The HTML element is used to enclose inline text which represents sample (or quoted) output from a computer program. Its contents are typically rendered using the browser's default monospaced font (such as Courier or Lucida Console).
Note the use of to allow customization of the appearance of specific portions of the sample text such as the shell prompts and the cursor. Note also the use of to represent the command the user entered at the prompt in the sample text.
The correct solution is not to get the shell to split $text on spaces, as that would still also invoke filename globbing on the generated words. Since $text contains $1, $2 and $3, these values would also be split on spaces etc. when $text is split.
I saved the script as samp.sh and used the code: ./samp.sh, but got the error that: No such file or directory. I think the input and output file directories are correct, so how to solve it Thanks for your help.
I think you should check you input directory and output directory, and there access right also (we never know). You also could use absolute path for infile and outfile instead of relative path and setwd. Here,I am not sure you are writing your output inside the correct directory.
It is difficult to reproduce the error on our end because we don't have at hand all that is required. You may need to try debug further yourself. You can use logs to try debug your code. (message function for example)You can also use checks to try for file existence before reading for example.
For diagnosing problems with finding files, see if the script works using the full absolute path to the file first. If the full path to the file works. If that works, it indicates that the issue is with relative paths. Usually, because there's an assumption that the working directory of process isn't where you think it is.
Those are files, and you need to copy them into their spot (models folder). And if you can read english you can see it says you have a modified exe, so an unsupported one. Just download another exe (stock hoodlum exe for example). Good luck.
Passes a value (1) back to the place which called main to tell it what happened (the exact value passed here doesn't matter but in other places it does). You now have your first (very basic) script. If you select file->new in pawno it will give you a much bigger start point will all the callbacks in (see below), including main (which isn't technically a callback but acts like one).
As described in Starting out, this calls the function called \"print\" (defined in a_samp.inc, which is why you need to include it) and tells it to display something in the server console (the word hello).
As mentioned above a common type of array is the MAX_PLAYERS array. MAX_PLAYERS is not a variable, it's a define which is explained later, but for now accept that it is a constant number equal to the max number of players a server can hold (this by default is 500, even if you change the number in your server.cfg file). The following code uses normal variables to hold data for 4 players and do something with those players in a function (for simplicity's sake assume MAX_PLAYERS is 4 for now):
I am a total noob here trying to analyze my RNA seq data. I've just started trying to use Salmon for my quantifications, and wanted to use their suggested script for running all of the samples in a loop instead of having to type them all in.
I think you are executing script in wrong directory. Wherever you are executing, files are not there. Even if there are, script would fail due to other issues in the script. Please post output from pwd (bash/zsh), from which ever directory you are executing the script from. Please also post the output from find . -type f -name \"*_01.fq.gz\"
NIMH ML requires two files to run a task: conditions file & timing script. However, the conditions file can be replaced with a userloop function and there are two ways to write timing scripts (version 1 and version 2). So there are a total of four ways to create a task
The conditions file is a text file that specifies possible combinations of stimuli (\"TaskObjects\") within a trial. Each combination defines a \"condition\". During experiment execution, different conditions are selected to run as determined by the options in the Task submenu. Conditions can be grouped into blocks and selected collectively. The rule of selecting/switching blocks can also be determined on the menu as well (see [Blocks] pane).
Below is an example of a conditions file for a delayed match-to-sample (DMS) task using a total of 4 picture stimuli (A, B, C and D). A DMS task requires a subject to remember the sample stimulus and identify it from a set of stimuli presented subsequently. In this version, a trial begins with an eye fixation. When the subject successfully fixates on the white circle shown at the center of the screen, an image (\"sample\") is displayed briefly, turned off and followed by a delay period. At the end of the delay period, two images (\"sample\" and \"distractor\") are presented on either side of the screen and the subject is required to indicate a choice by making a saccade eye movement to the chosen target. If the choice matches the sample image, a reward is delivered. Then, an inter-trial interval begins. The conditions file for this task includes TaskObjects that represent the fixation dot, the sample image and the distractor image.
The Info column is used here to pass labels to the timing file about which image is the sample and where on the screen the matching image will be displayed; this column is intended to make deciphering the conditions easier for the user, and does not affect actual task execution. Users can access this information in the timing script, like Info.samp and Info.match. This column is optional and may be deleted from the header.
The Timing File refers to the timing script (MATLAB m-file) which calls up each stimulus and checks for fixation, target acquisition, etc. Each condition can be associated with a different timing file, if desired. See below for how to write timing files.
The conditions file requires defining all trial conditions explicitly. This may not be convenient sometimes, for example, when there are so many conditions and stimuli or when the task needs a flexible way of randomizing trial sequences and handling error trials.
The userloop function is a MATLAB function that feeds the information necessary to run the next trial, in lieu of the conditions file. It is called before each trial starts and allows users to determine which TaskObject and which timing script should be used for the upcoming trial on the fly.
The second return value (\"timingfile\") is the name(s) of the timing file(s). Note that the very first call to this function is made before the pause menu shows up and it is for retrieving all the timing file names that will be used. So there is no need to fill the TaskObject list (\"C\"), if it is the first call. The above code provides a tip for how to exit the function early without the TaskObject list. To use multiple timing scripts, put their names in a cell char array and assign it to \"timingfile\". Except the very first call, return only one timing script name for that particular condition.
Task sequences can be programmed with plain MATLAB scripts (\"timing scripts\") using a rich set of functions for stimulus presentation and behavior monitoring. Some functions (called \"timing script functions\") are available only while the timing scripts are executed. Native MATLAB commands and functions can be used as well. For the complete list of all timing script functions, see \"Timing script functions & Command window functions\".
In the conditions file example above, each condition defines 4 TaskObjects (fixation cue, sample, match and distractor) and they are controlled by the \"dms\" timing script. The following code snippet is a piece from the \"dms\" script and shows how to present TaskObject#1 and track eye movements. The complete script is in the \"task\\runtime v1\" directory of NIMH ML.
The timing script v2 takes a different approach. In this new framework, behavior tracking and stimulus presentation are both handled by one function, run_scene. In addition, samples collected during one refresh interval are all analyzed together at the beginning of the next refresh interval and the screen is redrawn based on the sample analysis.
Therefore, the cycle of [analyzing samples]-[drawing screen]-[presenting] is repeated each frame and, by tapping into this cycle, we evaluate the behavior and then decide what to display on the upcoming frame.
Multiple adapters can be concatenated as a chain to do more complex jobs. There are >50 built-in adapters available and users can craft custom adapters from the provided template (see the \"ext\\ADAPTER_TEMPLATE.m\" file).
All adapter chains must start with a special adapter called Tracker. There are currently 8 trackers in NIMH ML and they are pre-defined with reserved names: eye_, eye2_, joy_, joy2_, touch_, button_, mouse_ and null_. Each tracker reads new data samples from the device that its name designates. null_ does not read any data.
This section is for those who want to write their own adapters. All adapters have four common member functions: init, analyze, draw and fini (for the role of each function, see the ext/ADAPTER_TEMPLATE.m file). They all receive the same input argument, p, which is an instantiation of the RunSceneParam class. It has many useful methods, including access to some timing script functions within the adapter. 59ce067264