def issue_token(self, total_amount, issue_amount):
self.push_action(
'eosio.token', 'create',
"'[ \"eosio\", \"%s\", 0, 0, 0]' -p eosio.token" %
Asset(total_amount))
self.push_action(
'eosio.token', 'issue',
"'[ \"eosio\", \"%s\", \"memo\" ]' -p eosio" % Asset(issue_amount))
def get_metals():
return \
[
Asset(icon="icons/32x32/equitrader.png", name="Copper", ticker="HG:CMX", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
Asset(icon="icons/32x32/expanse.png", name="Silver", ticker="XAG", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
Asset(icon="icons/32x32/eztoken.png", name="Gold", ticker="GC:CMX", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
Asset(icon="icons/32x32/hycon.png", name="Aluminium", ticker="ALI:CMX", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
]
def post_sys_create(self, a, net, cpu, ram, creator='eosio'):
cmd = self.teclos + ' --url %s system newaccount %s --transfer %s %s --stake-net \"%s\" --stake-cpu \"%s\" --buy-ram \"%s\"'
net = Asset(net)
cpu = Asset(cpu)
ram = Asset(ram)
run_retry(cmd % (self.host_address, creator, a.name, a.keypair.public,
net, cpu, ram))
a.amount += cpu + net + ram
def create_tip5_wallets_from_snapshot(self, path_to_csv, contract_account,
min_tokens, max_tokens):
try:
with open(path_to_csv, 'r', newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
amt = randint(min_tokens, max_tokens)
self.create_allotment_tip5(contract_account,
row['account_name'],
Asset(amt, 'TTT', 2).__str__())
self.transferfrom_tip5(contract_account,
row['account_name'],
Asset(amt, 'TTT', 2).__str__())
except Exception as e:
raise e
def register_airtable():
app.logger.info("register_airtable(): POST /register_airtable")
app.logger.debug(f"{request.form=}")
posted_asset = request.form.to_dict()
if posted_asset:
registrable_asset = Asset(
title=posted_asset["title"],
asin=posted_asset["asin"],
url=posted_asset["url"],
images=[{"url": posted_asset["image_url"]}],
manufacture=posted_asset["manufacturer"],
contributor=posted_asset["contributors"],
product_group=posted_asset["product_group"],
publication_date=0 if posted_asset["publication_date"] else 0,
features=posted_asset["features"],
default_position=posted_asset["default_positions"],
current_position=posted_asset["current_positions"],
note=posted_asset["note"],
registrant_name=posted_asset["registrants_name"])
AirtableClient().register_asset(registrable_asset)
app.logger.info(f"Registration completed! {registrable_asset=}")
return FlashMessage.show_with_redirect("Registration completed!", FlashCategories.INFO, url_for("index"))
else:
context_dict = {
"product": session.get("asset", "")
}
app.logger.debug(f"{context_dict}=")
return FlashMessage.show_with_render_template("Registration failed.", FlashCategories.ERROR,
"registration.html", **context_dict)
def calcIDAdjustment(Corp, eta=0.4):
"""
Calculates the adjustment factors for the corporate and noncorporate debt
and interest.
eta: retirement rate of existing debt
"""
# Create Asset object
asset1 = Asset(data1.btax_defaults, Corp)
asset1.calc_all()
# Get asset forecast
forecast = asset1.get_forecast()
# Create Debt object
debt1 = Debt(data1.btax_defaults, forecast, corp=Corp)
debt1.calc_all()
# Get unscaled net interest deduction
NID_gross = debt1.NID[38:54]
# Get net interest deduction from historical IRS data
if Corp:
NID_irs = np.array(data1.debt_data_corp['NID_IRS'])[38:54]
else:
NID_irs = np.array(data1.debt_data_noncorp['ID_Scorp'][38:54] +
data1.debt_data_noncorp['ID_sp'][38:54] +
data1.debt_data_noncorp['ID_partner'][38:54])
NID_scale = sum(NID_irs / NID_gross) / 16.
return NID_scale
def load_assets (factors, data_path):
"""loads all factors files for all files that have close prices
factors - list of string
"""
assets_dict={}
asset = None
#load close prices
for file in os.listdir(data_path+'close'):
if(file.endswith('.csv')):
file_path = os.path.join(data_path+'close', file)
file_name=os.path.splitext(file)[0]
if((os.stat(file_path).st_size != 0)):
asset=Asset(file_name)
assets_dict[file_name]=asset
add_factor(asset, file_path, 'close')
#load factors from factors list
for factor_name in factors:
for file in os.listdir(data_path+factor_name):
if(file.endswith('.csv')):
file_path = os.path.join(data_path+factor_name, file)
file_name=os.path.splitext(file)[0]
if(file_name in assets_dict):
asset=assets_dict[file_name]
add_factor(asset, file_path, factor_name)
assets_list=list(assets_dict.values())
assets_list.sort(key=custom_key,reverse=False)
return assets_list
def create_portfolio(data):
print("CREATING PORTFOLIO")
pf = Portfolio()
for index, row in data.iterrows():
ticker = row['Verdipapir']
amount = float(row['Antall'].replace(',', '.').replace(' ', ''))
kurs = float(row['Kurs'].replace(',', '.').replace(' ', ''))
vekslingskurs = float(row['Vekslingskurs'].replace(',', '.').replace(
' ', ''))
transaksjonstype = row['Transaksjonstype']
belop = float(row['Beløb'].replace(',', '.').replace(' ', ''))
# Deposit and withdraw cash
if transaksjonstype == 'INNSKUDD' or transaksjonstype == 'UTTAK INTERNET':
if transaksjonstype == 'INNSKUDD':
pf.deposit(belop)
if transaksjonstype == 'UTTAK INTERNET':
pf.withdraw(belop)
if transaksjonstype == 'KJØPT' or transaksjonstype == 'SALG':
a = Asset(ticker)
pf.buy(a, amount,
(kurs * vekslingskurs)) if belop < 0 else pf.sell(
a, amount, (kurs * vekslingskurs))
return pf
def load_a_layer(self, day, df_inc):
layer_assets_1 = ['120000053', '120000056','120000058','120000073']
layer_assets_2 = ['MZ.FA0010', 'MZ.FA0050','MZ.FA0070']
layer_assets = layer_assets_1 + layer_assets_2
layer_assets = dict([(asset_id , Asset(asset_id)) for asset_id in layer_assets])
layer_bounds = {}
for asset in layer_assets.keys():
layer_bounds[asset] = self.bound[asset]
rp_allocate = RpAllocate('ALC.000001', layer_assets, self.index, self.lookback, bound = layer_bounds)
layer_ws, df_alayer_inc = rp_allocate.allocate_day(day)
df_inc['ALayer'] = df_alayer_inc
df_inc_layer = df_inc[df_inc.columns.difference(layer_assets)]
bound = []
allocate_asset_ids = []
for asset_id in df_inc_layer.columns:
asset_bound = AssetBound.get_asset_day_bound(asset_id, day, self.bound).to_dict()
if asset_bound['upper'] > 0:
bound.append(asset_bound)
# allocate_asset_ids.append(asset_id)
return df_inc_layer, layer_ws, bound
def main():
# Sanity checks
# asset = Asset(0)
# l = Loan(12, 0.01, 10000, asset)
# for i in range(12):
# print l.interestDue(i), l.principalDue(i), l.balance(i)
# vl = VariableRateLoan(12, 0.01, 10000, {0: 0.023, 5: 0.03, 10: 0.04}, asset)
# handle the TypeError and log it
try:
l = Loan(12, 0.01, 10000, 'wrong asset')
except TypeError:
logging.exception(
"TypeError is caught when trying to create a Loan type.")
# tests for 4.2.4
asset = Asset(20000)
l = Loan(20, 0.02, 10000, asset)
print "*** DEBUG level ***"
logging.basicConfig(level=logging.DEBUG)
l.balance(-1) # should trigger one info log and one warn log
print "*** ERROR level ***"
logging.basicConfig(level=logging.ERROR)
l.balance(-1) # no any log
def create_asset_objects(self):
"""
Pulls asset details inputted by user into budget_report_input.xlsx and creates an Asset object for each row of
asset details entered.
:return: List of Asset objects that correspond to input entered by user into budget_report_input.xlsx
"""
# Read asset details into dataframe
df = pd.read_excel(self.budget_report_input_file_path, sheet_name="User Input")
# Convert dataframe into dictionary
# Key: "df index"
# Value: ["model_num", "asset_description", "quantity", "health_auth", "site_code", "shop_code"]
assets_dict = df.T.to_dict("list")
# Iterate through assets_dict, create Asset object for each value, and append to assets list
assets = []
for asset, details in assets_dict.items():
assets.append(Asset(details[0], # model_num
details[1], # asset_description
details[2], # quantity
details[3], # health_auth
details[4], # site_code
details[5], # shop_code
self.sites_cost_centre_dict))
return assets
def create_fund_account(self):
a = self.account_factory.get_acc_obj('telosfundacc')
self.account_factory.post_sys_create(a, (self.token_issue * 0.15) / 2,
(self.token_issue * 0.15) / 2,
1000.0000)
BootStrapper.transfer(
self.host_address, 'eosio', a.name, Asset(1000.0000),
"Sending 15 percent stake and enough money to create new accounts")
def get_currency_balance(self, account, symbol='TLOS'):
try:
body = {'code': 'eosio.token', 'account': account, 'symbol': symbol}
response = self.api.chain.get_currency_balance(body=body, params={}, headers={})
if response.status_code == 200 and len(response.body) > 0:
return Asset.string_to_asset(response.body[0])
return Asset(0, 'TTT')
except ServerError as e:
raise e
def _build_assets(self):
"""
+ Description: build assets.
+ Input:
-
+ Output:
-
"""
self.assets = {}
for asset_id, asset_values in self.assets_elements.items():
self.assets[asset_id] = Asset(asset_id, asset_values)
def wavelet(ctx, startdate, enddate, viewid, idx, wavenum, max_wave_num,
wave_name):
if idx is None:
#idx = ['120000001', '120000002', '120000013', '120000014', '120000015', '120000080' ,'ERI000001', 'ERI000002']
idx = ['120000001']
for _id in idx:
trade_dates = ATradeDate.trade_date()
nav = Asset(_id).nav(reindex=trade_dates).fillna(method='pad').dropna()
dates = nav.index[1000:]
views = []
for d in dates:
_tmp_nav = nav[nav.index <= d]
wave_nav = wavefilter(_tmp_nav,
wavenum,
wname=wave_name,
maxlevel=max_wave_num)
inc = wave_nav.pct_change().fillna(0.0).iloc[-120:]
wave_diff_rolling = wave_nav.diff().rolling(5).mean()
views.append(wave_diff_rolling[-1])
#print(d, views[-1])
print(_id, d, inc.mean(), inc.std())
view_df = pd.DataFrame(views, index=dates, columns=['bl_view'])
view_df[view_df > 0] = 1.0
view_df[view_df < 0] = 0
view_df.index.name = 'bl_date'
view_df['globalid'] = viewid
view_df['bl_index_id'] = _id
view_df['created_at'] = datetime.now()
view_df['updated_at'] = datetime.now()
df_new = view_df.reset_index().set_index(
['globalid', 'bl_date', 'bl_index_id'])
#print(df_new.head())
db = database.connection('asset')
metadata = MetaData(bind=db)
t = Table('ra_bl_view', metadata, autoload=True)
columns = [
t.c.globalid,
t.c.bl_date,
t.c.bl_view,
t.c.bl_index_id,
t.c.created_at,
t.c.updated_at,
]
s = select(columns).where(t.c.globalid == viewid).where(
t.c.bl_index_id == _id)
df_old = pd.read_sql(s,
db,
index_col=['globalid', 'bl_date', 'bl_index_id'],
parse_dates=['bl_date'])
database.batch(db, t, df_new, df_old, timestamp=False)
def main():
print ( '========== Exercise 2.1.6 ==========')
myAsset = Asset(10000)
print ('Initial Value: {}'.format(myAsset.initialvalue))
print ('Monthly Depreciation: {}'.format(myAsset.monthlyDepriciation()))
print ('Annual Depreciation: {}'.format(myAsset.yearlyDepreciation()))
myAsset.initalvalue = 12000
print ('New Inital Value: {}'.format(myAsset.initialvalue))
print(myAsset.currentvalue(8))
def create_random_generator(self, num, min, max, base="acctname"):
min = int(min)
max = int(max)
for _ in range(num):
a = self.get_acc_obj(base + id_generator(), True)
self.post_sys_create(a, randint(min, max), randint(min, max),
randint(min, max))
toTransfer = Asset(randint(min, max))
BootStrapper.transfer(self.host_address, 'eosio', a.name,
toTransfer, "Initial TLOS Liquid")
a.amount += toTransfer
yield a
def get_appreciation_curve(value, appreciation_rate, mortgage):
# calculate appreciation
index = mortgage.amortization
appreciation_array = []
# create a time series for the amount the house appreciates
for time in index:
amount = (value * appreciation_rate) / 12
transfer = Asset("appreciation", "home appreciation", amount)
appreciation_array.append(transfer)
return pd.Series(appreciation_array, index=index, name="appreciation")
def test_asset(self):
if 1:
ast = TestAsset('test.asset')
loc = ast.location()
# self.tmpDirs = [loc]
logger.info(' Created temp dir: %s', loc)
ast.save()
self.assertTrue(os.path.isdir(loc))
if 1:
ast = Asset('test.asset')
self.assertTrue(isinstance(ast, TestAsset))
def main():
a = Asset(1000)
normal_loan = Loan(12, 0.3, 1000, a)
memoizable_loan = MemoizedLoan(12, 0.3, 1000, a)
print "First normal loan time cost:"
normal_loan.interestDue(10)
print "Second normal loan time cost:"
normal_loan.interestDue(10)
print "First memoizable loan time cost:"
memoizable_loan.interestDue(10)
print "Second memoizable loan time cost:"
memoizable_loan.interestDue(10)
def get_portfolio():
files = []
raw_data = {}
portfolio = Portfolio()
for dirname, dirnames, filenames in os.walk('raw_data'):
files = [os.path.join(dirname, filename) for filename in filenames]
for file_name in files:
# for every active
with open(file_name) as csv_file:
raw_data[file_name] = []
line_counter = 0
amount = Decimal(0)
csv_reader = csv.reader(csv_file, delimiter=';')
start_date = None
end_date = None
in_usd = True if file_name.startswith('raw_data/USD') else False
for row in csv_reader:
if line_counter == 0:
line_counter += 1
elif line_counter == 1:
line_counter += 1
start_date = datetime.strptime(row[0], '%m/%d/%Y')
rate = Decimal(1) if in_usd else get_rate(
row[0],
currency_name=file_name.split('/', 1)[1].split('_',
1)[0],
fake=True)
amount = Decimal(row[5].replace(',', '.')) * rate
raw_data[file_name].append((start_date, amount))
else:
end_date = datetime.strptime(row[0], '%m/%d/%Y')
rate = Decimal(1) if in_usd else get_rate(
row[0],
currency_name=file_name.split('/', 1)[1].split('_',
1)[0],
fake=True)
amount += Decimal(row[5].replace(',', '.')) * rate
raw_data[file_name].append((end_date, amount))
portfolio.append(
Asset(file_name, start_date, end_date, raw_data[file_name]))
return portfolio
def create(spec):
"""Creates ImageSpec.
:rtype: ImageSpec
"""
image_type = spec.get('imageType', 'MOSAIC')
if image_type == 'MOSAIC':
return _createMosaic(spec)
if image_type == 'CLASSIFICATION':
return Classification(spec, create)
if image_type == 'CHANGE_DETECTION':
return ChangeDetection(spec, create)
if image_type == 'ASSET':
return Asset(spec)
else:
raise Exception('Unexpected image image_type: ' + str(image_type))
def get_bin_options():
return \
[
Asset(icon="icons/32x32/constellation.png", name="Binary USD/JPY", ticker="USDJPY", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5), is_option=True),
Asset(icon="icons/32x32/consensus.png", name="Binary USD/GBP", ticker="GBPUSD", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5), is_option=True),
Asset(icon="icons/32x32/chesscoin.png", name="Binary EUR/USD", ticker="EURUSD", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5), is_option=True),
Asset(icon="icons/32x32/centurion.png", name="Binary USD/NZD", ticker="NZDUSD", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5), is_option=True),
Asset(icon="icons/32x32/bunnycoin.png", name="Binary AUD/CHF", ticker="AUDCHF", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5), is_option=True),
Asset(icon="icons/32x32/callisto-network.png", name="Binary NZD/AUD", ticker="AUDNZD", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5), is_option=True),
]
def get_otc_options():
return \
[
Asset(icon="icons/32x32/coinpoker.png", name="OTC USD/JPY", ticker="USDJPY", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5), is_option=True),
Asset(icon="icons/32x32/cybervein.png", name="OTC USD/GBP", ticker="GBPUSD", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5), is_option=True),
Asset(icon="icons/32x32/daneel.png", name="OTC EUR/USD", ticker="EURUSD", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5), is_option=True),
Asset(icon="icons/32x32/deuscoin.png", name="OTC USD/NZD", ticker="NZDUSD", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5), is_option=True),
Asset(icon="icons/32x32/daxxcoin.png", name="OTC AUD/CHF", ticker="AUDCHF", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5), is_option=True),
Asset(icon="icons/32x32/darsek.png", name="OTC NZD/AUD", ticker="AUDNZD", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5), is_option=True),
]
def get_forex():
return \
[
Asset(icon="icons/32x32/edu-coin.png", name="USD/JPY", ticker="USDJPY", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
Asset(icon="icons/32x32/embers.png", name="USD/GBP", ticker="GBPUSD", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
Asset(icon="icons/32x32/ejoy.png", name="EUR/USD", ticker="EURUSD", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
Asset(icon="icons/32x32/egretia.png", name="USD/NZD", ticker="NZDUSD", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
Asset(icon="icons/32x32/eaglecoin.png", name="AUD/CHF", ticker="AUDCHF", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
Asset(icon="icons/32x32/digital-money-bits.png", name="NZD/AUD", ticker="AUDNZD", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
]
def get_cryptos():
return \
[
Asset(icon="icons/32x32/bitcoin.png", name="Bitcoin", ticker="BTC-USD", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
Asset(icon="icons/32x32/ethereum.png", name="Ethereum", ticker="ETH-USD", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
Asset(icon="icons/32x32/bitcoin.png", name="Chainlink", ticker="LINK-USD", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
Asset(icon="icons/32x32/litecoin.png", name="Litecoin", ticker="LTC-USD", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
Asset(icon="icons/32x32/bitcoincash.png", name="Bitcoin cash", ticker="BCH-USD", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
Asset(icon="icons/32x32/ripple.png", name="Ripple", ticker="XRP-USD", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
]
def get_commodities():
return \
[
Asset(icon="icons/32x32/igtoken.png", name="Apple", ticker="AAPL", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
Asset(icon="icons/32x32/monero-classic.png", name="Adobe Systems,inc.", ticker="ADBE", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
Asset(icon="icons/32x32/ors-group.png", name="Nvidia LLC", ticker="NVDA", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
Asset(icon="icons/32x32/qwark.png", name="Dell Technologies", ticker="DELL", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
Asset(icon="icons/32x32/rentledger.png", name="Amazon", ticker="AMZN", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
Asset(icon="icons/32x32/reddcoin.png", name="Microsoft", ticker="MSFT", buy=r.randrange(100, 9999, 5), sell=r.randrange(100, 9999, 5)),
]
def my_form_post():
if request.method == 'POST':
if request.form['serial'] != "":
name = request.form['name']
serial = request.form['serial']
manufacturer = request.form['manufacturer']
print("hello")
newasset = Asset(name, serial, manufacturer)
message = str(newasset.get_asset_details())
return jsonify(results=message)
else:
text = request.form['text']
tx = asset.view_asset_by_tx(text)
return jsonify(results=tx)
def boxes(self):
LOGGER.info('Converting...')
convert = Convert(self.pdf_path)
LOGGER.info('Processing %i images...' % len(convert.images))
for image in convert.images:
asset = Asset(image['path'])
LOGGER.info('asset river counts, y=[%i], x=[%i]' %
(len(list(asset.river_y())), len(list(asset.river_x()))))
grid = GridFinder(asset)
grid.process()
if grid.cells:
LOGGER.info('Found %s cells', len(grid.cells))
yield {
'boxes': grid.cells,
'meta': image
}
else:
LOGGER.info('Found no cells')
def register_airtable():
app.logger.info("register_airtable(): POST /register_airtable")
app.logger.debug(f"{request.form=}")
posted_asset = request.form.to_dict() if request.form else {}
if not posted_asset:
return FlashMessage.show_with_redirect(
"Registration failed. Please try the procedure again from the beginning, sorry for the inconvenience.",
FlashCategories.WARNING, url_for("index"))
else:
registrable_asset = Asset(
title=posted_asset.get("title", None),
asin=posted_asset.get("asin", None),
url=posted_asset.get("url", None),
images=[{
"url": posted_asset.get("image_url", None)
}],
manufacture=posted_asset.get("manufacturer", None),
contributor=posted_asset.get("contributors", None),
product_group=posted_asset.get("product_group", None),
publication_date=posted_asset.get("publication_date", None),
features=posted_asset.get("features", None),
default_position=posted_asset.get("default_positions", None),
current_position=posted_asset.get("current_positions", None),
note=posted_asset.get("note", None),
registrant_name=posted_asset.get("registrants_name", None))
try:
AirtableClient().register_asset(registrable_asset)
app.logger.info(f"Registration completed! {registrable_asset=}")
return FlashMessage.show_with_redirect("Registration completed!",
FlashCategories.INFO,
url_for("index"))
except requests.exceptions.HTTPError as he:
app.logger.error(he)
return FlashMessage.show_with_redirect(
f"Registration failed. Please try the procedure again. Error message: {he}",
FlashCategories.ERROR, url_for("registration"))
except TypeError as te:
app.logger.error(te)
return FlashMessage.show_with_redirect(
f"Registration failed. Please try the procedure again. Error message: {te}",
FlashCategories.ERROR, url_for("registration"))
