In this section, we will proceed to explain in general terms the process followed for the project. The process is split into several stages to obtain as many insights as possible from the data:
The problem we want to tackle is a regression problem based on the spanish_used_car_market. This dataset contains used cars offers extracted from a famous Spanish advertisement platform called Milanuncios. The data was collected by the authors using web-scraping methods. The main objective of this project is to predict the value of the cars based on the several variables - brand, model, year, etc. The result of this project will help us understand the used-cars market in Spain by predicting how much a specific car would cost or helping us choosing the right price for our car.
| Variable | Value |
|---|---|
| ad_id | r361993571 |
| ad_type | Oferta |
| ad_time | 57 dias |
| ad_title | RENAULT - R4 |
| car_desc | Coche en perfectas condiciones, chapa ning… |
| car_km | 1982 |
| car_year | Manual |
| car_engine_type | 5 puertas |
| car_door_num | 5 puertas |
| car_power | Azul |
| car_price | 1500 |
| advertizer_type | Particular |
| image_url | https://img.milanuncios.com/fp/3619/93/3619935… |
| ts | 2020-11-03 18:19:09.473219 |
| region | caceres |
The table above represents a small sample of the dataset. As we can see, there are several variables which some of them are not self-explanatory. Because of that, we need to dig deeper into the dataset description to find out the meaning of these variables:
As we can see, most of the variables will give us some insights into the problem. However, it may seem that the data is not fully usable. There are some numeric variables represented as text, the brand and model is represented as natural language, etc. Besides, there are some misplacements in the data, and the values of different columns are mixed. For those reasons, we will need to apply several transformations to the data.
For the last transformation step, the advertisement title is tokenized by splitting the text into words using specific separators. Then, we assume that the first word is the brand and the rest is the model. To avoid having a very high cardinality for the model variable, it will contain two words at most, and words related to the mileage or power are filtered out. To compute the category variable, we will use another dataset. This dataset represents a database of around 10K models with its make and category. The table below shows a sample of this dataset.
| Category | Make | Model | Year | ||
|---|---|---|---|---|---|
| 0 | SUV | Audi | Q3 | 2020 | |
| 1 | Sedan | Chevrolet | Malibu | 2020 | |
| 2 | SUV | Cadillac | Escalade ESV | 2020 | |
| 3 | Coupe, Convertible | Chevrolet | Corvette | 2020 | |
| 4 | Sedan | Acura | RLX | 2020 | |
| 5 | Pickup | Chevrolet | Silverado 2500 HD Crew Cab | 2020 | |
| 6 | Sedan | BMW | 3 Series | 2020 | |
| 7 | Van/Minivan | Chrysler | Pacifica | 2020 | |
| 8 | Pickup | Chevrolet | Colorado Crew Cab | 2020 | |
| 9 | SUV | BMW | X3 | 2020 |
One of the main challenges when dealing with multiple datasets is the task of finding entities in one source that refers to the same entity in other sources. This task is also called record linkage. For our problem, we have to match brands and models from our original dataset to brands and models from the new dataset. To do so, we will utilize techniques of fuzzy string matching - for each sample, in the original dataset, we will find the most similar entry in terms of brand and model from the other dataset. The similarity between models and brand strings are measure by Levenshtein distance. Also, to avoid connecting unrelated entities, we use a threshold of 90% of similarity.
| Variable | Value |
|---|---|
| model | None |
| brand | RENAULT |
| category | None |
| car_desc | Coche en perfectas condiciones… |
| advertizer_type | Particular |
| image_url | https://img.milanuncios.com/fp/3619/93/3619935… |
| region | caceres |
| car_price | 1500 |
| years_since_manufacturing | 38 |
| car_km | NaN |
| car_engine_type | Manual |
| car_door_num | 5 |
| car_power | NaN |
The table above shows an example of the data after being transformed.
As we face a modelling problem, it is always good to have a holdout dataset to evaluate the performance of the final model. for this problem, the data has been split by a 75-25 ratio. The plot above shows the number of samples for each set.
The rest of the EDA will be carried on using the training set only to avoid introducing any bias in our modeling that may affect the generalization error.
For this dataset, only 5 out of 13 variables are numeric. The following table describes those variables briefly:
| car_km | car_door_num | car_power | car_price | years_since_manufacturing | |
|---|---|---|---|---|---|
| count | 184272 | 9598 | 164625 | 191651 | 189520 |
| mean | 138197 | 4.46 | 141.103 | 12195.2 | 9.784 |
| std | 185754 | 0.932 | 175.222 | 74237.5 | 7.569 |
| min | 1 | 2 | 1 | 0 | -1 |
| 25% | 58860.2 | 4 | 105 | 3499 | 4 |
| 50% | 130000 | 5 | 125 | 8400 | 9 |
| 75% | 199000 | 5 | 150 | 15900 | 15 |
| max | 1.67772e+07 | 5 | 65535 | 1.67772e+07 | 114 |
| counts | 184272 | 9598 | 164625 | 191651 | 189520 |
| uniques | 28745 | 4 | 524 | 5416 | 84 |
| missing | 7379 | 182053 | 27026 | 0 | 2131 |
| missing_perc | 3.85% | 94.99% | 14.10% | 0% | 1.11% |
| types | numeric | numeric | numeric | numeric | numeric |
In terms of nullity, every continuous variable contains null values, so we will have to impute the missing values for those columns, or remove the variable. The car_km variable ranges between 1-16M, and it is highly likely that the upper-extreme is an outlier. For that reason, we will remove any sample with mileage over 3M kilometres. The years_since_manufacturing variable contains almost no null values, but it ranges from -1 to 114. For the purpose of this analysis, we will only consider cars dated between 1990-2020 because our main objective is to predict the price of everyday cars, not collectable or extremely outdated cars.
On the other hand, the car_door_num variable is missing for the vast majority of samples. Thus, any imputation method we would use will introduce high bias making that variable harmful for predictive modelling. So, that variable will be removed. The car_power variable ranges from 1 to 65535. Both extremes are most probably outliers, as the values are quite far from Q1 and Q3. This variable will be restricted to a range between 40 and 800 HP.
Finally, the car_price variable ranges from 0 to 16M. This case is quite similar to the previous ones, we have two extremes far away from Q1 and Q3, so we will need to clip this variable too. The new range for this variable will be 500-200K.
Once we have removed all the outliers, a good way to interpret the distribution of the variables is by plotting histograms:
As we can see, no variable follows a priori, a Gaussian distribution, but car_power is very close. As a final part of our continuous variables analysis, we will see the different correlations between the variables to discover whether there is colinearity or not. Because, as we know, if two variables have an almost perfect correlation, we can eliminate one of them and not lose much information.
The figure above shows no strong correlation between the independent variables, so we do not have to remove any features due to multicollinearity. We have also calculated Spearman's correlation coefficients. Since no variable is pure Gaussian, it makes much more sense to perform a non-parametric test based on ranking. We can also see how the correlation between the independent variables and the dependent variable are noticeable. It is also worth noticing how the non-parametric correlation is higher for car_km and years_since_manufacturing. That means that we will approximate the problem better by using non-linear relationships between those variables and the labels.
Before converting the categorical variables to numerical, we will analyze them deeply to increase our understanding of the problem. First of all, as we did with the continuous variables, we will see if there are null values. The following table shows the percentage of null values for each variable:
| Missing % | |
|---|---|
| model | 5.536% |
| brand | 0.000% |
| category | 27.348% |
| region | 0.000% |
| car_engine_type | 0.000% |
As we can see, in this case, there are null values, specifically in the model and category variables. That will make us have to impute or eliminate them later.
The figure above shows the distribution of the car price depending on the region. We are not particularly interested in knowing the distribution of each region, but checking if there are differences in prices between them. As depicted in the figure, there are some differences in terms of mean and interquartile range, so we will keep this variable for predictive modelling.
The differences in prices between brands is more significant than in the previous case. The figure above shows how cars from luxury brands such as Maserati, Porsche, or Dodge, have higher prices than the rest. It also shows how BMW, Mercedes-Benz and Volkswagen are the predominant classes.
The differences in prices between categories are depicted in the figure above. Clearly, the predominant categories are SUV and Sedan. Some categories show higher average prices and extreme values, so this variable may be useful for predictive modelling.
Finally, the variable car_engine_type, which represents the transmission type, shows visible differences in prices. Automatic cars tend to be more expensive than manual ones. Besides, the most expensive cars are most probably automatic. This variable contains useful information for further analysis.
Finally, the variable car_engine_type, which represents the transmission type, shows visible differences in prices. Automatic cars tend to be more expensive than manual ones. Besides, the most expensive cars are most probably automatic. This variable contains useful information for further analysis.
The figure above represents the word cloud for the car model. Indeed, that variable is categorical, but due to its cardinality, any categorical plot (e.g. count-plot) would be unreadable. Thus, using a word cloud allow us to understand the frequency of specific models. For this case, words like Serie or “Clase” (Class) are the most popular. Both words refer to models by BMW and Mercedes-Benz, respectively.
For the advertisement description, we can see how words like “vehiculo” (vehicle) or “coche” (car) are the most common. The text data can be useful for predictive modelling, but it requires a complicated procedure to convert it to a vector, and it is out of the scope of this project. Thus, we will discard this feature. In terms of images, we can use the URL to fetch the thumbnails directly from the advertising platform.
The image above represents a small sample of the images we can find. As we can see, the images show variety in camera pose, lightning, etc.
| Missing Train | Missing Test | |
|---|---|---|
| model | 5.536% | 5.724% |
| brand | 0.000% | 0.000% |
| category | 27.348% | 27.798% |
| car_desc | 0.925% | 0.946% |
| advertizer_type | 0.000% | 0.000% |
| image_url | 2.085% | 1.998% |
| region | 0.000% | 0.000% |
| car_price | 0.000% | 0.000% |
| years_since_manufacturing | 0.000% | 0.000% |
| car_km | 0.000% | 0.000% |
| car_engine_type | 0.000% | 0.000% |
| car_power | 0.000% | 0.000% |
In this section, we will apply some transformations to the data. The table above shows the percentage of missing values after filtering according to the previous step. Imputing the model column could introduce bias, so we will remove those samples. We will also remove the samples whose image URL is missing, and impute the missing values for the category using the most frequent value.
Subsequently, and depending on the type of data of each feature, different transformations will be applied:
The main objective of this project is to create a predictor of prices based on car features. For that, we have several categorical and numerical variables. One of the main challenges we face with this dataset is the high cardinality of the categorical variables. The model variable contains ~8900 different values; there are 23 different categories and 90 brands. That makes the use of One-Hot Encoding infeasible, as the number of columns generated would go up to ~9K. With such dimensionality, it is extremely difficult to fit a performant model using limited resources. For that reason, we will train a neural network based on embedding layers.
Embedding layers are a powerful component that turns positive integers into dense vectors of fixed size. These layers contain trainable parameters that get updated while fitting. By using embeddings, we are able to represent categorical variables with high cardinality on a low dimensional space while keeping the semantics and relationship between values. Embedding layers were proposed originally for text data, where each word represents a different value of categorical type. In the image below, there is an example of embeddings:
The architecture we will train is depicted in the figure below. It represents a multi-input architecture where there is one embedding branch for each categorical variable and one input layer for the rest of the variables. The output from the embedding layers and the numerical input is merged into a single output and used to feed a regressor. The regressor is composed of two hidden layers with Dropout regularization and an output layer of a single unit.
The training plot shows that the learning process is unstable. However, the model is able to reduce both loss (MSE) and metric (MAE) considerably. For this project, the metric MAE is more important than the loss. The reason is that MAE is more robust to outliers, and the scale is the same as the labels, making the metric easily interpretable. For this training phase, an average error of ~1600 for the validation set is obtained.
The training plot shows that the learning process is unstable. However, the model is able to reduce both loss (MSE) and metric (MAE) considerably. For this project, the metric MAE is more important than the loss. The reason is that MAE is more robust to outliers, and the scale is the same as the labels, making the metric easily interpretable. For this training phase, an average error of ~1600 for the validation set is obtained.
## Unsupervised Learning.
As a secondary objective for this project, we will apply clustering techniques to find similarities between ads, with the purpose of creating an advertisement retrieval system. The main idea is to use the features generated by the embedding layers to cluster similar ads together. To do so, we need to remove the regression part of the model and create a feature extractor. The architecture of this new model is depicted below.
This new architecture can be used in the same way as the original one, and it does not need retraining as the weights are copied. After applying the feature extractor the training dataset, we will apply the algorithm HDBSCAN. “HDBSCAN is a clustering algorithm developed by Campello, Moulavi, and Sander. It extends DBSCAN by converting it into a hierarchical clustering algorithm, and then using a technique to extract a flat clustering based on the stability of clusters”. The benefits of this type of clustering algorithms are that they do not require specifying the number of clusters. Apart from grouping instances, HDBSCAN allows us to detect outliers in the data, or to retrieve similar entities given one example.
| model | brand | category | car_desc | advertizer_type | region | car_price | years_since_… | car_km | car_engine_type | car_power | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| CALIFORNIA | VOLKSWAGEN | SUV | Volkswagen… | Particular | canarias | 13900 | 25.0 | 345000 | Manual | 78 | ||
| CALIFORNIA | VOLKSWAGEN | SUV | Vehiculo nacional… | Particular | navarra | 21600 | 19.0 | 368000 | Manual | 104 | ||
| CALIFORNIA | VOLKSWAGEN | SUV | Vendo Volkswagen.. | Particular | burgos | 14000 | 27.0 | 220000 | Manual | 95 | ||
| CALIFORNIA | VOLKSWAGEN | SUV | VW T4 California… | Particular | tenerife | 19500 | 21.0 | 296000 | Manual | 102 | ||
| CALIFORNIA | VOLKSWAGEN | SUV | Vw transporter… | Particular | navarra | 20000 | 12.0 | 160000 | Manual | 105 |
The table above shows a sample of a specific group. As we can see, the similarities between those instances are noticeable. All of them are advertisements from the same model, the number of years, HP and mileage are similar, and all of them are manual cars from private owners. It is worth pointing out that the car price has not been used for clustering, although the prices are quite similar. Most of the advertising platforms allow searching by brand, model, HP, etc. One way to enhance the search system is by adding this type of methods. Apart from searching by specific fields, let the user find similar cars across different brands and models.
In the supervised learning section, the image data has not been used for regression due to resources limitations. Nevertheless, images may contain important information about the problem. For the last part of this section, we will apply HDBSCAN to image features extracted from a pretrained MobileNetV2. MobileNetV2 is a light CNN architecture used for classification on the ImageNet dataset. Pretrained CNN models used as feature extractors when pruning its classification or regression layers. Thankfully, Keras offers a module to load pretrained CNN and use it as feature extractors. The image below shows a subset of a cluster.
We can see how the algorithm has been able to group similar images. In particular, for the first set of images, it represents similar parts of the car, while the images from the second figure come from a common advertiser. Moreover, the pose and shape of those cars are similar too.
| Model | Metric | Value | |
|---|---|---|---|
| 0 | Embeddings + Regressor | MAE | 1619.526431 |
| 1 | Embeddings + Regressor | R2 Score | 0.915482 |
| 2 | HDBSCAN | Silhouette score | 0.245372 |
| 3 | HDBSCAN Images | Silhouette score | -0.166373 |
The results of the regression model are quite impressive. We obtained an R2 Score above 0.9, and an MAE score around 1620. Taking into account that cars are priced from 200 to 200K, an average absolute error below 2K is a fairly good result. For the clustering methods, the results are not that impressive. The silhouette score has been computed, as we do not have labels for clustering, so we can only use internal evaluation measures. For the tabular data clustering, none of the points was incorrectly assigned, but the clusters are not dense or nicely separated. For image clustering, the negative score means that some points were incorrectly assigned.
As a conclusion, we could say that this project has allowed us to understand the Spanish used-cars market deeply. In the EDA section, important insights were extracted from the data, which were applied in the preprocessing phase. In the modelling section, a neural network based on embedding was implemented, and it threw quite good results on both validation and test data. Apart from implementing a regressor, interesting findings have been collected from applying clustering techniques to both tabular and image data.


Taylor Swift Fans Quickly Noticed Her AMAs Performance Didn't Feature Songs From 3 Albums
by Karenna MeredithTaylor Swift celebrated her last decade of music at the American Music Awards on Sunday night, and it thrilled the audience to no end. The 29-year-old singer belted through a mashup of her greatest hits, starting with "The Man" from her latest album, Lover, followed by songs from albums like Fearless, Red, and 1989.
Diehard Swifties might've been disappointed to see Swift skip over tracks from Speak Now, Reputation, and her namesake album, but there was an overwhelmingly positive response from fans overall — especially when she brought Camila Cabello and Halsey out to sing "Shake It Off" with her. Selena Gomez sang along from the audience next to Swift's parents, returning the favor from her own AMAs performance earlier in the night. Gomez's reaction is by far one of the cutest of the night, but keep reading to see a few of our other favorite responses — ask and the internet will deliver!


US Navy moves to expel war crime-accused SEAL after Trump pardoned him
A panel of Navy SEAL officers would convene in December to decide whether Gallagher can remain in the unit, Lawrence said.
by AFPWashington: The US Navy on Wednesday began a procedure that could remove from its elite SEALs unit an officer accused of war crimes whom President Donald Trump pardoned last week.
Along with three other unidentified members of his unit, Edward Gallagher, 40, was "notified today that a process will start to evaluate and look at their continued service as SEALs," spokeswoman for US special forces Tamara Lawrence told AFP.
A panel of Navy SEAL officers would convene in December to decide whether Gallagher can remain in the unit, Lawrence said.
The panel will give its opinion to the commander of the US Navy special forces, Rear Admiral Collin Green, who will make recommendations to US Navy command, which will make a final decision, she added.
In July, Gallagher was acquitted of charges related to the stabbing death of a wounded ISIS prisoner in Iraq, and of other killings of civilians.
But he was convicted of posing with the slain fighter's body in a group picture with other SEALs.
Gallagher's case was championed by Fox News, which is closely followed by Trump, and last week the president reversed the military court's decision to reduce Gallagher's demotion following his conviction.
He also pardoned a former US soldier convicted of murder and a Green Beret charged with killing a suspected Taliban bomb-maker.
Lawrence declined to say whether the proceedings started on Wednesday had been opened in response to Trump's moves.
"Discussions have been ongoing since before the president's action," she said, but the sailors concerned "were notified today."
The procedure is administrative and not legal, and even if they are excluded from the SEALs, they will be able to continue serving in the Navy.
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How to use transparency info from a video on a website
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{% from "macros.html" import top %} {% from "macros.html" import usual_head %}
{{ usual_head() }}Pick a start date and end date and the exporter will retrieve all events for this range and convert them into an ical file which can be imported into most calendar apps. There is no limit to the date range, but large date ranges may be slow to export, and be aware that calendar apps may struggle to handle more than a few thousand events.
{% if link %} Download is ready; {{ count }} events exported successfully for range {{ start }} to {{ end }}. Download {% elif message %} {{ message }} {% endif %}


Wholesaler SSApp PDM&FC Description SSApp to simulate the Wholesaler's ERP system so we can push events to the blockchain Contributing


Hive Tables
Spark SQL also supports reading and writing data stored in Apache Hive. However, since Hive has a large number of dependencies, these dependencies are not included in the default Spark distribution. If Hive dependencies can be found on the classpath, Spark will load them automatically. Note that these Hive dependencies must also be present on all of the worker nodes, as they will need access to the Hive serialization and deserialization libraries (SerDes) in order to access data stored in Hive.
Configuration of Hive is done by placing your hive-site.xml
, core-site.xml
(for security configuration),
and hdfs-site.xml
(for HDFS configuration) file in conf/
.
When working with Hive, one must instantiate SparkSession
with Hive support, including
connectivity to a persistent Hive metastore, support for Hive serdes, and Hive user-defined functions.
Users who do not have an existing Hive deployment can still enable Hive support. When not configured
by the hive-site.xml
, the context automatically creates metastore_db
in the current directory and
creates a directory configured by spark.sql.warehouse.dir
, which defaults to the directory
spark-warehouse
in the current directory that the Spark application is started. Note that
the hive.metastore.warehouse.dir
property in hive-site.xml
is deprecated since Spark 2.0.0.
Instead, use spark.sql.warehouse.dir
to specify the default location of database in warehouse.
You may need to grant write privilege to the user who starts the Spark application.
import java.io.File
import org.apache.spark.sql.{Row, SaveMode, SparkSession}
case class Record(key: Int, value: String)
// warehouseLocation points to the default location for managed databases and tables
val warehouseLocation = new File("spark-warehouse").getAbsolutePath
val spark = SparkSession
.builder()
.appName("Spark Hive Example")
.config("spark.sql.warehouse.dir", warehouseLocation)
.enableHiveSupport()
.getOrCreate()
import spark.implicits._
import spark.sql
sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING) USING hive")
sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
// Queries are expressed in HiveQL
sql("SELECT * FROM src").show()
// +---+-------+
// |key| value|
// +---+-------+
// |238|val_238|
// | 86| val_86|
// |311|val_311|
// ...
// Aggregation queries are also supported.
sql("SELECT COUNT(*) FROM src").show()
// +--------+
// |count(1)|
// +--------+
// | 500 |
// +--------+
// The results of SQL queries are themselves DataFrames and support all normal functions.
val sqlDF = sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key")
// The items in DataFrames are of type Row, which allows you to access each column by ordinal.
val stringsDS = sqlDF.map {
case Row(key: Int, value: String) => s"Key: $key, Value: $value"
}
stringsDS.show()
// +--------------------+
// | value|
// +--------------------+
// |Key: 0, Value: val_0|
// |Key: 0, Value: val_0|
// |Key: 0, Value: val_0|
// ...
// You can also use DataFrames to create temporary views within a SparkSession.
val recordsDF = spark.createDataFrame((1 to 100).map(i => Record(i, s"val_$i")))
recordsDF.createOrReplaceTempView("records")
// Queries can then join DataFrame data with data stored in Hive.
sql("SELECT * FROM records r JOIN src s ON r.key = s.key").show()
// +---+------+---+------+
// |key| value|key| value|
// +---+------+---+------+
// | 2| val_2| 2| val_2|
// | 4| val_4| 4| val_4|
// | 5| val_5| 5| val_5|
// ...
// Create a Hive managed Parquet table, with HQL syntax instead of the Spark SQL native syntax
// `USING hive`
sql("CREATE TABLE hive_records(key int, value string) STORED AS PARQUET")
// Save DataFrame to the Hive managed table
val df = spark.table("src")
df.write.mode(SaveMode.Overwrite).saveAsTable("hive_records")
// After insertion, the Hive managed table has data now
sql("SELECT * FROM hive_records").show()
// +---+-------+
// |key| value|
// +---+-------+
// |238|val_238|
// | 86| val_86|
// |311|val_311|
// ...
// Prepare a Parquet data directory
val dataDir = "/tmp/parquet_data"
spark.range(10).write.parquet(dataDir)
// Create a Hive external Parquet table
sql(s"CREATE EXTERNAL TABLE hive_bigints(id bigint) STORED AS PARQUET LOCATION '$dataDir'")
// The Hive external table should already have data
sql("SELECT * FROM hive_bigints").show()
// +---+
// | id|
// +---+
// | 0|
// | 1|
// | 2|
// ... Order may vary, as spark processes the partitions in parallel.
// Turn on flag for Hive Dynamic Partitioning
spark.sqlContext.setConf("hive.exec.dynamic.partition", "true")
spark.sqlContext.setConf("hive.exec.dynamic.partition.mode", "nonstrict")
// Create a Hive partitioned table using DataFrame API
df.write.partitionBy("key").format("hive").saveAsTable("hive_part_tbl")
// Partitioned column `key` will be moved to the end of the schema.
sql("SELECT * FROM hive_part_tbl").show()
// +-------+---+
// | value|key|
// +-------+---+
// |val_238|238|
// | val_86| 86|
// |val_311|311|
// ...
spark.stop()
import java.io.File;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.List;
import org.apache.spark.api.java.function.MapFunction;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
public static class Record implements Serializable {
private int key;
private String value;
public int getKey() {
return key;
}
public void setKey(int key) {
this.key = key;
}
public String getValue() {
return value;
}
public void setValue(String value) {
this.value = value;
}
}
// warehouseLocation points to the default location for managed databases and tables
String warehouseLocation = new File("spark-warehouse").getAbsolutePath();
SparkSession spark = SparkSession
.builder()
.appName("Java Spark Hive Example")
.config("spark.sql.warehouse.dir", warehouseLocation)
.enableHiveSupport()
.getOrCreate();
spark.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING) USING hive");
spark.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src");
// Queries are expressed in HiveQL
spark.sql("SELECT * FROM src").show();
// +---+-------+
// |key| value|
// +---+-------+
// |238|val_238|
// | 86| val_86|
// |311|val_311|
// ...
// Aggregation queries are also supported.
spark.sql("SELECT COUNT(*) FROM src").show();
// +--------+
// |count(1)|
// +--------+
// | 500 |
// +--------+
// The results of SQL queries are themselves DataFrames and support all normal functions.
Dataset<Row> sqlDF = spark.sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key");
// The items in DataFrames are of type Row, which lets you to access each column by ordinal.
Dataset<String> stringsDS = sqlDF.map(
(MapFunction<Row, String>) row -> "Key: " + row.get(0) + ", Value: " + row.get(1),
Encoders.STRING());
stringsDS.show();
// +--------------------+
// | value|
// +--------------------+
// |Key: 0, Value: val_0|
// |Key: 0, Value: val_0|
// |Key: 0, Value: val_0|
// ...
// You can also use DataFrames to create temporary views within a SparkSession.
List<Record> records = new ArrayList<>();
for (int key = 1; key < 100; key++) {
Record record = new Record();
record.setKey(key);
record.setValue("val_" + key);
records.add(record);
}
Dataset<Row> recordsDF = spark.createDataFrame(records, Record.class);
recordsDF.createOrReplaceTempView("records");
// Queries can then join DataFrames data with data stored in Hive.
spark.sql("SELECT * FROM records r JOIN src s ON r.key = s.key").show();
// +---+------+---+------+
// |key| value|key| value|
// +---+------+---+------+
// | 2| val_2| 2| val_2|
// | 2| val_2| 2| val_2|
// | 4| val_4| 4| val_4|
// ...
from os.path import join, abspath
from pyspark.sql import SparkSession
from pyspark.sql import Row
# warehouse_location points to the default location for managed databases and tables
warehouse_location = abspath('spark-warehouse')
spark = SparkSession \
.builder \
.appName("Python Spark SQL Hive integration example") \
.config("spark.sql.warehouse.dir", warehouse_location) \
.enableHiveSupport() \
.getOrCreate()
# spark is an existing SparkSession
spark.sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING) USING hive")
spark.sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
# Queries are expressed in HiveQL
spark.sql("SELECT * FROM src").show()
# +---+-------+
# |key| value|
# +---+-------+
# |238|val_238|
# | 86| val_86|
# |311|val_311|
# ...
# Aggregation queries are also supported.
spark.sql("SELECT COUNT(*) FROM src").show()
# +--------+
# |count(1)|
# +--------+
# | 500 |
# +--------+
# The results of SQL queries are themselves DataFrames and support all normal functions.
sqlDF = spark.sql("SELECT key, value FROM src WHERE key < 10 ORDER BY key")
# The items in DataFrames are of type Row, which allows you to access each column by ordinal.
stringsDS = sqlDF.rdd.map(lambda row: "Key: %d, Value: %s" % (row.key, row.value))
for record in stringsDS.collect():
print(record)
# Key: 0, Value: val_0
# Key: 0, Value: val_0
# Key: 0, Value: val_0
# ...
# You can also use DataFrames to create temporary views within a SparkSession.
Record = Row("key", "value")
recordsDF = spark.createDataFrame([Record(i, "val_" + str(i)) for i in range(1, 101)])
recordsDF.createOrReplaceTempView("records")
# Queries can then join DataFrame data with data stored in Hive.
spark.sql("SELECT * FROM records r JOIN src s ON r.key = s.key").show()
# +---+------+---+------+
# |key| value|key| value|
# +---+------+---+------+
# | 2| val_2| 2| val_2|
# | 4| val_4| 4| val_4|
# | 5| val_5| 5| val_5|
# ...
When working with Hive one must instantiate SparkSession
with Hive support. This
adds support for finding tables in the MetaStore and writing queries using HiveQL.
# enableHiveSupport defaults to TRUE
sparkR.session(enableHiveSupport = TRUE)
sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING) USING hive")
sql("LOAD DATA LOCAL INPATH 'examples/src/main/resources/kv1.txt' INTO TABLE src")
# Queries can be expressed in HiveQL.
results <- collect(sql("FROM src SELECT key, value"))
Specifying storage format for Hive tables
When you create a Hive table, you need to define how this table should read/write data from/to file system,
i.e. the “input format” and “output format”. You also need to define how this table should deserialize the data
to rows, or serialize rows to data, i.e. the “serde”. The following options can be used to specify the storage
format(“serde”, “input format”, “output format”), e.g. CREATE TABLE src(id int) USING hive OPTIONS(fileFormat 'parquet')
.
By default, we will read the table files as plain text. Note that, Hive storage handler is not supported yet when
creating table, you can create a table using storage handler at Hive side, and use Spark SQL to read it.
| Property Name | Meaning |
|---|---|
fileFormat |
A fileFormat is kind of a package of storage format specifications, including "serde", "input format" and "output format". Currently we support 6 fileFormats: 'sequencefile', 'rcfile', 'orc', 'parquet', 'textfile' and 'avro'. |
inputFormat, outputFormat |
These 2 options specify the name of a corresponding InputFormat and OutputFormat class as a string literal,
e.g. org.apache.hadoop.hive.ql.io.orc.OrcInputFormat . These 2 options must be appeared in a pair, and you can not
specify them if you already specified the fileFormat option.
|
serde |
This option specifies the name of a serde class. When the fileFormat option is specified, do not specify this option
if the given fileFormat already include the information of serde. Currently "sequencefile", "textfile" and "rcfile"
don't include the serde information and you can use this option with these 3 fileFormats.
|
fieldDelim, escapeDelim, collectionDelim, mapkeyDelim, lineDelim |
These options can only be used with "textfile" fileFormat. They define how to read delimited files into rows. |
All other properties defined with OPTIONS
will be regarded as Hive serde properties.
Interacting with Different Versions of Hive Metastore
One of the most important pieces of Spark SQL’s Hive support is interaction with Hive metastore, which enables Spark SQL to access metadata of Hive tables. Starting from Spark 1.4.0, a single binary build of Spark SQL can be used to query different versions of Hive metastores, using the configuration described below. Note that independent of the version of Hive that is being used to talk to the metastore, internally Spark SQL will compile against built-in Hive and use those classes for internal execution (serdes, UDFs, UDAFs, etc).
The following options can be used to configure the version of Hive that is used to retrieve metadata:
| Property Name | Default | Meaning | Since Version |
|---|---|---|---|
spark.sql.hive.metastore.version |
2.3.7 |
Version of the Hive metastore. Available
options are 0.12.0 through 2.3.7 and 3.0.0 through 3.1.2 .
|
1.4.0 |
spark.sql.hive.metastore.jars |
builtin |
Location of the jars that should be used to instantiate the HiveMetastoreClient. This
property can be one of three options:
-Phive is
enabled. When this option is chosen, spark.sql.hive.metastore.version must be
either 2.3.7 or not defined.
|
1.4.0 |
spark.sql.hive.metastore.sharedPrefixes |
com.mysql.jdbc, |
A comma-separated list of class prefixes that should be loaded using the classloader that is shared between Spark SQL and a specific version of Hive. An example of classes that should be shared is JDBC drivers that are needed to talk to the metastore. Other classes that need to be shared are those that interact with classes that are already shared. For example, custom appenders that are used by log4j. |
1.4.0 |
spark.sql.hive.metastore.barrierPrefixes |
(empty) |
A comma separated list of class prefixes that should explicitly be reloaded for each version
of Hive that Spark SQL is communicating with. For example, Hive UDFs that are declared in a
prefix that typically would be shared (i.e. |
1.4.0 |


ArityException(message: String)
Thrown when the Arity of an Operation does not match the number of lgp.core.evolution.registers.Arguments given to it.
message - A message to annotate the exception.
message


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|
Hubiquitus Android
0.3
Android client for hubiquitus protocol
|
Classes | |
| class | TransportDelegate |
Public Member Functions | |
| HClient () | |
| void | connect (String publisher, String password, HOptions options) |
| void | disconnect () |
| void | onStatus (HStatusDelegate statusDelgate) |
| void | onMessage (HMessageDelegate messageDelegate) |
| ConnectionStatus | status () |
| void | command (HCommand cmd, HCommandDelegate commandDelegate) |
| void | subscribe (String chid, HCommandDelegate commandDelegate) |
| void | unsubscribe (String chid, HCommandDelegate commandDelegate) |
| void | publish (HMessage message, HCommandDelegate commandDelegate) |
| void | getLastMessages (String chid, int nbLastMsg, HCommandDelegate commandDelegate) |
| void | getLastMessages (String chid, HCommandDelegate commandDelegate) |
| void | getSubscriptions (HCommandDelegate commandDelegate) |
| void | getThread (String chid, String convid, HCommandDelegate commandDelegate) |
| void | getThreads (String chid, String status, HCommandDelegate commandDelegate) |
| HMessage | buildMessage (String chid, String type, HJsonObj payload, HMessageOptions options) throws MissingAttrException |
| HMessage | buildConvState (String chid, String convid, String status, HMessageOptions options) throws MissingAttrException |
| HMessage | buildAck (String chid, String ackid, HAckValue ack, HMessageOptions options) throws MissingAttrException |
| HMessage | buildAlert (String chid, String alert, HMessageOptions options) throws MissingAttrException |
| HMessage | buildMeasure (String chid, String value, String unit, HMessageOptions options) throws MissingAttrException |
Definition at line 58 of file HClient.java.
Definition at line 72 of file HClient.java.
| HMessage org.hubiquitus.hapi.client.HClient.buildAck | ( | String | chid, |
| String | ackid, | ||
| HAckValue | ack, | ||
| HMessageOptions | options | ||
| ) | throws MissingAttrException |
Helper to create hack
| chid | - channel id : mandatory |
| ackid | : mandatory |
| ack | : mandatory |
| options |
| MissingAttrException |
Definition at line 483 of file HClient.java.
| HMessage org.hubiquitus.hapi.client.HClient.buildAlert | ( | String | chid, |
| String | alert, | ||
| HMessageOptions | options | ||
| ) | throws MissingAttrException |
Helper to create halert
| chid | - channel id : mandatory |
| alert | : mandatory |
| options |
| MissingAttrException |
Definition at line 517 of file HClient.java.
| HMessage org.hubiquitus.hapi.client.HClient.buildConvState | ( | String | chid, |
| String | convid, | ||
| String | status, | ||
| HMessageOptions | options | ||
| ) | throws MissingAttrException |
Helper to create hconvstate
| chid | - channel id : mandatory |
| convid | - conversation id : mandatory |
| status | - status of the conversation |
| options |
| MissingAttrException |
Definition at line 448 of file HClient.java.
| HMessage org.hubiquitus.hapi.client.HClient.buildMeasure | ( | String | chid, |
| String | value, | ||
| String | unit, | ||
| HMessageOptions | options | ||
| ) | throws MissingAttrException |
Helper to create hmeasure
| chid | - channel id : mandatory |
| value | : mandatory |
| unit | : mandatory |
| options |
| MissingAttrException |
Definition at line 547 of file HClient.java.
| HMessage org.hubiquitus.hapi.client.HClient.buildMessage | ( | String | chid, |
| String | type, | ||
| HJsonObj | payload, | ||
| HMessageOptions | options | ||
| ) | throws MissingAttrException |
Helper to create hmessage
| chid | - channel id : mandatory |
| type | |
| payload | |
| options |
| MissingAttrException |
Definition at line 406 of file HClient.java.
| void org.hubiquitus.hapi.client.HClient.command | ( | HCommand | cmd, |
| HCommandDelegate | commandDelegate | ||
| ) |
Used to perform a command on an hubiquitus component : a hserver or a hubot.
| cmd | - name of the command |
| commandDelegate | - a delegate notified when the command result is issued. Can be null |
Definition at line 198 of file HClient.java.
| void org.hubiquitus.hapi.client.HClient.connect | ( | String | publisher, |
| String | password, | ||
| HOptions | options | ||
| ) |
Connect to server
| publisher | - user jid (ie : my_user/resource) |
| password | |
| callback | - client callback to get api notifications |
| options |
Definition at line 83 of file HClient.java.
Definition at line 143 of file HClient.java.
| void org.hubiquitus.hapi.client.HClient.getLastMessages | ( | String | chid, |
| int | nbLastMsg, | ||
| HCommandDelegate | commandDelegate | ||
| ) |
Demands the hserver a list of the last messages saved for a dedicated channel. The publisher must be in the channel’s participants list.
Nominal response : an hCallback with an hResult will be performed when the result is available. If the hResult had status 0, the user should expect to receive n calls to hCallback of type hMessage where n is equal to the number of messages retrieved with nbLastMsg as an upper limit.
| chid | - channel id |
| nbLastMsg | |
| commandDelegate | - a delegate notified when the command result is issued. Can be null |
Definition at line 302 of file HClient.java.
| void org.hubiquitus.hapi.client.HClient.getLastMessages | ( | String | chid, |
| HCommandDelegate | commandDelegate | ||
| ) |
| chid | - channel id |
| commandDelegate | - a delegate notified when the command result is issued. Can be null |
Definition at line 318 of file HClient.java.
| void org.hubiquitus.hapi.client.HClient.getSubscriptions | ( | HCommandDelegate | commandDelegate | ) |
Demands the server a list of the publisher’s subscriptions.
Nominal response : a hCallback with a hResult will be performed when the result is available with an array of channel id.
| commandDelegate | - a delegate notified when the command result is issued. Can be null |
Definition at line 329 of file HClient.java.
| void org.hubiquitus.hapi.client.HClient.getThread | ( | String | chid, |
| String | convid, | ||
| HCommandDelegate | commandDelegate | ||
| ) |
Demands to the hserver the list of messages correlated by the convid value on a dedicated channel chid.
Nominal response : hResult where the status is 0 with an array of hMessage.
| chid | - Channel id. Mandatory |
| convid | - Conversation id. Mandatory |
| commandDelegate | - a delegate notified when the command result is issued. Can be null |
Definition at line 342 of file HClient.java.
| void org.hubiquitus.hapi.client.HClient.getThreads | ( | String | chid, |
| String | status, | ||
| HCommandDelegate | commandDelegate | ||
| ) |
Demands to the hserver the list of convid where there is a hConvState with the status value searched on the channel chid.
Nominal response : hResult where the status is 0 with an array of convid.
| chid | - Channel id. Mandatory |
| status | - The status searched. Mandatory |
| commandDelegate | - a delegate notified when the command result is issued. Can be null |
Definition at line 372 of file HClient.java.
| void org.hubiquitus.hapi.client.HClient.onMessage | ( | HMessageDelegate | messageDelegate | ) |
Message delegate receive all incoming HMessage
| messageDelegate |
Definition at line 180 of file HClient.java.
| void org.hubiquitus.hapi.client.HClient.onStatus | ( | HStatusDelegate | statusDelgate | ) |
Status delegate receive all connection status events.
| statusDelgate |
Definition at line 172 of file HClient.java.
| void org.hubiquitus.hapi.client.HClient.publish | ( | HMessage | message, |
| HCommandDelegate | commandDelegate | ||
| ) |
Perform a publish operation of the provided hMessage to a channel.
| message | |
| commandDelegate | - a delegate notified when the command result is issued. Can be null |
Definition at line 266 of file HClient.java.
| void org.hubiquitus.hapi.client.HClient.subscribe | ( | String | chid, |
| HCommandDelegate | commandDelegate | ||
| ) |
Demands the server a subscription to the channel id. The hAPI performs a hCommand of type hsubscribe. The server will check if not already subscribed and if authorized and subscribe him.
| chid | - channel id |
| commandDelegate | - a delegate notified when the command result is issued. Can be null |
Definition at line 239 of file HClient.java.
| void org.hubiquitus.hapi.client.HClient.unsubscribe | ( | String | chid, |
| HCommandDelegate | commandDelegate | ||
| ) |
Demands the server an unsubscription to the channel id. The hAPI checks the current publisher’s subscriptions and if he is subscribed performs a hCommand of type hunsubscribe.
| chid | - channel id |
| commandDelegate | - a delegate notified when the command result is issued. Can be null |
Definition at line 253 of file HClient.java.


I enjoy analyzing complex problems and developing effective and innovative design solutions. I would integrate my skills in formulating business strategy and optimizing user experience to create value for users, stakeholders and companies.
• Conducted user research, interactive prototype and user testing for insurance company digitalization project
• Implemented website analysis and user interviews for childhood education company
• Organized enterprise design thinking workshops for business solution
• Planned and conducted head-to-toe UX design process and launched a new feature for student goal setting
• Organized cross functional strategy workshop to allocate development resource base on user value
• Collaborated with data team and create platform journey map by both qualitative and quantitive data
• Developed personas on technical learners and provide insights for product education website redesign
• Conducted focus group on Azure users and provided marketing strategy
• Provided website analysis and visualize report according to different department demand


|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |
| Packages that use SolidMAT | |
|---|---|
| dialogs.analysis | |
| dialogs.assign | |
| dialogs.display | |
| dialogs.file | |
| dialogs.help | |
| dialogs.library | |
| dialogs.model | |
| dialogs.view | |
| main | |
| visualize |
| Uses of SolidMAT in dialogs.analysis |
|---|
| Fields in dialogs.analysis declared as SolidMAT | |
|---|---|
protected SolidMAT |
RunAnalysis1.owner_
The owner frame of this dialog. |
private SolidMAT |
CheckModel1.owner_
The owner frame of this dialog. |
private SolidMAT |
AnalysisOptions1.owner_
The owner frame of this dialog. |
| Constructors in dialogs.analysis with parameters of type SolidMAT | |
|---|---|
AnalysisOptions1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
CheckModel1(SolidMAT owner)
Builds dialog, builds components, calls addComponent, sets layout and sets up listeners, calls initialize and visualize. |
|
RunAnalysis1(SolidMAT owner)
Builds dialog, builds components, calls addComponent, sets layout and sets up listeners, calls initialize and visualize. |
| Uses of SolidMAT in dialogs.assign |
|---|
| Fields in dialogs.assign declared as SolidMAT | |
|---|---|
private SolidMAT |
ASolidTempLoad1.owner_
The owner frame of this dialog. |
private SolidMAT |
ASolidSpring1.owner_
The owner frame of this dialog. |
private SolidMAT |
ASolidMechLoad1.owner_
The owner frame of this dialog. |
private SolidMAT |
ASolidMaterial1.owner_
The owner frame of this dialog. |
private SolidMAT |
ASolidMass1.owner_
The owner frame of this dialog. |
protected SolidMAT |
ANodeInfo1.owner_
The owner frame of this dialog. |
private SolidMAT |
ANodalSpring1.owner_
The owner frame of this dialog. |
private SolidMAT |
ANodalMechLoad1.owner_
The owner frame of this dialog. |
private SolidMAT |
ANodalMass1.owner_
The owner frame of this dialog. |
private SolidMAT |
ANodalLocalAxis1.owner_
The owner frame of this dialog. |
private SolidMAT |
ANodalDispLoad1.owner_
The owner frame of this dialog. |
private SolidMAT |
ALineTempLoad1.owner_
The owner frame of this dialog. |
private SolidMAT |
ALineSpring1.owner_
The owner frame of this dialog. |
private SolidMAT |
ALineSection1.owner_
The owner frame of this dialog. |
private SolidMAT |
ALineMechLoad1.owner_
The owner frame of this dialog. |
private SolidMAT |
ALineMaterial1.owner_
The owner frame of this dialog. |
private SolidMAT |
ALineMass1.owner_
The owner frame of this dialog. |
private SolidMAT |
ALineLocalAxis1.owner_
The owner frame of this dialog. |
private SolidMAT |
AInitialVelo1.owner_
The owner frame of this dialog. |
private SolidMAT |
AInitialDisp1.owner_
The owner frame of this dialog. |
protected SolidMAT |
AElementInfo1.owner_
The owner frame of this dialog. |
private SolidMAT |
AConstraint1.owner_
The owner frame of this dialog. |
private SolidMAT |
AAreaTempLoad1.owner_
The owner frame of this dialog. |
private SolidMAT |
AAreaSpring1.owner_
The owner frame of this dialog. |
private SolidMAT |
AAreaSection1.owner_
The owner frame of this dialog. |
private SolidMAT |
AAreaMechLoad1.owner_
The owner frame of this dialog. |
private SolidMAT |
AAreaMaterial1.owner_
The owner frame of this dialog. |
private SolidMAT |
AAreaMass1.owner_
The owner frame of this dialog. |
| Constructors in dialogs.assign with parameters of type SolidMAT | |
|---|---|
AAreaMass1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
AAreaMaterial1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
AAreaMechLoad1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
AAreaSection1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
AAreaSpring1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
AAreaTempLoad1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
AConstraint1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
AElementInfo1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
AInitialDisp1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
AInitialVelo1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ALineLocalAxis1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ALineMass1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ALineMaterial1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ALineMechLoad1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ALineSection1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ALineSpring1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ALineTempLoad1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ANodalDispLoad1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ANodalLocalAxis1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ANodalMass1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ANodalMechLoad1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ANodalSpring1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ANodeInfo1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ASolidMass1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ASolidMaterial1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ASolidMechLoad1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ASolidSpring1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ASolidTempLoad1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
| Uses of SolidMAT in dialogs.display |
|---|
| Fields in dialogs.display declared as SolidMAT | |
|---|---|
private SolidMAT |
DisplayVSolidResult1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayVNodeResult1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayVLineResult1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayVAreaResult1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayUndeformedShape1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayTSolidResult1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayTNodeResult1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayTLineResult1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayTAreaResult1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplaySolidPlot1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayPreferences1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayOptions1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayNodePlot1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayNodeMisc1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayNodalLoads1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayLinePlot1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayFunctionPlot1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayElementMisc1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayElementLoads1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayDeformedShape1.owner_
The owner frame of this dialog. |
private SolidMAT |
DisplayAreaPlot1.owner_
The owner frame of this dialog. |
| Constructors in dialogs.display with parameters of type SolidMAT | |
|---|---|
DisplayAreaPlot1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayDeformedShape1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayElementLoads1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayElementMisc1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayFunctionPlot1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayLinePlot1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayNodalLoads1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayNodeMisc1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayNodePlot1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayOptions1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayPreferences1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplaySolidPlot1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayTAreaResult1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayTLineResult1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayTNodeResult1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayTSolidResult1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayUndeformedShape1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayVAreaResult1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayVLineResult1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayVNodeResult1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DisplayVSolidResult1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
| Uses of SolidMAT in dialogs.file |
|---|
| Fields in dialogs.file declared as SolidMAT | |
|---|---|
private SolidMAT |
WriteOutput1.owner_
The owner frame of this dialog. |
private SolidMAT |
FileHandler2.owner_
|
| Methods in dialogs.file with parameters of type SolidMAT | |
|---|---|
static void |
FileHandler1.createNewFile(SolidMAT owner)
Creates new file. |
private static void |
FileHandler1.createNewFile1(SolidMAT owner)
Sub-method of createNewFile. |
static void |
FileHandler1.openFile(SolidMAT owner)
Opens existing file. |
private static void |
FileHandler1.openFile1(SolidMAT owner)
Sub-method of openFile. |
static void |
FileHandler1.saveFile(SolidMAT owner)
Saves existing file. |
static boolean |
FileHandler1.saveFileAs(SolidMAT owner)
Saves file to a new location. |
| Constructors in dialogs.file with parameters of type SolidMAT | |
|---|---|
FileHandler2(SolidMAT owner,
int type,
java.lang.String path)
Builds dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
WriteOutput1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
| Uses of SolidMAT in dialogs.help |
|---|
| Constructors in dialogs.help with parameters of type SolidMAT | |
|---|---|
KeyAssist1(SolidMAT owner)
Builds dialog, builds components, calls addComponent, sets layout and sets up listeners. |
| Uses of SolidMAT in dialogs.library |
|---|
| Fields in dialogs.library declared as SolidMAT | |
|---|---|
protected SolidMAT |
SolverLib1.owner_
The owner frame of this dialog. |
protected SolidMAT |
Section1.owner_
The owner frame of this dialog. |
protected SolidMAT |
NodalSpring1.owner_
The owner frame of this dialog. |
protected SolidMAT |
NodalMechLoad1.owner_
The owner frame of this dialog. |
protected SolidMAT |
NodalMass1.owner_
The owner frame of this dialog. |
protected SolidMAT |
Material1.owner_
The owner frame of this dialog. |
protected SolidMAT |
LocalAxis1.owner_
The owner frame of this dialog. |
protected SolidMAT |
InitialVelo1.owner_
The owner frame of this dialog. |
protected SolidMAT |
InitialDisp1.owner_
The owner frame of this dialog. |
protected SolidMAT |
Function1.owner_
The owner frame of this dialog. |
protected SolidMAT |
ElementTemp1.owner_
The owner frame of this dialog. |
protected SolidMAT |
ElementSpring1.owner_
The owner frame of this dialog. |
protected SolidMAT |
ElementMechLoad1.owner_
The owner frame of this dialog. |
protected SolidMAT |
ElementMass1.owner_
The owner frame of this dialog. |
private SolidMAT |
ElementLib1.owner_
The owner frame of this dialog. |
protected SolidMAT |
DispLoad1.owner_
The owner frame of this dialog. |
protected SolidMAT |
Constraint1.owner_
The owner frame of this dialog. |
private SolidMAT |
BoundaryCase1.owner_
The owner frame of this dialog. |
protected SolidMAT |
AnalysisCase1.owner_
The owner frame of this dialog. |
| Constructors in dialogs.library with parameters of type SolidMAT | |
|---|---|
AnalysisCase1(SolidMAT owner)
Builds dialog, builds components, calls addComponent, sets layout and sets up listeners, calls initialize and visualize. |
|
BoundaryCase1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
Constraint1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DispLoad1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ElementLib1(SolidMAT owner)
Builds dialog, builds components, calls addComponent, sets layout and sets up listeners, calls initialize and visualize. |
|
ElementMass1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ElementMechLoad1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ElementSpring1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ElementTemp1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
Function1(SolidMAT owner)
Builds dialog, builds components, calls addComponent, sets layout and sets up listeners, calls initialize and visualize. |
|
InitialDisp1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
InitialVelo1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
LocalAxis1(SolidMAT owner)
Builds dialog, builds components, calls addComponent, sets layout and sets up listeners, calls initialize and visualize. |
|
Material1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
NodalMass1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
NodalMechLoad1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
NodalSpring1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
Section1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
SolverLib1(SolidMAT owner)
Builds dialog, builds components, calls addComponent, sets layout and sets up listeners, calls initialize and visualize. |
| Uses of SolidMAT in dialogs.model |
|---|
| Fields in dialogs.model declared as SolidMAT | |
|---|---|
private SolidMAT |
Sweep1.owner_
The owner frame of this dialog. |
protected SolidMAT |
ShowNode1.owner_
The owner frame of this dialog. |
protected SolidMAT |
ShowElement1.owner_
The owner frame of this dialog. |
private SolidMAT |
ReplicateNode1.owner_
The owner frame of this dialog. |
private SolidMAT |
ReplicateElement1.owner_
The owner frame of this dialog. |
private SolidMAT |
RemoveNode1.owner_
The owner frame of this dialog. |
private SolidMAT |
RemoveElement1.owner_
The owner frame of this dialog. |
private SolidMAT |
MoveNode1.owner_
The owner frame of this dialog. |
private SolidMAT |
MoveElement1.owner_
The owner frame of this dialog. |
private SolidMAT |
MirrorNode1.owner_
The owner frame of this dialog. |
private SolidMAT |
MirrorElement1.owner_
The owner frame of this dialog. |
private SolidMAT |
MeshSolid1.owner_
The owner frame of this dialog. |
private SolidMAT |
MeshArea1.owner_
The owner frame of this dialog. |
private SolidMAT |
ImportModel1.owner_
The owner frame of this dialog. |
protected SolidMAT |
Group1.owner_
The owner frame of this dialog. |
private SolidMAT |
EditNode1.owner_
The owner frame of this dialog. |
private SolidMAT |
EditElement1.owner_
The owner frame of this dialog. |
private SolidMAT |
DivideLine1.owner_
The owner frame of this dialog. |
private SolidMAT |
Check1.owner_
The owner frame of this dialog. |
private SolidMAT |
AddNode1.owner_
The owner frame of this dialog. |
private SolidMAT |
AddElement1.owner_
The owner frame of this dialog. |
| Constructors in dialogs.model with parameters of type SolidMAT | |
|---|---|
AddElement1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
AddNode1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
Check1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
DivideLine1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
EditElement1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
EditNode1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
Group1(SolidMAT owner)
Builds dialog, builds components, calls addComponent, sets layout and sets up listeners, calls initialize and visualize. |
|
ImportModel1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
MeshArea1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
MeshSolid1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
MirrorElement1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
MirrorNode1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
MoveElement1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
MoveNode1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
RemoveElement1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
RemoveNode1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ReplicateElement1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ReplicateNode1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ShowElement1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
ShowNode1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
Sweep1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
| Uses of SolidMAT in dialogs.view |
|---|
| Fields in dialogs.view declared as SolidMAT | |
|---|---|
private SolidMAT |
MenuFormat.owner_
The owner frame of this dialog. |
private SolidMAT |
CustomizeToolbars1.owner_
The owner frame of this dialog. |
| Methods in dialogs.view with parameters of type SolidMAT | |
|---|---|
static void |
ViewColor.show(SolidMAT owner)
Shows color chooser for setting up the background color of viewer. |
| Constructors in dialogs.view with parameters of type SolidMAT | |
|---|---|
CustomizeToolbars1(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
MenuFormat(SolidMAT owner)
Builds dialog, builds child dialog, builds components, calls addComponent, sets layout and sets up listeners. |
| Uses of SolidMAT in main |
|---|
| Fields in main declared as SolidMAT | |
|---|---|
private SolidMAT |
Toolbars.owner_
Owner frame of toolbars. |
protected SolidMAT |
MenuBar.owner_
Owner frame of the menu bar. |
private SolidMAT |
ActionHandler.owner_
Owner frame of this action handler. |
| Methods in main with parameters of type SolidMAT | |
|---|---|
static void |
Panels.buildPanels(SolidMAT owner)
Builds panels for the main frame. |
| Constructors in main with parameters of type SolidMAT | |
|---|---|
ActionHandler(SolidMAT owner)
Creates ActionHandler object for handling action events of menubar and toolbar buttons. |
|
MenuBar(SolidMAT owner)
Creates menu bar. |
|
PopupMenu1(SolidMAT owner)
Creates popup menu. |
|
PopupMenu2(SolidMAT owner)
Creates popup menu. |
|
Toolbars(SolidMAT owner)
Creates toolbars for the main frame. |
| Uses of SolidMAT in visualize |
|---|
| Fields in visualize declared as SolidMAT | |
|---|---|
protected SolidMAT |
ContourScalor.owner1_
The owner of this dialog. |
| Methods in visualize with parameters of type SolidMAT | |
|---|---|
void |
PreVisualizer.draw(SolidMAT owner)
Draws undeformed shape of the structure depending on the display options for nodes and elements selected. |
protected void |
PreNodeDisplay.draw(SolidMAT owner,
boolean[] options,
double[] vizOptions,
java.util.Vector[] assigns)
Draws nodes and related objects. |
protected void |
PreElement3DDisplay.draw(SolidMAT owner,
boolean[] options,
double[] vizOptions,
java.util.Vector[] assigns)
Draws three dimensional elements and related objects. |
protected void |
PreElement2DDisplay.draw(SolidMAT owner,
boolean[] options,
double[] vizOptions,
java.util.Vector[] assigns)
Draws two dimensional elements and related objects. |
protected void |
PreElement1DDisplay.draw(SolidMAT owner,
boolean[] options,
double[] vizOptions,
java.util.Vector[] assigns)
Draws one dimensional elements and related objects. |
protected void |
PostNodeDisplay.draw(SolidMAT owner,
int shape,
int option,
int[] comp,
boolean isScaled)
Draws node results for post visualizer. |
protected void |
PostElement3DDisplay.draw(SolidMAT owner,
int shape,
int option,
int[] comp,
boolean isScaled)
Draws three dimensional elements and related results. |
protected void |
PostElement2DDisplay.draw(SolidMAT owner,
int shape,
int option,
int[] comp,
boolean isScaled)
Draws two dimensional elements and related results. |
protected void |
PostElement1DDisplay.draw(SolidMAT owner,
int shape,
int option,
int[] comp,
boolean isScaled)
Draws one dimensional elements and related results. |
private void |
PostNodeDisplay.drawContourScalor(SolidMAT owner,
java.lang.String option)
Draws contour scalor for numerical results. |
private void |
PostElement3DDisplay.drawContourScalor(SolidMAT owner,
java.lang.String option)
Draws contour scalor for numerical results. |
private void |
PostElement2DDisplay.drawContourScalor(SolidMAT owner,
java.lang.String option)
Draws contour scalor for numerical results. |
private void |
PostElement1DDisplay.drawContourScalor(SolidMAT owner,
java.lang.String option)
Draws contour scalor for numerical results. |
void |
PostVisualizer.drawDeformedShape(SolidMAT owner,
int option,
int[] comp,
boolean isScaled,
int step)
Draws results on deformed shape of the structure. |
private void |
PreElement1DDisplay.drawElement(SolidMAT owner,
Element1D e1D,
DVec[] pos,
double[] vizOptions,
boolean[] options,
java.util.Vector[] assigns)
Draws one dimensional elements as lines. |
private void |
PreElement2DDisplay.drawElement(SolidMAT owner,
Element2D e2D,
DVec[] pos,
double[] vizOptions,
boolean[] options,
java.util.Vector[] assigns)
Draws two dimensional elements as polygons. |
private void |
PreElement3DDisplay.drawElement(SolidMAT owner,
Element3D e3D,
DVec[] pos,
double[] vizOptions,
boolean[] options,
java.util.Vector[] assigns)
Draws three dimensional elements. |
private void |
PreElement1DDisplay.drawExtrude(SolidMAT owner,
Element1D e1D,
DVec[] pos,
double[] vizOptions,
boolean[] options,
java.util.Vector[] assigns)
Extrudes one dimensional elements. |
private void |
PreNodeDisplay.drawNode(SolidMAT owner,
Node node,
DVec pos,
double[] vizOptions,
boolean[] options,
java.util.Vector[] assigns)
Draws nodes as sphere. |
private void |
PostVisualizer.drawScalor(SolidMAT owner,
int step)
Draws contour scalor for the deformed shape option. |
private void |
PreVisualizer.drawScalor(SolidMAT owner,
java.util.Vector<java.lang.Object>[] assigns)
Draws contour scalor for the deformed shape option. |
void |
PostVisualizer.drawUndeformedShape(SolidMAT owner,
int option,
int[] comp)
Draws results on undeformed shape of the structure. |
private java.util.Vector<java.lang.Object>[] |
PreVisualizer.getAssigns(SolidMAT owner)
Stores colored assign names and related colors in vectors and returns array storing these vectors. |
private java.util.Vector<java.lang.Object>[] |
PreVisualizer.getCheckAssigns(SolidMAT owner)
Stores colored check assign names and related colors in vectors and returns array storing these vectors. |
private java.util.Vector<java.lang.Object>[] |
PreVisualizer.getColoredAssigns(SolidMAT owner)
Stores colored assign names and related colors in vectors and returns array storing these vectors. |
private java.util.Vector<java.lang.Object>[] |
PreVisualizer.getElementGroupAssigns(SolidMAT owner)
Stores colored element group assign names and related colors in vectors and returns array storing these vectors. |
private java.util.Vector<java.lang.Object>[] |
PreVisualizer.getNodeGroupAssigns(SolidMAT owner)
Stores colored node group assign names and related colors in vectors and returns array storing these vectors. |
private void |
PreElement2DDisplay.setColorForAspectRatioAssigns(SolidMAT owner,
Element2D e2D,
java.util.Vector[] assigns,
inf.v3d.obj.Polygons poly)
Sets color to element for the aspect ratio assign. |
private void |
PreElement3DDisplay.setColorForAspectRatioAssigns(SolidMAT owner,
Element3D e3D,
java.util.Vector[] assigns,
inf.v3d.obj.Polygons poly)
Sets color to element for the aspect ratio assign. |
private void |
PreElement1DDisplay.setColorForGroupAssigns(SolidMAT owner,
Element e,
java.util.Vector[] assigns,
inf.v3d.obj.Object3D object3D)
Sets color to element for the group-assign. |
private void |
PreElement3DDisplay.setColorForGroupAssigns(SolidMAT owner,
Element e,
java.util.Vector[] assigns,
inf.v3d.obj.Polygons poly)
Sets color to element for the group-assign. |
private void |
PreElement2DDisplay.setColorForGroupAssigns(SolidMAT owner,
Element e,
java.util.Vector[] assigns,
inf.v3d.obj.Polygons poly)
Sets color to element for the group-assign. |
private void |
PreNodeDisplay.setColorForGroupAssigns(SolidMAT owner,
Node node,
java.util.Vector[] assigns,
inf.v3d.obj.BoundingBox box)
Sets color to node for the group-assign. |
private void |
PreElement3DDisplay.setColorForInsideOutAssigns(SolidMAT owner,
Element3D e3D,
java.util.Vector[] assigns,
inf.v3d.obj.Polygons poly)
Sets color to element for the inside out assign. |
private void |
PreElement2DDisplay.setColorForUpsideDownAssigns(SolidMAT owner,
Element2D e2D,
java.util.Vector[] assigns,
inf.v3d.obj.Polygons poly)
Sets color to element for the upside down assign. |
private void |
PreElement3DDisplay.setColorForZeroVolumeAssigns(SolidMAT owner,
Element3D e3D,
java.util.Vector[] assigns,
inf.v3d.obj.Polygons poly)
Sets color to element for the zero volume assign. |
| Constructors in visualize with parameters of type SolidMAT | |
|---|---|
ContourScalor(SolidMAT owner1,
javax.swing.JFrame owner2,
java.lang.String name,
int type,
java.lang.Object[] objects)
Builds dialog, builds components, calls addComponent, sets layout and sets up listeners. |
|
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| PREV NEXT | FRAMES NO FRAMES |


{#-
This file was automatically generated - do not edit
-#}
{% if page.edit_url %}
{% set edit = "https://github.com/squidfunk/mkdocs-material/edit" %}
{% set view = "https://raw.githubusercontent.com/squidfunk/mkdocs-material" %}
{% include ".icons/material/file-edit-outline.svg" %}
{% include ".icons/material/file-eye-outline.svg" %}
{% endif %}


Alerts are available for any length of text and can be used for more than traditional alerts. Ideally they should be used when we want content or information to stand out, be separated from the "normal" state, highlight a change, make the user aware of important information or simply to convey the response from an action.
For proper styling, use one of the required contextual classes described below. Avoid using different contexts/colors as a means of separating categories and content of the same type as it might confuse users and make the real context less obvious.
| Context | Usage | Class name |
|---|---|---|
| Primary | For content that should stand out but lacks obvious meaning/context and is not a response to an action. | .alert-primary |
| Secondary | For secondary content that should stand out but lacks obvious meaning/context and is not a response to an action. | .alert-secondary |
| Info | Content that holds neutral information that we find useful for user and that we want to inform them about. | .alert-info |
| Success | Should be used as a response to an action or something that is considered to be positive or a success. | .alert-success |
| Warning | Not an error but important information for a user to consider or be aware of. | .alert-warning |
| Danger | Used for errors or when something goes wrong. | .alert-danger |
The examples below use the .alert-outline-*
class (replace * with context) for a more subtle message. The background is white to make it stick out when placed on grey backgrounds.
The examples below use the traditional .alert-*
class (replace * with context), for use cases where the message is really important and needs to stand out.
The examples below use the traditional .alert-*
class (replace * with context), together with .alert-icon
to add extra emphasis by displaying an icon next to the message. Note that only warning
and danger
alerts have icons at the moment.
Use the .alert-link
utility class to quickly provide matching colored links within any alert.
A more complex alert that can be dismissed (please note that JavaScript or framework is needed to dismiss alert messages!).
Opps, looks like you've encountered an error with an important alert message. This example text is going to run a bit longer so that you can see how spacing within an alert works with this kind of content.
Whenever you need to, be sure to use margin utilities to keep things nice and tidy.
Aww yeah, you successfully read this important alert message. This example text is going to run a bit longer so that you can see how spacing within an alert works with this kind of content.
Whenever you need to, be sure to use margin utilities to keep things nice and tidy.


| I l@ve RuBoard |
As we've seen, raise statements can pass an extra data item along with the exception for use in a handler. In general, the extra data allows you to send context information to a handler. In fact, every exception has the extra data; much like function results, it's the special None object if nothing was passed explicitly. The following code illustrates:
myException = 'Error' # string object def raiser1(): raise myException, "hello" # raise, pass data def raiser2(): raise myException # raise, None implied def tryer(func): try: func() except myException, extraInfo: # run func, catch exception + data print 'got this:', extraInfo % python >>> from helloexc import * >>> tryer(raiser1) # gets explicitly passed extra data got this: hello >>> tryer(raiser2) # gets None by default got this: None
As a special case, Python 1.5 introduced an assert statement, which is mostly syntactic shorthand for a raise. A statement of the form:
assert <test>, <data> # the <data> part is optional
works like the following code:
if __debug__: if not <test>: raise AssertionError, <data>
but assert statements may be removed from the compiled program's byte code if the -O command-line flag is used, thereby optimizing the program. Assertion-Error is a built-in exception, and the _ _debug__ flag is a built-in name which is automatically set to 1 unless the -O flag is used. Assertions are typically used to verify program conditions during development; when displayed, their message text includes source-code line information automatically.
Recently, Python generalized the notion of exceptions. They may now also be identified by classes and class instances. Like module packages and private class attributes, class exceptions are an advanced topic you can choose to use or not. If you're just getting started, you may want to mark this section as optional reading.
So far we've used strings to identify our own exceptions; when raised, Python matches the exception to except clauses based on object identity (i.e., using the is test we saw in Chapter 2). But when a class exception is raised, an except clause matches the current exception if it names the raised class or a superclass of it. The upshot is that class exceptions support the construction of exception hierarchies: by naming a general exception superclass, an except clause can catch an entire category of exceptions; any more specific subclass will match.
In general, user-defined exceptions may be identified by string or class objects. Beginning with Python 1.5, all built-in exceptions Python may raise are predefined class objects, instead of strings. You normally won't need to care, unless you assume some built-in exception is a string and try to concatenate it without converting (e.g., KeyError + "spam", versus str(KeyError) + "spam").
With the addition of class-based exceptions, the raise statement can take the following five forms: the first two raise string exceptions, the next two raise class exceptions, and the last is an addition in Python Version 1.5, which simply reraises the current exception (it's useful if you need to propagate an arbitrary exception you've caught in a except block). Raising an instance really raises the instance's class; the instance is passed along with the class as the extra data item (it's a good place to store information for the handler).
raise string # matches except with same string object raise string, data # optional extra data (default=None) raise class, instance # matches except with this class, or a superclass of it raise instance # same as: raise instance.__class__, instance raise # re-raise the current exception (new in 1.5)
For backward compatibility with Python versions in which built-in exceptions were strings, you can also use these forms of the raise statement:
raise class # same as: raise class() raise class, arg # all are really: raise instance raise class, (arg, arg,...)
These are all the same as saying raise class(arg...), and therefore the same as the raise instance form above (Python calls the class to create and raise an instance of it). For example, you may raise an instance of the built-in KeyError exception by saying simply raise KeyError, even though KeyError is now a class.
If that sounds confusing, just remember that exceptions may be identified by string, class, or class instance objects, and you may pass extra data with the exception or not. If the extra data you pass with a class isn't an instance object, Python makes an instance for you.
Let's look at an example to see how class exceptions work. In the following, we define a superclass General and one subclass of it called Specific. We're trying to illustrate the notion of exception categories here; handlers that catch General will also catch a subclass of it like Specific. We then create functions that raise instances of both classes as exceptions and a top-level try that catches General; the same try catches General and Specific exceptions, because Specific is a subclass of General:
class General: pass class Specific(General): pass def raiser1(): X = General() # raise listed class instance raise X def raiser2(): X = Specific() # raise instance of subclass raise X for func in (raiser1, raiser2): try: func() except General: # match General or any subclass of it import sys print 'caught:', sys.exc_type % python classexc.py caught: <class General at 881ee0> caught: <class Specific at 881100>
Since there are only two possible exceptions here, this doesn't really do justice to the utility of class exceptions; we can achieve the same effects by coding a list of string exception names in the except (e.g., except (a, b, c):), and passing along an instance object as the extra data item. But for large or high exception hierarchies, it may be easier to catch categories using classes than to list every member of a category in a single except clause. Moreover, exception hierarchies can be extended by adding new subclasses, without breaking existing code.
For example, the built-in exception ArithmeticError is a superclass to more specific exceptions such as OverflowError and ZeroDivisionError, but catching just ArithmeticError in a try, you catch any more specific kind of numeric error subclass raised. Furthermore, if you add new kinds of numeric error subclasses in the future, existing code that catches the ArithmeticError superclass (category) also catches the new specific subclasses without modification; there's no need to explicitly extend a list of exception names.
Besides supporting hierarchies, class exceptions also provide storage for extra state information (as instance attributes), but this isn't much more convenient than passing compound objects as extra data with string exceptions (e.g., raise string, object). As usual in Python, the choice to use OOP or not is mostly yours to make.
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#include <gomotion.h>
|
| go_result | init () |
| | Init the gomotion specification. If 'totalt' is positive, then it is the time for the move. Otherwise, it's automatically computed from the vel, acc and jerk, the usual case. Here we set 'totalt' to zero to get the usual case. More...
|
| |
| go_result | set_type (go_flag type) |
| | Depending upon whether you are doing joint interpolation or world coordinate interpolation (as specified by your call to go_motion_queue_set_type()), fill in joint[] or pose accordingly. More...
|
| |
| go_flag | get_type () |
| |
| go_result | set_id (go_integer id) |
| |
| go_integer | get_id () |
| |
| go_result | set_jpar (go_integer i, go_real vel, go_real acc, go_real jerk) |
| | Set a joint parameters. More...
|
| |
| go_result | set_tpar (go_real vel, go_real acc, go_real jerk) |
| | Set world Cartesian translation parameters. More...
|
| |
| go_result | set_rpar (go_real vel, go_real acc, go_real jerk) |
| | Set world Cartesian rotational parameters. More...
|
| |
| go_result | set_cpar (go_cart *center, go_cart *normal, go_integer turns) |
| | Set circular rotation Cartesian move parameters. More...
|
| |
| go_result | set_time (go_real time) |
| | Set time to positive real number. More...
|
| |
| go_result | set_end_position (go_position *end) |
| | If you are doing joint interpolation (as specified by your call to go_motion_queue_set_type()), et_end_position fills in joint[] accordingly. More...
|
| |
| go_result | set_end_pose (go_pose *end) |
| | If you are doing world coordinate interpolation (as specified by your call to go_motion_queue_set_type()), set_end_position fills in ending pose accordingly. More...
|
| |
| go_integer gomotion::go_motion_spec::get_id |
( |
| ) |
|
| go_flag gomotion::go_motion_spec::get_type |
( |
| ) |
|
| go_result gomotion::go_motion_spec::init |
( |
| ) |
|
Init the gomotion specification. If 'totalt' is positive, then it is the time for the move. Otherwise, it's automatically computed from the vel, acc and jerk, the usual case. Here we set 'totalt' to zero to get the usual case.
- Returns
- go_result success or failure
| go_result gomotion::go_motion_spec::set_cpar |
( |
go_cart * |
center, |
|
|
go_cart * |
normal, |
|
|
go_integer |
turns |
|
) |
| |
Set circular rotation Cartesian move parameters.
| go_result gomotion::go_motion_spec::set_end_pose |
( |
go_pose * |
end | ) |
|
If you are doing world coordinate interpolation (as specified by your call to go_motion_queue_set_type()), set_end_position fills in ending pose accordingly.
- Parameters
-
| end | is the end pose of the robot (position and orientation) |
- Returns
- go_result success or failure
| go_result gomotion::go_motion_spec::set_end_position |
( |
go_position * |
end | ) |
|
If you are doing joint interpolation (as specified by your call to go_motion_queue_set_type()), et_end_position fills in joint[] accordingly.
- Parameters
-
| end | is the end position of the joints |
- Returns
- go_result success or failure
| go_result gomotion::go_motion_spec::set_id |
( |
go_integer |
id | ) |
|
| go_result gomotion::go_motion_spec::set_jpar |
( |
go_integer |
i, |
|
|
go_real |
vel, |
|
|
go_real |
acc, |
|
|
go_real |
jerk |
|
) |
| |
Set a joint parameters.
- Parameters
-
| i | joint number. |
| vel | velocity limit UNITS? |
| acc | acceleration limit UNITS? |
| jerk | jerk limit UNITS? |
- Returns
- go_result success or failure
| go_result gomotion::go_motion_spec::set_rpar |
( |
go_real |
vel, |
|
|
go_real |
acc, |
|
|
go_real |
jerk |
|
) |
| |
Set world Cartesian rotational parameters.
- Parameters
-
| vel | velocity limit UNITS? |
| acc | acceleration limit UNITS? |
| jerk | jerk limit UNITS? |
- Returns
- go_result success or failure
| go_result gomotion::go_motion_spec::set_time |
( |
go_real |
time | ) |
|
Set time to positive real number.
- Parameters
-
- Returns
- go_result success or failure
| go_result gomotion::go_motion_spec::set_tpar |
( |
go_real |
vel, |
|
|
go_real |
acc, |
|
|
go_real |
jerk |
|
) |
| |
Set world Cartesian translation parameters.
- Parameters
-
| vel | velocity limit UNITS? |
| acc | acceleration limit UNITS? |
| jerk | jerk limit UNITS? |
- Returns
- go_result success or failure
| go_result gomotion::go_motion_spec::set_type |
( |
go_flag |
type | ) |
|
Depending upon whether you are doing joint interpolation or world coordinate interpolation (as specified by your call to go_motion_queue_set_type()), fill in joint[] or pose accordingly.
CIR: some circular params.
ALL: target position, joints or pose.
| go_integer gomotion::go_motion_spec::id |
ALL: id echoed as current move.
For joint moves, each pertains to the indexed joint.
For world moves, [0] pertains to trans, [1] pertains to rot, and the [2]-[*] are unused.
(ALL) start pose wrt world; prev end - computed for you
| go_real gomotion::go_motion_spec::totalt |
(ALL) total planned time for the motion - computed for you
| go_flag gomotion::go_motion_spec::type |
ALL: GO_MOTION_JOINT,LINEAR,CIRCULAR.
| union { ... } gomotion::go_motion_spec::u |
| go_quat gomotion::go_motion_spec::uquat |
(ALL) unit rotation from start to end - computed for you
The documentation for this struct was generated from the following file:
- /usr/local/michalos/nistfanuc_ws/src/gomotion/include/gomotion/gomotion.h


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