tsa | A TSA random arrow generator
kandi X-RAY | tsa Summary
kandi X-RAY | tsa Summary
This tool is a sophisticated line management and direction randomization assistant. It allows TSA agents to determine the optimal randomly assigned queue to send passengers to for their preflight screening.
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QUESTION
I am working on a project where I need to take groups of data and predict the next value for that group using a time series model. In my data, I have a grouping variable and a numeric variable.
Here is an example of my data:
...ANSWER
Answered 2021-Jun-13 at 19:46You can groupby
first and then iterate. We can store the results in a dict
and after the loop convert it to a DataFrame:
QUESTION
As I understand there are two ways doing this by
- adding DSS dictionary
- embedding CRLs or OCSP responses in the signature at signing time
The DSS way seems to work and Adobe recognizes the signature as LTV enabled. The second way would fit much more into our application so I still try to get it to work. I'm having issues adding the OCSP responses to the signature so I only try to add the certificates and the CRLs. Correct me if I'm wrong but as I understand, either the CRLs or the OCSP responses should be added to the signature. There is no need for both? I collect the signing certificate and its root certificate, also the TSA certificate and its root certificate. After that, I fetch all the CRLs. All this is added before the signing and the timestamp. Adding only the certificates and the CRLs doesn't seem to work because Adobe doesn't recognize the signature as LTV enabled. I don't really understand what I'm doing wrong so any help is appreciated!
...ANSWER
Answered 2021-Jun-03 at 21:05What you're doing wrong is putting the CRLs into the CMS signature container element generally used for CRLs. In case of integrated PDF signatures, though, the situation is different; here CRLs are expected in a special signed attribute instead.
Have a look at ISO 32000-1 where this attribute already is specified:
The PKCS#7 object should contain the following:
...
- Revocation information as an signed attribute (PDF 1.6): This attribute may include all the revocation information that is necessary to carry out revocation checks for the signer's certificate and its issuer certificates. Since revocation information is a signed attribute, it must be obtained before the computation of the digital signature. This means that the software used by the signer must be able to construct the certification path and the associated revocation information. If one of the elements cannot be obtained (e.g. no connection is possible), a signature with this attribute will not be possible.
...
12.8.3.3.2 Revocation InformationThe adbe Revocation Information attribute:
QUESTION
I'm trying to find right parameters for ARIMA but not able to use parameters higher than 4. Here is the code.
...ANSWER
Answered 2021-Jun-01 at 17:39I am the main contributor to this model.
Unfortunately, it is currently impossible to use values greater than 4 for these parameters due to implementation reasons.
I see that you have opened a GitHub issue, thanks for that. We will consider adding support for higher parameter values and keep you updated on the GitHub issue.
QUESTION
I am trying to estimate an ARIMAX model with p=96 and q=96 as only non-zero coefficients (meaning all lag orders <96 have coefficient zero). The doc of statsmodels.tsa.arima.model.ARIMA¶ states that a list can be supplied for the lag orders p and q. I first tried this using the code below.
...ANSWER
Answered 2021-May-30 at 17:26It looks like you are using the old model. You need to do either:
QUESTION
from bs4 import BeautifulSoup
import numpy as np
import requests
from selenium import webdriver
from nltk.tokenize import sent_tokenize,word_tokenize
html = webdriver.Firefox(executable_path=r'D:\geckodriver.exe')
html.get("https://www.tsa.gov/coronavirus/passenger-throughput")
def TSA_travel_numbers(html):
print('NASEEF')
soup = BeautifulSoup(html,'lxml')
print('naseef2')
for i,rows in enumerate(soup.find_all('tr',class_='view-content')):
print('naseef3')
for texts in soup.find('td',header = 'view-field-2021-throughput-table-column'):
print('naseef4')
number = texts.text
if number is None:
continue
print('Naseef')
TSA_travel_numbers(html.page_source)
...ANSWER
Answered 2021-May-11 at 08:50Your page does not contain tags with a
class
of view-content
, so find_all
is correctly returning no results. If you remove the class
restriction, you get many results:
QUESTION
from bs4 import BeautifulSoup
import numpy as np
import requests
from selenium import webdriver
from nltk.tokenize import sent_tokenize,word_tokenize
html = webdriver.Firefox(executable_path=r'D:\geckodriver.exe')
html.get("https://www.tsa.gov/coronavirus/passenger-throughput")
def TSA_travel_numbers(html):
soup = BeautifulSoup(html,'lxml')
for i,rows in enumerate(soup.find('div',class_='view-content'),1):
# print(rows.content)
for header in rows.find('tr'):
number = rows.find_all('td',class_='views-field views-field field-2021-throughput views-align-center')
print(number.text)
TSA_travel_numbers(html.page_source)
...ANSWER
Answered 2021-May-10 at 14:16As the error says, you can't iterate over an int
, which is your rows
.
Also, there's no need for a webdriver
as data on the page is static.
Here's my take on it:
QUESTION
I'm doing a project on data analysis with timeseries and forecasting. I have a dataframe which contains a lot of data from which I need to handle Covid cases
. The dataframe looks like that:
ANSWER
Answered 2021-May-04 at 19:52In several steps of your implementation, you are equalizing the column dataframe['forecast']
to the results of new calculations (besides predicting values two times for different models and concatenating dataframes with similarly named columns):
QUESTION
I would like to loop through the axes (0 to 5 & 0 to 1), as well as loop through the columns of my series table (Pandas).
My idea was to do something along the lines of the following:
...ANSWER
Answered 2021-May-04 at 19:08You can zip
the dataframe's columns and the flattened axis together and then run a loop:
QUESTION
I'm (out-of-sample) forecasting with ARIMA(0,1,0).
In python's statsmodels latest stable version 0.12. I calculate:
...ANSWER
Answered 2021-Mar-16 at 12:44The difference is due to whether the models include a "constant" term or not. For the first case i.e. older statsmodels.tsa.arima_model.ARIMA
, it automatically includes a constant term (and no option to turn on/off). If you have a differencing, it also includes it but does so in the differenced domain (otherwise it would be eliminated anyway). So here is its ARIMA(0, 1, 0) model:
QUESTION
Given a small test data as follows:
...ANSWER
Answered 2021-Apr-29 at 03:51You may try the following:
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