2/n74DBz20BftcAWNoka4vnRJJ54e0oHW/51qrYtQOTm42SkoITaanGDnq5t7FzA0ghpZqsGJeTZu3Oi7n7t6udeM5eWUU04JOkeomlpeQcmfmjVrkpSU5IvUEhEWLlzIBRdcwJEjR0hJSWHx4sVAXrOXF5vNFpCPx5sA0Gaz0aNHD1/kFcCsWbNo06aNT/Aqi+jPBEVRip0aFZNC+q9A8It/cVziRYTkhEC1fZXUBMZc04NJt+QVUEZm3c/nzn7FsJP8UQEn9unduzf9+vWjf//+QauXR5t69erRpEmTgLw7PXr0oEOHDvTt29fnpwMEzZTs5corr6RmzZoB4eMul4u0tDRcLhf16tUDrAKjY8aMKZ6TKSFUg6MoSlwRidnJ2/e0IBXAAWa4O7PZ1GG4Y4Z3RJH2Fi7+Pjjr3A1oabPMaudmPsV3SQ+XyB6U/LHb7QGh1sWNiPhqX1WpUoXMzEzmzp1L8+bNqV+/Pueffz5Op5OKFSty5plnhpwnKSmJW2+9FZfLxSeffMKmTZto3ry5z1eoS5cuuN1udu/eTeXKlTHG+H6AuFwu5s6dS7Nmzahfv37M53VSDY6iKKVCuLljIp43ytOWRt5Xt99X8z6Tk705g8Rg3ZVywqWXXkqzZs24+eabOfnkk8nIyGDv3r2AZdoaPnw45557bliVw+12O6effjo2m43Nmzf7TG579uzhuuuuo02bNmzZsiUgeeHcuXP57bff+OCDDwLy/OSHMYZdu3aVSgZlFXAURSkdYiBjfMS/P0voF6u/icr/adLoqvJNYmJigEMyWKakwnLw4EHcbjdOp5OTTz6ZHj16cOaZZ+JwODjxxBN9zs5evHl8ACZPnhyW0LJz507Gjh3LnDlzIioWGg1UwFEUpVQoLvkmxrXmYeEgx0nUJjEgCSoxhzcRX1ES8tWoUQObzYbb7UZEfEkBIcdR+ccff/Tl7alVK8eUm5mZGeAPFIp9+/Zx/Phxfvvtt4iKhUYDFXAURYkrTqiWWnAnD+EIQ4Fak5KRnm7t39x3P4H4qg+kRIc+ffowYMCAIjk422w2RASn08myZcsCjm3YsAGwBJkXXniBb775BpfL5SsaarPZwtLg1KhRA4fDQXJyMjVqlGyGbnUyVhSlzBCOGf+CLg3Yefg4fVrULLhzjFIpKSfSS2LBlqfEHHa7PWT4ebjUrVuXkSNHsnTpUgYNGhRw7Pzzzyc9Pd3nm7No0SJfIkF/U5WXrKwsJkyYwLBhwwJKVYgIDoeD3r17+xyZSwrV4CiKUioUl8+h3SbcdnpLujSKTuVxEwtVQRWlGBARGjZsyNlnn53HMdmbj+e+++7zRUtlZGRgt9t9tbG8ZGdn8+qrr7Jhwwbef/99tmzZwg8//EB6ejrfffcdxhj++OMPdu/eXWLnBqrBURSllCgwiiomhYni3ZPLCB+7BtOxxFZUlPxJTU2lX79+vqKeDRo0oEaNGrjdbp+JatKkSRw/fhywfG5Gjx6NMYYlS5b4ylb4V0svKeJKwBERG3AnVhHQJsBe4AvgEWPMsWiPF5GzgYeBTkAmMA243xizMXdfRVGU/NhrqnBG5gscpgJf+Al/QslGnihKbvr06YPL5WLfvn1ccMEFrFq1ioyMDP7880/279/vSxpYsWJFjh8/jsvlQkR8wo3NZuPkk0+mTp3gpUyKi7gScLAqmN8BTAJeAtp4HncRkTOMMQV9U4Q9XkSGABOBJcB9QBXgLuB3EelmjAmdSlJRlBihYP1IgJ6pmLVKh6kIgM3kOBarBkcpbex2O/379/c9rl69OiIS4JjscDg4/fTT+f777wECHJDdbjdz5swhLS2NM888s8SqlMeNgCMi7YDbga+NMUP92jcCrwPDgfHRGC8iCcAoYCvQ1xiT5mn/EfgbeAy4IYqnpyhxRynk/Ypp/H190iq38t1XJ2Ml1jh48GCeCKrevXtTrVq1PHWwunTpQtWqVZk9ezbz58+nUqVK9O3bt0T2GU9Oxpdi/dh5NVf7+0A6cEUUx58G1Ac+8Ao3AMaYxcAMYJhHCFIUJQRl5bJdUmHi/s9HdnL1MPaiKKVDu3btfBFRXgfkdevWMXv27Dx9MzIyOOWUU+jY0fIsCxaBVVzEjQYH6A64gb/8G40xGSKy2HM8WuO99+cFmecP4HSgFVCyWY0UJc6JtlAUS37MoYQX1eAosYbNZuOaa65hypQpDB48mM8//5wRI0YgIowfP57GjRtTuXJlNm7cyPnnn8+uXbtYvHgxdrud6tWDC+/FQTwJOPWBfcaYzCDHtgO9RSTRGJMVhfH1/dqD9QVogAo4ihKS0qhNEy51Kyez60hG3gPFIBFlmgSSJJt0kxT0eAzJYIriIyEhgYsuugiA6667ztc+cuRI3/2uXbsCsH//fl+ZhuXLl9OoUaMS2WM8CTipWJFMwcjw6xNKwIlkvDfeLVh//755EJEbUP8cRYkJQgkPsx/oj9NlaPPI1Hx6RYdbsu/g347x3JZ9R9DjqsFRyjrt2rXD5XKxY8eOPAkFi5N4EnDSgdohjiX79YnGeO9tsJ9c+a5ljHkPeE9EC8wo5ZuCFDilqblIsNtICBroEf1dTXN3ZVpW15Cr+N9XHxylLGKz2ejcuTOdO3cu2XVLdLXiZQdQU0SCCR0NsMxPobQ3kY7f4dcerC8EN18pilLGKG1LmubBUZTCEU8Cznys8+nh3ygiyUBnYEEUx8/33PYKMk9P4AiwNrxtK4pSWkikPjWx5JWsKEq+xJOAMwEryOKuXO3XY/nDfOptEJHmItK6sOOBmcBO4DoRqeg3byegH/ClMSa7kOehKOWC0taMlBVUpFKUwhE3PjjGmGUi8iZwm4h8DfxATibimQQm+ZsGNMbvuyOS8caYbBG5E0somi0i7wOVgbuxyjs8WmwnqihxQoG1qIKNKQWpqKTy4IRCnYwVpXDEjYDj4S5gE1aU0jnAPqyMw4+EUaYhovHGmC9F5DhWLaoXyalF9YAxRv1vFKUMYC8DOmx/AUdFHUUJn7gScIwxLqwaUi8V0K9JUcb79f8O+C6yXSqKAlCnshVwWBpuLXec3oK56/fTt2WtAvsGaHBKaLP+y6iJSlEKR1wJOIqilB2SE+wsfmQgiY6SV6PcM+hE7inxVRVFKUlUwFEUpdSompoY8lhsBiyV/KZsGiauKIWiDFigFUVRSo8Av5dSkLr8TWSa6E9RwkcFHEVRlBhGhRpFKRwq4CiKUmYojSii0nAyDlg/Nm11ihLzqICjKIqSD6WRh8ZfpkmwBy2KpShKAaiAoyiKEsNstp3gu6/mKkUJH42iUhRFiUHOyHyedrKJJsnQzzmntLejKGUO1eAoiqLEIP+Yhkxxn1La21CUMosKOIqiKDGMGqUUpXCogKMoSkyiF3ZFUYqCCjiKoigxhoQQ77TYpqKEjwo4iqKUGUw5vMKXw1NWlKigAo6iKIqiKHGHCjiKoigxjPoiKUrhUAFHURQlH2wxZCTSRH+KEj4q4CiKouSDXVylun7siFeKUrZQAUdRFCUfHLhLewuKohQCFXAURYlJJEaqaKeZ5NLegqIohUBrUSmKUoYoeYPNXqpxT9ZN7KUq40pozXpVc4SqwGrmsSH0KUpZQAUcRVGUAvjafWqJrte6bmXffRVpFKVwqIlKURQlhlEnY0UpHCrgKIqixDCmPKZvVpQooAKOoihKCKb/qx9PXdC+VPdgUxuVohQK9cFRFEUJQdOaFWJKgxJDW1GUmEc1OIqiKDGMKnAUpXCogKMoSkwS7MJePjUY5fKkFaXIqICjKIoSy5RPqU5RiowKOIqiKGUELbapKOGjAo6iKEoMI2qiUpRCoQKOoihKDKM6G0UpHCrgKIqixDSqwVGUwqACjqIoSgxTJTXRd199cBQlfFTAURSlzFAlJaG0txB1rurVOGj75Fv78N9z23JirdQS3pGixAcq4CiKEpOIn7LivRFduaVfc3o1r1F6GyomHjuvHfMeOp2z2tcNaO98QlWuPaUp6cl1Q4xUFCU/tFSDoigxz6B2dRnULj4v9CJCvSopIY9vrjOA/2Vfxjx32xLclaKUfVTAURRFiWXExvuucwGox/5S3oyilB3URKUoipIPsRTDFEt7UZRYRwUcRVGUGCaWqpkrSllCBRxFURRFUeKOuBJwRORKEVkkIsdFZLeIfCAitYpjDhEZLSImxN9F0TsrRVHKA6EUNSKa+0ZRCkPcOBmLyN3Ay8BM4E6gIXAP0EtEehhjjhXTHCOCtP1VuLNQFMVL7crJpb2FmMDfRKWJ/hQlfOJCwBGRmsBTwHxggDHG5WmfD3yDJaw8XRxzGGM+id6ZKIri5bH/a4cAV/dpUtpbURSlDBIvJqoLgFRglFcwATDGfAtsAK4orjnEorKIxMtzqSgxQa1KSbxx2Ul0bVy9tLeiKEoZJC40OEB3z+28IMf+AC4VkYrGmLRimOMwUAnIEpFZwMPGmD/D3bja1xWl7CDPle46mwmjuvjj+p2iKBA/Gpz6ntvtQY5tx/pOqB/kWFHm2AW8AtwMXIhlvuoGzBaRM0ItIiI3iMiCAvaiKIqiKEoRiCkNjohUBe6KYMjrxpgDWKYlgMwgfTI8twVVrItoDmPMg7n6TBaR8cBi4G2gZbBFjDHvAe8VsBelhBCRBcaYbqW9j/KOvg6xg74WsYG+DkUnpgQcoCrwaAT9PwEOAOmex0nA8Vx9vKEY6eRPkecwxqwTkS+AkSLSyhiztoA1FUVRFEUpBmLKRGWM2WSMkQj+/vEM3eG5bRBk2gZYGc53BDnmTzTmANjkua0ZRl9FURRFUYqBmBJwisB8z22vIMd6AmsKcDCO1hyQY5raHUZfpfRRc2FsoK9D7KCvRWygr0MRkXioc+LJNLwZWAb09sth839YOWz+a4x5yq9/Iyx/mvXGmOxI5xCRCoDLGOP1zfHO2wUr4mq9MaZtMZ6yoiiKoij5EBcCDoCI3Au8CMwAPsMyK90LbAW6+2tfRGQGcBrQ1BizKdI5RKQz8CMwGVgHHAM6AdcAbmCQMWZO8ZypoiiKoigFETcCDoCIjATuBk4EjgDfAQ8aY/bk6jeDIAJOuHOISF3gBazcOfWBFGAnMB14xhizOuonpyiKoihK2MSVgKMoiqIoigLx42SsKAGIyEMi8qWIbPBUeN9UQP+TReRXETkqIkdEZKrHFKkUARFpJSJPiMgfIrLX8/wuFpH/eHzZcvc/UUQmi8hBETkmIrNF5PTS2Hu84XluPxWRVSJyWETSRWS1iLwsIvVC9NfXogQQkVS/76o3ghzX16IQxFoeHEWJFk9j5UhaiJVfKSQi0hPL72o78Iin+TasrNS9jTHLim+bcc81wK1YjvqfAtlAf6zCtpeISE9jzHEAEWkOzAWcwPNYZVCuB34SkbOMMb+Wwv7jiYZAPWASsA3ree4A3AAMF5HOXlO8vhYlzhNArWAH9LUoPGqiUuISEWlmjNngub8cqGiMaRKi719Aa6CNMWa7p60BsAr4wxgzqGR2HX+ISDdgnTHmcK72p4D/ALcbY97wtH0BDAW6GmMWe9oqAiuwsom3NvqFFXVE5GLgC+ABY8zznjZ9LUoIETkJ+Au4H3gJeNMYc5vfcX0tComaqJS4xCvcFISItMByFv/SK9x4xm8HvgTO8DiVK4XAGLMgt3DjYYLntj34Ui+cB8zwfol7xqcBHwCtyCmIq0SXzZ7baqCvRUkiInbgfWAq8HWQ4/paFAEVcJTyTkFV5AXoWnLbKTc09Nx6E2J2xCqTEup1AP0ijwoikiwiNUWkoYgMAt71HPrBc6uvRclxN5b2+LYQx/W1KAIq4CjlnYKqyEPw8h1KIfH8av0vlk/BeE+zvg4lx3XAXqz8Xj9h+ahdYYyZ7Tmur0UJICJNgceBJ3KnK/FDX4sioE7GSnknGpXolch4Faskyr+NMWs8bfo6lByTgdVARaALlgnEv3aevhYlwzvABuDlfProa1EEVMBRyjv+VeRzE24leiVMRORJLHX8e8aYZ/wO6etQQhhjtmFFUQFMFpGvgPkikup5TfS1KGZE5ApgIHCqt1xQCPS1KAJqolLKOwVVkYfg6mElQkTkMeBh4GPgplyH9XUoJYwxS4FFwC2eJn0tihERScLS2vwA7BKRFp5gh8aeLlU8bVXR16JIqICjlHcKqiJvgL9LbjvxiUe4eRQYA1wXJKx1GZYaPtTrALCg2DaopADVPff1tSheUrBy3pyDVcvQ+zfDc/wKz+Pr0NeiSGgeHCXuCSMPznys2mOtjTE7PG31sfwU/jLGnFFSe41HROQRLGfKccBIY4w7RL8vgSHAScaYJZ42b76PTOBEzfdReESkrjFmV5D2/sCvWKHIAzxt+loUEyKSAJwf5FAt4C2skPEPgaXGmLX6WhQeFXCUuERERpCj8r0dSMRKogWw2Rgzzq9vb6xCqduAUX5j6gB9vF8qSuSIyK3AG8AWrMip3MLNbmPML56+LbASnmUDr2AVu70eK9vuOcaYn0pq3/GIiEzCymT8G1bum2SsFAjDsfw4+vklktPXooQRkSbARvIm+tPXopCogKPEJX4V44Mx0xjTL1f/XljlA07GMkvNBR4yxiwsxm3GPSIyGrgqny4Br4WItAGexXrtErFKbTym6eiLjohcAlwJdMLSFhgsQecX4AVjzJZc/fW1KEFCCTieY/paFAIVcBRFURRFiTvUyVhRFEVRlLhDBRxFURRFUeIOFXAURVEURYk7VMBRFEVRFCXuUAFHURRFUZS4QwUcRVEURVHiDhVwFEVRFEWJO1TAURSl2BCRGSKyqbT3ESkissmTLDIacz0nIhtFJDEa8/nN20REjKfOV9wgIueLSJaItCztvShlGxVwFCXGEJHBngvXU0GO9fQcyxSR1CDHp4qIW0Rqlsxuyy4icpeIjCzmNZoCdwJPGGOyinOteMEYMwWryORzpb0XpWyjAo6ixB5zACfQL8ix/p5jiUBv/wMi4gBOAZYbY/YV8x7jgbuAkcW8xoNYtYM+KYa5N2NVps4jCMcBrwEXiki70t6IUnZRAUdRYgxjTBowH+geREvTD6t20C7yCkDdgQrAjOLdoRIOIlIZuBz4zBiTHe35jUWGMcYZjfnEomI05ooCX2MVAL2ptDeilF1UwFGU2GQ6lpamj7fBo6HpA8z0/PXPNaaf31hEpIeIjBaRtSKSLiJHReR3EbnQf5DHR8SISMfcmxCRKiJyXEQm52o/Q0R+FpFDIpIhIktFJOyLkYi0FJFxIrLT42+xSUReEJEKufqN9uytioi8LSJ7POv9LiInB5m3hoh8JCL7RSRNRH4TkS65fYFExGBVmz/NM7/3r0mu+VqLyPee5+6wiEwUkbphnubZWALnD0H2OcNzzk1EZJLneTzoOd+KImITkX97fHcyRGShiPTJNUdIHxwRGepZ45DntV8jIq97/YBEpJ9n7EgRuVVEVgIZwL88xx0i8oCIrPSsv9+zzw6h9iAi54rIfE//nZ7X05GrfzsR+VJEtnvMrLtEZLqInOPfzyPkzwYuCvO5VpQ8OAruoihKKTAd+Dc5GhvI0dDMxDJ7vCYiFYwxxzzH+2FViJ7peXwh0Br4AsucUQOrsvfXInK5MWa8p98Y4H6sStP/yrWPS4BkTx8AROQG4B3gD+B/wDFgIPC2iDQ3xtyX34mJSFfgN+AQ8C6wHavC9R1AHxE5LYjG4ydgL/CE5zzuAb4XkabGmKOeeZOAX4HOwGjgL6Cjp+1ArvlGAK8A+zzn4GWv3/0GWNqwScB9nj3eCFQGBuV3jh681eznhzheAet5mIllyuoOXIP1fO/Hqmw/CkjAel2+FZHG3vMNhYj8D+u9s9JzjjuB5sBQ4BHA3xfoLqzn830sreBWT/unWK/9L8DbQF3gVmCeiPQ1xizKtezZwC1Y74uPgPM9ez4IPO3ZVw3P+eLptxmoCXTznOv3ueacB5wpIq2NMavzO2dFCYoxRv/0T/9i7A/LtyITmOvX9hBwFOuHSRssYWaQ55gDSAMW+/WvEGTeVGANsDJX+3xgB2DP1T4bSwhI9Dyuh/VLf3yQuV8DXEAzv7YZwKZc/ZYAq4FKudov9JzTSL+20Z62t3L1vdjTfqNf2y2etv/k6uttz72PTcCMEM//Js+YS3K1v+lpPzGM13AmcCDEsRmeee7L1f414AYWAAl+7ecFOd8mnrbH/Np6eNp+A5JzzS2AeO738/Q7ANTO1W+g59gEb39Peycs/6/ZQfZwDGiSa63lwM4g53BJsOckyHN0haf/0NL+POpf2fxTE5WixCDGmOPAn0A3P7NNPyyBx2mMWQXsIccs5dXuTPebw6vZQURSPb+gU7Eufm3E8hHxMgZLeBnoN6YplknsM5MTAXQRkAR8KCI1/f+Ab7HM3meEOi+PiaMjMB5IyjV+DtaFMph25JVcj72aAP9Q4v/DErBey9X3A+BwqD3lww5jzBdhrBuKWuTVHPnjwtLQ+DMbSzh4xwRqsWaHue7lntuHjDEZ/geMh1z9xxpj9uRq85ow/+ff3xizBOs1PkVEauUaM9kYs8l/Laz3Yl3J8evxvgZn5XrvhWK/57Z2GH0VJQ8q4ChK7DIdyzxxigT633iZRY4fTj/P7QzvQRGpLSLvichuLMFhH5YJxusrU9Vvrs+wTBdX+rVdiXWxHevX1sZz+6tnLv8/rymtTj7n5B3/eJDxe7CEtGDjN/g/MMZ4L341/JqbYgklabn6ZgEb89lTKDYEaQu2bigM1vMXip25hRAskw7k2q8xxtte0LotPesuCWN/AGuDtDXF0iKtCnJshV8ffwp8rowxM7HeSyOBfR4/qsdFpG2IvXmfu9xCmaKEhfrgKErsMh3LZ6Ifls+N1//Gy0zgFc8v5H5YF6VZYEXEAD9jCRSvYZk8DmNpDa4GLsPvB44xZr+I/ABcICKVjOXnMQJYZYzx9yHxXnSuxPLtCEawi13u8S8BU0P0OZi7wRjjKmC+4iDUmuGuuxfLrFOY+YtyvobwhYL0MPsVRFjPlTHmKhF5ATgL6AvcC/xHRO4yxryRa1x1z+1eFKUQqICjKLHLPCx/l/5YAs5xAh1WZ2J9hvthaXcW+/3S74h1cX3CGPOo/6Qicl2I9cYAFwAXi8gaLMfUB3P1Wee53WeM+TXyU/KNdxVyfH5sAs4QkYr+WhwRScDSOBzK1b+4NQPLsaK0apqSy0u0Fkt46ITlZF0YNmAJv22ApbmOebUthdGIAWCMWY713LwgIlWxTLHPisibuUxoLTy3ywu7llK+UROVosQoxphMLCGnK3AuMM8EZsNdjmUGuI+8+W+8v6gDfvGLSHtyfCxy8z2WGetKz5+bvAnqvsByfn5cRFJyTyBWOHdSPqe1yLPvm0SkWZDxDhGpnndYWHwL2LEyB/tzPVAlSP80crQExcEMz23PYlwjN97IuKclSGkIj2avICZ7bh/y7+9575wHzDHGRKxVEZHqIhJwzTHGHMISllKxosf86QnsNsasiXQtRQHV4ChKrDMdS4PTGwjQxBhjjIjMxtK6ePt6WYXlL3G/WMkC1wCtsMKcl2EJTQEYY7JF5DPgNs/xX40x23P12SYiN2M57q4SkXFY4b61gA6evbTF0qbkwbPnEVjOuktF5CPPPlOxfrEPwYoWG53/0xKUDzzn95SItCAnTPwS4B/yft/9AVwrIk9iPV9u4Ft/5+wiMhUr6u1s4LsozZkvxpi/ROQ54AFgoYhMwAr/borlIN6DvJqs3HP8IiJfAMOBaiLyHTlh4hlY4fyF4UrgbhGZhPV6ZGOF0p8JfOFxrAfAY3btixVyriiFQgUcRYlt/IWWmUGOz8QSKlzkRNpgjHF5kqe9iJX7pgKW5uQqLPNFHgHHwxjgdqAigc7FPowxH4vIWqw8JzdiOSvvwxKi/ot1QQ2JMWaxiHTBEmTOw3J6PoolFI0GpuU3Pp95M0VkAPACVh6WS7DMHwOwhJ/cWaH/g6XBudVzDoIlCERFwDHGpInIJ8Awj49JidSiMsY8KCJLsATV+7E09VuxEg6G63NzObAQyyH4JaznZCbwX2PMskJubQbQBUsbWQ/rPbsR632U2/9mKNbr9W4h11IUX04ERVGUuERE7FgC2J/GmMElvHYTrJw/txljPijJtcsyIrIQK2/RkNLei1J2UR8cRVHihmB+QVgaoqrkhLGXGJ7cMK8CDwfziVHyIiIXAO2xzGyKUmhUg6MoStzgMQklA3OxnKF7YYXErwdOMgWUOVAUJX5QAUdRlLhBRK7E8qlpheVHtBvL9+S/xpjdpbk3RVFKFhVwFEVRFEWJO9QHR1EURVGUuEMFHEVRFEVR4g4VcBRFURRFiTtUwFEURVEUJe5QAUdRFEVRlLjj/wHsyOay3B3Y1AAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Apply a 5 pixel boxcar smoothing to the spectrum\n", "spec_bsmooth = box_smooth(spec, width=5) \n", @@ -1142,7 +1054,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, diff --git a/jdat_notebooks/MRS_Mstar_analysis/requirements.txt b/jdat_notebooks/MRS_Mstar_analysis/requirements.txt index 9934ab33..c77fe36a 100644 --- a/jdat_notebooks/MRS_Mstar_analysis/requirements.txt +++ b/jdat_notebooks/MRS_Mstar_analysis/requirements.txt @@ -1,7 +1,6 @@ scipy >= 1.7.1 astropy >= 4.3.1 matplotlib == 3.4.3 -aplpy >= 2.0.3 git+https://github.com/radio-astro-tools/radio-beam.git spectral-cube >= 0.5.0 photutils == 1.1.0 From b8e687515b3ac9d460ecb29c263ce118d1762f3e Mon Sep 17 00:00:00 2001 From: ojustino Date: Wed, 15 Dec 2021 18:00:18 -0500 Subject: [PATCH 5/8] Initiated a technical notebook review --- .../JWST_Mstar_dataAnalysis_analysis.ipynb | 249 +++++++++++++++++- 1 file changed, 237 insertions(+), 12 deletions(-) diff --git a/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb b/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb index 5dca2329..e8699496 100644 --- a/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb +++ b/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb @@ -55,6 +55,46 @@ "## Import Packages" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# disable all imported packages' loggers\n", + "import logging\n", + "logging.root.manager.loggerDict = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enable PEP8 checker for this notebook\n", + "%load_ext pycodestyle_magic\n", + "%flake8_on --ignore E261,E501,W291,W293\n", + "\n", + "# only allow the checker to throw warnings when there's a violation\n", + "logging.getLogger('flake8').setLevel('ERROR')\n", + "logging.getLogger('stpipe').setLevel('ERROR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" + ] + }, { "cell_type": "code", "execution_count": null, @@ -63,7 +103,17 @@ "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1: E999 SyntaxError: invalid syntax\n", + "10:10: E128 continuation line under-indented for visual indent\n", + "11:10: E128 continuation line under-indented for visual indent\n" + ] + } + ], "source": [ "# Import useful python packages\n", "import numpy as np\n", @@ -123,7 +173,15 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4:1: E302 expected 2 blank lines, found 1\n" + ] + } + ], "source": [ "# Save and Load Objects Using Pickle\n", "import pickle\n", @@ -142,7 +200,15 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4:31: E251 unexpected spaces around keyword / parameter equals\n" + ] + } + ], "source": [ "def checkKey(dict, key):\n", " \n", @@ -447,7 +513,24 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "5:19: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:5:19: E251 unexpected spaces around keyword / parameter equals\n", + "5:21: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:5:21: E251 unexpected spaces around keyword / parameter equals\n", + "5:24: E231 missing whitespace after ','\n", + "INFO:pycodestyle:5:24: E231 missing whitespace after ','\n", + "10:15: E231 missing whitespace after ','\n", + "INFO:pycodestyle:10:15: E231 missing whitespace after ','\n", + "17:16: E231 missing whitespace after ','\n", + "INFO:pycodestyle:17:16: E231 missing whitespace after ','\n" + ] + } + ], "source": [ "# Apply a 5 pixel boxcar smoothing to the spectrum\n", "spec_bsmooth = box_smooth(spec, width=5) \n", @@ -654,7 +737,16 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1:1: E265 block comment should start with '# '\n", + "INFO:pycodestyle:1:1: E265 block comment should start with '# '\n" + ] + } + ], "source": [ "#rename blackbody.flux as ybest\n", "ybest = blackbody.flux" @@ -686,7 +778,16 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "15:22: E231 missing whitespace after ','\n", + "INFO:pycodestyle:15:22: E231 missing whitespace after ','\n" + ] + } + ], "source": [ "# Plot the spectrum & the model fit to the short wavelength region of the data.\n", "plt.figure(figsize=(8, 4))\n", @@ -835,7 +936,16 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2:1: E265 block comment should start with '# '\n", + "INFO:pycodestyle:2:1: E265 block comment should start with '# '\n" + ] + } + ], "source": [ "# Save if you so desire. Keep commented otherwise.\n", "#poly.write('poly.fits')" @@ -845,7 +955,84 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "4:19: E262 inline comment should start with '# '\n", + "INFO:pycodestyle:4:19: E262 inline comment should start with '# '\n", + "5:19: E262 inline comment should start with '# '\n", + "INFO:pycodestyle:5:19: E262 inline comment should start with '# '\n", + "6:19: E262 inline comment should start with '# '\n", + "INFO:pycodestyle:6:19: E262 inline comment should start with '# '\n", + "7:19: E262 inline comment should start with '# '\n", + "INFO:pycodestyle:7:19: E262 inline comment should start with '# '\n", + "15:1: E265 block comment should start with '# '\n", + "INFO:pycodestyle:15:1: E265 block comment should start with '# '\n", + "18:15: E221 multiple spaces before operator\n", + "INFO:pycodestyle:18:15: E221 multiple spaces before operator\n", + "18:44: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:18:44: E251 unexpected spaces around keyword / parameter equals\n", + "18:46: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:18:46: E251 unexpected spaces around keyword / parameter equals\n", + "18:76: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:18:76: E251 unexpected spaces around keyword / parameter equals\n", + "18:78: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:18:78: E251 unexpected spaces around keyword / parameter equals\n", + "18:123: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:18:123: E251 unexpected spaces around keyword / parameter equals\n", + "18:125: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:18:125: E251 unexpected spaces around keyword / parameter equals\n", + "19:44: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:19:44: E251 unexpected spaces around keyword / parameter equals\n", + "19:46: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:19:46: E251 unexpected spaces around keyword / parameter equals\n", + "19:76: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:19:76: E251 unexpected spaces around keyword / parameter equals\n", + "19:78: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:19:78: E251 unexpected spaces around keyword / parameter equals\n", + "19:125: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:19:125: E251 unexpected spaces around keyword / parameter equals\n", + "19:127: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:19:127: E251 unexpected spaces around keyword / parameter equals\n", + "21:1: E265 block comment should start with '# '\n", + "INFO:pycodestyle:21:1: E265 block comment should start with '# '\n", + "24:19: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:24:19: E251 unexpected spaces around keyword / parameter equals\n", + "24:21: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:24:21: E251 unexpected spaces around keyword / parameter equals\n", + "27:15: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:27:15: E251 unexpected spaces around keyword / parameter equals\n", + "27:17: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:27:17: E251 unexpected spaces around keyword / parameter equals\n", + "29:58: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:29:58: E251 unexpected spaces around keyword / parameter equals\n", + "29:60: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:29:60: E251 unexpected spaces around keyword / parameter equals\n", + "34:19: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:34:19: E251 unexpected spaces around keyword / parameter equals\n", + "34:21: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:34:21: E251 unexpected spaces around keyword / parameter equals\n", + "34:37: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:34:37: E251 unexpected spaces around keyword / parameter equals\n", + "34:39: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:34:39: E251 unexpected spaces around keyword / parameter equals\n", + "39:1: E265 block comment should start with '# '\n", + "INFO:pycodestyle:39:1: E265 block comment should start with '# '\n", + "42:19: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:42:19: E251 unexpected spaces around keyword / parameter equals\n", + "42:21: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:42:21: E251 unexpected spaces around keyword / parameter equals\n", + "42:24: E231 missing whitespace after ','\n", + "INFO:pycodestyle:42:24: E231 missing whitespace after ','\n", + "45:15: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:45:15: E251 unexpected spaces around keyword / parameter equals\n", + "45:17: E251 unexpected spaces around keyword / parameter equals\n", + "INFO:pycodestyle:45:17: E251 unexpected spaces around keyword / parameter equals\n" + ] + } + ], "source": [ "# Fit a local continuum between the flux densities at: 8.0 - 8.1 & 14.9 - 15.0 microns\n", "# (i.e. excluding the line itself)\n", @@ -905,7 +1092,16 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1:1: E265 block comment should start with '# '\n", + "INFO:pycodestyle:1:1: E265 block comment should start with '# '\n" + ] + } + ], "source": [ "#Load 10 um feature back into specviz and calculate the Line flux; Line Centroid; Equivalent width\n", "specviz = Specviz()\n", @@ -916,7 +1112,18 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1:39: E231 missing whitespace after ','\n", + "INFO:pycodestyle:1:39: E231 missing whitespace after ','\n", + "2:37: E231 missing whitespace after ','\n", + "INFO:pycodestyle:2:37: E231 missing whitespace after ','\n" + ] + } + ], "source": [ "specviz.load_spectrum(line_spec_consub,data_label='Continuum Subtraction')\n", "specviz.load_spectrum(line_spec_norm,data_label='Normalized')" @@ -947,7 +1154,16 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2:1: E265 block comment should start with '# '\n", + "INFO:pycodestyle:2:1: E265 block comment should start with '# '\n" + ] + } + ], "source": [ "# Alternative method to analyze the 10um line within the notebook.\n", "# Calculate the Line flux; Line Centroid; Equivalent width\n", @@ -985,7 +1201,16 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2:23: E231 missing whitespace after ','\n", + "INFO:pycodestyle:2:23: E231 missing whitespace after ','\n" + ] + } + ], "source": [ "# Plot the optical depth of the 10 micron region vs wavelength\n", "plt.figure(figsize=(10,6))\n", From 6145d30780a95e6018488b5504b0ccabb5ee9480 Mon Sep 17 00:00:00 2001 From: Ori Date: Thu, 16 Dec 2021 14:07:15 -0500 Subject: [PATCH 6/8] Tech Edits for Justin --- .../JWST_Mstar_dataAnalysis_analysis.ipynb | 722 +++++++++++------- 1 file changed, 454 insertions(+), 268 deletions(-) diff --git a/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb b/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb index e8699496..bb15354e 100644 --- a/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb +++ b/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -75,9 +75,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1: E999 SyntaxError: invalid syntax\n", + "3:13: E225 missing whitespace around operator\n", + "3:25: E231 missing whitespace after ','\n", + "3:30: E231 missing whitespace after ','\n", + "3:35: E231 missing whitespace after ','\n" + ] + } + ], "source": [ "# enable PEP8 checker for this notebook\n", "%load_ext pycodestyle_magic\n", @@ -97,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "slideshow": { "slide_type": "fragment" @@ -108,9 +120,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "1: E999 SyntaxError: invalid syntax\n", - "10:10: E128 continuation line under-indented for visual indent\n", - "11:10: E128 continuation line under-indented for visual indent\n" + "1: E999 SyntaxError: invalid syntax\n" ] } ], @@ -124,15 +134,15 @@ "\n", "# Set general plotting options\n", "params = {'legend.fontsize': '18', 'axes.labelsize': '18',\n", - " 'axes.titlesize': '18', 'xtick.labelsize': '18',\n", - " 'ytick.labelsize': '18', 'lines.linewidth': 2, 'axes.linewidth': 2, 'animation.html': 'html5'}\n", + " 'axes.titlesize': '18', 'xtick.labelsize': '18',\n", + " 'ytick.labelsize': '18', 'lines.linewidth': 2, 'axes.linewidth': 2, 'animation.html': 'html5'}\n", "plt.rcParams.update(params)\n", "plt.rcParams.update({'figure.max_open_warning': 0})" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "jupyter": { "outputs_hidden": false @@ -164,33 +174,31 @@ "from specutils.analysis import line_flux, centroid, equivalent_width\n", "from specutils.spectra import SpectralRegion\n", "from specutils import SpectrumList\n", + "from jdaviz import Specviz\n", + "from jdaviz import Cubeviz\n", "\n", "# To fit a curve to the data\n", - "from scipy.optimize import curve_fit" + "from scipy.optimize import curve_fit\n", + "\n", + "# Display the video\n", + "from IPython.display import HTML, YouTubeVideo" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "4:1: E302 expected 2 blank lines, found 1\n" - ] - } - ], + "outputs": [], "source": [ "# Save and Load Objects Using Pickle\n", "import pickle\n", "\n", + "\n", "def save_obj(obj, name):\n", " with open(name, 'wb') as f:\n", " pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n", "\n", - "\n", + " \n", "def load_obj(name):\n", " with open(name, 'rb') as f:\n", " return pickle.load(f)" @@ -198,22 +206,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "4:31: E251 unexpected spaces around keyword / parameter equals\n" - ] - } - ], + "outputs": [], "source": [ "def checkKey(dict, key):\n", " \n", " if key in dict.keys():\n", - " print(\"Present, \", end =\" \")\n", + " print(\"Present, \", end=\" \")\n", " print(\"value =\", dict[key])\n", " return(True)\n", " else:\n", @@ -223,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -233,9 +233,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pipeline 3 Data Exists\n" + ] + } + ], "source": [ "# Check if Pipeline 3 Reduced data exists and, if not, download it\n", "import os\n", @@ -272,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -322,19 +330,40 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Video1:\n", + "## Video 1:\n", " \n", "This Specviz Instructional Demo is from STScI's official YouTube channel and provides an introduction to Cubeviz." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/jpeg": 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\n", + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "from IPython.display import HTML, YouTubeVideo\n", - "\n", "vid = YouTubeVideo(\"zLyRnfG32Bo\")\n", "display(vid)" ] @@ -348,15 +377,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8f8d3cab791f45d4bebb894a7b3ea656", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Application(config='specviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_selected', 'd…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Open these spectra up in Specviz\n", - "from jdaviz import Specviz\n", - "\n", "specviz = Specviz()\n", "specviz.app" ] @@ -370,7 +412,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -391,33 +433,67 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Video2:\n", + "## Video 2:\n", " \n", "This Cubeviz Instructional Demo is from STScI's official YouTube channel and provides an introduction to Cubeviz." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/jpeg": 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\n", + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "from IPython.display import HTML, YouTubeVideo\n", - "\n", "vid = YouTubeVideo(\"HMSYwiH3Gl4\")\n", "display(vid)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ec50037a01ad40878cc34296aa5b7de9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Application(config='cubeviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_selected', 'd…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "from jdaviz import Cubeviz\n", - "\n", "cubeviz = Cubeviz()\n", "cubeviz.app" ] @@ -431,9 +507,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:specutils.spectra.spectrum1d:Input WCS indicates that the spectral axis is not last. Reshaping arrays to put spectral axis last.\n", + "WARNING:specutils.spectra.spectrum1d:Input WCS indicates that the spectral axis is not last. Reshaping arrays to put spectral axis last.\n", + "WARNING:specutils.spectra.spectrum1d:Input WCS indicates that the spectral axis is not last. Reshaping arrays to put spectral axis last.\n" + ] + } + ], "source": [ "# Here, we load the data into the Cubeviz app for visual inspection. \n", "# In this case, we're just looking at a single channel because, unlike Specviz, Cubeviz can only load a single cube at a time.\n", @@ -453,27 +539,40 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Video3:\n", + "## Video 3:\n", " \n", "Here is a video that quickly shows how to select a spatial region in Cubeviz." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Video showing the selection of the star with a circular region of interest\n", - "from IPython.display import HTML\n", "HTML('')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -497,7 +596,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -511,24 +610,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "5:19: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:5:19: E251 unexpected spaces around keyword / parameter equals\n", - "5:21: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:5:21: E251 unexpected spaces around keyword / parameter equals\n", - "5:24: E231 missing whitespace after ','\n", - "INFO:pycodestyle:5:24: E231 missing whitespace after ','\n", - "10:15: E231 missing whitespace after ','\n", - "INFO:pycodestyle:10:15: E231 missing whitespace after ','\n", - "17:16: E231 missing whitespace after ','\n", - "INFO:pycodestyle:17:16: E231 missing whitespace after ','\n" - ] + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ @@ -536,19 +631,19 @@ "spec_bsmooth = box_smooth(spec, width=5) \n", "\n", "# Plot the spectrum & smoothed spectrum to inspect features \n", - "plt.figure(figsize = (8,4))\n", + "plt.figure(figsize=(8, 4))\n", "plt.plot(spec.spectral_axis, spec.flux, label='Source')\n", "plt.plot(spec.spectral_axis, spec_bsmooth.flux, label='Smoothed')\n", "plt.xlabel('Wavelength (microns)')\n", "plt.ylabel(\"Flux ({:latex})\".format(spec.flux.unit))\n", - "plt.ylim(-0.05,0.15)\n", + "plt.ylim(-0.05, 0.15)\n", "\n", "# Overplot the original input spectrum for comparison\n", "origspecfile = fn = download_file('https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/MRS_Mstar_analysis/63702662.txt', cache=False)\n", "origdata = ascii.read(origspecfile)\n", "wlorig = origdata['col1']\n", "fnujyorig = origdata['col2']*0.001 # comes in as mJy, change to Jy to compare with pipeline output\n", - "plt.plot(wlorig,fnujyorig, '.', color='grey', markersize=1, label='Original Input')\n", + "plt.plot(wlorig, fnujyorig, '.', color='grey', markersize=1, label='Original Input')\n", "\n", "plt.legend(frameon=False, fontsize='medium')\n", "plt.tight_layout()\n", @@ -570,7 +665,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -588,11 +683,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "42ef0e684b1d4c8cafc96fcd9f20dfe8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Application(config='specviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_selected', 'd…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Open these spectra up in Specviz\n", "specviz = Specviz()\n", @@ -610,7 +720,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -633,16 +743,30 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Video4:\n", + "## Video 4:\n", " \n", "Here is a video that quickly shows how to smooth your spectrum in Specviz." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Video showing how to smooth a spectrum in Specviz\n", "HTML('')" @@ -675,18 +799,32 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Video5:\n", + "## Video 5:\n", " \n", "Here is a video that shows how to fit a blackbody model to the spectrum" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Video showing how to fit a blackbody \n", "HTML('')" @@ -694,9 +832,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:Warning: Applying the value from the redshift slider to the output spectra. To avoid seeing this warning, explicitly set the apply_slider_redshift argument to True or False.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Not present\n", + "No Blackbody\n" + ] + } + ], "source": [ "spectra = specviz.get_spectra()\n", " \n", @@ -712,9 +866,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The blackbody.fits file does not exist\n" + ] + } + ], "source": [ "# Delete any existing output in current directory\n", "if os.path.exists(\"blackbody.fits\"):\n", @@ -725,7 +887,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -735,20 +897,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "1:1: E265 block comment should start with '# '\n", - "INFO:pycodestyle:1:1: E265 block comment should start with '# '\n" - ] - } - ], + "outputs": [], "source": [ - "#rename blackbody.flux as ybest\n", + "# Rename blackbody.flux as ybest\n", "ybest = blackbody.flux" ] }, @@ -776,16 +929,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "15:22: E231 missing whitespace after ','\n", - "INFO:pycodestyle:15:22: E231 missing whitespace after ','\n" - ] + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ @@ -803,7 +972,7 @@ "plt.close()\n", "\n", "# Now subtract the BB and plot the underlying dust continuum\n", - "plt.figure(figsize=(8,4))\n", + "plt.figure(figsize=(8, 4))\n", "plt.plot(spec.spectral_axis, spec.flux.value - ybest.value, color='purple', label='Dust spectra')\n", "plt.axhline(0, color='r', linestyle='dashdot', alpha=0.5)\n", "plt.xlabel('Wavelength (microns)')\n", @@ -855,7 +1024,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -865,9 +1034,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fc53f952f78443fe8d9c7de90b520f99", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Application(config='specviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_selected', 'd…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "specviz = Specviz()\n", "specviz.app" @@ -875,7 +1059,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -886,16 +1070,30 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Video6:\n", + "## Video 6:\n", " \n", "Here is a video that shows how to fit a polynomial to two separate spectral regions within a single subset to remove more underlying continuum" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Video showing how to fit a polynomial to two separate spectral regions within a single subset\n", "HTML('')" @@ -903,9 +1101,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:Warning: Applying the value from the redshift slider to the output spectra. To avoid seeing this warning, explicitly set the apply_slider_redshift argument to True or False.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Not present\n", + "No Polyfit\n" + ] + } + ], "source": [ "spectra = specviz.get_spectra()\n", " \n", @@ -921,9 +1135,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The poly.fits file does not exist\n" + ] + } + ], "source": [ "# Delete any existing output in current directory\n", "if os.path.exists(\"poly.fits\"):\n", @@ -934,102 +1156,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2:1: E265 block comment should start with '# '\n", - "INFO:pycodestyle:2:1: E265 block comment should start with '# '\n" - ] - } - ], + "outputs": [], "source": [ "# Save if you so desire. Keep commented otherwise.\n", - "#poly.write('poly.fits')" + "# poly.write('poly.fits')" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 48, + "metadata": { + "scrolled": false + }, "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, { "name": "stderr", "output_type": "stream", "text": [ - "4:19: E262 inline comment should start with '# '\n", - "INFO:pycodestyle:4:19: E262 inline comment should start with '# '\n", - "5:19: E262 inline comment should start with '# '\n", - "INFO:pycodestyle:5:19: E262 inline comment should start with '# '\n", - "6:19: E262 inline comment should start with '# '\n", - "INFO:pycodestyle:6:19: E262 inline comment should start with '# '\n", - "7:19: E262 inline comment should start with '# '\n", - "INFO:pycodestyle:7:19: E262 inline comment should start with '# '\n", - "15:1: E265 block comment should start with '# '\n", - "INFO:pycodestyle:15:1: E265 block comment should start with '# '\n", - "18:15: E221 multiple spaces before operator\n", - "INFO:pycodestyle:18:15: E221 multiple spaces before operator\n", - "18:44: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:18:44: E251 unexpected spaces around keyword / parameter equals\n", - "18:46: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:18:46: E251 unexpected spaces around keyword / parameter equals\n", - "18:76: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:18:76: E251 unexpected spaces around keyword / parameter equals\n", - "18:78: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:18:78: E251 unexpected spaces around keyword / parameter equals\n", - "18:123: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:18:123: E251 unexpected spaces around keyword / parameter equals\n", - "18:125: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:18:125: E251 unexpected spaces around keyword / parameter equals\n", - "19:44: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:19:44: E251 unexpected spaces around keyword / parameter equals\n", - "19:46: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:19:46: E251 unexpected spaces around keyword / parameter equals\n", - "19:76: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:19:76: E251 unexpected spaces around keyword / parameter equals\n", - "19:78: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:19:78: E251 unexpected spaces around keyword / parameter equals\n", - "19:125: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:19:125: E251 unexpected spaces around keyword / parameter equals\n", - "19:127: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:19:127: E251 unexpected spaces around keyword / parameter equals\n", - "21:1: E265 block comment should start with '# '\n", - "INFO:pycodestyle:21:1: E265 block comment should start with '# '\n", - "24:19: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:24:19: E251 unexpected spaces around keyword / parameter equals\n", - "24:21: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:24:21: E251 unexpected spaces around keyword / parameter equals\n", - "27:15: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:27:15: E251 unexpected spaces around keyword / parameter equals\n", - "27:17: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:27:17: E251 unexpected spaces around keyword / parameter equals\n", - "29:58: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:29:58: E251 unexpected spaces around keyword / parameter equals\n", - "29:60: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:29:60: E251 unexpected spaces around keyword / parameter equals\n", - "34:19: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:34:19: E251 unexpected spaces around keyword / parameter equals\n", - "34:21: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:34:21: E251 unexpected spaces around keyword / parameter equals\n", - "34:37: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:34:37: E251 unexpected spaces around keyword / parameter equals\n", - "34:39: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:34:39: E251 unexpected spaces around keyword / parameter equals\n", - "39:1: E265 block comment should start with '# '\n", - "INFO:pycodestyle:39:1: E265 block comment should start with '# '\n", - "42:19: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:42:19: E251 unexpected spaces around keyword / parameter equals\n", - "42:21: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:42:21: E251 unexpected spaces around keyword / parameter equals\n", - "42:24: E231 missing whitespace after ','\n", - "INFO:pycodestyle:42:24: E231 missing whitespace after ','\n", - "45:15: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:45:15: E251 unexpected spaces around keyword / parameter equals\n", - "45:17: E251 unexpected spaces around keyword / parameter equals\n", - "INFO:pycodestyle:45:17: E251 unexpected spaces around keyword / parameter equals\n" + "33:15: W605 invalid escape sequence '\\m'\n", + "INFO:pycodestyle:33:15: W605 invalid escape sequence '\\m'\n", + "49:36: W605 invalid escape sequence '\\m'\n", + "INFO:pycodestyle:49:36: W605 invalid escape sequence '\\m'\n" ] } ], @@ -1037,10 +1210,10 @@ "# Fit a local continuum between the flux densities at: 8.0 - 8.1 & 14.9 - 15.0 microns\n", "# (i.e. excluding the line itself)\n", "\n", - "sw_region = 8.0 #lam0\n", - "sw_line = 8.1 #lam1\n", - "lw_line = 14.9 #lam2\n", - "lw_region = 15.0 #lam3\n", + "sw_region = 8.0 # lam0\n", + "sw_line = 8.1 # lam1\n", + "lw_line = 14.9 # lam2\n", + "lw_region = 15.0 # lam3\n", "\n", "# Zoom in on the line complex & extract\n", "line_reg_10 = SpectralRegion([(sw_region*u.um, lw_region*u.um)])\n", @@ -1048,37 +1221,37 @@ "polysub = extract_region(poly, line_reg_10)\n", "line_y_continuum = polysub.flux\n", "\n", - "#-----------------------------------------------------------------\n", + "# -----------------------------------------------------------------\n", "# Generate a continuum subtracted and continuum normalised spectra\n", "\n", - "line_spec_norm = Spectrum1D(spectral_axis = line_spec.spectral_axis, flux = line_spec.flux/line_y_continuum, uncertainty = StdDevUncertainty(np.zeros(len(line_spec.spectral_axis))))\n", - "line_spec_consub = Spectrum1D(spectral_axis = line_spec.spectral_axis, flux = line_spec.flux - line_y_continuum, uncertainty = StdDevUncertainty(np.zeros(len(line_spec.spectral_axis))))\n", + "line_spec_norm = Spectrum1D(spectral_axis=line_spec.spectral_axis, flux=line_spec.flux/line_y_continuum, uncertainty=StdDevUncertainty(np.zeros(len(line_spec.spectral_axis))))\n", + "line_spec_consub = Spectrum1D(spectral_axis=line_spec.spectral_axis, flux=line_spec.flux - line_y_continuum, uncertainty=StdDevUncertainty(np.zeros(len(line_spec.spectral_axis))))\n", "\n", - "#-----------------------------------------------------------------\n", + "# -----------------------------------------------------------------\n", "# Plot the dust feature & continuum fit to the region\n", "\n", - "plt.figure(figsize = (8, 4))\n", + "plt.figure(figsize=(8, 4))\n", "\n", "plt.plot(line_spec.spectral_axis, line_spec.flux.value,\n", - " label = 'Dust spectra 10 micron region')\n", + " label='Dust spectra 10 micron region')\n", "\n", - "plt.plot(line_spec.spectral_axis, line_y_continuum, label = 'Local continuum')\n", + "plt.plot(line_spec.spectral_axis, line_y_continuum, label='Local continuum')\n", "\n", "plt.xlabel('Wavelength (microns)')\n", "plt.ylabel(\"Flux ({:latex})\".format(spec.flux.unit))\n", "plt.title(\"10$\\mu$m feature plus local continuum\")\n", - "plt.legend(frameon = False, fontsize = 'medium')\n", + "plt.legend(frameon=False, fontsize='medium')\n", "plt.tight_layout()\n", "plt.show()\n", "plt.close()\n", "\n", - "#-----------------------------------------------------------------\n", + "# -----------------------------------------------------------------\n", "# Plot the continuum subtracted 10 micron feature\n", "\n", - "plt.figure(figsize = (8,4))\n", + "plt.figure(figsize=(8, 4))\n", "\n", "plt.plot(line_spec.spectral_axis, line_spec_consub.flux, color='green',\n", - " label = 'continuum subtracted')\n", + " label='continuum subtracted')\n", "\n", "plt.xlabel('Wavelength (microns)')\n", "plt.ylabel(\"Flux ({:latex})\".format(spec.flux.unit))\n", @@ -1090,43 +1263,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "1:1: E265 block comment should start with '# '\n", - "INFO:pycodestyle:1:1: E265 block comment should start with '# '\n" - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0d56a10783fd4c819d4e06d3924c9d95", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Application(config='specviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_selected', 'd…" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "#Load 10 um feature back into specviz and calculate the Line flux; Line Centroid; Equivalent width\n", + "# Load 10 um feature back into specviz and calculate the Line flux; Line Centroid; Equivalent width\n", "specviz = Specviz()\n", "specviz.app" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "1:39: E231 missing whitespace after ','\n", - "INFO:pycodestyle:1:39: E231 missing whitespace after ','\n", - "2:37: E231 missing whitespace after ','\n", - "INFO:pycodestyle:2:37: E231 missing whitespace after ','\n" - ] - } - ], + "outputs": [], "source": [ - "specviz.load_spectrum(line_spec_consub,data_label='Continuum Subtraction')\n", - "specviz.load_spectrum(line_spec_norm,data_label='Normalized')" + "specviz.load_spectrum(line_spec_consub, data_label='Continuum Subtraction')\n", + "specviz.load_spectrum(line_spec_norm, data_label='Normalized')" ] }, { @@ -1142,9 +1310,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Video showing how to measure lines within specviz\n", "HTML('')" @@ -1152,21 +1334,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "2:1: E265 block comment should start with '# '\n", - "INFO:pycodestyle:2:1: E265 block comment should start with '# '\n" + "Line_centroid: 10.7569 micron \n", + "Integrated line_flux: 6.65227e-15 W / m2 \n", + "Equivalent width: -15.0066 micron \n" ] } ], "source": [ - "# Alternative method to analyze the 10um line within the notebook.\n", - "# Calculate the Line flux; Line Centroid; Equivalent width\n", + "# Alternative method to analyze the 10um line within the notebook. Calculate the Line flux; Line Centroid; Equivalent width\n", "\n", "line_centroid = centroid(line_spec_consub, SpectralRegion(sw_line*u.um, lw_line*u.um))\n", "line_flux_val = line_flux(line_spec_consub, SpectralRegion(sw_line*u.um, lw_line*u.um))\n", @@ -1185,7 +1367,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -1199,21 +1381,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "2:23: E231 missing whitespace after ','\n", - "INFO:pycodestyle:2:23: E231 missing whitespace after ','\n" - ] + "data": { + "image/png": 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I0yMcs0ABsN5aW9oyQ2rbEowTgoNBOBiZExJqeySLiIiIiHea0+ZtTmsOpL0wxoC1oVKK4KRy8LNPAVlERETEU+opFmXBUgqfPxiQTeB4sMOFJ8MSERERkYBmbQttjEkCzsTZOa8T9QO2tdb+oGWG1jY5gdjWmylODARkv4qQRURERDzVnC4WnYEPgFE4pbPuBXvWdUwBuRGhJ6xOFwujRXoiIiIiMaE5JRa3A8OBy4FBOFnvJOAQ4N/AZ0CXlh5gWxMspQiVWBBeYqFFeiIiIiLeak5APhV42lr7T6AocMxnrf3GWnshziYhd7X0ANua2pni8EV6SYnOFzV+vxfDEhEREZGA5gTkXJxZYnD6HgOkuS5/DTi9BcbUptWdKQ6WXAS3mK6oVkAWERER8VJzAnIBkBn4uhioBvq6Lq/GWbgnjQhtNx3IwcEZ5NRk55+iotoX/UGJiIiISEijAdkY088Ykx74dhUwAsBa6we+BC4xxqQaYzKAGcC61hxsW1CvxCIQmdOSNYMsIiIiEgv2N4O8HqetG8B7wHeNMamB7/+C0+6tANgFjAf+2gpjbFOCfY/rtnkLBuT8ksrQsc83FvCnd1dS7VNoFhGR2FVSWcPtb6xg+bZ9Xg9FpEXsr82bobYq4E7gz9baSgBr7QvGmBrgQsAHvGStfb7VRtpGBDcKqdvmLa9LBgAb99Tu2H32Q/MByO2YzkVH9Y/aGEVERJrjrzNX8cS89Tw+bz0b7j7V6+GIHLTmbDVtgco6x14BXmnpQbVlddu8BXXKTAGgqqb+bPG2wvLWH5iIiMgB2pBfuv+TROKItpqOsj2lVQAUllcDtSUXKYnOP0VVhHIKU++IiIiIiLSWpswgHx3YYrpJrLVPH8R42o2PVu0GasNvMCBX+yx+vyUhQbFYRETig7a4kramKcH3isDH/gS3mlZAboZgDXJCgiElMYEqn58qn5+0hMR654iIiIhI62tKQH4UWNDaA2mv3Nk3JckVkJNdAVlFFiIiIiJR05SAPNda+1yrj6SdMq7p4ZSkBKiMvFBPRERERKJDi/Q85i6fCC3UqxOQVWIhIiIiEj0KyB6rW2IBEQJyFMcjIiLSXNZqmZ60LQrIMSQUkOu2etMUsoiIiEjUNFqDbK1VgG5t7hrkBkosRERERCR6FIA9FqnEolIlFiIiIiKeUUD2mLt6IjPVae1WUlnT4DnNsbOogu88+DFvLtl+oMMTERERaXcUkD3m7nHcq2M6AFv3lrfIbd87axVfbirkmue+aJHbExERiURL9KStUUD2mHt2uHOHFAD2llWFn3OARRZVNXrJEhEREWkuBWSPuaNvdloyAEUV1eHnHGCJRUqSqpdFREREmksB2WNJibX/BNnpTkAurqhp6PRmSUnUP6+IiIhIcylBeSzYuQIgI9lZpFde5Qs750Dngd23LSIiIiJNowTlsZTE2vibnhI5IH+2ce9+b+etpduZuWJn2LHEBP3zioiIiDRXoxuFSOtzz/IGA3JZdXhA/mjV7kZvo7LGx9XPOp0qNtx9aui41bpiERERkWbTFKPHkhMjlVg0rwa52tdAEFY+FhGRKLD6fSNtjGaQPeZeSJeR4vxzlNUpsQCw1mIaaGfh89e+Mvn9loQEda8QEZHW98d3VrJwfQGpyZpvk7ZFP9Eei1RiUV5dPyBX+fx8sWkvFzy+gDW7SsIucwdkn/6MFxGRKHnww7Us2riXBesKvB6KSItSQI6yeb+azoyJ/UPfZ6bWTuKHapArffj94UH3un8v5qwHP+HjNXu47j9fhl1W4/eHvvb5FZBFRCS6/JqckTZGATnK+nTK4LhDeoS+zwiEYoAumSkkJxp2FFXU203vneU7Ql/vLKoMu6zGVYPsfpHSy5WIiESD8rG0NQrIHpiQ1zn0dVpyYtjXvXPSAVi2rajB6+8pDQ/IYSUWLTCD/MWmvfzoX5+zY1/FQd+WiIiISLzRIj0PpKckcsmkPHYXV9IlMyXsspyMFNhTxsX/WNjg9ev+pV7ta9kSi7Me/CRwu5bHLx5/0LcnIiIi8W1XcQVY6J6d5vVQokIB2SO3nD4y4vFOGcn7vW7/Lhlh37f0DHLQziLNIIuIiLR3VTV+JtzxPllpSXz52xNISmz7BQht/xHGmZyMlP2eM6ZPTtj37j7I6mIhIiIizbG/ybXl2/YBUFxRQ1FF8/ZqiFdxHZCNMQnGmJ8aY1YaYyqMMZuNMfcYYzK9HtuBymnCDHJFnTZw4X2Qa49bhWURERFpxC3/W85ht77HtsLyBs9xNw4orqje721aa5uVQUoqa6hxlYvGgngvsfgr8BPgVeAe4JDA94cZY4631sbWs90EnZowg1y3T3K1u81b4Adya2E52wpVIiEiIiINe/KTDQD8Y956bv72iIjnFJXXzhoX15lBrvH52VdeTefMFIwxPP/ZJn718lIAThzRg8P7d2L97lLG9e/EuLxOdM9KJSstfDLwoQ/X8M+PN3DzqSM4/8h+LfjoDlzcBmRjzEjgx8Ar1tqzXcfXA/cB5wLPeTS8A9aUGeTK6vDcH1aD7HP+apt89+wWH5uIiIh4r6C0il+8+BWXTR7AlCFdm319v9+ybV85Wam1maOyxskW6/NLeWvpdsqrfAzp0YHJg7tS5Jo13lfufG2t5a2lO7jtjRXsKKpgQNdMzpvQlzvfWhk6970VO3lvxU4Anl+0OXQ8r0sGo3p35NpjBzM8N5uF6wsoq/LRIzu12Y+ltcRtQAbOAwxwb53jjwF3AxcSlwF5/zPIJZXhf72FdbGwlhptFiIiIhKTKqp9VPv89WZRm+POt75m9spdzF65i2W/P4kOqU2Lc9Za3l62gzve/JqtdUoqnlmwkWcWbNzvbVzw+Kcc3i+H4ooaVrt29l2fXxoWjgESDESKJBv2lLFhTxlvLd3ORUf157MNezEGxvfvXP9kj8RzQD4C8ANh/dCstRXGmMWBy+NOU7pYFFeG1//U7WKh3fRERERi09Q/fkBBaRXLbz2J1KTE/V8hgo17SkNfH3Xn+7z0o4kMz81u9DofrdrNbW+sCAu1DTkirxMpSQlsyC9j277yeu1lv9hUCDiZ5ecnDuPsw/vw3MJN3DtzFZU1fh6ZMY7pw7pTVFFNenIiyYkJoZrktbtL2Fdew4MfrOH9lbt4ar4Tysf0yaFjEzJQtMRzQO4F5FtrKyNcthWYZIxJsdaGbUlnjLkCuCIaAzwQOekNzyBfM30Qf/9gbVgtENTfSU8BWUREpOXU+Pws21bEqF7ZB9XirKLax65iJ7bkl1SFNgdrrmA5BDjvKv9rwUZuP3M01lo+Wp3Ppj2lTBzUlbwuGTy/aDNvL93BvDX5AKQnJ/KT44bwncN689zCTWSkJLJ0yz7Kq32s3lXM2Yf34brjhmCMAaCwrIr3lu8kMcGQkZJIanICiQkJlFbWMGlQl9A73z+YMoBzxvehvMoX6pWc7ZolD97e4O5ZADxxyRH8d/FWnpm/kZLKGm47Y9QBPRetJZ4DcgYQKRwDVLjOCQvI1tpHgUeNMTGZIhurQb7++KH8/YO1FFdUY60N/bDV1J1BVvcKERGRFvOHd1by2Nz1XD1tEGeM7c3CDQVceGS/0O/hpnLvUFtZZ8F9c2zdG14e8a8Fm6iq8bN4cyGrdjY8Q3z0kK48fOE4MgMlGT87Yeh+7ysnI4VzjujbpHFlpSU3q3TkjLG9OWNs7yafH03xHJDLgO4NXJbmOieudMpseAY5OTGBjJREyqp8/HXW6tAPdk2dnfR8PgVkERFpXf529G7lY3PXA/DwnLU8+OFaALLTkpod7tzt0sqqmh6QNxeU8c6yHcyY1J9lW4vYU+rczvfH92V3SSWzV+7ihUVbQucfOaAzS7fuo6zKR5fMFH59ynCmDetOt6zYWQQX6+I5IG8DRhhjUiOUWfTGKb+oinC9mJaZEl6PlJhgwkomstOSKavycd/7qzl2eHfG9s3RDLKIiERde/xd4/6bYP7aPc0OyKWVtaF4fX4p972/mqunD2Zs35x65z6zYCN5XTI4ekg3vv/IfLbtq+COt74OXX7ehL7cddahANz3/mr+MnMVABPyOvP8lRMprqjmw292c0ReZ3I7to/toVtSPG8U8hnO+Ce4Dxpj0oCxwCIPxnTQ6r5d07dTeH1S38613weL9Gvq9EFWDbKIiLQ2fzsKyIkJ9Uspiiubv6OcuwvV3W+v5L0VOznz7x+HnWOtZfm2ffz2tWVc9MRCqmr8bNsXvq/BmD4d+d1pI0PfXzI5j+nDuvHDowfwwlUTAafc4bQxvRSOD1A8zyA/D9wEXA/MdR3/IU7t8bMejKlFzL/xWBZt2EuXDinc9/5qNuyprRQZ2asjn23YC8B1/1nM9n0VdEyvrffxN9DFwl2zLCIicrD8cbcV14Gr+24u1N+ToClKXQHZ3Wbt4TlrWburhB1FFXy1uTBsO+ej/1h/X4PRfTqSllz7jnN2WjL/vHRCvfPkwMXtDLK1dinwd+AsY8wrxpjLjTH3AH8B5hCHPZCDenZM57QxvZg0qCsJ+wm1d7+9kk0FtQG6oTZvB9obeVdRBT9/4StWbCs6oOuLiEjb1J5mkJMizCDPW7ObimYstCsoreKlz7dEvOzut1fy4udbmLs6PywcA+wsqt+PIDMlnuc340O8P8PXAxtw2radCuQD9wP/F4/bTEdS922dC47sF9oWMmiLazVrQwG5ssZP8gG0pvnVy0v44JvdvLZ4K2vv/Fazry8iIm1Te6pBjvRQK6r9zPjHQh44/zC6Z4WXMewqruCTNXvo2zmdDfllvLZ4Kx+vyY+4aQbAwK6ZrMuv7W187PDuHDWwM5mpSfzm1WX1zk9PObD+ydJ0cR2QrbU+4J7AR5tUdwZ5SI8sVt52Mq9/tY3f/W85ZVU+9pTU/nXZ0E56ldW+Ju+04xacnVZds4iIuLXENNSNryyhd0461x475OBvrBXV/WNg4sAurN5VzML1BUy4430yUhIZlpvFEXmdGdkrm1+/vJTyOrPLSQmGCQM6kZKUyEerdgMwdWg3Th/Ti1NH92T87TMpDXS2+McltXudbS+s4IEP1oTdVoYCcquL64DcHkRaGJCWnMj3xvfljSXbmbNqd6jpODhBNtLbXu6m4k1lUSgWEZHIDnYGed7qfP69cDOApwH5g292sXhTIT85bki937kL1xdw5TOLqKrzO/QXJw0lNSmRW99YwZIthZRV+fhyUyFfBnaYc+vZMY1LJ+dx0shc+nfJBGBDfik7iiqYkNeZhMB9ThvWnTeXbq+3echPjhtCdnoSfTplcPWzXwCQrhKLVqdnOMY1VoPcIc3559vmKvT3Wxu2s17QpLtn8+ZPpjCyV8dm3b8isoiIRHKwNcj/WrCxhUayfxXVPlKTEsIWqxeWVXHrGyt45YutAHTPTuWCI/uHXe93/1vO3rLqereXkpjIqN4deeHKiVRU+9hbVsWbS7Zz+5tOG7bc7DTevu5oPvhmF9OGdadznT0O8rpmktc1M+zYXWePpltWKhce1S/8vpISuGLqIL7ZURw6lpGsGeTWFreL9NqLxsqGOwT+gnQ3G6/xNdzm7Y/vfNOs+66uUTwWEZHIDnajkANdPN5U1lpe+nwL5zwyn0P+7x0G3vQWD324FmstC9btYeJds0PhGOA3ry7j3Efns2zrPgBWbCvi6+3OAvUfTRvE6N61E0wpSbW/nNOSE+nZMZ3LJg9gTJ+ODOnegTk3TKNTZgpnHd6nXjhuSHZaMrecPjK0FXNdacm196kSi9anGeQY19gMckmEHozVvoY3Cmlul7dvdhYzsFvm/k8UEZF2p6HfNT6/jVgeWP+82rKFbYXl9KpTWnCgdhdXMuMfC0Ph1u0P76zkvvdXA1Be7aNnxzSumDqQWV/v5OM1e1iwroCf/PtLHrpwHDP+sRBwFsz96uThrNrxWeh23AE5KCHB8No1k7GWUNlES8p0rSPKOIA1RdI8mkGOY/vK67/tU17tC3vRcUtKaP4/9+7i+u1lREREGnq3sqaJDZLd1YAzV+xsiSHxzY5ifvj0olA4TjBw6xkjWXLLifz6lOGA83uyvNrH9GHdmHvDdC6dPIBnLz+Kj345HYB1+aV896FPyC+pZPLgLvz9/MMBSHXN4EYKyOBs9tUa4RgI2/MgXSUWrU4BOcY1NusbaRFdeVUNPtdr04QBnbly6kCABoNzXcmJtXdaXNH8nYJERKTtazAgR1gHE0mwkwNAUYQJn8bud9nWfWEL56y13PX215x070cs3lwIOGUIj1w0nhkT88hOS+bSyXmh8ztlJPOPS44gyVXH2K9LRqiMobiyhm+NzuWJi48ItVRLcZ2bcgBtUw+Wu1Vre+pB7RUF5DiW6JoRnjK4K+D8Zez+6/2wvjlMClzWUL3X5xsL2BDov+j3W6obeHH7+QtfsbOoIuJlIiLSvjT0O+VAaovvmbmKvF+/ycL1BY3fp8/PD59exLfvn8eFj39KdWBG6MVFW3hkzjqSEgwzJvZnwY3HseLWkzlhRI/QdVOTamddpw3rHnF3Wfc593xvbNhude7LGppBjpZUj++/PVARSxz77amHcMH2In5x0jB2F1cyb00+2/dVMKJn7UKC648fyhebnK2pI/21v7mgjLMfmg/AhrtPpaKm4V2BXv5iCwWlldrOUkREGpwprvE1/m5lsKNEJOc8Mp8Nd59a7/Y2FZSRlJDAuY/OZ9s+Z6Jm4YYChvzmbfp3yWDjHqdn/03fOoTLpgzY79ibsnCu7mYc7lDsVUC953tjWLZtH2P75nhy/+2JAnIcG9Iji09vOg5jDG8v3Q44u+oFZ5CnDu1GekpiaLFEpL/ql2/bF/a9uyNGJBv2lDV6uYiItA8N1Ro3VHpx4ytL+ffCTQBN2rhq5oqd3PHmCjYWlIXtZJeVmsQVUwfy8Jy1lFb5QuH4rMN7M2Ni/wZuzXHbGSO5d9ZqThvTK+LljS0udAdkL0osAM4e14ezx/Xx5L7bGwXkOBd8iygnw/lreOaKnXw38J8nWEoc3EM+0otW3f6OBaVVjd6fVd2TiIjQcBCu9lustRhjqPH5Wby5kOLKmlA4hshdmII2F5SR2zGNG19ZQn5J+O+kgV0zefWayXRMT+bKYwZx8t8+YuOeMn576iFcMnn/M8cXTczjwqP6RyyvgMZre93hubUW4knsUEBuIzpl1q5uDS58CNYoNzaDvLcs/MVny97GZ4gVj0VEBBquNZ5892wAzpvQLywUN9VJ937E0B5Z5JdU0TE9mXm/ms4zCzby0ard3HXWoaFuDilJCbx2zWTKq3z0yE5r8u03FI7h4Hs7S9uhgNxGdMqorafasMdZcBecOQ62d4vUxaKssrakwlrLox+ta/R+tHJWRESg4RnkoOaG44HdMlm3u5SyKh+LNxeSkpjAExePJystmaunDebqaYPrXSc7LZnstOQIt3ZgGvsVpznj9kUBOcaZJv6XdPdHXLbV6f8YnDkOzSBHWFBR7QrN763YyYJ1ja8gVj4WEZGSyhrOf2zBfs+7etogunZI5dY3VjR6XnBh3sufb+HnL35F1w4pPHD+4YzP69wi422q1t7dT+KHAnIbkZacyJTBXZm3Jj+0gUhwBW5SYuQa5JLKGh6ZUztj7N51KDUpgcqa+jPOje3sJyIi7cOtry+P2BL0y9+eQE5GMk/MW0+njJTQgrLGAvKim48PfX3W4b0Z2TubvC6ZYS3WoqW8upGF6vr1166okV4bcvfZo8O+D64STmxgkd5/F2+NeD5AXpfIW0y7NxEREZH2Z29pFa8t3hbxspyMZIwxXH70wCZ1W5h7w3S6dkgNfW+MYXhutifh2C3S77qmvqMrbYNmkNuQPp0y6JyZEupEkZ3m/PMmNbBIr+5W1e5devp2TuebncX17iPZo9Y2IiLiva82F3LG3z8OO9YhNYncjmkc2rtjgwvgVt1+CuvzSxmWm0Xer98MHY+13ylJCYYav6V7Vv1Ff3oDtX1RQG5junaoDcgd0hqfQS6ps410heutpX6dI88ge717kIiIRF95lY/b3lzBc5/WX3iXnZbEzJ9ObbQ7REpSAsNys+odT4ixXynPXn4kt725gj+cfajXQxGPKSC3Me63pTqkOgv3gl0s3E3diyuqefDDtWHXDW4ScunkvAZLKYK3/8WmvewqquDkUT1bbvAiIhJT7nhzBUu27GPZ1n2UujaSOv6QHsz6eicAFTX+RsNxY2JtXcuRA7vwxo+PjnhZbI1UWluM/e0m9TTzf+R1xw0Jfd3YDPIT89bXu25wBjk1KTG0sK+uYEA+68FPuOpfX7AjsOWniIi0DRXVPn7/+nJG/+5dHpu7nk/XF4SF4++O68PjF48PO/9AxVpAbsykQV0Bwmqmpe3SDHIbc+zw7qGv69Yg55dUhXY3irRjXnAGubE95mt8fqpc3S0a2w1JRETiS7XPz3mPLeDLTYURL597w3T6ds4IO5Z4ECE3nhZ+TxnSlRevmsigbh28HopEgQJyG2OM4Y7vjOLLTYVMGez8tZvoegF6YPYafnzcEHp2TK933WB7m7TkxAZb3dT4bGgjEiAsLIuISHxatnUfu0squeLpRWHt2/p1zuDEET1ITDSM6JkdFo5/ceJQ/vzeKu6q00FpfybkdWbhBqfnflYLbvIRDUdEuS+zeEcBuQ264Mj+XHBk/9D3Sa494++ZuYofHzcEG9g0+spjBvLy51vIL6mi3DWDvHpX/Q4W4Gws8s2O2svKq2uw1rKzqJLcjk3f6lNERLy1q6iC5MQENhWU1etM8aNpgzhjbC/6d84M9dSv66pjBnH2uD4RJ1wac9qYnqGALBKrFJDbgcSE+m9hBXfESzQmtLhid0klAKnJCQ3WWFX7/GzfVx76vrzKz19mruL+2Wu466zRnDehXwuPXkREmquwrIrvP7KA40d055cnDQ+7rKrGz29fW8bzizY3eP1TR/dkeG52o/eRlJjQ7HAMoL3qJB5okV47kBShj05wwV6CMewudoLxwvXOX/Q56SmcMz5yg/canyW/pLZ+udrn5/7ZawD468xVLTpuEZH2ZF9ZNX96dyXLtu4LHauq8VNUUR3x/B37KrjxlaVMuGMWD9XpSjR3dT7f7Czm7x+sZeH6AnYVOwuqfX7LY3PXNRiOJ+R15u3rjmZU744t9Kjqs0rIEgc0gxzjWmL5gnsCOT3QhWLe6vx6lwV16ZDC4O5Z9OucwaaCsrDLqnx+8gMzzeAE5KBIM9UiIlJrZ1EF7y3fwfr8MjplJHPMsG6M7t2Rt5ft4OpnvwDgxUVbuPvs0Tz5yUbmr80nKSGBmT+bSp9OtfW/d771NY9+tC70/d/eX0Velww2FpRx6eQ8lmwpDF12ziPzAcjrksGGPbWv6dlpSRQF+uFff/wQzhnfl54d0w64ZVtTWSVkiQMKyDGuJV5GjDGkJCZQ5fMzsFsma3aVhOq/Ir0Q9s5x3jKLtLq4xmdDM84Q3jountr1iIhEk99v+WxDAd9/dEHY8XtmrgoLqgC7iiu57MlFoe+rfT7O/Psn/P70kYzP68SjH60LtersnZPO1sJyKqr9/CgQsFduL4q4FbQ7HF86OY+TRuZybmA8XTqk0iun+eUSByIlydttpEWaQiUW7cQrV08CnLe23DMLdWd9ExMMPQOL7dxbgI7q7dSibS0sZ6VrkV61OyDrp0lE2pA1u4r52fOLeXvp9gbPWbZ1H4fe8i4vNFCykF9SyX8Xb2X6PR+GheOLjurPaWN6kZqUEBaOgzqkJnHxxP5cMikvdDvXP/8llz+1KBSOR/bK5sNfTuONH09hTJ/akohgOO6QGnkO7JzxfbjpW4fQOTMldKyL6+vWdtbhvTl6SFfu+M6oqN2nSHNpBrmdCPY2rqzxhZVI1K2K6JWTRlIgGLv7Id96xijOevATfP7wGeQaV4lFp4wUPvxmF796eQkPXTiOw/t1ao2HIiISFTe9soyFGwp45cut3H3WaM6d0I81u4q5/c2vueqYQRw1sAt/evcbiipquOGlJXRMTyY70LZsWG4WT32ygYc+XEtV4HUyMcHg81t+++0R/GDKAAC2FZYzd/VuRvbqyE/+8yVj++YwIa8zkwZ1pV+XDKy1HNYvh+v+s5hqn2VpoD75+uOH8ONjh5CYYBjVuyP/vXYKxRXV/OCpRaH1JGcf3pun5m8MPZ7Xr53CqN7ZoXcOczJqW6xFMyCnJSfyzA+OjNr9iRwIBeQY11JFC6mBt7Qqa/zsibBJSNBgVwN0d/lFbnbkFm7uDUeGdM/ikn9+BsDdb6/khSsnHtSYRUS8YK1ly97ysFZkD364lr6dM7jg8U8BWLOrhJk/PYaNrr7wVz7zeYO32TE9mbm/mh4K0EG9ctL5/hFO95/ZP59W73rGGM4Y25t/LdjIZxv2ho5ff/zQeudmpSXzwpUTWbKlkI9W7ebkUbmhgJycaBjdJ3zhXaeM2lDcKYoBWSQeKCC3E6nJwRlkP7uL3Ivswquc+3fJDH3t3hCkV046KUkJ9TYGcQdk98IL98yyiEg8KK6o5upnv2BuYBEzQEZKIkkJhk0FZaFwDLBlbzlT//RB6B218yb0c2qBq3wUVVTzzc5ihnTvwI+mDSInPYVuWan1wnFzuNd77M+hfXI4tE8OWwtrW3IGF2i7ucvoOisgi4RRQG4nguUSFdU+Fm2snYWo+5LbLau2/3HnjBQKy6o5pKdTf3zSyFxe/yp84ce63bUhusb1Al5W5eO3ry3jtDG9mDBAOw+JSOx74IM1YeEYnNeyiQO7MH/dHgC+NTqXRRv2squ4kt3FlQzPzeKv3x8bep0MqvH5Q+VqLeFAFmy7y+QyG6hH/vcPj2JfeXWDve9F2istq4px/hZqhxMssSiuqAlr3Va33Y47zN577lhOPbQnT156BAAZEWYg3lm+I/S1e4Zj5Y5inlmwkXMemU9xAz08RURihc9vefnzLYAz2xrs5vObbx3CcYd0B2BYjyz+9N0xdHGFyRevmlgvHAMtGo4BTh/Tq9nXcQfkhnbDmzioCyePyj3gcYm0VZpBjnEV1S1TqpCSFPnF2h3AB3bNDNtn/tA+Ofz9/MND37tfYLtnpbLLtVgPoMYfeawrdxRr/3oRiWmfrttDfkkVA7pmMvvnx2CMYXNBWSgoD8vN4tA+OWSmJoW1wMw6iLKJ5rhkUh6/f31Fs67jft3PaCAgi0hkmkGOcZU1vha5nYY28XBPIE8Z0rXR23C/wP5o2qB6l+8rjzxT7F4IIiISi94ItHI7dXTP0ALlvp0zSEgwJCQYjh7SjY7pThhuqXf2msMYw1mH9wbg++P7Nuk6Ka5Z7Nzs6PQ4FmkrNIMc41pqBtnt3u+P5frnFwPgXveRsp+3BN2LPCJtQ7ppT1m9YwCllfV7fIqIxIJH5qzl0/UFzF65C4BTD+253+t4tQb51jNGMX1Yd44/pEeTznd3InKvLxGR/dMMcoybNrQbQFgT+AP1u9NGMH1YN04ZXVtv5q5Bzk5v+luFw3Oz6h3btq8i4rmXPvkZizcXUl7VMrPhIiJNtbmgjFtfX8GOCK9P81bnc9fbK0PheGC3zIivbXX5m9FRoiV1SE3itDG9GqwnbkymSixEmkUBOcZdecwgHr7wcJ6+7OCbql86eQD/vHRCaMEehL9VOKZvTqPXT3TV3TW0Q1MkBaVVnPn3j7np1aVNH6yISDPV+Pw8+fF6vgns9llZ4+PoP37APz5ez1F3vc8z8zcwc8XO0MSAuytPXpcM/nD2oWGzrg2ZMak/AN8d16cVHkXLuvnUQxjQNZMrpg70eigicUUlFjEuJSmBk0ft/y2/A2UtzPzpVJZvK+KYwGx1Q2ZMzOPLTYWcNDK3Sb9E6nr1y63cfOohYSvARURayoMfruUvM1fRtUMqj1w0jsWbC8Mu/+1/lwPOLnTXHz+UlTuKAHjhyonNakd53hH9GNMnh6E99j/b7LXLjx7I5UcrHIs0lwJyO+e3MKRHFkOa8ELfITWJx2aMP6j7e/2rbVwyecBB3YaISCSfrnd6FeeXVHL2Q580eN69s1YzdWg3Nu91NtLo3yWjWfeTENjeWUTaLgXkds4eUPv5cCmJCVQ1cdXKNzuLD/r+REQiqWxgUfOrV08it2Maudlp/PT5xby2eBtnPegE6Ky0JLrpXS0RqUM1yO1cS3Qr6tOp6e2DNuRH7nQhItIYay1fbS6korp2se/jc9dx7XNfhNphbnB10snNTuP8I/tx2xkjOaxfJ3p2TMcYw51njQ673cmDupLQQBtMEWm/NIPcztXdSe9ADOnRgS2F5VTV7H8WecOeUv731TYmD+pClw6p7CyqICstiYwU/SiKtGd+v+XGV5bSpUMKvzxpWL11Dk/P38jv/ufUEH/70J68sWR76LI3lmxnTN8c8kuczYv+79sjOOeIvhEXE2ekJPHGj6fw7fvnkZacwHXHD2nFRyUi8cq0RECKR8YYCy0TEOPRe8t38PCctdx33mH06dS8+rugB2av5s/vreKflxzBTa8uZXsDbd4iSUlK4ONfHcsRd8wCYPUdp5Dcwluzikj8WLKlkNMf+BiA5354JJMGhW9cdPZDn/D5xr37vZ0h3Tsw82fH7Pe8DfmlZKQm0j0r7cAGLCJxL/iHuLW23ttImrZrp04cmcuJI3P3f2Ijrj12CBdNzKNjenKTZo/dqmr8YS2W/jFvPVceU393PhFpH95etiP09Qufba4XkDcV1C/P6tohlZLK6rANlQZ169Ck+8vrmnmAIxWR9kABWQ5KcOvVMtcmIMcN7877gcb7bgkmfOe+Fz/fEvp6+bai1hukiMQ0n9/yn4WbQt+/vWwHN5dU0jWweK6i2sfu4srQ5b84cSjXHltbGlFcUc1Rd75PaZWPyUPCg7WIyIGI24BsjLkSmAqMA4YACZGmyCU6ygMLZ4yBB84/nPX5pby2eCuPfrQudE5e10zW7S4Nff/19tpQXFBaFb3BikhM+WpLIXvLqunbOZ0h3bOYvXIX42+fRWKC4a6zRodK4Xp1TOO2M53tlt2y0pJ57odHsXxbEeeMj/3NO0Qk9sVtQAZuBLoAXwKZgF4VY0D3rFTSUxIZ0SubD1eFzyL365wRFpDdVu5Q+zeRtq6gtAqf3/L6V9s4Iq8zo/t0ZFdRBX+btRqA44b34IdTB/LNw/PZWliOz2+54aUloetPH96d4w7pEfG2x/TN2e9uoCIiTRXPAXkasMla6zfGvIECsqeOGtiZBesKOGNs79CxlDqL7vp1bngxYH5JJUUV1WSnJbfaGEXEO4/PXcftb34ddsyY8FaTJ47sQe+cdF760UT+8t6qsDKsGRP7c/OpI6I1XBFp5+I2IFtrN3g9Bqn16Izx/G/xNr47rvbvlEHdwxfL9O9SuyhmWI+sepuGrN5ZTFmVT31JRWLc0i37KKuq4ciBXULHVu4oYvGmQk4Z1ZOOGclYa3ln2Q5KKmv49qG9uH/2mnq34w7HP5gygKMGOLfXs2M6f/reGK6ZPpjb31zB0UO6cfGkvNZ+WCIiIW2izVtgBvnU5tQgt/c2b9Ey9DdvU+Xzc+qhPblm2mC+dd9cAE4b0yusiwXAoG6ZrN1dypXHDOTGUw7xYrgi0gR5v34TgOW/P4nM1CSe+3QTN726FIBuWancfdZoqmr8/OjZLwDITEmktMpHp4xkzhjbm6E9shjduyPPLNjA5oJybjtzJIO773+7exGRlqQ2by7GmCuAK7weR3ux6o5TQl/vdS3EGxihxdLaQH3yI3PWKSCLxKga17byheXVZKYmhcIxwO7iSn7w1CKSXO8ClQa63Hz70F7ccvrI0PE/fndMFEYsItJ8ngZkY0wOcH0zrnKftbbgYO7TWvso8GhwBlmiJyejtr64W1Zq6OvuWanscrVwAmdXLZVZiMSed5bX9iuefPfsBs+rCfR0/NboXN5a6lznlNEH13tdRCRavJ5BzgF+14zz/wUcVEAW77i3jg32TwY4dnh3/vPZ5rBzV+8qYViu3nIViTUvLNrS4GVj+nTkqmMGhUorjIG7zz6UaUO70y07td7mHyIiscrTvX2ttRustaYZH/VXeUhc+ev3x3DRUf05ZVQuvzxpGIf3y+GGk4eHBWZwFvzU9eWmvXy0ane0hioiETT2f/D+8w7nlNE9uXzKAADOGdeX7LRkzjmib73exSIisUyL9NrA428LznrwY77YVBj6/uppg7jh5OFh5wQXBi26+fjQDlsiEl3B/4eRbLj7VMB5Xd1aWE7vnPSwd45ERGJJY4v0PJ1BFgkaXKcl3DeNbByyvbCitYcjIhFUuxbo1XXdcbVbPxtj6NMpQ+FYROKW1zXIB8wYcxoQXAI9OHDs5sD3hdbaBzwZmByQegF5ZzGLNhTQKTOFQd06hM30l1bVRHt4Iu2OtRZjTGjGeOLALsxftyfsnGunD+bscX1ISjD0bWQjIBGReBO3ARk4G7i4zrHbAp83AgrIcWSIqwdqgoEte8v57sPzAVh357eocs1cNTaLJSIH7uE5a7n77ZURL6sbjgF+fuJQzRKLSJsUtyUW1tpLGlnMl+f1+KR5pgzpyvlH9uPWM0YytEd494olW/dRWVMbiqtqFJBFWlp+SWWD4TiSpy6boHAsIm1W3AZkaVuSExO48zujmTExjx7ZaWGXzVqxk4pqX+j7SgVkkRY1f+0ext8+q0nnjuqdzfNXHMUxQ7u18qhERLwTzyUW0kZ1zwrvUDHr650ce0hti6jKGl/dq4jIAXrty61c//ziJp//xo+Pbr3BiIjECM0gS8w5fkSPsO9X7ihmyebC0PcV1ZpBFmkpzQnHn998fOsNREQkhiggS8yp29EC4H9fbQt9XVhWHc3hiMS9A+n3/rMThvLFb08Iff/Gj6fQRf3HRaSdUImFxJxcVw1yx/Rk9pVXh20isq2w3INRicQfn9/yz4/X8+CHa7l4Yh7XHT+EGp+fn77wFXtLq3jwwsPDzj9pZA/eXb4TgMuPHkBGSlJo8w8RkfZEM8gSczJTa/9uGxJhNnnZtn3RHI5I3Hrko7Xc/ubXFJRW8ddZqwB49tNNvP7VNuatyefFRVvCzn/4wnGcM74Pj80YT0aK5k9EpP3SK6DEpK4dUsgvqWLasG5sLSxn+77a3fM27SnzcGQi8cFayzPzN4Yd+3p7Ebe/uSL0/cuf1wbkW88YiTGGP353DCIi7Z1mkCUm/e/aKdz0reFcMXUQh/XLCbtsT2kVJZXaTU+kMY98tC70h+XYvjkAnPK3uVT7auuRV2wvAmBMn47MmJgX7SGKiMQszSBLTOqVk84VUwcBzi/3t5buCLt8W2F5vQ1FRNqjHfsqeO7TjewqrmTDnlJ+ctwQzn/s09Dl9513GMcf0p0rn/mcuavzAbjzO6N5buFGlm11AnJ2erInYxcRiVUKyBLzxvXvVO/YNzuKFZClXbPWUlRew8X/WMg3O4tDxxesqw3HN596CKeP6QXAk5dO4H9fbaVTRgrThnVnWG4Hzn7I2c69c2ZKdAcvIhLjFJAl5o3pkxP6ekDXTNbnl/LyF1t4eM5aemSn8ehF40hKVLWQtC8vLNrMr15e2uDlvz5lOD+YMiD0fWKC4TuH9Ql9f1jfTgzPzWLljmKuOmZQq45VRCTemAPpj9kWGGMsHFh/UIm+Lzft5bMNBaQnJ/Lb/y4Pu+yPZx/KOUf09WhkIt7I+/WbDV722jWTQ3XHIiISmTEGAGutqXuZpt0kLhzWrxNXTB3EcYf0qHfZsws3eTAikdZX4/OzeHMhfn/tH/KfrtvD1c9+3uB1stKSFI5FRA6SSiwkrvTsmEZWWhLFFbVdLL7aXMj6/FIGdM30cGQiB+/VL7cw6+tdHDWgMx9+s5v3V+4CYOrQbjx0weFUVPv4wVOL6nVxOXlkLvPX7eG8Cf24bEqeByMXEWlbFJAlrhhjGNYji0Ub94Yd/+/irVx//FCPRiVy4D5dt4en52/kW6N78quXllLl8/Pmku1h53y0ajffum8uPzthKCWVNaQnJzJ5cBfW7i7lVycP4+RRPT0avYhI26Qa5Hb6+OPZza8t5V8LnLKK4CKjvp3TeeiCcdw/ezU3nzqCvp0zPB6lyP5tLSznW3+by77y6rDjvXPS2drIlupnHdabv3x/bCuPTkSkbWusBlkzyBJ3Du2TAzgB+cQRPcgvqWJzQTnfvn8eAF9uKmThb473boAijfhqcyG3vbECn7VsLigLheMOqUn4/JaHLxrHMUO78fpX23h83nr65KTTKTOZfy/cjC9Qi9wtK9XLhyAi0uYpIEvcOdy1s16HtCROH9OLf3y8PnRsV3ElxRXVZKVp8wOJLZ9v3Mu5j84P282uQ2oS7/50KrnZaYDTjg3gtDG9OC3QwxjgxlMOYeTv3gVA73uJiLQuBWSJOwO7dgh93SkjhRNH5IYFZICnPtnAtccOifbQRBq0bncJZz/0CQBHDezMpEFdKavycdbhvemdk77f62em1r5cd9HGHiIirUoBWeJOQoLhvvMOY/bXOzn10J5kpCQxqFsma3eXhs55bO56Lp6UhzGGzQVlHNIz28MRS3tlreWzDXv5dN0e7pu9OnT8ymMGMX1Y92bf3qMXjeO/X23joon9W3KYIiJShxbptdPH39Zc8+wXvLnUWfnft3M6mwvK+cWJQ9myt5z/fLaZe743hrPH9dnPrYg038odRTz04Vr+u3hb6Fj3rFSevHQCM1fs5K+zVoWd3yUzhTk3TKdDquYnRES81NgiPQXkdvr425p7Z63i3lnODN0/LhnPZU8uIicjmcIyZwFUl8wUPv/tCV4OUdqYGp+fu95eyT8/Xo8/wstIbnYae0orw+qNF//fCaQlJ5KWnBjFkYqISCTqYiFt3vDc2hKK6cO6c0ReJz7bUNsreU9pFZU1PlKTFEzkwBVXVLOnpIr+XTL4wzsreWLeehIMfHdcHwywr7ya4T2z+d/irWzYUwbUtmwbnptFToZqh0VE4oECsrQJJ4zowcUT+9O7UzrGGK47bigXPvFp2Dnz1+5h2gHUfUr7tnhzIUu2FFJV4+fvH6xhb1l1qIwnKcHw9GUTmDS4a9h1TjikB9975BMSjOFv546le5azA6SIiMQHlVi008ff1vn9lkN//17YlrwXHNmPO74zGoC3l24nv7SKCyb0IyGh3jsr0s7tK6vmw1W76JiezJXPfE5ljT/ieb8/fSQXT8qLeNmuogpSkhI0aywiEqNUYiHtTkKCYXhu+JbUs77eye1njqLaZ7nuP4up8vnp1iGVk0flejhSiSX7yqu54PEFLNtaFHY8KzWJihofEwd15YaThjFvTT5DunfguEN6NHhb3QN9jUVEJP4oIEubdXj/TqGA3KtjGtv2VfDu8h1MGNCFKp8zI/ju8h0KyO1IUUU16cmJJCcmhI7tLq5k2dZ95GQks7Oosl44PveIvtzxndGhDTwARvXuGLUxi4hI9CkgS5t18aQ8nv9sMyeM6MHo3h353f+W8/CcdWE9kd//eifVPn9YYJK26YVFm7nhpSUA3HfeYUwd0pU5q3Zz3X8Wh8657jhnc5mcjGR+ceIwThzRQzPBIiLtkGqQ2+njby+qfX4SjaGixsdRd75PUUUNMyb25+n5G0PnPHv5kUyus8hK2p7T7p/H0q37Gj0nNzuNHUUV3HXWaM6b0C9KIxMRES80VoOsaTNp05ITE0hIMGSkJHH+kc7uY+5wDPDOsh1eDE2ipNrn595ZqyKGY1PnJXFHUQUAo1VCISLSrmkGuZ0+/vaoxudn4t2z2V1cCUCP7FR2Fjlfz/rZMQzu3sHL4clB+np7EZc/tYiKah/nTejHT08YSmKC4YOVu7j0yc8A+NboXE4ckcs7y3YwY1J/BnfrQPfsNN5eup2rn/uC7lmpXDt9MBdNzPP2wYiISKvTTnoRKCC3T+t2l3DsPXMAOGNsL3YVVTJ/3R5G9MzmreuO9nh00hTf7Cima4cUunRIDR2z1vLzF7/ilS+2hp378a+P5a0l27njra85vF8OL141KWyxnVtRRTVZqUmhF0wREWnb1OZNJCCvS2Zo5nh4bjbXTu/OCX/9iBXbiygoraJzpnrWxqriimrueW8VT36ygU4ZyQzomskXmwp59vIjee7TTby5dDsAKUkJVAX6Fk++ezapSU4l2WljejUYjgGy05Jb/0GIiEhc0AxyO3387dnqncW8t2Inl0zKIzM1iXMfnc+CdQX8+pThXDZ5AL9+eQnDe2ZxxdRBXg9VXG5+bSn/WrCp0XNys9N472dTWb2zmLMfmh86PrBrJi/9aJL+ABIRkRCVWESggCxBwRrV3jnp3HbmSC57chEA/71mMmP65ng7uHbsPws38c7yHVTV+BnTN4fZX+/im53FAPTvkkFZlS9UTw4wPDeLF66aGJoJ3rGvgrvf/ppD++Rw4VH9SUnSmmQREamlgByBArIE+f2W4/4yh/X5pUwZ3JV5a/IBOHlkLg9fNM7j0bUPG/JLSUtOJLdjGkUV1Vzyj4V8sakw4rnzbzyWnh3T8fktP/n3l8xeuYvyah83njKcK4/RrL+IiDSNAnIECsji9uqXW/jp81/VO/70ZROYOrSbByOKXZc/9RlVPstTlx6BMYaqGv9Bzc4WV1Qz8a7ZlFTWcMPJw8hMSeJ3/1seuvysw3qzdOs+1ueXcuzw7jw6Y3zY9atq/Hy5aS+H9eukWWIREWkyBeQIFJDFzee3TPvzB2wuKA87npORzKLfHE+SdtoLyfv1mwCsvO1kvv/oAr7aXAjAhrtPbfA6e0uryE5PJjHB8MKizfzxnW+4bEoeUwZ35fQHPg47Nz05kfJqH9OGdeOe740Jdavw+W2ji+xERESaQxuFiOxHYoLhjDG9Q99/f3xfAArLqvn3Z5u9GlZMW7i+IBSOAZ78eD17SirDzlmzq4TRt7zLYbfN5Cf/+RJrLbe9voL8kkr++M43XPXM5/Vut7zaB8A54/uGtXJTOBYRkWhRQBYJmD68e+jrIT068PfzDwfggdmrqQiEtvbuXwtqdyF89cvwnsO3vL6CqX/8gM83FmCt5evtRRz/lzkUV9QA8OaS7WzZW05xZU3oOtv2OTvXffvQnqy87WQmDuwCQMf05NDXIiIi0aY+yCIBY10dK/p2zuCEQ3owomc2K7YX8a8FG7n86IHeDS5G3PzastDXdQMyQGmVj7Mfms/Abpn07ZRR7/IfPOXsaDe+fyf81oYW4l02ZQBpyYk8e/mRLN5SSG52Gp3Ukk1ERDyiGuR2+vglsu37yvlkzR5OH9uL5MQEZq3YyeVPL6JTRjLzbzyOtOREr4foCb/f+X8y8Ka3mn3dfp0z2FRQFnbsr98fwwkjcnl87jrSkxO5YupA7WAnIiJRpUV6ESggS1NYazn9gY9ZunUfV04dyGljejGiZzYJbagetrLGx/3vr2FU746cPCq33uXlVT4O+b93mnWbWalJoVKKz35zPEu3Fob6S4/qnc0bP9a23iIi4i0t0hM5QMYYLjqqPwCPfLSOb98/j5/850v+u3grP3t+MUUV1R6P8OA9MHsND3ywhquf/ZzSQKhdumUf972/Gmtts8Px3BumM+eG6Zx6aE9+fOxgumWlMn1Yd6YPc9rlnXVYnxZ/DCIiIi1JM8jt9PFL01lreWzuOl75YisrdxSHXfbtQ3vyQGAxXzyq8fk5/LaZFAUW0r3x4ymkJSdw/F8+ApwtmtfllzbrNhtq91ZSWcPcVbs5cWSuOlKIiIjn2lyJhTGmNzADOBkYCmQDG4C3gLuttXuacBsKyNJsl/xzIR9+szvs2JxfTqN/l0yPRnRwdhZVcOSd74e+f2zGeH749KJm387K205mx74KumenkpGitb8iIhL72mKJxWnALcAe4E/A9cAngc+LjTH1CylFWsBJI+v/aD09f2OEM2PTE/PWc/oD83jow7UA5NfpW9yUcJyalMDPThgadiwpwZDXNVPhWERE2oR4nUEeCeyx1u6oc/xy4DHgHmvtL/ZzG5pBlmZbs6uE4/8yp97x3jnp3P6dUUwf1j3CtaLvhUWbeW/5Du499zA6pDqhde3uEo67xxl7UoJh2e9PYvqfP2R7oBdxU7141USOyOsMOL2Nu3RI4Sj1LBYRkTjT5kosGmKMyQKKgHettSfv51wFZGm2Gp+fwb95O/T9tGHdwkouvn1oT6yFYblZ/PDogaSntE5buJU7ikhNSmRA1/qlHdZaBtzotGM7c2wv7j33MPx+yzXPfcHby2r/pvzz98bwixe/atL9Lf/9ScxdvZucDIVhERFpG9piiUVDgsvjd3o6CmmzkhITGOPaUKRuqcEbS7bz5tLt/GXmKs55ZH6oK0RLWrOrmNPun8fp98+L2EXDHdgXrnd2tfvVy0t4e9kO3Gvj3OG4a4dU/nbu2ND33bJSeeYHEwB46rIJZKYmcfKongrHIiLSLrS1GeQXgO8Bx1lrZzdwzhXAFcA40AyyNN/Oogqu+8+XfHdcX04c2YNDb3kPgMunDCA5KYGkBMNri7eyuaCc4blZ/OHsQ0OheuOeUrpnpTV7Zrm4oprH565n7e4S3l2+g2qf83P7t3PHcsbY3gBs2lPGL178ioUbCkLXS040nDgilzeXbic9OZGHLxrHU59sYPbKXaFzUpISePLSI5g0qCvvLt/B0/M38OD54+iYkXwwT5OIiEhMi9kSC2NMDs7Cuqa6z1pbEOkCY8zPgT8Dj1prr2zCfavEQlpE3q/fBJwNMbplpQLOLO8Fj3/KziJnEVxyomHasO7MXLGTfp0z+MPZhzKufydSkhLYXFBGTkYymSlJVNb4SUiALXvL6dUxnfdX7uQf89azbFsRVTX+evd98cT+/N9pI9lcUMb3HpnP7uLKeucEXX/8EK4/fig/fX5xaJvoN348hZG9srWLnYiItDuxHJDzgPXNuMoQa+2aCLdzOfAoTpu371hr97t7gwKytJS5q3dTUFoVmskN2ltaxS9fWsKsrxuu+MlKS6K4oobx/TvRPTuVt5buaPDczpkpDO3RgS4dUhnSvQP3zlpd75wJeZ3DZpCDRvTM5pWrJ5GWnMgzCzby29eWccmkPG45fWQzHqmIiEjbEbMBuSUYYy4DHgfeA86w1jY8hRZ+PQVkaXXWWh6es44/vLOSUb2zues7h/LU/A289PmWJt/GwK6Z3H7mKCYO6hL6z/zmku1c89wXYedlpCQy55fTeenzLWwtLOPFRVuorPGTkpTAN7edHDZLvK+sWiUUIiLSrrXZgOwKx7OA0621Te5XpYAs0VRaWUNGSmLoP+OG/FIWby4kIyWRD77ZxX8+20zwR/Gsw3pz0cT+LNtWxNQhXSNuQlJSWcNPn19MfkklX20u5PB+nfjJcUOYOrRb6JwHP1zD/e+v4cELD4+Z9nMiIiKxok0GZGPMJcATwAfAadba8mZeXwFZYsamPWXMXbObacO60zsnvcVut8bnJymxrTWrEREROXhtLiAbY04HXsXpeXwDUDccl1hrX9vPbSggi4iIiLRTjQXkeN0X9nCcHs45OIvz6toIvBbF8YiIiIhIGxGXM8gtQTPIIiIiIu1Xe9pJT0RERETkoCggi4iIiIi4KCCLiIiIiLgoIIuIiIiIuCggi4iIiIi4KCCLiIiIiLgoIIuIiIiIuCggi4iIiIi4KCCLiIiIiLgoIIuIiIiIuCggi4iIiIi4JHk9AK8F9+EWEREREQHNIIuIiIiIhDHWWq/H0G4YYxZZa8d7PY62QM9ly9Lz2bL0fLYcPZctS89ny9Lz2bJi6fnUDLKIiIiIiIsCsoiIiIiIiwJydD3q9QDaED2XLUvPZ8vS89ly9Fy2LD2fLUvPZ8uKmedTNcgiIiIiIi6aQRYRERERcVFAFhERERFxUUAWEREREXFRQG5FxpgOxpibjDFLjTHFxph8Y8wnxphLjLbwazZjTA9jzMPGmM3GmCpjzCZjzN+MMTlejy2WGWNuNMa8aIxZZ4yxxpgN+zn/SGPMrMDPbJEx5h1jzNjojDa2Nee5NMacEPh5/cwYUxE4f1rUBhsHmvp8GmPSjDE/NMb81xizwRhTHrjOv40xh0R52DGrmT+fdwd+H+0yxlQGXlff0M+oo7mvm3Wu+4fAdUpacYhxpZk/m08Gzon08d2ojVmL9FqHMSYBmANMAp4CFgAZwHnABOCP1tpfeTfC+GKM6Q4sBHoBjwDLgFHAlcByYLK1tsy7EcYuY4wFCoAvgHFAkbU2r4FzjwI+BLYCDwQOXwt0ByZZa5e29nhjWTOfyyeB83F+Vg0wFphurf0wCkONC019Po0xw4GvgXnAe8A2YCDwIyATONla+0GUhh2zmvnz+SGwElgN7AVygQuBQ4AZ1tpnojDkmNWc57LO9cYCnwEVOBmrQysOM24cwGvnxcBFES7+yFq7qZWGGT4OBeTWYYyZCHwC3Gut/anreArOi1Jna22OR8OLO8aYe4HrgPOttf92HT8PeA74rbX2do+GF9OMMQOttesCXy8DOjTywrQQGA4cYq3dGjjWGyecLLDWnhidUcemZj6XvYF8a22lMeYXwJ9QQA7T1OfTGNMF6GutXVzn+AjgS2BprOy+5aXm/Hw2cP0OwFpgj7V2ROuMMj4cyHNpjEnEmQzbDmQD4xWQHc187XwSuNha6+k77SqxaD3Zgc/b3AettVVAPlAa9RHFt+lAOfCfOsefx/lL/dKojyhOBF+U9scYMxg4AngxGI4D198KvAgcb4zJbZ1RxoemPpeBc7daaytbczzxrqnPp7V2T91wHDi+gtp3k9q95vx8NnD9EmAP0KllRhS/DvC5/AkwAvhxCw8n7h3I82kc2YF35KNOAbn1LAQKgRuMMd8zxvQzxgw3xtyF8/bCLV4OLg6lAhW2zlse1lo/TnAeaIzp6snI2o4jAp/nR7hsAU6ZwLjoDUekcYFfnD2BnV6PJV4ZY7oaY7obY8YYYx7AKbF4y+txxRtjTH/gNuD31tqNXo+njdgX+Cg3xsw0xhwZzTtPiuadtSfW2r3GmNOBx4EXXBcVA2dba1/zZGDxazkwzBgz1j2TFKj3Cs529MOZnZcD0yvweWuEy4LHekdpLCJNcRVOQL7N64HEo0BJxW7XoXKcncx+5s2I4tpDwDrgL14PpA3YAfwV+Bzn3fYxwPXAXGPMt6y1s6IxCAXk1lWC8/bf/3DqkTsD1wDPGWPOsNbO9HJwceZe4EzgBWPM9TjP68jA8WogGWcRpBy44PMXqSygos45Ip4yxkzCCSNfAXd6PJx4VQ6cgJMF+gMXAB1w/p+rDLCJAmthTgamWGtrvB5PvLPW/rrOodeMMc8Bi3H+EBkSjXGoxKKVGGNG44TimdbaX1prX7XWPgFMwfnr6LFAQb80gbV2LnAukAW8CWwEXgc+AN4InFbkzejajGAXkNQIl6XVOUfEM8aYcTivA9uAU621Ffu5ikRgrfVZa2dZa9+x1j6Cs9ajHzDbGJPs8fDigjGmM85EzRPW2k88Hk6bZa1djfNu/GBjzNBo3KcCcuv5KU6oeNF9MNCK7E2cv9bzoj+s+GWtfRHoAxwGTAV6WWuvChyrAdZ4OLy2ILigNFIZRfBYpPILkagxxhwOzMSpTZzuXlAqB8da6wOexVn0ONXj4cSL3+G0GnzMGDM4+AGk46wzG2yM6evtENuMDYHPUVlvpBKL1hMMFJFmiZPqfJYmCryALw5+H+iqcBgwR32QD9pngc8TcWrn3Y4CLE5NmIgnAuF4Fs5ajulaDNUq0gOfO3s6ivjRHycgf9rA5atx1tCo08rBC5ZWRGVRrmaQW8+KwOdL3AeNs+vbGTiN2TXjeRACK9jvw/kj5A6PhxP3rLVrgEXA94wxwQV7BL7+HjDbWrvDq/FJ+2aMOQxn5rgEJxyv93hIccsY0ynQk7/u8UzgB4AfpxOT7N8fcF4f636swFm78T2cd5SlCYwxmcaYtAjHD8N5Lr+21q6Nxlg0g9l67gVmAHcH6pE/xvmL/Ic4q66vCcyGShMEVlsvBF4F1gMdcXYlHAf8RrtoNcwYcxHOLAdANyDFGHNz4PuNdXbMug6nrnuuMeb+wLEf4/wx/fNojDeWNee5NMYcCpwe+HZy4PNFxpgpga/vt9bua+0xx7KmPp+BFlozcTrW3AdMCizSc3vVWtuuF5Y14+fzGOARY8zLOBM1xcAAnJ3L+qBWZU1+Lq21kdpiYoy5FuhvrX2p1QcbB5rxszkEeNsY8xrO7Huwi8VlgA+4ImqDttbqo5U+gEE420xvwem0UAR8BJzl9dji7QNIAf6NE44rcLasfBc4yeuxxfoHztbRtoGPDyOcPxF4H2emrjjwPB/u9eOIhY/mPJc47x41dK4F8rx+PF5/NPX5BKbt57nU89m853MQThnVCpxa7mqcxeOv4yx69PyxeP3R3NfNBq5f4vXjiJWPZvxs5gLP4Ow4XBT42dwUyFLDozlmbTUtIiIiIuKiGmQRERERERcFZBERERERFwVkEREREREXBWQRERERERcFZBERERERFwVkEREREREXBWQRERERERcFZBERERERFwVkEREREREXBWQRkRhjjJlmjLHGmEu8HktzGGMuCYx7Wgvc1ihjTI0x5oSDH9lBjeMMY0yVMWaIl+MQkehSQBaRNsEYc3IgnN0e4bKjApdVGmMyIlz+jjHGb4zpGp3Rxi9jzFhjzC3GmLxWvqu/AB9ba2e28v00ylr7X2Ap8AcvxyEi0aWALCJtxTygBpgW4bLpgctSgEnuC4wxScAUYJm1Nr+Vx9gWjAV+B+S11h0YYyYCJ+CE5FjwN+A7xpiRXg9ERKJDAVlE2gRrbQnwGXBEhFniacBMYAf1A/QRQCbwYeuOUJrhaiAfeMvrgQS8ApQBV3k9EBGJDgVkEWlLPsCZJZ4cPBCYIZ4MzAl8TK9znWmu62KMyTLG3G6M+dQYkx8oy1hjjLnbHbyNMacEyjZ+Emkgxpj5xpjdxphk17FUY8xNxpjlxpgKY0yhMeZ1Y8xhTXlwTb2+qxb4WGPML4wxawOPY5Ux5uIGbjvPGPOyMaYo8PFfY8wAY8wGY8yHgXNuAf4ZfL4C92GNMU/WubmEpt5vhHEkAWcCs6y11XUuuyVwf3kRrhcaZ4Tn4ThjzP8ZYzYaY8oD/7ZHBc45xhgzzxhTaozZboz5bd3bDvzxNRf4blMeg4jEvySvByAi0oI+AG6idsYYameI5wBFwN+MMZnW2tLA5dMAG7gcoDdwOfAy8BxOacYxwA3AYcBJgfPew5mRngHc5x5EYEHXUcB9wZAXCMrv4JR4PAM8AHQEfgh8bIyZaq1d1NADO8Dr3wmkA48AlcCPgCeNMWustR+7brsLTgDsATwMfA0cHXg+M1239wrQE7gicNtfB46vPZD7bcA4oAOwcD/nNcfdQCJOqUQK8HPgPWPMDOAJ4FHgWeAc4FZjzHpr7b/q3MZ84CRjzHBr7coWHJuIxCAFZBFpSz4GqgifJZ4GlACLgH1AMs6M8nuu2eUl1tqCwPnrgL51Zi//boy5DbjZGDPBWrvQWuszxvwL+IUxZoS1doXr/BmBz0+5jl0bGMvJ1tp3gweNMQ8Cy4A/E7l++mCunwocYa2tCpz7UuDxXYvzXAX9CugDXGitfTZw7CFjzB+BXwZPstYuMcbMxwnIM621HzYw1qbebyQjAp/rhu6DkQgc5RrPCuC/wIvAxOAfFsaYJ4CNwDVA3YAcHM9IQAFZpI1TiYWItBnW2nLgU2C8MSY48zkN+MRaW2Ot/RrYRW2QDM4uf+C6jSrXrG+SMaZToLvFrMApR7ruMhiAg4EYY4wBLsRZ9PeF69wLcYLV58aYrsEPnBnNmcAUY0x6Iw/vQK7/YDAUBh7bVmAVULdl2WnAduDfdY7/uZHxNKap9xtJt8DngkbPap6H3OPBmS0H+NQ96x44ZyGRx7kn8Ll7C45LRGKUZpBFpK35AKc8YIox5n2cGeI7XZd/RO0M87TA5w/dN2CMuRpnQdZI6k8kdAp+Ya1dZoz5ArjAGHOTtdYPTMXp8HBDnesdglN2sLuRsXcFNjdw2YFcf12Ec/YA/escGwAsDIw/xFq7yxhT2Mj9NaSp9xuJDXw2B3C/DQkbj7V2r/N3DOsjnLsX6BLheHA8NsJlItLGKCCLSFvzAfB/OOG3iNr646A5wF+NMR0C5/hxQjMAxpifAffg1BjfB2zDKdvoDTxJ/cD8NHAvcCzOLPMMwEf9t+gNTj/dnzUy9sbC74Fc39fIbbWmg7nf4GPo3Mz7bOy2GxpPQ8cjCY6nsX8jEWkjFJBFpK2ZD1TgzBIXAeU47d+C5uC89k3DmV1ebK3d67r8ImADcIp7RtUYc3ID9/cc8CdghjHmY5xOBzOttdvrnLcap3xgdt2Z2iY62Os3ZgMw2BiTUOcxdwdy6pzb2jOoywKfGyvHyHZ/Eyhr6UHkGeGWMjjweVmjZ4lIm6AaZBFpU6y1lTgheRzwbWB+nfrTZThv9/+SyP2PfTghMDQjGVjM9+sG7m838DZwFnABTnh7KsKpTwO5NDADbIzp0fgjO+jrN+Z1nO4U59U5/osI55YEPjd3hrepvsT5w+aoRs45qc73Z+IsDGzNmfGjgJ3W2m9a8T5EJEZoBllE2qIPcGaQJ+Hs+hZirbXGmLk4oSp4rttLwF3A28aYV3AC7/lANQ17CjgdpzRjH/BahHP+hrM73J+MMccCs3GCYD/gOGpnvRtysNdvzB9wHuM/jTETcBYDHo3z/OUTPmv8GU5Zym+MMZ2AUmC9tfbTA7zvMIHuIK8AZxpjUgN/8NR1kzGmL84fO0Nx2sgVAqONMT+z1rboDnyBcpyjgX+05O2KSOzSDLKItEXu0DsnwuXBYz5qOxoE/Qmnl/JAnFB6DU498gwa9gZO14Vs4EVrbUXdEwKdMU4FrsMplfg98Ffg+ziLyO5q7AEd7PX3c9v5ONttvwFchhOYM3ECt8EpUwmeuylwTjrwEE7nix8d6H034CGc0o5vN3D5FTjh/X7gbJzn5Hqcjh4XtPBYCNxHBk5fZxFpB4y1WpArIiL1BTYQyQcesdZGdZtlY8w7QKa19mjXsVtw3hEYYK3dEMWxfAFssNaeFa37FBFvaQZZRERooAdzsO56ZoTLWtvPgYnGmBM9uO8QY8yZwCiczVREpJ1QDbKIiAC8ZYzZCHyBM3lyHE6JwydErqluVdba5cTA7yhr7Ws4pRsi0o54/uIjIiIx4Q2cOuvv4NQXb8FZdPh7a21z+gWLiMQ91SCLiIiIiLioBllERERExEUBWURERETERQFZRERERMRFAVlERERExEUBWURERETERQFZRERERMRFAVlERERExOX/AQ6FItDqEsdvAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ "# Plot the optical depth of the 10 micron region vs wavelength\n", - "plt.figure(figsize=(10,6))\n", + "plt.figure(figsize=(10, 6))\n", "plt.plot(optdepth_spec.spectral_axis, optdepth_spec.flux)\n", "plt.xlabel(\"Wavelength ({:latex})\".format(spec.spectral_axis.unit))\n", "plt.ylabel('Tau') \n", @@ -1279,7 +1465,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, From 2ede68241cfd7d0e81e902169a6897563fc7b530 Mon Sep 17 00:00:00 2001 From: Ori Date: Thu, 16 Dec 2021 14:08:55 -0500 Subject: [PATCH 7/8] Cleared Cells --- .../JWST_Mstar_dataAnalysis_analysis.ipynb | 545 +++--------------- 1 file changed, 86 insertions(+), 459 deletions(-) diff --git a/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb b/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb index bb15354e..ce4f4abd 100644 --- a/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb +++ b/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -75,21 +75,9 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "1: E999 SyntaxError: invalid syntax\n", - "3:13: E225 missing whitespace around operator\n", - "3:25: E231 missing whitespace after ','\n", - "3:30: E231 missing whitespace after ','\n", - "3:35: E231 missing whitespace after ','\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# enable PEP8 checker for this notebook\n", "%load_ext pycodestyle_magic\n", @@ -109,21 +97,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "1: E999 SyntaxError: invalid syntax\n" - ] - } - ], + "outputs": [], "source": [ "# Import useful python packages\n", "import numpy as np\n", @@ -142,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "jupyter": { "outputs_hidden": false @@ -186,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -206,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -223,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -233,17 +213,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pipeline 3 Data Exists\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Check if Pipeline 3 Reduced data exists and, if not, download it\n", "import os\n", @@ -280,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -337,32 +309,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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nisOpR1rUVlkuM6fB/FGUvjda2bMW+TbUXVlLc3Sp/wCqTRjx+y3FynRTqUszXqq8oNPWMl3F+zJQjh6sqmZRhUpzbX3mvZgu+4bZx1WOIeSpKMbRlBReVWkr379bkzf5LivXhNcmnSlJ2jGUnyUW2eh2Pho4ZynVa7ZRTyXv2cW0lfvZyJ7XxMlZ1p27rL6o6eDrurhcteb+0qxp058U0syzPirpL5j+Xyz2n4/HXI2jHLiKy5VJ/macD6mHxFV/ej2Me9y9r6EY2jOvjakYRalKdrP7tkk2+4Xa9ZRccPTd4UU0379T70v7F7snJZlvR9tfxl+FS/Sc5m7bkvto/hUv0mFMr4/6xPf9qDoYD16GJp8oKtH4xev0Oeb9h/zCjwnGpB/OLDv+tHH9nOzK49SXIzTlZ68hZTOTuf7no/F1vMWZtdRqjutDNnuXwQK3GacGJSzXNdRCXtuFaqXVlKk73bFxUlwKqtVviZpVuBnm1d6mZF8TbTmmtd5ghO3eXqonwI6mq5qypUy7iueIT0a+YspXEcCcVe/0JIZsLWFBCGEQlw1+XIhMmiL4oSURoTFnIjWuelbYrRMxFIqEmQuoOQJjDk3JuK2Sjo1xLIGuU07aJWilpx72ZaSLFIo56WZiVIquMmOFWiEVvGbKIzHzk2Vrz1EyEbBsFER7pEx1ElIdQIvS+fj0sS5MrUS6lTuRa08fxEwQyixow4Fj0QvL2rxyK1dK/wD9kqpzQSktO92JnDQ34rm7n6XQd9EV4mSWgKVkraNvf3CyoXNfynfTPmuyyIlSlkZZGOhPXY5jt7C9ur/l635IxRZq8npfvEIvdNTh1izLZp2e9aM6Pju/9RwfNPf/AJq25IkB7GjBvl/JR/zD/QV7MxboVoz+7ul/S/8Ad/kWz/ko/jv9Bz2TzNllVblldvaLp1M1CbVKWftFNL7KrdaSfLQq2ls6rKnQlGPaONPJJ03mXqvR9GUJdvhrb6lDVc5UX4MKU5PBzyyadKrGV4tp5ZK3DvRnObz9fhpep19/lljs6u3pRqf6GdLF4DLQoQq1I0YxU5TvrPNJ7lFb9DledVpNR7Wo7tJJ1JPV/M1bTpyrY3soa2y0l3ZVq/lqX15bNqecy46MtqqNKrXpxy5rU4SkvtKtRJLM+SSXU8yzdtOvGU1Tp/wqKyQ7396XzZikV8XM5m/svk6246G3P48fwqX6TCjdtz+PH8Kl+kwIfx/1ie/7VNzfsL+bo/GX6Wc9s37Edq0p8KdKrN/6bf3H3/Wjj+0cjEophJX1LMRJZd7zaWVtLfEx3Obuf7q7fjt8Y2xilxGc3cy0mzTFXI+mv2ts5JFNWNhs9tCJzurEVUYZyu94rgy2cEhXInVJps0RfNFKkrbtS+L0ItaT0ZRTGcLFV7A8RpzRN05h2JJMtWVrR9d5Eoknms5KLOzHjEVonNVPRCXLpoqmSositjsQIdKPGIKJYkFo5jiDIVDI644liQyIiNYZAlAkPGI5RiEOixU7LUUNPxsRlHITJRnWnN9lQ6kwYRkjOtZcPT7zQpXKqcbmunh2+RnbJ9tZaiEL8RK0bM2Rw8ktwypuKbZE+SKvOzHPumtPiNKWo1Za3t8ipSXzOvjpy9/a6MdC6D0tb5lFN3NUKdzS3Yznq+mXFxzLTgV0oaHWWDursR4ZLcjn6+SNJNZ8HUdOpGS3xlGXRnQ2rQUcRNr2Z2qRfNS18SKOypSl8rvu7jVVh2mGT+/h3kkuLpt6P5PQ6f8AT/LOp6/Dj/1HGVy8oRnccR7zujhrozl+5R/Hf6DnM31P5GP+Yf6DFGneyWrb3LVsnj8/8q6/CzDV5U5KcHaS6PufcdfB9hWVaMPsqlWm7wetPMtcyfD4GN4OnSSeInke9UoetUt38I/MswOPoQr0+zoJXkouc5uU0npu3Iz7zqby04njfadnbNXb05OvQkovO1GeaTS13WKMRjqcO07BuVSq5Z60ll0bu1BcPib6WJ7PzpujQXZJwWWDi23KyTd91kcxVcLU0nSnRfvUpZ4/OL/sKbbt9nckyMAsjZidnyhHtISVWj78OH9S3xMb3HRLL9MbLPt0du/x4/hUv0nOOht1/bx/CpfpOfcn4/6w+/7VB0cO+zwdepxqyjRj8F60vAwQg5NRiryk0kubZq29VUMlCLvGjHK3zqPWb/t8hd38HxPy4lSd2K4gokxOS3a7+ZkxZDdY0Um0U04m2NNWJtacxTUjexnqNpG1x0M87O6M9GZWCdTmI5F1aFjOw1UOpX0LaWnMog9S9VbbxVcW7yLBCWg7jfuI3F5qI67ixXFp+r3kt8Raqc4nOMqhU2Fyaa9zXIpbRGcRsWQ7UytYrZYI4hUxMR2xUrEpmda8uKkOgSHSO+PNERyEiSglMthIqSHQsGrXMrlIgVsVPTXGiJceLJpynv8AMVIdIbIzKtp7XUGb6W/TuMNJG+hbic3yOvi+nRg242W8y121ozVQepGNsraLU5/j6zouqwSoXV7GNUjt0YXEr7P4o7OPmm5XP3zrHQS4l1PfbvLFhLbmR2L3o28pYyz26dOlmskPLDKC15lGF2hGKs1u4m/tadSHtKz79UcN56txtbkX0oWV+epgxH2FZVLXhK8akecXvR0E8qSXBWKKyU00+J2/BxeLv4cXzdTqZ+XFx+F7KejvCSzU5e9HxMU3qdeDUU6Fb+G3eE/8OXP4czm47CzozcZrXemt0lzTPU56/FcN5/MaKjvgYf5l/oLZ1PM4qKs8TJetLf2MX91f8xXgNpQpQjGdLtMlTtYvPlSla262pMsZhpycpYebcm5Nuu9W/kTl3LPS9mbvtz531bbberb1bYq5p68PidRV8K//AE0/+8/AXtML/wANP/vS8C/K/r/4jJ+2/bFlRc1/6mcKn/SoL+7PPSOxX2lQnCEZYeTjTTjH7Z6J/LuMrxOE/wCGn/334EfHvMyxXedXdZsJi50ZZoPuaesZLk1xRfj8PCVPziirU28s4f4U+XwfAbznCf8ADT/778C2jtHDwjOMcNLLUjlmnWbTXTf3jtu7IUkzLVW3f48fwqX6TnGraGLVarnUcqyxio3zblbeW4LBxce2rXjQi/8Aqqy92PiVzfHmaVnl1cWYFeb0niZe27xoRfvbnP4I8/Xnnlvv/dm3am0JVpt6LTLGK9mEFuijmxWph31/26vi4/KU+BfSo3ZRKI6nfRmFdC9SysuqYlcOBkavZj06TJpy2fS1Ym+gkkSsO14itO7M9XnolSKZjqxtobUrd5VXphKcjGmOpiT0IUirVSL4zLI1DE56l0HoRVa050wdQpQrlZkq2tGZk3MzqDRlcMLWnLcVxFSsWKXMmqntEQkTYGtCaqRW2FyAbIq3OSJQEpHovLMSiEMhmZIkEAtPESEYzYoqEIdC2GRJr6bNDkrJIywL4GfTXirabNtMy0zTCRz/ACR08OhhxcdU1iJTqleJd7HPxx/uLvrGrD1LGynK+9nMpTNMJtF34/bG/JGqVJFUIZW+8mNUsk0a882Mf5IoxOFTWaPDeuZyM0lLj8DuTqZV3HIxE7yuipMVOtdLCV6iirSsrbnqdDCYjtLxek10a5o89TxluA0sa8yadn3G3P8AhHUl+3oa+HzqzXzOXPFKm3h8RFzpKzi906d+MX/YmG0puPtu5Hm/nKyt2nZ2l3nRPkye4w/h9+qz4nZsox7Sk+1o+/HfHulHemZIsro46ph6jtJwknZtfk1xOjHaOHr/AMallk99Sh6r+cXodPPyb/lh38Wf4Y8w8ahq9H0p/wALE0n/AMtW9KX10YehMTwp5lzhOMl+Zfnyz8KxzkVs3LY2K/wZ/TxHWxay1n2dNc51Iofnz+y8Ov05xMYuTSSbb3JK7Zv82w1P+JXdR+5Rjp/qehE9r5IuOHpxori161V/GT/sTfk/SpxqyGCp0LSxOs98aEX6z/ra9lGLG4ydefrWSirRilaEFySMLxLbvd7+OrbNdOqpPVb7HN38v6dPHxftjqUmm9CiMLs34z1W7O6ZltfcYTpvUUqd2WzpK2m8toUHJWS1HlDIpKUbuStf3XzJvSsY403u6GmlG7sxI6cNxdTlfUVpxNeWWy4GeVSzLqruZKkUSvTuRTUGuRJ6CPWOtTtqZmbqu4xyRcpkii5ysitAgOejqTFbC5EmIb6WQZbAzwZbnFT5XZiblKZYpEmsjMfMUJhcitJVjFJSGymTSe2CwDWIPVeSEMmQRci1UWwvL1UrvfZK73CtiKQXEaSUKhkGhIIARNVFkC+DKIstiyKuNEGaaauY4PU3UJJLd/4Muo08miKsV13uHb0Kashc8e9Y99ngzRGZihIuUzXxc3XTbTkXxloYaUzRCZc5c96q2VHNxKZYK3AvjMsjMfhFT5bjl1dnyvpqV+jaz3RO2pk9ryRc5F+a/lx8Ps2vmy5Wube6x06kVhKTqZs090eCvzNlKbe88ftOpUVWalJyak1cOp+G3wW921lxlbNNy56i0Z99gilxIc1wItdeftfOc47ndBDGu/BfQKMnLVW5D1cMsspS0svqVPk6n5ZX4+L+B6Qknq3b4s6GGqRlF/Dfc89fgaMFiHFtc+Y78nf7L+Lifhrx0pQyO+/6lqlb1ly1RlxNO9ldvS/wNOBksrT5byOurZ7PmSCaWja0CnNJ2RMo5k9dzKktSYOvtZjJaa8UVUJNNaXL8Qs8VdbuI1ClazQC1tpVVBvRWaJlHPw6hVilCMr6p6rhYmjNO9iFRRWo2WhVFafI1VVcpyk2qzWKtfgVOTa1W46FSnvMkuQ5dOzGaU9R+BRX0diac9Ao5RU3GaSNF9TNN6scWWxDJchGygABSC4DEg2SToSZE2jRGpoUbmXWTFRDpjLeVrQdE05V8GWSiUQZqpaow6mOjm65YAQz1K8qIZBLFIqkkkAAMSKSgMwIESTTlMh0ytG3ZWCeJxFOinbPLV8orVvoLBooRb0inJ8km2aFJxdmmnyasz6BJ4bZ9C9lTpqy0V5Tl+bZkW2MDi6c1OzUIuUo1I5ZKPNf+CbNGvHqYs3qeg8msfhacayqOMU6l4KazSyW01selwzo1Y56ahKOquoreGYzzXzi48Lnu6m0MHCTjKVNSi7NZdz6HG8mNlxqZq1RZkpZYRe6/FsaOuPeOTThK18src8rt1LoS0PVYrbNCjPs5N5lvUY3Ufic3b0qEoQlTSdSeqcdPV43K5rL5PjmbK5UZFmYogbdm1IxrQc2sqve+7czVy5bcVKeg0JnrIRg4qSUcrV07Lcea2jWhOtKVNpxtG1tFuDnvfWNfk+Hwm6spZsspJXcYyla9r2Vzw2JruU3JvVts9dTxq7WVFO7VCvKS5epojx2HpZtXuM7dru+KeHDqUPJvF1qEa1OMZRkrxWdKbXw/wDJTgfJ3FYiDnSgnFScXeai8y36M9FhNmecUKM8PiMtelSyOnmaS1fLVXuaNl4KrPZlWjD1KvauOsnHK4yjfVfBk2r8ni9oYGthZKNeDg3qtU4tc01oaMds7EYehTqVksla2X1rtaXs+Tt+R6/aOCjXWCwlSaq1YNSqu93kUfWv8RtrtY7D42hFLNQknStxcVf81JfMSvJ4LZ+z62Jnkowc5b3bRRXNt6I6GP8AJnFYaKqVIwyuUY3jO9pN2V93E62GxDwewlWou1SvOzmt8btr8lb5nnMLjqspRhKrUlCdSk5RlNyTammnrxKTa9PHyaxWR3hFPgs8Wc3D4OTmrLVtRt37j2W1cOpVW3ilSeRLJms9713r/aPJYeq03a5A1s/ZnFKd8iy/1xKqPk/iZ3cYKylKPtx9qLs/yZ2Yyl6Mm88s2f2szze0uI2Bpdps2KlW7JupJuo5W1zvjdbwLHLhsas5uk4LOoqbWZezexnw+FnVkqdNJz1e9LRbzubDpZMXVj23bfYp5734rTezD5OL99X9NQArr7KrUqcp1IJRW+0k7cBsJsSvKCnGCSauk5Wk18B9rYVRVWXniqXm/sc12ry3WzcPhwOjWjDHRpyo13Tq01pC9rP4f3QrDnpxYYepKbpxg3O7WXk1vuW4nYlenB1JKNlq7Su0jqYKMsPhcRUa+2UnFvfbcv73OH53UbadSVp6Su200zO+mk9rcHsutXV4R9Xdmk7K5g2psqvh7OpD1W7KUXmjfl3HoPKTFSo9lQptwhkv6rs3wtf5fUq8nsRLE08Rh6zc4KCactXG99L/ACuhyZ6Fv5cCPk3i60I1KdNOMkpL14ptfNmWjsLE1KsqCp2qwWaUZNR9Xnfcz1M8BXxOzsAsPU7OUck5SzuHq5Xy3620OjRxNOptNxhJSlTwuWbWuudaf75lp14jG7CxOFpOpWhGMbqN1NSd3u3HElLU9Dt/Zyp0nOO0FiPX/hdpe2/W2Z7vgeabDFSmAVEZhq0xDBMiTEDKQ5nbBSDCli7NYuizOpF9O3MmqjQo3G7Mq7VLRESqMnKv1FmewdvLemUpsfhoKyLnVV2FZYxGd1ebCMglikKSSQAjMiRbhcAZMm4tyAJamdryUxUaWOpOTtGWaF3wclZfW3U4cR0GFr6nt/ZCxlFQzZJRlmi7XV7NWfdqeE2hsbEYX+LD1ffj60OvD5mzZXlbiKCUJpVoLRZnaaX9XH5ntdn4+ljKGeCvF3jKMlqnxTRPuH66fNIyPf8Akk74KP8AVP8AM8XtvBrD4qrTj7Kace6LV0vlex7LyPf7jD+up+oL9J5mV5ba8v3uv+JL8z1XkpWTw2RP1oSd13PVP/fI8htmX73X/Fn+ZVg8XUozU6cnGS4riuTXEPFPllet2psGUqkqtJp5nmcHo79zObQwVSdTssrU+KlplXNm/ZnlSpuMK8VFtpZ4+zfvXA9HbW/EXuH4c9+441Lydgl61STf/Kkl9bnK2rguwmo5syaurqzWpdtXaM51ZpScYxk4pJ23aXZzsTi5zy55Zsqsm99jTjXP8t4+pHscJ/LU/wAKP6TxdOep7PBP91p/hR/SeGgx/H+T+f65LsXBVFi6snudHEa97iYMPh3GCuej2RL7SX4VX9Jz68Vlug8cq58t6k10tjrC0MuJdZ9pkadJa2b+pFXHwngcRFytUq1XNR1vrNPeVYPYNWpSjUTh6yvGLk1JroU4PAzrTyQje3tN+zFfEzrfat8nsRSw8K9aTXa5XGnBp3b3/V26FWzPLOt5xGOK7ONF3UpRg04u2j3viWbQ2HVw8c7lCUMyjo3dNuy4GTF+TdWddUPs+0dPtL5nly3tvtvEc0+E2tg4+cYGs82DnNzo1Ip+opPNbmrPczFjMHs7DwUqOKdeq6tJx1TUIqacm8q5X3lG0/JXE4am6tR0silGLyzbd5NJaW7zNtnYdbBVIxq2kpq8ZxvlfNa8Sob2mLxGAxVXtfOmnkULRTtZN813nHwiSu9+rMuA2XUWFeI9XJF2erzXulu+Z2dk7Kq1YZklGD3Sk7X+BJLKmNprATpZvtHK6jre10GAxWGlgY4etWySzSk7J39ptcDPitiVniOyWTM4OaeZ5XFO3IzY3YFehTdWo6eVWXqybertyEbqbLr4bD4meWtem6Vs0k/azbtENhJYTD141I13JNTUsy0V18DhYHDSrTjThbNK9ruy0V/7Gv0LWqRrKOVyovK4pu8na+gGbadLB2qVaeIlOo5ZslrJ3lrw+J0cJLAqcMRCq4ZVfsnvzWt8TyuEoyq1YU42zTdlm0V+86OH2RWliJ4dKOeFs7v6iXxt3ipuxhdswlVrKqn2NZ7t7jpb8kjPjMLg4U5yhXc5tPJG+599kJPYdaEoRzU5Z5OKkpaZrN2encxqnkziHxp/6n4Ee1TF/n+GxlGMMTN06sN091+++7XkynFbSw2Ew86OEk6lSppKpy4Xv+SRhhsCvPsrOH2sXKN5PclfXTvHreS+JjGUm6Vopt+u9yV+Q/YuKNr46lPZ2EownepTtnirpxWVor8kMdSw2IqSrTUIunlTab1zLkZtpbLqYbJ2mX7SLlHK29NN+neW0vJrEVFRlHs7VouULya0tfXQY/CjbGCwMKTnh8VOrUzL1JJJZXvfso4Ekenr+ReMipTfY5Ypyf2kr2X/AEk0vIrFyipJ0bSSa9d7n/0jDyyIsbcRhXTnOErXhKUJW3XTs7Gdxsw1WKUCLnTFcQLCJDZFYgfgKq5hEiSWTCN94FYspuPFDZkVSWpFww/JdKaIUipMaIrFTrVsmVtjNiM6rXFAyLDAyFFsSAtwBkSLcEhBIyQJDJDASO15MbNpYnEqFWVopZlDc6lvu3OOkW0puLUotqSd01o0+aAnq9teSFTtXPCqLpy17O6i4Pu4WPQeTWypYTD5JtOcpOcrapaJW+h5zCeWlaEUqtONRr7yeRv46NGfaXlXXrxcIpUoPR5W3JrlfwJynsntl8ocWq2Mqzi7xuop81FWv9Gev8jv5GP9dT9R8+SOzszyirYakqVONNxTbvJNu7d+Y7Ey+9UbY/m8R+LP8z0Ox9g0a2Dcs2apUXt/4TXBL8zyuJxEqtSdSW+Tcnbcvgatl7Vq4aTdOWj9qL1jL/fMeekbN9uthvJfEdrFTyqCabkpXuu5bz2d0rLoeS/bGdv4Eb887t0sczE7UrVqiqSm1KPs5fVUPgKy37Py54+nf2psOrKrKdHK4yd3FuzT4nMxOx6katKjmj2lSLe95Vv0v8i/DeU1aMWpqM3bR2s799jFU2vVlXjWeVygmoq3qpa+I5sZ9+F9vaYWg4UIU3bNGnGLtuulY8RjMLKhUdObTkrXy3a1VzcvKnEe7T/0vxMGLxcq9R1JpKTtu0WisPnYXy9c9SY17Gf2svwqv6THJXVi/Z1eNOcnLc6c47r6taFCKZT6jsYbZ9PEUqahVy16cMtu678d6Gk54fZrcdKkqjhNrevWcd/wViNn1sNQSquU3VUWsiTtfu0/uZ8FteP2tPExzUqspSdtcjbv0Ma7eb6ceLkna7tKUbq+j1TVz103/wDl4r/2j/WcPaDwUKf2MpzqZoNOSl6sVJN8FwHqeUmGW0VXzS7JYd028kr5897W+AKjjbcwWEg5TpYztKvbK9K3OevQ9XtytRrYhYCuku1pqpRqe7VTen0/NcTx+1nsxxlUw9Su67qRnacZKNnO8t65XLfKbalHGYmFSjKTUKaV3FwakpN8fkM3oqeElS2bVo1FZqvGL+DnDVfJieVGIlGrSoK8aUaakktE3dr6WMtfyjhiNnzpVG1iPVW52naSea60WhdDauFxVKEcZmhVgrKpFN3Xyv0Ygv8AJxt4lXbdqckr8FdbjmbXweHgpzhie0qZ39na2969Do4TaGEo4mLpuXZqlKLk4yblNtf2OTth4HLOVGdV1nK6Uk1HV68PiIz+TM/32j8Z/oZ6DB4h0/SNRK7hUlJJ7naN7HkdhY6FHFU6lRtQjmu0r74tbvmdentqgo4+OZ3ryk6fqvW8ba8gJr8whPFYbGUF9nOp9pHjCdn/AL+PxIjtSnh9oYyFa6hVyLMvu2jx48TnbD2x5tU9ZvsZ+2lrlfCSQ88dgquKxLrKThVy5KyTvCyXDetVyA1m0dkKjSVajV7Sg3pr7Lel9N/I1bUd9m4a2nrc+6Rlx+0MPHCxwuGcpxzZpTkmuN+KXHuDFY+nPBUKMW88GnJWaW58fmRVxZ5QL9wwVvdX6EJ5Vp+bYL8N/pgJtbG06mFw1OLeamkpXTS9m28XbmPp1qOGjBtunBqV01bSK/sxeRzk/lnHXC/hS/8AiTtr/wDWYH4L9BM9o4PF0aUcVOdOpSVs0U3mVlyT32Rk2/tOnWVKlQTVGlG0W1ZydrfkgtEX+VtJSwmBv/hy/TA6e3cFh6scN2+I7Fxp2irXzK0bnH25tCnXoYaFNtypwaleLVnlivnuZ0Mbj9n4mNLtZ1U6ccvqxkt9r8O4NPPp5HEUkpzUXmipSUZe8r6P5mStDidOuoZ5ZLuGaWVve430v8iicSPJp4+nPIyl86WouSxepxncBoQGqERbGJcqIRvvLFGxCjYlSArVVWJW2bMl0Z50rDlTYRIlS1GjEhxA4dsLE2IN3KghksUSoAYWGURGhIdIWwyAjJDpEIdDIJDpEIYCCJAlICCHRFguBU4IKclfVXXK9gGlZEupooiXQkGs+oeTJQo0UCTRRdBFS+pbCXDiCadoVSsF7iSHowymJVXIVsZu8X8COm/x3PtVUguDbVt9rHPq0E73NdOondcUV1Fclu5lSlyK6LtI21DHPeOG0UX6x0INM51E1KVmFDXB2M+NjqWSluZXV1iQbJFjuQnEZFCNMZaFb0ZMZ2TVlw14oSTuSpfTepqpMwxkaqL4mfTSNFW1jHVnoapS0MdXVEcrqh6jKRWSmaVEXxYzKYSHzGdjSUzEaJcgJXFU4XIyq35lthGipSqmVMr7OzNREolSpsUKJDgWtANOKdxE1dFrQriB4oSImPIia4jShkDEWOpyIFsOFhAqRJNgFhgZIEhkI0xRYkKhswyTYEQMgJKHQqJuIJZFwCwFiUyxISI6GmniMIhkxJOmOmVoa4IsWZmNErix7jTi1SIlIrTJYaJEDQlZlZMVqK1cZa6UamnHUKlbKTtGi9JpmFzckTHTFkqid+ZRKi3qX0IbyyUbBqmKDe40OWglWCWq4kJ6C1TT2l1YWm7lVORphCzETNJWY1i6rT1EsPSBKgTTQ3cSol7FtCfApmiIO0hWelRtquyKGx3O5VJkSNKVojKDYyiFuCTSZGmMNcVk6rAi2Cu0ioeDCqi/FUHTk4ytdcnddTOxm2yBHiu+oyFktSbjJDQi3lkpuyXBbipsZUzK5DXIkgCpiSLZRElErU4UGYfPJcl9Q88lyj9Tp8o4/GtqRJi89lyj0ZHnkuUfqHlB41uAw+ey5R+pPnsuUejF5HjekSc/z6XKPR+IefS5R6PxFp46IJnP8+nyj0fiHn0+Uej8Q0Y6aHRyltCfKPR+IekZ8o9H4hpY64HKW058o9H4h6Tnyj0fiLTx1iUcj0nPlHo/En0pU5R6PxDS8XXQ8WcX0rU5Q6PxJ9LVOUOj8R6m813UrjJHBW2Knuw6PxJ9NVfdh0fiPyiP4+nfsFjhenKvuw6PxB7cq+7Do/EVsH8fTvRYyZ5703V92HR+Iem6vuw6PxFo/jr0QI896dq+7Do/En07V92n0fiGifHXoBos856dq+7Do/En07V92n0fiKqnx13sb/DZx09TPU23VkrOMLfB+JnWPkne0ej8RT01x2qY8jjLatTlDo/EFtWpyh0fiMY6VZaFFjHLac3wj0fiIsdPlHo/EA6VJG2F7HCjtKa4R6PxLFtiovuw6PxEHdmrozcTmemavuw6PxEe1Kj4Q6PxDDdiO8mZxltWpyj0fiS9rVHwh0fiLFa7C1RU1ZnLjtWouEej8SXtao/uw6PxFlVsdTMDdzk+k58o9H4h6Tnyj0fiGUeUdUm5yfSc+Uej8SPSU+Uej8SbzVTuR12MzkelanKHR+JPpap7sOj8SfCq/k5dVIeJxvStTlDo/EPStTlHo/EfhS/kjt3FbOP6WqcodH4ivalTlHo/EX8dV/Ly68hTlek6nKPR+JHpKfKPR+I/Cl/Jy6jYrRzfSM+Uej8Q9Iz5R6PxDwo/k5dFMZo5fpGfKPR+JPpKfKPR+IeFH8nLoWIejMHpKfKPR+JEtoTfCPR+I/Gl58sgABqwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAH/2Q==\n", - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "vid = YouTubeVideo(\"zLyRnfG32Bo\")\n", "display(vid)" @@ -377,26 +326,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8f8d3cab791f45d4bebb894a7b3ea656", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Application(config='specviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_selected', 'd…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Open these spectra up in Specviz\n", "specviz = Specviz()\n", @@ -412,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -440,32 +374,9 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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\n", - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "vid = YouTubeVideo(\"HMSYwiH3Gl4\")\n", "display(vid)" @@ -473,26 +384,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ec50037a01ad40878cc34296aa5b7de9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Application(config='cubeviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_selected', 'd…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "cubeviz = Cubeviz()\n", "cubeviz.app" @@ -507,19 +403,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:specutils.spectra.spectrum1d:Input WCS indicates that the spectral axis is not last. Reshaping arrays to put spectral axis last.\n", - "WARNING:specutils.spectra.spectrum1d:Input WCS indicates that the spectral axis is not last. Reshaping arrays to put spectral axis last.\n", - "WARNING:specutils.spectra.spectrum1d:Input WCS indicates that the spectral axis is not last. Reshaping arrays to put spectral axis last.\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Here, we load the data into the Cubeviz app for visual inspection. \n", "# In this case, we're just looking at a single channel because, unlike Specviz, Cubeviz can only load a single cube at a time.\n", @@ -546,25 +432,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Video showing the selection of the star with a circular region of interest\n", "HTML('')" @@ -572,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -596,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -610,22 +482,9 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Apply a 5 pixel boxcar smoothing to the spectrum\n", "spec_bsmooth = box_smooth(spec, width=5) \n", @@ -665,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -683,26 +542,11 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "42ef0e684b1d4c8cafc96fcd9f20dfe8", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Application(config='specviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_selected', 'd…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Open these spectra up in Specviz\n", "specviz = Specviz()\n", @@ -720,7 +564,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -750,23 +594,9 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Video showing how to smooth a spectrum in Specviz\n", "HTML('')" @@ -806,25 +636,11 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Video showing how to fit a blackbody \n", "HTML('')" @@ -832,25 +648,9 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:Warning: Applying the value from the redshift slider to the output spectra. To avoid seeing this warning, explicitly set the apply_slider_redshift argument to True or False.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not present\n", - "No Blackbody\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "spectra = specviz.get_spectra()\n", " \n", @@ -866,17 +666,9 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The blackbody.fits file does not exist\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Delete any existing output in current directory\n", "if os.path.exists(\"blackbody.fits\"):\n", @@ -887,7 +679,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -897,7 +689,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -929,34 +721,9 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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qr8Jasx40xsyN+wgURVEUJcNJo5CkzBFJxpgK7JxtD0ep1zVM+Sif+1mN95xtiqIoipKR7K7pOjMlJklRFEVRFCWhqEhSPLn//vvp168fAwcOZPDgwfzwg6/ZVhRFUZQMJJqHLFoKgHQN4s4Yd5uSOCZNmsTHH3/MTz/9RH5+PuvXr6e0tHZppsrLy8nJ0dtNURRFSR/UkqTUYNWqVbRq1Yr8fJubs1WrVnTo0IGxY8cyZMgQBgwYwCWXXEJJiZ0qr2vXrqxfvx6AKVOmMGrUKADuuusuzj//fEaOHMn555/PmjVrOPnkkxk0aBCDBg3iu+++A+D1119nv/32Y/DgwVxxxRVUVFTU/UEriqIkiXSynIQjkTFJ6ZRMUv/a12MCc+YkmqKHjo24/ogjjuCee+6hV69eHHbYYZx55pkMGzaMiy66iLFjx9KrVy8uuOACnnnmGW688caIbc2ePZtvv/2WBg0acOaZZ3LwwQfzn//8h4qKCrZv386cOXN4++23mThxIrm5uVx99dW88cYbXHDBBQk8YkVRFKU2pI+sSSxqSVJq0LhxY6ZOncrzzz9P69atOfPMM3nuuefo1q0bvXrZaekuvPBCvv7666htnXDCCTRo0ACAcePGcdVVVwGQnZ1NYWEhY8eOZerUqey7774MHjyYsWPHsnjx4uQdnKIoSh2zu44MywTUklSPiWbxSSbZ2dmMGjWKUaNGMWDAAJ5++umwdXNycqistKmoiouLg9Y1atQo4n6MMVx44YU8+OCDte+0oihKPSSZVph5q7dx8Us/8sej+3Di4I5J2080oScZKgXVkqTUYN68eSxYsKDq87Rp0+jevTtFRUUsXLgQgNdee42DD7a5N7t27crUqVMB+Pe//x223dGjR/PMM88AUFFRwZYtWxg9ejTvvfcea9euBWDjxo0sXbo0KcelKIqSadz6/i+s3FLMDf+aluquRCE9HXYqkpQabN++nQsvvJC+ffsycOBAZs+ezUMPPcRLL73E6aefzoABA8jKyuLKK68E4M477+SGG25gn332ITs7O2y7jz/+OF999RUDBgxg6NChzJ49m759+3LfffdxxBFHMHDgQA4//HBWrVpVV4eqKIqSdJJpY6kr6RHPfsJtk0Zx2+puU2oydOjQqpFnbkaPHs3PP/9co/zAAw9k/vz5NcrvuuuuoM9t27blv//9b416Z555JmeeeWb8HVYURVGUJKAiSVESwHtTl1NWUUnDvGyGdmlO26YF7Cyt4Ku5azl+UAcueXkyPds05vbj+qa6q4qiKDGT0BQAaeR6U5GkKLVk5oot/OHd6WHXPzN+EfPWbGPC/HUqkhRFSUtqm3E7XdGYJEWpJVt2lUVcP2/Ntqr3U4o2snZrcYTaiqIo6Ue0OKOgaUnSx5CkliRFqS1NCvx/jU57dhIA8+47irIKQ0FOFmUVhgZ54QPeFUVJb5KpCdJJcKQjKpIUpRZsLS7jhKcmxrzd0g07OeJRm4yzcX4O/75qBL3bNUl09xRFUeoEdbcpilKDgXd9Edd2Zz//fdX77SXl3PHBTF74ejFTl25KVNcURaknJFM/pKM4SSfjl1qSlBpkZ2czYMAAjDFkZ2fz1FNPMWLECIqKithrr73o3bs3xhgaNWrESy+9RO/evVPd5ZRQWRn/V33DjtKgz1N/3cSPRRsBWPTAMWRnpeEvn8KWXWVsKy6jU/OGqe6KUo9Qd1sw6dRntSQpNWjQoAHTpk1j+vTpPPjgg9x6661V67p371617sILL+SBBx5IYU9TS3F5RcLaqnAJrq1RAsGV+sugu7/ggL98xfrtJanuStx8v3gDH05fmepuKPWM2v5tSyNdFIRakpSIbN26lebNm8e8bneguKwyKe3uKqsg3Fldu7WY0opKtVTUcxav20Grxvmp7kZcnOW4gvfp0pwOzRqkuDeZQSbYhdNV5NQWFUn1mbsKk9Tuloird+3axeDBgykuLmbVqlWMGzeuat2iRYsYPHgw27ZtY+fOnfzwww/J6WMasKsscZYkN8VOu//8dgn3fDybZ88bylH92wGw3wNjATs6Lj9HR8TVV9IxTiSUzTvLVCQliN1BYHje8mH8aumUTFLdbUoNAu62uXPn8tlnn3HBBRdgnJs94G5btGgRjz32GJdffnmKe5s65q7aGlP9d67Yn/tO6h+13qEPT+Avn83lno9nA3Dl61Nr1NlRkhyBpiSGDNBIiqKglqT6TRSLT12w//77s379etatW1dj3QknnMDFF1+cgl6llmUbd3LgX7+KaZuih44FYL9uLRi8RzOOe/LbiPWfGb+oRplJp2hHJe0J/be/ZWcZTRvkIJlgJqtj9IylL2pJUiIyd+5cKioqaNmyZY113377Ld27d09Br1KLl0Dq064Jr1yyn6/t+3csZPwfRjHz7iOZdfeRvrZZu7U4KLi7vDI58VBKYsg0HTF75VYG3fMFl79W06qpREf/3qQvaklSahCISQJrvXjllVfIzrbxL4GYJGMMeXl5vPjiiynsad1TWu4tTj678aCY2unaqlHV+5uO7M2Uoo3cf/IARjw0zrP+Za9O4ZnzhlZ9LqvQn10luYjL/vH+T8sBGDN7Taq6o6Q5Oi2JkjFUVHjHu3Tt2pVdu3bVcW/qFxtd+Y16tmlMq8b5nDu8c1XZc+cPZcbyLTz11ULfbV5zSI+q90UPHcvYOWu49JUpQXV+Wb6FovU7qj6HE2tKfSHDTEmKEg0v82kGmFRVJClKDFz1RrW74YNrRtIoP/grdGS/dhzZr12QSBrSuVlM+zi0TxvP8nNfrB5JuGrzLrq5rFGKoij1jnQyGYVBY5IUJQZ+/nVz1ftQgeTmkN6tq97/5+qRMe1DRFh4/9FMv/MI7jq+r2edc178gb99Ppfisgpe/GYxq7cUx7QPJblkwB9oRUka6TQIRS1JiuKTsopqF9eXvzs4Yt3GBbm12ldOdhaFDbIY2aNV2DpPf7WIpRt28vEvq/hmwXrfgeNK8km0RiotryRL7H1RV6RTLhtFSRZqSVIUn3w4rXqqhu6tI7u6LjugGwAXjehaq332bNuEdk0Lwq7/dMYqACbMr5miQckMKisNQ+8dw8i/eAf1K/UfNSymr+hWS5Ki+OT3706veh8tV8ygPZox556jaJBX+6zYr166H0c8+rXnukBWgBydELdekchcQjtKy9lWYl9KepKe8iB5pJG3TUWSovjB7UPf22cgdiIEEkCvtk349o+HMHvlVqYv38zTX9VMNKlkLtkpEsCi9g8lSaSRRlJ3m6L4YYNr6H8qYn86NW/IEf3acfrQPTzXN0yQIFMSQyLlRZbLKpVOAa9KNbuD3PQ6xky4W1UkKYoPFq+zOYoG7dGMJrUMyq4NXcMM+99arK6YVGKM4dwXv6/6nKzRbe6s63VJXe/1g59XcM2bP2VMPrBMEAu7KyqSFMUHgUSO3Vo2THFPbMJKL16auKSOe6IEmDB/HRMXbqj6nCxXVcVuYkm68e1pfPLLKj6YtiLVXVGSQDrdxiqSFMUHSzdakdSlZeoTOB7Zrx13HFczf9LdH83m2wXrU9AjZfPOsqDPlUl6CtTlw8U9GilV7qLtaiFNa9z3TToJIzcqkhQlCu9MWVYVLN21VeotSQAl5d5Tx5z3jx88y5XkkszkkckSXOnA7nvk/tFzlFwySiSJSJaI/FZE5opIsYgsE5GHRcTX338RuVVE3hWRxSJiRKQoSv1hIvKliGwTka0i8pmIDE7EsSj1h5vf+6Xq/V7tm6awJ9VcuH/XVHdBiUAiH1wL125PYGvphQaqpw9efxTCXb10ypmUUSIJeBR4BJgNXAe8C1wPfCQifo71AeBQYBGwKVJFERkOTAC6AX8G7gR6At+IyIB4D0Cp33Rv3TjVXQDslCivXzpM52+rpyTS+lNWkfoHSqp6sDtb0fySjiPnNrlGC9d3MkYkiUg/rDB63xhzijHmBWPM74DfAYcAZ/loprsxpqUx5nBgZZS6TwClwEHGmEeNMY8CB2F/Tx6O+0CUek1uHU4LEY0DerbiybOH1Cj/fvEGj9pKXZLIZ3t5RWpGeNWHPEmqkaKTjqfolUlLU90F39SfX/zaczZWVD8WUv4CsBM4L1oDxpjFfnYkIj2AfYF3jTFVwy+c9+8Ch4lIO3/dVuozn81cXfW+d9smKeyJN/07FtYoO+v57z1qKskkNMN2PBaQzTtL+X7xBiorDQf99Ste/34pe93xGWe6rmefOz6rdV8j4XZv1QeXSOp7oCSKdL2WmZRxe1+gEvjRXWiMKRaRac76RO4LYJLHuu+BS4ChwCcJ3KeSAq58fWrV+7+eNjCFPVHSiXjyGd303i+Mmb2GvOwsSisquf2DmUnoWWTc2q4+WHHU3aakmkyyJHUA1htjSjzWrQBaiUheAvcVaNdrXwAdvTYUkctFZEqC+qHUIYP2aJbqLvhmzdbiVHdhtyLUMVUZRiSt3lLMH96dztzVW2usGzN7DQClUdxr4dpOBOFaTpXjTTVS+pB652xyyCSR1BDwEkgAxa46idoXYfYXcV/GmOeNMfskqB+Kwpx7juLWo/sElQ17YCzFZd5pApTEEzqyJ1zSx5v//QvvTV3O6c8GG6HLYog7mrI04piSWuF2t1UGud4UJTJe90gmiNxMEkk7gfww6wpcdRK1L8LsL9H7UpSINMjL5oqDu9cov+K1qR6148cYw6yVW1R8+SCcu22RM5x/W3E54+etrSqfvmxzrdtOBO6WE/2AKy6rYPWW2CycybSaKYofMikmaSXQV0TyPVxuHbGuuESNOwyMfPNyqQXKNJ++klImzF/H/DXb6JWggPP/zVzN1W/8xH5dW/DOlfsnpM1MxW2F2VFSzhWvTWVXWQUrNu+qKr/opckAfPvHQzjtWa/wRm+SGVAdFJOU4LYDQecTbhrlO3N9pmikTHVFuYl2jOma8yqTLEmTsccTNEW7iBQAg4FExgFNdpZeT4rh2N+XxP6NV5Q4ePiLeazcvIsb//Uz02KwVnjxyS+rAPixaGMCepZZhA6XD3jPvpq7lr9+NpdvF65nahg32QF/+Sq2nSXxWeMWYMl6qN3kSs4ajfowwi4RZMRRxHEQycxEX1dkkkh6G3sZbwwp/w02PuiNQIGIdBeRPsSJMWYhVnSdLiKBIG6c96cD44wxq8Ntr6Qfe7RokOouxMXns9Yw4qFxfDBtJSc9PbFWbWXCD15dUVFpeOSLeVz88uSE54RJ1AO3tLySf3y7hEXrqjN6J9OSFCAwWbQfMsWSFA5jDEvW70hbK8vuQMaIJGPMDOBp4BQReV9ELhORh7EZuCcAb7qqjwXmhLYhIueLyO0icjvQGigMfBaR80Oq34CNSfpGRG4UkRuBb7Dn9PeJPj6l7nEH075/1cgU9sQf/TpEnzLlslem8Id3p8fVfpaqpLCEWjx+XLKRJ8YtjKmNIZ2b+aqXqGHxr04q4t6PZzP64QlVZXWRAkDvo2oeGTOfQ/4+nifGxnavpITd9LJlUkwSWCtSEXA5cCywHngS+LMxxs/wkUuBg0PK7nWWE4DXAoXGmO9EZBRwn/MywHfA6caY+J5CSr0iEEybkyW0bhJuTED9IScr+q/Yl3PsMPMHTh7Ad4vWM3zPlhTkZvtq30fzuw1Tijby18/nMbloI9cf2pMebYKnq/nnxCVht+3VtjGVxiaPXL+9Okwyr46zuc9bva1GWV2422K6jzLcwvKkI6QfHzufGw7rmeLeRCGOS5EJly+jRJIxpgI7JUjEaUGMMV3DlI+KcX+TgNGxbKOkD8s32SDb3u3qX6ZtL9xi54qD9uS5r8MnkH9nyjJu/2Amo/u04R8Xhc+zWllpeHzsAobt2cKXBWDLrjKKyypo27Qgat10pLLSkJUlQYHWj49dwBMe08MAHNWvHVccvCd7tmrMis27+HrBOn5z4J5kO0qh6y3V+WbXbQuXwSSYRD14vK5nXbjbQrOTK5nhVszU65ox7jZFSTSrnYSM++/ZMsU98ccDpwygR5vGPHve3tx6zF4sfuAYurf2HkX0lPMPduzctZ7rA3w5Zw2Pj13AOS/8EPQj+OiY+WzZVVaj/qC7v2DYA2PZXlJeiyOpnzz+5QIG3fNF0Ai1AAvX1LTKADx21mCGdG5OYcNc+nZoypUHd68SSKH4daP5rffdwvWs2lKzrwGyPH79k5kCIEC44/ciA7RDRDJUV2QUKpIUJQyBnC7tCtPDKtK9dWO+/N3BHNW/PQBZWUJJubeXeXVIRu5Jizbw4fSaczqv215t3XA/2x4fu4B7Ppodti/RMn6v3LyLlyYuYVdp+uRcevTL+WwrLudKj/xT4eKP/LoyAfq0ix5TBv6Ew0+/buKcF39g/wfHha3j9c8/aO62euBuy/RpSdIqPiuNuppIMsrdpiiJJCCS0tl1VFzmL5Pz2S/YSVT36dKcDs2qR/JNXlI93D/0B33Gis1x9+vk/5vImq0lrNy8i9uO7Rt3O3VBeUVlkDtkxootvrZ7+PRBvvfRvXUjThjcgc9m+RgU60M3zFlVc9oTN6u27OLNH36N2HSy5ElaCYMkkyVQX/4m3PHBTH7duJOXL97X23WW2Xo1LCqSFMWDLTvLqh5Y6WJJ8qJjswLWO9agJgU5bCuO7AbbuKM0SCR9MK3auhTqnon0Jz+aAWDNVtuncLmD6gvj5q7hkpfDp1jr064Jcz0CoMf+/mC6t27ssYU314/u6Vs8+MkdFC0I/G+fzfNuuw5Gt8WikTLckORc8/pxkK99b1NVFG3YSbdW/pJ97g6ou01RPDj3H99XvW+XxpakB08ZyH7dWvDBNSOZcdeRnnWWb6qeQSfSHGJfzFoT9DkRrpD68XgITySBBPDZjQfx0bUHMKhTYVC5X4H076v25/eH9+L4gR18iwc/pz03ikgK54YNcrcl6eq4xeA7U5Z5WrSq+5DZxBKfVVeEdbNG6arX6kzI/6SWJEXxYOaKandFOrvb+nZoyjtXRJ5C5PEvF1S9L48wzGbDjuBZfeKZQ6yy0lT9Y63vzF0d2WUVYECnQv577QH0vO1TyipiOydDu7RgaJcWMW3j57mTFefDt67zJN3sZN8+fZ9OnsIuA56xEUkr12Mtr0W6Xku1JClKFPJyMudr8uApA2qUNcirDi4OtSQ1b5gbtq1YBQHAJzNWceeHs6o+19cfzqL1OzjqsW8i1jll7+CpG2s78azfx6UfC140jRTOSuQuXb/dX0qCWPEMdwlzSJkyLUk40kkjxUMmpAWIy5IkIr2AfkAb7PdqHTDTGLMg4oaKoqSUs/frzGlDO7F80y5+WLyBW96fETQtRXmI8DlxcEde/q7Isy2vofABwv02Ll7nf0qKuqa8opKLXprMz79uYodr1N05wzpXuYSO6teOv58xiMb5NX86a5vrxu/2fqq5LRRPjVvAtYf6S1Todo/c8K9ptG6Sz4jurfx1zCde1pOwz9LM1kiJGTCW4H8aGX7KY8a3SBKRvYArgdOAdoFiZ2mcOmuAd4DnjDE1pv1QlHSgMhMyu0UgNzuLbq0asXCtFUcTF26oWnfBP38E4KWL9uW2/8xg5ZbIQ/mnLdvMorXb2bN1I4Z0bl5VHjasIeSpUF/O9Nqtxez3wFjPdX88qg8jurdk5oqt/OGIXuQkKTN2bra/R+ZbP/7Kkf3aBZV9Pms105Zt5uYje7Ni8y4+d42S+/sX8/2LpJDPb/zwa+JFknP63IIs0zTS1KWbaOMjS39aWVrSqKuJJKpIEpHuwF+Ak4Fd2PnJngMWARuwp64F0AMYDlwGXCci7wN/NMaET/urKPWQzR5JEjORFo3ywq67+OXJvtpwT5pb9NCxUevXwzhVAK596+ew6wob5HLcwA4cN7BD2DqJwO32jMT4eesoLqsIzrDu5G4a0b0lT41byA+u1A1gXYFZUv1QDuveqgNVErBW1ldXa235dcNOTn3mu7rbYV0JrbimJUn/i+zHkjQbmAFcBLxvjIloLxeRRlhr0w3Otukb9arslrinh7ixvs+nVAtaNQ4vkiLx/PlDudwjoWLwD6L3j2ONf84eP6Jrtxbz23emcdkBe3JInzZx9dEv67eX8ND/5vJjiKgIcPKQjp7lyWBYt+DM7pNvO4x97//Ss+7tH8ykU/MGrNpczK6yatfghu2lNQQSwKEPj6dtkwLeuTJyEH9oDFAyHr9zV29jzqqt9GobfbqfVD9kd5SUs6OknDYxDN5YvH579EoOCdE3iXa3JemUp2t8mR+RdLox5kO/DToi6hXgFRE5Me6eKUqKCASstmqcz/U+3RTpSOcWDX3V+/3hvbhyVHeMMylrm6YFnDe8M69/Hzx02+2lDOexDB3y7FXvof/NZeLCDUxcuMGXdao2XPnaVKa4cjU1yM2uEh0L7j/a16TBiSI7S7jsgG68+K2dHLd1k3yKHjo2aH63AO9NXe7Zxo1vT/MsX7phJ0s3VKd6CPsgrKPn2KuTirjnxP5R66Xa8z3k3jGUllfy0x2HR7S8uklPKVB70slzGAtRneuxCCSPbf8b77aKkioClqThe7aIeyh1OiAi/D0kK/T1h/bgxz+NZs/WjTh7v87cfuxeXDe6J7nZWeTlZFX9o/794b1rtOcedRVuBFao6PD6d7m1uO7cnVNCklnedKQ9rjOcIel1HTOS7N1FCraHmqIkWcdvTHq420qdfFKB+L1EUz9/XdLgwtQhmidJUVxUVhpuem86YC1Jmc5BPVvRrGEuZ+yzB7ce3afqoTju96Mibte8UR5/PW1gVZ4bgHNf/KHq/bRfN9O1ZaMac5eFWpLqOk/MrJVbuO6tn/nXb4bTMuT6ZglcckA39uvWIqZs2XXJaUM7hbUi+WHkQ+N4+py9w64PmxogCc9NP+6XdBBSNYihz2kVuL2bEvMwDRGZLyJ/FJF20WsrSnrxzcL1Vfl/WvsYnZLutGlawE+3Hx4kkPxyxj57BH12x/Xc8v4Mfv/u9KD1xWUVviwVyXhwBGJbjn3iWxav28F+D4zlPJeoAziwZ2sA+ncs9B1EXdfcdsxetW7jmjd/CpsoM1SUJOsRHmpJqgijhupLHEtMU6nUkz4riSGesaxlwIPAryLygYgcJyKZk21P2a2ZtbJ68tLWu4ElCWx25niFycCQ6TjcfPLLKq54bQo3/OtnthaXMfCuL7j349lBdbz2mugH879+/JW97x1TQxhMWrwh6HOf9tEDif2QTONA85C4mF5tqy1eE2851Hc7Ra74JDehj/dtjusz0cf09pRlQSLp1n/P8O5PhuuN+mhHCnvO4+hsJly+mMWNMaYfMAIbnH0I8F9gmYjc76QLUJS0Ze3W6pFtu4MlqbYM2aNZxPWfz1rDf6et5MrXplLqMS9cXYR83fL+DDbtLOOOD2Z6rv/rqQO5/tAeXFfLIP03LxtG+8IC3rhsWNxt+BGrvZ1RYdcf2gNxPbk6uiYmBhgU5dp4ETqa7Kt56wD4ev66mNuKui/XI/T9n1fwj2+XRI2ZShWx3KZ1LuwSoGB9jSKMUkWinKV0FbxxWYCMMd8bY34DtMfmRVoC3ArMF5FxInKOiOgTRkk7Vm2p/pHeHWKSass1h/TwVW/e6m2e5cVllSx2Mn6XlFdw+wcz+GL2Gs+6tWVy0aYaZVeN6s4Z++7B747o7ZlFOxZG9GjFpFtHJzz5Yiif3nAgv9x1BL87ombwvJu/njrQd5vXv/Uz05dtDvsgWxBn4PIjX8zjwf955xUO3de9H8/mZFfeLVsnvifrph2lPDl2QdD3uTYkyzpYX1IABM3ZV+vWMotaucmMMTuNMS8ZYw4A+gD/AkYBrwErReQxEelc+24qSt2wcnN1hmm1JEUnP8df7E7o5LgBZq/ayqEPT2By0Ube/OHXGmkFasOjY+Zz23+83TgBbj4ystCoj2RnCU0L7Jx6kR6yXVo2pJ3P/D4fTl/JiSECJYCfDPSzVm6pMdfb9GWbeWLcQp6bsNgzjYHXCMi124LbiPeB/cd//8LDY+ZzoZNBvi6JTbOkzuE2adGGqj8v7i77mRtwd6LWo9tEJBs4AbgUOAp7vr8CSoBrgd+IyDmaDkCp76zZWsyMFdUxSS3jTLa4WxHhN/7+k/vzzuRlTF++JXwlh9OfnVSrbpRVVLJ+ewn/+XkFlx7QjfycbB4fG30qyUweXZSTJUGjCS8/aE+WbdzJiB6twroen52wqEZZcXmFR81qlm7YwbFPfEuWwOIHq/NahRNdARLxKC6rqMSYmpNQT3VSO8xfk6ih+/7vk3SQGGu3FXP2C98DNlO+22JXWdMrnhB+/nVzchpOMnGLJBHpgxVG52Mnul0L/B14wRizyKnTAzuX21+xsUuKUm9xWx2ePmdvcpM0R9fuwln7dubcYV248J8/MiHOmBZjTFQh889vl3CPKyD8l2VbeOyswXHtL9UkUrJlZwl7tW9SFedz1cHdad4oD2MMzRrkcp3HVCxv/FDTkrerNLJIWrzeTsIQa+JHX2EwHnWmFG0kNzuLQXs0Y7/7v2TTzjKWPHhM0H2SLtaQVGl0d+wlhCaCTc65e+37pUlpN9nEkwLgUhGZCMwCfgdMB04HOhljbgkIJABjzELgCUADupV6z5dz1la9P2aAZrjwQ6Qf+YAV48UL92Hvzs3icl92u/VTfg0zEmtnaTnGmCCBBPDZrNXsfe+YmPeVaYgIDfOq/wcHRsaJCMcP6sD0O4/gybOHRG1nZxSRlB/vnwlfscLBlUrKKzjt2Umc+PREjDFs2mlH37m/u6FNl1VUctkrk3ltUlF8/STGFABpItDcJCRtgcc5SsNTUYN4LEkvAKuBh7BWo6Io9WdjY5QUJW3IZDdMIvFzlnKzs3j/6pEAfLNgHeUVJmgC3bn3HkWfOz4Lu/1Bf/uKFy/Yh8P6tmXdthJG/mVcVSbkcER7sAPcenQfH71Pb3Kyw1+hwga5HD+og6dFqUNhASu32Pi80FQJW3aWUdgwt+pzvFnpy3z4dUIfssVl1duUu8wfL3+3hMP7tvXc7n8zV/PlnLV8OWct5+/fNa6+xjS6LUnt1pbKSsPYuWvZr2uLGutMIixJGSCIvIhHJJ0CfGSMif4rBBhjfgTqPnpOUWIg2kNX8SZWMRlI2OgmNCu3F5e9OsVX+6N6t2b8PH+uvSsOrn8G7rYxTKTqBz9zz917Uv8aMUoBgQQEZVUHuOm96Txy5mAAGufnBCeFrDQ1sqqHY5/7vCfvdRP63HUHkRe7JvYNJICtqufqVLEPwZwq6vK/2LNfL+Kvn83jiL5tuX50cLqLYJFUd31KB+LJk/SBX4GkKOnCn//rHciqRCb0N75DYe0e8t1aNWL+fUfHvN2blw3j2z8ewssX78cdx/X1rPOXUwdw/vAutepfsjlveBcuGtGVt34zPCHtXXlwd3KyhOsODZ+q4aCesaUs+GL2Ggbc9Tmj/vYVlZWGCtdTNdF/NkLvrzJXrq1dLpFUEfpkDxrSXrdP/frqYvrPTysAPFNsuM9R3O7CDDW++7IkicjvYmy3AtgMzDDG/BRrpxSlrvnX5GVV77+5+ZAU9iS9CP0nHDrKKFYO7dOmRhvPnLs3V71R82fkiL5tKcjN5vGzBgdZtC49oBuXHtCN7xau56NfVvHWj9XByH6tHKkiLyeLu07ol7D29mzdmDn3HhVxEEK4+fP+dtpA/m/8IpY4gdl52VlVCUGNgfXbS/nfzNU0yq+2BJaWVyZ0SpfQvpW4RFhxqcv1FpKo1P2YT4Roic1i6n+H0RIwJpJIvapUS1JY/Lrb/h5n+0ZEfgJOMMasirMNRalT9mjRMNVdSBtCf+SfOmdvrn3zJ+48Pr4Hfegj46IRXTl6QHvPus9fsE/Etkb0aMWIHq2CRNLuGGoWbZRmOOF4+j57cPo+e1TlONqna3Pmrt7GRlfOq2ve/ImbXLmmSioqgNzQpuIm9Hq5LUk7y8pd5eHdbfE+890WFWMMC9dup1PzBlHdw16iLJxQqy/3Y+ixJgqvLPvpht+/fYfE+DoUOAk79H8Q8HAiO60oiWRhnNmElZo/8v07FjL+pkM4pE8bX9tfekC3oM992jcFoHvrRgCcN9zmoh3apXkte2rJri9PpRh4sxbTnPjBr3XNGLjbw8r1t8/nVb0vLa/EGJOwB22oJckthtzB+eUhQeBBGaTj7IrbhTdx4XoOe2RCrfN5pZJI1yRZlqSH/jc3cY2lCF+WJGPMhDjb/1BE8oAL49xeUZLOBf+ong2+s1qR6oR7T+zHonU7uP1YO6v9l787mB+WbOCUIR0B+OT6A1m3raTKqvfPC/fl+yUb+HXDTu7/dE5VvViJdyRWKhnRoxX/vWYk7WOI9zq6v/8UFuHcbV40iGJFmbtqG3f+d1bcebGi4bYkFQeJpOAnuzvGJt7RWu4mP52xGiAo2Ww4vPYW7hTXm7sxSFSqv81NrTNu+2AKcHEd7EdR4sI9kueDa0amsCfpR7yGmdCh2D3aNKZHm+oZ7Qtys4PcnoUNczmyXzsqKw37d29J73ZN4tpvLIKgPhHrZLWxHGYscVqRUgqA/1GIkVi7rfr7GHq93O6bIEtSiLstEXORucVVLEKrrjVGPLszIaIoWFTG149Id0Y6C6+o7jYRGR1v4yJymDHmLWNMzcQMilIPadFIpyKJhboMPAVrCerfsTDubOi7SxL1WK5LLC7InKzgE9i3fVM+vu4A+sQpWr044cnq6Uz+OXEJyzbaZKI7Ssp5eWJR1bqfl1VPWBwpcDte1RJus9cTmDm6LvOxuQ+ntCJ4ZGBlki1J6RwM7ucn4zMRGScixznztEVERHJF5GQRmQB8WvsuKopSX0k3w8zR/W0Q+D4JinFKNWEtOzFcF4lBOIZe79ycLPp3LPTfgA9Wby0O+nzRSz8yf802+t35OR9OX1lV/vRX1fPMlVUaPp2xirmrt9qCJFqSbg8z7131/upWEcTzFTz1merYqkoTEridgD6Fki7TxHjhx902BHgE+BBYJyJfYpNDLgI2Yq9RC6AnMBwYDTQDvgAGJ7zHipJAdpRUj5A5sl/bCDUVL9JMI9G/YyE//Gk0LTPEYvj30wdx+atT+dMxewWVJ+O6iNS0rjTItQrLbYF97MzBvDRxia+Jjf2waN0Ojnj064h11m0r4WonTUTRQ8eG5P2Jb7/BIsn/dpH2Z4zhln/PoE/7Jlw8slv4ijFQW/lRaYJlXTIETUaLJGPMTOAIEdkfuBo4ETibmtdGgK3A+8AzxpjJKEo950+uSW2fPHvvFPYkPUnH6VsSndU6lfRp15SvPfJ6xXJd3DVbNc5n/fYSjh/UoUY9Y2paSR44eQBA0BxxJw3pSMfmDVI2EqwyQe4j94C5RD3kZ67YyttTbE62i0d2qxeWWOtuq31MUiTSWCP5D9w2xkwCJjkut6FAX6A1ViytA2YCPxtj0j8xgrLb8N9p1eb72iZC3B2pB7/xSi1xC6o/HdOH5o3yGN6tpa9t92wdCLYPfgru27UFL128L4vWbue+T+bE3Kcf/zSaZg3zuP+T2bwyqToG6KlzhnBQr9Z8PX8dR/dvT/c/1Yzo+N/M1UHD96M9n79ZsI5b35/Bo2cOZl/XvGaVcUZ/R6o6c2Wwda1ORVKYjlVa9Rv82YsofY10LBltSQrFmZJE52NT0p5120pS3YW0pz78E1ZqEu9lyc/J5pDe4XNcxRLkfUjvNhzSuw1bdpXx5LiFvrZp1jCXd6/YnzaOte/yg7sHiaR9urSgaUEuxw2saekKcM2bwdnZa0xZEsJFL02motJw8UuTmXn3kVXl8Y9uC1/3q7lrfbfjF79XJFyvKitDrEfxaaSIZHrgdlogIlki8lsRmSsixSKyTEQeFpFGid5eRMaLiAnzipwGWKk3/O3z9E90lmrSwd121r570CgvmyP7+c8dlO7EclncVaNtt1+32Acq//6I3tErORzSuw0921aPlOvYrEHQ+sIGsWXzzsmSoLQBlRGe1ttd8YkQ/GAP3SpWF16geuj5rUyA3yUxMUnRBWFtvuq7lSWpHvMocD3wH2yG772cz0OcVATRbsdYt18P/NajncXxH4JSlxSt35nqLih1wEOnDuT+kwfU+3nbEkm8+aCibZWTnUWW1LQMhE4LkizCzQvXvrCAVVuKa5SXVxr++ll1RvAKY8gKOcrsLPG0NgVP1RG8rqLSRM0Z5cXWXcFCLBHD7WvbRIUxQW2UhJmkuDbpPtI5CCcjLEki0g+4DnjfGHOKMeYFY8zvgN9hp0k5Kwnb7zDGvO7x2pjQg1OSRtVwYSXj2Z0EEsTmGnHrKT/ayst6GDotSChn7btHDD3yJtSqBHDh/l0Y0LGQr28+hCP6Rh+des0bPzF16aagsnCHXBHB3VZeaZi/ZhtPjF1AcVlF6KY1CJyySYs3BJUnQlrWNuVApQkO3L7aYzJpqJ0lqSKNLUkZIZKwo+0EeCyk/AVgJ3BeMrZ3XHRNJR18DkoQO0rK2VpcHr2ioqQjMbnbghxuYesFHsZeNf58XD/aFxbw+FmDPbeNx+rih7tP7M9H1x1AbnYWd3nMKxfKF7PXcNqz3/lq253F28uSdMSjX/PImPk8O2ER05dtrkonEssEt4nQDn5dduGsVpWV/voR7xV8+It57H3vmDi3Tj2ZIpL2BSoJCSY3xhQD05z1id6+I7Ad2AJsF5H3RaRPHH1XUsCPRdUGv7ZN83n78uEp7I2iJJZ4XSP+LEk1y3q3a8KkW0dz4mDvOfXC9SeWuRKjuaY6eFiavNvxtz/3fHCh+3ave2rcQk58emJVygO/lp3Q6UDipbbxPn6tPPHaAvwG7ddXMiUmqQOw3hjjNVxpBTBCRPKMMaUJ2n4JMBH4BagAhgHXAqNF5ABjzAyPdgAQkcuBy30dlZIUdpSUc/FL1Wm83r58f7q28hXfrygZR5C7zUf9eJ7J4bydDcPEGHnh5yHdvXUjFq3bEbVe11s+oU+7JmzaWRo2Bsc91UloyJI7hikgmGavCu++9+p6pakfo74qQ/IkhSPa2a/rKYrqikwRSQ2BcOO5i111womkmLY3xoRO2PueiHwIjMdmJz88XEeNMc8Dz4tIPfh67J7c+Pa0oM/tm2VOcsFUsGfrRiz28WBS6o54AwAiCZHaPATDtRtLgLmfqrHEns1dvS3iencweqjFJ1IMll93mzEmIYHbfs9h2BQAIYHbAV6euCToHJRVVnLUY18zrFsL7j6xfxw9TU9idreJyF2RYnBEpIWIfFCrXsXOTiA/zLoCV51kbY8x5hvga+AQEfFn91XqnJkrtjBm9pqqz60a55Gf4//frFKTnN0sKDodaJwf3/9fX5akuFr2JhZR40cLeAmGO4/vy42H9fS1j1KXVckthCJZkkLxEhzllZU1BFFZRbA4WbxuO5e9MoVZIQkno5FbyyS41qIV3DdjDHd9NJv7P61OBFpcVsnc1duC8lbtDsRzdv8MfCUiNZzPInIwMB04urYdi5GVQCsR8RI6HbGutHBWpERsH6AIyAbSbvbMFZt3MW3Z5lR3I6ksWLON4578NqjscB8jYhQlXXj6nL0Z1q0F14/2JwogWHxkJSlK1Uvg7NW+aVg3nBd+LCZedfbt2oIbD+vFX04dEHX7Xrf/j6H3juH8f/zArtLqUWuh+ZVWbt4Vtg0v+bR5Zxndbg3ODj7w7s/Z6drHGc99z5dz1nDhP2vO6PXe1OW8+M1iT8tTbpiTOGvlFi55eTIL124P21ewgi+01XlrIlvZdifi+UpciQ1kni4iJ0LVKK97gS+BcuDgxHXRF5Oxx7Kfu1BECrCT7E5J8vYBemKPv16mASguqwg7XHXkQ+M46emJLNsYf+6gX5Zv5o/v/cKmHX70ZN1zuMckmVeP6pGCnmQWaTy6N+M4dmB73r5i/6AJZ2Mhkkst8CiNx0V06QE1J3P95LoD4p5jLhyRLFOnDw1OQ9CmibfzYMOOUr5ZsJ4zn/8+qMzN79+Z7qM3kSmrMOxy/R6v315StZy9citlTkzUpzNW8Yd3p3PfJ3M4+f++Y/WW4qprsKu0gnlh3IYXvTSZcXPXctgjE3jrx1/Dxh15uf2OeuybWh8fQIPc9LfSxyySnJia/YDVwPsi8izWzXQbNhHjYGPM9xGaSAZvYwX8jSHlv8HGEr0RKBCR7h6j0GLZvtCZvy4IETkWGAmMcUbFpZyZK7bwyndF7Cgpp7S8kj53fEafOz4Lmo5jctHGoEyzyzbtDNr+ybELqr6s0TjhqYm8PWUZQ+4dkxBfeyLxyok0/c4j2COG0TWKN/XrSiuxEiSMkuQ57dS8Id+ETMSblSUxWZL8CKosjwYD1qWsLKFZw+qM3Z/deJD/nYdQtMH7z2RFZWLijI554ht63vY/ut7ySVDeomnLNjP8wbF0u/VTnhi7gN++PY1trt/vjS4x5/6dv/X9GSzb6G39qjAGnz/xVazYvIuZK6LPQ9e8YWwZ0usjcTmujTGznOk3vsQKCYA/GWMeSljPYuvPDBF5GrhWRN4HPqU6Y/YE4E1X9bFAF1w/BzFufwjwiIh8hM2uXY4Vjedhs3DfmIxjjIWKSsPYOWu4/LWpANz54ayg9fve/yVgVf6uEMtSvsu/HXBNNW2Qy4UjusbUh63F5TFPIZBMjnvi2xpl9al/ipIqYh7dFud+vKw8oWWRBIavwG2POu7t3G6zFo3y+Pi6A/jHt0s4b3hnTn1mUvQdROHYJ76JGhCeKB4ZM79G2QF/Gcc/L9qXVo39WxL/PXU5Jw3xTt0QjpEPjQPgx9tG06ZJAVuLy/hu0YYa9dI5iWSAuESSiOQCfwVGAIuAzliBMckYMyGB/YuFG7ExQZcDx2IFy5PAn31MSRLL9vOw7rfjgLZALrAceBZ4wBizotZHUgs2PjSAkl3b6WOyGZuXQyk5lJLLVtOQLTRms2nEFhqxyTRhTUVzVkkLVtOSNaY5ZeTw7pTlNX4s7vxwFvPWbGN0nzb0atvE0/ry76nLgz6XlFfw3aItDO/W0vPfXbx8/MtKmhbkclCv1hHrbdlZxh/em86xA9rToVmDoLwmAI+dOThhfVKUTMGPtSbe555X07GMmIs3JsldFNr1/h0LedTjt+D1S4dx3j9+8N23APEKpDuO68u9H8+Oa1s3O0srOOv52Bw5L3yzhMlFm6JX9GC/+8dy7rDOQRYsN9EmF04HYhZJItIL+Bc2VucZ7NQdg4G3gC9F5EHgLp/CJGEYYyqwc649HKVe11puPwc4I75eJp/8XWtoISVxmc3XmUJWTG/FsNx2FFW2Y4lpR5HzevOHX3nzh18BuP/k/pSVV3Lu8C4Idq6f378b7KPf7/6xAFxx0J4c0a8tDfNy2Kt906r1FZUGAW77YCZDuzTntKGdovZv045Srn3zZwCKHjo2Yt3r//UzE+avCxrJ5ubEweFnEVdio765VpXY8JdvOxH7iSxg7OdIKQii42WtcgsnP7fqyB4tg+aIu2pUd54Zv8jH3uPn0gO6MbRLc/49dTmvfe89eiyZqTZqM2jnDee54EXopMHpSDyWpJ+w+YJONcb8xyn7QUQGYafxuB0YBcTv8FXi5uCSx8ilnFwpt0sqKKCUprKDZuxwlttpKVtpK5toJxtpJxtpyyZayxZayxYGs8iO0XOxwTRhoenIvMo9mPNhJ+ZV7kFWyWgem7gu7L8IgOe+XsxzX9s5fz+8diQDOzXj1vdn8J+fl/OXUwfy1o+/8taPv/oSSTtK/X3hyioqmTB/Xdj1X/1hVFrMXp8udGjWwFcCP6V+4v4uRPpa1FYLexmUQ0VNpP37sSQN7dKcH5YEj5txb+VH0JdXmKB+9WzTOGj9oX3aMG7u2qjtxMrgPZohEFYkDdmjOduKy4Nijeo7xWVpPLOtQzwiaRpwjjEmSD4aY7YBZ4nIGODxBPRNiZGKSsN6Cu0HA3t3bsYdx/Wla8tGNG+UR3lFJT1v/x+52VncfUI/rn5/BscObM8nv6wimwpas5k9ZB1ds1bTTVZzUuditiyfSxdZQ0vZRkuZy7CsudU7/PoejjDNmZ/biXlmD+aZPZhV2ZUFpiPlHrfWCU9N5L6T+vPWj/bWueFf0xJy3JWVhr9+Po9he7bgkN5tIo7QO2yvNnTT7NoJ5W+nDeKBT+dw+UF7prorSi3xsvZ0admQpRt2MqJ7q1q13dQjBjBU+LRtGj6xq5//NdeP7knrJvm0L2zAla9PrbGdX53n1m6hOaeG79miSiSdPKQje7ZqxMMe8UF++c2B1SP/BnYq5IqD9+S5CYtr1DMYJt92GGAzhit1Qzwi6WDHNeWJMeYfIlIzSlZJOtlZwvtXj+Cs57/n8TMHc/SA9kHrc7Kz+P7W0VRUGjo0a8DZ+3UG4KqDtzBp0Qbu/3QOq01LJlf04aIRXelwQj/ytpfQ974xtGMjPbNW0EuW0VuW0ytrGb1kBe1kE+2yN3EQ1TOxlJhc5plOzKrsyizTlVmVXZljOlNMPrd/MNP38QRGiuRkRx6EOWH+Op6dsIhnJyzird8M5+wXwvvkX7hgH9/7V/zRrrCAJ84ekupuKHES5G7zECLvXTmCbxeu49gBtXNR53p8j93727tzM64a1T3s9n6svwW52Vw8slvQyCuJ0d0GweItdHLeo/q154FP7Z/F/Jwsrhvds1Yi6U/H7BXU11uP3stTJMUSMX/i4A58Pmt1RlhyUk3MIimSQHLVmRdfd5Tasnfn5sy/L3wuT69/av07FtK/YyEje7Ti0lcm8+TZQxjaxebDbNU4n+l/PpJJi9fz/eKN/OO7oqrthEr2kHX0lmX0lmX0yfqV/lJE16w1DJQlDMxaUlW3wgiLTQdmOqLJiqcubKVxaHf4cvYaLnu1OjXV4D2acf/J/cOORtu8q9rdF0kg/f30QepmU5QIeH09WjfJ5+Qh0d3h8eAWI2/+ZjgFEfLqxPLNdbfr3q5Ly4bMXb2NjlEmww3ePnjPea4RwIn4PfHbRjSN9PDpg6piQ0f1bs3jZw1Ri1MCiCdwe5yPasYYMzqO/igppG+Hpky6teZlK2yYy1H923NU//b069CUm977BQBDFr+atvxq2jKGfexUv0ATdrKXLKV/VhH9soroK0X0yV5JT7OCnqzg5OyJVW0vq2zNTNOVx//8X4447Ei6DRjBZa/+FLT/acs2c9yT3/LV70dVlT01bgHnDe9Cs4Z5fDN/va/jOyXGYa6KsjsQnAIgsdOERCOWaUliyQYedEyuD8+dP5RHx8zn2kPDZyQ3IfsKPc5cl2WptgN3Txjk3zoXLZ7KPQdlXra32GxakMPWYhvb+eG1IznhqYme9ZRq4nG37UlNUZsDtMcmp1wPaBRnhnLa0E70adeU458K71Ed0H0PXrv0NLr/6VOogD8f15e9hrXnozFj+G7iV9w6uJT8DbPIWTuLPVjHHqzjaCbDuLdhHPyQ34yZld2qrE4zK7ux0rRk1N/HV+3j71/M5+9fRDZxX3doD54ctxCAyw7oltBUBIqSKfgN3K71fjzKYkomGWe6APc+urRsxGNnRXcNZwdtH7xf91xpgXWXjOzGPycu4YbRPWlfWMAt79vwg39etA+XvBx+wgav9APhiDaavkl+taU9L8x8bvm52eCIpC4tG/HJ9QdwrCuH3Nn7dea4ge0598XY0x9kKvG427p6lTvznv0OuJi6n5ZEqSNEhAGdCrloRFcmLlzPe1eNYNDdXwBwyt4deeSMwTW2GbRHIeQWcPwxx3P8McdXr6go5/Db/0F/WUL/rCL6Zy2hryylrWymbfbPjObnqqobTWPHTdeNmZVdmWm6stS0xURIGj+0S3Nev3QYn89azc1HhSZZVxQllFg0UiL0VCzuqtjmeXPtI46eRhKO+UEiyS5vOrI3p+zdkf4dC6msNFUiKdrxxWJJi+Zua5hfbT0KiKThe7bg+8XVo/3yXHFh2VlCvw6FQW387vBetA4zXcvuSnxTRXtgjCkBHhSRvsAjwNmJalupf9x1Qr+q92N/fzDfLljPecO7hKkd5ocgO4eBQ4bz75868Z/KA52alXSWtfQXK5r6OcsWsp0Ds2dyINWB39tMA2abLo61qSv99zmIA/YfwezVO1m7rZiDe7VGRDigZ+1G5SiKkhwSPS1JdV3v974wwf0KtSS5hUagTw3ysunfsbDmvmPcdSSinSu3eAv08R8X7ssvy7dUxWq6g9BzPBp054fy4sL9u/DKJO8UBZlKwkSSi2+BB5PQrlJP6d66Md1b1wzA9sPDZwzi4TMGAXZYqyGLpaYdS007Pqkc7tQytGcj/bOsxamfY3lqJ5sY5k5L8MuzMLuAXm37QftB8NMgu2zTF3L035GiJJJEBC3HYkmJZXe17Zu7X5ESXkbrv5/cTn7o3KIhNx3Z23Nd26b5DO3SPCgYPRA31Sg/h/27t3SV13QVdmregOWb7Lxu+WHcdAGaN8rjsTMHc+Pb0+I6jnQkGSKpGxDfFNSKArx52TBG9GiFMQYR4ZT/m8hPvwqrKlsyprJ6CH8rttDPEU03DSxGVv8Cm4pgxVT7CpCVA232gnaOaGo/CNr1hzzNl6QobpI5+rO205LE6wqMJxYx0ui24Hoe+05AjFeHwgJWbqmeJ/3rkMmB3dxzYn+O7NcuuF9hjtltPQoIvLd+M5wD//oVUC2iDturDV/OqZkwUxBOGtJRRVIkRKRzmFUtgMOwk8KOr0WflAxhz1aNWLx+B73bNfFV/5ubD2Hu6m2M6GHdY4Efm7cuH86z4xfTuCCHM/bpxKRFG7j8tamsp5Cf84Zy4VmXIn3a2kZ2bYLVM2DVdOf1C6yfb8tWz4Bprzt7E2jZA9r2hTb9oG0/+75Z19iG0SjKbkpiYpL8143FKhMuBYAfDCaoX269cf2hPWLqUzzxUABv/GY4//x2Ca0a53NonzYR63r1wcuVBqGWJLsMTZYJ8PS5e1O0fidHPvZ1UHm0S9CrbWPmr9keuVKaEY8lqYjwMWSCnQD2+ng7pGQOX/z2IEorKmmY5+8226NFQ8/Jc/NzsrnhsOohu0f0a8eR/dpSUWl44YJ9gv/9NmgO3Q6yrwAl22HNrGrhtHo6rJ0DGxbY1+z/VtfNbQRt+lgXXZu+1SKqceQJdRUlE6jrNGKxzKsWr6CK55jcvyluq0xoF6NZ3uI9n91aNeLek/r7quulh3LC/NHLCnIj2vfNG+XxwMkDgvLQ5edke/65jXY4mSaQID6RdA817xUDbATmA1/W9eS2Sv0kJzsrarbseHnu/BgyZ+c3hs7D7CtAeQmsmwtrZsPaWc5yNmxbVdNdB9CotSOa+lULqFY9oaApipIpxOTSSoCgSlacUbAlKI7Rba73kbx1Sfp5iwn38Z0zrDML1mwLa70PdyjnDPN2EL1x2TBe/q6oaqLwSKfypMEd+GDaSl99TifiSQFwVxL6oSh1S05+dXySm50brVgKEk9zYMc6WDLBvtw06QCte0Gr3q5lbyuqNLu3ksHE6kryEjnuEhNlkHtsI+G89xHP9pFaiO5uSz7uLjxw8oCIdWMNzxrZoxV7tm7kEknhGxjYqZmKJEXJeBq2gK4H2FcAY2Dzr454mmWX6+bB+gWwbaV9LR4f3E5BMyuWWvVylo6IKuysMU9KvSUmXZ8ABdC3Q9OqB2s0d1ssFiGphUoK7UckYVEfpjmKLddU7P3N9rlNuJF+LRvlsWFHqee6dCCqSBKRC+Jp2BjzajzbKUq9QwSad7Gv3q558SorYPNSWDcf1s8LXhZvhmU/2JebnAJosad9tewOLbpXL5u0U+uTslsxeI/mVe+jJUuMLSbJ/T4ed5srJilC3FRUy0wdfJ0HdSqMXskhnpF+fkfrhWs63X/S/FiSXsbev7EcqgFUJCmZTVZ2teDpfVR1uTGwfY1jbZrvLB3xtH21tUStnV2zvdxGjnjaM1g8teyu7jul3lFXd+MNo3vy+NgF3DC6l+9tajO6Dfwno4xmZYl3dJtfurVqRLOG0TPunDykI1/MWs0hvdvw45KNUeu78Zu9PLwAS+/fLT8iKXyCBkVRaiJirUJN2sGeITP0FG+FjYtgwyLYuNhZOp93bYQ1M+wrlLwm0LyrY9HqCs0cy1azLtCsM+TVHBWoKLGTugdauAlcf3t4L64a1Z2C3MjZoN24jyJWl1ioRSCSJSqaZSbZ/2uaFPiLmHn0zMGUV1Tyr8nLYt5HpJGCudlCWYWpUc9Nuv+3i3qGjTETotVRFMUnBU2hwxD7CmXXJtiw2CWiXMviLeEFFECjNt4CqnkXaNoJsjX8UEksiR7dFsndFotAsu263WUxdqpGW/Gtg+RLzljaj3ekcVDagJB1udlZlFVU2HphOpPu84r7+uUUkf2AhcaY2Ox0iqL4p0Fz6DTUvtwYY0fdbS6CTUttVvHNS+37zUth8zLYsda+lk+u2a5kQ9MO0LQjFHaCwo5QuIfrcye773T/y6fUmrq6Bc7cZ48aZdECt2OhthPcEmb7toUFIfvJ/O9M0LkMOdy8nCx2lgZEkv9zkZ0lPH3OEK58/adEdDGp+P17OQk4H3gTQEQaA88D9xljPIIrFEVJGCLQqKV9dRxac31lhc3vFBBNm4pc75fadVuW2Vc4a3tuw8giqkl7m29KURxqE2/To03Ne6mpT9eRH2ozus1u4rJEZcHblw9n7Ny1nLVvsLiLZiWpD6Pf3IRzaUYikvgJl7TSjdd9kpedxV7t0yPHnN+7MvQo84GzgBcBFUmKkkqysqvFDCNrri8vga0rYMsK2LIcti63y8DnLcuhdFt1BvJw5DWpjrVq0j78MrcgfBtKvaauHuleOZESKSiCR7fFtq2dM9K9vTBsz5YM27NljbpR8yQl3d8W2w7KK2snkkIFj3v3lWEEWD3TiTGjgQqKkunk5FePwgtH8RZvEbV1hbVAbVvtCKltkYUUWNddqHhq3M5O7dKoDTRuY0frFRSm/y9ohhFvVuu491f7JsK06w42jicFQDW1yZNU3+7uinhEkstYFOlwwwkwr03S6WuvIklRFCtYCgrtXHVeGGNzP21bbd13nkvntWuTfXmlOXCTnWfFUqPWjnBq4wip1q73jqhq0EKTcGYQR/dvn9T2xf1gr31rYdekOig5Pye270RtLUmRCCfA6pvLMVZUJCmKEh0RayFq0Bza7BW+XmWlTWXgJaR2rIPt62yA+fZ11jK11bFWRd1/FjRsZQVTwxbQsKXHq4UVU4HPmhYhZmKa+qMW+2mQF9totVjJChrdFkcKAJ+j42rjbusTZn61WIh11N++XZsD/lMHQGgKgPAHFF4keZSR/BxSiSIWkXSMiLRz3jfE3kuni8hgj7rGGPNobTunKEqakZUFjVrZV7vI80hRtgu2r7Xiacc65/3aYCG1w3m/a1P1CD6/5DSoFk9ByxBR1aCZnUamQTPIL9ytLVadmvsXlomwECTLyBCcJ8nfNvt2bc7kok0c1a9dwvIkgdClZUOWbthZY01ejFYgN6cP7cS7U5dzyciuMW03tEsL3r96BF1bNvK9TfBIwfCUVyRweGI9IhaRdI7zcnNFmLoGUJGkKEp4chtUT/cSjfJS2LneCqldG21KhJ0bnJfX+/VQvsvGV21dHkOnxOayKmjmWM6aVQuowLJB85plBc0gv2naCqx3rtifHaXltGgUPXtzgPpsB4hnaP4/L9qXn3/dzIjuLdleUl5VHqmp1o3zI7Ypkpw0AX85dSA3HdWbNk1iHySxd+fmMdV3i+FIMiicKy/d0yT4FUmHJLUXiqIokcjJc3I9dfBX3xgo3VEtnmoIK5eoKt4Mu7bYZclWG8RevMWmUIgFyXJiu5pZoZXvvAqaQn4T13vnc0Gh671Tntc4JUJrv24t6nyfySR0dJofmhTkclCv1jXKvbZ/7dL9+H7xBo7o2zamviSKrCyJSyDVlkgpBCoqKz3L01wj+RNJmnVbUZS0QsTmdcpv7M9SFaCi3BFJm2HXZije5Cydz7s2udZtCV5Xuq06aD3+jocRVK73+U3tceU1sqIqz3mf3zj4c16j5D6hatF0IhNHeuF37rWw2weNjqu5/sCerTmwZ01BVbOd+m1xSySxjW6TtBFPGritKIoSIDunOnFnrFSUVQunkq2OVWorlGxzvY9Uvg1Kt1fX2VrbgxGXkHKLKHdZk8ifcxtat2huQxsIn5voYPjkPClrO8FtUMbtWjzN031kVyiRxG1FpSFLwM8AunQ6KyqSFEVREkF2bnXQerxUVlQLpuJQQbXFvi/dbl2JJdud92E+l+2s/pxA5udnU0w+u8iHJ5o7QsoRU3mNqkWVR9kpWfPZRT55SwSaNqXhhgp6yHJ2mXzYsd7WyymwCVJrgd/A67Db1yIZZWg/0j0mx024hJFgLUlZIjXqpPvxq0hSFEWpL2RlV6daqC2VFVYsle6oFksl24M/hxVb2+zow9KdVmyV7XQ+7yCPCvLYSVN2wsbYXIuPBOLC338cgL2ALwOxz3+7obpiVq4jmPLtKMXcAiuecgqc9866gKgKKc/KKeDs7IUUmzxkdqkj0txthLbdwIpcjwd6bR7yyQrcThVeVqIL9+/CK5OWcs5+nXnxm8U1K3j62xLetaShIklRFCUTycp2RuolcI4sY+h564c0oJh2DSr54pp9XSJqp4ew2ul83gVlO3j/x4U0pIRDujcmv7KEnTu2snLdRhpICR0bGluvfBdUlkFJGZTEeejAg7nOh/ee8bmVWPGUnUfj7Dy+yTOUkkOLV5tDXoEVVdl5rqVXWT5k53N59iJKyaXF3KVcXrid8eu22M+FjVm6pYJSk0Pnsuawpjlk59uBCUHL/Fpb05KB13Qyd5/Yn9uP60tudpYTyxVcJ430kCcqkhRFURR/iFBGDmU0JltyoVWPmDb/3cRPAPjx1NG0aVLA3F83ccr/fQdA0d3H2krGQEWpI5iK7aus2IqnsmJX2S47L2GY8o1btpBVUUKz3HJbr0Z7IW1XllcJuyxgj8Agw/WrYj5NfwoItG+hE3BqwIK2Cwi83wJE0m+S7Vi7HAGWnWetXVm51e+z3e/zICsnuG6N9aFludX1I2zbV4ooJYfCnQ1hcz6t2UQZOTQ0lVBeQm6WPWDPxJFpbklTkaQoiqLETNIefiLV4qAWxJzUoKLcEU4l7Ny1gyMf/pI8yvn3b/amWR5WaFWU2JxdFSWOQAsts8vnv5pLHmWc0L8lUlHKxLkryKOMQ3s248cFq8iTcgpzK+nZMs9pozS4rfJiMBXV1rgU82ngUvxoX5MD2QfKgfuc95LN9OwsyrJzKCebcrIpIxvZlsvOvCwqyKKcHMrIxpBD6/da8HruNsrJodxZF9im3DhLcqggizLXuj1XLAKur7NjV5GkKIqixEx62wc8yM6BbCdtRG4hy4zNgWTaDoQYkmwCPDDGWsz2OfAAAK6d8S0AM888knPu/ByAAa0L+ejqA7wbMMZatgICqqLUjp50LyvLXGWlVuS561aWeWznql8ZUr9G+9Xr5yzfQA4VtGkkFObCui3byKWcXKmgUXalrWcqKJAKCigLORas/zOUlXBAHB7FVStWoCIpTkQkC7gBmwm8K7AOeAf4szFmR6K3F5FjgNuBQVjv+VjgZmPMkgQcjqIoSr2lVoYkJ2wlHYRWbY4z0rYR2xWpdnnVA46+xYq+W0b34cqDu7Ov87lNk3x+vO2wKlG3z92fUFJq7T45VJBLBb1aFbBswxayqSSXCnIopzAfHj61H799a4pTt9Kpb7fLkYqqunZddXvDOgwmudMjB5NRIgk7Fcr1wH+Ah7GDJ64HhojIYcYY75SgcWwvIqcA7wHTgZuAQuBGYKKI7GOMWZnIA1MURalPpHusSSSCk0nW7jiD2nKVJzuhZjIIPRNVh+CIul0UsINgYdckuwmLTOOgskLJpaTTCL6tjD0y/75O/RkR81bxkzEiSUT6AdcB7xtjTnWVLwGeAM4C3kzE9iKSCzwJLAMONMZsd8r/B0wF7gIuT+DhKYqi1CsyVyIlMk9S+mSWThbpfvzpORujN2djv7ePhZS/AOwEzkvg9gcDHYAXAwIJwBgzDRgPnOkIKUVRlIwkEQ+/dLBG1S7jdvqLhEj4OTSv82fPS3qcmEwSSfsCldj4+yqMMcXANGd9orYPvJ/k0c73QFOgl79uK4qipB9SC1tSOnmaamNJatU4n/aFDRLXmRQTqmtCr6OnIEped+qEjHG3YS07640xXk7OFcAIEckzxpQmYPsOrnKvugAdgVmhK0XkctQVpyhKmpMmhoC4CHa3xX6gn994EJt2ltK6Se3SGGQC6X6fZJJIakj4/KzFrjrhRFIs2wdmefSq765bA2PM88DzIpJOf6YURVGCSMSzr74+P2tjJQPo3a5JgnqS/ngmmKz7bsRNJrnbdgLhZHuBq04itg8sver72ZeiKEpaky4xJfFQW0uSUk04wZkuZzWTRNJKoJWIeAmXjlhXWjgrUqzbr3SVe9UFb1ecoijKbk99H/7ufoDXJiZpd8N4XFiv85dOAjuTRNJk7PHs5y4UkQJgMDAlgdtPdpb7e7QzHNgKzPfXbUVRlPQjjZ5ztUItSdXU1g2ZjmSSSHobG2x/Y0j5b7DxQW8ECkSku4j0iXd7YAKwCrhMRKqyZInIIGAU8K4xJiQ3u6IoSuaQCPFQX/WH29JRX/tYH/G0EKX5CcyYwG1jzAwReRq4VkTeBz6lOmP2BIITSY4FuuCyqsayvTGmTERuwAqrb0TkBeyw/99ipzK5M2kHqiiKUg9I82dfRNyHlk6uoWQTeir8nJlw7sp0Oa2ZZEkCawX6A9APeBqbJftJ4DgfU5LEtL0x5l3gBOwIt78DfwS+AUYaYzQeSVGUjOTZ84bStmk+T5w1JO42TD3PlJSVJdx0ZG/+eFSow6F21O+jjh0/x+Olhfzoo4+uDTP5bx2TMZYkAGNMBXbOtYej1Otam+1d9T8GPo6tl4qiKOnLUf3bcVT/dglpqz7HuFxzSI9Ud2G3pmsrzyw6dU6mWZIURVEUpV5SfyVhfPhzt3lPS+KHo/olRozXBhVJiqIoilIHZLq7zUv7tGiU51EqvqyI2dmpl5UqkhRFUZQ6pb7nSVJqz2F7teXIfm350zF7xd9IPbhPMiomSVEURUkf0mWEUzKo78Hrfoh0+Ub2aMnFI7uxtTi9s+GoJUlRFEVRlJhJusyrByJaRZKiKIqi1AHuaTvq88i+cCQqZ5RI+lgRVSQpiqIodUr6O5pqT6a72zIFFUmKoiiKUgdkfPZur1lJ/FWrt6hIUhRFUZQ6IO3dbQmsmy5HryJJURRFUeqYTHC3RTqC9D86i4okRVEUpU4JWFQy3fuUaSTqeqXTdVeRpCiKoihKzETSOoF16R6HpSJJURRFUeqATHFBBfAzLYkXgqRNUJKKJEVRFCUlpGPw8u7M7ni1VCQpiqIodcruOndbpomMeI8nnTxwKpIURVEURYkZP1o3jfSQJyqSFEVRFKUO2E0NaJ6ki6tVRZKiKIqSEtLJ7aLUJPTyPXzG4FR0I6moSFIURVEUJSrZWZFV7eF929Yo8xLC6aSNVSQpiqIoSh2Qleams3TPeRQPOanugKIoirJ7srs8c393eC92lJTTOD+9H7mxXK9IgkpEfLVVH26P9L5iiqIoilLPuX50z1R3ISHUdbB1fQh0V3eboiiKUqfsrnmS0p0oIUmehBNW9cFK5AcVSYqiKIqiRCXdY6riQUWSoiiKkhLSJVeO4pDAy5UuQeAqkhRFURSljklHl2MslqRIVf02Ux9klIokRVEUpU4x9SIkV4mV0JikSFcxIALDCSI/Aqg+3CUqkhRFUZSUkCYel6SQjseeqJikdDp2FUmKoiiKUseko7stVNxE0jrRhFC6CCUVSYqiKIqiRCUnK7pkaJCbDcDenZvXen/1QUdpMklFURSlTglYUdo0yU9tR1JIulhSAH5/eC8mzF/HYX3bRK076dZDWb5pF/07Fkasly4jG1UkKYqiKCmhWcM8Prn+AJoW5Ka6K3VOOrnbrhvdk+t8Zg1v1jCPZg3zItbxK5DqwylSkaQoiqKkjH4dIlsclMxDhPrhS/OBxiQpiqIodUp9sBAo9Z/6oKNUJCmKoiiKkhTC5kmqDwrIByqSFEVRFEVRPMgokSQiF4jIzyKyS0TWiMiLItI6GW2IyMsiYsK8TkvcUSmKoihK5pBGIUmZE7gtIr8FHgEmADcAnYDfAfuLyH7GmB1JauN8j7If4zsKRVEURVHqCxkhkkSkFXAfMBkYbYypcMonAx9iBc8DyWjDGPN64o5EURQlczlnWGeWbdxJ15YNU90VpY7wGu4vIkiaBCVlirvtJKAh8GRA3AAYYz4CFgPnJasNsTQVkUw5l4qiKEnhgZMH8Nqlw9LmAakoGGPS/gU8hx1V2sNj3RtAJdA4kW0ALzv1tzrLEmAMMMxnn42+9KUvfelLX/qqHy+vZ3WmWD86OMsVHutWYGPEOnisq00bq4FHgauAk7GuuH2Ab0TksHA7EZHLRWRKlL4oiqIoipJi6lVMkog0A26MYZMnjDEbsW4ysNacUIqdZTQneExtGGNuCanzgYi8CUwDngE8c7gbY54Hno/Sl4QgIlOMMfvUxb6UavS8pwY976lBz3tq0PNeN9QrkQQ0A+6Mof7rwEZgp/M5H9gVUqfAWe4kMrVuwxizQETeAS4SkV7GmPlR9qkoiqIoSj2lXrnbjDFFxhiJ4bXQ2XSls+zo0WxHrL9xpcc6N4loA6DIWbbyUVdRFEVRlHpKvRJJtWCys9zfY91wYJ4xZnsdtAHVbrY1Puommzpx6yk10POeGvS8pwY976lBz3sdIM5Iq7TGyYi9FJgBjHDlODoem+PoDmPMfa76nbHxRYuMMWWxtiEijYAKY0wgVinQ7hDge6fdvkk8ZEVRFEVRkkxGiCQAEfk98HdgPPAW1kX2e2AZsK/bCiQi44GDgW7GmKJY2xCRwcD/gA+ABcAOYBBwCTZVwBHGmG+Tc6SKoiiKotQFGSOSAETkIuC3QG9s/qKPgVuMMWtD6o3HQyT5bUNE2gF/A/bFpgVoAKwCvgIeNMbMTfjBKYqiKIpSp2SUSFIURVEURUkUmRK4rTiIiAnz8hN0rkRBRG4VkXdFZLFzXoui1B8mIl+KyDYR2SoinznuWsUnsZxzEXk5wnfgtDrsdtojIr1E5B4R+V5E1jn38DQRuc2Jywyt31tEPhCRTSKyQ0S+EZFDU9H3dCWWcy4id0W41/+QqmPINOpbniQlMXxDzZEPZanoSAbyADY310/YvF5hEZHh2Pi2FcCfneJrsVnZRxhjZiSvmxmF73Pu4nyPsh8T1aHdhEuAa7ADV97A/oYcgp0I/AwRGW6M2QUgIt2B74By4K/AFuA3wOcicrQx5ssU9D8d8X3OXfwWWB9SNjXZHd1dUHdbhiEiBnjFGHNRqvuSiYjInsaYxc77mdj5/LqGqfsj0AfYyxizwinrCMwBvjfGHFE3vU5vYjznLwMXGmN0BtVaIiL7AAuMMVtCyu8DbgOuM8Y85ZS9A5wKDDXGTHPKGgOzsDMW9DH6sIlKjOf8Lmzy5RqxtUriUHdbhiIiec6PlJJAAg/raIhID2xg/7sBgeRsvwJ4FzjMGQCgRMHvOXcjlqYior9xcWKMmRL6sHZ421n2h6qUKCcA4wMCydl+O/Ai0Av7XVCi4Pech+Lc6+oZSgL6A5KZnIadQmWbiKwVkSdFpDDVndrNCDwUJnms+x47YfLQuuvObscW57VLRMaIyLBUdyiD6OQsAwlzB2Kncwp3r4OKpNoSes7d/IK914tF5DsRObruupX5qPLMPH7EWioWAk2BY7BxMAc7cTAawF03dHCWKzzWBcq8psBRasdq4FFsTEYgf9mN2DiwYzQ2pnaISDZwBzb26E2nWO/1JBLmnANsxsaefgdswqatuRH4REQuMca8XKcdzVBUJGUYxpjQf8yvisgvwP3ADc5SST4NnWWJx7rikDpKgjDG3BJS9IGIvAlMA56hetogJT4ew07d9CdjzDynTO/15PIYNc85xpjHQiuKyD+BmcCjIvKe/imuPepu2z34G1AKHJvqjuxG7HSW+R7rCkLqKEnEGLMAeAfoISK9Ut2fdEVE7sVapZ83xjzoWqX3epKIcM49McZsAJ7FjgIdkdze7R6oSNoNcOanWwm0SnVfdiNWOksvN0OgzMs9oSSHImep34E4cEZS3Q68BFwZslrv9SQQ5ZxHoshZ6r2eAFQk7QaISAE28M8r6E9JDpOd5f4e64YDBs1lUpcE3Gz6HYgR11DzV4DLPIbyz8C62sLd6wBTktbBDMTHOY+E3usJREVSBiEiLcOsuhcbf/ZRHXZnt8YYsxD7YDhdRAKBrTjvTwfGGWNWp6p/mYiINHL+EISWD8Ge8znGmEV137P0RUT+jH1YvwZcYoypDK3jxL18BIwSkUGubRsDl2EnAddEnj7xc85FJMdrxLKI7AFcBWzABnQrtUSTSWYQIvIo9p/bV8CvQGPs6LZDgB+AQzyytSoxICLnA12cj9cBecDDzuelxpjXXHVHYK/FcuBJ1zZtgZHGmOl10uk0x+85d6Z7+R/wAfbBHBjddglQCRxhjPm2zjqe5ojINcBT2N+SO7Dn0M0aY8wYp24PrBAqw44u3IrNuD0AONYY83ld9Tud8XvORaQZsAR7r8+henTbZdjf/bONMe/WUbczGhVJGYSInAhcjU041hKowD4s3gEeMcYUR9hc8YGIjAcODrN6gjFmVEj9/bFTCgzDuti+A241xvyUxG5mFH7PuZOc82/YnDwdgAbAKqxQfdAYMzfpnc0gAtnLI1QJut9FZC/gIey1ysNOI3OXpl3wj99zLiL5wNPY35VOWGG0HpgI/NUYo5a7BKEiSVEURVEUxQONSVIURVEURfFARZKiKIqiKIoHKpIURVEURVE8UJGkKIqiKIrigYokRVEURVEUD1QkKYqiKIqieKAiSVEURVEUxQMVSYqipBQRGS8iRanuR6yISJGT6DIRbf1FRJaISF4i2nO121VEjDMXWMYgIieKSKmI9IxeW1HiR0WSoqQhInKU8/C7z2PdcGddiYg09Fj/mYhUiojOEh4FEblRRC5K8j66ATcA9xhjSpO5r0zBGPNf7MS6f0l1X5TMRkWSoqQn3wLlwCiPdYc46/KAEe4VIpIDHADMNMasT3IfM4EbgYuSvI9bsHOdvZ6Etpdip2epIaYzgMeBk0WkX6o7omQuKpIUJQ1xZl6fDOzrYS0aBYwBVlNTRO0LNALGJ7eHih9EpClwLvCWMaYs0e0bS7ExpjwR7YmlcSLaSgDvAzuBK1PdESVzUZGkKOnLV1hr0chAgWMpGglMcF6HhGwzyrUtIrKfiLwsIvNFZKeIbBORiSJysnsjJ2bGiMjA0E6ISKGI7BKRD0LKDxORL0Rks4gUi8gvIuL7gSYiPUXkNRFZ5cSfFInI30SkUUi9l52+FYrIMyKy1tnfRBEZ5tFuSxH5p4hsEJHtIjJORIaExkaJiAG6AAc77QdeXUPa6yMinzjnbouIvOdMtuuHY7Ci9VOPfo53jrmriPzHOY+bnONtLCJZIvInJ5apWER+EpGRIW2EjUkSkVOdfWx2rv08EXkiEBclIqOcbS8SkWtEZDZQDPzBWZ8jIn8UkdnO/jc4/RwQrg8icpyITHbqr3KuZ05I/X4i8q6IrHBcxqtF5CsROdZdz/mj8A1wms9zrSgxkxO9iqIo9ZSvgD9RbTmCakvRBKwL53ERaWSM2eGsHwUYZz3AyUAf4B2sa6Yldhby90XkXGPMm069V4CbgQtwHpIuzgAKnDoAiMjlwLPA98D9wA7gcOAZEelujLkp0oGJyFBgHLAZeA5YAQwCrgdGisjBHpaXz4F1wD3OcfwO+EREuhljtjnt5gNfAoOBl4EfgYFO2caQ9s4HHsXOrn6/q3yd631HrFXuP8BNTh+vAJoCR0Q6RoeDneXkMOsbYc/DBKxbbl/gEuz53oCdBf5JIBd7XT4SkS6B4w2HiNyPvXdmO8e4CugOnAr8GXDHRt2IPZ8vYK2Ty5zyN7DXfgzwDNAOuAaYJCIHGmN+DtntMcDV2Pvin8CJTp83AQ84/WrpHC9OvaVAK2Af51g/CWlzEnCkiPQxxsyNdMyKEhfGGH3pS19p+MLGmpQA37nKbgW2Yf8A7YUVREc463KA7cA0V/1GHu02BOYBs0PKJwMrgeyQ8m+wQiLP+dwea3F406Ptx4EKYE9X2XigKKTedGAu0CSk/GTnmC5ylb3slP1fSN3TnfIrXGVXO2W3hdQNlIf2owgYH+b8FznbnBFS/rRT3tvHNZwAbAyzbrzTzk0h5e8DlcAUINdVfoLH8XZ1yu5yle3nlI0DCkLaFkCc96OcehuBNiH1DnfWvR2o75QPwsbDfePRhx1A15B9zQRWeRzDGV7nxOMcnefUPzXV30d9ZeZL3W2KkqYYY3YBPwD7uFxQo7CiqdwYMwdYS7WLLWBl+srVRsDChIg0dP7JN8Q+QPcSGzMT4BWsADrctU03rHvvLVM9Mus0IB/4h4i0cr+Aj7Bu/sPCHZfjrhkIvAnkh2z/LfZh62WleTTkc8Ai4R4mfjxWpD0eUvdFYEu4PkVgpTHmHR/7DUdralqw3FRgLUVuvsEKjGdNsDXtG5/7PddZ3mqMKXavMA4h9V81xqwNKQu4Y+931zfGTMde4wNEpHXINh8YY4rc+8Lei+2kOs4pcA2ODrn3wrHBWbbxUVdRYkZFkqKkN19hXS0HSHA8UoCvqY5LGuUsxwdWikgbEXleRNZgxcd6rDspEDvUzNXWW1g3zAWusguwD+xXXWV7Ocsvnbbcr4BbsG2EYwpsf7fH9muxQs9r+8XuD8aYwAO0pau4G1bYbA+pWwosidCncCz2KPPabzgM9vyFY1WokMG6pyCkv8aYQHm0/fZ09jvdR/8A5nuUdcNas+Z4rJvlquMm6rkyxkzA3ksXAeuduLK7RaRvmL4Fzl2osFOUhKAxSYqS3nyFjSEZhY1BCsQjBZgAPOr8Ux+FfbB9DXakEvAFVpQ8jnXfbMFaLy4GzsH1R8oYs0FEPgVOEpEmxsa9nA/MMca4Y2oCD64LsLEuXng9MEO3fxj4LEydTaEFxpiKKO0lg3D79LvfdVgXVTzt1+Z4Df6FxU6f9aLh61wZYy4Ukb8BRwMHAr8HbhORG40xT4Vs18JZrkNRkoCKJEVJbyZh438OwYqkXQQHAU/Afs9HYa1M01wWh4HYB/Q9xpg73Y2KyGVh9vcKcBJwuojMwwb73hJSZ4GzXG+M+TL2Q6raviLO7SNRBBwmIo3d1iQRycVaPjaH1E+2hWImdvRcK1N3eavmYwXIIGzgejwsxgrovYBfQtYFrD7xWOYAMMbMxJ6bv4lIM6xb+SEReTrEHdjDWc6Md1+KEgl1tylKGmOMKcEKpaHAccAkE5y1eSbWpXETNfMjBf7ZB1keRKQ/1TEnoXyCdcld4LwqqZkE8R1sQPndItIgtAGxQ/XzIxzWz06/rxSRPT22zxGRFjU388VHQDY2w7Wb3wCFHvW3U22tSAbjneXwJO4jlMCIxQfEYxoUx8IYjQ+c5a3u+s69cwLwrTEmZuuOiLQQkaDnkjFmM1ZwNcSO6nMzHFhjjJkX674UxQ9qSVKU9OcrrCVpBBBkETLGGBH5Bmv9CdQNMAcbP3Kz2ISU84Be2CHsM7DCKwhjTJmIvAVc66z/0hizIqTOchG5ChsMPUdEXsMO5W4NDHD60hdr1amB0+fzsQHQv4jIP51+NsRaDk7BjuJ7OfJp8eRF5/juE5EeVKcAOANYSM3fxO+BS0XkXuz5qgQ+cge815LPsKMRjwE+TlCbETHG/CgifwH+CPwkIm9jh/Z3wwbd70dNi1poG2NE5B3gLKC5iHxMdQqAYmyqhni4APitiPwHez3KsGkSjgTecQYrAOC4kA/EphNQlKSgIklR0h+38JngsX4CVphUUD0CCmNMhZOg7+/Y3EiNsBacC7GumBoiyeEV4DqgMcEB21UYY14SkfnYPDhXYAPA12OF2B3Yh3JYjDHTRGQIVgydgA0k34YVVi8DYyNtH6HdEhEZDfwNm6fnDKwrZzRWQIVmL78Na0m6xjkGwYqJhIgkY8x2EXkdONOJuamTuduMMbeIyHSs2L0Z61VYhk1q6TcG6VzgJ2yQ9cPYczIBuMMYMyPOro0HhmCtou2x9+wS7H0UGo90KvZ6PRfnvhQlKmJqjPZUFEXZvRCRbKyI+8EYc1Qd77srNifUtcaYF+ty3+mMiPyEzWt1Sqr7omQuGpOkKMpuhVecFNZS1YzqFAV1hpM76DHgdq8YIaUmInIS0B/rMlSUpKGWJEVRdisc91YB8B02wHx/bLqDRcDeJsqUHoqi7D6oSFIUZbdCRC7Axhj1wsZVrcHG4txhjFmTyr4pilK/UJGkKIqiKIrigcYkKYqiKIqieKAiSVEURVEUxQMVSYqiKIqiKB6oSFIURVEURfFARZKiKIqiKIoH/w8aUhuTZbD6ZQAAAABJRU5ErkJggg==\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Plot the spectrum & the model fit to the short wavelength region of the data.\n", "plt.figure(figsize=(8, 4))\n", @@ -1024,7 +791,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1034,24 +801,9 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "fc53f952f78443fe8d9c7de90b520f99", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Application(config='specviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_selected', 'd…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "specviz = Specviz()\n", "specviz.app" @@ -1059,7 +811,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1077,23 +829,9 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Video showing how to fit a polynomial to two separate spectral regions within a single subset\n", "HTML('')" @@ -1101,25 +839,9 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:Warning: Applying the value from the redshift slider to the output spectra. To avoid seeing this warning, explicitly set the apply_slider_redshift argument to True or False.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Not present\n", - "No Polyfit\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "spectra = specviz.get_spectra()\n", " \n", @@ -1135,17 +857,9 @@ }, { "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The poly.fits file does not exist\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Delete any existing output in current directory\n", "if os.path.exists(\"poly.fits\"):\n", @@ -1156,7 +870,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1166,46 +880,11 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "33:15: W605 invalid escape sequence '\\m'\n", - "INFO:pycodestyle:33:15: W605 invalid escape sequence '\\m'\n", - "49:36: W605 invalid escape sequence '\\m'\n", - "INFO:pycodestyle:49:36: W605 invalid escape sequence '\\m'\n" - ] - } - ], + "outputs": [], "source": [ "# Fit a local continuum between the flux densities at: 8.0 - 8.1 & 14.9 - 15.0 microns\n", "# (i.e. excluding the line itself)\n", @@ -1263,24 +942,9 @@ }, { "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0d56a10783fd4c819d4e06d3924c9d95", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Application(config='specviz', events=['call_viewer_method', 'close_snackbar_message', 'data_item_selected', 'd…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Load 10 um feature back into specviz and calculate the Line flux; Line Centroid; Equivalent width\n", "specviz = Specviz()\n", @@ -1289,7 +953,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1310,23 +974,9 @@ }, { "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Video showing how to measure lines within specviz\n", "HTML('')" @@ -1334,19 +984,9 @@ }, { "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Line_centroid: 10.7569 micron \n", - "Integrated line_flux: 6.65227e-15 W / m2 \n", - "Equivalent width: -15.0066 micron \n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Alternative method to analyze the 10um line within the notebook. Calculate the Line flux; Line Centroid; Equivalent width\n", "\n", @@ -1367,7 +1007,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1381,22 +1021,9 @@ }, { "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Plot the optical depth of the 10 micron region vs wavelength\n", "plt.figure(figsize=(10, 6))\n", From d0f052ae25fdedd82b274e6eeadcc84fda8ec4bf Mon Sep 17 00:00:00 2001 From: Ori Date: Thu, 16 Dec 2021 14:10:05 -0500 Subject: [PATCH 8/8] Removed Pep 8 Check Commands --- .../JWST_Mstar_dataAnalysis_analysis.ipynb | 40 ------------------- 1 file changed, 40 deletions(-) diff --git a/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb b/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb index ce4f4abd..5442f1d3 100644 --- a/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb +++ b/jdat_notebooks/MRS_Mstar_analysis/JWST_Mstar_dataAnalysis_analysis.ipynb @@ -55,46 +55,6 @@ "## Import Packages" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete below when finished)

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# disable all imported packages' loggers\n", - "import logging\n", - "logging.root.manager.loggerDict = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# enable PEP8 checker for this notebook\n", - "%load_ext pycodestyle_magic\n", - "%flake8_on --ignore E261,E501,W291,W293\n", - "\n", - "# only allow the checker to throw warnings when there's a violation\n", - "logging.getLogger('flake8').setLevel('ERROR')\n", - "logging.getLogger('stpipe').setLevel('ERROR')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Reviewer note: Begin PEP8 check cells (delete above when finished)

" - ] - }, { "cell_type": "code", "execution_count": null,