def otherFinancialDetails():
design.design()
df, start_date = getCompanyData()
df = df.sort_values(by=['Date'], ascending=[True])
df.set_index('Date', inplace=True)
df = df.resample('D').ffill().reset_index()
closecost = df['Close']
opencost = df['Open']
value = input(
"\n1. Moving Average\n2. Weighted Moving average\n3. MACD(Moving Average Conversion Diversion)\n4. Coefficient of Variation\n5. Go Back To previous Menu\n6. Exit\n\n"
)
if (value == "1"):
moving_average(closecost)
elif (value == "2"):
weighted_moving_average(closecost)
elif (value == "3"):
macd(closecost)
elif (value == "4"):
cv(closecost)
elif (value == "5"):
financeMenu()
elif (value == "6"):
print("\n************** THANK YOU **************")
sys.exit()
else:
print("\nWrong Choice !!! Please retry")
otherFinancialDetails()
otherFinancialDetails()
def getMenuDetails():
design.design()
print("Please select from the below menu option :\n")
value = input("1. More Details of the Company\n2. Exit\n\n")
if value == "1":
selectMenu()
elif value == "2":
print("\n************** THANK YOU **************")
sys.exit()
else:
print("Wrong Choice!!!! Please retry")
getMenuDetails()
def roi(regr, date_test, currentcost):
design.design()
investment = float(
input("\nPlease Enter the Amount You want to invest: $"))
shares = (investment / currentcost)
investment_period = input(
"Enter till when you want to invest in the format MM/DD/YYYY :")
value = datecal(investment_period)
futureval = (prediction(regr, date_test, number=indx[0] + value))
roi = (shares * futureval)
print("\nCurrent Stock Price: $", currentcost)
print("\nFuture Stock Price: $", futureval)
print("\nRoi is: $", roi)
print("\nProfit is: $", (roi - investment))
def selectMenu():
design.design()
value = input("1. Financial Details\n2. Prediction Model\n3. Exit\n\n")
design.design()
if value == "1":
financial_indicators.main()
elif value == "2":
prediction.flow()
elif value == "3":
print("\n************** THANK YOU **************")
sys.exit()
else:
print("Wrong Choice!!! Please retry")
selectMenu()
def moving_average(closecost):
design.design()
try:
n = int(
input("\nEnter the value of n for calculating moving average: "))
except:
print("\nWrong Entry Please Try Again")
otherFinancialDetails()
value = pd.rolling_mean(closecost, n)
ax = value.plot()
plt.xlabel('Days')
plt.ylabel('Stock Price')
plt.suptitle('Moving Average')
plt.show()
def execute(niters):
#assign new random seed
if parallelize: np.random.seed()
naccept = 0
samples = np.zeros((D, niters + 1))
while True:
d = design.design(size=(ado.N_categories,
ado.N_features)) #first design is random
u = ado.global_utility(d)
if not u == 0: break
accepted_u = []
accepted_d = []
iterator = range(niters)
if ado.logging: iterator = tqdm.tqdm(iterator)
for i in iterator:
# j = J(i+1)
d_i = proposal_distribution(d) #random walk
u_i = ado.global_utility(d_i)
rho = min(1, u_i / u) #**j consider sim. annealing
if np.random.uniform() < rho:
naccept += 1 #number accepted
d = d_i
u = u_i
accepted_d.append(d)
accepted_u.append(u)
ado.log('\t### MCMC acceptance rate: %.2f ###' %
float(1.0 * naccept / niters))
idx_best_d = np.nanargmax(accepted_u)
return accepted_d[idx_best_d], accepted_u[idx_best_d]
def execute(niters):
#assign new random seed
if parallelize: np.random.seed()
xt = np.empty((niters, n_particles), dtype=np.object)
ut = np.zeros((niters, n_particles))
wt = np.zeros((niters, n_particles))
wt[0, :] = np.ones(n_particles) / n_particles
for i in np.arange(n_particles):
while True:
xt[0, i] = design.design(
size=(ado.N_categories,
ado.N_features)) #first design is random
ut[0, i] = ado.global_utility(xt[0, i])
if not ut[0, i] == 0: break
iterator = np.arange(start=1, stop=niters)
# if ado.logging: iterator = tqdm.tqdm(iterator)
#smc
for t in iterator:
xt_ = np.empty(n_particles, dtype=np.object)
ut_ = np.zeros(n_particles)
wt_ = np.zeros(n_particles)
#time update
for i in np.arange(n_particles):
xt_[i] = proposal_distribution(xt[t - 1, i]) #random walk
#measurement update
ut_[i] = ado.global_utility(xt_[i])
wt_[i] = wt[t - 1, i] * ut_[i]
#resample
idx = np.random.choice(a=n_particles,
size=n_particles,
p=wt_ / np.sum(wt_))
xt[t, :] = xt_[idx]
ut[t, :] = ut_[idx]
wt[t, :] = np.ones(n_particles) / n_particles
print(np.max(ut))
# #mcmc
# for i in iterator:
# # j = J(i+1)
# d_i = proposal_distribution(d) #random walk
# u_i = ado.global_utility(d_i)
# rho = min(1, u_i / u) #**j consider sim. annealing
# if np.random.uniform() < rho:
# naccept += 1 #number accepted
# d = d_i
# u = u_i
# accepted_d.append(d)
# accepted_u.append(u)
ado.log('\t### SMC sampling finished! ###')
idx_best_d = np.nanargmax(ut)
return xt.flatten()[idx_best_d], ut.flatten()[idx_best_d]
def evaluate_N_random(n): np.random.seed() #randomly generate and evaluate #'n_iterations' designs: randomDesigns = [design.design(size=(ado.N_categories, ado.N_features), alpha=alpha) for i in np.arange(n)] U = [ado.global_utility(d) for d in randomDesigns] try: idx_best_d = np.nanargmax(U) except Exception: d = design.design(size=(ado.N_categories, ado.N_features), alpha=alpha) return d, ado.global_utility(d) else: idx_best_d = np.nanargmax(U) return randomDesigns[idx_best_d], U[idx_best_d]
def optimize(ado, **kwargs):
N_designs = kwargs.get('n_designs', 100)
ado.results.d_evaluated.append(N_designs)
alpha = kwargs.get('alpha', 1.0)
parallelize = kwargs.get('parallelize', False)
if parallelize: N_threads = kwargs.get('N_threads', 4)
def evaluate_N_random(n):
np.random.seed()
#randomly generate and evaluate #'n_iterations' designs:
randomDesigns = [design.design(size=(ado.N_categories,
ado.N_features), alpha=alpha) for i in np.arange(n)]
U = [ado.global_utility(d) for d in randomDesigns]
try:
idx_best_d = np.nanargmax(U)
except Exception:
d = design.design(size=(ado.N_categories,
ado.N_features), alpha=alpha)
return d, ado.global_utility(d)
else:
idx_best_d = np.nanargmax(U)
return randomDesigns[idx_best_d], U[idx_best_d]
class MultiProcess(Process):
def __init__(self, n):
Process.__init__(self)
self.n = n
self.queueD = Queue()
self.queueU = Queue()
def run(self):
d, u = evaluate_N_random(self.n)
self.queueD.put(d)
self.queueU.put(u)
if parallelize:
split_N = np.array([int(N_designs / N_threads)] * N_threads)
split_N[0] += N_designs - np.sum(split_N)
jobs = [MultiProcess(n) for n in split_N]
for job in jobs: job.start()
for job in jobs: job.join()
#find best design among jobs!!
D = [job.queueD.get() for job in jobs]
U = [job.queueU.get() for job in jobs]
try:
idx_best_d = np.nanargmax(U)
except Exception:
d = design.design(size=(ado.N_categories,
ado.N_features), alpha=alpha)
return d, ado.global_utility(d)
else:
idx_best_d = np.nanargmax(U)
return D[idx_best_d], U[idx_best_d]
else:
return evaluate_N_random(N_designs)
def optimize(ado, **kwargs):
alpha = kwargs.get('alpha', 1.0)
ado.results.d_evaluated.append(1)
while(True):
d = design.design(size=(ado.N_categories, ado.N_features), alpha=alpha)
u = ado.global_utility(d)
if not math.isnan(u):
break
return d, u
def financeMenu():
plt.clf() # To close all the opened graphs from memory
design.design()
print("\nPlease select from the below options to see finance details : \n")
value = input(
"1. Basic Financial Details\n2. Other Financial Details \n3. Go back to previous menu\n4. Exit\n\n"
)
if (value == "1"):
basicFinancialDetails()
elif (value == "2"):
otherFinancialDetails()
elif (value == "3"):
company_details.selectMenu()
elif (value == "4"):
print("\n************** THANK YOU **************")
sys.exit()
else:
print("\nWrong Choice!!! Please retry")
financeMenu()
def proposal_distribution(d, sigma=sigma):
new_prior = np.absolute(
np.add(d.prior, np.random.normal(0, sigma, size=d.prior.shape)))
new_prior /= np.sum(new_prior)
while True:
new_lk = np.add(
d.likelihoods,
np.random.normal(0, sigma, size=d.likelihoods.shape))
new_lk = norm_probabilities(new_lk)
if np.argwhere(np.sum(new_lk, axis=1) % 3 == 0).size == 0: break
return design.design(pr=new_prior[:-1], lk=new_lk)
def basicFinancialDetails():
design.design()
value = input(
"\n1. Last 1 Year\n2. Last 6 Months\n3. Last 3 Months\n4. Go back to previous menu\n5. Exit\n\n"
)
design.design()
df, start_date = getCompanyData()
if value == "1":
end_date = start_date - dateutil.relativedelta.relativedelta(months=12)
elif value == "2":
end_date = start_date - dateutil.relativedelta.relativedelta(months=6)
elif value == "3":
end_date = start_date - dateutil.relativedelta.relativedelta(months=3)
elif value == "4":
financeMenu()
elif value == "5":
print("\n************** THANK YOU **************")
sys.exit()
else:
print("\nWrong Choice !!! Please retry")
basicFinancialDetails()
if value == "1" or value == "2" or value == "3":
print("Trading at ", df['Open'][1], "as of ", df['Date'][1])
design.design()
mask = (df['Date'] > end_date) & (df['Date'] < start_date)
df = df.loc[mask]
financialDetails = df.describe()
print(financialDetails)
basicFinancialDetails()
def utility(x_init):
x = np.copy(x_init)
penalty = 0
for i, p in enumerate(x): #penalize when
x[i] = min(1.0, x[i]) #out of bounds
x[i] = max(0.0, x[i])
penalty += abs(x[i] - x_init[i])
prior = x[:ado.N_categories - 1]
prior_sum = np.sum(prior)
if (prior_sum - 1.0) > 0.0:
for i, _ in enumerate(prior):
x[i] /= prior_sum
penalty += abs(prior_sum - 1.0)
likelihood = x[ado.N_categories - 1:]
d = design.design(pr=prior,
lk=np.reshape(likelihood,
(ado.N_features, ado.N_categories)))
utility = ado.global_utility(d) - 1000 * penalty
return d, utility
def run_step_simulation(self,
prior=None,
custom_design=None,
response=None,
last_step=0):
""" Run simulations/DO only for one time step, allows for custom design, prior, and response. """
subPath = '' #'data/'
self.max_steps = last_step + 1
self.step = last_step
self.stepwise = True
# CUSTOM PRIOR
if prior != None: #TODO: check if prior matches !!!
print "Loading prior from file!!!"
self.prior = np.load(subPath + prior)
self.priorName = 'custom'
# CUSTOM DESIGN
if custom_design != None:
print "Loading design from file (using pre-specified environment)!!!"
self.method = 'pre'
d = np.load(subPath + custom_design)
size = list(d.shape)[::-1]
size[1] = int(np.log2(size[1]))
self.N_categories, self.N_features = size
joint = d.flatten()
self.step_design = design.design(joint=joint, size=size)
# CUSTOM RESPONSE
if response != None:
print "Response pre-specified"
self.set_response = response
self.simulation = False
return self.execute()
def runTest(self):
config_file = "{}/tests/configs/config".format(
os.getenv("OPENRAM_HOME"))
globals.init_openram(config_file)
import wire
import tech
import design
layer_stacks = [tech.poly_stack] + tech.beol_stacks
for reverse in [False, True]:
for stack in layer_stacks:
if reverse:
layer_stack = stack[::-1]
else:
layer_stack = stack
# Just make a conservative spacing. Make it wire pitch instead?
min_space = 2 * (
tech.drc["minwidth_{}".format(layer_stack[0])] +
tech.drc["minwidth_{}".format(layer_stack[2])])
position_list = [[0, 0], [0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space],
[4 * min_space, 0], [7 * min_space, 0],
[7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space],
[-1 * min_space, 0]]
position_list = [[x - min_space, y - min_space]
for x, y in position_list]
w = design.design("wire_test_{}".format("_".join(layer_stack)))
wire.wire(w, layer_stack, position_list)
self.local_drc_check(w)
globals.end_openram()
def runTest(self):
globals.init_AMC("config_20_{0}".format(OPTS.tech_name))
global calibre
import calibre
OPTS.check_lvsdrc = False
import wire
import tech
import design
min_space = 2 * (tech.drc["minwidth_poly"] +
tech.drc["minwidth_metal1"])
layer_stack = ("poly", "contact", "metal1")
old_position_list = [[0, 0], [0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space],
[4 * min_space, 0], [7 * min_space, 0],
[7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space],
[-1 * min_space, 0]]
position_list = [[x - min_space, y - min_space]
for x, y in old_position_list]
w = design.design("wire_test1")
wire.wire(w, layer_stack, position_list)
self.local_drc_check(w)
min_space = 2 * (tech.drc["minwidth_poly"] +
tech.drc["minwidth_metal1"])
layer_stack = ("metal1", "contact", "poly")
old_position_list = [[0, 0], [0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space],
[4 * min_space, 0], [7 * min_space, 0],
[7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space],
[-1 * min_space, 0]]
position_list = [[x + min_space, y + min_space]
for x, y in old_position_list]
w = design.design("wire_test2")
wire.wire(w, layer_stack, position_list)
self.local_drc_check(w)
min_space = 2 * (tech.drc["minwidth_metal2"] +
tech.drc["minwidth_metal1"])
layer_stack = ("metal1", "via1", "metal2")
position_list = [[0, 0], [0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space], [4 * min_space, 0],
[7 * min_space, 0], [7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space], [-1 * min_space, 0]]
w = design.design("wire_test3")
wire.wire(w, layer_stack, position_list)
self.local_drc_check(w)
min_space = 2 * (tech.drc["minwidth_metal2"] +
tech.drc["minwidth_metal1"])
layer_stack = ("metal2", "via1", "metal1")
position_list = [[0, 0], [0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space], [4 * min_space, 0],
[7 * min_space, 0], [7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space], [-1 * min_space, 0]]
w = design.design("wire_test4")
wire.wire(w, layer_stack, position_list)
self.local_drc_check(w)
min_space = 2 * (tech.drc["minwidth_metal2"] +
tech.drc["minwidth_metal3"])
layer_stack = ("metal2", "via2", "metal3")
position_list = [[0, 0], [0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space], [4 * min_space, 0],
[7 * min_space, 0], [7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space], [-1 * min_space, 0]]
position_list.reverse()
w = design.design("wire_test5")
wire.wire(w, layer_stack, position_list)
self.local_drc_check(w)
min_space = 2 * (tech.drc["minwidth_metal2"] +
tech.drc["minwidth_metal3"])
layer_stack = ("metal3", "via2", "metal2")
position_list = [[0, 0], [0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space], [4 * min_space, 0],
[7 * min_space, 0], [7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space], [-1 * min_space, 0]]
position_list.reverse()
w = design.design("wire_test6")
wire.wire(w, layer_stack, position_list)
self.local_drc_check(w)
# return it back to it's normal state
OPTS.check_lvsdrc = True
globals.end_AMC()
def runTest(self):
config_file = "{}/tests/configs/config".format(
os.getenv("OPENRAM_HOME"))
globals.init_openram(config_file)
import wire_path
import tech
import design
min_space = 2 * tech.drc["minwidth_m1"]
layer_stack = ("m1")
# checks if we can retrace a path
position_list = [[0, 0], [0, 3 * min_space],
[4 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space], [0, 3 * min_space],
[0, 6 * min_space]]
w = design.design("path_test0")
wire_path.wire_path(w, layer_stack, position_list)
self.local_drc_check(w)
min_space = 2 * tech.drc["minwidth_m1"]
layer_stack = ("m1")
old_position_list = [[0, 0], [0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space],
[4 * min_space, 0], [7 * min_space, 0],
[7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space],
[-1 * min_space, 0]]
position_list = [[x + min_space, y + min_space]
for x, y in old_position_list]
w = design.design("path_test1")
wire_path.wire_path(w, layer_stack, position_list)
self.local_drc_check(w)
min_space = 2 * tech.drc["minwidth_m2"]
layer_stack = ("m2")
old_position_list = [[0, 0], [0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space],
[4 * min_space, 0], [7 * min_space, 0],
[7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space],
[-1 * min_space, 0]]
position_list = [[x - min_space, y - min_space]
for x, y in old_position_list]
w = design.design("path_test2")
wire_path.wire_path(w, layer_stack, position_list)
self.local_drc_check(w)
min_space = 2 * tech.drc["minwidth_m3"]
layer_stack = ("m3")
position_list = [[0, 0], [0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space], [4 * min_space, 0],
[7 * min_space, 0], [7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space], [-1 * min_space, 0]]
# run on the reverse list
position_list.reverse()
w = design.design("path_test3")
wire_path.wire_path(w, layer_stack, position_list)
self.local_drc_check(w)
globals.end_openram()
def webhook():
global reqContext
print(request.get_json(silent=True, force=True))
reqContext = request.get_json(silent=True, force=True)
print("webhook---->" + reqContext.get("result").get("action"))
if reqContext.get("result").get("action") == "input.welcome.edwin":
return welcome()
elif reqContext.get("result").get("action") == "showpopularcategories":
return showpopularcategories()
elif reqContext.get("result").get("action") == "professionalcourses":
return professional_courses.professionalcourses()
elif reqContext.get("result").get("action") == "professionalcertificates":
return professional_courses.professionalcertificates()
elif reqContext.get("result").get("action") == "careeradvancement":
return professional_courses.careeradvancement()
elif reqContext.get("result").get("action") == "micromastersprograms":
return professional_courses.micromastersprograms()
elif reqContext.get("result").get("action") == "advancedskillsetcourses":
return professional_courses.advancedskillsetcourses()
elif reqContext.get("result").get("action") == "popularcourses":
return popular_course.popularcourselist()
elif reqContext.get("result").get("action") == "computerscience":
return computer_science.computerscience()
elif reqContext.get("result").get("action") == "data_and_statistics":
return data_and_statistics.data_and_statistics()
elif reqContext.get("result").get("action") == "business_management":
return business_management.business_management()
elif reqContext.get("result").get("action") == "language":
return language.language()
elif reqContext.get("result").get("action") == "economics_and_finance":
return economics_and_finance.economics_and_finance()
elif reqContext.get("result").get("action") == "engineering":
return engineering.engineering()
elif reqContext.get("result").get("action") == "humanities":
return humanities.humanities()
elif reqContext.get("result").get("action") == "life_sciences":
return life_sciences.life_sciences()
elif reqContext.get("result").get("action") == "science_courses":
return science_courses.science_courses()
elif reqContext.get("result").get("action") == "chemistry":
return chemistry.chemistry()
elif reqContext.get("result").get("action") == "electronics":
return electronics.electronics()
elif reqContext.get("result").get("action") == "environmental_studies":
return environmental_studies.environmental_studies()
elif reqContext.get("result").get("action") == "mathematics":
return mathematics.mathematics()
elif reqContext.get("result").get("action") == "medicine":
return medicine.medicine()
elif reqContext.get("result").get("action") == "physics":
return physics.physics()
elif reqContext.get("result").get("action") == "social_science":
return social_science.social_science()
elif reqContext.get("result").get("action") == "energy_and_earth_science":
return energy_and_earth_science.energy_and_earth_science()
elif reqContext.get("result").get("action") == "art_courses":
return art_courses.art_courses()
elif reqContext.get("result").get("action") == "architecture":
return architecture.architecture()
elif reqContext.get("result").get("action") == "art_and_culture":
return art_and_culture.art_and_culture()
elif reqContext.get("result").get("action") == "communication":
return communication.communication()
elif reqContext.get("result").get("action") == "design":
return design.design()
elif reqContext.get("result").get("action") == "food_and_nutrition":
return food_and_nutrition.food_and_nutrition()
elif reqContext.get("result").get("action") == "health_and_safety":
return health_and_safety.health_and_safety()
elif reqContext.get("result").get("action") == "history":
return history.history()
elif reqContext.get("result").get("action") == "music":
return music.music()
elif reqContext.get("result").get("action") == "law":
return law.law()
elif reqContext.get("result").get("action") == "BacktoWelcomeContent":
return showpopularcategories()
elif reqContext.get("result").get("action") == "BackToProfessionalCourses":
return professional_courses.professionalcourses()
else:
print("Good Bye")
from design import design
from box import print_msg_box
from birthmonth import month_of_birth
from todaymonth import today_month
from dayoftheYear import day_year
import time
exit_press = True
design()
# Loop our program using While loop...
while exit_press:
print_msg_box(msg="""Follow this order: Month Date Year
Example: October 20 2000""")
birth_month, birth_date, birth_year = [
str(x) for x in input("Enter Birthday: ").split()
]
recent_month, recent_date, recent_year = [
str(x) for x in input("Enter Today's date: ").split()
]
birth_month = birth_month.capitalize()
recent_month = recent_month.capitalize()
print("Processing...")
time.sleep(2)
# Check if our birth date and recent date is in range of calendars date.
if int(birth_date) > 31:
print_msg_box(msg="Invalid Birth date", title="Process Failed!")
exit()
elif int(recent_date) > 31:
print_msg_box(msg="Invalid Today's date", title="Process Failed!")
def runTest(self):
globals.init_AMC("config_20_{0}".format(OPTS.tech_name))
global calibre
import calibre
OPTS.check_lvsdrc = False
import path
import tech
import design
min_space = 2 * tech.drc["minwidth_metal1"]
layer = ("metal1")
# checks if we can retrace a path
position_list = [[0, 0], [0, 3 * min_space],
[4 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space], [0, 3 * min_space],
[0, 6 * min_space]]
w = design.design("path_test0")
path.path(w, layer, position_list)
self.local_drc_check(w)
min_space = 2 * tech.drc["minwidth_metal1"]
layer = ("metal1")
old_position_list = [[0, 0], [0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space],
[4 * min_space, 0], [7 * min_space, 0],
[7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space],
[-1 * min_space, 0]]
position_list = [[x + min_space, y + min_space]
for x, y in old_position_list]
w = design.design("path_test1")
path.path(w, layer, position_list)
self.local_drc_check(w)
min_space = 2 * tech.drc["minwidth_metal2"]
layer = ("metal2")
old_position_list = [[0, 0], [0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space],
[4 * min_space, 0], [7 * min_space, 0],
[7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space],
[-1 * min_space, 0]]
position_list = [[x - min_space, y - min_space]
for x, y in old_position_list]
w = design.design("path_test2")
path.path(w, layer, position_list)
self.local_drc_check(w)
min_space = 2 * tech.drc["minwidth_metal3"]
layer = ("metal3")
position_list = [[0, 0], [0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space], [4 * min_space, 0],
[7 * min_space, 0], [7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space], [-1 * min_space, 0]]
# run on the reverse list
position_list.reverse()
w = design.design("path_test3")
path.path(w, layer, position_list)
self.local_drc_check(w)
# return it back to it's normal state
OPTS.check_lvsdrc = True
globals.end_AMC()
def runTest(self):
globals.init_openram("config_{0}".format(OPTS.tech_name))
import wire_path
import tech
import design
min_space = 2 * tech.drc["minwidth_metal1"]
layer_stack = ("metal1")
# checks if we can retrace a path
position_list = [[0, 0], [0, 3 * min_space],
[4 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space], [0, 3 * min_space],
[0, 6 * min_space]]
w = design.design("path_test0")
wire_path.wire_path(w, layer_stack, position_list)
self.local_drc_check(w)
min_space = 2 * tech.drc["minwidth_metal1"]
layer_stack = ("metal1")
old_position_list = [[0, 0], [0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space],
[4 * min_space, 0], [7 * min_space, 0],
[7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space],
[-1 * min_space, 0]]
position_list = [[x + min_space, y + min_space]
for x, y in old_position_list]
w = design.design("path_test1")
wire_path.wire_path(w, layer_stack, position_list)
self.local_drc_check(w)
min_space = 2 * tech.drc["minwidth_metal2"]
layer_stack = ("metal2")
old_position_list = [[0, 0], [0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space],
[4 * min_space, 0], [7 * min_space, 0],
[7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space],
[-1 * min_space, 0]]
position_list = [[x - min_space, y - min_space]
for x, y in old_position_list]
w = design.design("path_test2")
wire_path.wire_path(w, layer_stack, position_list)
self.local_drc_check(w)
min_space = 2 * tech.drc["minwidth_metal3"]
layer_stack = ("metal3")
position_list = [[0, 0], [0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space], [4 * min_space, 0],
[7 * min_space, 0], [7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space], [-1 * min_space, 0]]
# run on the reverse list
position_list.reverse()
w = design.design("path_test3")
wire_path.wire_path(w, layer_stack, position_list)
self.local_drc_check(w)
globals.end_openram()
def runTest(self):
config_file = "{}/tests/configs/config".format(os.getenv("OPENRAM_HOME"))
globals.init_openram(config_file)
import wire
import tech
import design
min_space = 2 * (tech.drc["minwidth_poly"] +
tech.drc["minwidth_metal1"])
layer_stack = ("poly", "contact", "metal1")
old_position_list = [[0, 0],
[0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space],
[4 * min_space, 0],
[7 * min_space, 0],
[7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space],
[-1 * min_space, 0]]
position_list = [[x-min_space, y-min_space] for x,y in old_position_list]
w = design.design("wire_test1")
wire.wire(w, layer_stack, position_list)
self.local_drc_check(w)
min_space = 2 * (tech.drc["minwidth_poly"] +
tech.drc["minwidth_metal1"])
layer_stack = ("metal1", "contact", "poly")
old_position_list = [[0, 0],
[0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space],
[4 * min_space, 0],
[7 * min_space, 0],
[7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space],
[-1 * min_space, 0]]
position_list = [[x+min_space, y+min_space] for x,y in old_position_list]
w = design.design("wire_test2")
wire.wire(w, layer_stack, position_list)
self.local_drc_check(w)
min_space = 2 * (tech.drc["minwidth_metal2"] +
tech.drc["minwidth_metal1"])
layer_stack = ("metal1", "via1", "metal2")
position_list = [[0, 0],
[0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space],
[4 * min_space, 0],
[7 * min_space, 0],
[7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space],
[-1 * min_space, 0]]
w = design.design("wire_test3")
wire.wire(w, layer_stack, position_list)
self.local_drc_check(w)
min_space = 2 * (tech.drc["minwidth_metal2"] +
tech.drc["minwidth_metal1"])
layer_stack = ("metal2", "via1", "metal1")
position_list = [[0, 0],
[0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space],
[4 * min_space, 0],
[7 * min_space, 0],
[7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space],
[-1 * min_space, 0]]
w = design.design("wire_test4")
wire.wire(w, layer_stack, position_list)
self.local_drc_check(w)
min_space = 2 * (tech.drc["minwidth_metal2"] +
tech.drc["minwidth_metal3"])
layer_stack = ("metal2", "via2", "metal3")
position_list = [[0, 0],
[0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space],
[4 * min_space, 0],
[7 * min_space, 0],
[7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space],
[-1 * min_space, 0]]
position_list.reverse()
w = design.design("wire_test5")
wire.wire(w, layer_stack, position_list)
self.local_drc_check(w)
min_space = 2 * (tech.drc["minwidth_metal2"] +
tech.drc["minwidth_metal3"])
layer_stack = ("metal3", "via2", "metal2")
position_list = [[0, 0],
[0, 3 * min_space],
[1 * min_space, 3 * min_space],
[4 * min_space, 3 * min_space],
[4 * min_space, 0],
[7 * min_space, 0],
[7 * min_space, 4 * min_space],
[-1 * min_space, 4 * min_space],
[-1 * min_space, 0]]
position_list.reverse()
w = design.design("wire_test6")
wire.wire(w, layer_stack, position_list)
self.local_drc_check(w)
globals.end_openram()
def optimize(ado, **kwargs):
D = (ado.N_features + 1) * ado.N_categories - 1
n_designs = kwargs.get('n_designs', 100)
n_iterations = kwargs.get('n_iterations', 100)
n_particles = int(kwargs.get('n_iterations', n_designs / n_iterations))
parallelize = kwargs.get('parallelize', False)
sigma = kwargs.get('sigma', 0.3)
alpha = kwargs.get('sigma', 1.0)
ado.results.d_evaluated.append(n_designs)
if parallelize: N_threads = kwargs.get('N_threads', 4)
def norm_probabilities(array):
for index, _ in np.ndenumerate(array):
array[index] = min(1.0, array[index])
array[index] = max(0.0, array[index])
return array
def proposal_distribution(d, sigma=sigma):
new_prior = np.absolute(
np.add(d.prior, np.random.normal(0, sigma, size=d.prior.shape)))
new_prior /= np.sum(new_prior)
while True:
new_lk = np.add(
d.likelihoods,
np.random.normal(0, sigma, size=d.likelihoods.shape))
new_lk = norm_probabilities(new_lk)
if np.argwhere(np.sum(new_lk, axis=1) % 3 == 0).size == 0: break
return design.design(pr=new_prior[:-1], lk=new_lk)
def execute(niters):
#assign new random seed
if parallelize: np.random.seed()
xt = np.empty((niters, n_particles), dtype=np.object)
ut = np.zeros((niters, n_particles))
wt = np.zeros((niters, n_particles))
wt[0, :] = np.ones(n_particles) / n_particles
for i in np.arange(n_particles):
while True:
xt[0, i] = design.design(
size=(ado.N_categories,
ado.N_features)) #first design is random
ut[0, i] = ado.global_utility(xt[0, i])
if not ut[0, i] == 0: break
iterator = np.arange(start=1, stop=niters)
# if ado.logging: iterator = tqdm.tqdm(iterator)
#smc
for t in iterator:
xt_ = np.empty(n_particles, dtype=np.object)
ut_ = np.zeros(n_particles)
wt_ = np.zeros(n_particles)
#time update
for i in np.arange(n_particles):
xt_[i] = proposal_distribution(xt[t - 1, i]) #random walk
#measurement update
ut_[i] = ado.global_utility(xt_[i])
wt_[i] = wt[t - 1, i] * ut_[i]
#resample
idx = np.random.choice(a=n_particles,
size=n_particles,
p=wt_ / np.sum(wt_))
xt[t, :] = xt_[idx]
ut[t, :] = ut_[idx]
wt[t, :] = np.ones(n_particles) / n_particles
print(np.max(ut))
# #mcmc
# for i in iterator:
# # j = J(i+1)
# d_i = proposal_distribution(d) #random walk
# u_i = ado.global_utility(d_i)
# rho = min(1, u_i / u) #**j consider sim. annealing
# if np.random.uniform() < rho:
# naccept += 1 #number accepted
# d = d_i
# u = u_i
# accepted_d.append(d)
# accepted_u.append(u)
ado.log('\t### SMC sampling finished! ###')
idx_best_d = np.nanargmax(ut)
return xt.flatten()[idx_best_d], ut.flatten()[idx_best_d]
execute(n_iterations)
d = design.design(size=(ado.N_categories, ado.N_features), alpha=alpha)
return d, ado.global_utility(d)
def flow():
design.design()
data = pd.read_csv("file.csv", delimiter=",", na_values=['-'])
date = data['Date']
date = pd.to_datetime(date)
data['Date'] = pd.to_datetime(data['Date'])
data = data.sort_values(by=['Date'], ascending=[True])
data.set_index('Date', inplace=True)
data = data.resample('D').ffill().reset_index()
data1 = data.drop_duplicates(subset=['Volume', 'Open', 'Close'],
inplace=True)
date1 = data['Date']
opencost = data['Open']
closecost = data['Close']
timeGraph(opencost, date1, legend='Open Cost Trend')
timeGraph(closecost, date1, legend='Close Cost Trend')
test = np.array(range(val[0]))
test = test.reshape(val[0], 1)
indx.append(data.index[-1])
opencost = (opencost[np.isfinite(opencost)])
opencost = opencost[0:val[0]]
opencost_pr = opencost.values.reshape(val[0],
1) # RESHAPPING TO SAME SHAPE
global i
global size
if (i) == 1:
try:
size = float(
input(
"\nPlease enter Percentage of training size between (1 - 99): "
))
except:
print("\n Wrong Entry! Try again")
flow()
if size not in range(1, 100):
print("\nSorry Invalid Value please enter size between 0 to 100")
flow()
else:
i += 1
#TRAINING AND TESTING
date_train, date_test, opencost_pr_train, opencost_pr_test = train_test_split(
test, opencost_pr, random_state=1, train_size=(size) / 100)
regr = linear_model.LinearRegression()
regr.fit(date_train, opencost_pr_train)
answer = input(
"\n1. Check Cost Trend\n2. Regression Graph\n3. Predictor\n4. Calculate returns\n5. Go Back to previous menu\n6. Exit\n\n"
).upper()
if answer == "1":
plt.suptitle('Cost Trend')
plt.show()
elif answer == "2":
plt.close()
plt.scatter(test, opencost_pr, color='green')
plt.plot(date_test, regr.predict(date_test), color='blue', linewidth=3)
plt.xlabel('Months')
plt.ylabel('Stock Price')
plt.suptitle('Linear Regression')
plt.xticks(())
plt.yticks(())
plt.show()
elif answer == "3":
design.design()
date = enterdate()
try:
no_of_days = datecal(date)
except:
print("\nSorry Wrong Input Try Again")
flow()
value = (indx[0] + no_of_days)
predict = prediction(regr, date_test, number=value)
prediction_Model = regr.predict(date_test)
if predict < 0:
print("\n\nCompany Not Listed At that time")
else:
print("\nPredicted value at %s for selected company is $%f" %
(date, predict))
# FOR R**2 value
print("\nR Squared Value is : ",
metrics.r2_score(opencost_pr_test, prediction_Model))
# Mean Square value
print("\nMean Squared error Value is : ",
np.sqrt(((prediction_Model - opencost_pr_test)**2).mean()))
elif answer == "4":
try:
roi(regr, date_test, closecost.iloc[-1])
except:
print("\n Oops Wrong Data Entry Retry")
elif answer == "5":
company_details.selectMenu()
elif answer == "6":
quitting()
else:
if input("Wrong Input! Press Any Key To Try Again or press 0 to exit: "
) == "0":
print("\n************** THANK YOU **************")
sys.exit()
flow()
def optimize(ado, **kwargs):
D = (ado.N_features + 1) * ado.N_categories - 1
niters = kwargs.get('n_designs', 100)
parallelize = kwargs.get('parallelize', True)
sigma = kwargs.get('sigma', 0.4)
alpha = kwargs.get('sigma', 1.0)
ado.results.d_evaluated.append(niters)
if parallelize: N_threads = kwargs.get('N_threads', 4)
class MultiProcess(Process):
def __init__(self, iterations):
Process.__init__(self)
self.iters = iterations
self.queueD = Queue()
self.queueU = Queue()
def run(self):
d, u = execute(niters=self.iters)
self.queueD.put(d)
self.queueU.put(u)
def J(t):
return int(t * 0.04) + 1
def norm_probabilities(array):
for index, _ in np.ndenumerate(array):
array[index] = min(1.0, array[index])
array[index] = max(0.0, array[index])
return array
def proposal_distribution(d, sigma=sigma):
new_prior = np.absolute(
np.add(d.prior, np.random.normal(0, sigma, size=d.prior.shape)))
new_prior /= np.sum(new_prior)
while True:
new_lk = np.add(
d.likelihoods,
np.random.normal(0, sigma, size=d.likelihoods.shape))
new_lk = norm_probabilities(new_lk)
if np.argwhere(np.sum(new_lk, axis=1) % 3 == 0).size == 0: break
return design.design(pr=new_prior[:-1], lk=new_lk)
def execute(niters):
#assign new random seed
if parallelize: np.random.seed()
naccept = 0
samples = np.zeros((D, niters + 1))
while True:
d = design.design(size=(ado.N_categories,
ado.N_features)) #first design is random
u = ado.global_utility(d)
if not u == 0: break
accepted_u = []
accepted_d = []
iterator = range(niters)
if ado.logging: iterator = tqdm.tqdm(iterator)
for i in iterator:
# j = J(i+1)
d_i = proposal_distribution(d) #random walk
u_i = ado.global_utility(d_i)
rho = min(1, u_i / u) #**j consider sim. annealing
if np.random.uniform() < rho:
naccept += 1 #number accepted
d = d_i
u = u_i
accepted_d.append(d)
accepted_u.append(u)
ado.log('\t### MCMC acceptance rate: %.2f ###' %
float(1.0 * naccept / niters))
idx_best_d = np.nanargmax(accepted_u)
return accepted_d[idx_best_d], accepted_u[idx_best_d]
if parallelize:
iters = int(niters / N_threads)
jobs = [MultiProcess(iterations=iters) for i in np.arange(N_threads)]
for job in jobs:
job.start()
for job in jobs:
job.join()
D = [job.queueD.get() for job in jobs]
U = [job.queueU.get() for job in jobs]
try:
idx_best_d = np.nanargmax(U)
except Exception:
d = design.design(size=(ado.N_categories, ado.N_features),
alpha=alpha)
return d, ado.global_utility(d)
else:
return D[idx_best_d], U[idx_best_d]
else:
return execute(niters)
def getinfo(x):
companies = (companylist[(companylist == x).any(1)].stack())
if len(companies) == 0: # HANDLING ERROR
print("Company Not Found : *Please enter exact name or symbol* \n")
value = input(
"If you want to try more please enter Y or enter N to go back to previous menu: "
)
if value.upper() == "Y":
getdetails("2")
elif value.upper() == "N":
design.design()
mainmenu()
else:
print("\nWrong Choice !!!!!! Please retry")
getdetails("2")
elif len(companies) > 8:
print(
"\n Oops it does'nt look like a correct symbol - Looking For Anyone of these?"
)
design.design()
print(companies[0:], "\n")
design.design()
if input(
"\nif not in the list? Hit 0 to try again OR any key to continue: "
) == "0":
main()
else:
np
confirm = input("confirm the Company Symbol by entering : ").upper(
) #Upper to handle case sensitive execpts
design.design()
try:
x = (np.where(companylist.Symbol == confirm))
p = int(x[0])
print(companylist.iloc[p][0:7])
val = input(
"To visit company's Nasdaq site press Y or to continue press any other key: "
)
if (val.upper() == "Y"):
webbrowser.open(
companylist[7]) # To open the company link for more info
company_details.getFile(confirm)
main()
except:
print("\nInvalid Entry Try Again")
getdetails("2")
else:
design.design()
print((companies[0:7]), "\n") #For memory quote in companies[8]
val = input(
"To visit company's Nasdaq site press Y or to continue press any other key: "
).upper()
if (val == "Y"):
print("opening url")
webbrowser.open(
companies[7]) # To open the company link for more info
pih = 'PIH'
if (x == pih):
company_details.main(x)
else:
company_details.main(companies[0])
design.design()
