How to use get_random_number method in locust

Best Python code snippet using locust

ml_backend.py

Source:ml_backend.py Github

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...23 'Pts5', 'OR1', 'OR2', 'OR3', 'OR4', 'OR5', 'DR1', 'DR2', 'DR3',24 'DR4', 'DR5', 'FG2Pct', 'FG3Pct', 'FTPct', 'BlockPct',25 'OppFG2Pct', 'OppFG3Pct', 'OppFTPct', 'OppBlockPct', 'F3GRate',26 'OppF3GRate', 'ARate', 'OppARate', 'StlRate', 'OppStlRate']27def get_random_number():28 """generate random number between -1 and 1."""29 rand = random.random()30 rand = (rand * 2) - 131 return rand32def prepare_data(year1, team1, year2, team2, mongo):33 """Prepare data for prediction."""34 data = []35 team_year_info = mongo.db.basketball.find_one(36 {'TeamName': str(team1), 'Season': int(year1)})37 for col in cols:38 data.append(team_year_info[col])39 tc1 = team_year_info['color1']40 team_year_info = mongo.db.basketball.find_one(41 {'TeamName': str(team2), 'Season': int(year2)})42 for col in cols:43 data.append(team_year_info[col])44 tc2 = team_year_info['color1']45 data.append(home_court.get(str(team1), 0))46 data = np.array(data)47 return data, tc1, tc248def randomize_data(year1, team1, year2, team2, data):49 """randomize data for bootstrap predictions."""50 data_std = year_team_std[year1].get(team1, year_team_std['all'])51 cols_len = len(cols)52 data_copy = data.copy()53 data_copy[0] += get_random_number() * data_std['Pace']54 data_copy[1] += get_random_number() * data_std['ORtg']55 data_copy[2] += get_random_number() * data_std['DRtg']56 data_copy[3] += get_random_number() * data_std['OeFG%'] * 10057 data_copy[4] += get_random_number() * data_std['DeFG%'] * 10058 data_copy[5] += get_random_number() * data_std['OTOV%']59 data_copy[6] += get_random_number() * data_std['DTOV%']60 data_copy[7] += get_random_number() * data_std['OORB%']61 data_copy[8] += get_random_number() * data_std['DDRB%']62 data_copy[9] += get_random_number() * data_std['OFT/FGA'] * 10063 data_copy[10] += get_random_number() * data_std['DFT/FGA'] * 10064 data_copy[42] += get_random_number() * data_std['h3P%'] * 10065 data_copy[43] += get_random_number() * data_std['hFT%'] * 10066 data_copy[44] += get_random_number() * data_std['BLK%']67 data_copy[51] += get_random_number() * data_std['AST%']68 data_copy[53] += get_random_number() * data_std['STL%'] / 10069 data_std = year_team_std[year2].get(team2, year_team_std['all'])70 data_copy[0 + cols_len] += get_random_number() * data_std['Pace']71 data_copy[1 + cols_len] += get_random_number() * data_std['ORtg']72 data_copy[2 + cols_len] += get_random_number() * data_std['DRtg']73 data_copy[3 + cols_len] += get_random_number() * data_std['OeFG%'] * 10074 data_copy[4 + cols_len] += get_random_number() * data_std['DeFG%'] * 10075 data_copy[5 + cols_len] += get_random_number() * data_std['OTOV%']76 data_copy[6 + cols_len] += get_random_number() * data_std['DTOV%']77 data_copy[7 + cols_len] += get_random_number() * data_std['OORB%']78 data_copy[8 + cols_len] += get_random_number() * data_std['DDRB%']79 data_copy[9 + cols_len] += get_random_number() * data_std['OFT/FGA'] * 10080 data_copy[10 + cols_len] += get_random_number() * data_std['DFT/FGA'] * 10081 data_copy[42 + cols_len] += get_random_number() * data_std['h3P%'] * 10082 data_copy[43 + cols_len] += get_random_number() * data_std['hFT%'] * 10083 data_copy[44 + cols_len] += get_random_number() * data_std['BLK%']84 data_copy[51 + cols_len] += get_random_number() * data_std['AST%']85 data_copy[53 + cols_len] += get_random_number() * data_std['STL%'] / 10086 return data_copy87def bootstrap(year1, team1, year2, team2, mongo):88 """predict 100 random games."""89 output = {}90 data, tc1, tc2 = prepare_data(year1, team1, year2, team2, mongo)91 data_df = pd.DataFrame(data.reshape(1, 111))92 data_copy = data.copy()93 num_trials = 9994 for i in range(num_trials):95 rand_data = randomize_data(year1, team1, year2, team2, data_copy)96 data_df.loc[len(data_df)] = rand_data97 data_df -= ml_stats_mean98 data_df = data_df / ml_stats_std99 global graph...

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test_cached.py

Source:test_cached.py Github

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2import pytest3import random4import warnings5@cached6def get_random_number(a, b, *args, **kwars):7 print(a, b)8 return random.randint(a, b)9@cached10def get_number(a, b, *args, **kwars):11 return (a + b) // 212class TestCached:13 def initialize(self):14 random.seed(42)15 def test_simple(self):16 self.initialize()17 for i in range(100):18 assert get_random_number(1, 2) == get_random_number(1, 2)19 def test_hashable_types(self):20 self.initialize()21 tpl = (1, 2.0, '123', None, True)22 for i in range(100):23 assert get_random_number(1, 2, tpl) == get_random_number(1, 2, tpl)24 def test_runtime_warning_unhashable_types(self):25 unhashable = {1: 2, 3: 4}26 with pytest.warns(RuntimeWarning):27 get_number(1, 2, unhashable)28 def test_unhashable_types(self):29 warnings.simplefilter("ignore")...

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test_fixture.py

Source:test_fixture.py Github

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...16# assert os.path.exists(directory), 'dir not exist'171819@pytest.fixture(scope="session")20def get_random_number():21 number = random.randint(1, 100)22 print(number)23 return number242526def test_random_number(get_random_number):27 assert 0 < get_random_number <= 100282930def test_random_number_2(get_random_number):31 assert 0 < get_random_number <= 100323334class TestRandom: ...

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test_2.py

Source:test_2.py Github

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1import pytest2from proga import get_random_number3def test_get_random_number_1():4 assert isinstance(get_random_number(12), int)5def test_get_random_number_2():6 assert get_random_number(12) <= 127def test_get_random_number_3():8 assert isinstance(get_random_number(20), int)9def test_get_random_number_4():10 assert get_random_number(20) <= 2011def test_get_random_number_5():12 assert isinstance(get_random_number(123), int)13def test_get_random_number_6():...

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