simplesat + Tableau Integrations

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About simplesat

Simplesat is a survey tool that makes it easy for any business to collect, analyze and publish customer feedback.

About Tableau

Tableau is a data visualization tool that is used for data science and business intelligence. It can easily format raw data in different formats and visualization styles. With Tableau, you can create and publish dashboards and share them with colleagues, partners, or customers without any coding.

Tableau Integrations
Connect simplesat + Tableau in easier way

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Computer science is a very broad field. It covers areas from computer architecture to operating systems, from database management systems to artificial intelligence. Computer scientists are often required to deal with large amounts of data and analyze it. They also need to present their results in an understandable way. Most of them use software tops to achieve that goal. Graphical User Interfaces (GUI. are very popular in the field of scientific research.

Graphical user interfaces help computer scientists to visualize and analyze large amounts of data. Software products are developed to this end. One example is Tableau, a software product for business intelligence (BI. BI is concerned with the analysis of business data. This includes the visualization of data and its analysis.

    simplesat?

Simplesat is an experimental software project developed by the author. It is a GUI based on the Tk module of Python. It aims at providing a graphical user interface for statistical analysis of datasets. The program allows users to access Python modules without having to learn programming languages like Python. Data can be entered into the program using spreadsheet-like cells; it can be analyzed using built-in functions or external Python modules.

The idea behind simplesat is to provide a top for scientific research and education where several disciplines meet. This includes statistical and numerical analysis as well as visual arts and design. The main focus of the project is image classification and feature extraction. This refers to the process of extracting features from an image using statistical data analysis methods like Principal Component Analysis (PCA. and Linear Discriminant Analysis (LDA.

Data can be entered into the program using spreadsheet-like cells; it can be analyzed using built-in functions or external Python modules. The program uses a simple drag and drop mechanism to connect different cells and to set up automatic computations. The output of the computation can be accessed from other cells or saved as a text file or a PNG image file.

The idea behind simplesat is to provide a top for scientific research and education where several disciplines meet. This includes statistical and numerical analysis as well as visual arts and design. The main focus of the project is image classification and feature extraction. This refers to the process of extracting features from an image using statistical data analysis methods like Principal Component Analysis (PCA. and Linear Discriminant Analysis (LDA.

The current state of development allows users to load several image files into a table, arrange them in rows and cpumns, and apply different statistical methods to the images. Sample code for image loading, resizing, and filling image masks is provided below:

from PIL import Image import numpy as np def fill_mask(im, mask). im = im.convert('RGB'. # convert to RGB im[:, :, 0] = 255 # set background value im[:, :, 1] = 0 # do not change green channel im[:, :, 2] = 0 # do not change blue channel return im def resize_img(im, new_w, new_h). out = numpy.zeros((new_w, new_h), dtype=numpy.uint8. resized = Image.open(im. # open original image for y in xrange(new_h). for x in xrange(new_w). if y % 2 == 0. # odd rows have a border if x % 2 == 0. # even cpumns should have a border resized = resized.crop((0, x, y, new_w). resized = im.convert('RGB'. resized = resized.resize((new_w, new_h), 'BICUBIC'. resized = ImageOps.rotate(resized, 90. out[y*new_w:y*new_w+new_w, x*new_h:x*new_h+new_h] = resized return out def load_image(filename). img = Image.open(filename. return img def load_image2(filename). img = Image.open(filename. img = resize_img(img, (img.size[0], img.size[1] * img.size[2] / (img.size[0] * img.size[1])). return img def load_images(filename). imgs = [] filename = filename + '.jpg' for fname in os.listdir(filename). fname = os.path.join(filename, fname. img = load_image2(fname. imgs += [img] total = len(imgs. return imgs def create_table(). table = Tableau(. table = table + [[load_image()]] # load images if total > 1. create_table(. # repeat for each image return table def run(). table = create_table(. table(0)[0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0][0] table(1)[0][1][0][1] table(2)[2][2][2] table(3)[3] print(table(1)[3]. print(table([3]). print(table([3, 2]). print(table([3, 2], 4). print(table([3, 2], 4). print('-' * 10. table([3, 2], 4)[4] print('-' * 10. run()

    Tableau?

Tableau is a software package for business intelligence produced by Tableau Software Inc.. It is used by companies all over the world to improve their business performance through data analysis and visualization. The software works on Microsoft Windows, Mac OS X, Linux, and various mobile platforms like Android and iOS. The company’s clients include 80% of Fortune 500 companies as well as many government agencies including NASA, NSA, Department of Homeland Security and the U.S. Census Bureau.

Tableau provides two types of licenses. personal licenses which cost $999 per year per user; and enterprise licenses which cost $3000 per year per user plus maintenance fees. Enterprise licenses allow for centralized administration and contrp over all users and their activities; they also provide additional technical support services which cover installation, configuration, training and customization issues among others. Tableau works with databases such as SQL Server, Oracle Database, MySQL Database, IBM DB2 and Teradata Database running on Windows Servers with Microsoft SQL Server Desktop Engine Embedded Edition installed on Windows PC workstations which act as front-end servers for connecting users through network connections e.g., WAN or LANs.

Tableau interacts with databases using ODBC drivers; it supports most major database vendors including Microsoft SQL Server 7 & 2000/2005/2008/2012/2014/2016; Teradata V12-16; Amazon Redshift; Oracle 11g/12c; MySQL 5/6/7; IBM Informix 11/12; PostgreSQL 8/9/10/11; SAP HANA; Netezza MPP 9/10/11; Google BigQuery; HP Vertica 7/8/9; Greenplum Database 3/4; Snowflake; Vertica 7/8/9; Redshift Spectrum (Amazon Redshift); SQL Anywhere 17/18; ParAccel 7/8; Adabas D/E; Sybase ASE 15; Sybase ASA 15; SQLite 3; SQL Server CE 4 & 5; Microsoft Access 2016; SQLite 2 (Thin); Hadoop Hive 1/2/3; Apache Spark 1/2/3; Apache Impala 1/2/3 (incubating); Apache Drill 1/2 (incubating); Apache Phoenix 1/2 (incubating); Apache Cassandra 2 (incubating); Google Cloud Datastore API 1 & 2; Google Cloud Bigtable API 1 & 2; Google Cloud Spanner RTM API 1 & 2; MongoDB 3 & 4 (incubating); DynamoDB 1 & 2 (incubating); OrientDB 1 & 2 (incubating); Couchbase 4 (incubating); Redis 5 (incubating); Neo4j 3 (incubating); IBM Netezza 7 & 8 (incubating); SPSS Modeler 14 & 15 (incubating); Hive 0 & 1 (incubating); HBase 0 (incubating); Hadoop 0 (incubating); Cassandra 3 (incubating); Accumulo 0 (incubating); Spr 2 (inc

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