reframed | ReFramed : metabolic modeling package
kandi X-RAY | reframed Summary
kandi X-RAY | reframed Summary
ReFramed implements many constraint-based simulation methods (see list below), and contains interfaces to other libraries of the COBRA ecosystem including escher, cobrapy, and optlang.
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Top functions reviewed by kandi - BETA
- Marge a model
- Returns a dictionary of metabolite reactions
- Return a solver instance
- Get the default solver
- Solve the objective function
- Build problem problem
- Add constraint
- Add variables to the problem
- Construct a SteadierComposition
- Print exchange rates
- Simulate an ensemble
- Computes the flux of a gene expression
- Convert a model to CobraPy
- Fit an abundance model to a community
- Sample a community
- Calculates the steady biomass variability of a community
- Add a constraint to the problem
- Extract the values from the model
- Convert a CBR model to a CBRA model
- R Calculates the energy of a model
- Solve the problem
- Compute the TFA
- Compute a GIMMEA model for a given gene expression
- Solves the optimizer
- Plot flux envelope
- R Compute all heterozygous switches
reframed Key Features
reframed Examples and Code Snippets
Lines of Code : 35License : Strong Copyleft (CC BY-SA 4.0)
Copydef math_qns(input):
new_list = []
for x in input:
if x[0] == "+":
math = sum(x[1:])
str_math = " + ".join([str(i) for i in x[1:]])
elif x[0] == "-":
math = x[1]
# invert scaling for forecast
inv_yhat = concatenate((yhat, test_X[:, -7:]), axis=1)
inv_yhat = scaler.inverse_transform(inv_yhat)
inv_yhat = inv_yhat[:,0]
# invert scaling for actual
test_y = test_y.reshape((len(test_y), 1))
inv_y = concawith open ('test.csv', 'r') as file:
reader = csv.reader(file)
for i in range(5):
try:
csv_row = ' '.join(next(reader))
print(csv_row)
except StopIteration:
break
for key in mydict:
try:
print("The currency is", key)
except:
print("Oops!",sys.exc_info()[0],"occured.")
break
# observed data (already considered zero-inflated)
Y = np.random.binomial(1, 0.5, size=10)
with pm.Model() as zero_inflated_beta_bernoulli:
# true_model_prior
p = pm.Beta('p', alpha=1, beta=1)
# dropout rate
d = 0.1
def sliding_windows(inputs):
target, query = inputs
target_length = K.shape(target)[1] # variable-length sequence, shape is a TF tensor
query_length = K.int_shape(query)[1]
num_windows = target_length - query_length + 1 #Community Discussions
Trending Discussions on reframed
QUESTION
I am having this code and its running as expected. However I am trying to find a better way to rewrite the following query as it can be seen that the dates and account codes are repeated all the time.
The data is being extracted from 3 Databases i.e. Db1, Db2 and Db3. The tables of each Database are similar. Even the AcctCodes to be extracted are similar.
So,I am wondering if the following code can be rewritten in few lines.
Since, the AcctCodes are similar, so adding an empty row with database name as Headers between each query helps me to identify them.
Select 'Outlet1','0','0' from Dummy
So if there is a better version of the following code, please let me know. Thanks.
...ANSWER
Answered 2021-Jun-14 at 08:04Implementing this "merge results from n different DBs" is rather common. Most of the times, this is done by means of a data warehouse.
HANA allows creating virtual tables that represent tables or views in remote systems - which is the basis for an integration scenario very popular with HANA sales folks: "...simply integrate all your DBs in HANA... no data warehouse and heavy data lifting required..."
I assume this is one of those scenarios.
So, what options are there to only have to specify the selection parameters once?
A simple approach would be to use query parameters. This can be done either via user defined table functions or parameterized views (yes, also via calculation views and parameters, but I will skip this here).
So, with this one could write something like this:
QUESTION
I have created a web app using Flutter and it is running fine. I wanted to create a mobile app out of it with as little modification to the code as possible and wanted to run it in an Android Emulator, so I installed Android Studio and then a device in ADV Manager. The code did not show any errors but Android Studio is not able to locate the newly installed device. Am I missing something?
The question could also be reframed as: how to convert a Flutter web app into a mobile app.
...ANSWER
Answered 2020-Aug-13 at 08:50Source of this answer is :here
Can you try to run the below command in terminal:
QUESTION
I am following the object_detection_tutorial.ipynb tutorial.
Here is the code ( I only put parts which are needed, the rest of the code is the same as the notebook):
...ANSWER
Answered 2020-May-13 at 21:12First import six
Add get_classes_name_and_scores method, before def show_inference(model, image_path):
get_classes_name_and_scores method returns {'name': 'person', 'score': '91%'}
QUESTION
I am having a LSTM which will predict the value for the next timestep .So my inputs are scaled into the LSTM like below:
...ANSWER
Answered 2020-May-18 at 10:32You're trying to do the inverse transform on your y data when you only applied the transform to the x data. The difference in the number of features (17 for x; 1 for y) is why you get that error
EDIT:
You need to create separate scalers for your x and y data e.g.
QUESTION
I've created a UITableView which has number of cells. All the cells are the same, I want to change the cell's subviews layout and the cell's height after tapping the cell. It's pretty easy to change the height of the cell itself, but I can't figure out how to change the cell's subviews layout...
Should I reload a new custom cell instead the tapped cell or just update the cell subviews frame?
This is the normal layout:
This is the new layout:
The constraints are not an issue, my question is how to add elements which were unused in a normal cell and now they needed to be reframed and enlarge (such as the main UIImageView and UILabel's)
...ANSWER
Answered 2020-Apr-16 at 19:28I'd have different cells and reload them as the cells are clicked.
QUESTION
I'm trying to use Keras to make simultaneous predictions for multiple variables. Using this example here, I want to predict values for all features including pm 2.5, DEWP, TEMP etc. instead of just pollution (pm 2.5). Essentially, this is, given all the variables, build a model to predict all variables as time series, not just forecasting one variable.
I modified the original sample code by using the reshaped 3D data, but got an error. Here's the code:
...ANSWER
Answered 2020-Mar-18 at 18:09I've found a solution here (under "Multiple Parallel Series"). We just need to reshape the features and labels and feed in the network, it'll just work! The features should have the shape of (n_steps, n_features) while the labels should have the shape (n_samples, n_features) (if we are predicting 1 timestep).
QUESTION
I have a flask app which is used to take user input as images and run a model and save it in a folder, it initially takes a single image and run the app. However I would like my app to take multiple images, run the model on these images and then save it to the folder. With all the answers I found on this StackOverflow question: Uploading multiple files with Flask none of them worked for my usecase. Please help me throw some light on where i am going wrong. here's my flask file
...ANSWER
Answered 2020-Feb-12 at 07:39You are not getting the list of files that is why you do not get multiple files. You need to access the list of files from the form which comes from the username input.
QUESTION
I am reading the fix-point of SICP:
ANSWER
Answered 2019-Dec-27 at 08:09It's the opposite: it's cleaner and more efficient with try because it doesn't need to redefine the good-enough-p.
(also, you're not supposed to use recursion in Python).
The version with try is better than the version which calls the top function, fixed-point, because fixed-point contains inner definitions, of the functions good-enough-p and try. A simple-minded compiler would compile it so that on each call it actually makes those definitions anew, again and again, on each call. With try there's no such concern as it is already inside the fixed-point's inner environment where good-enough-p is already defined, and so try can just run.
(correction/clarification: the above treats your code as if it were Scheme, with internal defines instead of the Common Lisp with defuns as you show. SICP is Scheme, after all. In Common Lisp / ELisp there's not even a question -- the internal defuns will always be performed, on each call to the enclosing function, just (re)defining the same functions at the top level over and over again.)
Incidentally, I like your Python loop translation, it is a verbatim translation of the Scheme's tail-recursive loop, one to one.
Your while translation is exactly what a Scheme compiler is supposed to be doing given the first tail-recursive Scheme code in your question. The two are exactly the same, down to the "horrible while True ... with an escape" which, personally, I quite like for its immediacy and clarity. Meaning, I don't need to keep track of which value gets assigned to what variable and which variable gets returned in the end -- instead, a value is just returned, just like it is in Scheme.
QUESTION
I'm trying to implement the code from 'object_detection_tutorial.ipynb' on my local machine to change some parts and play around. This tutorial is a huge mess and I'm trying really hard to fix any problem I came across but for this one I had no clue. So, here I am.
I'm using Windows 10 and Visual Studio 2019 Professional. Any package related to Tensorflow is up to date and I have another Machine Learning application running with no problems.
I'd like to point out that, I converted this code from its original format which is 'ipynb'. (save as .py)
If you need any extra information please ask me because I really need to understand this concept on a working code.
num_detections = int(output_dict.pop('num_detections')) this part gives the error:
Error int() argument must be a string, a bytes-like object or a number, not 'Tensor'
...ANSWER
Answered 2019-Nov-04 at 08:59Guys I've fixed the problem. Apparently, I had a problem with my Tensorflow installation. So, I've deleted all the related installation and re-installed everything.
The problem should be related to this because TF v2.0 has Tensor to int conversion already.
QUESTION
I'd like to train my TensorFlow object_detection model on black and white images, because the objects I'm trying to detect do not require color. However, when I train my model on black and white images, whenever I try and test them out with the image testing script I adapted, I get the following error: ValueError: Cannot feed value of shape (1, 1080, 1920) for Tensor 'image_tensor:0', which has shape '(?, ?, ?, 3)'
I can't say a lot, but my model is supposed to detect deformities along pipes, and that it's based on ssd_mobilenet_v1_coco_2018_01_28. Most of the deformities are visually detected by an altered shape, which is why color would just get in the way of the object detection process. (At least I think so, it would make sense to me that only detecting shape would increase accuracy).
TensorBoard Model Graph
I've tried reshaping the image to (1, 1080, 1920, 3), 1080x1920 being my image resolution.
Here's the code:
...ANSWER
Answered 2019-Jun-27 at 13:29Your input image is gray scale so you should make appropriate changes while running your model. Take a look at this code:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install reframed
You can use reframed 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.
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