def wrapper(self, event, corpus, **kwargs):
print "I am here"
is_met = \
self.deps_met_.get((event.fs_name(), corpus.fs_name()), False)
if is_met is False and kwargs.get('no_resolve', False) is False:
for dc in self.dependencies():
print self, dc
dep = dc()
coverage_per = dep.check_coverage(event, corpus, **kwargs)
if coverage_per != 1.:
sys.stdout.write(
u"{}: Incomplete coverage (%{:0.2f}), "
u"retrieving...\n"
.format(dep, coverage_per * 100.))
print dep.get
if dep.get(event, corpus, **kwargs) is True:
sys.stdout.write('OK!\n')
sys.stdout.flush()
else:
sys.stdout.write('FAILED!\n')
sys.flush()
sys.stderr.write(
'Failed to retrieve necessary resource:\n')
sys.stderr.write('\t{} for {} / {}'.format(
dep, event.query_id, corpus.fs_name()))
sys.stderr.flush()
sys.exit(1)
self.deps_met_[(event.fs_name(), corpus.fs_name())] = True
return fun(self, event, corpus, **kwargs)
def SunState(): UTCnow = datetime.datetime.utcnow() PSTnow = datetime.datetime.now() # Moved Lat & Long to top of script a = Astral() SunAngle = a.solar_elevation(UTCnow, latitude, longitude) #SunAngleMQTT = SunAngle # SunAngle is angle (+)above/(-)below horizon if SunAngle < 0.1: SunState = 'Night' else: SunState = 'Day' if ForceNight is True: SunState = 'Night' if DebugUTC is True: print 'PSTnow =', print (PSTnow), print 'UTCnow =', print (UTCnow), print 'SunState =', print (SunState), print 'SunAngle =', print (SunAngle), print 'ForceNight =', print (ForceNight) sys.flush() return SunState
def wrapper(self, event, corpus, **kwargs):
print "I am here"
is_met = \
self.deps_met_.get((event.fs_name(), corpus.fs_name()), False)
if is_met is False and kwargs.get('no_resolve', False) is False:
for dc in self.dependencies():
print self, dc
dep = dc()
coverage_per = dep.check_coverage(event, corpus, **kwargs)
if coverage_per != 1.:
sys.stdout.write(
u"{}: Incomplete coverage (%{:0.2f}), "
u"retrieving...\n".format(dep,
coverage_per * 100.))
print dep.get
if dep.get(event, corpus, **kwargs) is True:
sys.stdout.write('OK!\n')
sys.stdout.flush()
else:
sys.stdout.write('FAILED!\n')
sys.flush()
sys.stderr.write(
'Failed to retrieve necessary resource:\n')
sys.stderr.write('\t{} for {} / {}'.format(
dep, event.query_id, corpus.fs_name()))
sys.stderr.flush()
sys.exit(1)
self.deps_met_[(event.fs_name(), corpus.fs_name())] = True
return fun(self, event, corpus, **kwargs)
def wbyte(self, reg, value):
# Write a single byte <value> to register <reg>.
if self.DEBUG >= 32:
sys.stdout.write(
' write x{:02X} to chip x{:02X} register {}\n'.format(
value, self.address, reg))
sys.flush()
self.device.write_byte_data(self.address, reg, value)
return
# Main program:
try:
if __name__ == '__main__':
# Create NeoPixel object with appropriate configuration.
strip = Adafruit_NeoPixel(LED_COUNT, LED_PIN, LED_FREQ_HZ, LED_DMA, LED_INVERT, LED_BRIGHTNESS)
# Intialize the library (must be called once before other functions).
strip.begin()
strip.show()
blackout()
SunStateMQTT = SunState()
MQTT1()
MQTT2("First Run")
# Set LastOn and LastOff in event of exit before reaching either state
if Debug is True: print 'Debug is ON'
if Debug is False: print 'Debug is OFF'
sys.flush()
if Debug is True:
print ('Press \033[1m Ctrl-C \033[0m to quit.')
print (PBlue),
print ('It is currently'),
print (PYellow),
print SunState(),
print (PBlue),
print ('on'),
print (PPurple),
print Holiday(),
print (POff),
sys.flush()
while True:
#AllTest() # Functions setup for individual testing
while Holiday() != 'None':
def main():
"""
Get fact about number passed from command line
"""
if "-t" in argumentList:
if not update_token(argumentList[argumentList.index("-t") + 1]):
sys.stdout.write(
"Unable to update token, it may be invalid or file may not be written properly.\nCheck token validity again!\n"
)
sys.stdout.flush()
return
headers["x-rapidapi-key"] = argumentList[argumentList.index("-t") + 1]
argumentList.pop(argumentList.index("-t"))
argumentList.pop(argumentList.index(headers["x-rapidapi-key"]))
if len(argumentList) > 2:
if argumentList[1] == "-f":
querystring = {"fragment": "true", "json": "true"}
url = f"https://numbersapi.p.rapidapi.com/{argumentList[2]}/math"
response = requests.request("GET",
url,
headers=headers,
params=querystring)
data = response.json()
sys.stdout.write(f"{str(data['text'])}\n")
elif argumentList[1] == "-ft":
querystring = {
"fragment": "true",
"notfound": "floor",
"json": "true"
}
url = f"https://numbersapi.p.rapidapi.com/{argumentList[2]}/trivia"
response = requests.request("GET",
url,
headers=headers,
params=querystring)
data = response.json()
sys.stdout.write(f"{str(data['text'])}\n")
elif argumentList[1] == "-fy":
querystring = {"fragment": "true", "json": "true"}
url = f"https://numbersapi.p.rapidapi.com/{argumentList[2]}/year"
response = requests.request("GET",
url,
headers=headers,
params=querystring)
data = response.json()
if "date" in data:
sys.stdout.write(
f"{str(data['date'])}, {str(data['number'])} - {str(data['text'])}\n"
)
else:
sys.stdout.write(
f"{str(data['number'])} - {str(data['text'])}\n")
elif argumentList[1] == "-fd":
if "/" not in str(argumentList[2]):
sys.stdout.write("Please provide date in the form of - mm/dd")
sys.flush()
return
querystring = {"fragment": "true", "json": "true"}
url = f"https://numbersapi.p.rapidapi.com/{argumentList[2]}/date"
response = requests.request("GET",
url,
headers=headers,
params=querystring)
data = response.json()
sys.stdout.write(f"{str(data['year'])} - {str(data['text'])}\n")
else:
sys.stdout.write(
"Please provide options in - '-f', '-ft', '-fy' or '-fd'")
elif len(argumentList) > 1:
if argumentList[1] == "-h":
sys.stdout.write("Command line options - \n")
options = [("<none>", "get trivia fact about random number"),
("-f", "get math fact about a number. Ex. -f 29"),
("-ft", "get trivia fact about a number. Ex. -ft 7"),
("-fy", "get fact about year. Ex. -fy 2003"),
("-fd", "get fact about date. Ex. -fd 5/23")]
for pair in options:
sys.stdout.write(f"\t{pair[0]}\t{pair[1]}\n")
sys.stdout.flush()
return
querystring = {"fragment": "true", "json": "true"}
url = f"https://numbersapi.p.rapidapi.com/{argumentList[1]}/math"
response = requests.request("GET",
url,
headers=headers,
params=querystring)
data = response.json()
sys.stdout.write(f"{str(data['text'])}\n")
else:
url = "https://numbersapi.p.rapidapi.com/random/trivia"
querystring = {
"max": "20",
"fragment": "true",
"min": "10",
"json": "true"
}
response = requests.request("GET",
url,
headers=headers,
params=querystring)
data = response.json()
sys.stdout.write(f"{str(data['number'])} - {str(data['text'])}\n")
sys.stdout.flush()
return
def calibrate(self, numsteps):
"""
Calculates the coefficient and power terms for the sensor by recording
voltage measurements at known distances and then performing an
exponential curve fit on the data.
numsteps: the number of measurements to take from min to max distance
more steps will give a more accurate curve
"""
import numpy as np
from scipy.optimize import curve_fit
distances = range(self.min, self.max+1, (self.max-self.min)/numsteps)
voltages = []
def func(x, a, b):
"""
Curve fitting function. Used to fit data points to the curve
y = a * x ^ b
"""
return a * x ** b
print('IR Calibration')
print('\nThe calibration process requires you to place an object ' \
'(a flat piece of card is good) at a variety of distances from ' \
'the minumum distance to the maximum distance.\n\n' \
'Voltage readings will be taken at (cm):')
print distances
for d in distances:
print("Measure @ %d cm" % (d))
sleep(3)
for x in range(5):
sys.stdout.write("%d " % (5-x))
sys.flush()
sleep(1)
print("\nReading @ %d cm" % (d))
value = 0.0
total = 0.0
for x in range(5):
value = ADC.read(self.AIN) * 1.8 * self.scale
total = total + value
print("#%d - %2.4f" % (x+1, value))
sleep(1)
print("%d cm Average: %2.4f" % (d, total/5))
print"=" * 15
voltages.append(total/5)
# fits measured voltages to y = a * x ^ B curve
popt, pcov = curve_fit(func, np.array(voltages), np.array(distances))
self.coeff = popt[0]
self.power = popt[1]
print "Calibration Data"
print "-" * 15
print "Coefficient :", self.coeff
print "Power :", self.power
print "Err 1 std dev:", np.sqrt(np.diag(pcov))
print "\nEquation : distance = %2.2f * scaled_voltage ** %2.2f" % (self.coeff, self.power)
print "-" * 15
print "Example use:"
print ' IR = Adafruit_SharpIR("P9_36", scale=1560.0/560.0, coeff=%2.6f, power=%2.6f)' % (self.coeff, self.power)
print "-" * 15
def calibrate(self, numsteps):
"""
Calculates the coefficient and power terms for the sensor by recording
voltage measurements at known distances and then performing an
exponential curve fit on the data.
numsteps: the number of measurements to take from min to max distance
more steps will give a more accurate curve
"""
import numpy as np
from scipy.optimize import curve_fit
distances = range(self.min, self.max+1, (self.max-self.min)/numsteps)
voltages = []
def func(x, a, b):
"""
Curve fitting function. Used to fit data points to the curve
y = a * x ^ b
"""
return a * x ** b
print('IR Calibration')
print('\nThe calibration process requires you to place an object ' \
'(a flat piece of card is good) at a variety of distances from ' \
'the minumum distance to the maximum distance.\n\n' \
'Voltage readings will be taken at (cm):')
print distances
for d in distances:
print("Measure @ %d cm" % (d))
sleep(3)
for x in range(5):
sys.stdout.write("%d " % (5-x))
sys.flush()
sleep(1)
print("\nReading @ %d cm" % (d))
value = 0.0
total = 0.0
for x in range(5):
value = ADC.read(self.AIN) * 1.8 * self.scale
total = total + value
print("#%d - %2.4f" % (x+1, value))
sleep(1)
print("%d cm Average: %2.4f" % (d, total/5))
print"=" * 15
voltages.append(total/5)
# fits measured voltages to y = a * x ^ B curve
popt, pcov = curve_fit(func, np.array(voltages), np.array(distances))
self.coeff = popt[0]
self.power = popt[1]
print "Calibration Data"
print "-" * 15
print "Coefficient :", self.coeff
print "Power :", self.power
print "Err 1 std dev:", np.sqrt(np.diag(pcov))
print "\nEquation : distance = %2.2f * scaled_voltage ** %2.2f" % (self.coeff, self.power)
print "-" * 15
print "Example use:"
print ' IR = Adafruit_SharpIR("P9_36", scale=1560.0/560.0, coeff=%2.6f, power=%2.6f)' % (self.coeff, self.power)
print "-" * 15
def import_data(save=True,try_from_csv=False,saved_csvs=['MaleRankCount.csv','FemaleRankCount.csv',
'MaleRankIDs.csv','FemaleRankIDs.csv',
'male_name_ids.csv','female_name_ids.csv']):
cwd = os.getcwd()
subdir = 'data'
path = os.path.join(cwd,subdir)
dir_info = os.walk(path)
(_,_,files) = dir_info.__next__()
csv_files = set([file for file in files if '.csv' in file])
txt_files = [file for file in files if '.txt' in file]
name_id2str = {'M':{},'F':{}}
name_str2id = {'M':{},'F':{}}
ranks_count = {'M':None,'F':None}
ranks_ids = {'M':None,'F':None}
if try_from_csv and len(saved_csvs) == 6 and csv_files.issuperset(saved_csvs):
try:
sys.stdout.write('\nImporting from CSV files...\n')
sys.stdout.flush()
show_status(0./6)
with open(os.path.join(path,saved_csvs[0])) as f:
df = pd.read_csv(f)
try:
df.columns = [int(yr) for yr in df.columns]
except:
pass
ranks_count['M'] = df
show_status(1./6)
with open(os.path.join(path,saved_csvs[1])) as f:
df = pd.read_csv(f)
try:
df.columns = [int(yr) for yr in df.columns]
except:
pass
ranks_count['F'] = df
show_status(2./6)
with open(os.path.join(path,saved_csvs[2])) as f:
df = pd.read_csv(f)
try:
df.columns = [int(yr) for yr in df.columns]
except:
pass
ranks_ids['M'] = df
show_status(3./6)
with open(os.path.join(path,saved_csvs[3])) as f:
df = pd.read_csv(f)
try:
df.columns = [int(yr) for yr in df.columns]
except:
pass
ranks_ids['F'] = df
show_status(4./6)
with open(os.path.join(path,saved_csvs[4])) as f:
curr_reader = csv.reader(f)
for id,name in curr_reader:
name_id2str['M'][int(id)] = name
name_str2id['M'][name] = int(id)
show_status(5./6)
with open(os.path.join(path,saved_csvs[5])) as f:
curr_reader = csv.reader(f)
for id,name in curr_reader:
name_id2str['F'][int(id)] = name
name_str2id['F'][name] = int(id)
show_status(1)
return {'id2str':name_id2str,'str2id':name_str2id}, ranks_count, ranks_ids
except:
sys.stdout.write("\nError: Could not import from CSV. Attempting import from txt files\n")
sys.flush()
rankings = {i:defaultdict(list) for i in range(len(txt_files))}
mmax_len = 0; fmax_len = 0; high_prime = 104729 # 449563
sys.stdout.write('Extracting Data From txt files...\n')
sys.stdout.flush()
for i in range(len(txt_files)):
show_status(float(i+1)/len(txt_files))
file = txt_files[i]
with open(os.path.join(path,file)) as f:
data = list(csv.reader(f))
for name,gender,births in data:
h = str2hash(name,maxval=high_prime)
hval = name_id2str[gender].get(h,'-1')
count = 1
while name != hval and hval != '-1':
h = (h**count + ord(name[0])**count + ord(name[-1])**count) % high_prime
count+=1
hval = name_id2str[gender].get(h,'-1')
if name != hval:
name_id2str[gender][h] = name
name_str2id[gender][name] = h
rankings[i][gender].append((h,births))
mmax_len = max(mmax_len,len(rankings[i]['M']))
fmax_len = max(fmax_len,len(rankings[i]['F']))
ranks_count = {'M':pd.DataFrame(columns=list(range(len(rankings.keys()))), index=list(range(mmax_len)))
, 'F':pd.DataFrame(columns=list(range(len(rankings.keys()))), index=list(range(fmax_len)))}
ranks_ids = {'M':pd.DataFrame(columns=list(range(len(rankings.keys()))), index=list(range(mmax_len)))
, 'F':pd.DataFrame(columns=list(range(len(rankings.keys()))), index=list(range(fmax_len)))}
sys.stdout.write('\nConstructing DataFrame...\n')
sys.stdout.flush()
for year in rankings.keys():
#show_status(float(year + 1)/len(rankings.keys()))
for gender in rankings[year].keys():
size = len(rankings[year][gender])
ranks_ids[gender].loc[:size-1,year] = [rankings[year][gender][i][0] for i in range(size)]
ranks_count[gender].loc[:size-1,year] = [int(rankings[year][gender][i][1]) for i in range(size)]
if save:
sys.stdout.write('\nSaving Data...\n')
sys.stdout.flush()
with open(os.path.join('data','male_name_ids.csv'),'w',newline='') as fout:
w = csv.writer(fout)
w.writerows(name_id2str['M'].items())
with open(os.path.join('data','female_name_ids.csv'),'w',newline='') as fout:
w = csv.writer(fout)
w.writerows(name_id2str['F'].items())
export_data(df=ranks_count['M'],filename='MaleRankCount.csv',path='data')
export_data(df=ranks_count['F'],filename='FemaleRankCount.csv',path='data')
export_data(df=ranks_ids['M'],filename='MaleRankIDs.csv',path='data')
export_data(df=ranks_ids['F'],filename='FemaleRankIDs.csv',path='data')
return {'id2str':name_id2str,'str2id':name_str2id},ranks_count,ranks_ids