Great day! Welcome to a teeny tiny corner of the vast interwebs. My hope is that you find this particular corner useful. I got tired of hunting down color codes and syntax, saw that therewere a surprising number of searches for "latex color,"whence the solution seemed obvious.
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I would like to have voltage and current sources filled with color inside them as in v_s and \betav_\pi automatically by defining macros in the preamble. Can anyone suggest me how to do it? By default, they don't fill with color and coloring the way I have done below will simply make file bigger with codes if I have many dependent and independent sources:
Use the following table to identify and resolve issues when configuring the Azure Arc-enabled servers agent. You will need the AZCM0000 ("0000" can be any four digit number) error code printed to the console or script output.
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option_context() context manager has been exposed throughthe top-level API, allowing you to execute code with given option values. Option valuesare restored automatically when you exit the with block:
Some East Asian countries use Unicode characters whose width corresponds to two Latin characters.If a DataFrame or Series contains these characters, the default output mode may not align them properly.
The directory simulation_ws is also called simulation workspace, it is supposed to contain the code and scripts relevant for simulation. For all other files we have the catkin_ws (or catkin workspace). A few more terminologies to familiarize are xacro and macro, basically xacro is a file format encoded in xml. xacro files come with extra features called macros (akin functions) that helps in reducing the amount of written text in writing robot description. Robot model description for Gazebo simulation is described in URDF model format and xacro files simplify the process of writing elaborate robot description.
Going further we will now remove more code from the m2wr.xacro file and place it in a new file. Create a new file with name m2wr.gazebo inside the urdf directory. We will move all the gazebo tags from the m2wr.xacro file to this new file. We will need to add the enclosing
In the above code we have defined three macros, their purpose is to take parameters and create the required element (`link` element). The first macro is named link_wheel and it accepts only one parameter name. It creates a wheel link with the name passed in the parameter. The second macro accepts three parameters name, child and origin_xyz and it creates a joint link.
The link element we just added (for acting as sensor) has random inertia property (see line 5 of the above code). We can write some sane values using a macro that will calculate the inertia values using cylinder dimensions. For this we add a new macro to our macro.xacro script. Add the following macro to the file
This code specifies many important parameters Update rate : Controls how often (how fast) the laser data is captured samples : Defines how how many readings are contained in one scan resolution : Defines the minimum angular distance between readings captured in a laser scan range : Defines the minimum sense distance and maximum sense distance. If a point is below minimum sense its reading becomes zero(0) and if a point is further than the maximum sense distance its reading becomes inf. The range resolution defines the minimum distance between 2 points such that two points can be resolved as two separate points. noise : This parameter lets us add gaussian noise to the range data captured by the sensor topicName : Defines the name which is used for publishing the laser data frameName : Defines the link to which the plugin has to be applied
These commands will create a directory (named scripts) inside the motion_plan package. This directory will contains a python scripts (reading_laser.py) that we will use to read the laser scan data coming on the /m2wr/laser/scan topic (we created this topic in last part). Add the following code to the reading_laser.py file
The above code converts the 720 readings contained inside the LaserScan msg into five distinct readings. Each reading is the minimum distance measured on a sector of 60 degrees (total 5 sectors = 180 degrees).
At the time of creation of these video tutorials, RDS got upgraded from ROS Indigo to ROS Kinetic. While the code we have written in previous parts works fine, we can improve upon the organization and improve usability of the code. Thus, we have made the following changes to the project since the last part.
Next, we will integrate the robot spawning code into the simulation launch file. This will make the job of starting a simulation easier and faster as now the robot will automatically get spawned in a desired location when simulation starts.
This script (bug2.py) contains the code for the new Bug 2 navigation algorithm. Lets us analyze the contents of this script. Here are the contents of this file for reference
When a ProduceRequest is successfully handled by the broker and a ProduceResponse is received (also called the ack) without an error code the messages from the ProduceRequest are enqueued on the delivery report queue (if a delivery report callback has been set) and will be passed to the application on the next invocation rd_kafka_poll().
The message could not be successfully transmitted before message.timeout.ms expired, typically due to no leader being available or no broker connection. The message may have been retried due to other errors but those error messages are abstracted by the ERR__MSG_TIMED_OUT error code.
If a new transactional producer instance is started with the same transactional.id, any previous still running producer instance will be fenced off at the next produce, commit or abort attempt, by raising a fatal error with the error code set to RD_KAFKA_RESP_ERR__FENCED.
If the broker fails to respond to the ApiVersionRequest librdkafka will assume the broker is too old to support the API and fall back to an older broker version's API. These fallback versions are hardcoded in librdkafka and is controlled by the broker.version.fallback configuration property.
If a new consumer joins the group with same group.instance.id as an existing consumer, the existing consumer will be fenced and raise a fatal error. The fatal error is propagated as a consumer error with error code RD_KAFKA_RESP_ERR__FATAL, use rd_kafka_fatal_error() to retrieve the original fatal error code and reason.
If a consumer application subscribes to non-existent or unauthorized topics a consumer error will be propagated for each unavailable topic with the error code set to either RD_KAFKA_RESP_ERR_UNKNOWN_TOPIC_OR_PART or a broker-specific error code, such as RD_KAFKA_RESP_ERR_TOPIC_AUTHORIZATION_FAILED.
When a fatal error has occurred the application should call rd_kafka_flush() to wait for all outstanding and queued messages to drain before terminating the application. rd_kafka_purge(RD_KAFKA_PURGE_F_QUEUE) is automatically called by the client when a producer fatal error has occurred, messages in-flight are not purged automatically to allow waiting for the proper acknowledgement from the broker. The purged messages in queue will fail with error code set to RD_KAFKA_RESP_ERR__PURGE_QUEUE.
librdkafka's string-based key=value configuration property interface controls most runtime behaviour and evolves over time. Most features are also only configuration-based, meaning they do not require a new API (SSL and SASL are two good examples which are purely enabled through configuration properties) and thus no changes needed to the binding/application code.
If your language binding/applications allows configuration properties to be set in a pass-through fashion without any pre-checking done by your binding code it means that a simple upgrade of the underlying librdkafka library (but not your bindings) will provide new features to the user.
The error constants, both the official (value >= 0) errors as well as the internal (value 2ff7e9595c
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