IIabm/test.ipynb

In [14]:

import numpy as np

np.random.randint(0.5, 3.5)
p_remove = 0.9
np.random.choice([True, False], p=[p_remove, 1-p_remove])
rng = np.random.default_rng(0)
for _ in range(10):
    print(rng.integers(0,100))
np.random.choice([1, 2, 3], 2, p=[0.4, 0.4, 0.2])

Out [14]:

85
63
51
26
30
4
7
1
17
81

array([2, 2])

In [19]:

share = 0.8
list_succ_firms = [1, 1]
round(share * len(list_succ_firms)) if round(share * len(list_succ_firms)) > 0 else 1

Out [19]:

2

In [4]:

import math
size = [18,20,21,22,23]
p = [s / sum(size) for s in size]
print(p)
for beta in [0.1, 0.2, 0.3]:
    damp_size = [math.exp(beta*s) for s in size]
    print([s / sum(damp_size) for s in damp_size])

[0.17307692307692307, 0.19230769230769232, 0.20192307692307693, 0.21153846153846154, 0.22115384615384615]
[0.14899116146026878, 0.1819782155490595, 0.20111703154812216, 0.22226869439668717, 0.24564489704586234]
[0.10801741721030356, 0.16114305076975205, 0.19682056666851946, 0.2403971829915773, 0.29362178235984765]
[0.07643198434626533, 0.13926815562848321, 0.18799234648997357, 0.25376312466637047, 0.34254438886890737]

In [10]:

import math
size = [18,20,21,22,23]
p = [(s - min(size) + 1)/(max(size)-min(size)+1) for s in size]
print(p)
for beta in [0.1, 0.5, 0.8]:
    p = [((s - min(size) + 1)/(max(size)-min(size)+1))**beta for s in size]
    print(p)

[0.16666666666666666, 0.5, 0.6666666666666666, 0.8333333333333334, 1.0]
[0.8359588020779368, 0.9330329915368074, 0.960264500792218, 0.9819330445619127, 1.0]
[0.408248290463863, 0.7071067811865476, 0.816496580927726, 0.9128709291752769, 1.0]
[0.23849484685087588, 0.5743491774985174, 0.7229811807984657, 0.8642810744472068, 1.0]

In [1]:

import multiprocess as mp

print(mp.cpu_count())

32

In [18]:

from orm import engine
import pandas as pd
import pickle
str_sql = "select e_id, count, max_max_ts, dct_lst_init_remove_firm_prod from iiabmdb.without_exp_experiment as a " \
"inner join " \
"(select e_id, count(id) as count, max(max_ts) as max_max_ts from iiabmdb.without_exp_sample as a " \
"inner join (select s_id, max(ts) as max_ts from iiabmdb.without_exp_result where ts > 0 group by s_id) as b " \
"on a.id = b.s_id " \
"group by e_id) as b " \
"on a.id = b.e_id " \
"order by count desc;"
result = pd.read_sql(sql=str_sql, con=engine)
result['dct_lst_init_remove_firm_prod'] = result['dct_lst_init_remove_firm_prod'].apply(lambda x: pickle.loads(x))
# print(result)
list_dct = result.loc[result['count']>=9, 'dct_lst_init_remove_firm_prod'].to_list()
print(len(list_dct))

71

In [2]:

import numpy as np

np.random.choice([1], p=[0.9])

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[2], line 3
      1 import numpy as np
----> 3 np.random.choice([1], p=[0.9])

File mtrand.pyx:933, in numpy.random.mtrand.RandomState.choice()

ValueError: probabilities do not sum to 1

In [46]:

prob_remove = 0
prob_remove = np.random.uniform(
    prob_remove - 0.1, prob_remove + 0.1)
prob_remove = 1 if prob_remove > 1 else prob_remove
prob_remove = 0 if prob_remove < 0 else prob_remove
prob_remove

Out [46]:

0.004495606232695251

In [66]:

nprandom = np.random.default_rng(0)
lst_choose_firm = nprandom.choice(range(10),
                                       1,
                                       replace=False
                                       )
print(lst_choose_firm)

[8]

In [9]:

nprandom = np.random.default_rng(0)
lst_choose_firm = nprandom.choice([1,2],
                                  3,
                                  replace=False
                                  )
lst_choose_firm

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[9], line 2
      1 nprandom = np.random.default_rng(0)
----> 2 lst_choose_firm = nprandom.choice([1,2],
      3                                   3,
      4                                   replace=False
      5                                   )
      6 lst_choose_firm

File _generator.pyx:753, in numpy.random._generator.Generator.choice()

ValueError: Cannot take a larger sample than population when replace is False

In [3]:

for j in range(3):
    for k in range(3):
        print(j, k)
        if j == k == 1:
            print('break')
            break
    else:
        continue
    break

0 0
0 1
0 2
1 0
1 1
break

In [4]:

print(27 / (4 * 3))
print(27 / 4 / 3)

2.25
2.25

In [2]:

for i in range(1,1):
    print(i)

In [6]:

lst = list(range(1,11))
print(lst)

lst[-5:]

Out [6]:

[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

[6, 7, 8, 9, 10]

In [7]:

with open('SQL_export_high_risk_setting.sql', 'r') as f:
    contents = f.read()

import pandas as pd
from orm import engine
result = pd.read_sql(sql=contents, con=engine)
result

Out [7]:

	e_id	n_disrupt_sample	total_n_disrupt_firm_prod_experiment	dct_lst_init_disrupt_firm_prod
0	383	50	300.0	b'\x80\x05\x95\x17\x00\x00\x00\x00\x00\x00\x00...
1	227	50	250.0	b'\x80\x05\x95\x16\x00\x00\x00\x00\x00\x00\x00...
2	83	50	200.0	b'\x80\x05\x95\x16\x00\x00\x00\x00\x00\x00\x00...
3	135	50	200.0	b'\x80\x05\x95\x16\x00\x00\x00\x00\x00\x00\x00...
4	179	50	200.0	b'\x80\x05\x95\x16\x00\x00\x00\x00\x00\x00\x00...
...	...	...	...	...
90	76	24	56.0	b'\x80\x05\x95\x14\x00\x00\x00\x00\x00\x00\x00...
91	89	24	54.0	b'\x80\x05\x95\x16\x00\x00\x00\x00\x00\x00\x00...
92	90	24	54.0	b'\x80\x05\x95\x16\x00\x00\x00\x00\x00\x00\x00...
93	335	24	54.0	b'\x80\x05\x95\x17\x00\x00\x00\x00\x00\x00\x00...
94	449	24	53.0	b'\x80\x05\x95\x15\x00\x00\x00\x00\x00\x00\x00...

95 rows × 4 columns

In [5]:

import networkx as nx

G = nx.MultiDiGraph()
G.add_edge(1, 2)
nx.draw(G)
G.has_edge(1, 2, 1)

Out [5]:

False

In [17]:

s = set()
s.add(1)
s.add(2)
s.add(1)
len(s)
if s:
    print(1)

1

In [ ]: