def add():
form = RecordForm()
if form.validate_on_submit():
record = Record()
record.id = uuid.uuid4()
record.value = request.form['value']
record.timestamp = datetime.now()
RECORDS.append(record)
return redirect(url_for('home'))
return render_template('add.html', form=form)
def post(self):
name = self.request.headers["mapreduce-id"]
if not name:
name = "NAME NOT FOUND"
logging.info("MR CALLBACK " + name)
q = Record.all()
q.filter('mr_id =', name)
q.fetch(1)
if q:
for ii in q:
t = memcache.get('fsm_mapper_cleanup')
if not t:
logging.error("Unable to get datetime from memcache")
return False
dt, msec = t.split(".")
dt = datetime.datetime.strptime(dt, '%Y-%m-%d %H:%M:%S')
msec = datetime.timedelta(microseconds = int(msec))
fullDatetime = dt + msec
ii.end = fullDatetime
delta = (ii.end - ii.start)
ii.total = float(delta.days * 86400 + delta.seconds) + float(delta.microseconds)/1000000
ii.state = "Done"
ii.put()
logging.info("updated: record for MR job id %s"%name)
else:
logging.info("Unable to find record for MR job id %s"%name)
def recordsHandler():
if not g.user:
return redirect('/')
if request.method == "POST":
app.logger.info(request.method)
req = request.form
# Logout
if 'logoutBtn' in req:
app.logger.info("Logout user: "******"No medical records found.")
return redirect('/medic')
resList = []
for res in result:
print(res)
resList.append(Record(res[0], res[1], res[2], res[3]))
return render_template('records.html',
list=resList,
client_name=resList[0].name,
client_tel=resList[0].tel)
def get(self):
user = users.get_current_user()
if not user:
self.redirect(users.create_login_url(dest_url="/"))
return
q = SubSetDataSet.all()
q.order('-start')
results = q.fetch(1000)
datasets = [result for result in results]
datasets_len = len(datasets)
q = Record.all()
q.filter('benchmark =', "subset")
q.order('-start')
results = q.fetch(1000)
records = [result for result in results]
records_len = len(records)
fsm_count = get_fsm_count()
pl_count = get_pl_count()
mr_count = get_mr_count()
self.response.out.write(template.render("templates/subset.html",
{"user": user.email(),
"datasets_len" : datasets_len,
"datasets" : datasets,
"fsm_count" : fsm_count,
"pl_count" : pl_count,
"mr_count" : mr_count,
"records": records,
"records_len" : records_len}))
def encode_decode(headers, data):
from records import Record, print_records, dump_records
from io import StringIO, BytesIO
if not data:
return headers, data
#print headers
if 'X-WCF-Encode' in headers:
from xml2records import Parser
p = Parser()
print data
print '##################################'
p.feed(data)
data = dump_records(p.records)
print data.encode('hex')
del headers['X-WCF-Encode']
headers['Content-Type'] = 'application/soap+msbin1'
else:
if 'Content-Type' not in headers or headers[
'Content-Type'] != 'application/soap+msbin1':
return headers, data
fp = BytesIO(data)
data = Record.parse(fp)
fp.close()
fp = StringIO()
print_records(data, fp=fp)
data = fp.getvalue()
fp.close()
headers['X-WCF-Encode'] = '1'
headers['Content-Type'] = 'text/soap+xml'
return headers, data
def encode_decode(headers, data):
from records import Record, print_records, dump_records
from io import StringIO, BytesIO
if not data:
return headers, data
#print headers
if 'X-WCF-Encode' in headers:
from xml2records import Parser
p = Parser()
print data
print '##################################'
p.feed(data)
data = dump_records(p.records)
print data.encode('hex')
del headers['X-WCF-Encode']
headers['Content-Type'] = 'application/soap+msbin1'
else:
if 'Content-Type' not in headers or headers['Content-Type'] != 'application/soap+msbin1':
return headers, data
fp = BytesIO(data)
data = Record.parse(fp)
fp.close()
fp = StringIO()
print_records(data, fp=fp)
data = fp.getvalue()
fp.close()
headers['X-WCF-Encode'] = '1'
headers['Content-Type'] = 'text/soap+xml'
return headers, data
def query(self, sql, columns=None, **kwargs):
headers = {
"Accept":
"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8",
"Accept-Language":
"zh-CN,zh;q=0.8",
"Connection":
"keep-alive",
"Host":
"192.168.0.159:8007",
"Referer":
"http://192.168.0.159:8007/clustering",
"User-Agent":
"Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.79 Safari/537.36"
}
self.params.update({"q": sql})
rep = requests.get(self.db_url, params=self.params, headers=headers)
content = rep.text.split('\n')
rows_gen = (Record(json.loads(row).keys(),
json.loads(row).values()) for row in content
if row.strip())
results = RecordCollection(rows_gen)
return results
def get(self):
results = []
for jt in JOB_TYPES:
for e in ENGINES:
for js in JOB_SIZE:
q = Record.all()
q.filter('benchmark =', jt)
q.filter('engine_type =', e)
q.filter('num_entities =', js)
# hope you didnt run 1k+ jobs
ents = q.fetch(1000)
if len(ents) == 0:
continue
n = 0
sum_x = 0
mean = 0
stdev = 0
maximum = 0
minimum = 99999999
points = []
for ii in ents:
if ii.total: # some jobs may have failed
n += 1
points.append(ii.total)
if ii.total > maximum:
maximum = ii.total
if ii.total < minimum:
minimum = ii.total
if n != 0:
sum_x = getTotal(points)
mean = getAverage(points, sum_x)
stdev = getStDev(points, mean)
results.append(Job(n, sum_x, mean, maximum, minimum, stdev, jt, e, js))
self.response.out.write(template.render('templates/jobs.html', {'jobs':results, 'jobs_len':len(results)}))
return
def export_all(format, path, datas):
"""
将所有结果数据导出到一个文件
:param str format: 导出文件格式
:param str path: 导出文件路径
:param list datas: 待导出的结果数据
"""
format = check_format(format, len(datas))
timestamp = get_timestamp()
name = f'all_subdomain_result_{timestamp}'
path = check_path(path, name, format)
logger.log('INFOR', f'所有主域的子域结果 {path}')
row_list = list()
for row in datas:
row.pop('header')
row.pop('response')
row.pop('module')
row.pop('source')
row.pop('elapsed')
row.pop('count')
keys = row.keys()
values = row.values()
if format in {'xls', 'xlsx'}:
values = check_value(values)
row_list.append(Record(keys, values))
rows = RecordCollection(iter(row_list))
content = rows.export(format)
save_data(path, content)
def query(self, sql, columns=None, **kwargs):
rows = self.conn.execute(sql)
row_gen = (Record(columns, row) for row in rows)
# Convert psycopg2 results to RecordCollection.
results = RecordCollection(row_gen)
# # # Fetch all results if desired.
# if fetchall:
# results.all()
return results
def finalized(self):
plid = self.pipeline_id
q = Record.all()
q.filter('pipeline_id =',plid)
items = q.fetch(1)
for ii in items:
ii.end = datetime.datetime.now()
delta = (ii.end - ii.start)
ii.total = float(delta.days * 86400 + delta.seconds) + float(delta.microseconds)/1000000
ii.state = "Done"
ii.put()
logging.info("Updated aggregate pipeline record")
logging.info("Done with aggregate pipeline")
def iquery(self, query, batches=100):
cursor = self._conn.execute(text(query))
columns = cursor.keys()
history = []
for i, row in enumerate(cursor, start=1):
history.extend(
list(RecordCollection(
(Record(columns, _row) for _row in (row, )))))
if i % batches == 0:
yield history
history.clear()
if history:
yield history
def checkDuplicates(self, line):
# Returns true for unique records, stores duplicates
ret = True
cancer = False
s = line.strip().split(self.d)
if self.col.Patient and s[self.col.Patient] in self.reps.ids:
if self.col.Code and "8" in s[self.col.Code]:
cancer = True
# Sort duplicates and store for later
rec = Record(s[self.col.Sex], s[self.col.Age], s[self.col.Patient],
s[self.col.Species], cancer, s[self.col.ID])
self.reps.sortReplicates(rec)
self.dups[s[self.col.ID]] = line
ret = False
return ret
def query(self, sql, columns=None, **kwargs):
try:
dsl = json.loads(sql)
index_name = kwargs.pop("index_name", None)
type_name = kwargs.pop("type_name", None)
data_gen = (Record(line['_source'].keys(),
line['_source'].values())
for line in self.db.search(body=dsl,
index=index_name,
doc_type=type_name,
_source_include=columns)
['hits']['hits'])
result = RecordCollection(data_gen)
return result
except Exception as e:
print(e)
def export_all_results(path, name, format, datas):
path = check_path(path, name, format)
logger.log('ALERT', f'The subdomain result for all main domains: {path}')
row_list = list()
for row in datas:
if 'header' in row:
row.pop('header')
if 'response' in row:
row.pop('response')
keys = row.keys()
values = row.values()
if format in {'xls', 'xlsx'}:
values = check_value(values)
row_list.append(Record(keys, values))
rows = RecordCollection(iter(row_list))
content = rows.export(format)
save_data(path, content)
def post(self):
name = self.request.headers["mapreduce-id"]
if not name:
name = "NAME NOT FOUND"
logging.info("MR CALLBACK " + name)
q = Record.all()
q.filter('mr_id =', name)
q.fetch(1)
if q:
for ii in q:
ii.end = datetime.datetime.now()
delta = (ii.end - ii.start)
ii.total = float(delta.days * 86400 + delta.seconds) + float(delta.microseconds)/1000000
ii.state = "Done"
ii.put()
logging.info("updated: record for MR job id %s"%name)
else:
logging.info("Unable to find record for MR job id %s"%name)
def fsm_calculate_run_time():
""" Fantasm does not give call backs when its done. Must figure it out
with another job using the last modified date on output entities
"""
# Get the last job which was run for subset /fsm
q = Record.all()
q.filter('engine_type =','fsm')
q.filter('benchmark =','subset')
q.order('-start')
results = q.fetch(1)
if len(results) == 0:
logging.error("Unable to find a record for fsm/subset")
return False
q = None
record = None
for ii in results:
if ii.state == "Done":
logging.error("Last FSM end time has already been calculated")
logging.info(str(ii.num_entities))
q = SSFSMSimpleCounterShard.all()
if not q:
logging.error("No query returned for SubSet results")
return False
record = ii
max_date = None
while True:
results = q.fetch(1000)
for ii in results:
date = ii.modified
if max_date == None or max_date < date:
max_date = date
if len(results) < 1000:
break;
if not max_date:
logging.error("Unable to calculate the max date for FSM/subset")
return False
record.state = "Done"
record.end = max_date
delta = (record.end - record.start)
record.total = float(delta.days * 86400 + delta.seconds) + float(delta.microseconds)/1000000
record.put()
return True
def process_docs(docs_dataset):
invert_index = {}
for row in docs_dataset:
record = Record(keys=docs_dataset.headers, values=row)
logging.info("处理文档: %s" % record.id)
if not record.doc.strip():
logging.warning("文档内容为空")
continue
# 分词并获取词性
words_pos = word_segment(record.doc)
# 清洗单词
words = clean_words(words_pos)
word_frequency = get_word_frequency(words)
logging.info("文档词频统计结果: %s" % word_frequency)
for word, frequency in word_frequency.items():
if word in invert_index:
invert_index[word].append((record.id, frequency))
else:
invert_index[word] = [(record.id, frequency)]
return invert_index
def giveInsulin(self, amount: float):
print("Trying to deliver {} units of insulin.".format(amount))
from records import Record
r = Record()
scrollRate = r.getScrollRate()
lastDC = r.getDutyCycle()
ratio = .03 # Not a set ratio, I have to design the gearbox first.
try:
import RPi.GPIO as GPIO
GPIO.setmode(GPIO.BCM)
GPIO.setup(17, GPIO.OUT)
servo = GPIO.PWM(17, 50)
servo.start(lastDC)
for i in range(amount / scrollRate):
dutycycle = lastDC + (i * ratio)
servo.ChangeDutyCycle(dutycycle)
print(
"Servo dutycycle is now {}.\n{} units out of {} of insulin delivered as of now."
.format(dutycycle, i * scrollRate, amount))
r.setDutyCycle(dutycycle)
sleep(.5)
servo.stop()
GPIO.cleanup()
except ImportError or ModuleNotFoundError:
print(
"This is likely not running on a Raspberry Pi.\nIf it is, make sure RPi is installed for Python 3.\n\nRunning print loop now instead of sending servo commands."
)
print(amount / scrollRate)
for i in range(int(amount / scrollRate)):
dutycycle = lastDC + (i * ratio)
print(
"Servo dutycycle is now {}.\n{} units out of {} of insulin delivered as of now."
.format(dutycycle, i * scrollRate, amount))
r.setDutyCycle(dutycycle)
sleep(.5)
def visit(elem, depth=0):
content = elem[CONTENT_INDEX]
# 解析名字
name = content.text
if name is None or name.strip() == "":
name = get_name()
# 如果是a标签, 则需提取url
if content.tag == "a":
urls = content.get("href"),
url = urls[0]
createds = content.get("add_date"),
created = createds[0]
record = Record(keys=["url", "created"], values=[url, created])
else:
record = None
elem_obj = Element(name=name, data=record)
for child in elem.findall(CHILD_XPATH):
elem_obj.add_child(visit(child, depth + 1))
return elem_obj
def export_all(format, datas):
format = check_format(format, len(datas))
dpath = check_dpath()
timestamp = get_timestamp()
fpath = dpath.joinpath(f'all_subdomain_{timestamp}.{format}')
row_list = list()
for row in datas:
row.pop('header')
row.pop('response')
row.pop('module')
row.pop('source')
row.pop('elapsed')
row.pop('count')
keys = row.keys()
values = row.values()
if format in {'xls', 'xlsx'}:
values = check_value(values)
row_list.append(Record(keys, values))
rows = RecordCollection(iter(row_list))
content = rows.export(format)
save_data(fpath, content)
def fsm_calculate_run_time():
""" Fantasm does not give call backs when its done. Must figure it out
with another job using the last modified date on output entities
"""
# Get the last job which was run for grep/fsm
q = Record.all()
q.filter('engine_type =','fsm')
q.filter('benchmark =','grep')
q.order('-start')
results = q.fetch(1)
if len(results) == 0:
logging.error("Unable to find a record for fsm/grep")
return False
for ii in results:
ii.state = "Calculating time"
ii.put()
shards = ii.num_entities/1000
if shards < 1:
shards = 1
if shards > 256:
shards = 256 # max amount of shards allowed
kind = "GrepResults" #get_output_class(ii.num_entities)
mapreduce_id = control.start_map(
name="FSM Grep cleanup",
handler_spec="grep.fsm_mapper",
reader_spec="mapreduce.input_readers.DatastoreInputReader",
mapper_parameters={
"entity_kind": "data.grep."+kind,
"processing_rate": 500
},
mapreduce_parameters={model.MapreduceSpec.PARAM_DONE_CALLBACK:
'/grep/fsm/callback'},
shard_count=shards,
queue_name="default",
)
ii.mr_id = mapreduce_id
ii.put()
return True
def get(self):
user = users.get_current_user()
if not user:
self.redirect(users.create_login_url(dest_url="/"))
return
q = GrepDataSet.all()
q.order('-start')
results = q.fetch(1000)
datasets = [result for result in results]
datasets_len = len(datasets)
q = Record.all()
q.filter('benchmark =', "grep")
q.order('-start')
results = q.fetch(1000)
records = [result for result in results]
records_len = len(records)
self.response.out.write(template.render("templates/grep.html",
{"user": user.email(),
"datasets_len" : datasets_len,
"datasets" : datasets,
"records": records,
"records_len" : records_len}))
def parse(self,file):
delivery = None
section = None
for line in file:
rec = Record(line)
if rec.is_type(Record.NOT_IMPLEMENTED):
raise PBSParseException("Unimplemented record: "+line)
if rec.is_type(Record.DELIVERY_START):
delivery = rec
continue
if (rec.is_type(Record.DELIVERY_END)):
delivery.end(rec)
self._deliveries.append(delivery)
delivery == None
continue
if delivery is None:
raise PBSParseException("File contains content outside of delivery start/end")
if (rec.is_type(Record.SECTION_START)):
section = rec
continue
if (rec.is_type(Record.SECTION_END)):
section.end(rec)
delivery.append(section)
section = None
continue
if section is None:
raise PBSParseException("File contains content outside of section start/end")
if (rec.is_type(Record.PAYLOAD)):
section.append(rec)
continue
raise PBSParseException("File contains unrecognized content")
def post(self):
if self.request.get("fsm_cleanup"):
if fsm_calculate_run_time():
self.redirect("/subset")
else:
self.response.out.write("Error calculating run time of FSM/subset")
if self.request.get("reset_fsm_count"):
for c in SSFSMSimpleCounterShard.all():
c.delete()
self.redirect('/subset')
return
if self.request.get("reset_mr_count"):
for c in SSMRSimpleCounterShard.all():
c.delete()
self.redirect('/subset')
return
if self.request.get("reset_pl_count"):
for c in SSPLSimpleCounterShard.all():
c.delete()
self.redirect('/subset')
return
if self.request.get("compute"):
engine = self.request.get("engine")
dataset = self.request.get("dataset")
user = self.request.get('user')
data = SubSetDataSet.get_by_key_name(dataset)
record = Record(engine_type=engine,
dataset=dataset,
benchmark="subset",
num_entities=data.num_entries,
entries_per_pipe=data.entries_per_pipe,
user=user,
state="Running")
if engine == "fsm":
record.put()
# reset count
for c in SSFSMSimpleCounterShard.all():
c.delete()
context = {}
context['user'] = str(user)
context['num_entries'] = int(data.num_entries)
fsm.startStateMachine('SubSet', [context])
self.redirect('/subset')
elif engine == "pipeline":
for c in SSPLSimpleCounterShard.all():
c.delete()
mypipeline = SubSetPipeline(data.num_entries)
mypipeline.start()
record.pipeline_id = mypipeline.pipeline_id
record.put()
self.redirect('/subset')
#self.redirect(mypipeline.base_path + "/status?root=" + mypipeline.pipeline_id)
elif engine == "mr":
for c in SSMRSimpleCounterShard.all():
c.delete()
# Why 1k each per shard or less? is this ideal?
if data.num_entries > 1000: shards = data.num_entries/1000
else: shards = 1
kind = get_class(data.num_entries)
mapreduce_id = control.start_map(
name="Wordcount with just mappers",
handler_spec="subset.mr.subset_mapper",
reader_spec="mapreduce.input_readers.DatastoreInputReader",
mapper_parameters={
"entity_kind": "data.subset."+kind,
"processing_rate": 500
},
mapreduce_parameters={model.MapreduceSpec.PARAM_DONE_CALLBACK:
'/subset/mr/callback'},
shard_count=shards,
queue_name="default",
)
record.mr_id = mapreduce_id
record.put()
self.redirect('/subset')
def recordsPage(current_page):
current_page.frame.pack_forget()
current_page = Record(window, patientInfoPage)
def fsm_calculate_run_time():
""" Fantasm does not give call backs when its done. Must figure it out
with another job using the last modified date on output entities
"""
# Get the last job which was run for aggregate /fsm
q = Record.all()
q.filter('engine_type =','fsm')
q.filter('benchmark =','aggregate')
q.order('-start')
results = q.fetch(1)
# There is a second type of fsm job that has a fan in state
q2 = Record.all()
q2.filter('engine_type =','fsm_fan_in')
q2.filter('benchmark =','aggregate')
q2.order('-start')
results2 = q2.fetch(1)
if len(results) == 0 and len(results2) == 0:
logging.error("Unable to find a record for fsm/aggregate")
return False
# Take only the one which ran last
if len(results) == 0:
results = results2 #fsm with fan in ran last
elif len(results2) == 0:
pass
elif results[0].start > results2[0].start:
pass
else:
results = results2 #fsm with fan in ran last
q = None
record = None
# There should only be one result
for ii in results:
if ii.state == "Done":
logging.error("Last FSM end time has already been calculated")
logging.info(str(ii.num_entities))
q = FSMSimpleCounterShard.all()
if not q:
logging.error("No query returned for Aggregate results")
return False
record = ii
max_date = None
while True:
results = q.fetch(1000)
for ii in results:
date = ii.modified
if max_date == None or max_date < date:
max_date = date
if len(results) < 1000:
break;
if not max_date:
logging.error("Unable to calculate the max date for FSM/aggregate")
return False
record.state = "Done"
record.end = max_date
delta = (record.end - record.start)
record.total = float(delta.days * 86400 + delta.seconds) + float(delta.microseconds)/1000000
record.put()
return True
def post(self):
""" Generate data sets here """
if self.request.get("fsm_cleanup"):
if fsm_calculate_run_time():
self.redirect('/grep')
else:
self.response.out.write("Error calculating fsm/grep")
return
if self.request.get("compute"):
engine = self.request.get("engine")
dataset = self.request.get("dataset")
user = self.request.get('user')
needle = self.request.get('needle')
data = GrepDataSet.get_by_key_name(dataset)
record = Record(engine_type=engine,
dataset=dataset,
benchmark="grep",
num_entities=data.num_entries,
#shard_count=data.num_pipelines,
entries_per_pipe=data.entries_per_pipe,
user=user,
char_per_word=data.char_per_word,
state="Running")
if engine == "fsm":
record.put()
context = {}
context['user'] = str(user)
context['num_entries'] = int(data.num_entries)
context['needle'] = needle
fsm.startStateMachine('Grep', [context])
self.redirect('/grep')
elif engine == "pipeline":
mypipeline = GrepPipelineLoop(data.num_entries, needle)
mypipeline.start()
record.pipeline_id = mypipeline.pipeline_id
record.put()
self.redirect('/grep')
#self.redirect(mypipeline.base_path + "/status?root=" + mypipeline.pipeline_id)
return
elif engine == "mr":
# Why 1k each per shard or less? is this ideal?
if data.num_entries > 1000:
shards = data.num_entries/1000
shards = min(256, shards)
else: shards = 1
kind = getKindString(data.num_entries)
mapreduce_id = control.start_map(
name="Grep",
handler_spec="grep.mr.grep_mapper",
reader_spec="mapreduce.input_readers.DatastoreInputReader",
mapper_parameters={
"entity_kind": "data.grep."+kind,
"processing_rate": 500,
"needle":needle,
},
mapreduce_parameters={model.MapreduceSpec.PARAM_DONE_CALLBACK:
'/grep/mr/callback'},
shard_count=shards,
queue_name="default",
)
record.mr_id = mapreduce_id
record.put()
self.redirect('/grep')
dist = 50
limit = 100
color_list = ("b.", "r.", "g.", "m.", "c.", "y.")
with open('jsondata.json') as f:
x = json.load(f, object_hook=lambda d: SimpleNamespace(**d))
print(x.features[2].properties.filename)
X = np.zeros((1, 2))
list_rec=[]
weight_list = []
for i in range(len(x.features)):
list_rec.append(Record(i, x.features[i].properties))
X = np.vstack((X, [list_rec[i].lat, list_rec[i].lon]))
list_rec[i].calc_weight()
weight_list.append(list_rec[i].weight)
X = X[1:-1]
weight_list = weight_list[0:-1]
#print(np.unique(cat_list,return_index=True))
#print(x.features[6].properties)
cluster = sklearn.DBSCAN(eps=0.0001, min_samples=100).fit(X,y=None,sample_weight=weight_list)
dumps = []
cluster_count = np.unique(cluster.labels_)