def fromCSV(TimeData,dataPath1,dataPath2, dataName):
"""Prepares data of survival over time for model definition. Initialize
from a csv file using class method fromCSV.
Input:
- dataPath1, dataPath2 (str): paths to csv files corresponding to the survival
over time of each group. The csv files should have the days of observations in
each column, starting from 0 (which will indicate how many hosts were challenged
in each dose) in second column (the first column will have the doses). The last
column will indicate the last day of observation and the number of hosts which
survived each of the challenges. The results from each of the challenge doses
should be put in each line, starting from the second line (the first line will
have the days of observation). The doses and final day of observation should be
the same for both groups.
- dataName (str): a text string which should have no spaces and should be
descriptive of the data. Will be used to name folder and files of saved results
(ex: 'survWolb2012').
Returns a Data object.
"""
(timesDeath1,survivors1,tmax1,times1,doses1,ndoses1,nhosts1)=ut.readcsv(dataPath1)
(timesDeath2,survivors2,tmax2,times2,doses2,ndoses2,nhosts2)=ut.readcsv(dataPath2)
if ~((tmax1==tmax2)&(sum(times1==times2)==len(times1))):
raise DataError("Times of observation not the same in two datasets, please check the data in %s and %s"%(dataPath1,dataPath2))
if ~((ndoses1==ndoses2)&(sum(doses1==doses2)==ndoses1)):
raise DataError("Doses not the same in two datasets, please check the data in %s and %s"%(dataPath1,dataPath2))
return TimeData(timesDeath1,timesDeath2,survivors1,survivors2,nhosts1,nhosts2,tmax1,times1,doses1,ndoses1,dataName,dataPath1,dataPath2)
def generate_report(date):
script_path = os.path.dirname(os.path.abspath(__file__))
csv_file = f'watch-{date}.csv'
if not os.path.exists(os.path.join(script_path, 'csv_data', csv_file)):
return False
shutil.copy2(os.path.join(script_path, 'csv_data', csv_file),
os.path.join(script_path, 'web'))
report_date = datetime.strptime(date, '%m-%d-%Y').strftime("%A %-d %B %Y")
generation_date = datetime.now().strftime('%d %B %Y %H:%M')
csv_data = readcsv(os.path.join(script_path, 'csv_data', csv_file))
colors_data = readjson(os.path.join(script_path, 'web', 'colors.json'))
json_data = json.dumps({
'report_date': report_date,
'generation_date': generation_date,
'csv_file': csv_file,
'csv_data': csv_data,
'colors_data': colors_data
})
rendered = _render_template(json_data)
report_filename = os.path.join(script_path, 'web', f'report-{date}.html')
with open(os.path.join(report_filename), 'w') as f:
f.write(rendered)
return report_filename
def load_eval_data(data_path, w2v_path, w2v_dim, sequence_length, num_classes,
labels):
print("[LOADING] data: ", data_path)
data = utils.readcsv(data_path)
tagged_sents = []
e1_list = []
e2_list = []
y_list = []
for line in data:
tagged_s, e1, e2 = utils.pos_tag(line[0])
tagged_sents.append(tagged_s)
e1_list.append(e1)
e2_list.append(e2)
one_hot_label = one_hot_encodding(labels.index(line[1]), num_classes)
y_list.append(one_hot_label)
bow, embed = const_bow(tagged_sents, w2v_path, w2v_dim)
sentences = []
en1_pos = []
en2_pos = []
for i in range(len(tagged_sents)):
pos_tagged_sent = tagged_sents[i]
e1 = e1_list[i]
e2 = e2_list[i]
tmp_sent = []
tmp_pos1 = []
tmp_pos2 = []
for idx, token in enumerate(pos_tagged_sent):
if token in bow:
tmp_sent.append(bow[token])
tmp_pos1.append(pos_embed(e1 - idx))
tmp_pos2.append(pos_embed(e2 - idx))
sent_len = len(pos_tagged_sent)
while len(tmp_sent) != sequence_length:
tmp_sent.append(bow["<zero>"])
tmp_pos1.append(122)
tmp_pos2.append(122)
sent_len += 1
sentences.append(tmp_sent)
en1_pos.append(tmp_pos1)
en2_pos.append(tmp_pos2)
return sentences, en1_pos, en2_pos, y_list, bow, embed
def parse(path, label, output=None, recursive=None, clean=False):
"""
Read csv to extract and parse html from single or multiple files and write results to .txt.
@params:
path - Required : file or directory to parse (Str)
label - Required : csv variable name in which html is stored
output - Optional : output filename (use only if parsing single files) (Str)
recursive - Optional : recursive execution for directories (Bool)
clean - Optional : preprocessing of the input with utils.clean (Bool)
"""
output = output if output else path + '.html'
if recursive:
files = [f for f in os.listdir(path) if not f.startswith('.')]
files = [f for f in files if f.endswith('.csv')]
for index, filename in enumerate(files):
parse(path + filename, label, None, False, clean)
print 'Parsed document ' + str(index + 1) + ' of ' + str(
len(files))
else:
utils.progressbar_update(0, path)
read_size = 0
html_file = open(output, 'w+')
for index, row in enumerate(utils.readcsv(path)):
read_size += row['size']
row = row[label]
regex = re.compile(ur'[^\x00-\x7F]+', re.UNICODE)
row = re.sub(regex, '', row)
row = re.sub('[\n\t\r]', '', row)
row = re.sub('<article', '\n<article', row)
html_file.write(row)
utils.progressbar_update(read_size, path)
html_file.close()
html_parse.parse(path=output,
output=None,
recursive=False,
clean=clean)
def load_agent_test_data(data_path, sequence_length, num_classes, vocab_,
labels, entity2_dict):
print("[LOADING] data: ", data_path)
data = utils.readcsv(data_path)
sentences = []
en1_pos = []
en2_pos = []
y_list = []
entities = []
for line in tqdm(data):
sent = line[0]
entity1 = re.findall("<e1>.*?</e1>", sent)[0]
entity2 = re.findall("<e2>.*?</e2>", sent)[0]
entity1 = entity1.replace("<e1>", "").replace("</e1>", "")
entity2 = entity2.replace("<e2>", "").replace("</e2>", "")
one_hot_label = one_hot_encodding(labels.index(line[1]), num_classes)
pos_tagged_sent, e1, e2 = utils.pos_tag(sent)
tmp_sent = []
tmp_pos1 = []
tmp_pos2 = []
for idx, token in enumerate(pos_tagged_sent):
if token in vocab_:
tmp_sent.append(vocab_[token])
tmp_pos1.append(pos_embed(e1 - idx))
tmp_pos2.append(pos_embed(e2 - idx))
sent_len = len(pos_tagged_sent)
while len(tmp_sent) != sequence_length:
tmp_sent.append(vocab_["<zero>"])
tmp_pos1.append(122)
tmp_pos2.append(122)
sent_len += 1
sentences.append(tmp_sent)
en1_pos.append(tmp_pos1)
en2_pos.append(tmp_pos2)
y_list.append(one_hot_label)
entity_pair = (entity1, entity2)
entities.append(entity_pair)
Bags = constBag(entities, y_list, entity2_dict)
return sentences, en1_pos, en2_pos, y_list, Bags
predicted_y = []
probs=[]
for batch in tqdm(range(total_batch_size)):
st = batch*batch_size
en = min((batch+1)*batch_size,total_data_size)
batch_sents = sents_list[st:en]
batch_y = y_list[st:en]
batch_pos1 = en1_position_list[st:en]
batch_pos2 = en2_position_list[st:en]
feed_dict = {
cnn.input_text: batch_sents,
cnn.input_y: batch_y,
cnn.pos1: batch_pos1,
cnn.pos2: batch_pos2
}
prediction,probabilities = sess.run([cnn.prediction,cnn.probabilities], feed_dict=feed_dict)
for i in range(len(batch_sents)):
predicted_y.append(properties_list[prediction[i]])
probs.append(probabilities[i][prediction[i]])
result=[]
eval_data = utils.readcsv(test_data)
for i,line in enumerate(eval_data):
w = [line[0],predicted_y[i],line[2],line[3],line[4],probs[i]]
result.append(w)
utils.writecsv(output_path,result)
plugload_name = sys.argv[0];
state_fname = sys.argv[1];
postgresops.check_evil(plugload_name);
plugload_row = devicedb.get_devicemetas(where="key='PLUGLOAD' and value='%s'"%plugload_name,limit=1);
if ( len(plugload_row)==0 ):
print "Cannot find plugload metadata for "+plugload_name
sys.exit(1)
plugload_id = plugload_row[0].ID
else:
print "Not enough (or too many ) input arguments";
sys.exit(1);
if dtype=="GHMM":
data = utils.readcsv(state_fname);
if ( len(data) == 0 ):
print "No data in file";
sys.exit(1);
elif len(data[0]) != 4:
print "GHMM data file must be CSV with format \"state_no,counts,mean,variance\""
sys.exit(1);
print "Read %d states"%len(data);
if not pretend:
import learningdb
learningdb.connect()
learningdb.initdb();
learningdb.insertHMMGaussianEmissions(plugload_id,fromtime,totime,[i[0] for i in data],[i[1] for i in data],[i[2] for i in data],[i[3] for i in data]);
for g_line, p_line in zip(gold_data, pred_data):
rel = g_line[1]
if rel in dict_data:
dict_data[rel]["gold_data"].append(g_line[-1])
dict_data[rel]["pred_data"].append(p_line[-1])
else:
dict_data[rel] = {}
dict_data[rel]["gold_data"] = [g_line[-1]]
dict_data[rel]["pred_data"] = [p_line[-1]]
return dict_data
goldfile = "../data/ko/agent_test.csv"
predfile = "../result/agent_target_addBOTH_44_result.csv"
gold_data = utils.readcsv(goldfile)
pred_data = utils.readcsv(predfile)
gold_tags = exTag(gold_data)
pred_tags = exTag(pred_data)
print(classification_report(gold_tags, pred_tags, digits=4))
macro_p, macro_r, macro_f1, _ = precision_recall_fscore_support(
gold_tags, pred_tags, average="macro")
micro_p, micro_r, micro_f1, _ = precision_recall_fscore_support(
gold_tags, pred_tags, average="micro")
acc = accuracy_score(gold_tags, pred_tags)
print("Macro: {:.4f}\t{:.4f}\t{:.4f}".format(macro_p, macro_r, macro_f1))
print("Micro: {:.4f}\t{:.4f}\t{:.4f}".format(micro_p, micro_r, micro_f1))
print("acc: {:.4f}".format(acc))
dict_data = rel_data(gold_data, pred_data)
import numpy as np
import os
import sys
cwd = os.getcwd()
utilspath = cwd + '/../utils/'
sys.path.append(utilspath)
import utils
basefolder = cwd + '/../../data/filter/'
#first get the LNA gain:
datafolder2 = '../../data/gain2/'
snarray = ['022966','022967','022968','022969','022970','023230','023231','023232']
lnagain = []
for sn in snarray:
fgainname = datafolder2 + 'wenteqS21'+ sn + '.csv'
data = utils.readcsv(fgainname)
lnagain.append(data)
# get the on board filter data (CBP-B1230C+_Plus25DegC.s2p)
fref = 1400 #MHz freq to be normalized to (because resistor calibration was done at this frequency)
fname = '/CBP-B1230C+_Plus25DegC.s2p'
boardfilter = utils.readminicircuitdata(basefolder + fname) # (return [freq,gain])
attenatfref = np.interp([fref],boardfilter[0],boardfilter[1])
boardfilter[1] = boardfilter[1] - attenatfref # normalize the gain to the reference frequency
#get the biasT filter from data
measbiast = utils.getjacquesmeas(basefolder+'/scope_42.csv',basefolder+'/scope_43.csv')
#get the cable loss from data
meascable = utils.getjacquesmeas(basefolder+'/scope_43.csv',basefolder+'/scope_44.csv')