def main():
from results import get_results
stats, args = get_results()
plot_JS_EN_scatter_by_pairs(stats, **vars(args))
return 0
def result_page(year, *args, duration=False, **kwargs):
if lock.acquire(timeout=10):
if (datetime.now() - cache[duration][year]['ts']).seconds < LIMIT:
response = cache[duration][year]['data']
logging.info('Refresh too soon, using cache')
else:
total = results.get_results(year=year,
convert_ts=True,
duration=duration)
if total is None: # something failed
logging.error('Failed retrieving data, using cache')
response = cache[duration][year]['data']
else:
data = total.to_dict(orient='records')
columns = list(total.columns)
ts = datetime.now()
response = json.dumps(dict(data = data,
columns=columns,
ts=ts.strftime('%Y-%m-%d %H:%M')))
response = response.replace('NaN', 'null')
response = response.replace('NaT', '')
cache[duration][year]['data'] = response
cache[duration][year]['ts'] = ts
lock.release()
else:
logging.warning("Can't acquire lock, returning cached response")
response = cache[duration][year]['data']
return response
def main():
input_image = cv2.imread(
'image2.tiff', cv2.IMREAD_GRAYSCALE) # Read local image in grayscale
image_with_sp = add_sp_noise(input_image, prob_salt=0.2,
prob_pepper=0.2) # Add salt and pepper noise
cv2.imshow('Original Image', input_image) # Display Original image
cv2.imshow('Noisy Image',
image_with_sp) # Display image with Salt and Pepper Noise
num_passes = 5 # number of passes for filtering
# Make results directory with timestamp
timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
if not os.path.exists('results/'):
os.mkdir('results/')
os.mkdir('results/' + timestamp)
# Carry out median filtering
for i in range(num_passes):
if i == 0:
restored_image = median_filter(
image_with_sp) # apply filter to noisy image
else:
restored_image = median_filter(
restored_image) # apply filter to result of previous pass
mse, diff = compare_images(
input_image, restored_image
) # compare images to get mean square error and difference
# Call the get results function with the required data
get_results(input_image, image_with_sp, restored_image, diff, mse,
'Pass %d' % (i + 1),
'results/' + timestamp + '/pass_%d.png' % (i + 1))
cv2.imshow('Restored: Pass-%d' % num_passes,
restored_image) # Display the final restored image
cv2.imshow(
'Difference', diff
) # Display the difference between the original image and final restored image
cv2.waitKey(0) # Press any key to terminate window
cv2.destroyAllWindows() # Terminate all windows to end the program
def send_results(update, context):
context.user_data["keyword"] = update.message.text
context.user_data["chat_id"] = update.message.chat_id
msgs, markups = get_results(update, context)
context.user_data["msgs"] = msgs
context.user_data["markups"] = markups
context.user_data["current_page"] = 0
update.message.reply_text(msgs[0], reply_markup=markups[0])
def results():
auth = tweepy.OAuthHandler(config.consumer_key, config.consumer_secret)
auth.set_access_token(config.access_token, config.access_token_secret)
api = tweepy.API(auth)
try:
user = request.args.get('q')
return_json = jsonify(get_results(api, user))
except:
return (jsonify({'status': 0})) # 0 error, 1 passes
return (return_json)
def test_get_results():
'''Check that there are nfiles results, all successful.'''
run_empty()
results = get_results('empty.mutants', 'empty.results')
assert len(results) == nfiles
positions = [result[0] for result in results]
symptoms = [1 for result in results
if result[1] == 'silent' and result[2] == '0']
assert len(symptoms) == nfiles
assert set(positions) == set(range(nfiles))
shutil.rmtree('empty.mutants')
os.remove('empty.results')
def list_results():
win = Toplevel()
win.wm_title("resultslist")
txt = scrolledtext.ScrolledText(win, width=20, height=40, undo=True)
txt['font'] = ('consolas', '12')
txt.pack(expand=True, fill='both')
txt.grid(row=0, column=0)
txt.insert(1.0, results.get_results())
b = Button(win, text="Okay", command=win.destroy)
b.grid(row=1, column=0)
def get_results_request():
res = get_results()
return json.dumps(res)
# Clean Survey Results
import clean_last
new_drivers = clean_last.clean_last_qns(data)
import clean_others
new_df, new_features = clean_others.clean(data)
# Note: i_3 and o_3 are dropped for model improvements
new_features = new_features.drop(["i_3", "o_3"], axis=1)
# Load Prediction Model
rf = pickle.load(open("rf.sav", 'rb'))
# Execute Prediction Model on New Survey Results
import results
new_results_individual, new_results_department, new_results_job_level, new_results_age, new_results_organisation = results.get_results(
rf, new_df, new_features)
# Generate Report for Frontend & Storing to Database
import report
report_type_3_age, report_type_3_job_level, report_type_3_department, report_type_4_wellbeing, report_type_4_opinions, report_type_4_personality, report_type_4_core_values, report_type_5 = report.gen_report(
new_results_individual, new_results_age, new_results_job_level,
new_results_department)
# Convert Index to Column for Storage in MongoDB as unique identifier
new_results_age['Age Category'] = new_results_age.index
new_results_department['Department'] = new_results_department.index
new_results_job_level['Job Level'] = new_results_job_level.index
import data_upload
data_upload.upload(report_type_4_wellbeing, report_type_4_opinions,
report_type_4_personality, report_type_4_core_values,
def results_to_list(loci, mutation='point'):
'''return results as list'''
run_screen(loci, mutation)
rlist = get_results(mutation + '.mutants', mutation + '.results')
return rlist
def local_endpoint():
return get_results(API_URI)
def main():
from results import get_results
stats, args = get_results()
plot_ENS_hexbin(stats, **vars(args)) # vb_two
def main():
stats, args = get_results()
plot_bar(stats, **vars(args))
return 0
def main():
from results import get_results
stats, args = get_results()
plot_lrt_histograms(stats, **vars(args))
