def test_download_save_segments_timebounds(self, mock_updatedf, mock_insertdf, mseed_unpack,
db, tt_ak135_tts):
# prepare:
# mseed unpack takes no starttime and endtime arguments, so that
mseed_unpack.side_effect = lambda *a, **v: unpack(*a, **v)
mock_insertdf.side_effect = lambda *a, **v: insertdf(*a, **v)
mock_updatedf.side_effect = lambda *a, **v: updatedf(*a, **v)
# mock event response: it's the same as self._evt_urlread_sideeffect but modify the dates
# as NOW. This means, any segment downloaded later will
# be out-of-bound
utcnow = datetime.utcnow()
utcnow_iso = utcnow.isoformat().replace("T", " ")
urlread_sideeffect = """#EventID | Time | Latitude | Longitude | Depth/km | Author | Catalog | Contributor | ContributorID | MagType | Magnitude | MagAuthor | EventLocationName
20160508_0000129|%s|1|1|60.0|AZER|EMSC-RTS|AZER|505483|ml|3|AZER|CASPIAN SEA, OFFSHR TURKMENISTAN
20160508_0000004|%s|90|90|2.0|EMSC|EMSC-RTS|EMSC|505183|ml|4|EMSC|CROATIA
""" % (utcnow_iso, utcnow_iso)
events_df = self.get_events_df(urlread_sideeffect, db.session)
# restore urlread_side_effect:
urlread_sideeffect = None
net, sta, loc, cha = [], [], [], []
datacenters_df, eidavalidator = \
self.get_datacenters_df(urlread_sideeffect, db.session, self.service,
self.routing_service, net, sta, loc, cha,
db_bufsize=self.db_buf_size)
channels_df = self.get_channels_df(urlread_sideeffect, db.session,
datacenters_df,
eidavalidator,
net, sta, loc, cha, None, None, 10,
False, None, None, -1, self.db_buf_size)
# just to be sure. If failing, we might have changed the class default:
assert len(channels_df) == 12
# events_df
# id magnitude latitude longitude depth_km time
# 0 20160508_0000129 3.0 1.0 1.0 60.0 2016-05-08 05:17:11.500
# 1 20160508_0000004 4.0 2.0 2.0 2.0 2016-05-08 01:45:30.300
# channels_df (index not shown):
# columns:
# id station_id latitude longitude datacenter_id start_time end_time network station location channel
# data (not aligned with columns):
# 1 1 1.0 1.0 1 2003-01-01 NaT GE FLT1 HHE
# 2 1 1.0 1.0 1 2003-01-01 NaT GE FLT1 HHN
# 3 1 1.0 1.0 1 2003-01-01 NaT GE FLT1 HHZ
# 4 2 90.0 90.0 1 2009-01-01 NaT n1 s c1
# 5 2 90.0 90.0 1 2009-01-01 NaT n1 s c2
# 6 2 90.0 90.0 1 2009-01-01 NaT n1 s c3
# 7 3 1.0 1.0 2 2003-01-01 NaT IA BAKI BHE
# 8 3 1.0 1.0 2 2003-01-01 NaT IA BAKI BHN
# 9 3 1.0 1.0 2 2003-01-01 NaT IA BAKI BHZ
# 10 4 90.0 90.0 2 2009-01-01 NaT n2 s c1
# 11 4 90.0 90.0 2 2009-01-01 NaT n2 s c2
# 12 4 90.0 90.0 2 2009-01-01 NaT n2 s c3
assert all(_ in channels_df.columns for _ in [Station.network.key, Station.station.key,
Channel.location.key, Channel.channel.key])
chaid2mseedid = chaid2mseedid_dict(channels_df)
# check that we removed the columns:
assert not any(_ in channels_df.columns for _ in
[Station.network.key, Station.station.key,
Channel.location.key, Channel.channel.key])
# take all segments:
# use minmag and maxmag
ttable = tt_ak135_tts
segments_df = merge_events_stations(events_df, channels_df, dict(minmag=10, maxmag=10,
minmag_radius=10, maxmag_radius=10), tttable=ttable)
assert len(pd.unique(segments_df['arrival_time'])) == 2
h = 9
# segments_df (index not shown). Note that
# cid sid did n s l c ed event_id depth_km time <- LAST TWO ARE Event related columns that will be removed after arrival_time calculations
# 1 1 1 GE FLT1 HHE 0.0 20160508_0000129 60.0 2016-05-08 05:17:11.500
# 2 1 1 GE FLT1 HHN 0.0 20160508_0000129 60.0 2016-05-08 05:17:11.500
# 3 1 1 GE FLT1 HHZ 0.0 20160508_0000129 60.0 2016-05-08 05:17:11.500
# 7 3 2 IA BAKI BHE 0.0 20160508_0000129 60.0 2016-05-08 05:17:11.500
# 8 3 2 IA BAKI BHN 0.0 20160508_0000129 60.0 2016-05-08 05:17:11.500
# 9 3 2 IA BAKI BHZ 0.0 20160508_0000129 60.0 2016-05-08 05:17:11.500
# 4 2 1 n1 s c1 0.0 20160508_0000004 2.0 2016-05-08 01:45:30.300
# 5 2 1 n1 s c2 0.0 20160508_0000004 2.0 2016-05-08 01:45:30.300
# 6 2 1 n1 s c3 0.0 20160508_0000004 2.0 2016-05-08 01:45:30.300
# 10 4 2 n2 s c1 0.0 20160508_0000004 2.0 2016-05-08 01:45:30.300
# 11 4 2 n2 s c2 0.0 20160508_0000004 2.0 2016-05-08 01:45:30.300
# 12 4 2 n2 s c3 0.0 20160508_0000004 2.0 2016-05-08 01:45:30.300
# LEGEND:
# cid = channel_id
# sid = station_id
# scid = datacenter_id
# n, s, l, c = network, station, location, channel
# ed = event_distance_deg
# define a dc_dataselect_manager for open data only:
dc_dataselect_manager = DcDataselectManager(datacenters_df, Authorizer(None), False)
wtimespan = [1, 2] # in minutes
expected = len(segments_df) # no segment on db, we should have all segments to download
orig_segments_df = segments_df.copy()
segments_df, request_timebounds_need_update = \
prepare_for_download(db.session, orig_segments_df, dc_dataselect_manager, wtimespan,
retry_seg_not_found=True,
retry_url_err=True,
retry_mseed_err=True,
retry_client_err=True,
retry_server_err=True,
retry_timespan_err=True,
retry_timespan_warn=True)
# segments_df
# COLUMNS:
# channel_id datacenter_id network station location channel event_distance_deg event_id arrival_time start_time end_time id download_status_code run_id
# DATA (not aligned with columns):
# channel_id datacenter_id network station location channel event_distance_deg event_id arrival_time start_time end_time id download_status_code run_id
# GE.FLT1..HHE 1 1 GE FLT1 HHE 0.0 1 2016-05-08 05:17:12.500 2016-05-08 05:16:12 2016-05-08 05:19:12 None None 1
# GE.FLT1..HHN 2 1 GE FLT1 HHN 0.0 1 2016-05-08 05:17:12.500 2016-05-08 05:16:12 2016-05-08 05:19:12 None None 1
# GE.FLT1..HHZ 3 1 GE FLT1 HHZ 0.0 1 2016-05-08 05:17:12.500 2016-05-08 05:16:12 2016-05-08 05:19:12 None None 1
# IA.BAKI..BHE 7 2 IA BAKI BHE 0.0 1 2016-05-08 05:17:12.500 2016-05-08 05:16:12 2016-05-08 05:19:12 None None 1
# IA.BAKI..BHN 8 2 IA BAKI BHN 0.0 1 2016-05-08 05:17:12.500 2016-05-08 05:16:12 2016-05-08 05:19:12 None None 1
# IA.BAKI..BHZ 9 2 IA BAKI BHZ 0.0 1 2016-05-08 05:17:12.500 2016-05-08 05:16:12 2016-05-08 05:19:12 None None 1
# n1.s..c1 4 1 n1 s c1 0.0 2 2016-05-08 01:45:31.300 2016-05-08 01:44:31 2016-05-08 01:47:31 None None 1
# n1.s..c2 5 1 n1 s c2 0.0 2 2016-05-08 01:45:31.300 2016-05-08 01:44:31 2016-05-08 01:47:31 None None 1
# n1.s..c3 6 1 n1 s c3 0.0 2 2016-05-08 01:45:31.300 2016-05-08 01:44:31 2016-05-08 01:47:31 None None 1
# n2.s..c1 10 2 n2 s c1 0.0 2 2016-05-08 01:45:31.300 2016-05-08 01:44:31 2016-05-08 01:47:31 None None 1
# n2.s..c2 11 2 n2 s c2 0.0 2 2016-05-08 01:45:31.300 2016-05-08 01:44:31 2016-05-08 01:47:31 None None 1
# n2.s..c3 12 2 n2 s c3 0.0 2 2016-05-08 01:45:31.300 2016-05-08 01:44:31 2016-05-08 01:47:31 None None 1
# self._segdata is the folder file of a "valid" 3-channel miniseed
# The channels are:
# Thus, no match will be found and all segments will be written with a None
# download status code
# setup urlread: first three rows: ok
# rows[3:6]: 413, retry them
# rows[6:9]: malformed_data
# rows[9:12] 413, retry them
# then retry:
# rows[3]: empty_data
# rows[4]: data_with_gaps (but seed_id should notmatch)
# rows[5]: data_with_gaps (seed_id should notmatch)
# rows[9]: URLError
# rows[10]: Http 500 error
# rows[11]: 413
# NOTE THAT THIS RELIES ON THE FACT THAT THREADS ARE EXECUTED IN THE ORDER OF THE DATAFRAME
# WHICH SEEMS TO BE THE CASE AS THERE IS ONE SINGLE PROCESS
# self._seg_data[:2] is a way to mock data corrupted
urlread_sideeffect = [self._seg_data, 413, self._seg_data[:2], 413,
'', self._seg_data_gaps, self._seg_data_gaps,
URLError("++urlerror++"), 500, 413]
# Let's go:
ztatz = self.download_save_segments(urlread_sideeffect, db.session, segments_df,
dc_dataselect_manager,
chaid2mseedid,
self.run.id, False,
request_timebounds_need_update,
1, 2, 3, db_bufsize=self.db_buf_size)
# get columns from db which we are interested on to check
cols = [Segment.id, Segment.channel_id, Segment.datacenter_id,
Segment.download_code, Segment.maxgap_numsamples,
Segment.sample_rate, Segment.data_seed_id, Segment.data, Segment.download_id,
Segment.request_start, Segment.request_end, Segment.start_time, Segment.end_time
]
db_segments_df = dbquery2df(db.session.query(*cols))
assert Segment.download_id.key in db_segments_df.columns
OUTTIME_ERR, OUTTIME_WARN = s2scodes.timespan_err, s2scodes.timespan_warn
# assert no segment has data (time out of bounds):
assert len(db_segments_df.loc[(~pd.isnull(db_segments_df[Segment.data.key])) &
(db_segments_df[Segment.data.key].str.len() > 0),
Segment.data.key]) == 0
# assert the number of "correctly" downloaded segments, i.e. with data (4) has now
# code = TIMEBOUND_ERR
assert len(db_segments_df[db_segments_df[Segment.download_code.key] == OUTTIME_ERR]) == 4
# re-sort db_segments_df to match the segments_df:
ret = []
for cha in segments_df[Segment.channel_id.key]:
ret.append(db_segments_df[db_segments_df[Segment.channel_id.key] == cha])
db_segments_df = pd.concat(ret, axis=0)
# db_segments_df:
# id channel_id datacenter_id download_status_code max_gap_ovlap_ratio sample_rate data_seed_id data run_id start_time end_time
# 0 1 1 1 -3 0.0001 100.0 GE.FLT1..HHE b'' 1 2016-05-08 05:16:12 2016-05-08 05:19:12
# 1 2 2 1 -3 0.0001 100.0 GE.FLT1..HHN b'' 1 2016-05-08 05:16:12 2016-05-08 05:19:12
# 2 3 3 1 -3 0.0001 100.0 GE.FLT1..HHZ b'' 1 2016-05-08 05:16:12 2016-05-08 05:19:12
# 6 7 7 2 200.0 NaN NaN None 1 2016-05-08 05:16:12 2016-05-08 05:19:12
# 7 8 8 2 NaN NaN NaN None None 1 2016-05-08 05:16:12 2016-05-08 05:19:12
# 8 9 9 2 -3 20.0 20.0 IA.BAKI..BHZ b'' 1 2016-05-08 05:16:12 2016-05-08 05:19:12
# 3 4 4 1 -2.0 NaN NaN None None 1 2016-05-08 01:44:31 2016-05-08 01:47:31
# 4 5 5 1 -2.0 NaN NaN None None 1 2016-05-08 01:44:31 2016-05-08 01:47:31
# 5 6 6 1 -2.0 NaN NaN None None 1 2016-05-08 01:44:31 2016-05-08 01:47:31
# 9 10 10 2 -1.0 NaN NaN None None 1 2016-05-08 01:44:31 2016-05-08 01:47:31
# 10 11 11 2 500.0 NaN NaN None None 1 2016-05-08 01:44:31 2016-05-08 01:47:31
# 11 12 12 2 413.0 NaN NaN None None 1 2016-05-08 01:44:31 2016-05-08 01:47:31
# now modify the first row time bounds:
# first we need to assign the database id to our segments_df, to prevent
# db contraint error when writing to db:
# `download_save_segments` below needs toi UPDATE the segments and it does it by
# checking if an id is present.
# check that the channel_ids align:
assert (segments_df[Segment.channel_id.key].values ==
db_segments_df[Segment.channel_id.key].values).all()
# so that we can simply do this:
segments_df[Segment.id.key] = db_segments_df[Segment.id.key]
# first read the miniseed:
stream = read(BytesIO(self._seg_data))
tstart = stream[0].stats.starttime.datetime
tend = stream[0].stats.endtime.datetime
segments_df.loc[segments_df[Segment.channel_id.key] == 1,
Segment.request_start.key] = tstart
segments_df.loc[segments_df[Segment.channel_id.key] == 1,
Segment.request_end.key] = tstart + (tend-tstart)/2
segments_df.loc[segments_df[Segment.channel_id.key] == 2,
Segment.request_start.key] = tstart
segments_df.loc[segments_df[Segment.channel_id.key] == 2,
Segment.request_end.key] = tend
# build a segments_df of the three segments belonging to the same channel
# copy at the end to avoid pandas settingwithcopy warning
new_segments_df = \
segments_df.loc[segments_df[Segment.channel_id.key].isin([1, 2, 3]), :].copy()
# change urlread_side_effect to provide, for the first three segments, the same
# sequence of bytes. The sequence actually is OK, but in the first case it will be
# PARTIALLY saved in the second case TOTALLY, and in the thrid case NOT AT ALL:
urlread_sideeffect = [self._seg_data, self._seg_data, self._seg_data]
# define a dc_dataselect_manager for open data only:
dc_dataselect_manager = DcDataselectManager(datacenters_df, Authorizer(None), False)
ztatz = self.download_save_segments(urlread_sideeffect, db.session, new_segments_df,
dc_dataselect_manager,
chaid2mseedid,
self.run.id, False,
request_timebounds_need_update,
1, 2, 3, db_bufsize=self.db_buf_size)
db_segments_df = dbquery2df(db.session.query(*cols))
# re-sort db_segments_df to match the segments_df:
ret = [db_segments_df[db_segments_df[Segment.channel_id.key] == cha]
for cha in segments_df[Segment.channel_id.key]]
db_segments_df = pd.concat(ret, axis=0)
# assert the 1st segment whose time range has been modified has data, BUT
# download_status_code still TIMEBOUNDS_ERROR
df__ = db_segments_df.loc[db_segments_df[Segment.channel_id.key] == 1, :]
assert len(df__) == 1
row__ = df__.iloc[0]
assert row__[Segment.download_code.key] == OUTTIME_WARN
assert len(row__[Segment.data.key]) > 0
# assert the 2nd segment whose time range has been modified has data, AND
# download_status_code 200 (ok)
df__ = db_segments_df.loc[db_segments_df[Segment.channel_id.key] == 2, :]
assert len(df__) == 1
row__ = df__.iloc[0]
assert row__[Segment.download_code.key] == 200
assert len(row__[Segment.data.key]) > 0
# assert the 3rd segment whose time range has NOT been modified has no data,
# AND download_status_code is still TIMEBOUNDS_ERROR
df__ = db_segments_df.loc[db_segments_df[Segment.channel_id.key] == 3, :]
assert len(df__) == 1
row__ = df__.iloc[0]
assert row__[Segment.download_code.key] == OUTTIME_ERR
assert len(row__[Segment.data.key]) == 0
def test_download_save_segments(self, mock_updatedf, mock_insertdf, mseed_unpack, db,
tt_ak135_tts):
# prepare:
# mseed unpack takes no starttime and endtime arguments, so that
# we do not discard any correct chunk
mseed_unpack.side_effect = lambda *a, **v: unpack(a[0])
mock_insertdf.side_effect = lambda *a, **v: insertdf(*a, **v)
mock_updatedf.side_effect = lambda *a, **v: updatedf(*a, **v)
urlread_sideeffect = None # use defaults from class
events_df = self.get_events_df(urlread_sideeffect, db.session)
net, sta, loc, cha = [], [], [], []
datacenters_df, eidavalidator = \
self.get_datacenters_df(urlread_sideeffect, db.session, self.service,
self.routing_service, net, sta, loc, cha,
db_bufsize=self.db_buf_size)
channels_df = self.get_channels_df(urlread_sideeffect, db.session,
datacenters_df,
eidavalidator,
net, sta, loc, cha, None, None, 10,
False, None, None, -1, self.db_buf_size)
assert len(channels_df) == 12 # just to be sure. If failing, we might have changed the class default
# events_df
# id magnitude latitude longitude depth_km time
# 0 20160508_0000129 3.0 1.0 1.0 60.0 2016-05-08 05:17:11.500
# 1 20160508_0000004 4.0 2.0 2.0 2.0 2016-05-08 01:45:30.300
# channels_df (index not shown):
# columns:
# id station_id latitude longitude datacenter_id start_time end_time network station location channel
# data (not aligned with columns):
# 1 1 1.0 1.0 1 2003-01-01 NaT GE FLT1 HHE
# 2 1 1.0 1.0 1 2003-01-01 NaT GE FLT1 HHN
# 3 1 1.0 1.0 1 2003-01-01 NaT GE FLT1 HHZ
# 4 2 90.0 90.0 1 2009-01-01 NaT n1 s c1
# 5 2 90.0 90.0 1 2009-01-01 NaT n1 s c2
# 6 2 90.0 90.0 1 2009-01-01 NaT n1 s c3
# 7 3 1.0 1.0 2 2003-01-01 NaT IA BAKI BHE
# 8 3 1.0 1.0 2 2003-01-01 NaT IA BAKI BHN
# 9 3 1.0 1.0 2 2003-01-01 NaT IA BAKI BHZ
# 10 4 90.0 90.0 2 2009-01-01 NaT n2 s c1
# 11 4 90.0 90.0 2 2009-01-01 NaT n2 s c2
# 12 4 90.0 90.0 2 2009-01-01 NaT n2 s c3
assert all(_ in channels_df.columns for _ in [Station.network.key, Station.station.key,
Channel.location.key, Channel.channel.key])
chaid2mseedid = chaid2mseedid_dict(channels_df)
# check that we removed the columns:
assert not any(_ in channels_df.columns for _ in
[Station.network.key, Station.station.key,
Channel.location.key, Channel.channel.key])
# take all segments:
# use minmag and maxmag
ttable = tt_ak135_tts
segments_df = merge_events_stations(events_df, channels_df, dict(minmag=10, maxmag=10,
minmag_radius=10, maxmag_radius=10), tttable=ttable)
assert len(pd.unique(segments_df['arrival_time'])) == 2
h = 9
# segments_df (index not shown). Note that
# cid sid did n s l c ed event_id depth_km time <- LAST TWO ARE Event related columns that will be removed after arrival_time calculations
# 1 1 1 GE FLT1 HHE 0.0 20160508_0000129 60.0 2016-05-08 05:17:11.500
# 2 1 1 GE FLT1 HHN 0.0 20160508_0000129 60.0 2016-05-08 05:17:11.500
# 3 1 1 GE FLT1 HHZ 0.0 20160508_0000129 60.0 2016-05-08 05:17:11.500
# 7 3 2 IA BAKI BHE 0.0 20160508_0000129 60.0 2016-05-08 05:17:11.500
# 8 3 2 IA BAKI BHN 0.0 20160508_0000129 60.0 2016-05-08 05:17:11.500
# 9 3 2 IA BAKI BHZ 0.0 20160508_0000129 60.0 2016-05-08 05:17:11.500
# 4 2 1 n1 s c1 0.0 20160508_0000004 2.0 2016-05-08 01:45:30.300
# 5 2 1 n1 s c2 0.0 20160508_0000004 2.0 2016-05-08 01:45:30.300
# 6 2 1 n1 s c3 0.0 20160508_0000004 2.0 2016-05-08 01:45:30.300
# 10 4 2 n2 s c1 0.0 20160508_0000004 2.0 2016-05-08 01:45:30.300
# 11 4 2 n2 s c2 0.0 20160508_0000004 2.0 2016-05-08 01:45:30.300
# 12 4 2 n2 s c3 0.0 20160508_0000004 2.0 2016-05-08 01:45:30.300
# LEGEND:
# cid = channel_id
# sid = station_id
# scid = datacenter_id
# n, s, l, c = network, station, location, channel
# ed = event_distance_deg
# define a dc_dataselect_manager for open data only:
dc_dataselect_manager = DcDataselectManager(datacenters_df, Authorizer(None), False)
wtimespan = [1,2]
expected = len(segments_df) # no segment on db, we should have all segments to download
orig_segments_df = segments_df.copy()
segments_df, request_timebounds_need_update = \
prepare_for_download(db.session, orig_segments_df, dc_dataselect_manager, wtimespan,
retry_seg_not_found=True,
retry_url_err=True,
retry_mseed_err=True,
retry_client_err=True,
retry_server_err=True,
retry_timespan_err=True,
retry_timespan_warn=True)
# segments_df
# COLUMNS:
# channel_id datacenter_id network station location channel event_distance_deg event_id arrival_time start_time end_time id download_status_code run_id
# DATA (not aligned with columns):
# channel_id datacenter_id network station location channel event_distance_deg event_id arrival_time start_time end_time id download_status_code run_id
# GE.FLT1..HHE 1 1 GE FLT1 HHE 0.0 1 2016-05-08 05:17:12.500 2016-05-08 05:16:12 2016-05-08 05:19:12 None None 1
# GE.FLT1..HHN 2 1 GE FLT1 HHN 0.0 1 2016-05-08 05:17:12.500 2016-05-08 05:16:12 2016-05-08 05:19:12 None None 1
# GE.FLT1..HHZ 3 1 GE FLT1 HHZ 0.0 1 2016-05-08 05:17:12.500 2016-05-08 05:16:12 2016-05-08 05:19:12 None None 1
# IA.BAKI..BHE 7 2 IA BAKI BHE 0.0 1 2016-05-08 05:17:12.500 2016-05-08 05:16:12 2016-05-08 05:19:12 None None 1
# IA.BAKI..BHN 8 2 IA BAKI BHN 0.0 1 2016-05-08 05:17:12.500 2016-05-08 05:16:12 2016-05-08 05:19:12 None None 1
# IA.BAKI..BHZ 9 2 IA BAKI BHZ 0.0 1 2016-05-08 05:17:12.500 2016-05-08 05:16:12 2016-05-08 05:19:12 None None 1
# n1.s..c1 4 1 n1 s c1 0.0 2 2016-05-08 01:45:31.300 2016-05-08 01:44:31 2016-05-08 01:47:31 None None 1
# n1.s..c2 5 1 n1 s c2 0.0 2 2016-05-08 01:45:31.300 2016-05-08 01:44:31 2016-05-08 01:47:31 None None 1
# n1.s..c3 6 1 n1 s c3 0.0 2 2016-05-08 01:45:31.300 2016-05-08 01:44:31 2016-05-08 01:47:31 None None 1
# n2.s..c1 10 2 n2 s c1 0.0 2 2016-05-08 01:45:31.300 2016-05-08 01:44:31 2016-05-08 01:47:31 None None 1
# n2.s..c2 11 2 n2 s c2 0.0 2 2016-05-08 01:45:31.300 2016-05-08 01:44:31 2016-05-08 01:47:31 None None 1
# n2.s..c3 12 2 n2 s c3 0.0 2 2016-05-08 01:45:31.300 2016-05-08 01:44:31 2016-05-08 01:47:31 None None 1
# self._segdata is the folder file of a "valid" 3-channel miniseed
# The channels are:
# Thus, no match will be found and all segments will be written with a None
# download status code
# setup urlread: first three rows: ok
# rows[3:6]: 413, retry them
# rows[6:9]: malformed_data
# rows[9:12] 413, retry them
# then retry:
# rows[3]: empty_data
# rows[4]: data_with_gaps (but seed_id should notmatch)
# rows[5]: data_with_gaps (seed_id should notmatch)
# rows[9]: URLError
# rows[10]: Http 500 error
# rows[11]: 413
# NOTE THAT THIS RELIES ON THE FACT THAT THREADS ARE EXECUTED IN THE ORDER OF THE DATAFRAME
# WHICH SEEMS TO BE THE CASE AS THERE IS ONE SINGLE PROCESS
# self._seg_data[:2] is a way to mock data corrupted
urlread_sideeffect = [self._seg_data, 413, self._seg_data[:2], 413,
'', self._seg_data_gaps, self._seg_data_gaps,
URLError("++urlerror++"), 500, 413]
# Let's go:
ztatz = self.download_save_segments(urlread_sideeffect, db.session, segments_df,
dc_dataselect_manager,
chaid2mseedid,
self.run.id, False,
request_timebounds_need_update,
1, 2, 3, db_bufsize=self.db_buf_size)
# get columns from db which we are interested on to check
cols = [Segment.id, Segment.channel_id, Segment.datacenter_id,
Segment.download_code, Segment.maxgap_numsamples, \
Segment.sample_rate, Segment.data_seed_id, Segment.data, Segment.download_id,
Segment.request_start, Segment.request_end, Segment.start_time, Segment.end_time
]
db_segments_df = dbquery2df(db.session.query(*cols))
assert Segment.download_id.key in db_segments_df.columns
# change data column otherwise we cannot display db_segments_df.
# When there is data just print "data"
db_segments_df.loc[(~pd.isnull(db_segments_df[Segment.data.key])) &
(db_segments_df[Segment.data.key].str.len() > 0),
Segment.data.key] = b'data'
# assert we have 4 segments with "data" properly set:
assert len(db_segments_df.loc[(~pd.isnull(db_segments_df[Segment.data.key])) &
(db_segments_df[Segment.data.key].str.len() > 0),
Segment.data.key]) == 4
# re-sort db_segments_df to match the segments_df:
ret = []
for cha in segments_df[Segment.channel_id.key]:
ret.append(db_segments_df[db_segments_df[Segment.channel_id.key] == cha])
db_segments_df = pd.concat(ret, axis=0)
# db_segments_df:
# id channel_id datacenter_id download_status_code max_gap_ovlap_ratio sample_rate data_seed_id data run_id start_time end_time
# 0 1 1 1 200.0 0.0001 100.0 GE.FLT1..HHE data 1 2016-05-08 05:16:12 2016-05-08 05:19:12
# 1 2 2 1 200.0 0.0001 100.0 GE.FLT1..HHN data 1 2016-05-08 05:16:12 2016-05-08 05:19:12
# 2 3 3 1 200.0 0.0001 100.0 GE.FLT1..HHZ data 1 2016-05-08 05:16:12 2016-05-08 05:19:12
# 6 7 7 2 200.0 NaN NaN None 1 2016-05-08 05:16:12 2016-05-08 05:19:12
# 7 8 8 2 NaN NaN NaN None None 1 2016-05-08 05:16:12 2016-05-08 05:19:12
# 8 9 9 2 200.0 20.0 20.0 IA.BAKI..BHZ data 1 2016-05-08 05:16:12 2016-05-08 05:19:12
# 3 4 4 1 -2.0 NaN NaN None None 1 2016-05-08 01:44:31 2016-05-08 01:47:31
# 4 5 5 1 -2.0 NaN NaN None None 1 2016-05-08 01:44:31 2016-05-08 01:47:31
# 5 6 6 1 -2.0 NaN NaN None None 1 2016-05-08 01:44:31 2016-05-08 01:47:31
# 9 10 10 2 -1.0 NaN NaN None None 1 2016-05-08 01:44:31 2016-05-08 01:47:31
# 10 11 11 2 500.0 NaN NaN None None 1 2016-05-08 01:44:31 2016-05-08 01:47:31
# 11 12 12 2 413.0 NaN NaN None None 1 2016-05-08 01:44:31 2016-05-08 01:47:31
assert len(ztatz) == len(datacenters_df)
assert len(db_segments_df) == len(segments_df)
assert mock_updatedf.call_count == 0
dsc = db_segments_df[Segment.download_code.key]
exp_dsc = np.array([200, 200, 200, 200, np.nan, 200, -2, -2, -2, -1, 500, 413])
assert ((dsc == exp_dsc) | (np.isnan(dsc) & np.isnan(exp_dsc))).all()
# as we have 12 segments and a buf size of self.db_buf_size(=1, but it might change in the
# future), this below is two
# it might change if we changed the buf size in the future
# test that we correctly called mock_insertdf. Note that we assume that the
# latter is called ONLY inside DbManager. To test that, as the number of stuff
# to be added (length of the dataframes) varies, we need to implement a counter here:
mock_insertdf_call_count = 0
_bufzise = 0
for c in mock_insertdf.call_args_list:
c_args = c[0]
df_ = c_args[0]
_bufzise += len(df_)
if _bufzise >= self.db_buf_size:
mock_insertdf_call_count += 1
_bufzise = 0
assert mock_insertdf.call_count == mock_insertdf_call_count
# assert data is consistent
COL = Segment.data.key
assert (db_segments_df.iloc[:3][COL] == b'data').all()
assert (db_segments_df.iloc[3:4][COL] == b'').all()
assert pd.isnull(db_segments_df.iloc[4:5][COL]).all()
assert (db_segments_df.iloc[5:6][COL] == b'data').all()
assert pd.isnull(db_segments_df.iloc[6:][COL]).all()
# assert downdload status code is consistent
URLERR_CODE, MSEEDERR_CODE = s2scodes.url_err, s2scodes.mseed_err
# also this asserts that we grouped for dc starttime endtime
COL = Segment.download_code.key
assert (db_segments_df.iloc[:4][COL] == 200).all()
assert pd.isnull(db_segments_df.iloc[4:5][COL]).all()
assert (db_segments_df.iloc[5:6][COL] == 200).all()
assert (db_segments_df.iloc[6:9][COL] == MSEEDERR_CODE).all()
assert (db_segments_df.iloc[9][COL] == URLERR_CODE).all()
assert (db_segments_df.iloc[10][COL] == 500).all()
assert (db_segments_df.iloc[11][COL] == 413).all()
# assert gaps are only in the given position
COL = Segment.maxgap_numsamples.key
assert (db_segments_df.iloc[:3][COL] < 0.01).all()
assert pd.isnull(db_segments_df.iloc[3:5][COL]).all()
assert (db_segments_df.iloc[5][COL] == 20).all()
assert pd.isnull(db_segments_df.iloc[6:][COL]).all()
# now mock retry:
segments_df, request_timebounds_need_update = \
prepare_for_download(db.session, orig_segments_df, dc_dataselect_manager, wtimespan,
retry_seg_not_found=True,
retry_url_err=True,
retry_mseed_err=True,
retry_client_err=True,
retry_server_err=True,
retry_timespan_err=True,
retry_timespan_warn=True)
assert request_timebounds_need_update is False
COL = Segment.download_code.key
mask = (db_segments_df[COL] >= 400) | pd.isnull(db_segments_df[COL]) \
| (db_segments_df[COL].isin([URLERR_CODE, MSEEDERR_CODE]))
assert len(segments_df) == len(db_segments_df[mask])
urlread_sideeffect = [413]
mock_updatedf.reset_mock()
mock_insertdf.reset_mock()
# define a dc_dataselect_manager for open data only:
dc_dataselect_manager = DcDataselectManager(datacenters_df, Authorizer(None), False)
# Let's go:
ztatz = self.download_save_segments(urlread_sideeffect, db.session, segments_df,
dc_dataselect_manager,
chaid2mseedid,
self.run.id, False,
request_timebounds_need_update,
1, 2, 3, db_bufsize=self.db_buf_size)
# get columns from db which we are interested on to check
cols = [Segment.download_code, Segment.channel_id]
db_segments_df = dbquery2df(db.session.query(*cols))
# change data column otherwise we cannot display db_segments_df. When there is data
# just print "data"
# db_segments_df.loc[(~pd.isnull(db_segments_df[Segment.data.key])) &
# (db_segments_df[Segment.data.key].str.len() > 0), Segment.data.key] = b'data'
# re-sort db_segments_df to match the segments_df:
ret = []
for cha in segments_df[Segment.channel_id.key]:
ret.append(db_segments_df[db_segments_df[Segment.channel_id.key] == cha])
db_segments_df = pd.concat(ret, axis=0)
assert (db_segments_df[COL] == 413).all()
assert len(ztatz) == len(datacenters_df)
assert len(db_segments_df) == len(segments_df)
# same as above: but with updatedf: test that we correctly called mock_insertdf_napkeys.
# Note that we assume that the latter is called ONLY inside download.main.DbManager.
# To test that, as the number of stuff to be added (length of the dataframes) varies,
# we need to implement a counter here:
mock_updatedf_call_count = 0
_bufzise = 0
for c in mock_updatedf.call_args_list:
c_args = c[0]
df_ = c_args[0]
_bufzise += len(df_)
if _bufzise >= self.db_buf_size:
mock_updatedf_call_count += 1
_bufzise = 0
assert mock_updatedf.call_count == mock_updatedf_call_count
assert mock_insertdf.call_count == 0
def test_retry2(self, mock_get_opener, mock_get_data_from_token,
mock_get_data_from_userpass,
mock_get_data_open, mock_updatedf, mock_insertdf, mock_mseed_unpack,
mock_download_save_segments, mock_save_inventories, mock_get_channels_df,
mock_get_datacenters_df, mock_get_events_df,
# fixtures:
db, clirunner, pytestdir, yamlfile):
mock_get_events_df.side_effect = lambda *a, **v: self.get_events_df(None, *a, **v)
mock_get_datacenters_df.side_effect = \
lambda *a, **v: self.get_datacenters_df(None, *a, **v)
mock_get_channels_df.side_effect = lambda *a, **v: self.get_channels_df(None, *a, **v)
mock_save_inventories.side_effect = lambda *a, **v: self.save_inventories(None, *a, **v)
RESPONSES = [URLError('abc')]
mock_download_save_segments.side_effect = \
lambda *a, **v: self.download_save_segments(RESPONSES, *a, **v)
# mseed unpack is mocked by accepting only first arg (so that time bounds are not
# considered)
mock_mseed_unpack.side_effect = lambda *a, **v: unpack(a[0])
mock_insertdf.side_effect = lambda *a, **v: insertdf(*a, **v)
mock_updatedf.side_effect = lambda *a, **v: updatedf(*a, **v)
# prevlen = len(db.session.query(Segment).all())
# patching class methods while preserving the original call requires storing once
# the original methods (as class attributes). Sets the side effect of the mocked method
# to those class attributes as to preserve the original functionality
# and be able to assert mock_* functions are called and so on
# For info see:
# https://stackoverflow.com/a/29563665
mock_get_data_open.side_effect = self.dc_get_data_open
mock_get_data_from_userpass.side_effect = self.dc_get_data_from_userpass
mock_get_data_from_token.side_effect = \
lambda *a, **kw: self.dc_get_data_from_token(['a:b', 'c:d'], *a, **kw)
# TEST 1: provide a file with valid token:
tokenfile = pytestdir.newfile(create=True)
with open(tokenfile, 'w') as fh:
fh.write('BEGIN PGP MESSAGE')
# mock yaml_load to override restricted_data:
# USERPASS good for both datacenter:
mock_get_data_open.reset_mock()
mock_get_data_from_token.reset_mock()
mock_get_data_from_userpass.reset_mock()
mock_get_opener.reset_mock()
mock_get_data_from_token.side_effect = \
lambda *a, **kw: self.dc_get_data_from_token(['uzer:pazzword', 'uzer:pazzword'],
*a, **kw)
yaml_file = yamlfile(restricted_data=os.path.abspath(tokenfile),
retry_client_err=False)
result = clirunner.invoke(cli, ['download',
'-c', yaml_file,
'--dburl', db.dburl,
'--start', '2016-05-08T00:00:00',
'--end', '2016-05-08T9:00:00'])
assert clirunner.ok(result)
# get db data, sort by index and reset index to assure comparison across data frames:
seg_df = dbquery2df(db.session.query(Segment.id, Segment.download_code,
Segment.queryauth, Segment.download_id))\
.sort_values(by=[Segment.id.key]).reset_index(drop=True)
# seg_df:
# id download_code queryauth download_id
# 1 -1 True 2
# 2 -1 True 2
# 3 -1 True 2
# 4 -1 True 2
# 5 -1 True 2
# 6 -1 True 2
# 7 -1 True 2
# 8 -1 True 2
# 9 -1 True 2
# 10 -1 True 2
# 11 -1 True 2
# 12 -1 True 2
urlerr, mseederr = s2scodes.url_err, s2scodes.mseed_err
# according to our mock, we should have all urlerr codes:
assert (seg_df[Segment.download_code.key] == urlerr).all()
assert (seg_df[Segment.queryauth.key] == True).all()
DOWNLOADID = 2
assert (seg_df[Segment.download_id.key] == DOWNLOADID).all()
# other assertions:
assert 'restricted_data: %s' % os.path.abspath(tokenfile) in result.output
assert 'STEP 5 of 8: Acquiring credentials from token' in result.output
# assert we print that we are downloading open and restricted data:
assert re.search(r'STEP 7 of 8\: Downloading \d+ segments and saving to db',
result.output)
assert not mock_get_data_open.called
assert mock_get_data_from_token.called
assert not mock_get_data_from_userpass.called
# no credentials failed:
assert "Downloading open data only from: " not in result.output
# Ok, test retry:
new_seg_df = seg_df.copy()
# first get run id
# we have 12 segments, change the download codes. The second boolean
# value denotes queryauth (True or False):
code_queryauth = [(204, False), (204, True), (404, False), (404, True),
(401, False), (401, True), (403, False), (403, True),
(400, True), (400, False), (None, False), (None, True)]
for id_, (dc_, qa_) in zip(seg_df[Segment.id.key].tolist(), code_queryauth):
seg = db.session.query(Segment).filter(Segment.id == id_).first()
seg.download_code = dc_
seg.queryauth = qa_
# set expected values (see also yamlfile below)
# remember that any segment download will give urlerr as code
expected_new_download_code = dc_
expected_download_id = DOWNLOADID
if dc_ in (204, 404, 401, 403) and qa_ is False:
# to retry becaue they failed due to authorization problems
# (or most likely they did)
expected_new_download_code = urlerr
expected_download_id = DOWNLOADID + 1
elif dc_ is None or (dc_ < 400 and dc_ >= 500):
# to retry because of the flags (see yamlfile below)
expected_new_download_code = urlerr
expected_download_id = DOWNLOADID + 1
expected_query_auth = qa_ if dc_ == 400 else True
new_seg_df.loc[new_seg_df[Segment.id.key] == id_, :] = \
(id_, expected_new_download_code, expected_query_auth, expected_download_id)
db.session.commit()
# re-download and check what we have retried:
yaml_file = yamlfile(restricted_data=os.path.abspath(tokenfile),
retry_seg_not_found=True,
retry_client_err=False)
result = clirunner.invoke(cli, ['download',
'-c', yaml_file,
'--dburl', db.dburl,
'--start', '2016-05-08T00:00:00',
'--end', '2016-05-08T9:00:00'])
DOWNLOADID += 1
assert clirunner.ok(result)
# get db data, sort by index and reset index to assure comparison across data frames:
seg_df2 = dbquery2df(db.session.query(Segment.id, Segment.download_code, Segment.queryauth,
Segment.download_id))\
.sort_values(by=[Segment.id.key]).reset_index(drop=True)
# seg_df2:
# id download_code queryauth download_id
# 1 -1 True 3
# 2 204 True 2
# 3 -1 True 3
# 4 404 True 2
# 5 -1 True 3
# 6 401 True 2
# 7 -1 True 3
# 8 403 True 2
# 9 400 True 2
# 10 400 False 2
# 11 -1 True 3
# 12 -1 True 3
pd.testing.assert_frame_equal(seg_df2, new_seg_df)
# Another retry without modifyiung the segments but setting retry_client_err to True
# re-download and check what we have retried:
yaml_file = yamlfile(restricted_data=os.path.abspath(tokenfile),
retry_seg_not_found=True,
retry_client_err=True)
result = clirunner.invoke(cli, ['download',
'-c', yaml_file,
'--dburl', db.dburl,
'--start', '2016-05-08T00:00:00',
'--end', '2016-05-08T9:00:00'])
DOWNLOADID += 1
assert clirunner.ok(result)
# get db data, sort by index and reset index to assure comparison across data frames:
seg_df3 = dbquery2df(db.session.query(Segment.id, Segment.download_code, Segment.queryauth,
Segment.download_id))\
.sort_values(by=[Segment.id.key]).reset_index(drop=True)
expected_df = seg_df2.copy()
# modify all 4xx codes as they are updated. Note that old urlerr codes have the old
# download id (do not override)
old_4xx = expected_df[Segment.download_code.key].between(400, 499.999)
expected_df.loc[old_4xx, Segment.download_id.key] = DOWNLOADID
expected_df.loc[old_4xx, Segment.queryauth.key] = True
expected_df.loc[old_4xx, Segment.download_code.key] = urlerr
# seg_df3:
# id download_code queryauth download_id
# 1 -1 True 3
# 2 204 True 2
# 3 -1 True 3
# 4 -1 True 4
# 5 -1 True 3
# 6 -1 True 4
# 7 -1 True 3
# 8 -1 True 4
# 9 -1 True 4
# 10 -1 True 4
# 11 -1 True 3
# 12 -1 True 3
pd.testing.assert_frame_equal(seg_df3, expected_df)
old_urlerr_segids = seg_df2[seg_df2[Segment.download_code.key] == urlerr][Segment.id.key]
new_urlerr_df = expected_df[expected_df[Segment.id.key].isin(old_urlerr_segids)]
assert (new_urlerr_df[Segment.download_id.key] == 3).all()
def test_retry(self, mock_get_opener, mock_get_data_from_token,
mock_get_data_from_userpass,
mock_get_data_open, mock_updatedf, mock_insertdf, mock_mseed_unpack,
mock_download_save_segments, mock_save_inventories, mock_get_channels_df,
mock_get_datacenters_df, mock_get_events_df,
# fixtures:
db, clirunner, pytestdir, yamlfile):
mock_get_events_df.side_effect = lambda *a, **v: self.get_events_df(None, *a, **v)
mock_get_datacenters_df.side_effect = \
lambda *a, **v: self.get_datacenters_df(None, *a, **v)
mock_get_channels_df.side_effect = lambda *a, **v: self.get_channels_df(None, *a, **v)
mock_save_inventories.side_effect = lambda *a, **v: self.save_inventories(None, *a, **v)
mock_download_save_segments.side_effect = \
lambda *a, **v: self.download_save_segments([URLError('abc')], *a, **v)
# mseed unpack is mocked by accepting only first arg (so that time bounds are
# not considered)
mock_mseed_unpack.side_effect = lambda *a, **v: unpack(a[0])
mock_insertdf.side_effect = lambda *a, **v: insertdf(*a, **v)
mock_updatedf.side_effect = lambda *a, **v: updatedf(*a, **v)
# prevlen = len(db.session.query(Segment).all())
# mock our opener
m = Mock()
mockopen = Mock()
mockopen.read = lambda *a, **v: b''
mockopen.msg = 'abc'
mockopen.code = 204
m.open = lambda *a, **v: mockopen
# m.read = lambda *a, **v: ''
mock_get_opener.side_effect = lambda *a, **v: m
# patching class methods while preserving the original call requires storing once
# the original methods (as class attributes). Sets the side effect of the mocked method
# to those class attributes as to preserve the original functionality
# and be able to assert mock_* functions are called and so on
# For info see:
# https://stackoverflow.com/a/29563665
mock_get_data_open.side_effect = self.dc_get_data_open
mock_get_data_from_userpass.side_effect = self.dc_get_data_from_userpass
mock_get_data_from_token.side_effect = \
lambda *a, **kw: self.dc_get_data_from_token([URLError('a'), 'abc'], *a, **kw)
# TEST 1: provide a file with valid token:
tokenfile = pytestdir.newfile(create=True)
with open(tokenfile, 'w') as fh:
fh.write('BEGIN PGP MESSAGE')
# mock yaml_load to override restricted_data:
# launch two download runs with different responses for token auth query:
for tokenquery_mocked_return_values, dc_token_failed in \
([[URLError('a'), 'uzer:pazzword'], "http://geofon.gfz-potsdam.de"],
[['uzer:pazzword', URLError('a')], 'http://ws.resif.fr']):
# set how many times self.mock_urlopen has been called:
mock_urlopen_call_count = self.mock_urlopen.call_count
# TEST 2: USERPASS good for just one datacenter:
mock_get_data_open.reset_mock()
mock_get_data_from_token.reset_mock()
mock_get_data_from_userpass.reset_mock()
mock_get_opener.reset_mock()
mock_get_data_from_token.side_effect = \
lambda *a, **kw: self.dc_get_data_from_token(tokenquery_mocked_return_values,
*a, **kw)
yaml_file = yamlfile(restricted_data=os.path.abspath(tokenfile),
retry_client_err=False)
result = clirunner.invoke(cli, ['download',
'-c', yaml_file,
'--dburl', db.dburl,
'--start', '2016-05-08T00:00:00',
'--end', '2016-05-08T9:00:00'])
assert clirunner.ok(result)
assert 'restricted_data: %s' % os.path.abspath(tokenfile) in result.output
assert 'STEP 5 of 8: Acquiring credentials from token' in result.output
# assert we print that we are downloading open and restricted data:
assert re.search(r'STEP 7 of 8\: Downloading \d+ segments and saving to db',
result.output)
assert not mock_get_data_open.called
assert mock_get_data_from_token.called
assert not mock_get_data_from_userpass.called
assert "Downloading open data only from: %s" % dc_token_failed
dc_token_ok = 'http://ws.resif.fr' \
if dc_token_failed == "http://geofon.gfz-potsdam.de" else \
"http://geofon.gfz-potsdam.de"
assert mock_get_opener.call_count == 1
assert mock_get_opener.call_args_list[0][0][:] == (dc_token_ok, 'uzer', 'pazzword')
dc_id = {Fdsnws(i[1]).site: i[0] for i in
db.session.query(DataCenter.id, DataCenter.dataselect_url)}
# assert urlopen has been called only once with query and not queryauth:
# get the segments dataframe we (re)downloaded:
segments_df_to_download = mock_download_save_segments.call_args_list[-1][0][1]
dc2download = pd.unique(segments_df_to_download['datacenter_id']).tolist()
# set the expected call count based on the datacenters of (re)downloaded segments:
if dc_id[dc_token_failed] not in dc2download:
assert self.mock_urlopen.call_count == 0
else:
assert self.mock_urlopen.call_count >= 1
for i in range(self.mock_urlopen.call_count):
i+=1
assert self.mock_urlopen.call_args_list[-i][0][0].get_full_url() == \
dc_token_failed + "/fdsnws/dataselect/1/query"
def test_restricted(self, mock_get_opener, mock_get_data_from_token,
mock_get_data_from_userpass,
mock_get_data_open, mock_updatedf, mock_insertdf, mock_mseed_unpack,
mock_download_save_segments, mock_save_inventories, mock_get_channels_df,
mock_get_datacenters_df, mock_get_events_df,
# fixtures:
db, clirunner, pytestdir, yamlfile):
mock_get_events_df.side_effect = lambda *a, **v: self.get_events_df(None, *a, **v)
mock_get_datacenters_df.side_effect = \
lambda *a, **v: self.get_datacenters_df(None, *a, **v)
mock_get_channels_df.side_effect = lambda *a, **v: self.get_channels_df(None, *a, **v)
mock_save_inventories.side_effect = lambda *a, **v: self.save_inventories(None, *a, **v)
mock_download_save_segments.side_effect = \
lambda *a, **v: self.download_save_segments(None, *a, **v)
# mseed unpack is mocked by accepting only first arg
# (so that time bounds are not considered)
mock_mseed_unpack.side_effect = lambda *a, **v: unpack(a[0])
mock_insertdf.side_effect = lambda *a, **v: insertdf(*a, **v)
mock_updatedf.side_effect = lambda *a, **v: updatedf(*a, **v)
# prevlen = len(db.session.query(Segment).all())
# patching class methods while preserving the original call requires storing once
# the original methods (as class attributes). Sets the side effect of the mocked method
# to those class attributes as to preserve the original functionality
# and be able to assert mock_* functions are called and so on
# For info see:
# https://stackoverflow.com/a/29563665
mock_get_data_open.side_effect = self.dc_get_data_open
mock_get_data_from_userpass.side_effect = self.dc_get_data_from_userpass
mock_get_data_from_token.side_effect = \
lambda *a, **kw: self.dc_get_data_from_token([URLError('a'), 'abc'], *a, **kw)
# TEST 1: provide a file with valid token:
tokenfile = pytestdir.newfile(create=True)
with open(tokenfile, 'w') as fh:
fh.write('BEGIN PGP MESSAGE')
# mock yaml_load to override restricted_data:
yaml_file = yamlfile(restricted_data=os.path.abspath(tokenfile))
# The run table is populated with a run_id in the constructor of this class
# for checking run_ids, store here the number of runs we have in the table:
runs = len(db.session.query(Download.id).all())
result = clirunner.invoke(cli, ['download',
'-c', yaml_file,
'--dburl', db.dburl,
'--start', '2016-05-08T00:00:00',
'--end', '2016-05-08T9:00:00'])
assert clirunner.ok(result)
assert 'Downloading 12 segments (open data only)' in result.output
assert 'STEP 5 of 8: Acquiring credentials from token' in result.output
# note that due to (probably) dict order in py2-3 we need to test both of these:
if not ('Downloading open data only from: http://geofon.gfz-potsdam.de, '
'http://ws.resif.fr (Unable to acquire credentials for restricted data)') in \
result.output:
assert ('Downloading open data only from: http://ws.resif.fr, '
'http://geofon.gfz-potsdam.de (Unable to acquire credentials for restricted data)') in \
result.output
# assert we print that we are downloading open data only (all errors):
assert 'STEP 7 of 8: Downloading 12 segments (open data only)' in result.output
assert not mock_get_data_open.called
assert mock_get_data_from_token.called
assert not mock_get_data_from_userpass.called
assert not mock_get_opener.called
# some assertions to check data properly written
# These are important because they confirm that data has been downloaded anyway
# (the test does not differentiate between restricted or open data)
assert len(db.session.query(Download.id).all()) == runs + 1
runs += 1
segments = db.session.query(Segment).all()
assert len(segments) == 12
segments = db.session.query(Segment).filter(Segment.has_data).all()
assert len(segments) == 4
assert len(db.session.query(Station).filter(Station.has_inventory).all()) == 2
assert mock_updatedf.called # called while saving inventories
assert mock_insertdf.called
def test_opendata_and_errors(self, mock_get_data_from_token, mock_get_data_from_userpass,
mock_get_data_open, mock_updatedf, mock_insertdf,
mock_mseed_unpack, mock_download_save_segments,
mock_save_inventories, mock_get_channels_df,
mock_get_datacenters_df, mock_get_events_df,
# fixtures:
db, clirunner, pytestdir, yamlfile):
mock_get_events_df.side_effect = lambda *a, **v: self.get_events_df(None, *a, **v)
mock_get_datacenters_df.side_effect = \
lambda *a, **v: self.get_datacenters_df(None, *a, **v)
mock_get_channels_df.side_effect = lambda *a, **v: self.get_channels_df(None, *a, **v)
mock_save_inventories.side_effect = lambda *a, **v: self.save_inventories(None, *a, **v)
mock_download_save_segments.side_effect = \
lambda *a, **v: self.download_save_segments(None, *a, **v)
# mseed unpack is mocked by accepting only first arg
# (so that time bounds are not considered)
mock_mseed_unpack.side_effect = lambda *a, **v: unpack(a[0])
mock_insertdf.side_effect = lambda *a, **v: insertdf(*a, **v)
mock_updatedf.side_effect = lambda *a, **v: updatedf(*a, **v)
# prevlen = len(db.session.query(Segment).all())
# patching class methods while preserving the original call requires storing once
# the original methods (as class attributes). Sets the side effect of the mocked method
# to those class attributes as to preserve the original functionality
# and be able to assert mock_* functions are called and so on
# For info see:
# https://stackoverflow.com/a/29563665
mock_get_data_open.side_effect = self.dc_get_data_open
mock_get_data_from_userpass.side_effect = self.dc_get_data_from_userpass
mock_get_data_from_token.side_effect = self.dc_get_data_from_token
# TEST 1: NORMAL CASE (NO AUTH):
# mock yaml_load to override restricted_data:
yaml_file = yamlfile(restricted_data='')
# The run table is populated with a run_id in the constructor of this class
# for checking run_ids, store here the number of runs we have in the table:
runs = len(db.session.query(Download.id).all())
result = clirunner.invoke(cli, ['download',
'-c', yaml_file,
'--dburl', db.dburl,
'--start', '2016-05-08T00:00:00',
'--end', '2016-05-08T9:00:00'])
assert clirunner.ok(result)
assert 'Downloading 12 segments (open data only)' in result.output
assert mock_get_data_open.called
assert not mock_get_data_from_token.called
assert not mock_get_data_from_userpass.called
# some assertions to check data properly written
assert len(db.session.query(Download.id).all()) == runs + 1
runs += 1
segments = db.session.query(Segment).all()
assert len(segments) == 12
segments = db.session.query(Segment).filter(Segment.has_data).all()
assert len(segments) == 4
assert len(db.session.query(Station).filter(Station.has_inventory).all()) == 2
assert mock_updatedf.called # called while saving inventories
assert mock_insertdf.called
# TEST 1: USERPASS AND EIDA (PROBLEM):
# test that we provide userpass and eida: error:
# mock yaml_load to override restricted_data:
mock_get_data_open.reset_mock()
mock_get_data_from_token.reset_mock()
mock_get_data_from_userpass.reset_mock()
yaml_file = yamlfile(restricted_data=['user', 'password'], dataws='eida')
result = clirunner.invoke(cli, ['download',
'-c', yaml_file,
'--dburl', db.dburl,
'--start', '2016-05-08T00:00:00',
'--end', '2016-05-08T9:00:00'])
assert not clirunner.ok(result)
assert ('Error: Invalid value for "restricted_data": '
'downloading from EIDA requires a token') in result.output
# TEST 2: TOKEN FILE NOT EXISTING
mock_get_data_open.reset_mock()
mock_get_data_from_token.reset_mock()
mock_get_data_from_userpass.reset_mock()
yaml_file = yamlfile(restricted_data='abcdg465du97_Sdr4fvssgflero',
dataws='eida')
result = clirunner.invoke(cli, ['download',
'-c', yaml_file,
'--dburl', db.dburl,
'--start', '2016-05-08T00:00:00',
'--end', '2016-05-08T9:00:00'])
assert not clirunner.ok(result)
assert ('Invalid token. If you passed a file path') in result.output
# TEST 2: TOKEN FILE EXISTS, INVALID (e.g. empty)
filepath = pytestdir.newfile(create=True)
mock_get_data_open.reset_mock()
mock_get_data_from_token.reset_mock()
mock_get_data_from_userpass.reset_mock()
yaml_file = yamlfile(restricted_data=os.path.abspath(filepath),
dataws='eida')
result = clirunner.invoke(cli, ['download',
'-c', yaml_file,
'--dburl', db.dburl,
'--start', '2016-05-08T00:00:00',
'--end', '2016-05-08T9:00:00'])
assert not clirunner.ok(result)
assert ('Invalid token. If you passed a file path') in result.output