pdd | Packet Dump Decode - one-click packet hexdump decode | Learning library

by pstavirs C++ Version: Current License: GPL-3.0

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kandi X-RAY | pdd Summary

pdd is a C++ library typically used in Tutorial, Learning applications. pdd has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

Packet Dump Decode - one-click packet hexdump decode

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Support

pdd has a low active ecosystem.

It has 15 star(s) with 3 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 6 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of pdd is current.

Quality

pdd has 0 bugs and 0 code smells.

Security

pdd has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pdd code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pdd is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

pdd releases are not available. You will need to build from source code and install.

Installation instructions are not available. Examples and code snippets are available.

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pdd Examples and Code Snippets

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Community Discussions

Trending Discussions on pdd

Extract NetCDF Variable and create New NetCDF

Python+tkinter: Text widget doesn't change text immidiately

Using function on the whole list of XTS objects

In R, how to make the jitter (geom_jitter()) stay inside its correspondant boxplot without extending over the neighboring boxplots?

SQL join two tables by modifying on columns

Converting Multiple existing xts objects to multiple data.frames

query to get all not fully paid invoices

SqlSessionTemplate is not serializable in flink

I am trying to use CNN for stock price prediction but my code does not seem to work, what do I need to change or add?

Writing pandas dataframe appended text on the top

QUESTION

Extract NetCDF Variable and create New NetCDF

Asked 2022-Feb-26 at 15:37

I need some help with manipulating NetCDF files. In total I have 10 files for 10 years respectively. Each year hast multiple (the same) variables, some of them also covering daily values. Here, I show you one example for the structure:

...

ANSWER

Answered 2022-Feb-26 at 15:37

The NCO ncecat command, documented here, does exactly what you seem to want:

Source https://stackoverflow.com/questions/70953036

QUESTION

Python+tkinter: Text widget doesn't change text immidiately

Asked 2022-Jan-06 at 22:27

I have a tk.Text() widget and a button. When the button is clicked, I want to change the text in the Text widget, then conduct a lengthy job. Here is code snippet from the button command function:

...

ANSWER

Answered 2022-Jan-06 at 22:27

The problem here is that the window is not being updated after the text is inserted. This is because events are only processed after a callback returns.

To force the window to process the events before the callback returns, you can call self.root.update_idletasks() (at CoolCloud's suggestion, you shouldn't use update() unless it's absolutely necessary). Here is what the do_get() should look like:

Source https://stackoverflow.com/questions/70609391

QUESTION

Using function on the whole list of XTS objects

Asked 2021-Dec-14 at 18:17

I'm trying to analyze candlestick formation Marubozu in R. So far I was able to download the different stocks data and find the formation using "Candlesticks" library in one stock data. I would like to automate that process so that I can run the CSPMarubozu function on many stocks at the same time.

My main problem is that I cannot really understand how can I pass the list of data to this function. While trying to do it with for loop (Try 1) I get following error: "Error in CSPMarubozu((names(stocks_list[i])), n = 20, ATRFactor = 0.8, : Price series must contain Open, High, Low and Close." I know, that I can't pass the character variable to this function, but I can't find the way to get index names without the "" mark. (ex. "AMZN" and I need just AMZN"

My other try (Try 2) was to do it with lapply() function but the same problem occurs

Here is my code:

...

ANSWER

Answered 2021-Dec-14 at 18:17

This downloads stocks and then shows three different equivalent ways of processing each stock. We use dim(...) but that would be replaced with whatever processing is desired. Note that if x is an xts object for a stock having OHLC as well as adjusted close and volume then Op(x), Hi(x), Lo(x), Cl(x), Ad(x) and Vo(x) are the vectors of Open, High, Low, Close, Adjusted Close and Volume.

Although the code below seems preferable getSymbols(stocks); L <- mget(stocks) also works to put the stocks loose into your workspace and then collect them into a list L.

Source https://stackoverflow.com/questions/70350246

QUESTION

In R, how to make the jitter (geom_jitter()) stay inside its correspondant boxplot without extending over the neighboring boxplots?

Asked 2021-Dec-02 at 15:22

I would like to find a way for the jitter to stay in its own boxplot, without extending over the neighboring boxplots.

So far, I looked at this answers:

but none of them really addressed my issue; the main difference is that I have 3 groups running through a timeline on the X-axis.

The code I have so far:

...

ANSWER

Answered 2021-Dec-01 at 18:02

Specify the dodge width

+ geom_jitter(width = 0.05)

or geom_point(position = position_jitter(width = 0.05))

Source https://stackoverflow.com/questions/70188410

QUESTION

SQL join two tables by modifying on columns

Asked 2021-Sep-27 at 14:14

I have 3 tables on PostgreSQL:

SPENDINGS:

...

ANSWER

Answered 2021-Sep-27 at 14:14

try something as follows (you can use what ever join you would like to in place of inner)

Source https://stackoverflow.com/questions/69347334

QUESTION

Converting Multiple existing xts objects to multiple data.frames

Asked 2021-Sep-19 at 19:58

There is already a thread asking how to convert multiple xts objects into as many data.frames here. Unfortunately, the solutions show how to do it for data that is being downloaded in .GlobalEnv. Moreover, the first answer of mentioned thread suggests to create a new environment, download the objects into it, and transform everything inside with the following code: stocks <- eapply(dataEnv, as.data.frame).

However, this creates a large list stored in the variable stocks, whereas I need the objects to remain discrete. Even when I run the code without generating a list (i.e., by just applying eapply(dataEnv, as.data.frame)), nothing happens. This has been documented here. In order to update the original object, the answer to this question was to use a code that looks like this: NKLA <- fortify.zoo(NKLA). This solution, which by the way works, is ok for a few objects that can be done manually and I need to automatise the process.

In my case, the objects are already downloaded and some of the them are data.frames, some are xts objects, and there might even be other objects.

What I need is to find the xts objects and transform them into data.frames.

In order to find the xts objects, I use the following code: xtsObjects <- which(unlist(eapply(.GlobalEnv, is.xts))), but applying xtsObjects <- fortify.zoo(xtsObjects) only creates yet another object called xtsObjects that contains, for example, 2 obs. of 2 variables (because there are 2 xts objects in the environment).

For example, the following code (which should be reproducible) does not change the discrete xts objects into discrete data.frames:

...

ANSWER

Answered 2021-Sep-19 at 19:58

Use the names(which()) and then lapply.

Source https://stackoverflow.com/questions/69245999

QUESTION

query to get all not fully paid invoices

Asked 2021-Apr-30 at 21:33

I have these tables invoices,payments,payments_details, the invoices table have all the invoices that the user should pay created when a contract is created, this contract may have 1 invoice ore more, the payments table have all the payments for a contract (user may pay more one payment for each invoice) and the last table payments_details have the details for each payment in the payments table E.G. the payment may have deffirent payment methods such as cash, or cash and visa, or chash and visa and cheques. I'm getting payment value by getting the sum for payment method values from payments_details`, here is my tables script :

...

ANSWER

Answered 2021-Apr-30 at 21:33

Try this:

Source https://stackoverflow.com/questions/67339188

QUESTION

SqlSessionTemplate is not serializable in flink

Asked 2021-Mar-21 at 09:26

My flink aplication throws such exception when it starts:

...

ANSWER

Answered 2021-Mar-21 at 09:26

Rather than instantiating the Mapper object in the constructor, you can do this in the sink's open method, and then make the Mapper transient.

The sink's constructor is called on the Flink client, and the sink has to be serialized and sent to the task managers. Whereas the sink's open method is called once in each task manager as the job begins.

Source https://stackoverflow.com/questions/66729576

QUESTION

I am trying to use CNN for stock price prediction but my code does not seem to work, what do I need to change or add?

Asked 2021-Jan-28 at 05:40

import math
import numpy as np
import pandas as pd
import pandas_datareader as pdd
from sklearn.preprocessing import MinMaxScaler
from keras.layers import Dense, Dropout, Activation, LSTM, Convolution1D, MaxPooling1D, Flatten
from keras.models import Sequential
import matplotlib.pyplot as plt

df = pdd.DataReader('AAPL', data_source='yahoo', start='2012-01-01', end='2020-12-31')
data = df.filter(['Close'])
dataset = data.values
len(dataset)

# 2265

training_data_size = math.ceil(len(dataset)*0.7)
training_data_size

# 1586

scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(dataset)
scaled_data

# array([[0.04288701],
#       [0.03870297],
#       [0.03786614],
#       ...,
#       [0.96610873],
#       [0.98608785],
#       [1.        ]])

train_data = scaled_data[0:training_data_size,:]
x_train = []
y_train = []
for i in range(60, len(train_data)):
    x_train.append(train_data[i-60:i, 0])
    y_train.append(train_data[i,0])
    if i<=60:
        print(x_train)
        print(y_train)  

'''
[array([0.04288701, 0.03870297, 0.03786614, 0.0319038 , 0.0329498 ,
       0.03577404, 0.03504182, 0.03608791, 0.03640171, 0.03493728,
       0.03661088, 0.03566949, 0.03650625, 0.03368202, 0.03368202,
       0.03598329, 0.04100416, 0.03953973, 0.04110879, 0.04320089,
       0.04089962, 0.03985353, 0.04037657, 0.03566949, 0.03640171,
       0.03619246, 0.03253139, 0.0294979 , 0.03033474, 0.02960253,
       0.03002095, 0.03284518, 0.03357739, 0.03410044, 0.03368202,
       0.03472803, 0.02803347, 0.02792885, 0.03556487, 0.03451886,
       0.0319038 , 0.03127613, 0.03274063, 0.02688284, 0.02635988,
       0.03211297, 0.03096233, 0.03472803, 0.03713392, 0.03451886,
       0.03441423, 0.03493728, 0.03587866, 0.0332636 , 0.03117158,
       0.02803347, 0.02897494, 0.03546024, 0.03786614, 0.0401674 ])]
[0.03933056376752886]
'''

x_train, y_train = np.array(x_train), np.array(y_train)
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
x_train.shape

# (1526, 60, 1)

model = Sequential()
model.add(Convolution1D(64, 3, input_shape= (100,4), padding='same'))
model.add(MaxPooling1D(pool_size=2))
model.add(Convolution1D(32, 3, padding='same'))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(1))
model.add(Activation('linear'))
model.summary()

model.compile(loss='mean_squared_error', optimizer='rmsprop', metrics=['accuracy'])
model.fit(X_train, y_train, batch_size=50, epochs=50, validation_data = (X_test, y_test), verbose=2)

test_data = scaled_data[training_data_size-60: , :]
x_test = []
y_test = dataset[training_data_size: , :]
for i in range(60, len(test_data)):
    x_test.append(test_data[i-60:i, 0])

x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions)
rsme = np.sqrt(np.mean((predictions - y_test)**2))
rsme

train = data[:training_data_size]
valid = data[training_data_size:]
valid['predictions'] = predictions
plt.figure(figsize=(16,8))
plt.title('PFE')
plt.xlabel('Date', fontsize=18)
plt.ylabel('Close Price in $', fontsize=18)
plt.plot(train['Close'])
plt.plot(valid[['Close', 'predictions']])
plt.legend(['Train', 'Val', 'predictions'], loc='lower right')
plt.show

import numpy as np

y_test, predictions = np.array(y_test), np.array(predictions)
mape = (np.mean(np.abs((predictions - y_test) / y_test))) * 100
accuracy = 100 - mape
print(accuracy)

...

ANSWER

Answered 2021-Jan-28 at 05:38

Your model doesn't tie to your data.

Change this line:

Source https://stackoverflow.com/questions/65931302

QUESTION

Writing pandas dataframe appended text on the top

Asked 2020-Oct-11 at 06:14

I have the pandas data frame. I want to write it in a text file and add text on the top of the frame.

e.g (this is the data.

...

ANSWER

Answered 2020-Oct-11 at 06:14

You can create a dataframe of the strings you want at the top and then append your main dataframe. Make sure the column names are the same before appending, so that it lines up (0 in my example):

Source https://stackoverflow.com/questions/64300984

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install pdd

You can download it from GitHub.

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For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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