What is a Stage Plot? A Stage Plot (or technical rider) is a document that details exactly what your band needs to make a show fantastic. It details the equipment and instruments of every band member, the input you will need from the venues that you're playing and how you want your stage to look and sound.
What Can You Include on a Tech Rider? If you're a small band and you don't have much equipment or instruments with you, your Stage Plot will most likely mainly cover the gear that you're using onstage. As your band grows, your Tech Rider will start to include any FOH consoles that you bring with you, the lighting rigs you're touring with and any pyrotechnics that you have onstage. A Stage Plot is constantly updated as you continue touring and continue growing as a band.
StagePlotPro is a program designed to create professional stage plots for stage managers and sound engineers. The text you enter into the Input List Window will be written to the section beneath the stage. In the preferences window, you may specify whether you want to group 8 lines of text per block, 10 lines, or use up to 3 columns of text per block.
Generate stage plots using various instruments and controlling their arrangement. Check, amend, copy and re-organize the input list and monitor mixes or stage layout. Integrate the plots with other stage management suites for complete environmental control.
StagePlotPro allows you to create professional, accurate, and easy-to-read stage plots for stage managers and sound engineers. Keep your stage layout, input list and monitor mixes all on one page to print or email as a JPEG. Finally, an application dedicated to creating stage plots for professionals.
Use our stage plan builder for musicians to create the exact stage plot for your gig. After that all you need to do is send the provided URL (or PDF with paid services) to the engineer of the venue. You can enter your instruments, monitors, DI boxes etc, scale and rotate and provide full details of what you want.
Use our stage plan builder for venues and managers to share plots with artists who either perform at your venue or who work with you. After you share the plot, the artist can then create their stage plot by adding musicians and equipment to the plan.
For any one who has a stage that artists perform on, using our pro services means you can create your stage (including stage dimensions and equipment like monitors, drum risers etc) and share it with artists who can add their equipment to your stage.
Resources:Stage plot software:Band Helper (Android app)Stage Plot Designer (free)Stage Plot Maker (for iPhone and iPad)StagePlot Pro (best software but costs ~$5)Tecrider (free)
It functions effortless and intuitive stage plot creation, customized images (equal to a video game), 100s of props included in the 4.99 pro-upgrade, ability to include lists, notes and contact info, and an ever-expanding prop library.
Napkin Draw, the app, is your solution To make a design, simply choose your stage size, move and fall components onto the draw and when youre done, either e-mail or save the sketch perfect to your video camera roll Google android Set Lists and Even more Even more for groups, but offers capability to design stage plots and input lists Computer Mac Simply no free types right here, but seems to become the almost all regarded, most full-featured, ánd with the fanciést and broadest choice of components: They have got a visual for everything you can believe of, though several think about it 3D depictions of everything to be completely overkill and unwanted detail (appears damn wonderful though Cost can be 39.99, but comes with a 30-day time free trial.
When you create a pivot chart, you can plot different data (such as Grand Totals) based on the cells you select. The pivot chart below displays the values in the Total rows (Electric and Manual) in the pivot table.
This paper is organized as follows: Section 2 briefly introduces the algorithm of Modal Expectation Maximi- zation (MEM) and builds the notion of mode association clustering technique. Section 3 describes a parallel computing framework of HMAC along with computing time comparisons. Section 4 illustrates the implemen- tation of clustering functions in the R package Modalclust along with examples of the plotting functions especially designed for objects of class hmac. Section 5 provides the conclusion and discussion. Comparison of Modal clustering with other popular model based and model free techniques are provided in the supplementary document.
Figure 1 shows one PHMAC example on the graph. In this figure, (a) shows the simulated data with four clusters along with the contour plot, where the color indicates the final clustering using PHMAC; (b) shows the four random partitions of the unlabeled data along with the modes (red asterisks) at each partition; (c) shows the mode obtained from the four partitions; (d) shows the final modes (green triangles) starting from the modes of the partitioned data. A demonstration of different steps of parallel clustering with four random partitions is given in Figure 1. The original data set is partitioned into 4 random subsets, and initial modal clustering is performed within the partitions. In the next step, the modes of each of these partitions are merged to form the overall modal clusters in Figure 1(c).
The R package Modalclust was created to implement the HMAC and PHMAC. There are also some plotting tools that give the user a comprehensive visual and understanding of the clustering result. Sources, binaries and documentation of Modalclust are available for download from the Comprehensive R Archive Network -project.org/ under the GNU Public License.
First, we provide an example of performing modal clustering to extract the subpopulations in the logcta20 data. The description of the dataset is given in the package. The scatter plot, along with its smooth density, is provided in Figure 3. First, we use the following command to download and install the package:
There are several plotting functions in Modalclust, which can be used to visualize the output from the function phmac. The plotting functions are defined on object class hmac, which is the default class of a phmac output. These plot functions will be illustrated through a data set named disc2d, which has 400 observations displaying the shape of two half discs. The scatter plot of disc2d along with its contour plot are given in Figure 5.
There are some other plotting functions that are designed mainly for visualizing clustering results for two dimensional data, although one can provide multivariate extensions of the functions by considering all possible pairwise dimensions. One can obtain the hard clustering of the data for each level using the command
Alternatively, the user can specify the hierarchical level or the number of desired clusters, and obtain the corresponding cluster membership (hard clustering) of the data. For example, the plot in Figure 7 can be obtained by either of the following two commands:
Another function, which allows the user to visualize the soft clustering of the data, is based on the posterior probabilities of each observation belonging to the clusters at a specified level. For example, the plot in Figure 8 can be obtained using
Modalclust performs a hierarchical model based clustering allowing for arbitrary density shapes. Parallel computing can dramatically increase the computing speed by splitting the data and running the HMAC simul- taneously on multi-core processors. Plotting functions give the user a comprehensive visualizing and under- standing of the clustering result. One future work from this stage would be to increase computing speed, especially for large data set. From the discussion in Section 3, it is clear to see, parallel computing increases the computing speed a lot. That relies on the computing equipment. If one user has no multicore or a few multicore processors available, it will take a lot of the computing resources when clustering large data sets. One potential way to solve the computing speed problem is using k-means or other faster clustering techniques initially, and using the HMAC from the centers of each cluster of initial clustering results. For example, if we have a data set with 20,000 observations, we can use k-means clustering and choose a certain number of centers, like 200 centers and run k-means clustering first. And then we start from the centers of 200 clusters and clustering by HMAC. Theoretically it is a sub-optimal way compared with running HMAC for all points. In practice, it is very useful to reduce the computing costs and still obtain the right clustering.
Apart from the actual design, the clever combination of information allows a variety of other data planning functions, like stock lists for example, to be created.StagePlotPro 2.9.5 - Professional stage plot creator for stage managers and sound engineers. This allows for designs of great accuracy in the shortest time, even complex trussing or lighting plans. This is based on an extensive library of over 20,000 2D and 3D symbols in areas such as lighting, audio, video, stage, trusses, rigging, studio and theatre and also banquet and catering.An integral component of AutoSTAGE is the large collection of tools. You can find both free softwares or free libraries for Autocad and paid softwares that allow you to design, simulate, plot, draft projects on paper.Is an efficient tool for the cost-effective and easy creation of professional CAD plans, engineering designs and visual images for fair, exhibition, theatre and live entertainment.AutoSTAGE provides the technical simulation of reality. Stage Light 3D models for download, files in 3ds, max, c4d, maya, blend, obj.Stage Lighting Design Cad Software is a collection reguarding useful softwares for the planning of lighting shows. 2b1af7f3a8