Get analysis of the model and the data the model was trained on.
Delete a trained model.
Check training status of your model.
Begin training your model.
list(pageToken=None, maxResults=None)
List available models.
list_next(previous_request, previous_response)
Retrieves the next page of results.
Submit model id and request a prediction.
Add new data to a trained model.
analyze(id)
Get analysis of the model and the data the model was trained on. Args: id: string, The unique name for the predictive model. (required) Returns: An object of the form: { "kind": "prediction#analyze", # What kind of resource this is. "errors": [ # List of errors with the data. { "a_key": "A String", # Error level followed by a detailed error message. }, ], "dataDescription": { # Description of the data the model was trained on. "outputFeature": { # Description of the output value or label. "text": [ # Description of the output labels in the data set. { "count": "A String", # Number of times the output label occurred in the data set. "value": "A String", # The output label. }, ], "numeric": { # Description of the output values in the data set. "count": "A String", # Number of numeric output values in the data set. "variance": 3.14, # Variance of the output values in the data set. "mean": 3.14, # Mean of the output values in the data set. }, }, "features": [ # Description of the input features in the data set. { "index": "A String", # The feature index. "text": { # Description of multiple-word text values of this feature. "count": "A String", # Number of multiple-word text values for this feature. }, "numeric": { # Description of the numeric values of this feature. "count": "A String", # Number of numeric values for this feature in the data set. "variance": 3.14, # Variance of the numeric values of this feature in the data set. "mean": 3.14, # Mean of the numeric values of this feature in the data set. }, "categorical": { # Description of the categorical values of this feature. "count": "A String", # Number of categorical values for this feature in the data. "values": [ # List of all the categories for this feature in the data set. { "count": "A String", # Number of times this feature had this value. "value": "A String", # The category name. }, ], }, }, ], }, "modelDescription": { # Description of the model. "confusionMatrixRowTotals": { # A list of the confusion matrix row totals "a_key": 3.14, }, "confusionMatrix": { # An output confusion matrix. This shows an estimate for how this model will do in predictions. This is first indexed by the true class label. For each true class label, this provides a pair {predicted_label, count}, where count is the estimated number of times the model will predict the predicted label given the true label. Will not output if more then 100 classes [Categorical models only]. "a_key": { "a_key": 3.14, }, }, "modelinfo": { # Basic information about the model. "kind": "prediction#training", # What kind of resource this is. "created": "A String", # Insert time of the model (as a RFC 3339 timestamp). "trainingComplete": "A String", # Training completion time (as a RFC 3339 timestamp). "storageDataLocation": "A String", # Google storage location of the training data file. "modelType": "A String", # Type of predictive model (classification or regression) "storagePMMLModelLocation": "A String", # Google storage location of the pmml model file. "trainingStatus": "A String", # The current status of the training job. This can be one of following: RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND "modelInfo": { # Model metadata. "numberLabels": "A String", # Number of class labels in the trained model [Categorical models only]. "meanSquaredError": 3.14, # An estimated mean squared error. The can be used to measure the quality of the predicted model [Regression models only]. "modelType": "A String", # Type of predictive model (CLASSIFICATION or REGRESSION) "numberInstances": "A String", # Number of valid data instances used in the trained model. "classWeightedAccuracy": 3.14, # Estimated accuracy of model taking utility weights into account [Categorical models only]. "classificationAccuracy": 3.14, # A number between 0.0 and 1.0, where 1.0 is 100% accurate. This is an estimate, based on the amount and quality of the training data, of the estimated prediction accuracy. You can use this is a guide to decide whether the results are accurate enough for your needs. This estimate will be more reliable if your real input data is similar to your training data [Categorical models only]. }, "storagePMMLLocation": "A String", # Google storage location of the preprocessing pmml file. "trainingInstances": [ # Instances to train model on. { "output": "A String", # The generic output value - could be regression or class label "csvInstance": [ # The input features for this instance "", ], }, ], "id": "A String", # The unique name for the predictive model. "selfLink": "A String", # A URL to re-request this resource. "utility": [ # A class weighting function, which allows the importance weights for class labels to be specified [Categorical models only]. { # Class label (string). "a_key": 3.14, }, ], }, }, "id": "A String", # The unique name for the predictive model. "selfLink": "A String", # A URL to re-request this resource. }
delete(id)
Delete a trained model. Args: id: string, The unique name for the predictive model. (required)
get(id)
Check training status of your model. Args: id: string, The unique name for the predictive model. (required) Returns: An object of the form: { "kind": "prediction#training", # What kind of resource this is. "created": "A String", # Insert time of the model (as a RFC 3339 timestamp). "trainingComplete": "A String", # Training completion time (as a RFC 3339 timestamp). "storageDataLocation": "A String", # Google storage location of the training data file. "modelType": "A String", # Type of predictive model (classification or regression) "storagePMMLModelLocation": "A String", # Google storage location of the pmml model file. "trainingStatus": "A String", # The current status of the training job. This can be one of following: RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND "modelInfo": { # Model metadata. "numberLabels": "A String", # Number of class labels in the trained model [Categorical models only]. "meanSquaredError": 3.14, # An estimated mean squared error. The can be used to measure the quality of the predicted model [Regression models only]. "modelType": "A String", # Type of predictive model (CLASSIFICATION or REGRESSION) "numberInstances": "A String", # Number of valid data instances used in the trained model. "classWeightedAccuracy": 3.14, # Estimated accuracy of model taking utility weights into account [Categorical models only]. "classificationAccuracy": 3.14, # A number between 0.0 and 1.0, where 1.0 is 100% accurate. This is an estimate, based on the amount and quality of the training data, of the estimated prediction accuracy. You can use this is a guide to decide whether the results are accurate enough for your needs. This estimate will be more reliable if your real input data is similar to your training data [Categorical models only]. }, "storagePMMLLocation": "A String", # Google storage location of the preprocessing pmml file. "trainingInstances": [ # Instances to train model on. { "output": "A String", # The generic output value - could be regression or class label "csvInstance": [ # The input features for this instance "", ], }, ], "id": "A String", # The unique name for the predictive model. "selfLink": "A String", # A URL to re-request this resource. "utility": [ # A class weighting function, which allows the importance weights for class labels to be specified [Categorical models only]. { # Class label (string). "a_key": 3.14, }, ], }
insert(body)
Begin training your model. Args: body: object, The request body. (required) The object takes the form of: { "kind": "prediction#training", # What kind of resource this is. "created": "A String", # Insert time of the model (as a RFC 3339 timestamp). "trainingComplete": "A String", # Training completion time (as a RFC 3339 timestamp). "storageDataLocation": "A String", # Google storage location of the training data file. "modelType": "A String", # Type of predictive model (classification or regression) "storagePMMLModelLocation": "A String", # Google storage location of the pmml model file. "trainingStatus": "A String", # The current status of the training job. This can be one of following: RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND "modelInfo": { # Model metadata. "numberLabels": "A String", # Number of class labels in the trained model [Categorical models only]. "meanSquaredError": 3.14, # An estimated mean squared error. The can be used to measure the quality of the predicted model [Regression models only]. "modelType": "A String", # Type of predictive model (CLASSIFICATION or REGRESSION) "numberInstances": "A String", # Number of valid data instances used in the trained model. "classWeightedAccuracy": 3.14, # Estimated accuracy of model taking utility weights into account [Categorical models only]. "classificationAccuracy": 3.14, # A number between 0.0 and 1.0, where 1.0 is 100% accurate. This is an estimate, based on the amount and quality of the training data, of the estimated prediction accuracy. You can use this is a guide to decide whether the results are accurate enough for your needs. This estimate will be more reliable if your real input data is similar to your training data [Categorical models only]. }, "storagePMMLLocation": "A String", # Google storage location of the preprocessing pmml file. "trainingInstances": [ # Instances to train model on. { "output": "A String", # The generic output value - could be regression or class label "csvInstance": [ # The input features for this instance "", ], }, ], "id": "A String", # The unique name for the predictive model. "selfLink": "A String", # A URL to re-request this resource. "utility": [ # A class weighting function, which allows the importance weights for class labels to be specified [Categorical models only]. { # Class label (string). "a_key": 3.14, }, ], } Returns: An object of the form: { "kind": "prediction#training", # What kind of resource this is. "created": "A String", # Insert time of the model (as a RFC 3339 timestamp). "trainingComplete": "A String", # Training completion time (as a RFC 3339 timestamp). "storageDataLocation": "A String", # Google storage location of the training data file. "modelType": "A String", # Type of predictive model (classification or regression) "storagePMMLModelLocation": "A String", # Google storage location of the pmml model file. "trainingStatus": "A String", # The current status of the training job. This can be one of following: RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND "modelInfo": { # Model metadata. "numberLabels": "A String", # Number of class labels in the trained model [Categorical models only]. "meanSquaredError": 3.14, # An estimated mean squared error. The can be used to measure the quality of the predicted model [Regression models only]. "modelType": "A String", # Type of predictive model (CLASSIFICATION or REGRESSION) "numberInstances": "A String", # Number of valid data instances used in the trained model. "classWeightedAccuracy": 3.14, # Estimated accuracy of model taking utility weights into account [Categorical models only]. "classificationAccuracy": 3.14, # A number between 0.0 and 1.0, where 1.0 is 100% accurate. This is an estimate, based on the amount and quality of the training data, of the estimated prediction accuracy. You can use this is a guide to decide whether the results are accurate enough for your needs. This estimate will be more reliable if your real input data is similar to your training data [Categorical models only]. }, "storagePMMLLocation": "A String", # Google storage location of the preprocessing pmml file. "trainingInstances": [ # Instances to train model on. { "output": "A String", # The generic output value - could be regression or class label "csvInstance": [ # The input features for this instance "", ], }, ], "id": "A String", # The unique name for the predictive model. "selfLink": "A String", # A URL to re-request this resource. "utility": [ # A class weighting function, which allows the importance weights for class labels to be specified [Categorical models only]. { # Class label (string). "a_key": 3.14, }, ], }
list(pageToken=None, maxResults=None)
List available models. Args: pageToken: string, Pagination token maxResults: integer, Maximum number of results to return Returns: An object of the form: { "nextPageToken": "A String", # Pagination token to fetch the next page, if one exists. "items": [ # List of models. { "kind": "prediction#training", # What kind of resource this is. "created": "A String", # Insert time of the model (as a RFC 3339 timestamp). "trainingComplete": "A String", # Training completion time (as a RFC 3339 timestamp). "storageDataLocation": "A String", # Google storage location of the training data file. "modelType": "A String", # Type of predictive model (classification or regression) "storagePMMLModelLocation": "A String", # Google storage location of the pmml model file. "trainingStatus": "A String", # The current status of the training job. This can be one of following: RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND "modelInfo": { # Model metadata. "numberLabels": "A String", # Number of class labels in the trained model [Categorical models only]. "meanSquaredError": 3.14, # An estimated mean squared error. The can be used to measure the quality of the predicted model [Regression models only]. "modelType": "A String", # Type of predictive model (CLASSIFICATION or REGRESSION) "numberInstances": "A String", # Number of valid data instances used in the trained model. "classWeightedAccuracy": 3.14, # Estimated accuracy of model taking utility weights into account [Categorical models only]. "classificationAccuracy": 3.14, # A number between 0.0 and 1.0, where 1.0 is 100% accurate. This is an estimate, based on the amount and quality of the training data, of the estimated prediction accuracy. You can use this is a guide to decide whether the results are accurate enough for your needs. This estimate will be more reliable if your real input data is similar to your training data [Categorical models only]. }, "storagePMMLLocation": "A String", # Google storage location of the preprocessing pmml file. "trainingInstances": [ # Instances to train model on. { "output": "A String", # The generic output value - could be regression or class label "csvInstance": [ # The input features for this instance "", ], }, ], "id": "A String", # The unique name for the predictive model. "selfLink": "A String", # A URL to re-request this resource. "utility": [ # A class weighting function, which allows the importance weights for class labels to be specified [Categorical models only]. { # Class label (string). "a_key": 3.14, }, ], }, ], "kind": "prediction#list", # What kind of resource this is. "selfLink": "A String", # A URL to re-request this resource. }
list_next(previous_request, previous_response)
Retrieves the next page of results. Args: previous_request: The request for the previous page. (required) previous_response: The response from the request for the previous page. (required) Returns: A request object that you can call 'execute()' on to request the next page. Returns None if there are no more items in the collection.
predict(id, body)
Submit model id and request a prediction. Args: id: string, The unique name for the predictive model. (required) body: object, The request body. (required) The object takes the form of: { "input": { # Input to the model for a prediction "csvInstance": [ # A list of input features, these can be strings or doubles. "", ], }, } Returns: An object of the form: { "kind": "prediction#output", # What kind of resource this is. "outputLabel": "A String", # The most likely class label [Categorical models only]. "id": "A String", # The unique name for the predictive model. "outputMulti": [ # A list of class labels with their estimated probabilities [Categorical models only]. { "score": 3.14, # The probability of the class label. "label": "A String", # The class label. }, ], "outputValue": 3.14, # The estimated regression value [Regression models only]. "selfLink": "A String", # A URL to re-request this resource. }
update(id, body)
Add new data to a trained model. Args: id: string, The unique name for the predictive model. (required) body: object, The request body. (required) The object takes the form of: { "output": "A String", # The generic output value - could be regression value or class label "csvInstance": [ # The input features for this instance "", ], "label": "A String", # The class label of this instance } Returns: An object of the form: { "kind": "prediction#training", # What kind of resource this is. "created": "A String", # Insert time of the model (as a RFC 3339 timestamp). "trainingComplete": "A String", # Training completion time (as a RFC 3339 timestamp). "storageDataLocation": "A String", # Google storage location of the training data file. "modelType": "A String", # Type of predictive model (classification or regression) "storagePMMLModelLocation": "A String", # Google storage location of the pmml model file. "trainingStatus": "A String", # The current status of the training job. This can be one of following: RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND "modelInfo": { # Model metadata. "numberLabels": "A String", # Number of class labels in the trained model [Categorical models only]. "meanSquaredError": 3.14, # An estimated mean squared error. The can be used to measure the quality of the predicted model [Regression models only]. "modelType": "A String", # Type of predictive model (CLASSIFICATION or REGRESSION) "numberInstances": "A String", # Number of valid data instances used in the trained model. "classWeightedAccuracy": 3.14, # Estimated accuracy of model taking utility weights into account [Categorical models only]. "classificationAccuracy": 3.14, # A number between 0.0 and 1.0, where 1.0 is 100% accurate. This is an estimate, based on the amount and quality of the training data, of the estimated prediction accuracy. You can use this is a guide to decide whether the results are accurate enough for your needs. This estimate will be more reliable if your real input data is similar to your training data [Categorical models only]. }, "storagePMMLLocation": "A String", # Google storage location of the preprocessing pmml file. "trainingInstances": [ # Instances to train model on. { "output": "A String", # The generic output value - could be regression or class label "csvInstance": [ # The input features for this instance "", ], }, ], "id": "A String", # The unique name for the predictive model. "selfLink": "A String", # A URL to re-request this resource. "utility": [ # A class weighting function, which allows the importance weights for class labels to be specified [Categorical models only]. { # Class label (string). "a_key": 3.14, }, ], }


This is space settlers, version 1. It is built on the same ideas as spacewar version 2. This code has been fully rewritten from the previous versions of spacewar. You are welcome to use the code and please cite the following paper if you publish anything based on the code.
McGovern, Amy and Trytten, Deborah. (2013). Making In-Class Competitions Desirable For Marginalized Groups. Proceedings of the 2013 Frontiers in Education Conference, pages 704-706.


Harry Styles rolls around with Kenan Thompson in 'SNL' promo
by Fred TopelNov. 13 (UPI) -- Saturday Night Live released a promo for this week's episode on Wednesday. Harry Styles will host and perform music on Saturday's show.
The behind-the-scenes promo shows Styles returning to Studio 8H where he's previously guest starred and performed music with One Direction. Kenan Thompson gives Styles a welcome back tour.
"I'm going to be some characters," Styles says. "I'm going to sing. I'm pretty much down for anything."
Thompson accepts Styles' challenge and asks, "Would you like to enjoy the magic of fall with me?"
Styles replies, "I would love to enjoy the magic of fall with you."
Conveniently, Thompson and Styles find a pile of leaves on the soundstage floor. After declaring "Classic fall fun!" Styles belly flops into the pile of leaves.
Styles and Thompson play in the leaves until a groundskeeper interrupts them with a leaf blower and kicks them out. At first, they can't hear him over the noise of the leaf blower, but when he threatens to call security the singer and actor bolt.
After SNL, Styles drops his second solo album in December and embarks on a world tour next year.
Saturday Night Live airs Saturday at 11:30 p.m. EDT on NBC.


DACO - main module¶
Todo
- add support for finding Kulback et.al. for categorical data
- sequential vs tabular data (long term)
- local and global metrics
- differential privacy (long term)
- privacy checks
- mean, variance, … ( this is available in pandas)
- plotting/smart plotting, i.e. show only anomalies
- pull plots based on the output from pd.dataframe.describe
- allow setting range, density, binning, etc. for each variable manually?
- set a random seed globally in class
- log-axes in plots
- IdentityDisclosure
- AttributeDisclosure
-
class
daco_main.
daco
(df1, df2, name1='df1', name2='df2', file_dir='plots/')[source]¶ Class for comparing two Pandas dataframes.
The purpose of this class is to easily compare datasets in different settings, e.g. check if a synthetic version of a dataset is good enoug for your use.
-
__init__
(df1, df2, name1='df1', name2='df2', file_dir='plots/')[source]¶ Parameters: - df1 (dataframe) – dataframe to be compared. Must have header and dtype for all columns.
- df2 (dataframe) – dataframe to be compared. Must have header and dtype for all columns. This is treated as the synthetic version of df1 throughout the module.
- file_dir (str) – path to directory where plots are saved
- name1 (str) – names of dataframes, used as keys in dictionaries containing info about dataframes
- name2 (str) – names of dataframes, used as keys in dictionaries containing info about dataframes
-
_checkDataframes
(df1, df2)[source]¶ Checking whether the dataframes provided fullfills the requirements:
- The input should be pandas dataframes
- Only float or categorical variables
- Same column names in both frames
-
_findAndNormalizeDistances
()[source]¶ Calculate and normalize distances for all numerical variables.
Returns: - kl_array (array) – Kullback-Leibler values
- bha_array (array) – Bhattacharyya values
- hel_array (array) – Hellinger values
-
_plotConfusionMatrixFromLogisticRegression
(conf_mat, title='', ax=None)[source]¶ Plotting method used in
logisticRegressionBenchmark
Parameters: - conf_mat (array) – confusion matrix output from
confusion_matrix
inSciPy
- title (str) – title of subplot
- ax (object) – axis object for plotting
- conf_mat (array) – confusion matrix output from
-
_trainSeveralModels
(name, data, models=None)[source]¶ Private method for
trainSynthTestReal
andsyntheticRankingAgreement
. Models and their scores are saved in dictionaries.Note
This method is constrained to only use models with a
.fit()
-method.Parameters: - name (str) – name of the entry in the
model_dict
andmodel_score
- data (tuple) – tuple with training and test data in the order (X_train, y_train, X_test, y_test)
- models (list) – list of tuples with models and a dict with parameters
Returns: - model_dict (dict) – dictionary containing all models trained
- model_scores (dict) – dictionary containing lists with scores for each model trained
- name (str) – name of the entry in the
-
_trainSingleModel
(model, params, X_train, y_train, X_test, y_test)[source]¶ Private method for training a single model and returning the model and its score
Parameters: - model (object) – ML-model-object with the methods
.fit()
and.predict()
- params (dict) – dictionary with hyperparameter to the model
- X_train (array) – numpy-array with training data
- y_train (array) – numpy-array with training targets
- X_test (array) – numpy-array with test data
- y_test (array) – numpy-array with test targets
Returns: - model_trained (object) – trained model
- scores (float) – test score for the model
- model (object) – ML-model-object with the methods
-
bhattacharyya
(var1)[source]¶ Calculate the Bhattacharyya distance for the distributions of var1 in the two dataframes. See https://en.wikipedia.org/wiki/Bhattacharyya_distance
Parameters: var1 (str) – name of variable in the datasets to do calculate the Bhattacharyya distance for. Returns: b_dis Return type: float
-
chisquare
(var1)[source]¶ Method for calculating the chisquare test using scipy.stats.chisquare.
Parameters: var1 (str) – name of variable in the dataset to do the chisquare test. Returns: - D (float) – The chi-squared test statistic
- p (float) – p-value
-
dataPrep
(target, features, test_size, eval_size, name)[source]¶ Data preparation for the ML-models used in DACO. Takes in the two dataframes applies one hot encoding on categorical variables, and splits them into train, test, and evaluation sets.
Parameters: - target (list) – target values
- features (list) – features to use in training/predictions
- test_size (float) – size of test set
- eval_size (float) – size of evaluation set
- name (str) – name of dataset to prepare
-
findDistributionOfNumericalVariable
(var, range_, bins_)[source]¶ Calculate distribution of a single variable.
-
findDistributions
(bins_='sturges')[source]¶ Find normalized distributions of all variables/columns in dataframes loaded into daco and save them in dictionaries.
The error is calculated as
\[\epsilon = \frac{1}{\sqrt{N}} \sqrt{\frac{n_i}{N}},\]where \(N\) is the number of data samples and \(n_i\) is the number of samples in the bin.
Parameters: - bins (int) – number of bins in histogram/distribution or how to find the number of bins.
- density (bool) – if True a normalised distribution is returned.
Returns: distributions – All normalized distributions gathered in a dict.
Return type: dict
Examples
By giving two Pandas dataframes we find the distributions:
>>> daco_obj = daco(df1,df2) >>> dist = daco_obj.findDistributions() >>> print(dist) {'df1': {'age': (array([0.01964384, 0.0258, ...]}, 'df2': {...}}
-
hellinger
(var1)[source]¶ Calculate the Hellinger divergence for the distributions of var1 in the two dataframes. See https://en.wikipedia.org/wiki/Hellinger_distance
Parameters: var1 (str) – name of variable in the datasets to do calculate the Hellinger divergence for. Returns: hellinger_div. Ouput value is in range [0, sqrt(2)]
.Return type: float
-
hellingerRowForRow
()[source]¶ Method for comparing finding the pair of rows in the datasets that have the best match.
Warning
Very slow implementation!
Todo
Parallellize?
-
ks2_test
(var)[source]¶ Method using the scipy.stats.ks_2samp for computing the Kolmogorov- Smirnov statistic on two samples. The result is added to a dictionary
ks2_test_val
keeping the results of all calculations.Parameters: var1 (str) – name of variable in the datasets to do the test on. Returns: - statistic (float) – KS statistic
- pvalue (float) – two-tailed p-value
-
kullbackleibler
(var1)[source]¶ Calculate Kullback-Leibler divergence for the distributions of var1 in the two dataframes with scipy.stats.entropy.
-<EMAIL_ADDRESS>- https://en.wikipedia.org/wiki/Kullback–Leibler_divergence
Parameters: var1 (str) – name of variable to do calculate the Kullback-Leibler divergence for. Must be contained in the dataframes. Returns: kb_div Return type: float
-
logisticRegressionBenchmark
(target, features, test_size=0.2, eval_size=0.2)[source]¶ Method for training a logistic regression-model on the datasets and investigate the differences in the model and predictions. The results and models are saved as class variables.
The following is done by this function:
- Training LR-models on synth. and real data (and save them in this class)
- Predict N samples
- Comparing the accuracy of the two models (several measures possible)
- Plotting confusion matrix
Parameters: - target (list) – target values
- features (list) – features to use in training/predictions
- test_size (float) – size of test set
- eval_size (float) – size of evaluation set
-
numericalComparing
()[source]¶ Compare the mean, variance, etc. of the numerical variable between the two dataframes. For each numerical variable we calculate the mean of the values in both dataframes, then we divide the mean value of the variable in the first dataframe on the second one to find how much they differ.
Returns: desc_compare – contains the relative mean values of all numerical variables in both dataframes. Return type: dict
-
printDistances
()[source]¶ Print a nice markdown table with the distance metrics for all numerical variables.
Returns: distance_values – a dict containing all values calculated Return type: dict
-
rowMatching
(atol_=0.1, rtol_=0.1)[source]¶ Method which loops over each row in synthetic dataset and finds the row in the original dataset with highest match in percent, and saves this number as an attribute to each row in the synthetic dataset. Using numpy.isclose as a matching measure for numerical values.
Parameters: - atol (float) – The relative tolerance parameter, see Numpy docs.
- rtol (float) – The absolute tolerance parameter, see Numpy docs.
Returns: match_values – array with dimensions (len(df2), 3) where each row contains
[<index in df2>, <index in df1>, match_value]
.Return type: array
-
syntheticRankingAgreement
(model_scores=None, target=None, features=None)[source]¶ Method for checking whether the synthetic dataset is useful in machine learning contexts. We use the “Synthetic Ranking Agreement”-method, SRA for short, see https://arxiv.org/pdf/1806.11345v1.pdf It is defined as
\[\text{SRA} = \frac{1}{k(k-1)} \sum^{k}_{i=1}\sum_{j\neq i} \mathbb{I}\left( (R_i - R_j) \times (S_i - S_j) \right)\]where \(R_k\) and \(S_k\) represents the performance score of algorithm \(k\) on the real dataset \(R\) and the synthetic dataset \(S\) respectively. The SRA can be thought of as the (emprical) probability of a comparison on the synthetic data beain “correct”.
Parameters: - model_scores (dict) – dict with scores for the different algorithms on the real and synthetic dataset.
- target (list) – list of training targets (if not
model_scores
is given) - features (list) – list of training features (if not
model_scores
is given)
Returns: sra – a single value which is the result of the formula given above.
Return type: float
-
trainAndTestModels
(target, features, models=None, test_size=0.2, eval_size=0.2)[source]¶ Method for training several ML-models on the two datasets given to this class and do testing as specified by the user. The models and their test scores are saved in dictionaries.
Parameters: - target (list) – target values
- features (list) – features to use in training/predictions
- models (list) – list of tuples with models and a dict with parameters
- test_size (float) – size of test set
- eval_size (float) – size of evaluation set
Returns: - model_dict (dict) – dictionary with the models trained
- model_scores (dict) – dictionary with test scores for the models trained
-


Man seriously injured after confronting intruder in his own home
The injured man, who is in his 60s, was taken by ambulance to Crosshouse Hospital in Kilmarnock.
by Lucinda CameronA man has been seriously injured after confronting an intruder in his house.
Police were called to the scene in Stronsay Court, Irvine , North Ayrshire at around 11.15am on Sunday.
The injured man, who is in his 60s, was taken by ambulance to Crosshouse Hospital in Kilmarnock , where medical staff describe his condition as serious but stable.
Police said that a 26-year-old man has been arrested in connection with the incident and has been taken to Crosshouse Hospital for assessment.
Investigations into the incident are continuing.


Why have we created this pattern?
- no GOV.UK pattern is available
Example component
{% include "includes/highlighting.html" %}
Interaction principles
- matches should be clear but not take away from the content on the page
- matches shouldn't decrease the colour contrast to fail AA
Accessibility principles
- Need to add some user research findings
User research
- users found the highlights helpful to make decisions
- users found that the highlights increased the speed of their checking
Prototype code
- Styles — highlights.scss
Services using this pattern
- Passport caseworking
- Application platform


The film met tremendous success at the Academy Awards, winning Best Picture and Best Actress (Tandy) while the two male leads received nominations (Freeman and Dan Akroyd, who in a supporting role plays Tandy's practical businessman son).
The story begins in the 1940s. Tandy is about seventy, and wrecks her new car backing out of the driveway. Now uninsurable, Akroyd hires Freeman to be her chauffeur and companion. This leads to a power struggle between Tandy and Freeman, as she refuses to use her hired hand, and tries to keep her independence. Freeman shows massive patience and finally overcomes Tandy's blustering. Freeman should have won the Oscar instead, as his character must ride the line between deference and defiance, and he does it well. Tandy plays the testy but vulnerable Miss Daisy well, but it is an easier role.
Racism is a background subject throughout the film. Tandy stubbornly resists treating Freeman as a friend and equal. She also makes several catty remarks about Christians. The audience is supposed to consider these remarks humorous, but the major difference between her remarks and those of the bigoted policeman is that she is not in a position of power.
Tandy and Freeman do not noticeable age as the first twenty years pass. It is sometimes difficult to tell which decade a scene is taking place in. Another minor problem I had: Tandy asks Freeman to attend a Martin Luther King dinner only at the last minute. Freeman's character shows his stubborness by refusing the invitation. You'd think he'd swallow his pride and attend the dinner. When would he get another chance, and it would be no humilation to attend.
Still, there are many scenes with depth in "Driving Miss Daisy", such as Tandy teaching the illiterate Freeman the connection between letters and words, and Freeman drawing the line with Tandy, telling her that he will park the car to "make water", despite her objections. The script is very good.
http://members.tripod.com/~Brian_Koller/movies.html
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Find a bunch of vocaloid and anime-related songs using our category system, or find the most popular tracks down below! Simply click on the category you'd like to listen to and a song will pop up underneath! Happy listening!
My Hero Academia
Sword Art Online
Detective Conan
Jojo's Bizarre Adventure
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{% include "includes/phase_banner_alpha.html" %}
Call details
Call details
| Call name |
Clifton CA Claim - Test Call |
| Current status |
ACTIVE |
| Access |
Open |
| Published Date |
04/05/2016 |
| Call Open Date |
07/03/2016 |
| Call Manager |
Another Person |
| Call Reference No. |
OC50R15P0020 |
| Published Date |
04/05/2016 |
| Type |
Project |
| Straight to Full Application |
Yes |
| Call Closed Date |
14/04/2017 |
| Call Document |
Download (17kb) |
| Call Manager E-mail |
<EMAIL_ADDRESS>|
| Summary Description |
One morning, when Gregor Samsa woke from troubled dreams, he found himself transformed in his bed into a horrible vermin. He lay on his armour-like back, and if he lifted his head a little he could see his brown belly, slightly domed and divided by arches into stiff sections. |
Priority Axis
| Fund |
ERDF |
| Programme |
Leverage agile frameworks |
| Programme Priority Axis |
To provide robust synopsis |
| Programme Investment Priority |
For high level overviews. Iterative approaches to corporate strategy |
Call Financial Details
| Interactice Budget Allocated to the Call |
£1000,000.00 |
| Maximum Funding Request Accepted |
£100,000.00 |
| Maximum Intervention Rate |
45% |
Local Enterprise Partnerships
| LEP |
15 - Greater Manchester |
Indicators
| Programme Indicators |
PIC1A |
Scoring Criteria
| Scoring Criteria |
Far far away, behind the word mountains, far from the countries Vokalia and Consonantia. |
Call Timeline Dates
| Timeline Date |
01/01/2001 |
| Timeline date description |
At the end of the day, going forward, a new normak |
Continue application
Cancel application and Exit
{% endblock %}


One of the strengths of Tornado is it’s ability to handle asynchronous requests and support for websockets. Top make Mojo more of a responsive framework, we’ve decided to bundle TornadIO2 functionality as part of the overall package.
For those that do not know what TornadIO2 is, it is a SocketIO server implementation written in Python for Tornado, and makes it transparent to implement SocketIO clients into your app.
By default, Mojo will start a TornadIO server for you that multiplexes a channel connection for each of the apps in your project.
So if you have 2 apps: App1 and App2, then you will be able to speak to either of these apps via SocketIO on the client by connecting to the /App1Socket or /App2Socket channels in your client side configuration.
To configure what commands the server should intitiate on connection, send event or receive event, simply edit the socket_handlers.py file in your app folder:
from Mojo.SocketHandlers.BaseSocketHandler import MojoSocketHandler, CURRENT_SESSIONS, LOGGED_IN_SESSIONS
#Setup your socket connections here
class SocketConnectionHandler(MojoSocketHandler):
def on_message(self, msg):
#Do something cool when a message arrives
pass
To make the most of TornadIO2 and SocketIO we’d recommend you read the documentation for both as their inner workings fall outside of the remit of the Mojo documentation.
Mojo does some handy tracking for you if you use the MojoSocketHandler to manage your socket connections. Mojo will automatically register all connections in two variables that enable you to communicate with users directly from the start, these variables are CURRENT_SESSIONS and LOGGED_IN_SESSIONS
This is a dictionary object that will track each connection by their Session ID. The session ID is a secure Tornado cookie that is called session_id, if you are using the MojoSessionMixin module, this will be set for you, however if you want to roll your own session management, you can just set this cookie and the user will be tracked by the ID in the SocketHandler.
This will enable you to do something like:
from Mojo.RequestHandlers.MojoHandler import MojoRequestHandler
from Mojo.SocketHandlers.BaseSocketHandler import CURRENT_SESSIONS
import json
class HelloWorldHandler(MojoRequestHandler):
def get(self, *args, **kwargs):
jsonObj = json.dumps({'data':'%s has joined the chat' % (self.session_id)})
for session in CURRENT_SESSIONS:
session.emit('joinedStatus', jsonObj)
self.render('hello.html')
Although the above code is not functional, the principle is valid - in this example, you could use the CURRENT_SESSIONS object to broadcast to all active users that a new user has joined the conversation.
If you are using the AuthMixin from Mojo, then it will set an encrypted cookie of the name logged_in with the user ID of the current user. Naturally, you can set this yourself to whatever identifier you like if you decide not use the mixins.
Any session that is identified as Logged in will be added to the LOGGED_IN_SESSIONS dictionary, and will enable you to interact with users. In a similar vein to the CURRENT_SESSIONS object, you would now be able to send specific messages only to logged in members of your app.


#include <nn_ops.h>
Finds values and indices of the k
largest elements for the last dimension.
If the input is a vector (rank-1), finds the k
largest entries in the vector and outputs their values and indices as vectors. Thus values[j]
is the j
-th largest entry in input
, and its index is indices[j]
.
For matrices (resp. higher rank input), computes the top k
entries in each row (resp. vector along the last dimension). Thus,
values.shape = indices.shape = input.shape[:-1] + [k]
If two elements are equal, the lower-index element appears first.
Arguments:
k
.Optional attributes (see Attrs
):
k
elements will be sorted by the values in descending order.Returns:
Output
values: The k
largest elements along each last dimensional slice.Output
indices: The indices of values
within the last dimension of input
.
Constructors and Destructors |
|
|---|---|
TopK(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input k)
|
|
TopK(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input k, const TopK::Attrs & attrs)
|
Public attributes |
|
|---|---|
indices
|
|
operation
|
|
values
|
Public static functions |
|
|---|---|
Sorted(bool x)
|
Structs |
|
|---|---|
|
tensorflow:: |
Optional attribute setters for TopK. |
::tensorflow::Output indices
Operation operation
::tensorflow::Output values
TopK( const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input k )
TopK( const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input k, const TopK::Attrs & attrs )
Attrs Sorted( bool x )


{% extends "layout.html" %} {% block content %}
This app shows the basic usage of Flask framework. It allows creation and deletion of users. A user can self register but will require and approval from administrator before the user can have access to database entries.
Once the user has access, (s)he can create a post and enter expenses. This functionality demonstrates the usage of wtforms
.
More specifically, usage of dropdown and display of date field and checkboxes.
Further activities to explore: * ~~ Create a grid editable form~~ - kind of done, can be improved. * Creation of subforms * Modals * ~~Creation and submission of forms using Modals.~~ - Works * ~~Validation to display in Modals and not take back to the page form. - Pending.~~ - Works * Make modal changes for Users table and Posts Table. * Make the button disappear for entries created by other users. * Clean Up code
pip install --upgrade pip pip install gunicorn flask run.py flask-bootstrap flask-mail flask-SQLAlchemy flask-bcrypt flask-table flask-wtf flask-login pillow
approot
on terminal / commandline and give the command python run.py
Ctrl+C
to stop the server.create_admin_user.py
python run.py
<EMAIL_ADDRESS>and password adminpassword
Admin
.


test¶
Important
Its a note! in markdown!
-
class
rl_coach.agents.dqn_agent.
DQNAgent
(agent_parameters, parent: Union[LevelManager, CompositeAgent] = None)[source]¶ -
act
(action: Union[None, int, float, numpy.ndarray, List] = None) → rl_coach.core_types.ActionInfo¶ Given the agents current knowledge, decide on the next action to apply to the environment
- Parameters
action – An action to take, overriding whatever the current policy is
- Returns
An ActionInfo object, which contains the action and any additional info from the action decision process
-
call_memory
(func, args=())¶ This function is a wrapper to allow having the same calls for shared or unshared memories. It should be used instead of calling the memory directly in order to allow different algorithms to work both with a shared and a local memory.
- Parameters
func – the name of the memory function to call
args – the arguments to supply to the function
- Returns
the return value of the function
-
choose_action
(curr_state)¶ choose an action to act with in the current episode being played. Different behavior might be exhibited when training or testing.
- Parameters
curr_state – the current state to act upon.
- Returns
chosen action, some action value describing the action (q-value, probability, etc)
-
collect_savers
(parent_path_suffix: str) → rl_coach.saver.SaverCollection¶ Collect all of agent’s network savers :param parent_path_suffix: path suffix of the parent of the agent (could be name of level manager or composite agent) :return: collection of all agent savers
-
create_networks
() → Dict[str, rl_coach.architectures.network_wrapper.NetworkWrapper]¶ Create all the networks of the agent. The network creation will be done after setting the environment parameters for the agent, since they are needed for creating the network.
- Returns
A list containing all the networks
-
freeze_memory
()¶ Shuffle episodes in the memory and freeze it to make sure that no extra data is being pushed anymore. :return: None
-
get_predictions
(states: List[Dict[str, numpy.ndarray]], prediction_type: rl_coach.core_types.PredictionType)¶ Get a prediction from the agent with regard to the requested prediction_type. If the agent cannot predict this type of prediction_type, or if there is more than possible way to do so, raise a ValueException.
- Parameters
states – The states to get a prediction for
prediction_type – The type of prediction to get for the states. For example, the state-value prediction.
- Returns
the predicted values
-
get_state_embedding
(state: dict) → numpy.ndarray¶ Given a state, get the corresponding state embedding from the main network
- Parameters
state – a state dict
- Returns
a numpy embedding vector
-
handle_episode_ended
() → None¶ Make any changes needed when each episode is ended. This includes incrementing counters, updating full episode dependent values, updating logs, etc. This function is called right after each episode is ended.
- Returns
None
-
improve_reward_model
(epochs: int)¶ Train a reward model to be used by the doubly-robust estimator
- Parameters
epochs – The total number of epochs to use for training a reward model
- Returns
None
-
init_environment_dependent_modules
()¶ Initialize any modules that depend on knowing information about the environment such as the action space or the observation space
- Returns
None
-
initialize_session_dependent_components
()¶ Initialize components which require a session as part of their initialization.
- Returns
None
-
learn_from_batch
(batch)[source]¶ Given a batch of transitions, calculates their target values and updates the network.
- Parameters
batch – A list of transitions
- Returns
The total loss of the training, the loss per head and the unclipped gradients
-
load_memory_from_file
()¶ Load memory transitions from a file.
- Returns
None
-
log_to_screen
() → None¶ Write an episode summary line to the terminal
- Returns
None
-
observe
(env_response: rl_coach.core_types.EnvResponse) → bool¶ Given a response from the environment, distill the observation from it and store it for later use. The response should be a dictionary containing the performed action, the new observation and measurements, the reward, a game over flag and any additional information necessary.
- Parameters
env_response – result of call from environment.step(action)
- Returns
a boolean value which determines if the agent has decided to terminate the episode after seeing the given observation
-
property
parent
¶ Get the parent class of the agent
- Returns
the current phase
-
property
phase
¶ The current running phase of the agent
- Returns
RunPhase
-
post_training_commands
() → None¶ A function which allows adding any functionality that is required to run right after the training phase ends.
- Returns
None
-
prepare_batch_for_inference
(states: Union[Dict[str, numpy.ndarray], List[Dict[str, numpy.ndarray]]], network_name: str) → Dict[str, numpy.core.multiarray.array]¶ Convert curr_state into input tensors tensorflow is expecting. i.e. if we have several inputs states, stack all observations together, measurements together, etc.
- Parameters
states – A list of environment states, where each one is a dict mapping from an observation name to its corresponding observation
network_name – The agent network name to prepare the batch for. this is needed in order to extract only the observation relevant for the network from the states.
- Returns
A dictionary containing a list of values from all the given states for each of the observations
-
register_signal
(signal_name: str, dump_one_value_per_episode: bool = True, dump_one_value_per_step: bool = False) → rl_coach.utils.Signal¶ Register a signal such that its statistics will be dumped and be viewable through dashboard
- Parameters
signal_name – the name of the signal as it will appear in dashboard
dump_one_value_per_episode – should the signal value be written for each episode?
dump_one_value_per_step – should the signal value be written for each step?
- Returns
the created signal
-
reset_evaluation_state
(val: rl_coach.core_types.RunPhase) → None¶ Perform accumulators initialization when entering an evaluation phase, and signal dumping when exiting an evaluation phase. Entering or exiting the evaluation phase is determined according to the new phase given by val, and by the current phase set in self.phase.
- Parameters
val – The new phase to change to
- Returns
None
-
reset_internal_state
() → None¶ Reset all the episodic parameters. This function is called right before each episode starts.
- Returns
None
-
restore_checkpoint
(checkpoint_dir: str) → None¶ Allows agents to store additional information when saving checkpoints.
- Parameters
checkpoint_dir – The checkpoint dir to restore from
- Returns
None
-
run_off_policy_evaluation
()¶ Run the off-policy evaluation estimators to get a prediction for the performance of the current policy based on an evaluation dataset, which was collected by another policy(ies). :return: None
-
run_pre_network_filter_for_inference
(state: Dict[str, numpy.ndarray], update_filter_internal_state: bool = True) → Dict[str, numpy.ndarray]¶ Run filters which where defined for being applied right before using the state for inference.
- Parameters
state – The state to run the filters on
update_filter_internal_state – Should update the filter’s internal state - should not update when evaluating
- Returns
The filtered state
-
save_checkpoint
(checkpoint_prefix: str) → None¶ Allows agents to store additional information when saving checkpoints.
- Parameters
checkpoint_prefix – The prefix of the checkpoint file to save
- Returns
None
-
set_environment_parameters
(spaces: rl_coach.spaces.SpacesDefinition)¶ Sets the parameters that are environment dependent. As a side effect, initializes all the components that are dependent on those values, by calling init_environment_dependent_modules
- Parameters
spaces – the environment spaces definition
- Returns
None
-
set_incoming_directive
(action: Union[int, float, numpy.ndarray, List]) → None¶ Allows setting a directive for the agent to follow. This is useful in hierarchy structures, where the agent has another master agent that is controlling it. In such cases, the master agent can define the goals for the slave agent, define it’s observation, possible actions, etc. The directive type is defined by the agent in-action-space.
- Parameters
action – The action that should be set as the directive
- Returns
-
set_session
(sess) → None¶ Set the deep learning framework session for all the agents in the composite agent
- Returns
None
-
setup_logger
() → None¶ Setup the logger for the agent
- Returns
None
-
sync
() → None¶ Sync the global network parameters to local networks
- Returns
None
-
train
() → float¶ Check if a training phase should be done as configured by num_consecutive_playing_steps. If it should, then do several training steps as configured by num_consecutive_training_steps. A single training iteration: Sample a batch, train on it and update target networks.
- Returns
The total training loss during the training iterations.
-
update_log
() → None¶ Updates the episodic log file with all the signal values from the most recent episode. Additional signals for logging can be set by the creating a new signal using self.register_signal, and then updating it with some internal agent values.
- Returns
None
-
update_step_in_episode_log
() → None¶ Updates the in-episode log file with all the signal values from the most recent step.
- Returns
None
-
update_transition_before_adding_to_replay_buffer
(transition: rl_coach.core_types.Transition) → rl_coach.core_types.Transition¶ Allows agents to update the transition just before adding it to the replay buffer. Can be useful for agents that want to tweak the reward, termination signal, etc.
- Parameters
transition – the transition to update
- Returns
the updated transition
-


Rosemead, California
Savannah Memorial Park
Blink and you may miss this rustic pioneer cemetery.
by J KuzelaBy street view, this cemetery almost looks like an ordinary park. But a closer inspection reveals history hidden in plain sight.
Savannah Memorial Park is also known as Savannah Pioneer Cemetery, El Monte Cemetery, and California Historical Landmark #1046. The cemetery was founded in 1851 with some graves dating back even further.
Due to recent California drought restrictions, the landscape of the park is not lush and green like a conventional cemetery. The results of these restrictions have transformed the land to resemble a more natural terrain similar to the pioneer era. Old trees scatter dead flowers like confetti over the weathered monuments and tombstones.
Most of the graves belong to pioneers of Rosemead and various other neighborhoods of the San Gabriel Valley. Decedents of these families are still being interred today. Local lore also suggests that Savannah Memorial Park is a site for unexplained paranormal activity.
Know Before You Go
There is no staff or caretaker on site.
Visit California with Atlas Obscura Trips
Desert Canvas: Art of Joshua Tree and the Mojave
Join us as we venture deep into the High Desert, glimpsing massive sculptures, revitalizing domes, and fluorescent painted mountains along the way.
Learn More »
Community Discussion
-
No Comments Yet
Start the discussion. forum Visit our forums.


There are five kinds of input: slider
, buttons
, number
, checkbox
and text
. The array of inputs in the MathCell()
command should contain a dictionary for each input desired, with type
set equal to one of these string values, followed by a comma-separated list of optional attributes. Entries in the array can also be arrays of inputs, following the logic of matrices, to allow for arbitrary arrangements.
All inputs have two additional string options: a name
that is used to identify the input in subsequent code, and a label
that appears next to the input. The label can be any valid HTML. Single letter labels are italicized by default.
All variables are assigned in code via the helper function
var x = getVariable( id, 'x' );
where 'x'
is the name
of the variable.
A slider
input has four numeric options with default values
min | 0 |
max | 1 |
step | .01 |
default | min
|
A buttons
input has four additional options: an array of numbers or strings, an optional array of string labels, a value in the first array and a CSS string with default values
values | [1,2,3] |
labels | values
|
default | values[0]
|
width | empty |
A number
input has three numeric options with default values
min | 0 |
max | 1 |
default | min
|
A checkbox
input has a single additional option default
, which can be set to either 'checked'
or true
to have the box initially checked.
A text
input has two additional options: an initial string and a CSS string with the default values
default | empty |
width | automatic |
The minimum and maximum values of slider and number inputs can be set via the helper function
setLimit( id, name, end, value );
where end
is either 'min'
or 'max'
. This can be used to avoid invalid numerical input.


Christian comedian John Crist cancels tour amid sexual harassment allegations
by WTVF StaffNASHVILLE, Tenn. — Christian Comedian John Crist has canceled his upcoming tour dates after he was accused of sexual misconduct.
Five women came forward to accuse Crist of sexual misconduct in an investigation published by Christian publication Charisma News,
Some allege Crist requested sexual favors in exchange for tickets.
There were several dates left on his current tour, two of which were in his home state of Tennessee.
At this point, it's unclear if his upcoming Netflix special will still be released.
In response to Charisma News' report, Crist said he was "not guilty of everything" of which he had been accused, but admitted he "treated relationships with women far too casually, in some cases even recklessly."
"I am sorry for the hurt and pain I have caused these women and will continue to seek their forgiveness. I have also hurt the name of Jesus and have sought His forgiveness," Crist wrote in a statement to Charisma News.
This story was originally published by Kasey Freeman on WTVF in Nashville, Tennessee.


Movie reviews: 2019 Cinema One Originals films, Part 2
Oggs Cruz reviews 'Lucid,' 'O,' 'Metamorphosis,' and 'Yours Truly Shirley'
by Oggs CruzSubscribe
Email Address
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- Glenda Gloria's Take this Tuesday
- Huddle by Miriam Grace Go
- The Newsbreak Agenda by Chay Hofileña
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@421
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6 years |
mmckerns |
cg & bfgs now work regardless if fprime provides list or ndarray
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@420
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6 years |
mmckerns |
minor edit to docs to denote likely performance increase w/ scipy …
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@419
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6 years |
mmckerns |
reduce scipy function copies (and likely delay due to importing)
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@418
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6 years |
mmckerns |
centralized support for (modified) scipy helper functions
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@417
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6 years |
mmckerns |
test_ncg uses mystic.models
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@416
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6 years |
mmckerns |
updated dejong to include rosen.der and rosen.hess
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@413
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6 years |
altafang |
Adding some comments on constraints tools
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@412
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6 years |
mmckerns |
added lines to setup for optional dependencies
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@411
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6 years |
altafang |
Improving snobfit, fixing typos
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@410
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6 years |
altafang |
Removing constrained_tests_other because it is too outdated, and adding …
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@409
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6 years |
altafang |
Adding a better adaptation of the Snobfit solver that seems to work better …
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@408
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6 years |
altafang |
Adding tests of Mystic performance where parameters are adjusted for best …
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@407
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6 years |
altafang |
Improving docstrings for math tools, mcdiarmid bound calculator, etc.
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@406
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6 years |
altafang |
Editing the defaults for the augmented Lagrangian and barrier methods of …
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@405
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6 years |
altafang |
Fixing a bug in constraint tools
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@404
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6 years |
altafang |
The gradient solvers don't handle box bounds well, as they are implemented …
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@403
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6 years |
altafang |
Reverting some changes in scipy_optimize and adding some comments on the …
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@402
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6 years |
altafang |
Updating and reorganizing constrained optimization tests
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@401
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6 years |
altafang |
Adding tests of constrained optimizations, and updating older tests
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@400
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6 years |
altafang |
Adding CRT for PowellDirectionalSolver? and making sure everything is using …
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@399
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6 years |
altafang |
Mostly just adding some comments, and beginning to make a few small …
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@398
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6 years |
altafang |
Fixing typos
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@397
|
6 years |
altafang |
Allowing the barrier method to handle equality constraints, and making it …
|
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@396
|
6 years |
altafang |
Fixing constraint tools and moving scipy_ncg back into the mystic-0.2a1 …
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@395
|
6 years |
altafang |
Adding tool for finding a feasible point given constraints, and allowing …
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@394
|
6 years |
altafang |
Fixing some typos in constraints code
|
|
|
|
@393
|
6 years |
altafang |
Changing the filtering method for imposing constraints to the logarithmic …
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|
|
|
@392
|
6 years |
altafang |
Cleaning up constraint tools code, adding examples and tests
|
|
|
|
@391
|
6 years |
altafang |
Making particle swarm more parallel-friendly, fixing typos in the …
|
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@390
|
6 years |
altafang |
Adding the actual cost functions to the mystic test suite file so it can …
|
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|
|
@389
|
6 years |
altafang |
Fixing a few things in the gradient solvers, (such as consistency in using …
|
|
|
|
@388
|
6 years |
mmckerns |
prepare for move out of "solver" directory -- don't use direct imports
|
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|
|
@387
|
6 years |
mmckerns |
further standardization of docs and settings
|
|
|
|
@386
|
6 years |
mmckerns |
partial standardization of docs for solvers
|
|
|
|
@385
|
6 years |
altafang |
Fixing up the solvers that still need more work/testing, and adding some …
|
|
|
|
@384
|
6 years |
altafang |
Fixing something from the last commit
|
|
|
|
@383
|
6 years |
altafang |
Adding a tentative particle swarm optimizer and organizing solvers that …
|
|
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|
@382
|
6 years |
altafang |
Fixing bugs in the constrained optimization example
|
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@381
|
6 years |
altafang |
Fixing a bug with using Rand1Bin with the augmented Lagrangian method
|
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@380
|
6 years |
altafang |
Fixing a bug in constraint tools
|
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|
@379
|
6 years |
altafang |
Improving the gaussian measure code
|
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|
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@378
|
6 years |
altafang |
Adding more inline comments to the gaussian OUQ example
|
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@377
|
6 years |
altafang |
Cleaning up files forgotten in the last commit
|
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|
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@376
|
6 years |
altafang |
Organizing the constraints tests into separate directories
|
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@373
|
6 years |
altafang |
Fixing minor things, adjusting termination conditions on tests
|
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@371
|
6 years |
altafang |
Fixing the matrix constraints input interface and adding another test.
|
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@369
|
6 years |
altafang |
Adding more tests of constrained optimizations and fixing typos
|
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@368
|
6 years |
altafang |
Fixing a bug with the interface for inputting constraints as functions
|
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@367
|
6 years |
altafang |
Adding interface for constraints that are functions, not just symbolic …
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@364
|
6 years |
altafang |
Improving matrix constraints interface and adding installation …
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@363
|
6 years |
altafang |
Adding a very tentative implementation of the OUQ code with Gaussian …
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@362
|
6 years |
altafang |
Adding logarithmic barrier option and more constrained optimization tests
|
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@361
|
6 years |
altafang |
Renaming the tutorial example and adding test cases (I found some more …
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@360
|
6 years |
altafang |
More testing of constraints tools and comparisons of different constraints …
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@359
|
6 years |
altafang |
Fixing the direct enforcing method for constraints
|
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@358
|
6 years |
altafang |
Adding tests of the direct enforcing method and of the range constraints …
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@357
|
6 years |
altafang |
Adding two more tests of constrained optimizations and improving the …
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@356
|
6 years |
altafang |
Adding more tests of constrained optimizations, and slightly adjusting the …
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@355
|
6 years |
altafang |
Cleaning up the tests of the constraints tools, and adjusted augmented …
|
|
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@354
|
6 years |
altafang |
Figured out why augmented Lagrange multiplier method wasn't working for …
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@351
|
6 years |
altafang |
Fixed handling of disp keyword in augmented lagrangian method; adding …
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@350
|
6 years |
altafang |
Fixed constraint tools to be able to parse constraints where ndim > 9 …
|
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@349
|
6 years |
altafang |
Added a test problem to the Mystic test suite, added more tests of the …
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@348
|
6 years |
altafang |
Changing the barrier method to a simple filtering method and so changing …
|
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@347
|
6 years |
altafang |
Removing constraints from one-line interfaces of solvers, for now. Fixing …
|
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@346
|
6 years |
altafang |
Fixing a mistake in the augmented lagrange multiplier method code and …
|
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@345
|
6 years |
altafang |
Fixing something from last commit
|
|
|
|
@344
|
6 years |
altafang |
Adding a better interface for inputting constraints, making it easier to …
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@343
|
6 years |
altafang |
Adding ability to impose constraints using the augmented Lagrange …
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@342
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6 years |
altafang |
Adding a commonly used test problem for constrained optimizations, the …
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@341
|
6 years |
altafang |
Adding more tests to the Mystic test suite, and adding more tests of …
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@340
|
6 years |
altafang |
Fixing a bad typo in the constraint tools
|
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@339
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6 years |
altafang |
Adding more test problems and tests to the Mystic test suite. Added …
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@338
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6 years |
altafang |
Found some good sources of optimization test problems, and translated some …
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@334
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6 years |
altafang |
Formatting the docstrings better on constraint_tools and moving …
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@333
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6 years |
altafang |
Cleaning up constraint_tools.py and polishing its docstrings so it's more …
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@332
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6 years |
altafang |
Adding an interface for entering linearequality and inequality constraints …
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@331
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6 years |
altafang |
Changed constraints back to a keyword rather than a solver attribute in …
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@330
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6 years |
altafang |
Fixing a bug with verifying if constraints are satisifed
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@329
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6 years |
altafang |
Adding another test case, a peaks function, to the Mystic test suite, and …
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@328
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6 years |
altafang |
Fixing typo in the last commit
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@327
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6 years |
altafang |
Adding the griewangk function and NelderMeadSimplexSolver? to the Mystic …
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@326
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6 years |
altafang |
Adding ability to impose constraints uisng the penalty method, which seems …
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@325
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6 years |
altafang |
Fixing something from the last commit -- accidentally deleted something …
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@324
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6 years |
altafang |
Adding more of the DE paper's test problems to the Mystic test suite.
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@323
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6 years |
altafang |
Adding some test problems found in books and on websites, adding …
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@322
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6 years |
mmckerns |
more use of defaults; RandomInitialPoints?
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@321
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6 years |
mmckerns |
tests use defaults; pass unless far off
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@320
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6 years |
altafang |
Starting code for a Mystic test suite for different problems. Currently …
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@318
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6 years |
altafang |
Adding another adapted OUQ test and fixing up the notes on comparing …
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@317
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6 years |
altafang |
Got rid of only imposing constraints if they don't violate bounds in the …
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@316
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6 years |
altafang |
Slight edits to the last commit of notes on how constraints are imposed.
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@315
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6 years |
altafang |
Adding some notes on the performance of the seesaw2d examples depending on …
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@314
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6 years |
altafang |
Changing differential evolution so that it always imposes constraints each …
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@313
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6 years |
altafang |
Put back something I removed in the last commit, to make differential …
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@312
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6 years |
altafang |
Fixed measure_methods and differential_evolution so that they work with …
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@311
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6 years |
mmckerns |
make Dirac_3D and measure_methods python2.5 compatable
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@310
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6 years |
altafang |
Improving efficiency of inequalities constraints function and of …
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@309
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6 years |
mmckerns |
make constraint_tools python2.5 compatable
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@308
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6 years |
altafang |
Adding ability to constrain the mean and range with inequalities, such as …
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|


Copyright © 2021 STMicroelectronics
STMCube is an STMicroelectronics original initiative to ease developers life by reducing development efforts, time and cost.
STM32Cube covers STM32 portfolio.
STM32Cube Version 1.x includes:
The STM32CubeMX, a graphical software configuration tool that allows to generate C initialization code using graphical wizards.
A comprehensive embedded software platform, delivered per series (such as STM32CubeU5 for STM32U5 series)
The STM32Cube HAL, an STM32 abstraction layer embedded software, ensuring maximized portability across STM32 portfolio
A consistent set of middleware libraries such as ThreadX, FileX, USBX, NetDuoX, OpenBootloader, USBPD, trustedfirmware, mbed-crypto, Network Library…
A full set of software projects (basic examples, applications, and demonstrations) for each board provided for this STM32 series.
A new LPBAM (Low Power Background Autonomous Mode) utility which is a software helper that assists STM32U5 users in the elaboration of LPBAM scenarios.
The STM32Cube firmware solution offers a straightforward API with a modular architecture, making it simple to fine tune custom applications and scalable to fit most requirements.
Both the HAL and LL APIs are production–ready, checked with CodeSonar® static analysis tool, and developed in compliance with MISRA C® guidelines, following a process certified according to IEC 61508 systematic capability 2 level (SC2). Reports are available on demand.
The drivers provided within this package support the STM32U575 / STM32U585 / STM32U595xx / STM32U5A5xx / STM32U599xx / STM32U5A9xx lines.
Patch release of STM32CubeU5 Firmware Package
Fixed bugs list
| Headline |
|---|
| Fix an issue to build the GTZC_MPCWM_IllegalAccess_TrustZone example after the regeneration on STM32U575I-EV board |
| Update internal connection description in OPAMP_Follower example readme.html file on NUCLEO-U575ZI-Q board |
The STM32CubeU5 Firmware package comes with template running on STMicroelectronics boards, organized by board and provided with preconfigured projects for the main supported toolchains The exhaustive list of projects is provided in this table STM32CubeProjectsList.html
| Name | Version | Release notes |
|---|---|---|
| Projects | V1.1.1 | release notes |
Notes:
Please refer to AN5347 for more details
The components flagged by “” have changed since the previous release. “” are new.
| Name | Version | Release note |
|---|---|---|
| Cortex-M CMSIS | V5.6.0_cm33 | release notes |
| STM32U5xx CMSIS | V1.1.0 | release notes |
| STM32U5xx HAL | V1.1.0 | release notes |
| STM32U5xx NUCLEO | V1.1.0 | release notes |
| BSP STM32U575I-EV | V1.1.0 | release notes |
| BSP B-U585I-IOT02A | V1.1.0 | release notes |
| BSP aps6408 | V1.0.1 | release notes |
| BSP Common | V7.2.0 | release notes |
| BSP cs42l51 | V2.0.3 | release notes |
| BSP hts221 | V5.3.2 | release notes |
| BSP hx8347i | V2.0.3 | release notes |
| BSP iis2mdc | V1.1.2 | release notes |
| BSP ism330dhcx | V1.1.3 | release notes |
| BSP lps22hh | V1.2.2 | release notes |
| BSP lsm6dso | V1.5.4 | release notes |
| BSP m24lr64 | V1.0.2 | release notes |
| BSP mfxstm32l152 | V4.0.0 | release notes |
| BSP mx_wifi | v2.1.12 | release notes |
| BSP mx25lm51245g | V2.0.8 | release notes |
| BSP ov5640 | V3.2.2 | release notes |
| BSP stm32wb_at | V1.0.11 | release notes |
| BSP sx8651 | V1.0.1 | release notes |
| BSP tcpp0203 | V1.2.2 | release notes |
| BSP veml6030 | V1.0.1 | release notes |
| BSP vl53l5cx | V1.0.1 | release notes |
| Name | Version | Release note |
|---|---|---|
| STM32 TouchSensing Library | V2.2.8 | release notes |
| STM32 USBPD Core Library | V4.1.0 | release notes |
| STM32 USBPD Device Library | u5_v3.1.0 | release notes |
| mbed-crypto | mbed-crypto-2.25.0 | ST release notes |
| STSAFE_A1xx | MW_V3.3.5 | ST release notes |
| SE_STSAFEA_API | SE_STSAFEA_API_V1.0.0 | ST release notes |
| mcuboot | V1.7.2 | ST release notes |
| trustedfirmware | tfm/v1.3.0 | ST release notes |
| OpenBootloader | V6.0.1 | release notes |
| Network Library | V2.2.0f | release notes |
| ThreadX | threadx-<IP_ADDRESS>513 | release notes |
| NetXduo | netxduo-<IP_ADDRESS>513 | release notes |
| USBX | usbx-<IP_ADDRESS>128 | release notes |
| FileX | filex-<IP_ADDRESS>128 | release notes |
| LevelX | levelx-<IP_ADDRESS>128 | release notes |
| cmsis_rtos_threadx | tx-cmsis-<IP_ADDRESS>108 | release notes |
| Name | Version | Release note |
|---|---|---|
| Common | V1.6.0 | release notes |
| Fonts | V2.0.3 | release notes |
| GUI_INTERFACE | GUI_V2.2.1 | release notes |
| lcd | V2.0.2 | release notes |
| lpbam | V1.1.1 | release notes |
| TRACER_EMB | V1.7.1 | release notes |
This release is compatible with the previous versions
Backward compatibility ensured by legacy defines
The STM32CubeU5 Firmware package comes with template running on STMicroelectronics boards, organized by board and provided with preconfigured projects for the main supported toolchains The exhaustive list of projects is provided in this table STM32CubeProjectsList.html
This release contains all HAL drivers, LL drivers, BSP drivers, templates, projects, demonstrations and examples
Remove WifiBasics Application on B-U585I-IOT02A board
| Name | Version | Release notes |
|---|---|---|
| Projects | V1.1.0 | release notes |
Notes:
Please refer to AN5347 for more details
The components flagged by “” have changed since the previous release. “” are new.
| Name | Version | Release note |
|---|---|---|
| Cortex-M CMSIS | V5.6.0_cm33 | release notes |
| STM32U5xx CMSIS | V1.1.0 | release notes |
| STM32U5xx HAL | V1.1.0 | release notes |
| STM32U5xx NUCLEO | V1.1.0 | release notes |
| BSP STM32U575I-EV | V1.1.0 | release notes |
| BSP B-U585I-IOT02A | V1.1.0 | release notes |
| BSP aps6408 | V1.0.1 | release notes |
| BSP Common | V7.2.0 | release notes |
| BSP cs42l51 | V2.0.3 | release notes |
| BSP hts221 | V5.3.2 | release notes |
| BSP hx8347i | V2.0.3 | release notes |
| BSP iis2mdc | V1.1.2 | release notes |
| BSP ism330dhcx | V1.1.3 | release notes |
| BSP lps22hh | V1.2.2 | release notes |
| BSP lsm6dso | V1.5.4 | release notes |
| BSP m24lr64 | V1.0.2 | release notes |
| BSP mfxstm32l152 | V4.0.0 | release notes |
| BSP mx_wifi | v2.1.12 | release notes |
| BSP mx25lm51245g | V2.0.8 | release notes |
| BSP ov5640 | V3.2.2 | release notes |
| BSP stm32wb_at | V1.0.11 | release notes |
| BSP sx8651 | V1.0.1 | release notes |
| BSP tcpp0203 | V1.2.2 | release notes |
| BSP veml6030 | V1.0.1 | release notes |
| BSP vl53l5cx | V1.0.1 | release notes |
| Name | Version | Release note |
|---|---|---|
| STM32 TouchSensing Library | V2.2.8 | release notes |
| STM32 USBPD Core Library | V4.1.0 | release notes |
| STM32 USBPD Device Library | u5_v3.1.0 | release notes |
| mbed-crypto | mbed-crypto-2.25.0 | ST release notes |
| STSAFE_A1xx | MW_V3.3.5 | ST release notes |
| SE_STSAFEA_API | SE_STSAFEA_API_V1.0.0 | ST release notes |
| mcuboot | V1.7.2 | ST release notes |
| trustedfirmware | tfm/v1.3.0 | ST release notes |
| OpenBootloader | V6.0.1 | release notes |
| Network Library | V2.2.0f | release notes |
| ThreadX | threadx-<IP_ADDRESS>128 | release notes |
| NetXduo | netxduo-<IP_ADDRESS>128 | release notes |
| USBX | usbx-<IP_ADDRESS>128 | release notes |
| FileX | filex-<IP_ADDRESS>128 | release notes |
| LevelX | levelx-<IP_ADDRESS>128 | release notes |
| cmsis_rtos_threadx | tx-cmsis-<IP_ADDRESS>108 | release notes |
| Name | Version | Release note |
|---|---|---|
| Common | V1.6.0 | release notes |
| Fonts | V2.0.3 | release notes |
| GUI_INTERFACE | GUI_V2.2.1 | release notes |
| lcd | V2.0.2 | release notes |
| lpbam | V1.1.0 | release notes |
| TRACER_EMB | V1.7.1 | release notes |
The STM32CubeU5 Firmware package comes with template running on STMicroelectronics boards, organized by board and provided with preconfigured projects for the main supported toolchains. The exhaustive list of projects is provided in this table STM32CubeProjectsList.html
This release contains all HAL drivers, LL drivers, BSP drivers, templates, projects and examples.
| Name | Version | Release notes |
|---|---|---|
| Projects | V1.0.1 | release notes |
Notes
Please refer to AN5347 for more details.
The components flagged by “” have changed since the previous release. “” are new.
| Name | Version | Release note |
|---|---|---|
| Cortex-M CMSIS | V5.6.0_cm33 | release notes |
| STM32U5xx CMSIS | V1.0.1 | release notes |
| STM32U5xx HAL | V1.0.2 | release notes |
| STM32U5xx NUCLEO | V1.0.1 | release notes |
| BSP STM32U575I-EV | V1.0.1 | release notes |
| BSP B-U585I-IOT02A | V1.0.1 | release notes |
| BSP aps6408 | V1.0.0 | release notes |
| BSP Common | V7.1.0 | release notes |
| BSP cs42l51 | V2.0.2 | release notes |
| BSP hts221 | V5.3.0 | release notes |
| BSP hx8347i | V2.0.2 | release notes |
| BSP iis2mdc | V1.1.0 | release notes |
| BSP ism330dhcx | V1.1.1 | release notes |
| BSP lps22hh | V1.2.0 | release notes |
| BSP lsm6dso | V1.5.1 | release notes |
| BSP m24lr64 | V1.0.1 | release notes |
| BSP mfxstm32l152 | V4.0.0 | release notes |
| BSP mx_wifi | V2.1.11 | release notes |
| BSP mx25lm51245g | V2.0.6 | release notes |
| BSP ov5640 | V3.2.1 | release notes |
| BSP stm32wb_at | V1.0.8 | release notes |
| BSP sx8651 | V1.0.0 | release notes |
| BSP tcpp0203 | V1.2.1 | release notes |
| BSP veml6030 | V1.0.0 | release notes |
| Name | Version | Release note |
|---|---|---|
| STM32 TouchSensing Library | V2.2.6 | release notes |
| STM32 USBPD Core Library | V4.0.0 | release notes |
| STM32 USBPD Device Library | u5_v3.0.0 | release notes |
| mbed-crypto | mbed-crypto-1.1.6 | ST release notes |
| mcuboot | V1.0.13 | ST release notes |
| trustedfirmware | tfm/v1.0.15 | ST release notes |
| OpenBootloader | V5.0.0 | release notes |
| Network Library | V2.2.0f | release notes |
| ThreadX | threadx-<IP_ADDRESS>903 | release notes |
| NetXduo | netxduo-<IP_ADDRESS>903 | release notes |
| USBX | usbx-<IP_ADDRESS>903 | release notes |
| FileX | filex-<IP_ADDRESS>903 | release notes |
| LevelX | levelx-<IP_ADDRESS>903 | release notes |
| Name | Version | Release note |
|---|---|---|
| Common | V1.4.1 | release notes |
| Fonts | V2.0.2 | release notes |
| GUI_INTERFACE | GUI_V2.1.0 | release notes |
| lcd | V2.0.1 | release notes |
| lpbam | V1.0.1 | release notes |
| TRACER_EMB | V1.6.1 | release notes |
The STM32CubeU5 Firmware package comes with template running on STMicroelectronics boards, organized by board and provided with preconfigured projects for the main supported toolchains. The exhaustive list of projects is provided in this table STM32CubeProjectsList.html
This release contains all HAL drivers, LL drivers, BSP drivers, templates, projects and examples.
| Name | Version | Release notes |
|---|---|---|
| Projects | V1.0.1 | release notes |
Notes
Please refer to AN5347 for more details.
The components flagged by “” have changed since the previous release. “” are new.
| Name | Version | Release note |
|---|---|---|
| Cortex-M CMSIS | V5.6.0_cm33 | release notes |
| STM32U5xx CMSIS | V1.0.1 | release notes |
| STM32U5xx HAL | V1.0.1 | release notes |
| STM32U5xx NUCLEO | V1.0.1 | release notes |
| BSP STM32U575I-EV | V1.0.1 | release notes |
| BSP B-U585I-IOT02A | V1.0.1 | release notes |
| BSP aps6408 | V1.0.0 | release notes |
| BSP Common | V7.1.0 | release notes |
| BSP cs42l51 | V2.0.2 | release notes |
| BSP hts221 | V5.3.0 | release notes |
| BSP hx8347i | V2.0.2 | release notes |
| BSP iis2mdc | V1.1.0 | release notes |
| BSP ism330dhcx | V1.1.1 | release notes |
| BSP lps22hh | V1.2.0 | release notes |
| BSP lsm6dso | V1.5.1 | release notes |
| BSP m24lr64 | V1.0.1 | release notes |
| BSP mfxstm32l152 | V4.0.0 | release notes |
| BSP mx_wifi | V2.1.11 | release notes |
| BSP mx25lm51245g | V2.0.6 | release notes |
| BSP ov5640 | V3.2.1 | release notes |
| BSP stm32wb_at | V1.0.8 | release notes |
| BSP sx8651 | V1.0.0 | release notes |
| BSP tcpp0203 | V1.2.1 | release notes |
| BSP veml6030 | V1.0.0 | release notes |
| Name | Version | Release note |
|---|---|---|
| STM32 TouchSensing Library | V2.2.6 | release notes |
| STM32 USBPD Core Library | V4.0.0 | release notes |
| STM32 USBPD Device Library | u5_v3.0.0 | release notes |
| mbed-crypto | mbed-crypto-1.1.6 | ST release notes |
| mcuboot | V1.0.13 | ST release notes |
| trustedfirmware | tfm/v1.0.15 | ST release notes |
| OpenBootloader | V5.0.0 | release notes |
| Network Library | V2.2.0f | release notes |
| ThreadX | threadx-<IP_ADDRESS>903 | release notes |
| NetXduo | netxduo-<IP_ADDRESS>903 | release notes |
| USBX | usbx-<IP_ADDRESS>903 | release notes |
| FileX | filex-<IP_ADDRESS>903 | release notes |
| LevelX | levelx-<IP_ADDRESS>903 | release notes |
| Name | Version | Release note |
|---|---|---|
| Common | V1.4.1 | release notes |
| Fonts | V2.0.2 | release notes |
| GUI_INTERFACE | GUI_V2.1.0 | release notes |
| lcd | V2.0.1 | release notes |
| lpbam | V1.0.1 | release notes |
| TRACER_EMB | V1.6.1 | release notes |
The STM32CubeU5 Firmware package comes with template running on STMicroelectronics boards, organized by board and provided with preconfigured projects for the main supported toolchains. The exhaustive list of projects is provided in this table STM32CubeProjectsList.html
This release contains almost HAL drivers, LL drivers, BSP drivers, templates, projects and examples.
| Name | Version | Release notes |
|---|---|---|
| Projects | V1.0.0 | release notes |
Notes
Please refer to AN5347 for more details.
The components flagged by “” have changed since the previous release. “” are new.
| Name | Version | Release note |
|---|---|---|
| Cortex-M CMSIS | V5.6.0_cm33 | release notes |
| STM32U5xx CMSIS | V1.0.0 | release notes |
| STM32U5xx HAL | V1.0.0 | release notes |
| STM32U5xx NUCLEO | V1.0.0 | release notes |
| BSP STM32U575I-EV | V1.0.0 | release notes |
| BSP B-U585I-IOT02A | V1.0.0 | release notes |
| BSP aps6408 | V1.0.0 | release notes |
| BSP Common | V7.1.0 | release notes |
| BSP cs42l51 | V2.0.2 | release notes |
| BSP hts221 | V5.3.0 | release notes |
| BSP hx8347i | V2.0.2 | release notes |
| BSP iis2mdc | V1.1.0 | release notes |
| BSP ism330dlc | V1.2.0 | release notes |
| BSP lps22hh | V1.2.0 | release notes |
| BSP lsm6dso | V1.5.1 | release notes |
| BSP m24lr64 | V1.0.1 | release notes |
| BSP mfxstm32l152 | V3.0.3 | release notes |
| BSP mx_wifi | V2.1.11 | release notes |
| BSP mx25lm51245g | V2.0.6 | release notes |
| BSP ov5640 | V3.2.1 | release notes |
| BSP stm32wb_at | V1.0.6 | release notes |
| BSP sx8651 | V1.0.0 | release notes |
| BSP tcpp0203 | V1.2.1 | release notes |
| Name | Version | Release note |
|---|---|---|
| STM32 TouchSensing Library | V2.2.6 | release notes |
| STM32 USBPD Core Library | V4.0.0 | release notes |
| STM32 USBPD Device Library | u5_v3.0.0 | release notes |
| mbed-crypto | mbed-crypto-1.1.6 | ST release notes |
| mcuboot | V1.0.13 | ST release notes |
| trustedfirmware | tfm/v1.0.15 | ST release notes |
| OpenBootloader | V5.0.0 | release notes |
| Network Library | V2.2.0c | release notes |
| ThreadX | threadx-<IP_ADDRESS>621 | release notes |
| NetXduo | netxduo-<IP_ADDRESS>621 | release notes |
| USBX | usbx-<IP_ADDRESS>621 | release notes |
| FileX | filex-<IP_ADDRESS>621 | release notes |
| LevelX | levelx-<IP_ADDRESS>621 | release notes |
| Name | Version | Release note |
|---|---|---|
| Common | V1.4.1 | release notes |
| Fonts | V2.0.2 | release notes |
| GUI_INTERFACE | GUI_V2.0.0 | release notes |
| lcd | V2.0.1 | release notes |
| lpbam | V1.0.0 | release notes |
| TRACER_EMB | V1.6.1 | release notes |


dart:core library
Built-in types, collections, and other core functionality for every Dart program.
This library is automatically imported.
Some classes in this library, such as String and num, support Dart's built-in data types. Other classes, such as List and Map, provide data structures for managing collections of objects. And still other classes represent commonly used types of data such as URIs, dates and times, and errors.
Numbers and booleans
int and double provide support for Dart's built-in numerical data types: integers and double-precision floating point numbers, respectively. An object of type bool is either true or false. Variables of these types can be constructed from literals:
int meaningOfLife = 42;
double valueOfPi = 3.141592;
bool visible = true;
Strings and regular expressions
A String is immutable and represents a sequence of characters.
String shakespeareQuote = "All the world's a stage, ...";
StringBuffer provides a way to construct strings efficiently.
StringBuffer moreShakespeare = new StringBuffer();
moreShakespeare.write('And all the men and women ');
moreShakespeare.write('merely players; ...');
The String and StringBuffer classes implement string concatenation, interpolation, and other string manipulation features.
String philosophy = 'Live on ';
String get palindrome => philosophy + philosophy.split('').reversed.join();
RegExp implements Dart regular expressions, which provide a grammar for matching patterns within text. For example, here's a regular expression that matches a string of one or more digits:
var numbers = new RegExp(r'\d+');
Dart regular expressions have the same syntax and semantics as JavaScript regular expressions. See ecma-international.org/ecma-262/5.1/#sec-15.10 for the specification of JavaScript regular expressions.
Collections
The dart:core library provides basic collections, such as List, Map, and Set.
A List is an ordered collection of objects, with a length. Lists are sometimes called arrays. Use a List when you need to access objects by index.
List superheroes = [ 'Batman', 'Superman', 'Harry Potter' ];
A Set is an unordered collection of unique objects. You cannot get an item by index (position). Adding a duplicate item has no effect.
Set villains = new Set();
villains.add('Joker');
villains.addAll( ['Lex Luther', 'Voldemort'] );
A Map is an unordered collection of key-value pairs. Maps are sometimes called associative arrays because maps associate a key to some value for easy retrieval. Keys are unique. Use a Map when you need to access objects by a unique identifier.
Map sidekicks = { 'Batman': 'Robin',
'Superman': 'Lois Lane',
'Harry Potter': 'Ron and Hermione' };
In addition to these classes, dart:core contains Iterable, an interface that defines functionality common in collections of objects. Examples include the ability to run a function on each element in the collection, to apply a test to each element, to retrieve an object, and to determine length.
Iterable is implemented by List and Set, and used by Map for its keys and values.
For other kinds of collections, check out the
dart:collection
library.
Date and time
Use DateTime to represent a point in time and Duration to represent a span of time.
You can create DateTime objects with constructors or by parsing a correctly formatted string.
DateTime now = new DateTime.now();
DateTime berlinWallFell = new DateTime(1989, 11, 9);
DateTime moonLanding = DateTime.parse("1969-07-20");
Create a Duration object specifying the individual time units.
Duration timeRemaining = new Duration(hours:56, minutes:14);
In addition to DateTime and Duration, dart:core contains the Stopwatch class for measuring elapsed time.
Uri
A Uri object represents a uniform resource identifier, which identifies a resource on the web.
Uri dartlang = Uri.parse('http://dartlang.org/');
Errors
The Error class represents the occurrence of an error during runtime. Subclasses of this class represent specific kinds of errors.
Other documentation
For more information about how to use the built-in types, refer to Built-in Types in Chapter 2 of Dart: Up and Running.
Also, see dart:core - Numbers, Collections, Strings, and More for more coverage of classes in this package.
The Dart Language Specification provides technical details.
Classes
-
BidirectionalIterator<
E> - An Iterator that allows moving backwards as well as forwards.
- BigInt
- An arbitrarily large integer.
- bool
-
The reserved words
true
andfalse
denote objects that are the only two instances of this class. [...] -
Comparable<
T> - Interface used by types that have an intrinsic ordering. [...]
- DateTime
- An instant in time, such as July 20, 1969, 8:18pm GMT. [...]
- Deprecated
-
The annotation
@Deprecated('migration')
marks a feature as deprecated. [...] - double
- A double-precision floating point number. [...]
- Duration
- A span of time, such as 27 days, 4 hours, 12 minutes, and 3 seconds. [...]
-
Expando<
T> - An Expando allows adding new properties to objects. [...]
- Function
- The base class for all function types. [...]
-
Future<
T> - An object representing a delayed computation. [...]
- int
- An integer number. [...]
- Invocation
- Representation of the invocation of a member on an object. [...]
-
Iterable<
E> - A collection of values, or "elements", that can be accessed sequentially. [...]
-
Iterator<
E> - An interface for getting items, one at a time, from an object. [...]
-
List<
E> - An indexable collection of objects with a length. [...]
-
Map<
K, V> - A collection of key/value pairs, from which you retrieve a value using its associated key. [...]
-
MapEntry<
K, V> - A key/value pair representing an entry in a Map.
- Match
- A result from searching within a string. [...]
- Null
-
The reserved word
null
denotes an object that is the sole instance of this class. [...] - num
- An integer or floating-point number. [...]
- Object
- The base class for all Dart objects. [...]
- Pattern
- An interface for basic searches within strings.
- pragma
- A hint to tools. [...]
- Provisional
- An annotation class that was used during development of Dart 2. [...]
- RegExp
- A regular expression pattern. [...]
- RegExpMatch
- A regular expression match. [...]
- RuneIterator
- Iterator for reading runes (integer Unicode code points) out of a Dart [...]
- Runes
- The runes (integer Unicode code points) of a String.
-
Set<
E> - A collection of objects in which each object can occur only once. [...]
-
Sink<
T> - A generic destination for data. [...]
- StackTrace
- An interface implemented by all stack trace objects. [...]
- Stopwatch
- A simple stopwatch interface to measure elapsed time.
-
Stream<
T> - A source of asynchronous data events. [...]
- String
- A sequence of UTF-16 code units. [...]
- StringBuffer
- A class for concatenating strings efficiently. [...]
- StringSink
- Symbol
- Opaque name used by mirrors, invocations and Function.apply.
- Type
- Runtime representation of a type.
- Uri
- A parsed URI, such as a URL. [...]
- UriData
-
A way to access the structure of a
data:
URI. [...]
Constants
- deprecated → const Deprecated
-
Marks a feature as Deprecated until the next release.
const Deprecated("next release")
- override → const Object
-
The annotation
@override
marks an instance member as overriding a superclass member with the same name. [...]const _Override()
- provisional → const Null
-
An annotation that was used during development of Dart 2. [...]
null
- proxy → const Object
-
This annotation is deprecated and will be removed in Dart 2. [...]
const _Proxy()
Functions
Typedefs
-
Comparator<
T> (T a, T b) → int - The signature of a generic comparison function. [...]
Exceptions / Errors
- AbstractClassInstantiationError
- Error thrown when trying to instantiate an abstract class.
- ArgumentError
- Error thrown when a function is passed an unacceptable argument.
- AssertionError
- Error thrown by the runtime system when an assert statement fails.
- CastError
- Error thrown by the runtime system when a cast operation fails.
- ConcurrentModificationError
- Error occurring when a collection is modified during iteration. [...]
- CyclicInitializationError
- Error thrown when a lazily initialized variable cannot be initialized. [...]
- Error
- Error objects thrown in the case of a program failure. [...]
- Exception
- A marker interface implemented by all core library exceptions. [...]
- FallThroughError
- Error thrown when control reaches the end of a switch case. [...]
- FormatException
- Exception thrown when a string or some other data does not have an expected format and cannot be parsed or processed.
- IndexError
-
A specialized RangeError used when an index is not in the range
0..indexable.length-1
. [...] - IntegerDivisionByZeroException
- NoSuchMethodError
-
Error thrown by the default implementation of
noSuchMethod
on Object. - NullThrownError
-
Error thrown when attempting to throw
null
. - OutOfMemoryError
- RangeError
- Error thrown due to an index being outside a valid range.
- StackOverflowError
- StateError
- The operation was not allowed by the current state of the object. [...]
- TypeError
- Error thrown by the runtime system when a type assertion fails.
- UnimplementedError
- Thrown by operations that have not been implemented yet. [...]
- UnsupportedError
- The operation was not allowed by the object. [...]


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Now you can, through SoundBetter.
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Alooma JavaScript client
Base tests
Dev (unminified)
Production (minified)
Other implementation scenarios
localStorage instead of cookie
Multiple lib instances
Alooma calls in <head>
API down
Cookie-setting fails
Loaded as module
AMD/RequireJS
CommonJS/Webpack
ES2015/Babelify


In Pictures: Oman lights up for 49th National Day
by Times News ServiceSeveral areas in Muscat have been lit up in national colours as Oman gear-up to celebrate its 49th National Day.
Several areas in Muscat have been lit up in national colours as Oman gear-up to celebrate its 49th National Day.


Our Story
See how it all got started.
-
Fall 2016
First Hack Potsdam
After the ACM President, Jesse, had been to Hack Upstate, he had the crazy idea to help bring one to Potsdam. With the tremendous help of the e-board members and a lot of other people we can't fit on this line to thank, that crazy idea came to life.
-
Spring 2017
The Collaboration Continues
With the wild success of the first hackathon, SUNY Potsdam's ACM and Clarkson's COSI decide to collaborate on the spring iteration of the event.


v2.5.1
- Updated Support: Debugger
- Notable changes:
- Fixed Stack Frame colors for Frames not on the top.
- Fixed Evaluated Expression Text on hover.
v2.5
- Added Features: NEW 2017.3 features
- Updated Themes: All
- Notable changes:
- Added Marked for removal methods. (2017.3)
- Added Inline parameter hints. (2017.3)
- Added Diff separator color. (2017.3)
- Added Visual indent guide. (2017.3)
- Brought feature parity to all themes.
- Breadcrumbs have a new style that is compatible with Android Studio and IntelliJ IDEs.
- Debugger has a new style. New colors and breakpoint lines are bold.
- Method separators are now visible if enabled.
- Added bracket support to ignore files.
- Annotations are italic, and attribute names are purple.
- Diff & Merge have darker error stripe marks for conflicts and deleted text
v2.1.1
- Updated Support: Ignore Files
- Notable changes:
- Fixed issue with ignore file headers and sections being incorrectly formatted. (Closes #35)
- Fixed issue with ignore file negations being incorrectly formatted. (Closes #36)
v2.1
- Updated Languages: Groovy
- Notable changes:
- Fixed regression with Gradle DSL not displayed as green. (Closes #31)
- Fixed regression with Groovy Unknown Symbol having a gray boxed effect. (Closes #32)
- Changed Unused Symbols to have yellow underlines (Closes #33)
- Updated Build System to use Gradle
v2.0
- Added Themes: Darker, Darcula-compatible
- Added Languages: Kotlin, Markdown
- Other Notable changes:
- Standardized colors across all languages.
- Added Breadcrumb color support to the editor.
- Enhanced Injected language color support.
- Added Read-only color support.
- Modified HTML Tree Tag level colors.
- Updated all warnings and errors to use italics.
- Unused identifiers are enhanced to prevent further issues.
- Enhanced Java instance field colors.
- Android Logcat's Assert are now purple.
- Tear line (plus collapse / expand buttons) are now green.
- Fixed regression with not highlighting all instances of the current identifier under the carat.
v1.1.1
- v1.1 did not work when deployed. This is a quick-fix.
v1.1
- Added Languages: Android Native (C / C++)
- Added Support: Debugger, ProGuard files, Ignore files
- Other Notable changes:
- Simplified the color scheme.
- Folded text is a different color.
- Numbers and Strings are the same color.
- Annotation colors are similar in style to comments.
v1.0
- Added Color Scheme: Default (dark)
- Added Languages: Java, Groovy, XML, JSON, HTML
- Added Support: Android, Android Manifest, Android Resources, Gradle files, LogCat, Property files, and
diffs


{% extends 'surveys/user_survey.html' %} {% load static %} {% block fields %}
How important are the following management goals or objectives are to you?