Add Experimental GUI Tool for Quick XRT Data Preview

examples/README.txt

@@ -3,3 +3,12 @@ Example Gallery
 ***************
 
 The gallery contains examples of how to use XRTpy.
+========================
+XRT Stuff
+========================
+- **Removing Light Leak**
+========================
+XRT FOV GUI
+========================
+
+- **FOV tool test**

examples/preview_observation.py

@@ -0,0 +1,82 @@
+"""
+=============================
+FOV tool test
+=============================
+
+We are just testing stuff here
+"""
+
+import time as timer
+import matplotlib.pyplot as plt
+from astropy.io import fits
+import astropy.time
+import astropy.units as u
+
+import sunpy
+import ipywidgets as widgets
+from ipywidgets import Layout, interact, IntSlider,IntProgress, RadioButtons, FloatSlider,FloatRangeSlider
+import numpy as np
+import matplotlib.pyplot as plt
+from ipywidgets import interact
+import numpy as np
+from sunpy.net import Fido
+from sunpy.net import attrs as a
+import matplotlib.dates as mdates
+
+import sys
+sys.path.append('/Users/ntrueba/SOLAR/code/GIT/xrtpy/xrtpy/visualization/fov/') #
+import metadata_manager as ObsMeta
+
+##############################################################################
+# FIDO SEARCH
+# The tool is meant to preview metadata within the fido ecosystem - this means that fido is the slowest part of the code unless you are looking at a very small time frame
+
+# Define the time range of interest for solar observations
+time_range_1 = a.Time("2011-06-07 06:00:00", "2011-06-07 06:45:54")
+time_range_2 = a.Time("2007-12-17 10:40:00", "2007-12-17 13:00:54")
+
+# Specify the instrument as 'xrt' to search for Hinode X-Ray Telescope data
+instrument = a.Instrument("xrt")
+
+
+# This will return a catalog of available XRT data during t≠he specified period
+xrt_downloaded_files_1 = Fido.search(time_range_1, instrument)
+xrt_downloaded_files_2 = Fido.search(time_range_2, instrument)
+
+
+
+##############################################################################
+#Metadata extraction and the XRT_meta structure
+#The first function accepts the output of the fido search as an input and retrieves the corresponding metadata without having to download the data.
+#By default, it downloads the level0 metadata (same as SSWIDL) which is very quick when dealing with many files. If you want the level 1 metadata, the syntax is (.., fast_bool = False) - this is only recommended for short observations (< 1hr), as it can retrieve metadata at a rate 5 obs/second
+#The second function creates a handy metadata object for all the observations
+#it contains a list of headers (xmeta.head_lis)
+#and a dictonary (xmeta.metadata) containing important filter-separated quantities
+#This could be super handy, as you get much more information than what fido gives you, so you can download observations that meet very specific conditions - let me know if you want something specific included here
+
+
+
+xrt_dset1 = ObsMeta.DatasetMetaManager(xrt_downloaded_files_1)
+xrt_dset2 = ObsMeta.DatasetMetaManager(xrt_downloaded_files_2)##
+
+##############################################################################
+#Plotting backend
+#The %matplotlib inline works best when using notebooks to avoid flickering. Ipywidgets is not available with HTML, but you can uncomment this line when you download the notebook.
+#%matplotlib inline
+###
+ani = xrt_dset2.plot_preview(ani_bool = False, d_mode=False)
+plt.show()
+
+##############################################################################
+###
+#We can also do nightmode 
+
+ani = xrt_dset1.plot_preview(ani_bool = False, d_mode=True, vertical_plot=True)##
+plt.show()
+
+##############################################################################
+###
+#We can also do a horizontal version
+
+ani = xrt_dset2.plot_preview(ani_bool = False, d_mode=True, vertical_plot=False)
+plt.show()

pyproject.toml

@@ -45,6 +45,7 @@ dependencies = [
   "scikit-image>=0.21",
   "scipy>=1.11.1",
   "sunpy[map]>=5.1",
+  "ipywidgets>=8.0.0",
 ]
 
 optional-dependencies.dev = [

visualization/fov/metadata_downloader.py

@@ -0,0 +1,174 @@
+from astropy.io import fits
+import astropy.units as u
+
+import numpy as np
+
+from astropy.coordinates import SkyCoord
+
+from sunpy.coordinates import frames
+from astropy.time import Time
+import scipy.io as sio
+from scipy.io import readsav
+import urllib.request
+
+
+def download_metadata(xrt_downloaded_files, filen, overwrite=False):
+    url_lis = xrt_downloaded_files[0][:]['fileid']
+    url_str_lis = []
+    for i in range(len(url_lis)):
+        url_str_lis.append(url_lis[i])
+    primary_hdu = fits.PrimaryHDU(data=np.ones((3, 3)))
+    c1 = fits.Column(name='URL', array=url_str_lis, format='100A')
+    c2 = fits.Column(name='header_int', array=np.asarray(range(len(url_str_lis)))+2, format='J')
+    table_hdu = fits.BinTableHDU.from_columns([c1, c2])
+
+    hdul2 = fits.HDUList([primary_hdu, table_hdu])
+    #hlis = []
+
+    for i in range(len(url_str_lis)):
+        if (i%10 == 0):
+            print(int(1000.0*i/len(url_str_lis))/10.0,'%')
+        fsspec_kwargs = {"block_size": 100_000, "cache_type": "bytes"}
+        with fits.open(url_lis[i], use_fsspec=True, fsspec_kwargs=fsspec_kwargs) as hdul:
+            #hlis.append(hdul[0].header)
+            # Download a single header
+            t_header = hdul[0].header
+            image_hdu = fits.ImageHDU(data=np.ones((100, 100)), header=t_header,name="header"+str(i))
+        hdul2.append(image_hdu)        
+    return hdul2
+
+    #hdul2.writeto(filen,overwrite=overwrite)
+
+def date_to_meta(xrt_download_list):
+    time_lis = xrt_download_list[0]['Start Time']
+    year_lis = xrt_download_list[0]['Start Time'].ymdhms.year
+    month_lis = xrt_download_list[0]['Start Time'].ymdhms.month
+    day_lis = xrt_download_list[0]['Start Time'].ymdhms.day
+    new_date = []
+    file_lis = []
+    for i in range(len(time_lis)):
+        year_str  = str(year_lis[i]) 
+        month_str = str(month_lis[i])
+        day_str   = str(day_lis[i])  
+        if len(day_str) < 2:
+            day_str = '0'+day_str
+        if len(month_str) < 2:
+            month_str = '0'+month_str
+        ndatei =  year_str + month_str+ day_str  
+        if ndatei in file_lis:
+            new_date.append(file_lis.index(ndatei))
+            #new_date[file_lis.index(ndatei)].append(ndatei)
+        else:
+            file_lis.append(ndatei)
+            new_date.append(file_lis.index(ndatei))
+            #print(ndatei,file_lis.index(ndatei))
+    return file_lis, new_date
+
+def get_urls(file_n_lis, ggg):
+    nfile = len(file_n_lis)
+    geny_lis = []
+    for i in range(nfile):
+        find_url = 'xrt'+file_n_lis[i]
+        findex = ggg.find(find_url)
+        gen_fn = ggg[findex:findex+35]
+        findex2 = gen_fn.find('geny')
+        gen_fn = gen_fn[:findex2+4]
+        geny_lis.append(gen_fn)
+    return geny_lis
+
+def get_metafile(geny_lis):
+    url_start = 'https://sot.lmsal.com/data/sot/metadata/sswdb/hinode/xrt/xrt_genxcat/'
+    ngeny = len(geny_lis)
+    meta_lis = []
+    for i in range(ngeny):
+        print(i)
+        gen_fn = geny_lis[i]
+        f, h = urllib.request.urlretrieve(url_start + gen_fn)
+        print(i)
+        data2 = readsav(f)["p0"]
+        data_dict2 = {k : data2[k] for k in data2.dtype.names}
+        meta_lis.append(data_dict2)
+    return meta_lis
+
+#def mk_meta_header(meta_lis):
+        #print(data_dict2['DATE_OBS'])
+
+def meta_to_dict(data_dict, di):
+    dkeys = data_dict.keys()
+    hdict = {}
+    for dki in dkeys:
+        try:
+            hdict[dki] = data_dict[dki][di].decode('ascii')
+        except:
+            hdict[dki] = data_dict[dki][di]
+    return hdict
+
+def match_vso_to_cat(data_dict_lis, cat_fi, xrt_download):
+
+    n_dict = len(data_dict_lis)
+    cat_time_lis = []
+    for i in range(n_dict):
+        data_dict = data_dict_lis[i]
+        date_obs_cat = data_dict['DATE_OBS']
+        cat_len = len(date_obs_cat)
+        cat_str = []
+        for cat_bin in date_obs_cat:
+            cat_str.append(cat_bin.decode('ascii'))
+        cat_time = Time(np.asarray(cat_str), format='isot', scale='utc')
+        cat_time_lis.append(cat_time)
+    #print('yo')
+    min_ti_lis = []
+    delt_lis = []
+    delt_lisp = []
+    delt_lism = []
+    header_lis = []
+    for i in range(len(xrt_download[0]['Start Time'])):
+        cat_time = cat_time_lis[cat_fi[i]]
+        stime = xrt_download[0]['Start Time'][i]
+        delt = cat_time - stime
+        delt = delt.value*24.0*3600.0
+        min_ti = np.argmin(np.abs(delt))
+        min_ti_lis.append(min_ti)
+        delt_lis.append(delt[min_ti])
+        try:
+            delt_lisp.append(delt[min_ti+1])
+            delt_lism.append(delt[min_ti-1])
+        except:
+            print()
+        header_lis.append(meta_to_dict(data_dict_lis[cat_fi[i]],min_ti))
+    return header_lis
+
+def get_html_lis():
+    url_start = 'https://sot.lmsal.com/data/sot/metadata/sswdb/hinode/xrt/xrt_genxcat/'
+    with urllib.request.urlopen(url_start) as response:  
+
+        html = response.read()
+        #print(response.info())
+        #print()
+        ggg = html.decode('utf-8')
+    return ggg
+
+def download_metadata_fast(xrt_downloaded_files, ggg=None):
+    if (ggg == None):
+        ggg = get_html_lis()
+    file_lis, new_date = date_to_meta(xrt_downloaded_files)
+    genyl = get_urls(file_lis, ggg)
+    print('downloading')
+    tmeta_lis = get_metafile(genyl)
+    print('done')
+    hlis3 = match_vso_to_cat(tmeta_lis, new_date, xrt_downloaded_files)
+    return hlis3
+
+def fetch_metadata(xrt_downloaded_files, fast_bool = True):
+    if fast_bool:
+        print('Fast Metadata (Level 0)')
+        return download_metadata_fast(xrt_downloaded_files, ggg=None)
+    else:
+        print('Slow Metadata (Level 1)')
+        hdul = download_metadata(xrt_downloaded_files,'')
+        hlis = []
+        for i in range(len(xrt_downloaded_files[0])):
+            hlis.append(hdul[i+2].header)
+        return hlis
+
+