def dispatch_request(ip, request):
method = REQUEST_TABLE[request['request']]
spec = inspect.getargspec(method)
# figure out what args are required
if spec[3] is not None:
required_args = set(spec[0][:-len(spec[3])]) # set of args with no default value
else:
required_args = set(spec[0]) # all args are required
# remove compulsory stuff from request dict, add IP
del request['version']
del request['api_key']
del request['nonce']
del request['signature']
del request['timestamp']
del request['request']
request['ip'] = ip
# check required args are provided and we haven't been sent superfluous args
keys = set(request.keys())
if not keys.issubset(spec[0]):
raise errors.InvalidArgumentError() # some request args aren't in method args
elif not required_args.issubset(keys):
raise errors.InvalidArgumentError() # some required method args aren't in request args
return method(**request) # call into target method
def market_depth(ip, step=0.05):
try:
step = Decimal(str(step))
except:
raise errors.InvalidArgumentError()
if step <= 0:
raise errors.InvalidArgumentError()
cursor_bid = run_market_depth_query(step, 'bid')
cursor_ask = run_market_depth_query(step, 'ask')
return {
'bid': compile_market_depth_results(cursor_bid, step),
'ask': compile_market_depth_results(cursor_ask, step)
}
def run_market_depth_query(step, column):
if column == 'bid':
offer_currency = 'GBP'
elif column == 'ask':
offer_currency = 'BTC'
else:
raise errors.InvalidArgumentError()
query = """SELECT
CAST(txdb_order.%s / %s AS UNSIGNED INTEGER) * %s as 'price',
SUM(txdb_order.want_amount) as 'volume'
FROM txdb_order
INNER JOIN txdb_balance
ON txdb_order.balance_id = txdb_balance.id
INNER JOIN txdb_currency
ON txdb_balance.currency_id = txdb_currency.id
WHERE txdb_order.filled = 0 AND txdb_order.cancelled = 0
AND txdb_currency.code = %s
GROUP BY CAST(txdb_order.%s / %s AS UNSIGNED INTEGER)
ORDER BY price ASC""" % (column, '%s', '%s', '%s', column, '%s')
cursor = connection.cursor()
cursor.execute(query, [step, step, offer_currency, step])
return cursor
def one_task_per_interval(
interval_count,
validate_task_output,
if_sequence=0,
and_end_task=True,
reuse_tasks=True,
interval_list_param="interval_list",
oldest_git_commit_to_reuse='6ca726fc265f9e55765bf1fdf71b86285b8a0ff2',
task_key_params=['name', 'inputs', 'interval', 'ref'],
script=arvados.current_job()['script']):
"""
Queue one task for each of interval_count intervals, splitting
the genome chunk (described by the .interval_list file) evenly.
Each new task will have an "inputs" parameter: a manifest
containing a set of one or more gVCF files and its corresponding
index.
Each new task will also have a "ref" parameter: a manifest
containing the reference files to use.
Note that all gVCFs not matching the group_by_regex are ignored.
if_sequence and and_end_task arguments have the same significance
as in arvados.job_setup.one_task_per_input_file().
"""
if if_sequence != arvados.current_task()['sequence']:
return
interval_list_file = gatk_helper.mount_single_gatk_interval_list_input(
interval_list_param=interval_list_param)
interval_reader = open(interval_list_file, mode="r")
lines = interval_reader.readlines()
sn_intervals = dict()
sns = []
total_len = 0
for line in lines:
if line[0] == '@':
# skip all lines starting with '@'
continue
fields = line.split("\t")
if len(fields) != 5:
raise errors.InvalidArgumentError(
"interval_list %s has invalid line [%s] - expected 5 fields but got %s"
% (interval_list_file, line, len(fields)))
sn = fields[0]
start = int(fields[1])
end = int(fields[2])
length = int(end) - int(start) + 1
total_len += int(length)
sn_intervals[sn] = (start, end)
sns.append(sn)
print "Total chunk length is %s" % total_len
interval_len = int(total_len / interval_count)
intervals = []
print "Splitting chunk into %s intervals of size ~%s" % (interval_count,
interval_len)
for interval_i in range(0, interval_count):
interval_num = interval_i + 1
intervals_count = 0
remaining_len = interval_len
interval = []
while len(sns) > 0:
sn = sns.pop(0)
if not sn_intervals.has_key(sn):
raise errors.ValueError(
"sn_intervals missing entry for sn [%s]" % sn)
start, end = sn_intervals[sn]
if (end - start + 1) > remaining_len:
# not enough space for the whole sq, split it
real_end = end
end = remaining_len + start - 1
assert ((end - start + 1) <= remaining_len)
sn_intervals[sn] = (end + 1, real_end)
sns.insert(0, sn)
interval.append("%s:%s-%s" % (sn, start, end))
remaining_len -= (end - start + 1)
intervals_count += 1
if remaining_len <= 0:
break
if intervals_count > 0:
intervals.append(interval)
else:
print "WARNING: skipping empty intervals for %s" % interval_input_name
print "Have %s intervals" % (len(intervals))
if reuse_tasks:
# get candidates for task reuse
job_filters = [
['script', '=', script],
['repository', '=',
arvados.current_job()['repository']],
['script_version', 'in git', oldest_git_commit_to_reuse],
[
'docker_image_locator', 'in docker',
arvados.current_job()['docker_image_locator']
],
]
reusable_tasks = get_reusable_tasks(if_sequence + 1, task_key_params,
job_filters)
print "Have %s potentially reusable tasks" % (len(reusable_tasks))
for interval in intervals:
interval_str = ' '.join(interval)
print "Creating task to process interval: [%s]" % interval_str
new_task_params = arvados.current_task()['parameters']
new_task_params['interval'] = interval_str
if reuse_tasks:
task = create_or_reuse_task(if_sequence + 1, new_task_params,
reusable_tasks, task_key_params,
validate_task_output)
else:
task = create_task(if_sequence + 1, new_task_params)
if and_end_task:
print "Ending task %s successfully" % if_sequence
arvados.api().job_tasks().update(uuid=arvados.current_task()['uuid'],
body={
'success': True
}).execute()
exit(0)
def one_task_per_group_combined_inputs(ref_input,
job_input,
interval_lists,
group_by_regex,
if_sequence=0,
and_end_task=True,
create_task_func=create_task):
"""
Queue one task for each group of gVCFs and corresponding interval_list
in the inputs_collection, with grouping based on three things:
- the stream in which the gVCFs are held within the collection
- the value of the named capture group "group_by" in the
group_by_regex against the filename in the inputs_collection
Each new task will have an "inputs" parameter: a manifest
containing a set of one or more gVCF files and its corresponding
index.
Each new task will also have a "ref" parameter: a manifest
containing the reference files to use.
Note that all gVCFs not matching the group_by_regex are ignored.
if_sequence and and_end_task arguments have the same significance
as in arvados.job_setup.one_task_per_input_file().
"""
if if_sequence != arvados.current_task()['sequence']:
return
group_by_r = re.compile(group_by_regex)
# prepare interval_lists
il_cr = arvados.CollectionReader(interval_lists)
il_ignored_files = []
interval_list_by_group = {}
for s in il_cr.all_streams():
for f in s.all_files():
m = re.search(group_by_r, f.name())
if m:
group_name = m.group('group_by')
interval_list_m = re.search(r'\.interval_list', f.name())
if interval_list_m:
if group_name not in interval_list_by_group:
interval_list_by_group[group_name] = dict()
interval_list_by_group[group_name][s.name(), f.name()] = f
continue
# if we make it this far, we have files that we are ignoring
il_ignored_files.append("%s/%s" % (s.name(), f.name()))
# prepare gVCF input collections
cr = arvados.CollectionReader(job_input)
ignored_files = []
last_stream_name = ""
gvcf_by_group = {}
gvcf_indices = {}
for s in sorted(cr.all_streams(), key=lambda stream: stream.name()):
stream_name = s.name()
# handle each stream name separately
if stream_name != last_stream_name:
if last_stream_name != "":
print "Done processing files in stream %s" % last_stream_name
one_task_per_gvcf_group_in_stream_combined_inputs(
last_stream_name,
gvcf_by_group,
gvcf_indices,
interval_list_by_group,
if_sequence,
ref_input,
create_task_func=create_task_func)
# now that we are done with last_stream_name, reinitialise dicts to
# process data from new stream
print "Processing files in stream %s" % stream_name
gvcf_by_group = {}
gvcf_indices = {}
last_stream_name = stream_name
# loop over all the files in this stream (there may be only one)
for f in s.all_files():
if re.search(r'\.tbi$', f.name()):
gvcf_indices[s.name(), f.name()] = f
continue
m = re.search(group_by_r, f.name())
if m:
group_name = m.group('group_by')
gvcf_m = re.search(r'\.vcf\.gz$', f.name())
if gvcf_m:
if group_name not in gvcf_by_group:
gvcf_by_group[group_name] = dict()
gvcf_by_group[group_name][s.name(), f.name()] = f
continue
interval_list_m = re.search(r'\.interval_list', f.name())
if interval_list_m:
if group_name not in interval_list_by_group:
interval_list_by_group[group_name] = dict()
if (s.name(),
f.name()) in interval_list_by_group[group_name]:
if interval_list_by_group[group_name][s.name(
), f.name()].as_manifest() != f.as_manifest():
raise errors.InvalidArgumentError(
"Already have interval_list for group %s file %s/%s, but manifests are not identical!"
% (group_name, s.name(), f.name()))
else:
interval_list_by_group[group_name][s.name(),
f.name()] = f
continue
# if we make it this far, we have files that we are ignoring
ignored_files.append("%s/%s" % (s.name(), f.name()))
# finally, process the last stream
print "Processing last stream"
one_task_per_gvcf_group_in_stream_combined_inputs(
stream_name,
gvcf_by_group,
gvcf_indices,
interval_list_by_group,
if_sequence,
ref_input,
create_task_func=create_task_func)
# report on any ignored files
if len(ignored_files) > 0:
print "WARNING: ignored non-matching files in inputs_collection: %s" % (
' '.join(ignored_files))
# TODO: could use `setmedian` from https://github.com/ztane/python-Levenshtein
# to print most representative "median" filename (i.e. skipped 15 files like median), then compare the
# rest of the files to that median (perhaps with `ratio`)
if and_end_task:
print "Ending task %s successfully" % if_sequence
arvados.api().job_tasks().update(uuid=arvados.current_task()['uuid'],
body={
'success': True
}).execute()
exit(0)
def chunked_tasks_per_cram_file(
ref_input,
job_input,
interval_lists,
validate_task_output,
if_sequence=0,
and_end_task=True,
reuse_tasks=True,
reuse_tasks_retrieve_all=True,
interval_list_param="interval_list",
oldest_git_commit_to_reuse='6ca726fc265f9e55765bf1fdf71b86285b8a0ff2',
script=arvados.current_job()['script']):
"""
Queue one task for each cram file in this job's input collection.
Each new task will have an "input" parameter: a manifest
containing one .cram file and its corresponding .crai index file.
Files in the input collection that are not named *.cram or *.crai
(as well as *.crai files that do not match any .cram file present)
are silently ignored.
if_sequence and and_end_task arguments have the same significance
as in arvados.job_setup.one_task_per_input_file().
"""
if if_sequence != arvados.current_task()['sequence']:
return
# prepare interval lists
cr = arvados.CollectionReader(interval_lists)
chunk_interval_list = {}
chunk_input_pdh_names = []
for s in cr.all_streams():
for f in s.all_files():
if re.search(r'\.interval_list$', f.name()):
chunk_interval_list[s.name(), f.name()] = f
for ((s_name, f_name),
chunk_interval_list_f) in sorted(chunk_interval_list.items()):
chunk_input = chunk_interval_list_f.as_manifest()
try:
r = arvados.api().collections().create(body={
"manifest_text": chunk_input
}).execute()
chunk_input_pdh = r["portable_data_hash"]
chunk_input_name = os.path.join(s_name, f_name)
chunk_input_pdh_names.append((chunk_input_pdh, chunk_input_name))
except:
raise
if len(chunk_input_pdh_names) == 0:
raise errors.InvalidArgumentError(
"No interval_list files found in %s" % (interval_lists))
# prepare CRAM input collections
cr = arvados.CollectionReader(job_input)
cram = {}
crai = {}
for s in cr.all_streams():
for f in s.all_files():
if re.search(r'\.cram$', f.name()):
cram[s.name(), f.name()] = f
elif re.search(r'\.crai$', f.name()):
crai[s.name(), f.name()] = f
for ((s_name, f_name), cram_f) in cram.items():
crai_f = crai.get(
(s_name, re.sub(r'cram$', 'crai', f_name)),
crai.get((s_name, re.sub(r'cram$', 'cram.crai', f_name)), None))
task_input = cram_f.as_manifest()
if crai_f:
task_input += crai_f.as_manifest()
else:
# no CRAI for CRAM
raise errors.InvalidArgumentError(
"No correponding CRAI file found for CRAM file %s" % f_name)
# Create a portable data hash for the task's subcollection
try:
r = arvados.api().collections().create(body={
"manifest_text": task_input
}).execute()
task_input_pdh = r["portable_data_hash"]
except:
raise
if reuse_tasks:
task_key_params = ['input', 'ref', 'chunk']
# get candidates for task reuse
job_filters = [
['script', '=', script],
['repository', '=',
arvados.current_job()['repository']],
['script_version', 'in git', oldest_git_commit_to_reuse],
[
'docker_image_locator', 'in docker',
arvados.current_job()['docker_image_locator']
],
]
if reuse_tasks_retrieve_all:
# retrieve a full set of all possible reusable tasks
reusable_tasks = get_reusable_tasks(if_sequence + 1,
task_key_params,
job_filters)
print "Have %s tasks for potential reuse" % (
len(reusable_tasks))
else:
reusable_task_jobs = get_jobs_for_task_reuse(job_filters)
print "Have %s jobs for potential task reuse" % (
len(reusable_task_jobs))
reusable_task_job_uuids = [
job['uuid'] for job in reusable_task_jobs['items']
]
for chunk_input_pdh, chunk_input_name in chunk_input_pdh_names:
# Create task for each CRAM / chunk
new_task_params = {
'input': task_input_pdh,
'ref': ref_input,
'chunk': chunk_input_pdh
}
print "Creating new task to process %s with chunk interval %s " % (
f_name, chunk_input_name)
if reuse_tasks:
if reuse_tasks_retrieve_all:
task = create_or_reuse_task(if_sequence + 1,
new_task_params,
reusable_tasks,
task_key_params,
validate_task_output)
else:
task = create_or_reuse_task_from_jobs(
if_sequence + 1, new_task_params,
reusable_task_job_uuids, task_key_params,
validate_task_output)
else:
task = create_task(if_sequence + 1, new_task_params)
if and_end_task:
print "Ending task 0 successfully"
arvados.api().job_tasks().update(uuid=arvados.current_task()['uuid'],
body={
'success': True
}).execute()
exit(0)
def one_task_per_gvcf_group_in_stream_combined_inputs(
stream_name,
gvcf_by_group,
gvcf_indices,
interval_list_by_group,
if_sequence,
ref_input_pdh,
create_task_func=create_task):
"""
Process one stream of data and launch a subtask for handling it
"""
print "Finalising stream %s" % stream_name
for group_name in sorted(gvcf_by_group.keys()):
print "Have %s gVCFs in group %s" % (len(
gvcf_by_group[group_name]), group_name)
# require interval_list for this group
if group_name not in interval_list_by_group:
raise errors.InvalidArgumentError(
"Inputs collection did not contain interval_list for group %s"
% group_name)
interval_lists = interval_list_by_group[group_name].keys()
if len(interval_lists) > 1:
raise errors.InvalidArgumentError(
"Inputs collection contained more than one interval_list for group %s: %s"
% (group_name, ' '.join(interval_lists)))
interval_list_manifest = interval_list_by_group[group_name].get(
interval_lists[0]).as_manifest()
# "combined_inputs" style is to have interval_list and inputs in same collection
task_inputs_manifest = interval_list_manifest
for ((s_name, gvcf_name), gvcf_f) in gvcf_by_group[group_name].items():
task_inputs_manifest += gvcf_f.as_manifest()
gvcf_index_f = gvcf_indices.get(
(s_name, re.sub(r'vcf.gz$', 'vcf.tbi', gvcf_name)),
gvcf_indices.get(
(s_name, re.sub(r'vcf.gz$', 'vcf.gz.tbi', gvcf_name)),
None))
if gvcf_index_f:
task_inputs_manifest += gvcf_index_f.as_manifest()
else:
# no index for gVCF - TODO: should this be an error or warning?
print "WARNING: No correponding .tbi index file found for gVCF file %s" % gvcf_name
#raise errors.InvalidArgumentError("No correponding .tbi index file found for gVCF file %s" % gvcf_name)
# Create a portable data hash for the task's subcollection
try:
r = arvados.api().collections().create(
body={
"manifest_text": task_inputs_manifest
}).execute()
task_inputs_pdh = r["portable_data_hash"]
except:
raise
# Create task to process this group
name_components = []
if len(stream_name) > 0 and stream_name != ".":
name_components.append(stream_name)
if len(group_name) > 0:
name_components.append(group_name)
if len(name_components) == 0:
name = "all"
else:
name = '::'.join(name_components)
print "Creating task to process %s" % name
new_task_params = {
'inputs': task_inputs_pdh,
'ref': ref_input_pdh,
'name': name
}
task = create_task_func(if_sequence + 1, new_task_params)
def historical_prices(ip, start_date=None, end_date=None, scope='daily'):
try:
start_date = int(start_date) if start_date is not None else None
end_date = int(end_date) if end_date is not None else None
except:
raise errors.InvalidArgumentError()
# are both valid unix timestamps?
if start_date is not None and (start_date < 0 or start_date > 0x7FFFFFFF):
raise errors.InvalidArgumentError()
elif end_date is not None and (end_date < 0 or end_date > 0x7FFFFFFF or end_date <= start_date):
raise errors.InvalidArgumentError()
# adjust scope of query
if scope == 'monthly':
group_by = 'YEAR(txdb_transaction.executed), MONTH(txdb_transaction.executed)'
elif scope == 'weekly':
group_by = 'YEARWEEK(txdb_transaction.executed)'
elif scope == 'hourly':
group_by = 'DATE(txdb_transaction.executed), HOUR(txdb_transaction.executed)'
elif scope == '15mins':
group_by = 'DATE(txdb_transaction.executed), HOUR(txdb_transaction.executed), CAST(MINUTE(txdb_transaction.executed) / 15 AS INTEGER)'
else: # scope == 'daily'
group_by = 'DATE(txdb_transaction.executed)'
# construct where clause limiting date range
date_limits = ""
date_params = []
if start_date:
date_limits += " AND txdb_transaction.executed >= FROM_UNIXTIME(%s)"
date_params.append(start_date)
if end_date:
date_limits += " AND txdb_transaction.executed <= FROM_UNIXTIME(%s)"
date_params.append(end_date)
# abusing MySQL since 2011
query = """SELECT
txdb_transaction.executed as 'timestamp',
SUBSTRING_INDEX(GROUP_CONCAT(txdb_order.bid ORDER BY txdb_transaction.executed ASC), ',', 1) AS 'open',
SUBSTRING_INDEX(GROUP_CONCAT(txdb_order.bid ORDER BY txdb_transaction.executed DESC), ',', 1) AS 'close',
SUBSTRING_INDEX(GROUP_CONCAT(txdb_order.bid ORDER BY txdb_order.bid ASC), ',', 1) AS 'low',
SUBSTRING_INDEX(GROUP_CONCAT(txdb_order.bid ORDER BY txdb_order.bid DESC), ',', 1) AS 'high',
AVG(txdb_order.bid) as 'mean',
SUM(linked_transaction.amount) as 'volume'
FROM txdb_transaction
INNER JOIN txdb_balance from_balance
ON txdb_transaction.from_balance_id = from_balance.id
INNER JOIN txdb_currency from_currency
ON from_balance.currency_id = from_currency.id
INNER JOIN txdb_transaction linked_transaction
ON txdb_transaction.linked_transaction_id = linked_transaction.id
INNER JOIN txdb_order
ON txdb_transaction.order_id = txdb_order.id
WHERE txdb_transaction.reversed = 0 AND from_currency.code = 'GBP'
%s
GROUP BY %s
ORDER BY txdb_transaction.executed ASC
LIMIT %d""" % (date_limits, group_by, settings.HISTORICAL_PRICES_ROW_LIMIT)
cursor = connection.cursor()
cursor.execute(query, date_params)
results = {
'timestamp': [],
'open': [],
'close': [],
'low': [],
'high': [],
'mean': [],
'volume': []
}
# format the results into something usable by ChartDirector
row = cursor.fetchone()
while row:
ts = calendar.timegm(floor_datetime(row[0], scope).timetuple())
results['timestamp'].append(ts)
results['open'].append(str(row[1]))
results['close'].append(str(row[2]))
results['low'].append(str(row[3]))
results['high'].append(str(row[4]))
results['mean'].append(str(row[5]))
results['volume'].append(str(row[6]))
row = cursor.fetchone()
return results
