Make sure data examples achieve a single task

examples/README.txt

@@ -2,10 +2,13 @@
 Example Gallery
 ***************
 
-This gallery contains examples of how to use SunPy. Most of these examples
-require the SunPy sample data, which you can download by running::
+The gallery contains examples of how to use sunpy.
+Each example is a short and self contained how-to guide for performing a specific task.
+
+Sample data
+===========
+Some of these examples require the SunPy sample data, which you can download by running::
 
      >>> import sunpy.data.sample
 
-Once downloaded the data will be stored in your user directory and you will not
-need to download it again.
+Once downloaded the data will be stored in your user directory and you will not need to download it again.

examples/acquiring_data/downloading_cutouts.py

@@ -4,7 +4,7 @@
 ===============================================
 
 This example shows how to request a cutout of a series of
-AIA images from the JSOC and animate the resulting sequence.
+AIA images from the JSOC.
 """
 # sphinx_gallery_thumbnail_number = 2
 import os
@@ -14,7 +14,6 @@
 import astropy.time
 import astropy.units as u
 from astropy.coordinates import SkyCoord
-from astropy.visualization import ImageNormalize, SqrtStretch
 
 import sunpy.map
 from sunpy.net import Fido
@@ -92,23 +91,4 @@
 
 files = Fido.fetch(query)
 files.sort()
-
-#####################################################
-# Now that we've downloaded the files, we can create
-# a `~sunpy.map.MapSequence` from them.
-
-sequence = sunpy.map.Map(files, sequence=True)
-
-#####################################################
-# Finally, we can construct an animation in time from
-# our stack of cutouts and interactively flip through
-# each image in our sequence. We first adjust the plot
-# settings on each image to ensure the colorbar is the
-# same at each time step.
-
-for each_map in sequence:
-    each_map.plot_settings['norm'] = ImageNormalize(vmin=0, vmax=5e3, stretch=SqrtStretch())
-plt.figure()
-ani = sequence.plot()
-
-plt.show()
+print(files)

examples/acquiring_data/fido_metadata_queries.py

@@ -1,7 +1,7 @@
 """
-====================================
-Querying Metadata clients using Fido
-====================================
+=========================
+Querying Metadata clients
+=========================
 
 This example shows how to search and retrieve metadata using `~sunpy.net.Fido`.
 Fido supports searching metadata from services like `~sunpy.net.hek.HEKClient`,

examples/acquiring_data/goes_xrs_example.py

@@ -1,7 +1,7 @@
 """
-=====================================================
-Retrieving and analyzing GOES X-Ray Sensor (XRS) data
-=====================================================
+=======================================
+Retrieving GOES X-Ray Sensor (XRS) data
+=======================================
 
 The X-ray Sensor (XRS) on board the GOES series of satellites
 have provided soft X-ray measurements in two broadband energy
@@ -27,10 +27,7 @@
 example, there are times when GOES 13, 14 and 15 overlap and such data is
 available from each satellite. Similarly there are times when GOES 16 and 17 overlap.
 """
-import matplotlib.pyplot as plt
-import numpy as np
 
-from astropy.visualization import time_support
 
 from sunpy import timeseries as ts
 from sunpy.net import Fido
@@ -69,37 +66,6 @@
 goes_15 = ts.TimeSeries(file_goes15)
 goes_15.peek()
 
-#############################################################
-# The resulting `~sunpy.timeseries.TimeSeries` can be filtered by GOES quality flags. For more information
-# refer to the `GOES Data Guide <https://satdat.ngdc.noaa.gov/sem/goes/data/science/xrs/GOES_13-15_XRS_Science-Quality_Data_Readme.pdf>`__.
-
-df = goes_15.to_dataframe()
-df = df[(df["xrsa_quality"] == 0) & (df["xrsb_quality"] == 0)]
-goes_15 = ts.TimeSeries(df, goes_15.meta, goes_15.units)
-
-###############################################################
-# We can also pull out the individual GOES chanels and plot. The 0.5-4 angstrom
-# channel is known as the "xrsa" channel and the 1-8 angstrom channel is known
-# as the "xrsb" channel.
-
-goes_15.plot(columns=["xrsb"])
-plt.show()
-
-###############################################################
-# We can also truncate the data for the time of the large flare,
-# and analyze the different channels. For example, we can plot the
-# derivative which is useful in terms of the Neupert effect when analyzing
-# flares.
-
-goes_flare = goes_15.truncate("2015-06-21 09:35", "2015-06-21 10:30")
-
-time_support()
-fig, ax = plt.subplots()
-ax.plot(goes_flare.time, np.gradient(goes_flare.quantity("xrsb")))
-ax.set_ylabel("Flux (Wm$^{-2}$$s^{-1}$)")
-fig.autofmt_xdate()
-plt.show()
-
 ###############################################################
 # GOES 16 and 17 data
 # -------------------

examples/acquiring_data/querying_the_GOES_event_list.py

@@ -1,7 +1,7 @@
 """
-==================================================
-Querying the GOES flare event list through the HEK
-==================================================
+==================================
+Querying the GOES flare event list
+==================================
 
 How to retrieve the GOES flare event list through use of
 sunpy's Heliophysics Event Knowledgebase (HEK) client.

examples/acquiring_data/downloading_hmi.py → examples/map_transformations/downloading_hmi.py

@@ -1,9 +1,10 @@
 """
-===========================================
-Downloading and plotting an HMI magnetogram
-===========================================
+==============================================
+Rotating HMI maps so they're not 'upside-down'
+==============================================
 
-This example shows how to download a HMI magnetogram data with Fido and make a plot.
+This example shows how to rotate a HMI magnetogram, so when you plot it
+it appears with solar North puting up, and not upside down!
 """
 import matplotlib.pyplot as plt
 
@@ -12,28 +13,15 @@
 from sunpy.net import attrs as a
 
 ###############################################################################
-# To download the required data, we use `sunpy.net.Fido`, a downloader client,
-# to query the Virtual Solar Observatory to acquire HMI data.
+# To download the required data, we use `sunpy.net.Fido`.
 
 result = Fido.search(a.Time('2020/01/20 00:00:00', '2020/01/20 00:01:00'),
                      a.Instrument.hmi, a.Physobs.los_magnetic_field)
 
 ###############################################################################
-# Now we can see what results we obtained from our search.
+# Now we can see what results we obtained from our search, and download them.
 
 print(result)
-
-###############################################################################
-# We can look at the values of specific keywords from this result.
-
-jsoc_result = result[0]
-print(jsoc_result.show('T_REC', 'CROTA2'))
-
-###############################################################################
-# The following shows how to download the results. If we
-# don't provide a path it will download the file into the sunpy data directory.
-# The output provides the path of the downloaded files.
-
 downloaded_file = Fido.fetch(result)
 print(downloaded_file)