def read_json():
return_dict = read_excel('test_down')
time = return_dict['time']
#
with open('y_pred_speed.json', 'r') as f:
pred_speed = json.load(f)
with open('y_pred_flow.json', 'r') as f:
pred_flow = json.load(f)
with open('y_true_speed.json', 'r') as f:
true_speed = json.load(f)
with open('y_true_flow.json', 'r') as f:
true_flow = json.load(f)
cut = np.where(
time == np.datetime64('2019-04-08T08:15:00'))[0].tolist()[0] + 1
# speed = return_dict['speed']
# flow = return_dict['flow']
pred_speed = np.array(pred_speed).reshape(-1, 1)
pred_flow = np.array(pred_flow).reshape(-1, 1)
true_speed = np.array(true_speed).reshape(-1, 1)
true_flow = np.array(true_flow).reshape(-1, 1)
return time[cut:], pred_speed, pred_flow, true_speed, true_flow
def data_uploader():
# Uploading data
uploaded_file = st.file_uploader(
"Upload the Sensor Data file (only .XLSX format)", type="xlsx")
if uploaded_file is not None:
return ut.read_excel(uploaded_file)
else:
return None
def get_final_weight():
return_dict = read_excel('train_up')
time = return_dict['time']
speed = return_dict['speed']
flow = return_dict['flow']
speed_scaler = return_dict['scaler_speed']
flow_scaler = return_dict['scaler_flow']
speed = speed_scaler.inverse_transform(speed)
flow = flow_scaler.inverse_transform(flow)
_, mw, ew, ow = cal_entropy_weight(time, speed, flow)
return mw, ew, ow
def create_vids_from_excel(inpfile, sheet, tmpdir, story, first_slide, last_slide, voice, user):
mdir = os.getcwd()
files = ['pup.js', 'blank.mp3', 'blank_long.mp3', 'logo.png']
for f in files:
shutil.copy(f, "{0}/{1}".format(tmpdir, f))
os.chdir(tmpdir)
os.mkdir('images')
sheet = read_excel(inpfile, sheet)
create_intro_video(sheet, voice)
normal_videos = []
slow_videos = []
split_videos = []
start = int(first_slide)
end = int(last_slide)+1
for i in range(start, end):
if not sheet.cell(row=i, column=1).value:
continue
para = sheet.cell(row=i, column=2).value.strip()
(json_data, json_data_slow, json_data_split) = create_audio_data(para, voice)
images_text = ' '.join(para.strip().replace('*', '').replace('\n', ' \n ').split(' '))
# print("images text: " + images_text)
images = create_images(images_text, sheet.cell(row=i, column=3).value, story)
json_data = process_json_data(json_data, voice)
json_data_slow = process_json_data(json_data_slow, voice)
json_data_split = process_json_data(json_data_split, voice)
create_para_vid(1, i-start-1, json_data, images, 'audio.mp3', "vid{}".format(str(i-start+1)))
create_para_vid(0.75, i-start-1, json_data_slow, images, 'audio_slow.mp3', "vid{}-slow".format(str(i-start+1)))
create_para_vid(1, i-start-1, json_data_split, images, 'audio_split.mp3', "vid{}-split".format(str(i-start+1)))
normal_videos.append("vid{}.mp4".format(str(i-start+1)))
slow_videos.append("vid{}-slow.mp4".format(str(i-start+1)))
split_videos.append("vid{}-split.mp4".format(str(i-start+1)))
os.chdir(os.path.join(mdir, 'static', 'videos', user))
if story=='1':
os.chdir("Stories")
else:
os.chdir("Other Videos")
os.mkdir(output_name)
print(output_name)
os.chdir(os.path.join(os.getcwd(), output_name))
print(os.getcwd())
concatenate_videos(["intro.mp4"]+normal_videos, "{}-fast.mp4".format(output_name), tmpdir)
concatenate_videos(["intro.mp4"]+slow_videos, "{}-medium.mp4".format(output_name), tmpdir)
concatenate_videos(["intro.mp4"]+split_videos, "{}-slow.mp4".format(output_name), tmpdir)
def rebalance(spread_sheet_name, idx_name):
portfolio_fname = 'portfolio-analysis-' + idx_name + '.p'
bad_fname = 'not-portfolio-analysis-' + idx_name + '.p'
port_df = u.read_pickle(portfolio_fname)
bad = u.read_pickle(bad_fname)
spread_sheet = u.read_excel(spread_sheet_name)
# get current allocation
current_alloc = s.get_portfoilo_alloc(spread_sheet)
tickers = spread_sheet.ticker.to_list()
bad_list = []
new_port_df = pd.DataFrame()
# kick out current port members that did not make the cut
for ticker in tickers:
if len(bad[bad.ticker == ticker]) == 0: # not found
tmp_df = port_df[port_df.ticker == ticker]
if len(tmp_df) == 0:
log.info('ticker not found {}'.format(ticker))
bad_list.append({'ticker': ticker, 'notes': 'Not found'})
continue
new_port_df = new_port_df.append(tmp_df)
else:
bad_list.append({'ticker': ticker, 'notes': 'SELL'})
log.info('ticker {} did not make the cut. dropping from portfolio'.
format(ticker))
# replace kicked out entries
if len(bad_list):
not_in_port = port_df[~port_df.ticker.isin(new_port_df.ticker)].dropna(
)
new_members = not_in_port[:len(bad_list)].copy()
new_members['notes'] = 'NEW BUY'
new_port_df = new_port_df.append(new_members)
r.do_allocation(new_port_df, len(new_port_df))
new_port_df = new_port_df.append(bad_list, ignore_index=True)
sheet_name = 'portfolio-' + str(date.today())
# add sheets to spreadsheet
u.append_worksheet_to_excel(spread_sheet_name, sheet_name, new_port_df)
u.append_worksheet_to_excel(spread_sheet_name, 'current-alloc',
current_alloc)
def create_vids(inpfile, sheet_name, tmpdir, story, first_slide, last_slide,
audio_in, user):
mdir = os.getcwd()
files = ['pup.js', 'blank.mp3', 'blank_long.mp3', 'logo.png']
for f in files:
shutil.copy(f, "{0}/{1}".format(tmpdir, f))
os.chdir(tmpdir)
os.mkdir('images')
audio_files = split_audio_on_silence(audio_in)
print(audio_files)
sheet = read_excel(inpfile, sheet_name)
create_intro_video(sheet, audio_files[0])
page_videos = []
start = int(first_slide)
end = int(last_slide) + 1
for i in range(start, end):
if not sheet.cell(row=i, column=1).value:
continue
page_no = i - start + 1
para = sheet.cell(row=i, column=2).value.strip()
image_text = ' '.join(para.strip().replace('*', '').replace(
'\n', ' \n ').split(' '))
image = create_image(image_text,
sheet.cell(row=i, column=3).value, page_no, story)
audio_file = audio_files[page_no]
create_page_vid(page_no, audio_file, image)
page_videos.append("{}.mp4".format(page_no))
os.chdir(os.path.join(mdir, 'static', 'videos', user))
if story == '1':
os.chdir("Stories")
else:
os.chdir("Other Videos")
os.mkdir(output_name)
os.chdir(os.path.join(os.getcwd(), output_name))
concatenate_videos(["intro.mp4"] + page_videos,
"{}.mp4".format(output_name), tmpdir)
def get_S(fname):
df = read_excel(fname, header=[0])
df.head()
freq = np.asarray(df.index)
dx = 0.01
# gT = graphTool((2,1), (10,10))
S11_DB = df[('log_s11')]
S11_phase = df[(('phase_s11'))]
S11 = get_S_parameter(S11_DB, S11_phase)
S11 = S11.reshape(1, 801)
S21_DB = df[('log_s21')]
S21_phase = df[(('phase_s21'))]
S21 = get_S_parameter(S21_DB, S21_phase)
S21 = S21.reshape(1, 801)
return freq, S11, S21
help = "Columnfile in which the value of each column is specified (needed for " +
".xls(x) and .txt files, not for .json files")
parser.add_argument('--header', action = 'store_true',
help = "Specify if the file contains a header")
args = parser.parse_args()
# read in file
if args.i[-3:] == "xls" or args.i[-4:] == "xlsx":
# make sure date and time fields are correctly processed
indexline = utils.read_columnfile(args.c)
date, time = False, False
if indexline[3] != "-":
date = indexline[3]
if indexline[4] != "-":
time = indexline[4]
lines = utils.read_excel(args.i, args.header, date, time)
elif args.i[-4:] == "json":
csvrows = utils.read_json(args.i)
else: # txt file
with open(args.i, encoding="utf-8") as fn:
lines = [x.strip().split("\t") for x in fn.readlines()]
if args.header:
lines = lines[1:]
# with open(args.o, 'w') as csvfile:
# writer = csv.writer(csvfile)
# for line in lines:
# writer.writerow(line)
# quit()
# set columns of lines in right order
import pandas as pd
import numpy as np
from utils import read_excel, graphTool
df = read_excel('NRW~1\MeasData(19-12-23 17-10-06)_CNT.xls')
df.columns
gT = graphTool(figshape=(1,1), figsize= (5,5))
freq = np.asarray(df.index)
S11 = np.exp(df[('LOG MAG [dB]', 'S11')].to_numpy())
S11_i =
S21 = np.exp(df[('LOG MAG [dB]', 'S21')].to_numpy())
gT.plot(ax_num=(0,0), x = freq, y = S11_logMagnitude, c='r', label ='S11')
gT.plot(ax_num=(0,0), x = freq, y = S21_logMagnitude, c='b', label ='S21')
**2
S11**2