blam | longer maintained. Check out fSpy instead | Graphics library
kandi X-RAY | blam Summary
kandi X-RAY | blam Summary
This is BLAM, a camera and video projector calibration toolkit for Blender in the form of an add-on, written in python, that facilitates modeling based on photographs. To get development progress updates, either check back here regularly or follow me on Twitter.
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Top functions reviewed by kandi - BETA
- Checks the current scene
- Compute camera rotation matrix
- Align the coordinate axes
- Compute the focal length of a flow
- Execute the action
- Find all faces connected to a mesh
- Get mesh faces
- Determine if a mesh is a quadrilateral
- Cancels the current object
- Performs a high quality projection on a mesh
- Compute the projection matrix for a given camera
- Projects the uvs onto the view
- Computes the eigenvalue of the matrix
- Compute the Hessian of the matrix
- Finds all elements that satisfy a predicate
- Convert a function to a vector
- LU
- Solve the linear system
- Solve the matrix b
- Calculate the ratio of the polynomial
- Return the conjugate of a matrix
- Determine if a predicate exists
- Augment two matrices together
blam Key Features
blam Examples and Code Snippets
Community Discussions
Trending Discussions on blam
QUESTION
I have a large dataframe consisting of tweets, and keyword dictionaries loaded as values that have words associated with morality (kw_Moral
) and emotion (kw_Emo
). In the past I have used the keyword dictionaries to subset a dataframe to get only the tweets that have one or more of the keywords present.
For example, to create a subset with only those tweets that have emotional keywords, I loaded in my keyword dictionary...
...ANSWER
Answered 2018-Dec-12 at 14:02Your requirement would seem to lend itself to a matrix type output, where, for example, the tweets are rows, and each term is a column, with the cell value being the number of occurrences. Here is a base R solution using gsub
:
QUESTION
I have a large dataframe consisting of tweets, and a keyword dictionary loaded as a list that has words and word stems associated with emotion (kw_Emo
). I need to find a way to count how many times any given word/word stem from kw_Emo
is present each tweet. In kw_Emo
, word stems are marked with an asterisk ( * ). For example, one word stem is ador*
, meaning that I need to account for the presence of adorable
, adore
, adoring
, or any pattern of letters that starts with ador…
.
From a previous Stack Overflow discussion (see previous question on my profile), I was greatly helped with the following solution, but it only counts exact character matches (Ex. only ador
, not adorable
):
Load relevant package.
library(stringr)
Identify and remove the
*
from word stems inkw_Emo
.for (x in 1:length(kw_Emo)) { if (grepl("[*]", kw_Emo[x]) == TRUE) { kw_Emo[x] <- substr(kw_Emo[x],1,nchar(kw_Emo[x])-1) }
}Create new columns, one for each word/word stem from
kw_Emo
, with default value 0.for (x in 1:length(keywords)) { dataframe[, keywords[x]] <- 0}
Split each Tweet to a vector of words, see if the keyword is equal to any, add +1 to the appropriate word/word stems' column.
for (x in 1:nrow(dataframe)) { partials <- data.frame(str_split(dataframe[x,2], " "), stringsAsFactors=FALSE) partials <- partials[partials[] != ""] for(y in 1:length(partials)) { for (z in 1:length(keywords)) { if (keywords[z] == partials[y]) { dataframe[x, keywords[z]] <- dataframe[x, keywords[z]] + 1 } } } }
Is there a way to alter this solution to account for word stems? I'm wondering if it's possible to first use a stringr pattern to replace occurrences of a word stem with the exact characters, and then use this exact match solution. For instance, something like stringr::str_replace_all(x, "ador[a-z]+", "ador")
. But I'm unsure how to do this with my large dictionary and numerous word stems. Maybe the loop removing [*]
, which essentially identifies all word stems, can be adapted somehow?
Here is a reproducible sample of my dataframe, called TestTweets
with the text to be analysed in a column called clean_text
:
dput(droplevels(head(TestTweets, 20)))
ANSWER
Answered 2019-Jan-08 at 12:17So first of all I would get rid of some of the for
loops:
QUESTION
I have an array of arrays. I am trying to split them up into 4 groups of 3 each. Here is the code...
...ANSWER
Answered 2018-Oct-24 at 16:01Sticking to python standard library (instead of pandas
which would provide much more elegant solution) you should probably go like this:
QUESTION
I'm writing a decorator that adds keyword arguments to the wrapped function. In this example, the decorator add keywords deco0
and deco1
ANSWER
Answered 2018-Sep-05 at 16:43i think you'd have to do something like modify kwargs
inside the inner function:
QUESTION
I search for some files and I want to replace old content with new content from other files, so I have
...ANSWER
Answered 2018-Apr-24 at 11:45The problem here is that > {}
is not part of the find
command. The script is first interpreted by the shell, and the meaning of >
is handled by the shell before passing the stuff around it as arguments to execute:
QUESTION
The script that I am working on currently performs three regular expression searches in a file; consider the following as input:
...ANSWER
Answered 2018-Jan-22 at 14:38Regex isn't intended to group data; it's intended to find data. Use regex to extract the values, and then use code to group them:
QUESTION
I'm iterating a function through each parameter value of Lx for each value of Ly. The function produces a list and then the lists are put into a matrix. I'm trying to give names to the resultant lists so that I know what combination of parameters (Lx and Ly) was used for each run of the function. See reproducible example below:
...ANSWER
Answered 2017-Dec-07 at 01:12You can use lapply
instead of sapply
.
This will produce a nested list:
QUESTION
array_pop()
is returning the last item of the array instead of deleting the last item of the array. Why is this happening?
ANSWER
Answered 2017-Nov-14 at 19:50You're overriding the variable. If you need the removed value, change it to
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