Animate gallery example

changelog/323.doc.rst

@@ -0,0 +1 @@
+Improved the "Requesting specific AIA images from the JSOC" Example to animate and use Fido instead of DRMS.

examples/download_specific_data.py

@@ -5,128 +5,54 @@
 
 This example shows how to request a specific series of AIA images from the JSOC.
 
-We will be filtering the data we require by keywords and requesting short exposure images from a recent flare.
-
-Unfortunately, this can not be done using the sunpy downloader `~sunpy.net.Fido`
-and instead we will use the `drms <https://docs.sunpy.org/projects/drms/en/stable/>`__ Python library directly.
+We will be filtering the data we require by keywords and requesting short exposure images from a recent (at time of writing) flare.
 """
-
 import os
-from pathlib import Path
 
 import astropy.units as u
-import drms
 import matplotlib.pyplot as plt
 import sunpy.map
+from astropy.coordinates import SkyCoord
+from astropy.visualization import AsinhStretch, ImageNormalize
+from sunpy.net import Fido, attrs
 
 from aiapy.calibrate import correct_degradation, register, update_pointing
 from aiapy.calibrate.util import get_correction_table, get_pointing_table
 
 #####################################################
 # Exporting data from the JSOC requires registering your
-# email first. Please replace this with your email
-# address once you have registered.
-# See `this page <http://jsoc.stanford.edu/ajax/register_email.html>`__
-# for more details.
+# email first. Please replace the text after the ``=``
+# with your email address once you have registered.
+# `See this page for more details. <http://jsoc.stanford.edu/ajax/register_email.html>`__
 
 jsoc_email = os.environ.get("JSOC_EMAIL")
 
 #####################################################
-# Our goal is to request data of a recent (of time of writing)
-# X-class flare. However, we will request the explanation of
-# the keywords we want from the JSOC.
-
-client = drms.Client(email=jsoc_email)
-keys = ["EXPTIME", "QUALITY", "T_OBS", "T_REC", "WAVELNTH"]
-
-print("Querying series info")
-# We plan to only use the EUV 12s data for this example.
-series_info = client.info("aia.lev1_euv_12s")
-for key in keys:
-    linkinfo = series_info.keywords.loc[key].linkinfo
-    note_str = series_info.keywords.loc[key].note
-    print(f"{key:>10} : {note_str}")
-
-#####################################################
-# We will construct the query. The X-class flare occurred
-# on the 2021/07/03 at 14:30:00 UTC. We will focus on the 5 minutes
-# before and after this time.
-
-qstr = "aia.lev1_euv_12s[2021-07-03T14:25:00Z-2021-07-03T14:35:00Z]"
-print(f"Querying data -> {qstr}")
-results = client.query(qstr, key=keys)
-print(f"{len(results)} records retrieved.")
-
-#####################################################
-# As you can see from the output, we have received a
-# a list of AIA images that were taken during the flare.
-# What we want to do now is to filter the list of images
-# to only include shorter expsoures.
-# However, before we do this, let us check what the exposure times are.
-
-# Filter out entries with EXPTIME > 2 seconds
-results = results[results.EXPTIME < 2]
-print(results)
-
-#####################################################
-# This style of filtering can be done to any column
-# in the results. For example, we can filter the WAVELNTH
-# column to only include 171 data with short expsoures.
-
-# Only use entries with WAVELNTH == 211
-results = results[results.WAVELNTH == 211]
-print(results)
-
-#####################################################
-# .. note::
-#   **Only complete searches can be downloaded from JSOC**,
-#   this means that no slicing operations performed on the results object
-#   will affect the number of files downloaded.
-#
-# We can filter and do analysis on the metadata that was returned.
-# The issue is is that if we only want this data, you can not use
-# this "filtered results" to download only the data we want.
-# To do this, we will have to do a second query to the JSOC,
-# this time using the query string syntax the
-# `lookdata <http://jsoc.stanford.edu/ajax/lookdata.html>`__ web page.
-# You can use the website to validate the string before you export the query.
-
-updated_qstr = "aia.lev1_euv_12s[2021-07-03T14:25:00Z-2021-07-03T14:35:00Z][? EXPTIME<2.0 AND WAVELNTH=211 ?]{image}"
-print(f"Querying data -> {updated_qstr}")
-# The trick here is to use the "image" keyword for ``seg`` to only download the
-# image data only and this gives us direct filenames as well.
-records, filenames = client.query(updated_qstr, key=keys, seg="image")
-print(f"{len(records)} records retrieved. \n")
-
-# We do a quick comparision to ensure the final results are the same.
-# For this to work, we just need to deal with the different indexes.
-print("Quick Comparison")
-print(results.reset_index(drop=True) == records.reset_index(drop=True))
-
-#####################################################
-# From here you can now request (export) the data.
-# This will download this specific subset of data to your
-# local machine when the export request has been completed.
-# Depending on the status of the JSOC, this might take a while.
-#
-# Please be aware the script will hold until the export is complete.
-
-export = client.export(updated_qstr, method="url", protocol="fits")
-files = export.download(Path("~/sunpy/").expanduser().as_posix())
+# Our goal is to request data of a recent X-class flare.
+# The X-class flare occurred on the 2021/07/03 at 14:30:00 UTC.
+# We will focus on the 5 minutes before and after this time.
+# What we want to do is only get the shorter exposures,
+# which are going to be the flare related.
+
+query = Fido.search(
+    attrs.Time("2021-07-03 14:25:00", "2021-07-03 14:35:00"),
+    attrs.jsoc.Series("aia.lev1_euv_12s"),
+    attrs.Wavelength(211 * u.AA),
+    attrs.jsoc.Notify(jsoc_email),
+    attrs.jsoc.Keyword("EXPTIME") <= 2,
+    attrs.jsoc.Segment("image"),
+)
+
+print(query)
 
 #####################################################
-# With AIA files, it is possible to bypass the export stage.
-# We can manually construct the URLS of the the data.
-# Be aware that each file will have the same filename based on the URL.
-# You will have to then use your preferred downloader to download the files.
+# Now we will download the data and "aia prep" the
+# data with every feature of `aiapy` and plot the
+# data sequence using `sunpy`.
 
-urls = [f"http://jsoc.stanford.edu{filename}" for filename in filenames.image]
+files = Fido.fetch(query)
 
-#####################################################
-# Now we will "prep" the data with every feature of
-# `aiapy` and plot the data sequence using `sunpy`.
-
-level_1_maps = sunpy.map.Map(files.download.to_list())
+level_1_maps = sunpy.map.Map(files)
 # We get the pointing table outside of the loop for the relevant time range.
 # Otherwise you're making a call to the JSOC every single time.
 pointing_table = get_pointing_table(level_1_maps[0].date - 3 * u.h, level_1_maps[-1].date + 3 * u.h)
@@ -139,8 +65,18 @@
     map_registered = register(map_updated_pointing)
     map_degradation = correct_degradation(map_registered, correction_table=correction_table)
     map_normalized = map_degradation / map_degradation.exposure_time
-    level_15_maps.append(map_normalized)
+    bottom_left = SkyCoord(500 * u.arcsec, 100 * u.arcsec, frame=map_normalized.coordinate_frame)
+    top_right = SkyCoord(1500 * u.arcsec, 700 * u.arcsec, frame=map_normalized.coordinate_frame)
+    map_cropped = map_normalized.submap(bottom_left, top_right=top_right)
+    level_15_maps.append(map_cropped)
+
+#####################################################
+# Finally, we create a sequence of maps and animate it
+
 sequence = sunpy.map.Map(level_15_maps, sequence=True)
-sequence.peek()
+
+fig = plt.figure()
+ax = fig.add_subplot(projection=sequence.maps[0])
+ani = sequence.plot(axes=ax, norm=ImageNormalize(vmin=0, vmax=1e3, stretch=AsinhStretch()))
 
 plt.show()