def create_hybrid_per_total_percent_month():
"""create graph"""
tmp = []
for i, j in list(zip(np(total_by_month)[-1],
np(total_by_month)[0]))[1:-3]:
tmp.append((i / j) * 100 // 0.01 / 100)
line_chart = pygal.Line(x_label_rotation=90)
line_chart.title = "เปอเซ็นของรถยนต์ไฮบริดในแต่ละเดือนเทียบกับรถยนต์ทุกประเภท"
line_chart.x_labels = all_months()
line_chart.add("hybrid", tmp)
line_chart.render_to_file('All_hybrid_percent_month.svg')
def create_hybrid_change_percent():
"""create graph"""
tmp = [0]
for i in range(2, len(np(total_by_month)[-1][:-3])):
tmp.append(
np(total_by_month)[-1][i] - np(total_by_month)[-1][i - 1])
line_chart = pygal.Line(x_label_rotation=90)
line_chart.title = "อัตราการเปล่ยนแปลงของรถยนต์ไฮบริดจากเดือนก่อนหน้า"
line_chart.x_labels = all_months()
line_chart.add("change from previous month", tmp)
line_chart.render_to_file('Change_hybrid_prevmonth.svg')
def matrixReshape(nums, r, c):
"""
:type nums: List[List[int]]
:type r: int
:type c: int
:rtype: List[List[int]]
"""
len_of_orig_mat = len(nums) * len(nums[0])
len_of_output_mat = r * c
if len_of_orig_mat != len_of_output_mat:
return nums
else:
print(type(np(nums, r * c).tolist()))
return np(nums, r * c)
def create_all_sum():
"""create graph"""
bar_chart = pygal.Bar(x_label_rotation=90)
bar_chart.title = "ผลรวมของรถยนต์แต่ละยี่ห้อ"
for i, j in zip(all_by_month.index.values, np(all_by_month)):
bar_chart.add(i, sum(j))
bar_chart.render_to_file('All_cars_each_brand.svg')
def create_hybrid_sum():
"""create graph"""
bar_chart = pygal.Bar(x_label_rotation=90)
bar_chart.title = "ผลรวมรถยนต์ไฮบริดแต่ละประเภท"
for i in np(hybrid_by_month):
bar_chart.add(i[0], sum(i[1:]))
bar_chart.render_to_file('All_hybrid_each_type.svg')
def create_hybrid_month():
"""create graph"""
bar_chart = pygal.StackedBar(x_label_rotation=90)
bar_chart.title = "ผลรวมของรถยนต์ไฮบริดแต่ละประเภทของแต่ละเดือน"
bar_chart.x_labels = all_months()
for i in np(hybrid_by_month):
bar_chart.add(i[0], i[1:])
bar_chart.render_to_file('All_hybrid_each_month.svg')
def create_all_month():
"""create graph"""
bar_chart = pygal.Bar(x_label_rotation=90)
bar_chart.title = "ผลรวมของรถยนต์แต่ละเดือน"
bar_chart.x_labels = all_months()
tmp = np(total_by_month)[0]
bar_chart.add(tmp[0], tmp[1:])
bar_chart.render_to_file('All_cars_each_month.svg')
def megaFaker():
with open('1000.json', 'r') as handle:
milSemillas = js(handle)
palabras = [
"como ", "cuando ", "donde queda ", "como encontrar ", "que hacer en ",
"obtener "
]
categoria = [
"animal", "carmodle", "moviesTitle", "NameOfCompany", "Drug", "uni",
"apps"
]
rCategoria = (str(categoria[rd(0, 6)]))
rSemilla = (np(milSemillas)[rd(1, 100)][rCategoria])
rPalabras = (str(palabras[rd(0, 5)]))
return (str(rPalabras + rSemilla))
def merge(trovo, daily):
'''need size from solve
'''
factorImport = open(glob(r'\\xsqnfs2.nyc.ime.reuters.com\TRPS\Bank Loans\Auto 2.0\Factor*.csv')[0], 'r')
readFactors = csv.reader(factorImport)
staleFactor = 10 #default
for row in readFactors:
if "Stale" in row[0] and row[1] not in (None, "", " "):
staleFactor = float(row[1])
factorImport.close()
concatIndex = len(trovo.columns)
trovo.insert(concatIndex, 'Concatenated', 1) #initializing stale column
trovo.iloc[:, concatIndex] = trovo['lin'] + '_' + trovo['dealer'] #making unique lin for identification
trovo = pd.merge(trovo, daily, how = 'left', on = 'Concatenated') #merging days unchanged/size/stale data
unchangedIndex = trovo.columns.tolist().index('unchanged_for')
daysStale = trovo.columns.tolist().index('days_stale')
lastBidDate = trovo.columns.tolist().index('last_bid_date')
trovo.iloc[:, daysStale] = trovo['days_stale'] + 1
trovo.iloc[:, unchangedIndex] = trovo['unchanged_for'] + 1
'''need size data still'''
staleIndex = len(trovo.columns)
trovo.insert(staleIndex, 'Not Stale', 1) #initializing stale column
trovo.insert(staleIndex + 1, 'Color', 0) #initializing color column
now = timeComp.now()
year = now.year
month = now.month
day = now.day
trovo.iloc[:, lastBidDate] = toDateTime(trovo['last_bid_date'], errors = 'coerce')
trovo.iloc[:, daysStale] = [0 if ~(x == date64('NaT')) and (x.year == year) and (x.month == month) # updating quotes from today to be stale 0 days
and (x.day == day) else y for (x, y) in list(zip(trovo['last_bid_date'], trovo['days_stale']))]
trovo.iloc[:, staleIndex] = np((trovo['days_stale'] > staleFactor), 0, 1) #killing quotes stale for more than 10 days
#outputting intermediate file after cleansing
trovo.to_csv(r'\\xsqnfs2.nyc.ime.reuters.com\TRPS\Bank Loans\Auto 2.0\Place Results Here\FilterFile' + dateLabel.today().strftime('%m-%d-%Y') + '.csv', index = False)
return trovo
def predictModel(categories):
#using the trained model to predict
#categories=["Graphics cards", "Monitors", "Processors", "Mouse", "Keyboards"]
num_cat = []
prediction = []
#TODO: change this to search for the tags/categories? (Data which user clicks on)
for cat in categories:
if cat == "Graphics cards":
num_cat.append("1")
if cat == "Monitors":
num_cat.append("2")
if cat == "Processors":
num_cat.append("3")
if cat == "Mouse":
num_cat.append("4")
if cat == "Keyboards":
num_cat.append("5")
else:
num_cat.append("6")
while len(num_cat) < 18:
num_cat.append("0")
num_cat_numpy = []
num_cat_numpy.append(num_cat)
num_cat_numpy = np(num_cat_numpy)
num_cat_numpy = nump.reshape(
num_cat_numpy, (num_cat_numpy.shape[0], num_cat_numpy.shape[1]))
print("0: ", num_cat_numpy.shape[0], "1: ", num_cat_numpy.shape[1])
prediction = model.predict(num_cat_numpy).argmax()
print("Prediction number: ", prediction)
predict = NumberToPredCat(prediction)
#predict=InvertPred(cat)
return InvertPred(predict)
def main():
"""Flatten"""
import pandas as pd
from numpy import array as np
import pygal
df = pd.read_excel("data.xlsx")
df.set_index("BRAND", inplace=True)
hybrid_types = ("Electric", "Petrol Hybrid", "Diesel Hybrid",
"Plug-In Petrol Hybrid")
total_by_month = df.loc["TOTAL", ["TYPE"] + all_months()]
total_by_year = df.loc["TOTAL", ["TYPE"] +
[str(i) + "-YTD" for i in range(2012, 2018)]]
all_by_month = df.loc["ALFA ROMEO":"YAMAHA", all_months()]
hybrid_by_month = total_by_month.loc[total_by_month["TYPE"].isin(
hybrid_types)]
brand_hybrid_by_month = df.loc[df["TYPE"].isin(hybrid_types),
["TYPE"] + all_months()]
sum_brand_hybrid = [(i, j[0], sum(j[1:])) for i, j in \
zip(brand_hybrid_by_month.index.values,np(brand_hybrid_by_month)) if i != "TOTAL"]
def create_brand_hybrid_sum():
"""create graph"""
bar_chart = pygal.Bar(x_label_rotation=90)
bar_chart.title = "ผลรวมของรถยนต์ไฮบริดแต่ละรุ่น"
for i, j, k in sum_brand_hybrid:
bar_chart.add(i + " " + j, k)
bar_chart.render_to_file('All_brands_of_hybrids.svg')
create_brand_hybrid_sum()
def create_all_month():
"""create graph"""
bar_chart = pygal.Bar(x_label_rotation=90)
bar_chart.title = "ผลรวมของรถยนต์แต่ละเดือน"
bar_chart.x_labels = all_months()
tmp = np(total_by_month)[0]
bar_chart.add(tmp[0], tmp[1:])
bar_chart.render_to_file('All_cars_each_month.svg')
create_all_month()
def create_all_sum():
"""create graph"""
bar_chart = pygal.Bar(x_label_rotation=90)
bar_chart.title = "ผลรวมของรถยนต์แต่ละยี่ห้อ"
for i, j in zip(all_by_month.index.values, np(all_by_month)):
bar_chart.add(i, sum(j))
bar_chart.render_to_file('All_cars_each_brand.svg')
create_all_sum()
def create_hybrid_month():
"""create graph"""
bar_chart = pygal.StackedBar(x_label_rotation=90)
bar_chart.title = "ผลรวมของรถยนต์ไฮบริดแต่ละประเภทของแต่ละเดือน"
bar_chart.x_labels = all_months()
for i in np(hybrid_by_month):
bar_chart.add(i[0], i[1:])
bar_chart.render_to_file('All_hybrid_each_month.svg')
create_hybrid_month()
def create_hybrid_sum():
"""create graph"""
bar_chart = pygal.Bar(x_label_rotation=90)
bar_chart.title = "ผลรวมรถยนต์ไฮบริดแต่ละประเภท"
for i in np(hybrid_by_month):
bar_chart.add(i[0], sum(i[1:]))
bar_chart.render_to_file('All_hybrid_each_type.svg')
create_hybrid_sum()
def create_hybrid_per_total_percent_month():
"""create graph"""
tmp = []
for i, j in list(zip(np(total_by_month)[-1],
np(total_by_month)[0]))[1:-3]:
tmp.append((i / j) * 100 // 0.01 / 100)
line_chart = pygal.Line(x_label_rotation=90)
line_chart.title = "เปอเซ็นของรถยนต์ไฮบริดในแต่ละเดือนเทียบกับรถยนต์ทุกประเภท"
line_chart.x_labels = all_months()
line_chart.add("hybrid", tmp)
line_chart.render_to_file('All_hybrid_percent_month.svg')
create_hybrid_per_total_percent_month()
def create_hybrid_change_percent():
"""create graph"""
tmp = [0]
for i in range(2, len(np(total_by_month)[-1][:-3])):
tmp.append(
np(total_by_month)[-1][i] - np(total_by_month)[-1][i - 1])
line_chart = pygal.Line(x_label_rotation=90)
line_chart.title = "อัตราการเปล่ยนแปลงของรถยนต์ไฮบริดจากเดือนก่อนหน้า"
line_chart.x_labels = all_months()
line_chart.add("change from previous month", tmp)
line_chart.render_to_file('Change_hybrid_prevmonth.svg')
create_hybrid_change_percent()
def megaFaker():
try:
with open('1000.json', 'r') as milSemillas:
milSemillas = js(milSemillas)
except Exception as e:
print(str(e) + "< Archivo no existe!")
exit()
categoria = [
"animal", "carmodle", "moviesTitle", "NameOfCompany", "uni", "apps"
]
rCategoria = (str(categoria[rd(0, (len(categoria)) - 1)]))
#####################Entrenar IA mejor dese txt#############
if (rCategoria == "animal"):
palabras = [
"donde encontar ",
"habita ",
"comida ",
"peloigro de extinción ",
"venenoso ",
"composicion osea ",
]
elif (rCategoria == "carmodle"):
palabras = [
"que es ", "a que sabe ", "donde queda ", "como encontrar ",
"ver "
]
elif (rCategoria == "moviesTitle"):
palabras = [
"fecha de lanzamiento de ",
"reparto en ",
"secuelas de ",
"resumen ",
"protagonista en ",
"elenco de ",
"descargar de ",
"animacion de ",
"calidad de ",
"cinematic de ",
"musica de ",
"esenario en ",
"Historia en",
"catetgoria de la ",
"errores de la ",
"trama de",
"arte de ",
"libro de ",
"escritor de ",
"gion de ",
"etapas de ",
]
elif (rCategoria == "NameOfCompany"):
palabras = [
"fecha de creacion ",
"competencia de la ",
"estrategia de la ",
"vision de la ",
"mision de la ",
"capacidad de trabajadores ",
"beneficios de la ",
]
elif (rCategoria == "uni"):
palabras = [
"requisitos para entara en ", "carrera ", "donde queda la ",
"como encontrar la ", "que hacer en la ", "obtener beca en la "
]
if (rCategoria == "apps"):
palabras = [
"Version ", "git ", "licencia ", "como encontrar ",
"que hacer en la ", "animacion ", "keys ", "free ", "gratis ",
"obtener ", "descargar "
]
rSemilla = np(milSemillas)[rd(0, 999)][rCategoria]
rPalabras = str(palabras[rd(0, (len(palabras)) - 1)])
#################### Documentacion #######################
global rn
doc = """
--------------------Thankas for play!-----------
Al azar python3
Tiempo segunso = [{}]
Tiempo minutos = [{}]
Tema = [{}]
Categoria = [{}]
Accion a concatenar = [{}]
cantidad de palabras = [{}]
------------------------------------------------
""".format(rn(), (str(rn() / 60)), rCategoria, rSemilla, rPalabras,
(len(milSemillas)))
print(doc)
##########################################################
palaGenerada = str(rPalabras + rSemilla)
#lis.append(palaGenerada)
return (str(rPalabras + rSemilla))
def display():
print("\x1b[1;1H( WORKING )\n")
if MAKE_DISPLAY_VIDEO or SHOW_DISPLAY:
frame = []
for i in range(len(field)):
frame_row = []
for j in range(len(field[i])):
f = field[i][j]
# choose colored cell for graphics
graphic = "--"
if f == 0: # water
if (i <= LIGHT_HEIGHT):
graphic = "\x1b[38;2;40;40;255m" # light
frame_row.append([200, 200, 120])
else:
graphic = "\x1b[38;2;0;0;200m" # shadow
frame_row.append([120, 120, 60])
elif f == 1: # cell
for k in cells:
if k.cords == [i, j]:
if k.kind == 0: # fotosynth
graphic = "\x1b[38;2;0;255;0m"
frame_row.append([80, 180, 80])
elif k.kind == 1: # organics
graphic = "\x1b[38;2;255;200;0m"
frame_row.append([70, 200, 200])
elif k.kind == 2: # carnivorous
graphic = "\x1b[38;2;255;0;0m"
frame_row.append([20, 20, 120])
break
elif f == 2: # food
graphic = "\x1b[38;2;100;100;100m"
frame_row.append([150, 150, 150])
elif f == 3: # posion
graphic = "\x1b[38;2;200;0;255m"
frame_row.append([250, 50, 250])
if SHOW_DISPLAY: print(graphic + "@@", end="")
frame.append(frame_row)
if SHOW_DISPLAY: print()
if MAKE_DISPLAY_VIDEO:
npframe = cv2.resize(np(frame, dtype=uint8),
dim,
interpolation=cv2.INTER_AREA)
video.write(npframe)
if SHOW_STAT or MAKE_DISPLAY_VIDEO or MAKE_STAT_VIDEO:
print("\x1b[0m\x1b[J")
if MAKE_DISPLAY_VIDEO:
print("Display video\n- size :",
round(Path(DISPLAY_VIDEO_NAME).stat().st_size / 1000000, 2),
"MB\n- duration :", cycle // 120, "s")
if MAKE_STAT_VIDEO:
print("Statistics video\n- size :",
round(Path(STAT_VIDEO_NAME).stat().st_size / 1000000, 2),
"MB\n- duration :", cycle // 120, "s")
if SHOW_STAT or MAKE_STAT_VIDEO:
print("\nLife cycle : ", cycle)
print("Amount of cells : ", len(cells))
age = [0]
for i in cells:
age.append(i.age)
print("\nLongest living cell\n- Age : ", max(age))
gen_s = []
kind = 0
for i in cells:
if i.age == max(age):
gen_s = i.gen_seq
kind = i.kind
break
print("- Kind :", kind)
print("- Agorithm :")
print(" ", gen_s)
amount = [0, 0, 0]
for i in [0, 1, 2]:
for j in cells:
if j.kind == i:
amount[i] += 1
print(i, "- Amount :", amount[i])
gray = 0
pink = 0
for i in range(len(field)):
for j in range(len(field[i])):
f = field[i][j]
if f == 2: gray += 1
elif f == 3: pink += 1
print("food - Amount :", gray)
print("posion - Amount :", pink)
if MAKE_STAT_VIDEO:
for i in range(PER[0]):
new = [0, 0, 0]
for j in range(PER[1])[::-1]:
new, frame_stat[i][j] = frame_stat[i][j], new
if pink:
frame_stat[-1 - int(pink / (FIELD_SIZE[0] * FIELD_SIZE[1]) *
PER[0])][-1] = [250, 50, 250]
if gray:
frame_stat[-1 - int(gray / (FIELD_SIZE[0] * FIELD_SIZE[1]) *
PER[0])][-1] = [150, 150, 150]
if amount[0]:
frame_stat[-1 -
int(amount[0] / (FIELD_SIZE[0] * FIELD_SIZE[1]) *
PER[0])][-1] = [80, 180, 80]
if amount[1]:
frame_stat[-1 -
int(amount[1] / (FIELD_SIZE[0] * FIELD_SIZE[1]) *
PER[0])][-1] = [70, 200, 200]
if amount[2]:
frame_stat[-1 -
int(amount[2] / (FIELD_SIZE[0] * FIELD_SIZE[1]) *
PER[0])][-1] = [20, 20, 120]
npframe = cv2.resize(np(frame_stat, dtype=uint8),
dim_stat,
interpolation=cv2.INTER_AREA)
video_stat.write(npframe)
def megaFaker():
with open('1000.json', 'r') as milSemillas:
milSemillas=js(milSemillas)
categoria =[
"animal",
"carmodle",
"moviesTitle",
"NameOfCompany",
"uni",
"apps"
]
rCategoria = (str(categoria[rd(0,(len(categoria))-1)]))
#####################Entrenar IA mejor dese txt#############
if(rCategoria=="animal"):
palabras=[
"donde encontar ",
"habita ",
"comida ",
"peloigro de extinción ",
"venenoso ",
"composicion osea ",
]
elif(rCategoria=="carmodle"):
palabras=[
"que es ",
"a que sabe ",
"donde queda ",
"como encontrar ",
"ver "]
elif(rCategoria=="moviesTitle"):
palabras=[
"fecha de lanzamiento de ",
"reparto en ",
"secuelas de ",
"resumen ",
"protagonista en ",
"elenco de ",
"descargar de ",
"animacion de ",
"calidad de " ,
"cinematic de ",
"musica de " ,
"esenario en ",
"Historia en" ,
"catetgoria de la " ,
"errores de la " ,
"trama de" ,
"arte de " ,
"libro de " ,
"escritor de " ,
"gion de " ,
"etapas de ",
]
elif(rCategoria=="NameOfCompany"):
palabras=[
"fecha de creacion ",
"competencia de la ",
"estrategia de la ",
"vision de la ",
"mision de la ",
"capacidad de trabajadores ",
"beneficios de la ",
]
elif(rCategoria=="uni"):
palabras=[
"requisitos para entara en ",
"carrera ",
"donde queda la ",
"como encontrar la ",
"que hacer en la ",
"obtener beca en la "
]
if(rCategoria=="apps"):
palabras=[
"Version ",
"git ",
"licencia ",
"como encontrar ",
"que hacer en la ",
"animacion ",
"keys ",
"free ",
"gratis ",
"obtener ",
"descargar "
]
rSemilla = np(milSemillas)[rd(0,999)][rCategoria]
rPalabras = str(palabras[rd(0,(len(palabras))-1)])
return(str(rPalabras+rSemilla))
"""
list of all constants used across all game python scripts
"""
from numpy import array as np
STARTING_LEVEL = 5
# screen size (currently 720p)
SCREEN_HEIGHT = 720
SCREEN_WIDTH = 1280
# a 1 pixel-wide circle to be enlarged by multiplying it by a desired number size
CIRCLE = np([(1, 0), (0.966, 0.259), (0.866, 0.5), (0.707, 0.707),
(0.5, 0.866), (0.259, 0.966), (0, 1), (-0.259, 0.966),
(-0.5, 0.866), (-0.707, 0.707), (-0.866, 0.5), (-0.966, 0.259),
(-1, 0), (-0.966, -0.259), (-0.866, -0.5), (-0.707, -0.707),
(-0.5, -0.866), (-0.259, -0.966), (0, -1), (0.259, -0.966),
(0.5, -0.866), (0.707, -0.707), (0.866, -0.5), (0.966, -0.259)])
CIRCLE_LARGE = list(CIRCLE * 30)
# the color white in RGBA
WHITE = [255, 255, 255, 255]
# frame rate of game (60)
FRAME_RATE = 1 / 60
# screen window title (working title is used
SCREEN_TITLE = "Color seeker!"
# angle offset for converting tmx angle to Sprite angles
import csv
import pandas as pd
from sklearn.decomposition import PCA
from numpy import zeros as np
file_=open('1train_dorothea.csv')
w_file=open('VV_train_dorothea.csv','w')
X=np((800,100000),dtype=int)
#print X[1]
row=0
for line in file_:
line=line.strip()
line_list=line.split(',')
for count in line_list:
count=int(count)
X[row][count-1]=1
row=row+1
#print X[0]
df = pd.DataFrame(data=X)
df = df.transpose()
pca = PCA(n_components=500)
pca.fit(df)
#print pca.components_
Y=pca.components_
Y=Y.transpose()
print X.shape,Y.shape
row =0
cloumn=0
while row < 800:
cloumn=0
while cloumn < 500:
w_file.write(str(Y[row][cloumn]))
import testX, time
from numpy import genfromtxt as np
data2= np('book.csv', delimiter=',')
def Main():
ms = time.time()*1000.0
#data2= np.genfromtxt('book.csv', delimiter=',')
t = 1 # panjang waktu sinyal reference (sekond)
fs = 256 # frekuensi sampling
f = 8.5 # frekusampling data dari epoch ke 0
t1 = 20*256 # sampling data berawal di epoch 0
t2 = 21*256 # sampling data berakhir di epoch ke 255
lowcutoff = 8
highcutoff = 30
data = testX.Bandpass(data2,lowcutoff,highcutoff,fs)
Ref = testX.refSignal(t,fs,f) # Reference Signal
ms = time.time()*1000.0
r = testX.cca(data,Ref,t1,t2)#(B,C,0,5)#
print "Hasil Korelasi CCA =", max(r)
print "Waktu Hitung (ms) =", time.time()*1000.0 - ms
Main()
def select(clean, hierarchy):
#default values, will update if values present in excel file
timeDecayF = 30 #volatile, drop this number - days unchanged > 30 gets killed
stdDevTol = 1.28 #tolerance for number of std devs
staleF = 10 #standard in Trovo
factorImport = open(glob(r'\\xsqnfs2.nyc.ime.reuters.com\TRPS\Bank Loans\Auto 2.0\Factor*.csv')[0], 'r')
readFactors = csv.reader(factorImport)
for row in readFactors:
if "Time" in row[0] and row[1] not in (None, "", " "):
timeDecayF = float(row[1])
if "Standard" in row[0] and row[1] not in (None, "", " "):
stdDevTol = float(row[1])
if "Stale" in row[0] and row[1] not in (None, "", " "):
staleF = float(row[1])
factorImport.close()
hierarchyDict = {}
for row in hierarchy.iterrows(): #inserting dealer rankings into dictionary
hierarchyDict.update({row[1][0] : row[1][1]})
finalFile = pd()
clean.insert(len(clean.columns), 'DealerW', 0) #initializing dealer weight
clean.insert(len(clean.columns), 'UnchangedW', 0) #initializing days unchanged weight
clean.insert(len(clean.columns), 'StaleW', 0) #initializing stale weight
clean.insert(len(clean.columns), 'Weight', 0) #initializing weights
clean.insert(len(clean.columns), 'StdDev', 0) #initializing stdDev
clean.insert(len(clean.columns), 'Calculated Bid', 0) #initializing avg bid
clean.insert(len(clean.columns), 'Calculated Offer', 0) #initializing avg offer
select = len(clean.columns)
clean.insert(select, 'Select', 0) #initializing selection column
staleIndex = clean.columns.tolist().index('Not Stale')
clean.iloc[:, select] = clean.iloc[:, staleIndex] #copying over initial select values from stale values
columns = clean.columns.tolist()
stdDevIndex = columns.index('StdDev')
weightIndex = columns.index('Weight')
dealerIndex = columns.index('dealer')
lastBidIndex = columns.index('last_bid')
lastOfferIndex = columns.index('last_offer')
colorIndex = columns.index('Color')
unchangedIndex = columns.index('unchanged_for')
calcBid = columns.index('Calculated Bid')
calcOffer = columns.index('Calculated Offer')
days_stale = columns.index('days_stale')
dealerWIndex = columns.index('DealerW')
unchangedWIndex = columns.index('UnchangedW')
staleWIndex = columns.index('StaleW')
bidDate = columns.index('last_bid_date')
lins = clean.groupby('lin') #grouping quotes by their LINs
hasColor = False #color is midpoint
now = timeComp.now()
year = now.year
month = now.month
day = now.day
for group, quotes in lins: #iterating through each LIN to do quote selection
colorQuote = 0 #color quote price
midpoint = 0 #midpoint price
midpointW = 0 #midpoint weight
stdDev = 0 #variable to get std dev
hasColor = False
numIncluded = 0 #number of quotes considered
stdDevList = [] # keep track of bids to avoid double dataFrame loop
tempFrame = pd()
bestStaleBid = 0
bestStaleAsk = 0
bidPx = 0
offerPx = 0
stdDevDen = 0
bestStaleW = 0
if len(quotes) == 1: #if only quote, must use it
for row in quotes.iterrows():
data = row[1].to_frame().transpose() #skipping over index column
data.iloc[0, select] = 1
dealer = data.iloc[0, dealerIndex]
lastBid = data.iloc[0, lastBidIndex]
weight = 0
dealerCheck = hierarchyDict.get(dealer) #dealer val
staleVal = data.iloc[0, days_stale] #increment days stale (yesterday's data)
daysStale = 0
if nancheck(staleVal):
daysStale = 1
else:
daysStale = staleVal
if dealerCheck == None:
dealerW = 0.75
else:
dealerW = 1 - (hierarchyDict.get(dealer) - 1)*.05 #dealer weighting
unchVal = data.iloc[0, unchangedIndex] #increment days unchanged (yesterday's data)
daysU = 0
if nancheck(unchVal):
daysU = 1
elif unchVal < daysStale: #error checking because Trovo isn't good
daysU = daysStale
else:
daysU = unchVal #increment days unchanged (yesterday's data)
updateW = 0
if timeDecayF < 1:
date = data.iloc[0, bidDate]
if daysStale != 0:
updateW = 0.25
else:
diff = 0
diff += 60 * date.hour
diff += now.minute - date.minute
updateW = (timeDecayF * 400) / (diff + timeDecayF * 400) #400 is max in a trading day, will act nicely
elif daysU >= 8 * timeDecayF: #days unchanged cutoff
updateW = 0.2
else: #days unchanged weighting
updateW = (3 * timeDecayF) / (daysU + (3 * timeDecayF))
'''need size weight'''
staleW = (3 * staleF) /(daysStale + (3 * staleF)) #days stale weight
weight = dealerW * updateW * staleW
data.iloc[0, dealerWIndex] = '{0:.5g}'.format(dealerW) #dealer weighting
data.iloc[0, staleWIndex] = '{0:.5g}'.format(staleW) #stale weighting
data.iloc[0, unchangedWIndex] = '{0:.5g}'.format(updateW) #unchanged weighting
data.iloc[0, weightIndex] = '{0:.5g}'.format(weight) #final weighting
tempFrame = concat([tempFrame, data.iloc[0, :].to_frame().transpose()])
midpoint = data.iloc[0, lastBidIndex]
bidPx = midpoint
offerPx = data.iloc[0, lastOfferIndex]
else:
for row in quotes.iterrows(): #otherwise, iterate through all quotes and do selection process
data = row[1].to_frame().transpose() #skipping over index column
dealer = data.iloc[0, dealerIndex]
lastBid = data.iloc[0, lastBidIndex]
weight = 0
if isinstance(dealer, float): #has color, mark it- dealer is NaN
data.iloc[0, lastOfferIndex] = data.iloc[0, lastBidIndex] #need to set offer price for calculations
if hasColor: #if two with color, make midpoint average of two
colorQuote = (colorQuote + lastBid) / 2
else:
hasColor= True
colorQuote = lastBid
weight = 1
data.iloc[0, colorIndex] = 1
data.iloc[0, dealerWIndex] = '{0:.5g}'.format(1) #dealer weighting
data.iloc[0, staleWIndex] = '{0:.5g}'.format(1) #stale weighting
data.iloc[0, unchangedWIndex] = '{0:.5g}'.format(1) #unchanged weighting
data.iloc[0, weightIndex] = '{0:.5g}'.format(weight) #final weighting
else:
dealerCheck = hierarchyDict.get(dealer) #dealer val
staleVal = data.iloc[0, days_stale] #increment days stale (yesterday's data)
daysStale = 0
if nancheck(staleVal):
daysStale = 1
else:
daysStale = staleVal
if dealerCheck == None:
dealerW = 0.75
else:
dealerW = 1 - (hierarchyDict.get(dealer) - 1)*.05 #dealer weighting
unchVal = data.iloc[0, unchangedIndex] #increment days unchanged (yesterday's data)
daysU = 0
if nancheck(unchVal):
daysU = 1
data.iloc[0, unchangedIndex] = 1
elif unchVal < daysStale: #error checking because Trovo isn't good
daysU = daysStale
data.iloc[0, unchangedIndex] = daysU
else:
daysU = unchVal #increment days unchanged (yesterday's data)
updateW = 0
if timeDecayF < 1:
try:
date, time, timeZone = data.iloc[0, bidDate].split(" ")
if int(date.split("-")[0]) != year or int(date.split("-")[1]) != month or int(date.split("-")[2]) != day:
updateW = 0.25
else:
diff = 0
time = time.split(":")
diff += 60 * (now.hour - int(time[0]))
diff += now.minute - int(time[1])
updateW = (timeDecayF * 400) / (diff + timeDecayF * 400) #400 is max in a trading day, will act nicely
except:
updateW = 0.25 #either no date of quote or it was before we kept track of time, very old
elif daysU >= 8 * timeDecayF: #days unchanged cutoff
updateW = 0.2
else: #days unchanged weighting
updateW = (3 * timeDecayF) / (daysU + (3 * timeDecayF))
'''need size weight'''
'''IF THERE IS SIZE< TIGHTEN STD DEV'''
staleW = (3 * staleF) /(daysStale + (3 * staleF)) #days stale weight
weight = dealerW * updateW * staleW
data.iloc[0, dealerWIndex] = '{0:.5g}'.format(dealerW) #dealer weighting
data.iloc[0, staleWIndex] = '{0:.5g}'.format(staleW) #stale weighting
data.iloc[0, unchangedWIndex] = '{0:.5g}'.format(updateW) #unchanged weighting
data.iloc[0, weightIndex] = '{0:.5g}'.format(weight) #final weighting
if data.iloc[0, select] == 1:
if weight > midpointW:
midpoint = lastBid
midpointW = weight
numIncluded += 1 #increment n
if weight > 0.6 or dealerW == 1:
stdDevList.append(lastBid)
stdDevDen += 1
if weight > bestStaleW:
bestStaleBid = lastBid
bestStaleAsk = data.iloc[0, lastOfferIndex]
bestStaleW = weight
tempFrame = concat([tempFrame, data])
if hasColor: #if color, auto midpoint
midpoint = colorQuote
if numIncluded == 0: #all stale
if len(quotes) > 2:
numCalc = np(True, tempFrame['unchanged_for'], 0)
midpointUnch = min(numCalc)
midpointDraw = np(midpointUnch == tempFrame['unchanged_for'], tempFrame['last_bid'], 0)
midpoint = max(midpointDraw)
stdDev = 2
tempFrame.iloc[:, select] = np((abs(tempFrame['last_bid'] - midpoint) <= (stdDev*stdDevTol)), 1, 0)
numCalc = np((tempFrame['Select'] == 1), tempFrame['last_bid'], 0)
numInc = nonZ(numCalc)
bidPx = sum(numCalc)/numInc
offerPx = sum(np((tempFrame['Select'] == 1), tempFrame['last_offer'], 0))/numInc
else:
tempFrame.iloc[:, select] = np((abs(tempFrame['last_bid'] - midpoint) <= (stdDev*stdDevTol)), 1, 0)
bidPx = bestStaleBid
offerPx = bestStaleAsk
midpoint = bestStaleBid
stdDev = 0
else:
'''IF THERE IS SIZE, TIGHTEN STD DEV, else do everything below'''
if stdDevDen == 0:
stdDev = .25 #make very tight parameter but allow for depth
else:
stdDev = ((sum([(x - midpoint)**2 for x in stdDevList])/(stdDevDen))**(1/2))
if stdDev < 0.25:
stdDev = 0.25
if stdDev > 5.0:
if midpoint < 80:
stdDev = 5.0
else:
stdDev = 3.0
elif stdDev > 3.0:
if midpoint >= 80:
stdDev = 3.0
tempFrame.iloc[:, select] = np((abs(tempFrame['last_bid'] - midpoint) <= (stdDev*stdDevTol)) & tempFrame['Not Stale'] == 1, 1, 0)
numCalc = np((tempFrame['Select'] == 1), tempFrame['last_bid'], 0)
numInc = nonZ(numCalc)
if numInc < 3 and stdDev == 0.25:
tempFrame.iloc[:, select] = np(((abs(tempFrame['last_bid'] - midpoint) <= (0.25*stdDevTol))), 1, 0) #too tight, add depth, stdDev 0.25
numCalc = np((tempFrame['Select'] == 1), tempFrame['last_bid'], 0)
numInc = nonZ(numCalc)
bidPx = sum(numCalc)/numInc
offerPx = sum(np((tempFrame['Select'] == 1), tempFrame['last_offer'], 0))/numInc
tempFrame.iloc[:, stdDevIndex] = stdDev
tempFrame.iloc[:, calcBid] = '%.3f' % bidPx
tempFrame.iloc[:, calcOffer] = '%.3f' % offerPx
finalFile = concat([finalFile, tempFrame])
return finalFile
if weights[0] > 0:
# use the weights to create the profile
for j in range(length):
weightsSum = 0
# calculate weights sum and entropy
for i in range(sequences):
if not j < extremities[i][0] and not j > extremities[i][1]:
result[j][aa[lines[i + 1][j]]] += weights[i]
for i in range(21): # Gaps are normalised separately
weightsSum += result[j][i]
# normalize result
for i in range(21):
if result[j][i] != 0:
result[j][i] = result[j][i] / weightsSum
if result[j][21] != 0:
result[j][21] = result[j][21] / (weightsSum + result[j][21])
elif sequences > 1:
# 1 aligned sequence diverge only with gaps so no weights/entropy but gaps found
result = frequencies
# clip to 1 the frequence of the AA presents in the profiled protein
for j in range(length):
result[j][aa[lines[1][j]]] = 1.0
# convert result (a list of lists) in a string and write out the result
f.write(" ".join(map(str, np(result))) + "\n")
f.close()
except ValueError:
for m in range(0,len(licz[i])):
num[i] = num[i] + float(licz[i][m])*10**(len(licz[i])-m-1)
wynik = num[0]/num[1] + num[2]/num[3]
print("Twój wynik to: ",wynik)
else:
dziel = list(input('''Wprowadz ułamki w postaci:a/b + c/d:
'''))
print()
num = [0,0,0,0]
licz = list()
licz.append(dziel[:dziel.index('/')])
licz.append(dziel[(dziel.index('/')+1):(dziel.index('+')-1)])
del dziel[dziel.index('/')]
licz.append(dziel[(dziel.index('+')+2):dziel.index('/')])
licz.append(dziel[(dziel.index('/')+1):(len(dziel))])
for i in range(0,4):
for m in range(0,len(licz[i])):
num[i] = num[i] + int(licz[i][m])*10**(len(licz[i])-m-1)
z = np(num[1], num[3])
for i in range(0,3,2):
num[i] = num[i] * int((z/num[i+1]))
num[0] = num[0] + num[2]
print("Twój wynik to: ", num[0],"/",z)
