FAST | Hazus - Flood Assessment Structure Tool | Hacking library

 by   nhrap-hazus Python Version: 0.0.7 License: GPL-3.0

kandi X-RAY | FAST Summary

kandi X-RAY | FAST Summary

FAST is a Python library typically used in Manufacturing, Utilities, Construction, Security, Hacking applications. FAST has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However FAST build file is not available. You can download it from GitHub.

Hazus - Flood Assessment Structure Tool

            kandi-support Support

              FAST has a low active ecosystem.
              It has 18 star(s) with 5 fork(s). There are 2 watchers for this library.
              It had no major release in the last 12 months.
              There are 6 open issues and 1 have been closed. On average issues are closed in 25 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of FAST is 0.0.7

            kandi-Quality Quality

              FAST has 0 bugs and 0 code smells.

            kandi-Security Security

              FAST has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              FAST code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              FAST is licensed under the GPL-3.0 License. This license is Strong Copyleft.
              Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

            kandi-Reuse Reuse

              FAST releases are available to install and integrate.
              FAST has no build file. You will be need to create the build yourself to build the component from source.
              It has 1218 lines of code, 31 functions and 10 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed FAST and discovered the below as its top functions. This is intended to give you an instant insight into FAST implemented functionality, and help decide if they suit your requirements.
            • Check if the tool is up to date
            • Try to connect to the proxy
            • Update the tool
            • Parse version from init
            • Set the proxy values
            • Check if we are installing
            • Installs HazPy
            • Create aazpy environment
            • Determine if a conda is on the PATH
            • Creates a grid form
            • Run the hazus script
            • Calculate flood damage loss
            • Run flood damage
            • Creates a popup dialog
            • Start the app
            • Check if the internet connection is connected
            Get all kandi verified functions for this library.

            FAST Key Features

            No Key Features are available at this moment for FAST.

            FAST Examples and Code Snippets

            No Code Snippets are available at this moment for FAST.

            Community Discussions


            Special Number Count
            Asked 2022-Mar-09 at 04:56

            It is a number whose gcd of (sum of quartic power of its digits, the product of its digits) is more than 1. eg. 123 is a special number because hcf of(1+16+81, 6) is more than 1.

            I have to find the count of all these numbers that are below input n. eg. for n=120 their are 57 special numbers between (1 and 120)

            I have done a code but its very slow can you please tell me to do it in some good and fast way. Is there is any way to do it using some maths.



            Answered 2022-Mar-06 at 18:14

            The critical observation is that the decimal representations of special numbers constitute a regular language. Below is a finite-state recognizer in Python. Essentially we track the prime factors of the product (gcd > 1 being equivalent to having a prime factor in common) and the residue of the sum of powers mod 2×3×5×7, as well as a little bit of state to handle edge cases involving zeros.

            From there, we can construct an explicit automaton and then count the number of accepting strings whose value is less than n using dynamic programming.



            Efficient summation in Python
            Asked 2022-Jan-16 at 12:49

            I am trying to efficiently compute a summation of a summation in Python:

            WolframAlpha is able to compute it too a high n value: sum of sum.

            I have two approaches: a for loop method and an np.sum method. I thought the np.sum approach would be faster. However, they are the same until a large n, after which the np.sum has overflow errors and gives the wrong result.

            I am trying to find the fastest way to compute this sum.



            Answered 2022-Jan-16 at 12:49

            (fastest methods, 3 and 4, are at the end)

            In a fast NumPy method you need to specify dtype=np.object so that NumPy does not convert Python int to its own dtypes (np.int64 or others). It will now give you correct results (checked it up to N=100000).



            Why is WSL extremely slow when compared with native Windows NPM/Yarn processing?
            Asked 2022-Jan-06 at 00:43

            I am working with WSL a lot lately because I need some native UNIX tools (and emulators aren't good enough). I noticed that the speed difference when working with NPM/Yarn is incredible.

            I conducted a simple test that confirmed my feelings. The test was running npx create-react-app my-test-app and the WSL result was Done in 287.56s. while GitBash finished with Done in 10.46s..

            This is not the whole picture, because the perceived time was higher in both cases, but even based on that - there is a big issue somewhere. I just don't know where. The project I'm working on uses tens of libraries and changing even one of them takes minutes instead of seconds.

            Is this something that I can fix? If so - where to look for clues?

            Additional info:

            • my processor: Processor AMD Ryzen 7 5800H with Radeon Graphics, 3201 Mhz, 8 Core(s), 16 Logical Processors

            • I'm running Windows 11 with all the latest updates to both the system and the WSL. The chosen system is Ubuntu 20.04

            • I've seen some questions that are somewhat similar like 'npm install' extremely slow on Windows, but they don't touch WSL at all (and my pure Windows NPM works fast).

            • the issue is not limited to NPM, it's also for Yarn

            • another problem that I'm getting is that file watching is not happening (I need to restart the server with every change). In some applications I don't get any errors, sometimes I get the following:



            Answered 2021-Aug-29 at 15:40

            Since you mention executing the same files (with proper performance) from within Git Bash, I'm going to make an assumption here. Correct me if I'm wrong on this, and I'll delete the answer and look for another possibility.

            This would be explained (and expected) if your files are stored on /mnt/c (a.k.a. C:, or /C under Git Bash) or any other Windows drive, as they would likely need to be to be accessed by Git Bash.

            WSL2 uses the 9P protocol to access Windows drives, and it is currently known to be very slow when compared to:

            • Native NTFS (obviously)
            • The ext4 filesystem on the virtual disk used by WSL2
            • And even the performance of WSL1 with Windows drives

            I've seen a git clone of a large repo (the WSL2 Linux kernel Github) take 8 minutes on WSL2 on a Windows drive, but only seconds on the root filesystem.

            Two possibilities:

            • If possible (and it is for most Node projects), convert your WSL to version 1 with wsl --set-version 1. I always recommend making a backup with wsl --export first.

              And since you are making a backup anyway, you may as well just create a copy of the instance by wsl --importing your backup as --version 1 (as the last argument). WSL1 and WSL2 both have their uses, and you may find it helpful to keep both around.

              See this answer for more details on the exact syntax..

            • Or just move the project over to somewhere under the WSL root, such as /home/username/src/.



            Why set the stop flag using `memory_order_seq_cst`, if you check it with `memory_order_relaxed`?
            Asked 2022-Jan-05 at 15:38

            Herb Sutter, in his "atomic<> weapons" talk, shows several example uses of atomics, and one of them boils down to following: (video link, timestamped)

            • A main thread launches several worker threads.

            • Workers check the stop flag:



            Answered 2022-Jan-05 at 14:48
            mo_relaxed is fine for both load and store of a stop flag

            There's also no meaningful latency benefit to stronger memory orders, even if latency of seeing a change to a keep_running or exit_now flag was important.

            IDK why Herb thinks shouldn't be relaxed; in his talk, his slides have a comment that says // not relaxed on the assignment, but he doesn't say anything about the store side before moving on to "is it worth it".

            Of course, the load runs inside the worker loop, but the store runs only once, and Herb really likes to recommend sticking with SC unless you have a performance reason that truly justifies using something else. I hope that wasn't his only reason; I find that unhelpful when trying to understand what memory order would actually be necessary and why. But anyway, I think either that or a mistake on his part.

            The ISO C++ standard doesn't say anything about how soon stores become visible or what might influence that, just Section Forward progress

            18. An implementation should ensure that the last value (in modification order) assigned by an atomic or synchronization operation will become visible to all other threads in a finite period of time.

            Another thread can loop arbitrarily many times before its load actually sees this store value, even if they're both seq_cst, assuming there's no other synchronization of any kind between them. Low inter-thread latency is a performance issue, not correctness / formal guarantee.

            And non-infinite inter-thread latency is apparently only a "should" QOI (quality of implementation) issue. :P Nothing in the standard suggests that seq_cst would help on an implementation where store visibility could be delayed indefinitely, although one might guess that could be the case, e.g. on a hypothetical implementation with explicit cache flushes instead of cache coherency. (Although such an implementation is probably not practically usable in terms of performance with CPUs anything like what we have now; every release and/or acquire operation would have to flush the whole cache.)

            On real hardware (which uses some form of MESI cache coherency), different memory orders for store or load don't make stores visible sooner in real time, they just control whether later operations can become globally visible while still waiting for the store to commit from the store buffer to L1d cache. (After invalidating any other copies of the line.)

            Stronger orders, and barriers, don't make things happen sooner in an absolute sense, they just delay other things until they're allowed to happen relative to the store or load. (This is the case on all real-world CPUs AFAIK; they always try to make stores visible to other cores ASAP anyway, so the store buffer doesn't fill up, and

            See also (my similar answers on):

            The second Q&A is about x86 where commit from the store buffer to L1d cache is in program order. That limits how far past a cache-miss store execution can get, and also any possible benefit of putting a release or seq_cst fence after the store to prevent later stores (and loads) from maybe competing for resources. (x86 microarchitectures will do RFO (read for ownership) before stores reach the head of the store buffer, and plain loads normally compete for resources to track RFOs we're waiting for a response to.) But these effects are extremely minor in terms of something like exiting another thread; only very small scale reordering.

            because who cares if the thread stops with a slightly bigger delay.

            More like, who cares if the thread gets more work done by not making loads/stores after the load wait for the check to complete. (Of course, this work will get discarded if it's in the shadow of a a mis-speculated branch on the load result when we eventually load true.) The cost of rolling back to a consistent state after a branch mispredict is more or less independent of how much already-executed work had happened beyond the mispredicted branch. And it's a stop flag so the total amount of wasted work costing cache/memory bandwidth for other CPUs is pretty minimal.

            That phrasing makes it sound like an acquire load or release store would actually get the the store seen sooner in absolute real time, rather than just relative to other code in this thread. (Which is not the case).

            The benefit is more instruction-level and memory-level parallelism across loop iterations when the load produces a false. And simply avoiding running extra instructions on ISAs where an acquire or especially an SC load needs extra instructions, especially expensive 2-way barrier instructions, not like ARM64 ldapr.

            BTW, Herb is right that the dirty flag can also be relaxed, only because of the thread.join sync between the reader and any possible writer. Otherwise yeah, release / acquire.

            But in this case, dirty only needs to be atomic<> at all because of possible simultaneous writers all storing the same value, which ISO C++ still deems data-race UB. e.g. because of the theoretical possibility of hardware race-detection that traps on conflicting non-atomic accesses.



            Fast idiomatic Floyd-Warshall algorithm in Rust
            Asked 2021-Nov-21 at 22:48

            I am trying to implement a reasonably fast version of Floyd-Warshall algorithm in Rust. This algorithm finds a shortest paths between all vertices in a directed weighted graph.

            The main part of the algorithm could be written like this:



            Answered 2021-Nov-21 at 19:55

            At first blush, one would hope this would be enough:



            Inverting an Order-Preserving Minimal Perfect Hash Function in Better than O(K*lg N) Running Time
            Asked 2021-Nov-20 at 16:05

            I am trying to find a more efficient solution to a combinatorics problem than the solution I have already found.

            Suppose I have a set of N objects (indexed 0..N-1) and wish to consider each subset of size K (0<=K<=N). There are S=C(N,K) (i.e., "N choose K") such subsets. I wish to map (or "encode") each such subset to a unique integer in the range 0..S-1.

            Using N=7 (i.e., indexes are 0..6) and K=4 (S=35) as an example, the following mapping is the goal:
            0 1 2 3 --> 0
            0 1 2 4 --> 1
            2 4 5 6 --> 33
            3 4 5 6 --> 34

            N and K were chosen small for the purposes of illustration. However, in my actual application, C(N,K) is far too large to obtain these mappings from a lookup table. They must be computed on-the-fly.

            In the code that follows, combinations_table is a pre-computed two-dimensional array for fast lookup of C(N,K) values.

            All code given is compliant with the C++14 standard.

            If the objects in a subset are ordered by increasing order of their indexes, the following code will compute that subset's encoding:



            Answered 2021-Oct-21 at 02:18

            Take a look at the recursive formula for combinations:

            Suppose you have a combination space C(n,k). You can divide that space into two subspaces:

            • C(n-1,k-1) all combinations, where the first element of the original set (of length n) is present
            • C(n-1, k) where first element is not preset

            If you have an index X that corresponds to a combination from C(n,k), you can identify whether the first element of your original set belongs to the subset (which corresponds to X), if you check whether X belongs to either subspace:

            • X < C(n-1, k-1) : belongs
            • X >= C(n-1, k-1): doesn't belong

            Then you can recursively apply the same approach for C(n-1, ...) and so on, until you've found the answer for all n elements of the original set.

            Python code to illustrate this approach:



            firebase function with realtime database error
            Asked 2021-Nov-08 at 12:28

            I am new to firebase function and trying to use firebase function with Realtime database (Emulator suite).But when i try to set the value in firebase using the firebase function,it gives an error and doesn't set the value in database.




            Answered 2021-Nov-05 at 13:59

            I'm unsure as to the cause of that log message, but I do see that you are returning a response from your function before it completes all of its work. In a deployed function, as soon as the function returns, all further actions should be treated as if they will never be executed as documented here. An "inactive" function might be terminated at any time, is severely throttled and any network calls you make (like setting data in the RTDB) may never be executed.

            I know you are new to this, but its a good habit to get into now: don't assume the person calling your function is you. Check for problems like missing query parameters and dodgy data before you blindly action something. The Admin SDK bypasses your database's security rules and if you are not careful a malicious user can cause some damage (e.g. a user that updates /users/$theirUid/roles/admin to true).



            Difficulties installing Cro: "Failed to find dependencies: CBOR::Simple"
            Asked 2021-Nov-04 at 19:02

            I'm hoping to try out the Cro library in Raku:

            However, when I try to install it using zef, I get this output:



            Answered 2021-Nov-04 at 19:02

            I asked around on GitHub and IRC. The solution was to download and use rakudo-pkg to get a newer version of zef. The one that can be installed via apt is too old.



            Why is any (True for ... if cond) much faster than any (cond for ...)?
            Asked 2021-Sep-19 at 10:54

            Two similar ways to check whether a list contains an odd number:



            Answered 2021-Sep-06 at 05:17

            The first method sends everything to any() whilst the second only sends to any() when there's an odd number, so any() has fewer elements to go through.



            How is the s=s+c string concat optimization decided?
            Asked 2021-Sep-08 at 00:15

            Short version: If s is a string, then s = s + 'c' might modify the string in place, while t = s + 'c' can't. But how does the operation s + 'c' know which scenario it's in?

            Long version:

            t = s + 'c' needs to create a separate string because the program afterwards wants both the old string as s and the new string as t.

            s = s + 'c' can modify the string in place if s is the only reference, as the program only wants s to be the extended string. CPython actually does this optimization, if there's space at the end for the extra character.

            Consider these functions, which repeatedly add a character:



            Answered 2021-Sep-08 at 00:15

            Here's the code in question, from the Python 3.10 branch (in ceval.c, and called from the same file's implementation of the BINARY_ADD opcode). As @jasonharper noted in a comment, it peeks ahead to see whether the result of the BINARY_ADD will next be bound to the same name from which the left-hand addend came. In fast(), it is (operand came from s and result stored into s), but in slow() it isn't (operand came from s but stored into t).

            There's no guarantee this optimization will persist, though. For example, I noticed that your fast() is no faster than your slow() on the current development CPython main branch (which is the current work-in-progress toward an eventual 3.11 release).

            Should people rely on this?

            As noted, there's no guarantee this optimization will persist. "Serious" Python programmers should know better than to rely on dodgy CPython-specific tricks, and, indeed, PEP 8 explicitly warns against relying on this specific one:

            Code should be written in a way that does not disadvantage other implementations of Python (PyPy, Jython, IronPython, Cython, Psyco, and such).

            For example, do not rely on CPython's efficient implementation of in-place string concatenation for statements in the form a += b or a = a + b ...


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install FAST

            You can download it from GitHub.
            You can use FAST like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            Please see the Help folder for building data guidance, FAST case study information (coming soon), and a brief video demonstration of FAST:
            Find more information at:

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