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visualize with plotly: we make three diagrams: 1) a horizontal bar plot comparing the overall papers per db 2) a vertical bar plot differentiating time and db 3) a vertical bar plot differentiating tima and db with a logarithmic y-scale (allows for better inspection of smaller numbers)	#set data for horizontal bar plot: data = [go.Bar( x=[pd.DataFrame.sum(df2)['wos'],pd.DataFrame.sum(df2)['scopus'],pd.DataFrame.sum(df2)['ARTICLE_TITLE']], y=['Web of Science', 'Scopus', 'Total'], orientation = 'h', marker=dict( color=colorlist ...	1-number of papers over time/Creating overview bar-plots.ipynb	MathiasRiechert/BigDataPapers	gpl-3.0
Source localization with MNE/dSPM/sLORETA/eLORETA The aim of this tutorial is to teach you how to compute and apply a linear inverse method such as MNE/dSPM/sLORETA/eLORETA on evoked/raw/epochs data.	import numpy as np import matplotlib.pyplot as plt import mne from mne.datasets import sample from mne.minimum_norm import make_inverse_operator, apply_inverse	0.19/_downloads/ff83425ee773d1d588a6994e5560c06c/plot_mne_dspm_source_localization.ipynb	mne-tools/mne-tools.github.io	bsd-3-clause
Process MEG data	data_path = sample.data_path() raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif' raw = mne.io.read_raw_fif(raw_fname) # already has an average reference events = mne.find_events(raw, stim_channel='STI 014') event_id = dict(aud_l=1) # event trigger and conditions tmin = -0.2 # start of each epoc...	0.19/_downloads/ff83425ee773d1d588a6994e5560c06c/plot_mne_dspm_source_localization.ipynb	mne-tools/mne-tools.github.io	bsd-3-clause
Compute the evoked response Let's just use MEG channels for simplicity.	evoked = epochs.average().pick('meg') evoked.plot(time_unit='s') evoked.plot_topomap(times=np.linspace(0.05, 0.15, 5), ch_type='mag', time_unit='s') # Show whitening evoked.plot_white(noise_cov, time_unit='s') del epochs # to save memory	0.19/_downloads/ff83425ee773d1d588a6994e5560c06c/plot_mne_dspm_source_localization.ipynb	mne-tools/mne-tools.github.io	bsd-3-clause
Note that there is a relationship between the orientation of the dipoles and the surface of the cortex. For this reason, we do not use an inflated cortical surface for visualization, but the original surface used to define the source space. For more information about dipole orientations, see tut-dipole-orientations. No...	for mi, (method, lims) in enumerate((('dSPM', [8, 12, 15]), ('sLORETA', [3, 5, 7]), ('eLORETA', [0.75, 1.25, 1.75]),)): surfer_kwargs['clim']['lims'] = lims stc = apply_inverse(evoked, inverse_operator, lambda2, me...	0.19/_downloads/ff83425ee773d1d588a6994e5560c06c/plot_mne_dspm_source_localization.ipynb	mne-tools/mne-tools.github.io	bsd-3-clause
유의 확률의 값이 아주 작으면 귀무 가설이 맞다는 가정하에 계산된 검정 통계량이 나올 가능성이 희귀하다는 의미이다. 다시 예를 들자면 "어떤 병에 걸렸다"는 귀무 가설을 증명하기 위한 검정에서 혈액 검사를 사용하여 계산한 유의확률이 0.02%라는 의미는 실제로 병에 걸린 환자들 중 혈액 검사 수치가 해당 환자의 혈액 검사 수치보다 낮은 사람은 0.02% 뿐이었다는 뜻이고 "어떤 학생이 우등생이다."라는 귀무사설을 증명하기 위한 검정에서 시험 성적을 사용하여 계산한 유의확률이 0.3%라는 의미는 실제로 우등생의 성적을 분석해 보면 실수로 시험을 잘 못치른 경우를 포함...	1 - sp.stats.binom(15, 0.5).cdf(12-1)	12. 추정 및 검정/02. 검정과 유의 확률.ipynb	zzsza/Datascience_School	mit
이 값은 5% 보다는 작고 1% 보다는 크기 때문에 유의 수준이 5% 라면 기각할 수 있으며(즉 공정한 동전이 아니라고 말할 수 있다.) 유의 수준이 1% 라면 기각할 수 없다.(즉, 공정한 동전이 아니라고 말할 수 없다.) 문제2 <blockquote> 어떤 트레이더의 일주일 수익률은 다음과 같다.:<br> -2.5%, -5%, 4.3%, -3.7% -5.6% <br> 이 트레이더는 돈을 벌어다 줄 사람인가, 아니면 돈을 잃을 사람인가? </blockquote> 수익률이 정규 분포를 따른 다고 가정하면 이 트레이더의 검정통계량은 다음과 같이 계산된다. $$...	x = np.array([-0.025, -0.05, 0.043, -0.037, -0.056]) t = x.mean()/x.std(ddof=1)*np.sqrt(len(x)) t, sp.stats.t(df=4).cdf(t)	12. 추정 및 검정/02. 검정과 유의 확률.ipynb	zzsza/Datascience_School	mit
Incremental Incremental returns a sequence of numbers that increase in regular steps.	g = Incremental(start=200, step=4) print_generated_sequence(g, num=20, seed=12345)	notebooks/v6/Primitive_generators.ipynb	maxalbert/tohu	mit
Integer Integer returns a random integer between low and high (both inclusive).	g = Integer(low=100, high=200) print_generated_sequence(g, num=20, seed=12345)	notebooks/v6/Primitive_generators.ipynb	maxalbert/tohu	mit
Timestamp	g = Timestamp(start="2018-01-01 11:22:33", end="2018-02-13 12:23:34") type(next(g)) print_generated_sequence(g, num=10, seed=12345, sep='\n') g = Timestamp(start="2018-01-01 11:22:33", end="2018-02-13 12:23:34").strftime("%-d %b %Y, %H:%M (%a)") type(next(g)) print_generated_sequence(g, num=10, seed=12345, sep='\n...	notebooks/v6/Primitive_generators.ipynb	maxalbert/tohu	mit
Date	g = Date(start="2018-01-01", end="2018-02-13") type(next(g)) print_generated_sequence(g, num=10, seed=12345, sep='\n') g = Date(start="2018-01-01", end="2018-02-13").strftime("%-d %b %Y") type(next(g)) print_generated_sequence(g, num=10, seed=12345, sep='\n')	notebooks/v6/Primitive_generators.ipynb	maxalbert/tohu	mit
Ensure the two .xls data files are in the same folder as the Jupyter notebook Before we proceed, let's make sure the two .xls data files are in the same folder as our running Jupyter notebook. We'll use a Jupyter notebook magic command to print out the contents of the folder that our notebook is in. The %ls command lis...	%ls	content/code/matplotlib_plots/stress_strain_curves/stress_strain_curve_with_python.ipynb	ProfessorKazarinoff/staticsite	gpl-3.0
We can see our Jupyter notebook stress_strain_curve_with_python.ipynb as well as the two .xls data files aluminum6061.xls and steel1045.xls are in our current folder. Now that we are sure the two .xls data files are in the same folder as our notebook, we can import the data in the two two .xls files using Panda's pd.r...	steel_df = pd.read_excel("steel1045.xls") al_df = pd.read_excel("aluminum6061.xls")	content/code/matplotlib_plots/stress_strain_curves/stress_strain_curve_with_python.ipynb	ProfessorKazarinoff/staticsite	gpl-3.0
We can use Pandas .head() method to view the first five rows of each dataframe.	steel_df.head() al_df.head()	content/code/matplotlib_plots/stress_strain_curves/stress_strain_curve_with_python.ipynb	ProfessorKazarinoff/staticsite	gpl-3.0
We see a number of columns in each dataframe. The columns we are interested in are FORCE, EXT, and CH5. Below is a description of what these columns mean. FORCE Force measurements from the load cell in pounds (lb), force in pounds EXT Extension measurements from the mechanical extensometer in percent (%), strain in pe...	strain_steel = steel_df['CH5']0.01 d_steel = 0.506 # test bar diameter = 0.506 inches stress_steel = (steel_df['FORCE']0.001)/(np.pi((d_steel/2)2)) strain_al = al_df['CH5']0.01 d_al = 0.506 # test bar diameter = 0.506 inches stress_al = (al_df['FORCE']0.001)/(np.pi((d_al/2)**2))	content/code/matplotlib_plots/stress_strain_curves/stress_strain_curve_with_python.ipynb	ProfessorKazarinoff/staticsite	gpl-3.0
Build a quick plot Now that we have the data from the tensile test in four series, we can build a quick plot using Matplotlib's plt.plot() method. The first x,y pair we pass to plt.plot() is strain_steel,stress_steel and the second x,y pair we pass in is strain_al,stress_al. The command plt.show() shows the plot.	plt.plot(strain_steel,stress_steel,strain_al,stress_al) plt.show()	content/code/matplotlib_plots/stress_strain_curves/stress_strain_curve_with_python.ipynb	ProfessorKazarinoff/staticsite	gpl-3.0
We see a plot with two lines. One line represents the steel sample and one line represents the aluminum sample. We can improve our plot by adding axis labels with units, a title and a legend. Add axis labels, title and a legend Axis labels, titles and a legend are added to our plot with three Matplotlib methods. The me...	plt.plot(strain_steel,stress_steel,strain_al,stress_al) plt.xlabel('strain (in/in)') plt.ylabel('stress (ksi)') plt.title('Stress Strain Curve of Steel 1045 and Aluminum 6061 in tension') plt.legend(['Steel 1045','Aluminum 6061']) plt.show()	content/code/matplotlib_plots/stress_strain_curves/stress_strain_curve_with_python.ipynb	ProfessorKazarinoff/staticsite	gpl-3.0
The plot we see has two lines, axis labels, a title and a legend. Next we'll save the plot to a .png image file. Save the plot as a .png image Now we can save the plot as a .png image using Matplotlib's plt.savefig() method. The code cell below builds the plot and saves an image file called stress-strain_curve.png. The...	plt.plot(strain_steel,stress_steel,strain_al,stress_al) plt.xlabel('strain (in/in)') plt.ylabel('stress (ksi)') plt.title('Stress Strain Curve of Steel 1045 and Aluminum 6061 in tension') plt.legend(['Steel 1045','Aluminum 6061']) plt.savefig('stress-strain_curve.png', dpi=300, bbox_inches='tight') plt.show()	content/code/matplotlib_plots/stress_strain_curves/stress_strain_curve_with_python.ipynb	ProfessorKazarinoff/staticsite	gpl-3.0
Label Network Embeddings The label network embeddings approaches require a working tensorflow installation and the OpenNE library. To install them, run the following code: bash pip install networkx tensorflow git clone https://github.com/thunlp/OpenNE/ pip install -e OpenNE/src For an example we will use the LINE embe...	from skmultilearn.embedding import OpenNetworkEmbedder from skmultilearn.cluster import LabelCooccurrenceGraphBuilder graph_builder = LabelCooccurrenceGraphBuilder(weighted=True, include_self_edges=False) openne_line_params = dict(batch_size=1000, order=3) embedder = OpenNetworkEmbedder( graph_builder, 'LINE...	docs/source/multilabelembeddings.ipynb	scikit-multilearn/scikit-multilearn	bsd-2-clause
We now need to select a regressor and a classifier, we use random forest regressors with MLkNN which is a well working combination often used for multi-label embedding:	from skmultilearn.embedding import EmbeddingClassifier from sklearn.ensemble import RandomForestRegressor from skmultilearn.adapt import MLkNN clf = EmbeddingClassifier( embedder, RandomForestRegressor(n_estimators=10), MLkNN(k=5) ) clf.fit(X_train, y_train) predictions = clf.predict(X_test)	docs/source/multilabelembeddings.ipynb	scikit-multilearn/scikit-multilearn	bsd-2-clause
Cost-Sensitive Label Embedding with Multidimensional Scaling CLEMS is another well-perfoming method in multi-label embeddings. It uses weighted multi-dimensional scaling to embedd a cost-matrix of unique label combinations. The cost-matrix contains the cost of mistaking a given label combination for another, thus real-...	from skmultilearn.embedding import CLEMS, EmbeddingClassifier from sklearn.ensemble import RandomForestRegressor from skmultilearn.adapt import MLkNN dimensional_scaler_params = {'n_jobs': -1} clf = EmbeddingClassifier( CLEMS(metrics.jaccard_similarity_score, is_score=True, params=dimensional_scaler_params), ...	docs/source/multilabelembeddings.ipynb	scikit-multilearn/scikit-multilearn	bsd-2-clause
Scikit-learn based embedders Any scikit-learn embedder can be used for multi-label classification embeddings with scikit-multilearn, just select one and try, here's a spectral embedding approach with 10 dimensions of the embedding space:	from skmultilearn.embedding import SKLearnEmbedder, EmbeddingClassifier from sklearn.manifold import SpectralEmbedding from sklearn.ensemble import RandomForestRegressor from skmultilearn.adapt import MLkNN clf = EmbeddingClassifier( SKLearnEmbedder(SpectralEmbedding(n_components = 10)), RandomForestRegressor(...	docs/source/multilabelembeddings.ipynb	scikit-multilearn/scikit-multilearn	bsd-2-clause
On the GeoIDE Wiki they give some example CSW examples to illustrate the range possibilities. Here's one where to search for PACIOOS WMS services:	HTML('<iframe src=https://geo-ide.noaa.gov/wiki/index.php?title=ESRI_Geoportal#PacIOOS_WAF width=950 height=350></iframe>')	CSW/CSW_ISO_Queryables-IOOS.ipynb	rsignell-usgs/notebook	mit
Also on the GEO-IDE Wiki we find the list of UUIDs for each region/provider, which we turn into a dictionary here:	regionids = {'AOOS': '{1E96581F-6B73-45AD-9F9F-2CC3FED76EE6}', 'CENCOOS': '{BE483F24-52E7-4DDE-909F-EE8D4FF118EA}', 'CARICOOS': '{0C4CA8A6-5967-4590-BFE0-B8A21CD8BB01}', 'GCOOS': '{E77E250D-2D65-463C-B201-535775D222C9}', 'GLOS': '{E4A9E4F4-78A4-4BA0-B653-F548D74F68FA}', 'MARACOOS': '{A26F8553-798B-4B1C-8755-1031D752F7C...	CSW/CSW_ISO_Queryables-IOOS.ipynb	rsignell-usgs/notebook	mit
The filters can be passed as a list to getrecords2, with AND or OR implied by syntax: <pre> [a,b,c] --> a \|\| b \|\| c [[a,b,c]] --> a && b && c [[a,b],[c],[d],[e]] or [[a,b],c,d,e] --> (a && b) \|\| c \|\| d \|\| e </pre>	# try simple query with serviceType and keyword first csw.getrecords2(constraints=[[serviceType,keywords]],maxrecords=15,esn='full') for rec,item in csw.records.iteritems(): print item.title # check out references for one of the returned records csw.records['NOAA.NOS.CO-OPS SOS'].references # filter for GCOOS SOS...	CSW/CSW_ISO_Queryables-IOOS.ipynb	rsignell-usgs/notebook	mit
Implement Preprocessing Function Text to Word Ids As you did with other RNNs, you must turn the text into a number so the computer can understand it. In the function text_to_ids(), you'll turn source_text and target_text from words to ids. However, you need to add the <EOS> word id at the end of target_text. Th...	def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int): """ Convert source and target text to proper word ids :param source_text: String that contains all the source text. :param target_text: String that contains all the target text. :param source_vocab_to_int: Dictionar...	language-translation/dlnd_language_translation.ipynb	mu4farooqi/deep-learning-projects	gpl-3.0
Check Point This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk.	""" DON'T MODIFY ANYTHING IN THIS CELL """ import numpy as np import helper import problem_unittests as tests (source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()	language-translation/dlnd_language_translation.ipynb	mu4farooqi/deep-learning-projects	gpl-3.0
Check the Version of TensorFlow and Access to GPU This will check to make sure you have the correct version of TensorFlow and access to a GPU	""" DON'T MODIFY ANYTHING IN THIS CELL """ from distutils.version import LooseVersion import warnings import tensorflow as tf from tensorflow.python.layers.core import Dense # Check TensorFlow Version assert LooseVersion(tf.__version__) >= LooseVersion('1.1'), 'Please use TensorFlow version 1.1 or newer' print('Tensor...	language-translation/dlnd_language_translation.ipynb	mu4farooqi/deep-learning-projects	gpl-3.0
Build the Neural Network You'll build the components necessary to build a Sequence-to-Sequence model by implementing the following functions below: - model_inputs - process_decoder_input - encoding_layer - decoding_layer_train - decoding_layer_infer - decoding_layer - seq2seq_model Input Implement the model_inputs() fu...	def model_inputs(): """ Create TF Placeholders for input, targets, learning rate, and lengths of source and target sequences. :return: Tuple (input, targets, learning rate, keep probability, target sequence length, max target sequence length, source sequence length) """ input_data = tf.placehold...	language-translation/dlnd_language_translation.ipynb	mu4farooqi/deep-learning-projects	gpl-3.0
Encoding Implement encoding_layer() to create a Encoder RNN layer: * Embed the encoder input using tf.contrib.layers.embed_sequence * Construct a stacked tf.contrib.rnn.LSTMCell wrapped in a tf.contrib.rnn.DropoutWrapper * Pass cell and embedded input to tf.nn.dynamic_rnn()	from imp import reload reload(tests) def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, source_sequence_length, source_vocab_size, encoding_embedding_size): """ Create encoding layer :param rnn_inputs: Inputs for the RNN :param rnn_size: RNN Size ...	language-translation/dlnd_language_translation.ipynb	mu4farooqi/deep-learning-projects	gpl-3.0
Decoding - Training Create a training decoding layer: * Create a tf.contrib.seq2seq.TrainingHelper * Create a tf.contrib.seq2seq.BasicDecoder * Obtain the decoder outputs from tf.contrib.seq2seq.dynamic_decode	def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, target_sequence_length, max_summary_length, output_layer, keep_prob): """ Create a decoding layer for training :param encoder_state: Encoder State :param dec_cell: Decoder RNN Cell ...	language-translation/dlnd_language_translation.ipynb	mu4farooqi/deep-learning-projects	gpl-3.0
Decoding - Inference Create inference decoder: * Create a tf.contrib.seq2seq.GreedyEmbeddingHelper * Create a tf.contrib.seq2seq.BasicDecoder * Obtain the decoder outputs from tf.contrib.seq2seq.dynamic_decode	def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, max_target_sequence_length, vocab_size, output_layer, batch_size, keep_prob): """ Create a decoding layer for inference :param encoder_state: Encoder ...	language-translation/dlnd_language_translation.ipynb	mu4farooqi/deep-learning-projects	gpl-3.0
Build the Decoding Layer Implement decoding_layer() to create a Decoder RNN layer. Embed the target sequences Construct the decoder LSTM cell (just like you constructed the encoder cell above) Create an output layer to map the outputs of the decoder to the elements of our vocabulary Use the your decoding_layer_train(e...	def decoding_layer(dec_input, encoder_state, target_sequence_length, max_target_sequence_length, rnn_size, num_layers, target_vocab_to_int, target_vocab_size, batch_size, keep_prob, decoding_embedding_size): """ Create decoding layer ...	language-translation/dlnd_language_translation.ipynb	mu4farooqi/deep-learning-projects	gpl-3.0
Build the Neural Network Apply the functions you implemented above to: Encode the input using your encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, source_sequence_length, source_vocab_size, encoding_embedding_size). Process target data using your process_decoder_input(target_data, target_vocab_to_int, bat...	def seq2seq_model(input_data, target_data, keep_prob, batch_size, source_sequence_length, target_sequence_length, max_target_sentence_length, source_vocab_size, target_vocab_size, enc_embedding_size, dec_embedding_size, rnn_size, ...	language-translation/dlnd_language_translation.ipynb	mu4farooqi/deep-learning-projects	gpl-3.0
Neural Network Training Hyperparameters Tune the following parameters: Set epochs to the number of epochs. Set batch_size to the batch size. Set rnn_size to the size of the RNNs. Set num_layers to the number of layers. Set encoding_embedding_size to the size of the embedding for the encoder. Set decoding_embedding_siz...	# Number of Epochs epochs = 3 # Batch Size batch_size = 256 # RNN Size rnn_size = 256 # Number of Layers num_layers = 2 # Embedding Size encoding_embedding_size = 300 decoding_embedding_size = 300 # Learning Rate learning_rate = 0.01 # Dropout Keep Probability keep_probability = 0.75 display_step = 20	language-translation/dlnd_language_translation.ipynb	mu4farooqi/deep-learning-projects	gpl-3.0
Build the Graph Build the graph using the neural network you implemented.	""" DON'T MODIFY ANYTHING IN THIS CELL """ save_path = 'checkpoints/dev' (source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess() max_target_sentence_length = max([len(sentence) for sentence in source_int_text]) train_graph = tf.Graph() with train_graph.as_default():...	language-translation/dlnd_language_translation.ipynb	mu4farooqi/deep-learning-projects	gpl-3.0
Batch and pad the source and target sequences	""" DON'T MODIFY ANYTHING IN THIS CELL """ def pad_sentence_batch(sentence_batch, pad_int): """Pad sentences with <PAD> so that each sentence of a batch has the same length""" max_sentence = max([len(sentence) for sentence in sentence_batch]) return [sentence + [pad_int] * (max_sentence - len(sentence)) for...	language-translation/dlnd_language_translation.ipynb	mu4farooqi/deep-learning-projects	gpl-3.0
Train Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the forms to see if anyone is having the same problem.	""" DON'T MODIFY ANYTHING IN THIS CELL """ def get_accuracy(target, logits): """ Calculate accuracy """ max_seq = max(target.shape[1], logits.shape[1]) if max_seq - target.shape[1]: target = np.pad( target, [(0,0),(0,max_seq - target.shape[1])], 'constant'...	language-translation/dlnd_language_translation.ipynb	mu4farooqi/deep-learning-projects	gpl-3.0
Sentence to Sequence To feed a sentence into the model for translation, you first need to preprocess it. Implement the function sentence_to_seq() to preprocess new sentences. Convert the sentence to lowercase Convert words into ids using vocab_to_int Convert words not in the vocabulary, to the <UNK> word id.	def sentence_to_seq(sentence, vocab_to_int): """ Convert a sentence to a sequence of ids :param sentence: String :param vocab_to_int: Dictionary to go from the words to an id :return: List of word ids """ return [vocab_to_int.get(word, vocab_to_int['<UNK>']) for word in sentence.lower().spli...	language-translation/dlnd_language_translation.ipynb	mu4farooqi/deep-learning-projects	gpl-3.0
Translate This will translate translate_sentence from English to French.	translate_sentence = 'he saw a old yellow truck .' """ DON'T MODIFY ANYTHING IN THIS CELL """ translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int) loaded_graph = tf.Graph() with tf.Session(graph=loaded_graph) as sess: # Load saved model loader = tf.train.import_meta_graph(load_path +...	language-translation/dlnd_language_translation.ipynb	mu4farooqi/deep-learning-projects	gpl-3.0
We will create a Twitter API handle for fetching data Inorder to qualify for a Twitter API handle you need to be a Phone Verified Twitter user. Goto Twitter settings page twitter.com/settings/account Choose Mobile tab on left pane, then enter your phone number and verify by OTP Now you should be able to register ne...	# make sure to exclued this folder in git ignore path_to_cred_file = path.abspath('../restricted/api_credentials.p') # we will store twitter handle credentials in a pickle file (object de-serialization) # code for pickling credentials need to be run only once during initial configuration # fill the following dictionar...	sentiment_analysis/twitter_sentiment_analysis-jallikattu/code/twitter_sentiment_analysis-jallikattu_FINAL.ipynb	nixphix/ml-projects	mit
From second run you can load the credentials securely form stored file If you want to check the credentials uncomment the last line in below code block	# make sure to exclued this folder in git ignore path_to_cred_file = path.abspath('../restricted/api_credentials.p') # load saved twitter credentials twitter_credentials = pickle.load(open(path_to_cred_file,'rb')) #print("\n".join(["{:20} : {}".format(key,value) for key,value in twitter_credentials.items()]))	sentiment_analysis/twitter_sentiment_analysis-jallikattu/code/twitter_sentiment_analysis-jallikattu_FINAL.ipynb	nixphix/ml-projects	mit
Creating an Open Auth Instance With the created api and token we will open an open auth instance to authenticate our twitter account. If you feel that your twitter api credentials have been compromised you can just generate a new set of access token-secret pair, access token is like RSA to authenticate your api key.	# lets create an open authentication handler and initialize it with our twitter handlers api key auth = tweepy.OAuthHandler(twitter_credentials['api_key'],twitter_credentials['api_secret']) # access token is like password for the api key, auth.set_access_token(twitter_credentials['access_token'],twitter_credentials['...	sentiment_analysis/twitter_sentiment_analysis-jallikattu/code/twitter_sentiment_analysis-jallikattu_FINAL.ipynb	nixphix/ml-projects	mit
Twitter API Handle Tweepy comes with a Twitter API wrapper class called 'API', passing the open auth instance to this API creates a live Twitter handle to our account. ATTENTION: Please beware that this is a handle you your own account not any pseudo account, if you tweet something with this it will be your tweet This ...	# lets create an instance of twitter api wrapper api = tweepy.API(auth) # lets do some self check user = api.me() print("{}\n{}".format(user.name,user.location))	sentiment_analysis/twitter_sentiment_analysis-jallikattu/code/twitter_sentiment_analysis-jallikattu_FINAL.ipynb	nixphix/ml-projects	mit
Inspiration for this Project I drew inspiration for this project from the ongoing issue on traditional bull fighting AKA Jallikattu. Here I'm trying read pulse of the people based on tweets. We are searching for key word Jallikattu in Twitters public tweets, in the retured search result we are taking 150 tweets to do ...	# now lets get some data to check the sentiment on it # lets search for key word jallikattu and check the sentiment on it query = 'jallikattu' tweet_cnt = 150 peta_tweets = api.search(q=query,count=tweet_cnt)	sentiment_analysis/twitter_sentiment_analysis-jallikattu/code/twitter_sentiment_analysis-jallikattu_FINAL.ipynb	nixphix/ml-projects	mit
Processing Tweets Once we get the tweets, we will iterate through the tweets and do following oprations 1. Pass the tweet text to TextBlob to process the tweet 2. Processed tweets will have two attributes * Polarity which is a numerical value between -1 to 1, the sentiment of the text can be infered from this. * S...	# lets go over the tweets sentiment_polarity = [0,0,0] tags = [] for tweet in peta_tweets: processed_tweet = textblob.TextBlob(tweet.text) polarity = processed_tweet.sentiment.polarity upd_index = 0 if polarity > 0 else (1 if polarity == 0 else 2) sentiment_polarity[upd_index] = sentiment_polarity[upd...	sentiment_analysis/twitter_sentiment_analysis-jallikattu/code/twitter_sentiment_analysis-jallikattu_FINAL.ipynb	nixphix/ml-projects	mit
Sentiment Analysis We can see that majority is neutral which is contributed by 1. Tweets with media only(photo, video) 2. Tweets in regional language. Textblob do not work on our indian languages. 3. Some tweets contains only stop words or the words that do not give any positive or negative perspective. 4. Polarity ...	# lets process the hash tags in the tweets and make a word cloud visualization # normalizing tags by converting all tags to lowercase tags = [t.lower() for t in tags] # get unique count of tags to take count for each uniq_tags = list(set(tags)) tag_count = [] # for each unique hash tag take frequency of occurance for...	sentiment_analysis/twitter_sentiment_analysis-jallikattu/code/twitter_sentiment_analysis-jallikattu_FINAL.ipynb	nixphix/ml-projects	mit
Simple Word Cloud with Twitter #tags Let us viualize the tags used in for Jallikattu by creating a tag cloud. The wordcloud package takes a single string of tags separated by whitespace. We will concatinate the tags and pass it to generate method to create a tag cloud image.	# we will create a vivid tag cloud visualization # creating a single string of texts from tags, the tag's font size is proportional to its frequency text = " ".join(tags) # this generates an image from the long string, if you wish you may save it to local wc = WordCloud().generate(text) # we will display the image w...	sentiment_analysis/twitter_sentiment_analysis-jallikattu/code/twitter_sentiment_analysis-jallikattu_FINAL.ipynb	nixphix/ml-projects	mit
Masked Word Cloud The tag cloud can be masked using a grascale stencil image the wordcloud package neatly arranges the word in side the mask image. I have supreimposed generated word cloud image on to the mask image to provide a detailing otherwise the background of the word cloud will be white and it will appeare like...	# we can also create a masked word cloud from the tags by using grayscale image as stencil # lets load the mask image from local bull_mask = np.array(Image.open(path.abspath('../asset/bull_mask_1.jpg'))) wc_mask = WordCloud(background_color="white", mask=bull_mask).generate(text) mask_image = plt.imshow(bull_mask, cma...	sentiment_analysis/twitter_sentiment_analysis-jallikattu/code/twitter_sentiment_analysis-jallikattu_FINAL.ipynb	nixphix/ml-projects	mit
Gromov-Wasserstein example This example is designed to show how to use the Gromov-Wassertsein distance computation in POT.	# Author: Erwan Vautier <erwan.vautier@gmail.com> # Nicolas Courty <ncourty@irisa.fr> # # License: MIT License import scipy as sp import numpy as np import matplotlib.pylab as pl from mpl_toolkits.mplot3d import Axes3D # noqa import ot	docs/source/auto_examples/plot_gromov.ipynb	rflamary/POT	mit
Sample two Gaussian distributions (2D and 3D) The Gromov-Wasserstein distance allows to compute distances with samples that do not belong to the same metric space. For demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces.	n_samples = 30 # nb samples mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) mu_t = np.array([4, 4, 4]) cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) P = sp.linalg.sqrtm(cov_t) xt = np.random.randn(n_samples, 3).dot(P) + mu_t	docs/source/auto_examples/plot_gromov.ipynb	rflamary/POT	mit
Plotting the distributions	fig = pl.figure() ax1 = fig.add_subplot(121) ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') ax2 = fig.add_subplot(122, projection='3d') ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') pl.show()	docs/source/auto_examples/plot_gromov.ipynb	rflamary/POT	mit
Compute distance kernels, normalize them and then display	C1 = sp.spatial.distance.cdist(xs, xs) C2 = sp.spatial.distance.cdist(xt, xt) C1 /= C1.max() C2 /= C2.max() pl.figure() pl.subplot(121) pl.imshow(C1) pl.subplot(122) pl.imshow(C2) pl.show()	docs/source/auto_examples/plot_gromov.ipynb	rflamary/POT	mit
Compute Gromov-Wasserstein plans and distance	p = ot.unif(n_samples) q = ot.unif(n_samples) gw0, log0 = ot.gromov.gromov_wasserstein( C1, C2, p, q, 'square_loss', verbose=True, log=True) gw, log = ot.gromov.entropic_gromov_wasserstein( C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True) print('Gromov-Wasserstein distances: ' + str(log0['...	docs/source/auto_examples/plot_gromov.ipynb	rflamary/POT	mit
Resolviendo el problema en forma analítica con SymPy Para resolver el problema, debemos utilizar la La Ecuación diferencial de la ley del enfriamiento de Newton. Los datos que tenemos son: Temperatura inicial = 34.5 Temperatura 1 hora despues = 33.9 Temperatura del ambiente = 15 Temperatura normal promedio de un ser h...	# defino las incognitas t, k = sympy.symbols('t k') y = sympy.Function('y') # expreso la ecuacion f = k*(y(t) -15) sympy.Eq(y(t).diff(t), f) # Resolviendo la ecuación edo_sol = sympy.dsolve(y(t).diff(t) - f) edo_sol	content/notebooks/ecuaciones-diferenciales.ipynb	relopezbriega/mi-python-blog	gpl-2.0
Ahora que tenemos la solución de la Ecuación diferencial, despejemos constante de integración utilizando la condición inicial.	# Condición inicial ics = {y(0): 34.5} C_eq = sympy.Eq(edo_sol.lhs.subs(t, 0).subs(ics), edo_sol.rhs.subs(t, 0)) C_eq C = sympy.solve(C_eq)[0] C	content/notebooks/ecuaciones-diferenciales.ipynb	relopezbriega/mi-python-blog	gpl-2.0
Ahora que ya sabemos el valor de C, podemos determinar el valor de $k$.	eq = sympy.Eq(y(t), C * sympy.E*(kt) +15) eq ics = {y(1): 33.9} k_eq = sympy.Eq(eq.lhs.subs(t, 1).subs(ics), eq.rhs.subs(t, 1)) kn = round(sympy.solve(k_eq)[0], 4) kn	content/notebooks/ecuaciones-diferenciales.ipynb	relopezbriega/mi-python-blog	gpl-2.0
Ahora que ya tenemos todos los datos, podemos determinar la hora aproximada de la muerte.	hmuerte = sympy.Eq(37, 19.5 * sympy.E*(knt) + 15) hmuerte t = round(sympy.solve(hmuerte)[0],2) t h, m = divmod(t*-60, 60) print "%d horas, %d minutos" % (h, m)	content/notebooks/ecuaciones-diferenciales.ipynb	relopezbriega/mi-python-blog	gpl-2.0
Es decir, que pasaron aproximadamente 3 horas y 51 minutos desde que ocurrió el crimen, por lo tanto la hora del asesinato debio haber sido alredor de las 10:50 pm. Transformada de Laplace Un método alternativo que podemos utilizar para resolver en forma analítica Ecuaciones diferenciales ordinarias complejas, es utili...	# Ejemplo de transformada de Laplace # Defino las incognitas t = sympy.symbols("t", positive=True) y = sympy.Function("y") # simbolos adicionales. s, Y = sympy.symbols("s, Y", real=True) # Defino la ecuación edo = y(t).diff(t, t) + 3y(t).diff(t) + 2y(t) sympy.Eq(edo) # Calculo la transformada de Laplace L_edo = s...	content/notebooks/ecuaciones-diferenciales.ipynb	relopezbriega/mi-python-blog	gpl-2.0
Aquí ya logramos convertir a la Ecuación diferencial en una ecuación algebraica. Ahora podemos aplicarle las condiciones iniciales para resolverla.	# Definimos las condiciones iniciales ics = {y(0): 2, y(t).diff(t).subs(t, 0): -3} ics # Aplicamos las condiciones iniciales L_edo_4 = L_edo_3.subs(ics) # Resolvemos la ecuación y arribamos a la Transformada de Laplace # que es equivalente a nuestra ecuación diferencial Y_sol = sympy.solve(L_edo_4, Y) Y_sol # Por úl...	content/notebooks/ecuaciones-diferenciales.ipynb	relopezbriega/mi-python-blog	gpl-2.0
Las Transformadas de Laplace, pueden ser una buena alternativa para resolver Ecuaciones diferenciales en forma analítica. Pero aún así, siguen existiendo ecuaciones que se resisten a ser resueltas por medios analíticos, para estos casos, debemos recurrir a los métodos numéricos. Series de potencias y campos de direccio...	# Defino incognitas x = sympy.symbols('x') y = sympy.Function('y') # Defino la función f = y(x)2 + x2 -1 # Condición inicial ics = {y(0): 0} # Resolviendo la ecuación diferencial edo_sol = sympy.dsolve(y(x).diff(x) - f, ics=ics) edo_sol	content/notebooks/ecuaciones-diferenciales.ipynb	relopezbriega/mi-python-blog	gpl-2.0
El resultado que nos da SymPy, es una aproximación con Series de potencias (una serie de Taylor); y el problema con las Series de potencias es que sus resultados sólo suelen válidos para un rango determinado de valores. Una herramienta que nos puede ayudar a visualizar el rango de validez de una aproximación con Series...	# grafico de campo de dirección fig, axes = plt.subplots(1, 1, figsize=(7, 5)) campo_dir = plot_direction_field(x, y(x), f, ax=axes)	content/notebooks/ecuaciones-diferenciales.ipynb	relopezbriega/mi-python-blog	gpl-2.0
Rango de validez de la solución de series de potencia Ahora que ya conocemos a los Campos de direcciones, volvamos a la solución aproximada con Series de potencias que habiamos obtenido anteriormente. Podemos graficar esa solución en el Campos de direcciones, y compararla con una solución por método númericos. <img tit...	fig, axes = plt.subplots(1, 2, figsize=(10, 5)) # panel izquierdo - solución aproximada por Serie de potencias plot_direction_field(x, y(x), f, ax=axes[0]) x_vec = np.linspace(-3, 3, 100) axes[0].plot(x_vec, sympy.lambdify(x, edo_sol.rhs.removeO())(x_vec), 'b', lw=2) # panel derecho - Solución por método...	content/notebooks/ecuaciones-diferenciales.ipynb	relopezbriega/mi-python-blog	gpl-2.0
<center><h1>Soluciones numéricas con Python</h1> <br> <h2>SciPy<h2> <br> <a href="https://scipy.org/" target="_blank"><img src="https://www2.warwick.ac.uk/fac/sci/moac/people/students/peter_cock/python/scipy_logo.png?maxWidth=175&maxHeight=61" title="SciPy"></a> </center> # SciPy [SciPy](https://www.scipy.org/) es un...	# Defino la función f = y(x)**2 + x f # la convierto en una función ejecutable f_np = sympy.lambdify((y(x), x), f) # Definimos los valores de la condición inicial y el rango de x sobre los # que vamos a iterar para calcular y(x) y0 = 0 xp = np.linspace(0, 1.9, 100) # Calculando la solución numerica para los valores...	content/notebooks/ecuaciones-diferenciales.ipynb	relopezbriega/mi-python-blog	gpl-2.0
Los resultados son dos matrices unidimensionales de NumPy $yp$ y $yn$, de la misma longitud que las correspondientes matrices de coordenadas $xp$ y $xn$, que contienen las soluciones numéricas de la ecuación diferencial ordinaria para esos puntos específicos. Para visualizar la solución, podemos graficar las matrices $...	# graficando la solucion con el campo de direcciones fig, axes = plt.subplots(1, 1, figsize=(8, 6)) plot_direction_field(x, y(x), f, ax=axes) axes.plot(xn, yn, 'b', lw=2) axes.plot(xp, yp, 'r', lw=2) plt.show()	content/notebooks/ecuaciones-diferenciales.ipynb	relopezbriega/mi-python-blog	gpl-2.0
Sistemas de ecuaciones diferenciales En este ejemplo, solucionamos solo una ecuación. Generalmente, la mayoría de los problemas se presentan en la forma de sistemas de ecuaciones diferenciales ordinarias, es decir, que incluyen varias ecuaciones a resolver. Para ver como podemos utilizar a integrate.odeint para resolve...	# Definimos el sistema de ecuaciones def f(xyz, t, sigma, rho, beta): x, y, z = xyz return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z] # Asignamos valores a los parámetros sigma, rho, beta = 8, 28, 8/3.0 # Condición inicial y valores de t sobre los que calcular xyz0 = [1.0, 1...	content/notebooks/ecuaciones-diferenciales.ipynb	relopezbriega/mi-python-blog	gpl-2.0
Ecuaciones en derivadas parciales Los casos que vimos hasta ahora, se trataron de ecuaciones diferenciales ordinarias, pero ¿cómo podemos hacer para resolver ecuaciones en derivadas parciales? Estas ecuaciones son mucho más difíciles de resolver, pero podes recurrir a la poderosa herramienta que nos proporciona el Méto...	# Discretizando el problema N1 = N2 = 75 mesh = dolfin.RectangleMesh(dolfin.Point(0, 0), dolfin.Point(1, 1), N1, N2) # grafico de la malla. dolfin.RectangleMesh(dolfin.Point(0, 0), dolfin.Point(1, 1), 10, 10)	content/notebooks/ecuaciones-diferenciales.ipynb	relopezbriega/mi-python-blog	gpl-2.0
El siguiente paso es definir una representación del espacio funcional para las funciones de ensayo y prueba. Para esto vamos a utilizar la clase FunctionSpace. El constructor de esta clase tiene al menos tres argumentos: un objeto de malla, el nombre del tipo de función base, y el grado de la función base. En este caso...	# Funciones bases V = dolfin.FunctionSpace(mesh, 'Lagrange', 1) u = dolfin.TrialFunction(V) v = dolfin.TestFunction(V)	content/notebooks/ecuaciones-diferenciales.ipynb	relopezbriega/mi-python-blog	gpl-2.0
Ahora debemos definir a nuestra EDP en su formulación débil equivalente para poder tratarla como un problema de álgebra lineal que podamos resolver con el MEF.	# Formulación debil de la EDP a = dolfin.inner(dolfin.nabla_grad(u), dolfin.nabla_grad(v)) * dolfin.dx f = dolfin.Constant(0.0) L = f * v * dolfin.dx	content/notebooks/ecuaciones-diferenciales.ipynb	relopezbriega/mi-python-blog	gpl-2.0
Y definimos las condiciones de frontera.	# Defino condiciones de frontera def u0_top_boundary(x, on_boundary): return on_boundary and abs(x[1]-1) < 1e-8 def u0_bottom_boundary(x, on_boundary): return on_boundary and abs(x[1]) < 1e-8 def u0_left_boundary(x, on_boundary): return on_boundary and abs(x[0]) < 1e-8 def u0_right_boundary(x, on_boundar...	content/notebooks/ecuaciones-diferenciales.ipynb	relopezbriega/mi-python-blog	gpl-2.0
Ahora ya podemos resolver la EDP utilizando la función dolfin.solve. El vector resultante, luego lo podemos convertir a una matriz de NumPy y utilizarla para graficar la solución con Matplotlib.	# Resolviendo la EDP u_sol = dolfin.Function(V) dolfin.solve(a == L, u_sol, bcs) # graficando la solución u_mat = u_sol.vector().array().reshape(N1+1, N2+1) x = np.linspace(0, 1, N1+2) y = np.linspace(0, 1, N1+2) X, Y = np.meshgrid(x, y) fig, ax = plt.subplots(1, 1, figsize=(8, 6)) c = ax.pcolor(X, Y, u_mat, vmin=-...	content/notebooks/ecuaciones-diferenciales.ipynb	relopezbriega/mi-python-blog	gpl-2.0
Inintialize model and solve	# Input model parameters beta = 0.99 sigma= 1 eta = 1 omega= 0.8 kappa= (sigma+eta)(1-omega)(1-betaomega)/omega rhor = 0.9 phipi= 1.5 phiy = 0 rhog = 0.5 rhou = 0.5 rhov = 0.9 Sigma = 0.001np.eye(3) # Store parameters parameters = pd.Series({ 'beta':beta, 'sigma':sigma, 'eta':eta, 'omega':omega...	examples/nk_model.ipynb	letsgoexploring/linearsolve-package	mit
Compute impulse responses and plot Compute impulse responses of the endogenous variables to a one percent shock to each exogenous variable.	# Compute impulse responses nk.impulse(T=11,t0=1,shocks=None) # Create the figure and axes fig = plt.figure(figsize=(12,12)) ax1 = fig.add_subplot(3,1,1) ax2 = fig.add_subplot(3,1,2) ax3 = fig.add_subplot(3,1,3) # Plot commands nk.irs['e_g'][['g','y','i','pi','r']].plot(lw='5',alpha=0.5,grid=True,title='Demand shock'...	examples/nk_model.ipynb	letsgoexploring/linearsolve-package	mit
Construct a stochastic simulation and plot Contruct a 151 period stochastic simulation by first siumlating the model for 251 periods and then dropping the first 100 values. The seed for the numpy random number generator is set to 0.	# Compute stochastic simulation nk.stoch_sim(T=151,drop_first=100,cov_mat=Sigma,seed=0) # Create the figure and axes fig = plt.figure(figsize=(12,8)) ax1 = fig.add_subplot(2,1,1) ax2 = fig.add_subplot(2,1,2) # Plot commands nk.simulated[['y','i','pi','r']].plot(lw='5',alpha=0.5,grid=True,title='Output, inflation, and...	examples/nk_model.ipynb	letsgoexploring/linearsolve-package	mit
Filling in Missing Values	# Impute missing values # Manually set metadata properties, as current py_entitymatching.impute_table() # requires 'fk_ltable', 'fk_rtable', 'ltable', 'rtable' properties em.set_property(A, 'fk_ltable', 'id') em.set_property(A, 'fk_rtable', 'id') em.set_property(A, 'ltable', A) em.set_property(A, 'rtable', A) A_all_a...	stage4/stage4_report.ipynb	malnoxon/board-game-data-science	gpl-3.0
Generating Features Here, we generate all the features we decided upon after our final iteration of cross validation and debugging. We only use the relevant subset of all these features in the reported iterations below.	# Generate a set of features #import pdb; pdb.set_trace(); import py_entitymatching.feature.attributeutils as au import py_entitymatching.feature.simfunctions as sim import py_entitymatching.feature.tokenizers as tok ltable = A1 rtable = B1 # Get similarity functions for generating the features for matching sim_funcs...	stage4/stage4_report.ipynb	malnoxon/board-game-data-science	gpl-3.0
Cross Validation Method	def cross_validation_eval(H): cv_iter = pd.DataFrame(columns=['Precision', 'Recall', 'F1']) # Matchers matchers = [em.DTMatcher(name='DecisionTree', random_state=0), em.RFMatcher(name='RandomForest', random_state=0), em.SVMMatcher(name='SVM', random_state=0), em.NBMatcher(name='NaiveBayes'),...	stage4/stage4_report.ipynb	malnoxon/board-game-data-science	gpl-3.0
Iteration 1: CV	# Subset of features we used on our first iteration include_features = [ 'min_num_players_min_num_players_lev_dist', 'max_num_players_max_num_players_lev_dist', 'min_gameplay_time_min_gameplay_time_lev_dist', 'max_gameplay_time_max_gameplay_time_lev_dist', ] F_1 = F.loc[F['feature_name'].isin(include_fe...	stage4/stage4_report.ipynb	malnoxon/board-game-data-science	gpl-3.0
Iteration 2: Debug	PQ = em.split_train_test(H_1, train_proportion=0.80, random_state=0) P = PQ['train'] Q = PQ['test'] # Convert the I into a set of feature vectors using F # Here, we add name edit distance as a feature include_features_2 = [ 'min_num_players_min_num_players_lev_dist', 'max_num_players_max_num_players_lev_dist'...	stage4/stage4_report.ipynb	malnoxon/board-game-data-science	gpl-3.0
Iteration 3: CV	# Convert the I into a set of feature vectors using F # Here, we add name edit distance as a feature include_features_3 = [ 'min_num_players_min_num_players_lev_dist', 'max_num_players_max_num_players_lev_dist', 'min_gameplay_time_min_gameplay_time_lev_dist', 'max_gameplay_time_max_gameplay_time_lev_dis...	stage4/stage4_report.ipynb	malnoxon/board-game-data-science	gpl-3.0
Iteration 4: CV	# Convert the I into a set of feature vectors using F # Here, we add name edit distance as a feature include_features_4 = [ 'min_num_players_min_num_players_lev_dist', 'max_num_players_max_num_players_lev_dist', 'min_gameplay_time_min_gameplay_time_lev_dist', 'max_gameplay_time_max_gameplay_time_lev_dis...	stage4/stage4_report.ipynb	malnoxon/board-game-data-science	gpl-3.0
Train-Test Set Accuracy	# Apply train, test set evaluation I_table = em.extract_feature_vecs(I, feature_table=F_2, attrs_after='label', show_progress=False) J_table = em.extract_feature_vecs(J, feature_table=F_2, attrs_after='label', show_progress=False) matchers = [ #em.DTMatcher(name='DecisionTree', random_state=0), #em.RFMatcher(name='R...	stage4/stage4_report.ipynb	malnoxon/board-game-data-science	gpl-3.0
In that case libm_cbrt is expected to be a capsule containing the function pointer to libm's cbrt (cube root) function. This capsule can be created using ctypes:	import ctypes # capsulefactory PyCapsule_New = ctypes.pythonapi.PyCapsule_New PyCapsule_New.restype = ctypes.py_object PyCapsule_New.argtypes = ctypes.c_void_p, ctypes.c_char_p, ctypes.c_void_p # load libm libm = ctypes.CDLL('libm.so.6') # extract the proper symbol cbrt = libm.cbrt # wrap it cbrt_capsule = PyCapsul...	docs/examples/Third Party Libraries.ipynb	pombredanne/pythran	bsd-3-clause
The capsule is not usable from Python context (it's some kind of opaque box) but Pythran knows how to use it. beware, it does not try to do any kind of type verification. It trusts your #pythran export line.	pythran_cbrt(cbrt_capsule, 8.)	docs/examples/Third Party Libraries.ipynb	pombredanne/pythran	bsd-3-clause
With Pointers Now, let's try to use the sincos function. It's C signature is void sincos(double, double, double). How do we pass that to Pythran?	%%pythran #pythran export pythran_sincos(None(float64, float64, float64), float64) def pythran_sincos(libm_sincos, val): import numpy as np val_sin, val_cos = np.empty(1), np.empty(1) libm_sincos(val, val_sin, val_cos) return val_sin[0], val_cos[0]	docs/examples/Third Party Libraries.ipynb	pombredanne/pythran	bsd-3-clause
There is some magic happening here: None is used to state the function pointer does not return anything. In order to create pointers, we actually create empty one-dimensional array and let pythran handle them as pointer. Beware that you're in charge of all the memory checking stuff! Apart from that, we can now ca...	sincos_capsule = PyCapsule_New(libm.sincos, "unchecked anyway".encode(), None) pythran_sincos(sincos_capsule, 0.)	docs/examples/Third Party Libraries.ipynb	pombredanne/pythran	bsd-3-clause
With Pythran It is naturally also possible to use capsule generated by Pythran. In that case, no type shenanigans is required, we're in our small world. One just need to use the capsule keyword to indicate we want to generate a capsule.	%%pythran ## This is the capsule. #pythran export capsule corp((int, str), str set) def corp(param, lookup): res, key = param return res if key in lookup else -1 ## This is some dummy callsite #pythran export brief(int, int((int, str), str set)): def brief(val, capsule): return capsule((val, "doctor"), {"...	docs/examples/Third Party Libraries.ipynb	pombredanne/pythran	bsd-3-clause
It's not possible to call the capsule directly, it's an opaque structure.	try: corp((1,"some"),set()) except TypeError as e: print(e)	docs/examples/Third Party Libraries.ipynb	pombredanne/pythran	bsd-3-clause
It's possible to pass it to the according pythran function though.	brief(1, corp)	docs/examples/Third Party Libraries.ipynb	pombredanne/pythran	bsd-3-clause
With Cython The capsule pythran uses may come from Cython-generated code. This uses a little-known feature from cython: api and __pyx_capi__. nogil is of importance here: Pythran releases the GIL, so better not call a cythonized function that uses it.	!find -name 'cube' -delete %%file cube.pyx #cython: language_level=3 cdef api double cube(double x) nogil: return x x * x from setuptools import setup from Cython.Build import cythonize _ = setup( name='cube', ext_modules=cythonize("cube.pyx"), zip_safe=False, # fake CLI call script_name='...	docs/examples/Third Party Libraries.ipynb	pombredanne/pythran	bsd-3-clause
The cythonized module has a special dictionary that holds the capsule we're looking for.	import sys sys.path.insert(0, '.') import cube print(type(cube.__pyx_capi__['cube'])) cython_cube = cube.__pyx_capi__['cube'] pythran_cbrt(cython_cube, 2.)	docs/examples/Third Party Libraries.ipynb	pombredanne/pythran	bsd-3-clause
Document Table of Contents 1. Key Properties 2. Key Properties --> Flux Correction 3. Key Properties --> Genealogy 4. Key Properties --> Software Properties 5. Key Properties --> Coupling 6. Key Properties --> Tuning Applied 7. Key Properties --> Conservation --> Heat 8. Key Properties --> Conse...	# PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.model_overview') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s)	notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb	ES-DOC/esdoc-jupyterhub	gpl-3.0
1.2. Model Name Is Required: TRUE    Type: STRING    Cardinality: 1.1 Name of coupled model.	# PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.model_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s)	notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb	ES-DOC/esdoc-jupyterhub	gpl-3.0
2. Key Properties --> Flux Correction Flux correction properties of the model 2.1. Details Is Required: TRUE    Type: STRING    Cardinality: 1.1 Describe if/how flux corrections are applied in the model	# PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.flux_correction.details') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s)	notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb	ES-DOC/esdoc-jupyterhub	gpl-3.0
3. Key Properties --> Genealogy Genealogy and history of the model 3.1. Year Released Is Required: TRUE    Type: STRING    Cardinality: 1.1 Year the model was released	# PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.genealogy.year_released') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s)	notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb	ES-DOC/esdoc-jupyterhub	gpl-3.0
3.2. CMIP3 Parent Is Required: FALSE    Type: STRING    Cardinality: 0.1 CMIP3 parent if any	# PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.genealogy.CMIP3_parent') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s)	notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb	ES-DOC/esdoc-jupyterhub	gpl-3.0
3.3. CMIP5 Parent Is Required: FALSE    Type: STRING    Cardinality: 0.1 CMIP5 parent if any	# PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.genealogy.CMIP5_parent') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s)	notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb	ES-DOC/esdoc-jupyterhub	gpl-3.0
3.4. Previous Name Is Required: FALSE    Type: STRING    Cardinality: 0.1 Previously known as	# PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.genealogy.previous_name') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s)	notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb	ES-DOC/esdoc-jupyterhub	gpl-3.0
4. Key Properties --> Software Properties Software properties of model 4.1. Repository Is Required: FALSE    Type: STRING    Cardinality: 0.1 Location of code for this component.	# PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.software_properties.repository') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s)	notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb	ES-DOC/esdoc-jupyterhub	gpl-3.0
4.2. Code Version Is Required: FALSE    Type: STRING    Cardinality: 0.1 Code version identifier.	# PROPERTY ID - DO NOT EDIT ! DOC.set_id('cmip6.toplevel.key_properties.software_properties.code_version') # PROPERTY VALUE: # Set as follows: DOC.set_value("value") # TODO - please enter value(s)	notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb	ES-DOC/esdoc-jupyterhub	gpl-3.0

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