def write_messages_to_tsv(files, bucket_name, metadata_file=None):
"""
Consume the subscription and write results to tsv manifest
Args:
files(dict): a dictionary of object files
{
"url": "test_url",
"md5": "test_md5",
"size": 1
}
bucket_name(str): bucket for uploading the manifest to
metadata_file(str): metadata file for merging
"""
metadata_info = {}
# Default filenames without merging
fields = ["url", "size", "md5"]
# merge extra metadata info from file
if metadata_file:
with open(metadata_file, "rt") as csvfile:
csvReader = csv.DictReader(csvfile, delimiter="\t")
# Build a map with url as the key
for row in csvReader:
if "url" in row:
metadata_info[row["url"]] = {
k: v
for k, v in row.items() if k != "url"
}
# do merging if possible, and update fields
need_merge = False
first_row_need_merge = None
for row_num, fi in enumerate(files):
if fi["url"] in metadata_info:
need_merge = True
first_row_need_merge = first_row_need_merge or row_num
for k, v in metadata_info[fi["url"]].items():
fi[k] = v
if files and need_merge:
# add new fields
[
fields.append(k) for k in files[first_row_need_merge].keys()
if k not in ["url", "size", "md5"]
]
if len(files) > 0:
# part the url
parts = urlparse(files[0]["url"])
# generate unique manifest output
now = datetime.now()
current_time = now.strftime("%m_%d_%y_%H:%M:%S")
filename = "manifest_{}_{}.tsv".format(parts.netloc, current_time)
# write list of object metadata to a file
utils.write_tsv(filename, files, fields)
# Upload the file to google bucket
utils.upload_file(bucket_name, filename, filename)
logging.info("DONE!!!")
def main():
folder = "./data/transomcs/"
file = folder + "TransOMCS_full.txt"
data = read_csv(file, delimiter="\t")
confidences = {}
for d in data:
key = tuple_key(d)
confidences[key] = float(d[3])
human_eval_file = folder + "human_evaluation_tuples.tsv"
tuples = read_csv(human_eval_file, delimiter="\t", skip_header=True)
updated_t = [{
"head_event": t[0],
"relation": t[1],
"tail_event": t[2]
} for t in tuples if confidences[tuple_key(t)] >= 0.5]
dropped = [{
"head_event": t[0],
"relation": t[1],
"tail_event": t[2]
} for t in tuples if confidences[tuple_key(t)] < 0.5]
output_file = folder + "human_evaluation_tuples_v2.tsv"
write_tsv(output_file, updated_t)
output_file = folder + "dropped_human_evaluation_tuples_v2.tsv"
write_tsv(output_file, dropped)
def parse():
r = requests.get(URL)
if not r.ok:
print(f"Failed to fetch {URL}", file=sys.stderr)
exit(1)
r.close()
db = json.loads(r.text)
r.close()
# Convert to ready made TSVs
regions = defaultdict(list)
for row in db:
elt = [
int(row[X[c]]) if i > 0 else row[X[c]].split()[0]
for i, c in enumerate(cols)
]
regions[row[X["region"]]].append(elt)
regions = dict(regions)
# Sum all regions to obtain Italian data
dates = defaultdict(lambda: np.zeros(len(cols) - 1))
for data in regions.values():
for datum in data:
dates[datum[0]] += np.array(datum[1:])
regions["Italy"] = []
for date, counts in dates.items():
regions["Italy"].append([date] + [int(c) for c in counts])
for region, data in regions.items():
write_tsv(f"{LOC}/{region}.tsv", cols, data, "italy")
def parse():
r = requests.get(URL)
if not r.ok:
print(f"Failed to fetch {URL}", file=sys.stderr)
exit(1)
r.close()
db = json.loads(r.text)
r.close()
# Convert to ready made TSVs
regions = defaultdict(list)
for row in db:
date = str(row["date"])
date = f"{date[0:4]}-{date[4:6]}-{date[6:8]}"
elt = [ date, row["positive"], row["death"], None, None, None ]
regions[acronyms[row["state"]]].append(elt)
regions = dict(regions)
for region, data in regions.items():
write_tsv(f"{LOC}/{region}.tsv", cols, data, "unitedstates")
def parse():
r = requests.get(URL)
if not r.ok:
print(f"Failed to fetch {URL}", file=sys.stderr)
exit(1)
r.close()
regions = defaultdict(list)
fd = io.StringIO(r.text)
rdr = csv.reader(fd)
hdr = next(rdr)
for row in rdr:
date = row[0]
canton = cantonal_codes[row[1]]
regions[canton].append([date, to_int(row[2]), to_int(row[5]), to_int(row[6]), None, to_int(row[7])])
for region, data in regions.items():
if region != "Liechtenstein":
write_tsv(f"{LOC}/{region}.tsv", cols, data, "switzerland")
else:
write_tsv(f"{LOC2}/{region}.tsv", cols, data, "switzerland")
def calculate_all_outliers(samples,
thresholds,
filtered_genelist_path,
outliers_path,
verbose=False):
"""For each sample calculate up and down outlier genes based on the thresholds.
Then, apply expression & variance filters to mark 'noise' genes, and save as result file."""
print_v = print if verbose else lambda *a, **k: None
print_v("Calculating outliers for all samples.")
outliers = samples.apply(single_sample_outliers, args=[thresholds], axis=0)
# Apply the expression & variance filters that we calculated in step 1 to mark filtered
# genes as dropped due to Expression or Variance, or as Retained.
# gene_filter_status is a pd.Series with values eg ["E"]
gene_filter_status = utils.read_feather(
filtered_genelist_path)["status"].apply(list)
outliers = outliers.add(gene_filter_status, axis="index")
outliers = outliers.applymap("".join) # Concatenate all values to string
print_v("Writing outlier results to {}".format(outliers_path))
utils.write_tsv(outliers, outliers_path)
return outliers
def parse():
cases = retrieve_case_data()
cases = flatten(cases)
write_tsv(f"{LOC}/World.tsv", cols, cases, "world")
from utils import get_paragraph_words
from utils import make_transposition_pair_dataset
from utils import write_tsv
if __name__ == '__main__':
paragraphs = get_paragraph_words(500, 20, 60, 3, tags=[[0, 1, 2]])
train_data, validation_data = make_transposition_pair_dataset(
paragraphs, 128)
write_tsv(train_data, 'train.tsv')
write_tsv(validation_data, 'dev.tsv')
match_ids = get_match_ids(fn=match_ids_fn)
data_fn = '../output/match_player_data.tsv'
data = pd.read_csv(data_fn, sep='\t')f
dupes = data[data['match_id_slot'].duplicated()]
print(dupes)
data[data['match_id'] == 5536512014]
print(len(data))
print(len(data.drop_duplicates()))
import utils
match_kills_out_fn = '../output/match_player_data.tsv'
utils.write_tsv(data=data.drop_duplicates(), fn=match_kills_out_fn)
data_ids = set(data['match_id'])
missing_mIDs = set(match_ids) - data_ids
print(len(missing_mIDs))
match_ids.index(list(missing_mIDs)[0])
match_ids.index(list(missing_mIDs)[1])
# import csv
# # cw = csv.writer(open('../input/missing_mIDs.csv', 'w'))
# # cw.writerow(list(missing_mIDs))
# with open('../input/missing_mIDs.txt', 'w') as outfile:
# # write(list(missing_mIDs))
# writer = csv.writer(outfile)
# Update df with first blood killers
rm_no_kills.loc[rm_no_kills['match.id.slot'].isin(fb_kill_mID_slots),
'first.blood'] = 1
return rm_no_kills
if __name__ == '__main__':
json_in_fn = '../input/match_data.json'
matches = utils.load_match_json(json_in_fn=json_in_fn)
match_kills = get_player_data(matches=matches)
match_kills_out_fn = '../output/match_player_data_v2.tsv'
utils.write_tsv(data=match_kills, fn=match_kills_out_fn)
print("Player level data for each match:")
print(f" - Saved {len(matches)} matches to output folder")
# Junk:
# print(matches[0]['start_time'])
# matches = json.loads(json_file)
# print(matches[0]['match_id'])
# print(matches[1]['match_id'])
# matches[0].keys()
# type(matches)
# len(matches)
