Math Parser Compiler Emulator supports learning how a compiler creates an executable program file from source code. Using mathematical expressions as an input, Math Parser shows how the following stages of compilation are performed: Lexical Analysis Syntax Analysis Code Generation Optimisation In addition, Math Parser demonstrates: How mathematical expressions are formed into Reverse Polish Notation (RPN) for execution using the Shunting Yard algorithm and the Stack data structure. How an Abstract Syntax Tree can be created from the RPN, showing how a recursive algorithm is used in practice. The RPN and Data Dictionary from successfully compiled expressions can be saved and then evaluated using the Math Parser Virtual Machine. Math Parser supports implied multiplication (e.g. 5x) as well as explicit multiplication (e.g. 5 * x) in its input expressions. Fully supported by a comprehensive Help file, Math Parser includes all the algorithms used and explains all the technical terminology.

Python Resource Pack 3 is a set of coding challenges designed to support the teaching and learning of the Python programming language. This pack is designed for those who have some experience of programming in Python, such as completing the exercises in Resource Packs 1 and 2. It is suitable for teaching Python in schools from Year 8 onwards, tutors and learners in adult learning classes or for the hobbyist learning at home. The pack also encourages the learner to start to consider the whole system life cycle and program documentation by introducing the following requirements: Preparatory design before attempting to code the solution to the challenge. Full validation to prevent the wrong type of data or incorrect values from being entered. Full testing (both unsuccessful and successful tests, together with any corrective actions) to be recorded as a part of the solution alongside the code. The pack is designed to support learning that has previously taken place in the classroom, via self-directed study, or by following tutorials. The pack contains: A pdf file with four coding challenges. Fully commented example solutions to each of the challenges. The following topics are introduced: The regular expression module. The re.match command to determine if a string matches a format defined by a regular expression. .isspace() to detect strings made completely of spaces. The len command to find the length of a string. The lstrip() and rstrip() commands to remove spaces, newline and tab characters from the start and end of a string. Parameter passing to procedures/functions. The random.seed command to seed the random number generator. The random.randrange to set the range of random numbers to be generated. The try… except ValueError command for error trapping. Lists. The sum command to add the values in a list of numbers. The statistics module. The chr command to get a character from an ASCII value. The count() command to determine how many times one string appears in another. The challenges are designed so that self-directed research will be required to complete them; they are not a cut-and-paste tutorial.

This software models the passing of a voltage through metal wires of varying dimensions to see how resistance and current varies until a power equilibrium is reached, when the power going into the circuit equals the power coming out via heat emission. It is designed to support the teaching of Science and Physics at GCSE and A Level. The model takes into account the resistivity and temperature coefficient of the metal. The following parameters can be varied within the model: Voltage applied; Length and diameter of the wire; Room temperature; The required sample interval. As these parameters are varied, the number of samples that will be taken is displayed so that the optimum number for a particular experiment can be selected. The temperature, resistance, power in and out and current are calculated for each sample. Parameters can be adjusted and the metals list modified to the user’s specification. These new settings can be saved in a file for later use. The results can be printed and also exported as a CSV file for further analysis or graphing. The application is supported by a comprehensive help file which includes guidance on how to generate the required number of samples.

Electrolysis Solution Finder shows what ions from an ionic compound (either molten or dissolved in water) are attracted to the cathode and anode during electrolysis. It is designed to support the teaching of Science and Chemistry at GCSE and A Level. The following parameters can be varied within the model: The Ionic Compound to test; Whether the Ionic Compound is dissolved in water or molten. The ions and half equations involved are displayed, while in a graphic, solids forming on an electrode are demonstrated by an appropriate colour change at the electrode, liquid forming shown by a cloud forming around the electrode, and gas by a bubbling animation and sound effect. The application is supported by a comprehensive help file.

Python Resource Pack 2 is a set of coding challenges designed to support the teaching and learning of the Python programming language. This pack is designed for those who have some experience of programming in Python, such as completing the exercises in Resource Pack 1. It is suitable for teaching Python in schools from Year 7 onwards, tutors and learners in adult learning classes or for the hobbyist learning at home. The pack also encourages the learner to start to consider the whole system life cycle and program documentation by including a requirement for all testing (both unsuccessful and successful tests, together with any corrective actions) to be recorded as a part of the solution alongside the code. Some of the tasks also require preparatory design before attempting to code the solution to the challenge. The pack is designed to support learning that has previously taken place in the classroom, via self-directed study, or by following tutorials. The pack contains: A pdf file with five coding challenges. Fully commented example solutions to each of the challenges. The following topics are introduced: Combining individual instructions into more complex instructions. The .format command as an alternative to the str command when concatenating strings with numbers. The import command to access specialised commands in addition to the basic Python command set. The math.fmod command to perform modulo division (finding the remainder). The .upper() command to convert text to all capitals and its help with data entry verification. The use of logical or. The if… elif… else command for multiple options in a selection command. The use of functions to segregate programs and generate and return data. The random.randint command to generate random integers. Using while True to create endless loops. The .lower() command to convert text to all capitals and its help with data entry verification. Use of the comma to concatenate and space integers and strings. The challenges are designed so that some self-directed research will be required to complete them; they are not a cut-and-paste tutorial.

Python Resource Pack 1 is a set of coding challenges designed to support the teaching and learning of the Python programming language. This pack is designed for anyone who has no experience of programming in Python, or no experience of programming at all. It is suitable for teaching Python in schools from Year 7 onwards, tutors and learners in adult learning classes or for the hobbyist learning at home. The pack is designed to support learning that has previously taken place in the classroom, via self-directed study, or by following tutorials. The pack contains: A pdf file with eight coding challenges. Fully commented example solutions to each of the challenges. The following topics are introduced: Displaying text on the screen with the print command. The input command to accept data typed in by the program’s user. Using variables to store data. The int command to convert text strings to integers (whole numbers). The for… in range command to perform commands more than once in count controlled loops. The /n newline command. The .title() command to capitalize the first letter in a string. String concatenation. String replication. The str command to convert other data types into strings. Basic mathematics. The float command to convert text strings to real numbers (decimals). The if command to perform instructions if a condition is met. The if… else command to perform instructions if a condition is met and alternative instructions if not. The while command to perform commands more than once in condition controlled loops. The comparison operators. The break command to jump out of instructions. The challenges are designed so that a degree of self-directed research will be required to complete them; they are not a cut-and-paste tutorial.

This software contains two models: The Fundamental Frequency Of A String Or Wire Under Tension This software calculates the Fundamental Frequency of a string or wire under tension and plays a tone at that frequency. It is designed to support the teaching of Science and Physics at GCSE and A Level. The model takes into account the density of the material. The following parameters can be varied within the model: Length of the string or wire; Diameter of the string or wire; Tension the string or wire is under. The volume and mass of the string or wire are calculated as well as the Fundamental Frequency. Parameters can be adjusted and the materials list modified to the user’s specification. These new settings can be saved in a file for later use. The results are calculated in real time as the parameters are changed, and can be printed. A tone can be played at the currently calculated Fundamental Frequency if it is within the system boundaries. The Fundamental Frequency Of A Gas Filled Tube Or Pipe This software calculates the Fundamental Frequency of a gas filled tube or pipe and plays a tone at that frequency. It is designed to support the teaching of Science and Physics at GCSE and A Level. The model takes into account the Heat Capacity Ratio and the Molecular Mass of the gas. The following parameters can be varied within the model: Length of the tube or pipe; Temperature within the tube or pipe; Whether or not the tube or pipe has one end closed. The speed of sound is calculated as well as the Fundamental Frequency. Parameters can be adjusted and the gases list modified to the user’s specification. These new settings can be saved in a file for later use. The results are calculated in real time as the parameters are changed, and can be printed. A tone can be played at the currently calculated Fundamental Frequency if it is within the system boundaries. The application is supported by a comprehensive help file.

This software models the heating of liquids in an insulated container over time and is designed to support the teaching of Science and Physics at GCSE and A Level. The model takes into account the density, boiling point and specific heat capacity of the liquid. The following parameters can be varied within the model: Mass and initial temperature of the liquid; Room temperature; Resistance and current of the heater; Heating time; Container diameter and insulation thickness; The required sample interval. As these parameters are varied, the number of samples that will be taken is displayed so that the optimum number for a particular experiment can be selected. The energy used, liquid temperature and the temperature rise are calculated for each sample. Parameters can be adjusted and the liquids list modified to the user’s specification. These new settings can be saved in a file for later use. The results can be printed and also exported as a CSV file for further analysis or graphing. The application is supported by a comprehensive help file which includes guidance on how to generate the required number of samples.

Data Sorts provides theory notes and interactive models of four common data sorting routines used in computing: the Bubble Sort, Insertion Sort, Merge Sort and Quicksort. The models include a delay feature so sorting occurs at a speed to suit the user, or, alternatively, instantaneously. In addition, the element of the Sort that is currently being processed is highlighted, making it easier to see what is going on and to relate it to the algorithm being used. There is an option to switch on a step-by-step explanation of how each algorithm is functioning. Speed Trials generates a large data set and then allows the user to sort that data using one of the sorting algorithms, timing how long it took. The data can then be returned to its original order, and one of the other sorts used, allowing comparisons to be drawn. Further sets of data can then be generated to substantiate results. Unlike the standalone models, Speed Trials is not deliberately slowed down and has all unnecessary formatting code removed to optimise performance and therefore provide true comparative data. Alongside the five interactive models are a set of theory notes describing each of the sort algorithms and designed to be read before interacting with the models and to reinforce learning as they are used. Data Sorts is supported by a comprehensive help file.

Math Parser Virtual Machine supports learning how Reverse Polish Notation (RPN) is evaluated to produce answers. Using RPN files created using Math Parser Compiler Emulator (supplied) as input, Math Parser Virtual Machine shows how: The Stack is used in evaluation How the X Register in the CPU is used How the Y Register in the CPU is used How the Accumulator in the CPU is used How run-time errors are handled Fully supported by a comprehensive Help file, Math Parser Virtual Machine includes the algorithm used and explains all the technical terminology.

This software models spheres falling through different fluids. It also models their rising after bouncing, should they bounce. It is designed to support the teaching of Science and Physics at GCSE and A Level. The model takes into account the viscosity and density of the fluid. The following parameters can be varied within the model: Height from which the sphere falls; Mass and diameter of the sphere; Energy loss on impact; The required sample interval. As these parameters are varied, the number of samples that will be taken is displayed so that the optimum number for a particular experiment can be selected. The fall time, fall velocity and fall distance are calculated for each sample as the object falls. The rise time, rise velocity and rise distance are calculated for each sample as the object rises. If the object instead floats in the fluid when released, then this is reported. Parameters can be adjusted and the fluids list modified to the user’s specification. These new settings can be saved in a file for later use. The results can be printed and also exported as a CSV file for further analysis or graphing. The application is supported by a comprehensive help file which includes guidance on how to generate the required number of samples.

Payback Period Calculator models when an initial investment in a business will be repaid within a five-year period, if at all. Sales for each of the five years can be input, as can the initial investment, cost price, desired profit, and whether that profit will be a mark-up or a margin. The payback period is displayed as years and weeks as well as years and months. Revenue, costs, net return and outstanding repayment are calculated for each of the five years. Payback Period Calculator is fully supported by a comprehensive help file which includes an explanation of the difference between mark-up and margin when determining profit and also how to use the system to set a sale price.

Python Resource Pack 4 is a set of coding challenges designed to support the teaching and learning of the Python programming language. This pack is designed for those who have some experience of programming in Python, such as completing the exercises in Resource Packs 1, 2 and 3. It is suitable for teaching Python in schools from Year 8 onwards, tutors and learners in adult learning classes or for the hobbyist learning at home. The pack gives learners an opportunity to practice debugging (fixing) third party code rather than their own. It consists of six graduated programs, all with errors in them, which need to be studied and the bugs diagnosed and corrected. The pack is designed to support learning that has previously taken place in the classroom, via self-directed study, or by following tutorials. The pack contains: Six programs with bugs in them. Working solutions to each of the six programs. A pdf file ‘Cheat Sheet’ for teachers/tutors explaining where the bugs are.** The challenges are designed so that self-directed research will be required to complete them; they are not a cut-and-paste tutorial.

Python Resource Pack 7 is a set of coding challenges designed to support the teaching and learning of the Python programming language. This pack is designed for those who have some experience of programming in Python, such as completing the exercises in Resource Packs 1 - 6. It is suitable for teaching Python in schools at AS and A Level, for tutors and learners in adult learning classes or for the hobbyist learning at home. As part of the higher-level learning and exam preparation for which the pack is designed, these challenges necessitate a greater degree of design and coding ability than the challenges in Resource Pack 6. Emphasis is placed on the system life cycle and program documentation by requiring: Preparatory design before attempting to code the solution to the challenge. Full validation to prevent the wrong type of data or incorrect values from being entered. The use of meaningful names for variables, functions and procedures. Fully commenting code. Full testing (both unsuccessful and successful tests, together with any corrective actions) to be recorded as a part of the solution alongside the code. The pack is designed to support learning that has previously taken place in the classroom, via self-directed study, or by following tutorials. The pack contains nine challenges, each consisting of: A pdf file with one or more coding challenges and, in some cases, supplementary extension work. Fully commented example solutions to the challenge or challenges and any extensions. The following topics are introduced: The // mathematical operator to perform integer division. The [:n] string operator to get the leftmost n characters of a string. The [-n:] string operator to get the rightmost n characters of a string. The re.sub regular expression function. The chr() and ord() commands to get a character from an ASCII value. The pow() command to calculate powers. The math.sqrt() command for calculating square roots. The challenges are designed so that self-directed research will be required to complete them; they are not a cut-and-paste tutorial.

Python Resource Pack 8 is a set of coding challenges designed to support the teaching and learning of the Python programming language. This pack is designed for those who have some experience of programming in Python, such as completing the exercises in Resource Packs 1 - 7. It is suitable for teaching Python in schools at AS and A Level, for tutors and learners in adult learning classes or for the hobbyist learning at home. As part of the higher-level learning and exam preparation for which the pack is designed, these challenges necessitate a greater degree of design and coding ability than the challenges in Resource Packs 6 and 7. Emphasis is placed on the system life cycle and program documentation by requiring: Full design prior to coding (system flowchart, program flowchart, pseudocode, data dictionary). Full validation so only valid data can be entered by the user. Meaningful names for variables, procedures and functions. Full error trapping. Fully commented code. Complete evidence of the program being tested during development (Alpha Testing or White Box Testing). Complete evidence of the program being tested post development (Beta Testing or Black Box Testing). The pack is designed to support learning that has previously taken place in the classroom, via self-directed study, or by following tutorials. The pack contains five challenges, each consisting of: A pdf file with one or more coding challenges and, in some cases, supplementary extension work. Fully commented example solutions to the challenge or challenges and any extensions. The following topics are introduced: The datetime module, and specifically date() and datetime() within it. The dateutil add-on module, and specifically relativedelta() within it. The challenges are designed so that self-directed research will be required to complete them; they are not a cut-and-paste tutorial.

Python Resource Pack 6 is a set of coding challenges designed to support the teaching and learning of the Python programming language. This pack is designed for those who have some experience of programming in Python, such as completing the exercises in Resource Packs 1 - 5. It is suitable for teaching Python in schools at GCSE, AS and A Level, for tutors and learners in adult learning classes or for the hobbyist learning at home. As part of the higher-level learning and exam preparation for which the pack is designed, emphasis is placed on the system life cycle and program documentation by requiring: Preparatory design before attempting to code the solution to the challenge. Full validation to prevent the wrong type of data or incorrect values from being entered. The use of meaningful names for variables, functions and procedures. Fully commenting code. Full testing (both unsuccessful and successful tests, together with any corrective actions) to be recorded as a part of the solution alongside the code. The pack is designed to support learning that has previously taken place in the classroom, via self-directed study, or by following tutorials. The pack contains eleven challenges, each consisting of: A pdf file with one or more coding challenges and, in some cases, supplementary extension work. Fully commented example solutions to the challenge or challenges and any extensions. The following topics are introduced: Writing recursive as opposed to iterative solutions. The global command to create static variables. The round command to round numbers to a given degree of accuracy. The use of end= in print commands. The % mathematical operator to perform modulo division. The** .append()** command with lists. The ** function for calculating exponents; The math module; The import math command. The math.degrees() command to convert from radians to degrees. The math.asin() command (the equivalent of sin-1) The math.pi() command representing π. The math.sin() command. The math.cos() command. The in command for checking if an item is in a list. The pass command in error trapping. The match… case command to make selections based on the value of a variable. The not in command to search for strings within strings. The use of constants and a constants file. The challenges are designed so that self-directed research will be required to complete them; they are not a cut-and-paste tutorial.

Python Resource Pack 5 is a set of coding challenges designed to support the teaching and learning of the Python programming language. This pack is designed for those who have some experience of programming in Python, such as completing the exercises in Resource Packs 1 - 4. It is suitable for teaching Python in schools from Year 8 onwards, tutors and learners in adult learning classes or for the hobbyist learning at home. The pack gives learners an opportunity to investigate how to write, append and read text files using Python. It consists of eight programming challenges, each of which introduces different elements of file handling. The pack is designed to support learning that has previously taken place in the classroom, via self-directed study, or by following tutorials. The pack contains: Eight pdf files each containing a file handling coding challenge. Working solutions to each of the eight programs. The challenges are designed so that self-directed research will be required to complete them; they are not a cut-and-paste tutorial.

Data Structures provides interactive models and theory notes of four common data structures used in computing: the Stack, Circular Queue, Binary Search Tree and Linked List. Each is designed to prevent the user from performing illegal operations such as adding data to an already full structure and explains why such actions are being taken. The Binary Search Tree and Linked List models include a delay feature so that the Tree may be traversed, and the List read at a speed to suit the user, or, alternatively, instantaneously. In addition, the element of the Tree or List that is currently being accessed is highlighted, making it easier to see what is going on and to relate it to the algorithm being used. The Binary Search Tree includes removal of data from the tree, complete with a Structured English description and a Pseudocode algorithm in the theory notes of how it is accomplished. There is an option to switch on a step-by-step explanation of how data is entered into a Binary Search Tree and how the three traversal methods operate, and how data is inserted into, read from and removed from a Linked List. Alongside the four interactive models are a set of theory notes describing each of the data structures and designed to be read before interacting with the models and to reinforce learning as they are used. Data Structures is supported by a comprehensive help file.