\n",
" \n",
" 
\n",
"
\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Show some of the AIA channels and their temperature responses for illustration:\n",
"plt_emmax = 5.0e28\n",
"gfac = 1.0/2.2\n",
"\n",
"fig, axs = plt.subplots(2, 3)\n",
"axs[0,0].imshow(np.clip(maps[1].data/np.max(tresps[1]),0,plt_emmax)**gfac,cmap=plt.get_cmap('gray'))\n",
"axs[0,0].set(title='AIA 193 Angstrom')\n",
"axs[1,0].plot(trlogts[1],tresps[1]/1.0e-27)\n",
"axs[1,0].set(title='AIA 193 Temperature Response',xlabel='Temperature (dB Kelvin)',ylabel='Response (DN/s/(10$^9$ cm$^{-3})^2$/Mm)')\n",
"\n",
"axs[0,1].imshow(np.clip(maps[2].data/np.max(tresps[2]),0,plt_emmax)**gfac,cmap=plt.get_cmap('gray'))\n",
"axs[0,1].set(title='AIA 335 Angstrom')\n",
"axs[1,1].plot(trlogts[2],tresps[2]/1.0e-27)\n",
"axs[1,1].set(title='AIA 335 Temperature Response',xlabel='Temperature (dB Kelvin)',ylabel='Response (DN/s/(cm$^{-3})^2$/Mm)')\n",
"\n",
"axs[0,2].imshow(np.clip(maps[-1].data/np.max(tresps[-1]),0,plt_emmax)**gfac,cmap=plt.get_cmap('gray'))\n",
"axs[0,2].set(title='XRT Ti-Poly')\n",
"axs[1,2].plot(trlogts[-1],tresps[-1]/1.0e-27)\n",
"\n",
"axs[1,2].set(title='XRT Ti-Poly Temperature Response',xlabel='Temperature (dB Kelvin)',ylabel='Response (DN/s/(cm$^{-3})^2$/Mm)')\n",
"\n",
"# Adjust layout and show the plots\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "8473bb46",
"metadata": {},
"source": [
"## DEM Analysis"
]
},
{
"cell_type": "markdown",
"id": "d6c009ab",
"metadata": {},
"source": [
"### Compute the Differential Emission Measure"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3828a08f-77fd-4c6b-9688-3d14164bd9d3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Calculating sparse_nlmap_demsequence from scratch..."
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" 10%|▉ | 18090/189010 [00:10<01:53, 1505.29it/s]"
]
}
],
"source": [
"sparse_nlmap_demsequence, multi_out = autoloading_sparse_nlmap_dem_wrapper(datasequence, recalc=False, wrapargs={'norms':norms, 'overall_norm':overall_norm})\n",
"em_collection.add_model(sparse_nlmap_demsequence)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b3ff869f-2c80-4611-a047-c2f928fb1359",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "689d4d97",
"metadata": {},
"source": [
"### Run the Dashboard"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "53350f69",
"metadata": {},
"outputs": [],
"source": [
"# Get the most recent version from the tool kit:\n",
"from EMToolKit.visualization.dashboard import dashboard_object\n",
"importlib.reload(EMToolKit.visualization.dashboard)\n",
"from EMToolKit.visualization.dashboard import dashboard_object\n",
"\n",
"# Suppress specific SunpyMetadataWarning\n",
"import warnings\n",
"from sunpy.util.exceptions import SunpyMetadataWarning\n",
"warnings.filterwarnings(\"ignore\", category=SunpyMetadataWarning)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c5c30d46",
"metadata": {},
"outputs": [],
"source": [
"# Create the dashboard:\n",
"dash = dashboard_object(em_collection, xsize=8, ysize=4, fontsize=8)\n",
"dash.display(False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e53c3d1b-407b-4030-952e-68a45e1f21cd",
"metadata": {},
"outputs": [],
"source": [
"multi_synthmaps = [em_collection.synthesize_map(map) for map in em_collection.data()]\n",
"multi_synthdata = [map for map in multi_synthmaps]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a8005dd9",
"metadata": {},
"outputs": [],
"source": [
"def calc_resids(synthdata, em_collection):# Calculate the residuals and Chi squared:\n",
"\tndata = len(synthdata)\n",
"\tresids = []\n",
"\tdatasequence = em_collection.data()\n",
"\tchi2 = 0\n",
"\t[nx,ny] = datasequence[0].data.shape\n",
"\tfor seq in datasequence: [nx,ny] = [np.min([seq.data.shape[0],nx]),np.min([seq.data.shape[1],ny])]\n",
"\tfor i in range(0,ndata):\n",
"\t\tresids.append(((synthdata[i].data-datasequence[i].data)/datasequence[i].uncertainty.array)**2)\n",
"\t\tchi2 += np.mean(resids[i])/ndata\n",
"\treturn resids, chi2\n",
"\n",
"#spars_resids, spars_chi2 = calc_resids(spars_synthdata,em_collection)\n",
"#simpl_resids, simpl_chi2 = calc_resids(simpl_synthdata,em_collection)\n",
"multi_resids, multi_chi2 = calc_resids(multi_synthdata,em_collection)\n",
"print(\"Multi-instrument Chi squared = \",multi_chi2)\n",
"#print('simple_reg_dem Chi squared = ',simpl_chi2, 'spars_em Chi squared = ',spars_chi2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6b3ed125",
"metadata": {},
"outputs": [],
"source": [
"# Plot Residuals:\n",
"fig = plt.figure(figsize=[20,12])\n",
"plt.suptitle('Residuals for '+multi_algorithm)\n",
"for i in range(0,7):\n",
" ax1 = fig.add_subplot(2,4,i+1)\n",
" ax1.imshow(multi_resids[i],vmin=0,vmax=5)\n",
" ax1.set(title=em_collection.data()[i].meta['channel'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "52bcd063",
"metadata": {},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"from ipywidgets import interact, interactive, fixed, interact_manual\n",
"import ipywidgets as widgets\n",
"\n",
"\n",
"class dashboard_object(object):\n",
" def __init__(self,em_collection):\n",
" self.emc = em_collection\n",
"\n",
" def widgwrap(self, xpt, ypt, rtemp, gtemp, btemp, sigma, algorithm):\n",
" dashboard_object(self.emc, plotpoint=[xpt,ypt], temperatures=[rtemp,gtemp,btemp], sigmas=sigma, algorithm=algorithm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dbcf6d3e",
"metadata": {},
"outputs": [],
"source": [
"importlib.reload(EMToolKit.visualization.dashboard)\n",
"from EMToolKit.visualization.dashboard import dashboard_object\n",
"dash = dashboard_object(em_collection)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c296f04b",
"metadata": {},
"outputs": [],
"source": [
"[nx,ny] = dash.emc.collection[dash.emc.collection['models'][0]][0].data.shape\n",
"xpt=widgets.IntSlider(min=0, max=nx, value=10, step=1, description='xpt', continuous_update=False)\n",
"ypt=widgets.IntSlider(min=0, max=ny, value=100, step=1, description='ypt', continuous_update=False)\n",
"rtemp=widgets.FloatSlider(min=5, max=7, value=5.8, step=0.05, description='rtemp', continuous_update=False)\n",
"gtemp=widgets.FloatSlider(min=5, max=7, value=6.1, step=0.05, description='gtemp', continuous_update=False)\n",
"btemp=widgets.FloatSlider(min=5, max=7, value=6.4, step=0.05, description='btemp', continuous_update=False)\n",
"sigma=widgets.FloatSlider(min=0.025, max=0.5, value=0.125, step=0.01, description='sigma', continuous_update=False)\n",
"algorithm=widgets.Dropdown(options=dash.emc.collection['models'], description='algorithm', continuous_update=False)\n",
"ui = widgets.HBox([xpt,ypt,rtemp,gtemp,btemp,sigma,algorithm])\n",
"out = widgets.interactive_output(dash.widgwrap, {'xpt':xpt,'ypt':ypt,'rtemp':rtemp,'gtemp':gtemp,'btemp':btemp,'sigma':sigma,'algorithm':algorithm})\n",
"display(ui,out)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8a27721d-f578-4605-8cdc-c8b5133093b2",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
\n",
"