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provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator)
RepeatingInvoice(BaseModel)
openapi_types (dict)
attribute_map (dict)
type(self)
type(self, type)
contact(self)
contact(self, contact)
schedule(self)
schedule(self, schedule)
line_items(self)
line_items(self, line_items)
line_amount_types(self)
line_amount_types(self, line_amount_types)
reference(self)
reference(self, reference)
branding_theme_id(self)
branding_theme_id(self, branding_theme_id)
currency_code(self)
currency_code(self, currency_code)
status(self)
status(self, status)
sub_total(self)
sub_total(self, sub_total)
total_tax(self)
total_tax(self, total_tax)
total(self)
inclusive (i.e. SubTotal + TotalTax)
total(self, total)
inclusive (i.e. SubTotal + TotalTax)
repeating_invoice_id(self)
repeating_invoice_id(self, repeating_invoice_id)
id(self)
id(self, id)
has_attachments(self)
has_attachments(self, has_attachments)
attachments(self)
attachments(self, attachments)
__init__(self)
python_bitbankcc.public()
get_ticker(self, pair)
self.pub.get_ticker(pair)
print(e)
__init__(self)
python_bitbankcc.private(api_key, api_secret)
get_asset(self)
self.prv.get_asset()
print(e)
buy_order(self, order_price, amount)
self.prv.order('btc_jpy', order_price, amount, 'buy', 'limit')
print(e)
main()
pathlib.Path.home()
BitBankPubAPI()
BitBankPrvAPI()
pub_set.get_ticker('btc_jpy')
int(ticker['last'])
floor(amount * 10 ** 4 + 0.5)
datetime.now()
strftime('%Y-%m-%d %H:%M:%S')
log_file_path.exists()
open(log_file_path, 'a', newline='')
csv.writer(f)
writer.writerow([t, str(order_price)
str(amount)
str(last_price)
log_file_path.touch()
open(log_file_path, 'w+', newline='')
csv.writer(f)
writer.writerow(['time', 'order_price', 'amount', 'current_price'])
writer.writerow([t, str(order_price)
str(amount)
str(last_price)
prv_set.buy_order(order_price=str(order_price)
str(amount)
main()
get_stratified_by_area_folds(car_ids, n_splits, fold_id, random_state, min_bin_size=7)
cars (order matters!)
classes(bins)
pd.read_hdf(join(config.input_data_dir, 'areas_df.hdf5')
len(car_ids)
len(df)
np.histogram(df['sum'], bins=35)
len(freq)
max(cur_bin_right, bins[pos + 1])
len(freq)
new_bin_hs.append(cur_bin_h)
new_bins.append(cur_bin_right)
max(new_bins)
max(bins)
freq.sum()
np.sum(new_bin_hs)
len(df)
len(new_bin_hs)
len(new_bin_hs)
len(new_bin_hs)
pd.cut(df['sum'], new_bins, labels=False)
StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=random_state)
list(kf.split(X=np.arange(len(df)
CARVANA(Dataset)