mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-12-26 01:29:10 +00:00
Merge pull request #43 from titanscouting/master-staged
Pull changes from master staged to master for release
This commit is contained in:
Arthur Lu
GitHub
commit
b9a2e680bc
@ -24,5 +24,5 @@
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"ms-python.python",
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"waderyan.gitblame"
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],
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"postCreateCommand": "apt install vim -y && pip install -r data-analysis/requirements.txt && pip install -r analysis-master/requirements.txt && pip install tra-analysis"
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"postCreateCommand": "apt install vim -y ; pip install -r data-analysis/requirements.txt ; pip install -r analysis-master/requirements.txt ; pip install tra-analysis"
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}
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3
.gitignore
vendored
3
.gitignore
vendored
@ -37,4 +37,5 @@ analysis-master/tra_analysis/__pycache__
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analysis-master/tra_analysis/.ipynb_checkpoints
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.pytest_cache
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analysis-master/tra_analysis/metrics/__pycache__
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analysis-master/dist
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analysis-master/dist
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data-analysis/config/
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@ -11,9 +11,9 @@ after installing python, or with a package manager on linux. Refer to the [pip i
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### Standard Platforms
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For the latest version of tra-analysis, run `pip install tra-analysis` or `pip install tra_analysis`. The requirements for tra-analysis should be automatically installed.
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### Exotic Platforms (Android)
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[Termux](https://termux.com/) is recommended for a linux environemnt on Android. Consult the [documentation](https://titanscouting.github.io/analysis/installation#exotic-platforms-android) for advice on installing the prerequisites. After installing the prerequisites, the package should be installed normally with `pip install tra-analysis` or `pip install tra_analysis`.
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[Termux](https://termux.com/) is recommended for a linux environemnt on Android. Consult the [documentation](https://titanscouting.github.io/analysis/general/installation#exotic-platforms-android) for advice on installing the prerequisites. After installing the prerequisites, the package should be installed normally with `pip install tra-analysis` or `pip install tra_analysis`.
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## Use
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tra-analysis operates like any other python package. Consult the [documentation](https://titanscouting.github.io/analysis/) for more information.
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tra-analysis operates like any other python package. Consult the [documentation](https://titanscouting.github.io/analysis/tra_analysis/) for more information.
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# Supported Platforms
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Although any modern 64 bit platform should be supported, the following platforms have been tested to be working:
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* AMD64 (Tested on Zen, Zen+, and Zen 2)
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@ -8,7 +8,7 @@ with open("requirements.txt", 'r') as file:
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setuptools.setup(
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name="tra_analysis",
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version="2.0.3",
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version="2.1.0",
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author="The Titan Scouting Team",
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author_email="titanscout2022@gmail.com",
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description="Analysis package developed by Titan Scouting for The Red Alliance",
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@ -7,12 +7,23 @@
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# current benchmark of optimization: 1.33 times faster
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# setup:
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__version__ = "2.2.1"
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__version__ = "2.3.1"
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# changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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2.3.1:
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- fixed bugs in Array class
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2.3.0:
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- overhauled Array class
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2.2.3:
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- fixed spelling of RandomForest
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- made n_neighbors required for KNN
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- made n_classifiers required for SVM
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2.2.2:
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- fixed 2.2.1 changelog entry
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- changed regression to return dictionary
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2.2.1:
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changed all references to parent package analysis to tra_analysis
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- changed all references to parent package analysis to tra_analysis
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2.2.0:
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- added Sort class
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- added several array sorting functions to Sort class including:
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@ -424,7 +435,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
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X = np.array(inputs)
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y = np.array(outputs)
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regressions = []
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regressions = {}
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if 'lin' in args: # formula: ax + b
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@ -437,7 +448,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
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popt, pcov = scipy.optimize.curve_fit(lin, X, y)
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coeffs = popt.flatten().tolist()
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regressions.append(str(coeffs[0]) + "*x+" + str(coeffs[1]))
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regressions["lin"] = (str(coeffs[0]) + "*x+" + str(coeffs[1]))
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except Exception as e:
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@ -454,7 +465,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
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popt, pcov = scipy.optimize.curve_fit(log, X, y)
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coeffs = popt.flatten().tolist()
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regressions.append(str(coeffs[0]) + "*log(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
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regressions["log"] = (str(coeffs[0]) + "*log(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
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except Exception as e:
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@ -471,7 +482,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
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popt, pcov = scipy.optimize.curve_fit(exp, X, y)
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coeffs = popt.flatten().tolist()
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regressions.append(str(coeffs[0]) + "*e^(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
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regressions["exp"] = (str(coeffs[0]) + "*e^(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
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except Exception as e:
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@ -482,7 +493,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
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inputs = np.array([inputs])
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outputs = np.array([outputs])
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plys = []
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plys = {}
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limit = len(outputs[0])
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for i in range(2, limit):
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@ -500,9 +511,9 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
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for param in params:
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temp += "(" + str(param) + "*x^" + str(counter) + ")"
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counter += 1
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plys.append(temp)
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plys["x^" + str(i)] = (temp)
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regressions.append(plys)
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regressions["ply"] = (plys)
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if 'sig' in args: # formula: a tanh (b(x + c)) + d
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@ -515,7 +526,7 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
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popt, pcov = scipy.optimize.curve_fit(sig, X, y)
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coeffs = popt.flatten().tolist()
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regressions.append(str(coeffs[0]) + "*tanh(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
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regressions["sig"] = (str(coeffs[0]) + "*tanh(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
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except Exception as e:
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@ -642,7 +653,7 @@ def decisiontree(data, labels, test_size = 0.3, criterion = "gini", splitter = "
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class KNN:
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def knn_classifier(self, data, labels, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
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def knn_classifier(self, data, labels, n_neighbors, test_size = 0.3, algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, p=2, weights='uniform'): #expects *2d data and 1d labels post-scaling
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data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
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model = sklearn.neighbors.KNeighborsClassifier()
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@ -651,7 +662,7 @@ class KNN:
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return model, ClassificationMetric(predictions, labels_test)
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def knn_regressor(self, data, outputs, test_size, n_neighbors = 5, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
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def knn_regressor(self, data, outputs, n_neighbors, test_size = 0.3, weights = "uniform", algorithm = "auto", leaf_size = 30, p = 2, metric = "minkowski", metric_params = None, n_jobs = None):
|
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data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
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model = sklearn.neighbors.KNeighborsRegressor(n_neighbors = n_neighbors, weights = weights, algorithm = algorithm, leaf_size = leaf_size, p = p, metric = metric, metric_params = metric_params, n_jobs = n_jobs)
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@ -754,9 +765,9 @@ class SVM:
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return RegressionMetric(predictions, test_outputs)
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class RandomForrest:
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class RandomForest:
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def random_forest_classifier(self, data, labels, test_size, n_estimators="warn", criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None):
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def random_forest_classifier(self, data, labels, test_size, n_estimators, criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None):
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data_train, data_test, labels_train, labels_test = sklearn.model_selection.train_test_split(data, labels, test_size=test_size, random_state=1)
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kernel = sklearn.ensemble.RandomForestClassifier(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_samples_leaf = min_samples_leaf, min_weight_fraction_leaf = min_weight_fraction_leaf, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start, class_weight = class_weight)
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@ -765,7 +776,7 @@ class RandomForrest:
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return kernel, ClassificationMetric(predictions, labels_test)
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def random_forest_regressor(self, data, outputs, test_size, n_estimators="warn", criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False):
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def random_forest_regressor(self, data, outputs, test_size, n_estimators, criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False):
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data_train, data_test, outputs_train, outputs_test = sklearn.model_selection.train_test_split(data, outputs, test_size=test_size, random_state=1)
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kernel = sklearn.ensemble.RandomForestRegressor(n_estimators = n_estimators, criterion = criterion, max_depth = max_depth, min_samples_split = min_samples_split, min_weight_fraction_leaf = min_weight_fraction_leaf, max_features = max_features, max_leaf_nodes = max_leaf_nodes, min_impurity_decrease = min_impurity_decrease, min_impurity_split = min_impurity_split, bootstrap = bootstrap, oob_score = oob_score, n_jobs = n_jobs, random_state = random_state, verbose = verbose, warm_start = warm_start)
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@ -779,7 +790,7 @@ class CorrelationTest:
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def anova_oneway(self, *args): #expects arrays of samples
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results = scipy.stats.f_oneway(*args)
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return {"F-value": results[0], "p-value": results[1]}
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return {"f-value": results[0], "p-value": results[1]}
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def pearson(self, x, y):
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@ -978,81 +989,112 @@ class StatisticalTest:
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return {"z-score": results[0], "p-value": results[1]}
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class Array(): # tests on nd arrays independent of basic_stats
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|
||||
def __init__(self, narray):
|
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|
||||
self.array = np.array(narray)
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|
||||
def __str__(self):
|
||||
|
||||
return str(self.array)
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||||
|
||||
def elementwise_mean(self, *args): # expects arrays that are size normalized
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||||
def elementwise_mean(self, *args, axis = 0): # expects arrays that are size normalized
|
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if len(*args) == 0:
|
||||
return np.mean(self.array, axis = axis)
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else:
|
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return np.mean([*args], axis = axis)
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return np.mean([*args], axis = 0)
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def elementwise_median(self, *args, axis = 0):
|
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def elementwise_median(self, *args):
|
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if len(*args) == 0:
|
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return np.median(self.array, axis = axis)
|
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else:
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return np.median([*args], axis = axis)
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return np.median([*args], axis = 0)
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def elementwise_stdev(self, *args, axis = 0):
|
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|
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def elementwise_stdev(self, *args):
|
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if len(*args) == 0:
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return np.std(self.array, axis = axis)
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else:
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return np.std([*args], axis = axis)
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return np.std([*args], axis = 0)
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def elementwise_variance(self, *args, axis = 0):
|
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|
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def elementwise_variance(self, *args):
|
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if len(*args) == 0:
|
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return np.var(self.array, axis = axis)
|
||||
else:
|
||||
return np.var([*args], axis = axis)
|
||||
|
||||
return np.var([*args], axis = 0)
|
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def elementwise_npmin(self, *args, axis = 0):
|
||||
|
||||
def elementwise_npmin(self, *args):
|
||||
if len(*args) == 0:
|
||||
return np.amin(self.array, axis = axis)
|
||||
else:
|
||||
return np.amin([*args], axis = axis)
|
||||
|
||||
return np.amin([*args], axis = 0)
|
||||
def elementwise_npmax(self, *args, axis = 0):
|
||||
|
||||
def elementwise_npmax(self, *args):
|
||||
if len(*args) == 0:
|
||||
return np.amax(self.array, axis = axis)
|
||||
else:
|
||||
return np.amax([*args], axis = axis)
|
||||
|
||||
return np.amax([*args], axis = 0)
|
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def elementwise_stats(self, *args, axis = 0):
|
||||
|
||||
def elementwise_stats(self, *args):
|
||||
|
||||
_mean = self.elementwise_mean(*args)
|
||||
_median = self.elementwise_median(*args)
|
||||
_stdev = self.elementwise_stdev(*args)
|
||||
_variance = self.elementwise_variance(*args)
|
||||
_min = self.elementwise_npmin(*args)
|
||||
_max = self.elementwise_npmax(*args)
|
||||
_mean = self.elementwise_mean(*args, axis = axis)
|
||||
_median = self.elementwise_median(*args, axis = axis)
|
||||
_stdev = self.elementwise_stdev(*args, axis = axis)
|
||||
_variance = self.elementwise_variance(*args, axis = axis)
|
||||
_min = self.elementwise_npmin(*args, axis = axis)
|
||||
_max = self.elementwise_npmax(*args, axis = axis)
|
||||
|
||||
return _mean, _median, _stdev, _variance, _min, _max
|
||||
|
||||
def __getitem__(self, key):
|
||||
|
||||
return self.array[key]
|
||||
|
||||
def __setitem__(self, key, value):
|
||||
|
||||
self.array[key] == value
|
||||
|
||||
def normalize(self, array):
|
||||
|
||||
a = np.atleast_1d(np.linalg.norm(array))
|
||||
a[a==0] = 1
|
||||
return array / np.expand_dims(a, -1)
|
||||
|
||||
def add(self, *args):
|
||||
def __add__(self, other):
|
||||
|
||||
temp = np.array([])
|
||||
return self.array + other.array
|
||||
|
||||
for a in args:
|
||||
temp += a
|
||||
def __sub__(self, other):
|
||||
|
||||
return self.array - other.array
|
||||
|
||||
def __neg__(self):
|
||||
|
||||
return temp
|
||||
return -self.array
|
||||
|
||||
def mul(self, *args):
|
||||
def __abs__(self):
|
||||
|
||||
temp = np.array([])
|
||||
return abs(self.array)
|
||||
|
||||
for a in args:
|
||||
temp *= a
|
||||
|
||||
return temp
|
||||
def __invert__(self):
|
||||
|
||||
def neg(self, array):
|
||||
|
||||
return -array
|
||||
return 1/self.array
|
||||
|
||||
def inv(self, array):
|
||||
def __mul__(self, other):
|
||||
|
||||
return 1/array
|
||||
return self.array.dot(other.array)
|
||||
|
||||
def dot(self, a, b):
|
||||
def __rmul__(self, other):
|
||||
|
||||
return np.dot(a, b)
|
||||
return self.array.dot(other.array)
|
||||
|
||||
def cross(self, a, b):
|
||||
def cross(self, other):
|
||||
|
||||
return np.cross(a, b)
|
||||
return np.cross(self.array, other.array)
|
||||
|
||||
def sort(self, array): # depreciated
|
||||
warnings.warn("Array.sort has been depreciated in favor of Sort")
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
{
|
||||
"team": "",
|
||||
"competition": "",
|
||||
"competition": "2020ilch",
|
||||
"key":{
|
||||
"database":"",
|
||||
"tba":""
|
||||
|
||||
@ -1 +0,0 @@
|
||||
2020ilch
|
||||
@ -1,14 +0,0 @@
|
||||
balls-blocked,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
|
||||
balls-collected,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
|
||||
balls-lower-teleop,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
|
||||
balls-lower-auto,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
|
||||
balls-started,basic_stats,historical_analyss,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
|
||||
balls-upper-teleop,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
|
||||
balls-upper-auto,basic_stats,historical_analysis,regression_linear,regression_logarithmic,regression_exponential,regression_polynomial,regression_sigmoidal
|
||||
wheel-mechanism
|
||||
low-balls
|
||||
high-balls
|
||||
wheel-success
|
||||
strategic-focus
|
||||
climb-mechanism
|
||||
attitude
|
||||
@ -3,10 +3,12 @@
|
||||
# Notes:
|
||||
# setup:
|
||||
|
||||
__version__ = "0.6.2"
|
||||
__version__ = "0.7.0"
|
||||
|
||||
# changelog should be viewed using print(analysis.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
0.7.0:
|
||||
- finished implementing main function
|
||||
0.6.2:
|
||||
- integrated get_team_rankings.py as get_team_metrics() function
|
||||
- integrated visualize_pit.py as graph_pit_histogram() function
|
||||
@ -120,6 +122,65 @@ import matplotlib.pyplot as plt
|
||||
import time
|
||||
import warnings
|
||||
|
||||
def main():
|
||||
|
||||
warnings.filterwarnings("ignore")
|
||||
|
||||
while (True):
|
||||
|
||||
current_time = time.time()
|
||||
print("[OK] time: " + str(current_time))
|
||||
|
||||
config = load_config("config.json")
|
||||
competition = config["competition"]
|
||||
match_tests = config["statistics"]["match"]
|
||||
pit_tests = config["statistics"]["pit"]
|
||||
metrics_tests = config["statistics"]["metric"]
|
||||
print("[OK] configs loaded")
|
||||
|
||||
apikey = config["key"]["database"]
|
||||
tbakey = config["key"]["tba"]
|
||||
print("[OK] loaded keys")
|
||||
|
||||
previous_time = get_previous_time(apikey)
|
||||
print("[OK] analysis backtimed to: " + str(previous_time))
|
||||
|
||||
print("[OK] loading data")
|
||||
start = time.time()
|
||||
match_data = load_match(apikey, competition)
|
||||
pit_data = load_pit(apikey, competition)
|
||||
print("[OK] loaded data in " + str(time.time() - start) + " seconds")
|
||||
|
||||
print("[OK] running tests")
|
||||
start = time.time()
|
||||
matchloop(apikey, competition, match_data, match_tests)
|
||||
print("[OK] finished tests in " + str(time.time() - start) + " seconds")
|
||||
|
||||
print("[OK] running metrics")
|
||||
start = time.time()
|
||||
metricloop(tbakey, apikey, competition, previous_time, metrics_tests)
|
||||
print("[OK] finished metrics in " + str(time.time() - start) + " seconds")
|
||||
|
||||
print("[OK] running pit analysis")
|
||||
start = time.time()
|
||||
pitloop(apikey, competition, pit_data, pit_tests)
|
||||
print("[OK] finished pit analysis in " + str(time.time() - start) + " seconds")
|
||||
|
||||
set_current_time(apikey, current_time)
|
||||
print("[OK] finished all tests, looping")
|
||||
|
||||
clear()
|
||||
|
||||
def clear():
|
||||
|
||||
# for windows
|
||||
if name == 'nt':
|
||||
_ = system('cls')
|
||||
|
||||
# for mac and linux(here, os.name is 'posix')
|
||||
else:
|
||||
_ = system('clear')
|
||||
|
||||
def load_config(file):
|
||||
|
||||
config_vector = {}
|
||||
@ -148,6 +209,10 @@ def get_previous_time(apikey):
|
||||
|
||||
return previous_time
|
||||
|
||||
def set_current_time(apikey, current_time):
|
||||
|
||||
d.set_analysis_flags(apikey, "latest_update", {"latest_update":current_time})
|
||||
|
||||
def load_match(apikey, competition):
|
||||
|
||||
return d.get_match_data_formatted(apikey, competition)
|
||||
@ -402,4 +467,6 @@ def graph_pit_histogram(apikey, competition, figsize=(80,15)):
|
||||
|
||||
i+=1
|
||||
|
||||
plt.show()
|
||||
plt.show()
|
||||
|
||||
main()
|
||||
@ -1,378 +0,0 @@
|
||||
# Titan Robotics Team 2022: Superscript Script
|
||||
# Written by Arthur Lu & Jacob Levine
|
||||
# Notes:
|
||||
# setup:
|
||||
|
||||
__version__ = "0.0.5.002"
|
||||
|
||||
# changelog should be viewed using print(analysis.__changelog__)
|
||||
__changelog__ = """changelog:
|
||||
0.0.5.002:
|
||||
- made changes due to refactoring of analysis
|
||||
0.0.5.001:
|
||||
- text fixes
|
||||
- removed matplotlib requirement
|
||||
0.0.5.000:
|
||||
- improved user interface
|
||||
0.0.4.002:
|
||||
- removed unessasary code
|
||||
0.0.4.001:
|
||||
- fixed bug where X range for regression was determined before sanitization
|
||||
- better sanitized data
|
||||
0.0.4.000:
|
||||
- fixed spelling issue in __changelog__
|
||||
- addressed nan bug in regression
|
||||
- fixed errors on line 335 with metrics calling incorrect key "glicko2"
|
||||
- fixed errors in metrics computing
|
||||
0.0.3.000:
|
||||
- added analysis to pit data
|
||||
0.0.2.001:
|
||||
- minor stability patches
|
||||
- implemented db syncing for timestamps
|
||||
- fixed bugs
|
||||
0.0.2.000:
|
||||
- finalized testing and small fixes
|
||||
0.0.1.004:
|
||||
- finished metrics implement, trueskill is bugged
|
||||
0.0.1.003:
|
||||
- working
|
||||
0.0.1.002:
|
||||
- started implement of metrics
|
||||
0.0.1.001:
|
||||
- cleaned up imports
|
||||
0.0.1.000:
|
||||
- tested working, can push to database
|
||||
0.0.0.009:
|
||||
- tested working
|
||||
- prints out stats for the time being, will push to database later
|
||||
0.0.0.008:
|
||||
- added data import
|
||||
- removed tba import
|
||||
- finished main method
|
||||
0.0.0.007:
|
||||
- added load_config
|
||||
- optimized simpleloop for readibility
|
||||
- added __all__ entries
|
||||
- added simplestats engine
|
||||
- pending testing
|
||||
0.0.0.006:
|
||||
- fixes
|
||||
0.0.0.005:
|
||||
- imported pickle
|
||||
- created custom database object
|
||||
0.0.0.004:
|
||||
- fixed simpleloop to actually return a vector
|
||||
0.0.0.003:
|
||||
- added metricsloop which is unfinished
|
||||
0.0.0.002:
|
||||
- added simpleloop which is untested until data is provided
|
||||
0.0.0.001:
|
||||
- created script
|
||||
- added analysis, numba, numpy imports
|
||||
"""
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
"Jacob Levine <jlevine@imsa.edu>",
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"main",
|
||||
"load_config",
|
||||
"simpleloop",
|
||||
"simplestats",
|
||||
"metricsloop"
|
||||
]
|
||||
|
||||
# imports:
|
||||
|
||||
from tra_analysis import analysis as an
|
||||
import data as d
|
||||
import numpy as np
|
||||
from os import system, name
|
||||
from pathlib import Path
|
||||
import time
|
||||
import warnings
|
||||
|
||||
def main():
|
||||
warnings.filterwarnings("ignore")
|
||||
while(True):
|
||||
|
||||
current_time = time.time()
|
||||
print("[OK] time: " + str(current_time))
|
||||
|
||||
start = time.time()
|
||||
config = load_config(Path("config/stats.config"))
|
||||
competition = an.load_csv(Path("config/competition.config"))[0][0]
|
||||
print("[OK] configs loaded")
|
||||
|
||||
apikey = an.load_csv(Path("config/keys.config"))[0][0]
|
||||
tbakey = an.load_csv(Path("config/keys.config"))[1][0]
|
||||
print("[OK] loaded keys")
|
||||
|
||||
previous_time = d.get_analysis_flags(apikey, "latest_update")
|
||||
|
||||
if(previous_time == None):
|
||||
|
||||
d.set_analysis_flags(apikey, "latest_update", 0)
|
||||
previous_time = 0
|
||||
|
||||
else:
|
||||
|
||||
previous_time = previous_time["latest_update"]
|
||||
|
||||
print("[OK] analysis backtimed to: " + str(previous_time))
|
||||
|
||||
print("[OK] loading data")
|
||||
start = time.time()
|
||||
data = d.get_match_data_formatted(apikey, competition)
|
||||
pit_data = d.pit = d.get_pit_data_formatted(apikey, competition)
|
||||
print("[OK] loaded data in " + str(time.time() - start) + " seconds")
|
||||
|
||||
print("[OK] running tests")
|
||||
start = time.time()
|
||||
results = simpleloop(data, config)
|
||||
print("[OK] finished tests in " + str(time.time() - start) + " seconds")
|
||||
|
||||
print("[OK] running metrics")
|
||||
start = time.time()
|
||||
metricsloop(tbakey, apikey, competition, previous_time)
|
||||
print("[OK] finished metrics in " + str(time.time() - start) + " seconds")
|
||||
|
||||
print("[OK] running pit analysis")
|
||||
start = time.time()
|
||||
pit = pitloop(pit_data, config)
|
||||
print("[OK] finished pit analysis in " + str(time.time() - start) + " seconds")
|
||||
|
||||
d.set_analysis_flags(apikey, "latest_update", {"latest_update":current_time})
|
||||
|
||||
print("[OK] pushing to database")
|
||||
start = time.time()
|
||||
push_to_database(apikey, competition, results, pit)
|
||||
print("[OK] pushed to database in " + str(time.time() - start) + " seconds")
|
||||
|
||||
clear()
|
||||
|
||||
def clear():
|
||||
|
||||
# for windows
|
||||
if name == 'nt':
|
||||
_ = system('cls')
|
||||
|
||||
# for mac and linux(here, os.name is 'posix')
|
||||
else:
|
||||
_ = system('clear')
|
||||
|
||||
def load_config(file):
|
||||
config_vector = {}
|
||||
file = an.load_csv(file)
|
||||
for line in file:
|
||||
config_vector[line[0]] = line[1:]
|
||||
|
||||
return config_vector
|
||||
|
||||
def simpleloop(data, tests): # expects 3D array with [Team][Variable][Match]
|
||||
|
||||
return_vector = {}
|
||||
for team in data:
|
||||
variable_vector = {}
|
||||
for variable in data[team]:
|
||||
test_vector = {}
|
||||
variable_data = data[team][variable]
|
||||
if(variable in tests):
|
||||
for test in tests[variable]:
|
||||
test_vector[test] = simplestats(variable_data, test)
|
||||
else:
|
||||
pass
|
||||
variable_vector[variable] = test_vector
|
||||
return_vector[team] = variable_vector
|
||||
|
||||
return return_vector
|
||||
|
||||
def simplestats(data, test):
|
||||
|
||||
data = np.array(data)
|
||||
data = data[np.isfinite(data)]
|
||||
ranges = list(range(len(data)))
|
||||
|
||||
if(test == "basic_stats"):
|
||||
return an.basic_stats(data)
|
||||
|
||||
if(test == "historical_analysis"):
|
||||
return an.histo_analysis([ranges, data])
|
||||
|
||||
if(test == "regression_linear"):
|
||||
return an.regression(ranges, data, ['lin'])
|
||||
|
||||
if(test == "regression_logarithmic"):
|
||||
return an.regression(ranges, data, ['log'])
|
||||
|
||||
if(test == "regression_exponential"):
|
||||
return an.regression(ranges, data, ['exp'])
|
||||
|
||||
if(test == "regression_polynomial"):
|
||||
return an.regression(ranges, data, ['ply'])
|
||||
|
||||
if(test == "regression_sigmoidal"):
|
||||
return an.regression(ranges, data, ['sig'])
|
||||
|
||||
def push_to_database(apikey, competition, results, pit):
|
||||
|
||||
for team in results:
|
||||
|
||||
d.push_team_tests_data(apikey, competition, team, results[team])
|
||||
|
||||
for variable in pit:
|
||||
|
||||
d.push_team_pit_data(apikey, competition, variable, pit[variable])
|
||||
|
||||
def metricsloop(tbakey, apikey, competition, timestamp): # listener based metrics update
|
||||
|
||||
elo_N = 400
|
||||
elo_K = 24
|
||||
|
||||
matches = d.pull_new_tba_matches(tbakey, competition, timestamp)
|
||||
|
||||
red = {}
|
||||
blu = {}
|
||||
|
||||
for match in matches:
|
||||
|
||||
red = load_metrics(apikey, competition, match, "red")
|
||||
blu = load_metrics(apikey, competition, match, "blue")
|
||||
|
||||
elo_red_total = 0
|
||||
elo_blu_total = 0
|
||||
|
||||
gl2_red_score_total = 0
|
||||
gl2_blu_score_total = 0
|
||||
|
||||
gl2_red_rd_total = 0
|
||||
gl2_blu_rd_total = 0
|
||||
|
||||
gl2_red_vol_total = 0
|
||||
gl2_blu_vol_total = 0
|
||||
|
||||
for team in red:
|
||||
|
||||
elo_red_total += red[team]["elo"]["score"]
|
||||
|
||||
gl2_red_score_total += red[team]["gl2"]["score"]
|
||||
gl2_red_rd_total += red[team]["gl2"]["rd"]
|
||||
gl2_red_vol_total += red[team]["gl2"]["vol"]
|
||||
|
||||
for team in blu:
|
||||
|
||||
elo_blu_total += blu[team]["elo"]["score"]
|
||||
|
||||
gl2_blu_score_total += blu[team]["gl2"]["score"]
|
||||
gl2_blu_rd_total += blu[team]["gl2"]["rd"]
|
||||
gl2_blu_vol_total += blu[team]["gl2"]["vol"]
|
||||
|
||||
red_elo = {"score": elo_red_total / len(red)}
|
||||
blu_elo = {"score": elo_blu_total / len(blu)}
|
||||
|
||||
red_gl2 = {"score": gl2_red_score_total / len(red), "rd": gl2_red_rd_total / len(red), "vol": gl2_red_vol_total / len(red)}
|
||||
blu_gl2 = {"score": gl2_blu_score_total / len(blu), "rd": gl2_blu_rd_total / len(blu), "vol": gl2_blu_vol_total / len(blu)}
|
||||
|
||||
|
||||
if(match["winner"] == "red"):
|
||||
|
||||
observations = {"red": 1, "blu": 0}
|
||||
|
||||
elif(match["winner"] == "blue"):
|
||||
|
||||
observations = {"red": 0, "blu": 1}
|
||||
|
||||
else:
|
||||
|
||||
observations = {"red": 0.5, "blu": 0.5}
|
||||
|
||||
red_elo_delta = an.Metrics.elo(red_elo["score"], blu_elo["score"], observations["red"], elo_N, elo_K) - red_elo["score"]
|
||||
blu_elo_delta = an.Metrics.elo(blu_elo["score"], red_elo["score"], observations["blu"], elo_N, elo_K) - blu_elo["score"]
|
||||
|
||||
new_red_gl2_score, new_red_gl2_rd, new_red_gl2_vol = an.Metrics.glicko2(red_gl2["score"], red_gl2["rd"], red_gl2["vol"], [blu_gl2["score"]], [blu_gl2["rd"]], [observations["red"], observations["blu"]])
|
||||
new_blu_gl2_score, new_blu_gl2_rd, new_blu_gl2_vol = an.Metrics.glicko2(blu_gl2["score"], blu_gl2["rd"], blu_gl2["vol"], [red_gl2["score"]], [red_gl2["rd"]], [observations["blu"], observations["red"]])
|
||||
|
||||
red_gl2_delta = {"score": new_red_gl2_score - red_gl2["score"], "rd": new_red_gl2_rd - red_gl2["rd"], "vol": new_red_gl2_vol - red_gl2["vol"]}
|
||||
blu_gl2_delta = {"score": new_blu_gl2_score - blu_gl2["score"], "rd": new_blu_gl2_rd - blu_gl2["rd"], "vol": new_blu_gl2_vol - blu_gl2["vol"]}
|
||||
|
||||
for team in red:
|
||||
|
||||
red[team]["elo"]["score"] = red[team]["elo"]["score"] + red_elo_delta
|
||||
|
||||
red[team]["gl2"]["score"] = red[team]["gl2"]["score"] + red_gl2_delta["score"]
|
||||
red[team]["gl2"]["rd"] = red[team]["gl2"]["rd"] + red_gl2_delta["rd"]
|
||||
red[team]["gl2"]["vol"] = red[team]["gl2"]["vol"] + red_gl2_delta["vol"]
|
||||
|
||||
for team in blu:
|
||||
|
||||
blu[team]["elo"]["score"] = blu[team]["elo"]["score"] + blu_elo_delta
|
||||
|
||||
blu[team]["gl2"]["score"] = blu[team]["gl2"]["score"] + blu_gl2_delta["score"]
|
||||
blu[team]["gl2"]["rd"] = blu[team]["gl2"]["rd"] + blu_gl2_delta["rd"]
|
||||
blu[team]["gl2"]["vol"] = blu[team]["gl2"]["vol"] + blu_gl2_delta["vol"]
|
||||
|
||||
temp_vector = {}
|
||||
temp_vector.update(red)
|
||||
temp_vector.update(blu)
|
||||
|
||||
for team in temp_vector:
|
||||
|
||||
d.push_team_metrics_data(apikey, competition, team, temp_vector[team])
|
||||
|
||||
def load_metrics(apikey, competition, match, group_name):
|
||||
|
||||
group = {}
|
||||
|
||||
for team in match[group_name]:
|
||||
|
||||
db_data = d.get_team_metrics_data(apikey, competition, team)
|
||||
|
||||
if d.get_team_metrics_data(apikey, competition, team) == None:
|
||||
|
||||
elo = {"score": 1500}
|
||||
gl2 = {"score": 1500, "rd": 250, "vol": 0.06}
|
||||
ts = {"mu": 25, "sigma": 25/3}
|
||||
|
||||
#d.push_team_metrics_data(apikey, competition, team, {"elo":elo, "gl2":gl2,"trueskill":ts})
|
||||
|
||||
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
|
||||
|
||||
else:
|
||||
|
||||
metrics = db_data["metrics"]
|
||||
|
||||
elo = metrics["elo"]
|
||||
gl2 = metrics["gl2"]
|
||||
ts = metrics["ts"]
|
||||
|
||||
group[team] = {"elo": elo, "gl2": gl2, "ts": ts}
|
||||
|
||||
return group
|
||||
|
||||
def pitloop(pit, tests):
|
||||
|
||||
return_vector = {}
|
||||
for team in pit:
|
||||
for variable in pit[team]:
|
||||
if(variable in tests):
|
||||
if(not variable in return_vector):
|
||||
return_vector[variable] = []
|
||||
return_vector[variable].append(pit[team][variable])
|
||||
|
||||
return return_vector
|
||||
|
||||
main()
|
||||
|
||||
"""
|
||||
Metrics Defaults:
|
||||
|
||||
elo starting score = 1500
|
||||
elo N = 400
|
||||
elo K = 24
|
||||
|
||||
gl2 starting score = 1500
|
||||
gl2 starting rd = 350
|
||||
gl2 starting vol = 0.06
|
||||
"""
|
||||
@ -1,188 +0,0 @@
|
||||
import json
|
||||
import superscript as su
|
||||
import threading
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
)
|
||||
|
||||
class Tasker():
|
||||
|
||||
match_ = False
|
||||
metric_ = False
|
||||
pit_ = False
|
||||
|
||||
match_enable = True
|
||||
metric_enable = True
|
||||
pit_enable = True
|
||||
|
||||
config = {}
|
||||
|
||||
def __init__(self):
|
||||
|
||||
self.config = su.load_config("config.json")
|
||||
|
||||
def match(self):
|
||||
|
||||
self.match_ = True
|
||||
|
||||
apikey = self.config["key"]["database"]
|
||||
competition = self.config["competition"]
|
||||
tests = self.config["statistics"]["match"]
|
||||
|
||||
data = su.load_match(apikey, competition)
|
||||
su.matchloop(apikey, competition, data, tests)
|
||||
|
||||
self.match_ = False
|
||||
|
||||
if self.match_enable == True and self.match_ == False:
|
||||
|
||||
task = threading.Thread(name = "match", target = match)
|
||||
task.start()
|
||||
|
||||
def metric():
|
||||
|
||||
self.metric_ = True
|
||||
|
||||
apikey = self.config["key"]["database"]
|
||||
tbakey = self.config["key"]["tba"]
|
||||
competition = self.config["competition"]
|
||||
metric = self.config["statistics"]["metric"]
|
||||
|
||||
timestamp = su.get_previous_time(apikey)
|
||||
|
||||
su.metricloop(tbakey, apikey, competition, timestamp, metric)
|
||||
|
||||
self.metric_ = False
|
||||
|
||||
if self.metric_enable == True and self.metric_ == False:
|
||||
|
||||
task = threading.Thread(name = "match", target = metric)
|
||||
task.start()
|
||||
|
||||
def pit():
|
||||
|
||||
self.pit_ = True
|
||||
|
||||
apikey = self.config["key"]["database"]
|
||||
competition = self.config["competition"]
|
||||
tests = self.config["statistics"]["pit"]
|
||||
|
||||
data = su.load_pit(apikey, competition)
|
||||
su.pitloop(apikey, competition, data, tests)
|
||||
|
||||
self.pit_ = False
|
||||
|
||||
if self.pit_enable == True and self.pit_ == False:
|
||||
|
||||
task = threading.Thread(name = "pit", target = pit)
|
||||
task.start()
|
||||
|
||||
def start_match():
|
||||
task = threading.Thread(name = "match", target = match)
|
||||
task.start()
|
||||
|
||||
def start_metric():
|
||||
task = threading.Thread(name = "match", target = metric)
|
||||
task.start()
|
||||
|
||||
def start_pit():
|
||||
task = threading.Thread(name = "pit", target = pit)
|
||||
task.start()
|
||||
|
||||
def stop_match():
|
||||
self.match_enable = False
|
||||
|
||||
def stop_metric():
|
||||
self.metric_enable = False
|
||||
|
||||
def stop_pit():
|
||||
self.pit_enable = False
|
||||
|
||||
def get_match():
|
||||
return self.match_
|
||||
|
||||
def get_metric():
|
||||
return self.metric_
|
||||
|
||||
def get_pit():
|
||||
return self.pit_
|
||||
|
||||
def get_match_enable():
|
||||
return self.match_enable
|
||||
|
||||
def get_metric_enable():
|
||||
return self.metric_enable
|
||||
|
||||
def get_pit_enable():
|
||||
return self.pit_enable
|
||||
"""
|
||||
def main():
|
||||
|
||||
init()
|
||||
start_match()
|
||||
start_metric()
|
||||
start_pit()
|
||||
|
||||
exit = False
|
||||
while(not exit):
|
||||
|
||||
i = input("> ")
|
||||
cmds = i.split(" ")
|
||||
cmds = [x for x in cmds if x != ""]
|
||||
l = len(cmds)
|
||||
|
||||
if(l == 0):
|
||||
pass
|
||||
else:
|
||||
if(cmds[0] == "exit"):
|
||||
if(l == 1):
|
||||
exit = True
|
||||
else:
|
||||
print("exit command expected no arguments but encountered " + str(l - 1))
|
||||
if(cmds[0] == "status"):
|
||||
if(l == 1):
|
||||
print("status command expected 1 argument but encountered none\ntype status help for usage")
|
||||
elif(l > 2):
|
||||
print("status command expected 1 argument but encountered " + str(l - 1))
|
||||
elif(cmds[1] == "threads"):
|
||||
threads = threading.enumerate()
|
||||
threads = [x.getName() for x in threads]
|
||||
print("running threads:")
|
||||
for thread in threads:
|
||||
print(" " + thread)
|
||||
elif(cmds[1] == "flags"):
|
||||
print("current flags:")
|
||||
print(" match running: " + match_)
|
||||
print(" metric running: " + metric_)
|
||||
print(" pit running: " + pit_)
|
||||
print(" match enable: " + match_enable)
|
||||
print(" metric enable: " + metric_enable)
|
||||
print(" pit enable: " + pit_enable)
|
||||
elif(cmds[1] == "config"):
|
||||
print("current config:")
|
||||
print(json.dumps(config))
|
||||
elif(cmds[1] == "all"):
|
||||
threads = threading.enumerate()
|
||||
threads = [x.getName() for x in threads]
|
||||
print("running threads:")
|
||||
for thread in threads:
|
||||
print(" " + thread)
|
||||
print("current flags:")
|
||||
print(" match running: " + match_)
|
||||
print(" metric running: " + metric_)
|
||||
print(" pit running: " + pit_)
|
||||
print(" match enable: " + match_enable)
|
||||
print(" metric enable: " + metric_enable)
|
||||
print(" pit enable: " + pit_enable)
|
||||
elif(cmds[1] == "help"):
|
||||
print("usage: status [arg]\nDisplays the status of the tra data analysis threads.\nArguments:\n threads - prints the stuatus ofcurrently running threads\n flags - prints the status of control and indicator flags\n config - prints the current configuration information\n all - prints all statuses\n <name_of_thread> - prints the status of a specific thread")
|
||||
else:
|
||||
threads = threading.enumerate()
|
||||
threads = [x.getName() for x in threads]
|
||||
if(cmds[1] in threads):
|
||||
print(cmds[1] + " is running")
|
||||
|
||||
if(__name__ == "__main__"):
|
||||
main()
|
||||
"""
|
||||
@ -1,55 +0,0 @@
|
||||
import threading
|
||||
from multiprocessing import Process, Queue
|
||||
import time
|
||||
from os import system
|
||||
|
||||
class testcls():
|
||||
|
||||
i = 0
|
||||
j = 0
|
||||
|
||||
t1_en = True
|
||||
t2_en = True
|
||||
|
||||
def main(self):
|
||||
t1 = Process(name = "task1", target = self.task1)
|
||||
t2 = Process(name = "task2", target = self.task2)
|
||||
t1.start()
|
||||
t2.start()
|
||||
#print(self.i)
|
||||
#print(self.j)
|
||||
|
||||
def task1(self):
|
||||
self.i += 1
|
||||
time.sleep(1)
|
||||
if(self.i < 10):
|
||||
t1 = Process(name = "task1", target = self.task1)
|
||||
t1.start()
|
||||
|
||||
def task2(self):
|
||||
self.j -= 1
|
||||
time.sleep(1)
|
||||
if(self.j > -10):
|
||||
t2 = t2 = Process(name = "task2", target = self.task2)
|
||||
t2.start()
|
||||
"""
|
||||
if __name__ == "__main__":
|
||||
|
||||
tmain = threading.Thread(name = "main", target = main)
|
||||
tmain.start()
|
||||
|
||||
t = 0
|
||||
while(True):
|
||||
system("clear")
|
||||
for thread in threading.enumerate():
|
||||
if thread.getName() != "MainThread":
|
||||
print(thread.getName())
|
||||
print(str(len(threading.enumerate())))
|
||||
print(i)
|
||||
print(j)
|
||||
time.sleep(0.1)
|
||||
t += 1
|
||||
if(t == 100):
|
||||
t1_en = False
|
||||
t2_en = False
|
||||
"""
|
||||
@ -1,33 +0,0 @@
|
||||
import argparse
|
||||
from tasks import Tasker
|
||||
import test
|
||||
import threading
|
||||
from multiprocessing import Process, Queue
|
||||
|
||||
t = Tasker()
|
||||
|
||||
task_map = {"match":None, "metric":None, "pit":None, "test":None}
|
||||
status_map = {"match":None, "metric":None, "pit":None}
|
||||
status_map.update(task_map)
|
||||
|
||||
parser = argparse.ArgumentParser(prog = "TRA")
|
||||
subparsers = parser.add_subparsers(title = "command", metavar = "C", help = "//commandhelp//")
|
||||
|
||||
parser_start = subparsers.add_parser("start", help = "//starthelp//")
|
||||
parser_start.add_argument("targets", metavar = "T", nargs = "*", choices = task_map.keys())
|
||||
parser_start.set_defaults(which = "start")
|
||||
|
||||
parser_stop = subparsers.add_parser("stop", help = "//stophelp//")
|
||||
parser_stop.add_argument("targets", metavar = "T", nargs = "*", choices = task_map.keys())
|
||||
parser_stop.set_defaults(which = "stop")
|
||||
|
||||
parser_status = subparsers.add_parser("status", help = "//stophelp//")
|
||||
parser_status.add_argument("targets", metavar = "T", nargs = "*", choices = status_map.keys())
|
||||
parser_status.set_defaults(which = "status")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if(args.which == "start" and "test" in args.targets):
|
||||
a = test.testcls()
|
||||
tmain = Process(name = "main", target = a.main)
|
||||
tmain.start()
|
||||
@ -1,166 +0,0 @@
|
||||
import json
|
||||
import superscript as su
|
||||
import threading
|
||||
|
||||
__author__ = (
|
||||
"Arthur Lu <learthurgo@gmail.com>",
|
||||
)
|
||||
|
||||
match_ = False
|
||||
metric_ = False
|
||||
pit_ = False
|
||||
|
||||
match_enable = True
|
||||
metric_enable = True
|
||||
pit_enable = True
|
||||
|
||||
config = {}
|
||||
|
||||
def __init__(self):
|
||||
|
||||
global match_
|
||||
global metric_
|
||||
global pit_
|
||||
|
||||
global match_enable
|
||||
global metric_enable
|
||||
global pit_enable
|
||||
|
||||
config = su.load_config("config.json")
|
||||
|
||||
def match(self):
|
||||
|
||||
match_ = True
|
||||
|
||||
apikey = config["key"]["database"]
|
||||
competition = config["competition"]
|
||||
tests = config["statistics"]["match"]
|
||||
|
||||
data = su.load_match(apikey, competition)
|
||||
su.matchloop(apikey, competition, data, tests)
|
||||
|
||||
match_ = False
|
||||
|
||||
if match_enable == True and match_ == False:
|
||||
|
||||
task = threading.Thread(name = "match", target = match)
|
||||
task.start()
|
||||
|
||||
def metric():
|
||||
|
||||
metric_ = True
|
||||
|
||||
apikey = config["key"]["database"]
|
||||
tbakey = config["key"]["tba"]
|
||||
competition = config["competition"]
|
||||
metric = config["statistics"]["metric"]
|
||||
|
||||
timestamp = su.get_previous_time(apikey)
|
||||
|
||||
su.metricloop(tbakey, apikey, competition, timestamp, metric)
|
||||
|
||||
metric_ = False
|
||||
|
||||
if metric_enable == True and metric_ == False:
|
||||
|
||||
task = threading.Thread(name = "match", target = metric)
|
||||
task.start()
|
||||
|
||||
def pit():
|
||||
|
||||
pit_ = True
|
||||
|
||||
apikey = config["key"]["database"]
|
||||
competition = config["competition"]
|
||||
tests = config["statistics"]["pit"]
|
||||
|
||||
data = su.load_pit(apikey, competition)
|
||||
su.pitloop(apikey, competition, data, tests)
|
||||
|
||||
pit_ = False
|
||||
|
||||
if pit_enable == True and pit_ == False:
|
||||
|
||||
task = threading.Thread(name = "pit", target = pit)
|
||||
task.start()
|
||||
|
||||
def start_match():
|
||||
task = threading.Thread(name = "match", target = match)
|
||||
task.start()
|
||||
|
||||
def start_metric():
|
||||
task = threading.Thread(name = "match", target = metric)
|
||||
task.start()
|
||||
|
||||
def start_pit():
|
||||
task = threading.Thread(name = "pit", target = pit)
|
||||
task.start()
|
||||
|
||||
def main():
|
||||
|
||||
init()
|
||||
start_match()
|
||||
start_metric()
|
||||
start_pit()
|
||||
|
||||
exit = False
|
||||
while(not exit):
|
||||
|
||||
i = input("> ")
|
||||
cmds = i.split(" ")
|
||||
cmds = [x for x in cmds if x != ""]
|
||||
l = len(cmds)
|
||||
|
||||
if(l == 0):
|
||||
pass
|
||||
else:
|
||||
if(cmds[0] == "exit"):
|
||||
if(l == 1):
|
||||
exit = True
|
||||
else:
|
||||
print("exit command expected no arguments but encountered " + str(l - 1))
|
||||
if(cmds[0] == "status"):
|
||||
if(l == 1):
|
||||
print("status command expected 1 argument but encountered none\ntype status help for usage")
|
||||
elif(l > 2):
|
||||
print("status command expected 1 argument but encountered " + str(l - 1))
|
||||
elif(cmds[1] == "threads"):
|
||||
threads = threading.enumerate()
|
||||
threads = [x.getName() for x in threads]
|
||||
print("running threads:")
|
||||
for thread in threads:
|
||||
print(" " + thread)
|
||||
elif(cmds[1] == "flags"):
|
||||
print("current flags:")
|
||||
print(" match running: " + match_)
|
||||
print(" metric running: " + metric_)
|
||||
print(" pit running: " + pit_)
|
||||
print(" match enable: " + match_enable)
|
||||
print(" metric enable: " + metric_enable)
|
||||
print(" pit enable: " + pit_enable)
|
||||
elif(cmds[1] == "config"):
|
||||
print("current config:")
|
||||
print(json.dumps(config))
|
||||
elif(cmds[1] == "all"):
|
||||
threads = threading.enumerate()
|
||||
threads = [x.getName() for x in threads]
|
||||
print("running threads:")
|
||||
for thread in threads:
|
||||
print(" " + thread)
|
||||
print("current flags:")
|
||||
print(" match running: " + match_)
|
||||
print(" metric running: " + metric_)
|
||||
print(" pit running: " + pit_)
|
||||
print(" match enable: " + match_enable)
|
||||
print(" metric enable: " + metric_enable)
|
||||
print(" pit enable: " + pit_enable)
|
||||
elif(cmds[1] == "help"):
|
||||
print("usage: status [arg]\nDisplays the status of the tra data analysis threads.\nArguments:\n threads - prints the stuatus ofcurrently running threads\n flags - prints the status of control and indicator flags\n config - prints the current configuration information\n all - prints all statuses\n <name_of_thread> - prints the status of a specific thread")
|
||||
else:
|
||||
threads = threading.enumerate()
|
||||
threads = [x.getName() for x in threads]
|
||||
if(cmds[1] in threads):
|
||||
print(cmds[1] + " is running")
|
||||
|
||||
if(__name__ == "__main__"):
|
||||
main()
|
||||
Loading…
Reference in New Issue
Block a user