efb | EFB for FlyByWire projects | Frontend Framework library
kandi X-RAY | efb Summary
kandi X-RAY | efb Summary
This repo contains the EFB prototype for FlyByWire Simulations' projects.
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QUESTION
I have a dataframe that has n number of columns. These contain letters, the amount of letters a column contains varies and a letter can appear in various amounts of columns. I need the code for a pandas dataframe to convert the sheet to columns starting with the letters, the rows should contain the numbers of the columns that that letter was in.
- ...
ANSWER
Answered 2021-Feb-21 at 18:29Use DataFrame.stack with DataFrame.reset_index for reshape, then DataFrame.sort_values and aggregate lists, last create DataFrame by constructor with transpose:
QUESTION
Box1's source code, written in the R, should meet the following specifications;
- The data are output in a matrix.
- The values in columns 2 through 4 were chosen so that the marginal total are constant when fitted to a 2×2 table. (The marginal total are predetermined by n_A, n_B, n_T and n_F.)
- Chi-square values, calculated from the 2-5th column's value, are listed in the 6th column.
- Fisher's exact probability, calculated from the 2-5th column's value, are listed in the 6th column.
There are some points of concern from above view-point, but I want to focus on the following My Questions first.
My Questions
In addition to the above functionality, I would like to add the following specifications;
- Sorting all columns by the 6th column's value, so as to value of the 6th column value increase as you go down.
- The values in column 8 should be the values in column 7 added together from the top.
- Then, write a graph with column 6 on the x-axis and column 7 on the y-axis.
In other words, we want the the table and graph like following table 1 and Fig.1, from the result of Box1's code.
Right now, I'm executing this process in Excel every time I change the settings, but is it possible to complete it in R?
Box1
...ANSWER
Answered 2020-Nov-12 at 17:50It's not a very smart code, but I figured it out on my own. The part under the "#answer of this question" is the essential part; this part is intended to sort all rows of the matrix, so as to the 6th column's value increase as you go down. And write a graph for column 6 vs. column 7 Other parts are as same as the code described in the Box1 of the question.
Box
QUESTION
I've been searching for a couple of days to add a fade transition effect on the backgroundImage that I'm changing through a Vue app.
Here is the code snippet I've been testing on:
...ANSWER
Answered 2020-Apr-22 at 19:49For Vue Transitions to work, you need to change the DOM elements. So this way would work if you were changing actual images out. In your example, you're only changing an attribute value. The DOM doesn't trigger a transition since its the same element.
However, you can use the :key attribute to convince VUE to replace the element thus giving you a transition between 2 elements.
You can also set the image with inline CSS like you're doing in the example. You'll still have to create the transition in your CSS.
Here's an example using Vue Transition
QUESTION
I have a redux persist configuration like this:
...ANSWER
Answered 2020-Feb-22 at 20:31Since localStorage persists data in strings, you'll need to stringify the JSON before it gets stored, and then parse it when you retrieve it.
QUESTION
I have the following dataframe snippet:
...ANSWER
Answered 2019-Mar-11 at 12:49Use groupby with DataFrameGroupBy.resample and aggregate SeriesGroupBy.nunique with count by DataFrameGroupBy.size :
QUESTION
I was going through the JohnSnowLabs SpellChecker here.
I found the Norvig's algorithm implementation there, and the example section has just the following two lines:
ANSWER
Answered 2018-Nov-21 at 23:26spark-nlp are designed to be used in its own specific pipelines and input columns for different transformers have to include special metadata.
The exception already tells you that input to the NorvigSweetingModel should be tokenized:
Make sure such columns have following annotator types: token
If I am not mistaken, at minimum you'll have assemble documents and tokenized here.
QUESTION
I'm using verilator for linting my design with this commands :
...ANSWER
Answered 2018-Dec-05 at 07:40According to official verilator forum, the only way to do it is to make «null module».
QUESTION
I need to read a bunch of files from a grouped list, and combine them based on the group (files from the same group will have the same columns and thus can be reduced with bind_rows().
I can't seem to get a handle on how the data is changing as I move through the purrr::map() function as I keep geting warnings that I can't use the $ on atomic vectors.
The first thing I do is split by the group so that I get a list of a list of the files I want to read within each group. Then I use map to go through each item in that list, and a second map to go through the rows on each sublist to read the files. However something happens at that level where it no longer treats the data the same as if I was just working with a single group at the top level.
(The lack of being able to debug and look at my environment inside a map function is really an issue in understanding the mechanics.)
...ANSWER
Answered 2018-Nov-12 at 22:44The issue is that we need to loop through the individual files in the column instead of the looping through the columns in the dataset. In the OP's post, the second map loops through the data.frame with a single column. Here, the basic unit is a data.frame with one column. If the column was extracted as a vector, the unit becomes vector and it loops through each element of the vector
QUESTION
I have a sample dataframe as follows:
...ANSWER
Answered 2018-Nov-06 at 07:44You cannot mix Strings and Columns in a select statement.
This will work:
QUESTION
I have a dummy dataframe as follows:
...ANSWER
Answered 2018-Nov-05 at 13:23scala> import org.apache.spark.sql.catalyst.encoders.RowEncoder
import org.apache.spark.sql.catalyst.encoders.RowEncoder
scala> val df = Seq((Seq("abc", "cde"), 19, "red, abc"), (Seq("eefg", "efa", "efb"), 192, "efg, efz efz")).toDF("names", "age", "color")
df: org.apache.spark.sql.DataFrame = [names: array, age: int ... 1 more field]
scala> implicit val encoder = RowEncoder(df.schema)
encoder: org.apache.spark.sql.catalyst.encoders.ExpressionEncoder[org.apache.spark.sql.Row] = class[names[0]: array, age[0]: int, color[0]: string]
scala> val df2 = df.map(x => x).toDF
df2: org.apache.spark.sql.DataFrame = [names: array, age: int ... 1 more field]
scala> df2.collect
res0: Array[org.apache.spark.sql.Row] = Array([WrappedArray(abc, cde),19,red, abc], [WrappedArray(eefg, efa, efb),192,efg, efz efz])
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